case study about acute myeloid leukemia

• Get new issue alerts Get alerts

Secondary Logo

Journal logo.

Colleague's E-mail is Invalid

Your message has been successfully sent to your colleague.

Save my selection

Acute myeloid leukemia in an 86-year-old man with AML1/ETO treated with Homoharringtonine and Arsenic Trioxide

A case report.

Editor(s): NA.,

Department of Hematology, Union Hospital, Fujian Medical University, Fujian Institute of Hematology, Fujian Provincial Key Laboratory of Hematology, Fuzhou, China.

∗Correspondence: Yong Wu, Department of Hematology, Fujian Medical University Union Hospital, Fujian Institute of Hematology, 29 Xinquan Road, Fuzhou 350001, China (e-mail: [email protected] ).

Abbreviations: AML = acute myeloid leukemia, AML-M2 = AML with maturation, As 2 O 3 = Arsenic Trioxide, Hb = hemoglobin, HHT = Homoharringtonine, PLT = platelet, WBC = white blood cell.

Current publication is supported by grants from the Construction project of Fujian Medical Center of Hematology (Min201704), National and Fujian Provincial Key Clinical Specialty Discipline Construction Program, China (2011-1006, 2012-149).

The authors have no conflicts of interest to disclose.

This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc/4.0

Rationale: 

Acute myeloid leukemia (AML) is a malignantly clonal and highly heterogeneous disease. Although the treatment of AML has brought promising outcomes for younger patients, prognosis of the elderly remains dismal. Innovative regimens are increasingly necessary to be investigated.

Patient concerns: 

We present an 86-year-old AML patient with fever, cough, and sputum production.

Diagnoses: 

A diagnosis of AML with maturation (AML-M2) and AML1/ETO was made.

Interventions: 

The patient was treated with a regimen of Homoharringtonine coupled with arsenic trioxide.

Outcomes: 

The AML-M2 patient with AML1/ETO achieved incomplete remission, but showed few toxic side effects and improved survival. Besides, we analyzed the dynamic counts of complete blood cells during the treatment. The count of white blood cell had a positive correlation with the percentage of blast cells ( r = 0.65), both of which had a negative correlation with the percentage of segmented neutrophils ( r = –0.63, –0.89).

Lessons: 

Homoharringtonine and arsenic trioxide may induce both the apoptosis and differentiation of leukemic cells in AML-M2 with AML1/ETO.

1 Introduction

Acute myeloid leukemia (AML) is a malignantly clonal disorder characterized by blockage of differentiation in the myeloid lineage and an accumulation of its immature progenitors in bone marrow, leading to hematopoietic failure. [1] In China, it was predicted that there were about 75,300 newly diagnosed leukemia cases in 2015; meanwhile, it was estimated that about 53,400 Chinese died of leukemia in 2015. [2] Age has been recommended as one of the poorest prognostic indicators for overall survival over the past decades. Although the changing treatment schedules and transplantation have shown benefits in AML of younger patients, response to treatment and survival in older ones remains dismal. [3] Here, we reported a successful case of 86-year-old man with AML treated with traditional Chinese medicines (TCM), Homoharringtonine and Arsenic, showing few toxic side effects and improved survival.

This study was approved by Ethical Committee of Union Hospital Affiliated to Fujian Medical University (2018YF037-02), and written informed consent was obtained from the patient's family for publication of this case report and accompanying images.

3 Case presentation

An 86-year-old man with fever, cough and sputum production for 7 days, was admitted to our hospital in November 2016. The medical history revealed the patient diagnosed with malignant lymphoma by the biopsy of cervical lymph node 4 years ago, had received 6 courses of standard chemotherapy (CHOP regimen), and had 5 years history of diabetes. Apart from the signs of anemia in the aged man, peripheral blood counts revealed white blood cells (WBC) 40.05 × 10 9 /L, segmented neutrophils 2%, hemoglobin (Hb) 76.0 g/L, platelet (PLT) 74.0 × 10 9 /L, and blast cells accounted for 90% of nucleated cells. Bone marrow was examined in an effort to establish the diagnosis, showing a marked hypercellularity with 68% myeloblasts, the occurrence of Auer rods, and 100% positive myeloperoxidase staining. AML1-ETO fusion gene was also detected. Consequently, the elderly patient was diagnosed with AML-M2 based on French–American–British classification.

He was treated with Homoharringtonine 2 mg/d and arsenic trioxide (As 2 O 3 ) 10 mg/d after the initial diagnosis. But Homoharringtonine and As 2 O 3 were replaced by supportive therapy due to overt myelosuppression 4 days later. Peripheral blood examination revealed WBC 1.71 × 10 9 /L (myeloblasts decreased to 25% and segmented neutrophils increased to 51% of all nucleated cells), Hb 44.0 g/L, and PLT 13.0 × 10 9 /L. Surprisingly, no myeloblast was detected and segmented neutrophils were 34% at day 9 after the chemotherapy. Whereas the follow-up count of WBC increased to 73.43 × 10 9 /L and myeloblasts increased to 97% at day 47 after his first chemotherapy. The initial regimen of Homoharringtonine and As 2 O 3 were reused. The count of WBC returned to normal 3 days later and the chemotherapy was then discontinued. In order to reduce the degree of myelosuppression, we chose the regimen of As 2 O 3 between 5 mg × 7 day and 10 mg × 7 day, alternately. Meanwhile, the regimen of Homoharringtonine between 0.5 mg × 7 day and 1 mg × 7 day was adopted, alternately. No myeloblast was detected in the peripheral blood cell smear with myelocytes 23%, metamyelocytes 22%, and segmented neutrophils 51% after 2 courses of the regimen above.

Analyzing the correlations among complete blood cell counts with Spearman test [4] in our case, we found some features as follows: The patient displayed an abnormally elevated count of WBC, and aberrantly decreased counts of PLT and Hb at his first visit, which was consistent with pathological feature of AML. Besides, the count of WBC had a positive correlation with the percentage of blast cells ( r = 0.65), but a negative correlation with the percentage of segmented neutrophils ( r = –0.63). The percentage of blast cells had a negative correlation with the percentage of segmented neutrophils ( r = –0.89). It may be explained by the differentiation from blast cells to segmented neutrophils after chemotherapy. However, the counts of PLT and Hb had no correlation with the other parameters above ( Fig. 1 ).

4 Discussion

Usually, AML patients have no evident causes. Exposure to chemotherapy is 1 risk factor associated with increased incidence with age. In our case, the patient with lymphoma had received chemotherapy for 6 cycles before the diagnosis of AML-M2, the cause of which may be the chemotherapy. In addition, AML1-ETO fusion gene was found in the case diagnosed with AML-M2. Whether the occurrence of AML1-ETO gene is before lymphoma or not, is not known. AML1-ETO gene is the product of t(8;21)(q22;q22) translocation in AML patients. AML1-ETO keeps the function of DNA binding sites in AML1 and the ability to recruit relevant cofactors through ETO, promoting granulopoiesis with inhibition of erythropoiesis in bone marrow. [5]

Older AML patients (age >60 years) have always been one of the most challenging group to treat. These patients have different tolerance of toxicity, and treatments are hardly curative. Treatment-related mortality of elderly patients with intensive treatment is more common (10%–40%) than that of younger patients (<10%). [1] Innovative chemotherapy regimens are thus necessary to be investigated. A randomized controlled, phase 3 trial study in 609 AML patients (14–59 years old) from China reported that the Homoharringtonine based HAA regimen (Homoharringtonine, aclarubicin, and cytarabine) had a higher complete remission (CR) rate and survival advantage than the daunorubicin and cytarabine regimen. [6] More recently, a retrospective research of 140 patients (16–60 years old) with t(8;21)AML revealed that the HAA regimen provided good molecular response and achieved much higher CR rate after 1 cycle of induction treatment, compared to other regimens reported in t(8;21)AML. [7] Thus, the Homoharringtonine based regimen may be a better choice in AML, especially in t (8; 21) AML. Arsenic, with a 500-year history in TCM, was successfully used to treat acute promyelocyte leukemia (APL) in TCM principle of counteracting one toxin with another. [8,9] In 2000, Chinese researchers reported that the CR rate and the 5-year survival rate of 136 APL patients treated with As 2 O 3 were 87.9% and 92.0%, respectively. [10]

On the basis of the encouraging results above, we selected Homoharringtonine coupled with As 2 O 3 to treat the 86-year-old case diagnosed as AML-M2 with AML1-ETO fusion gene, and a good response was achieved after the 2-cycle chemotherapy. We observed leukocytes decreased rapidly and blast cells differentiated to segmented neutrophils after chemotherapy. The antileukemic effects of Homoharringtonine mainly depended on inhibiting protein synthesis to inhibit proliferation, induce differentiation, and promote apoptosis of leukemic cells, leukemia stem cells included too. [11–15] Moreover, Chen et al [9] found that As 2 O 3 mediated a dual effect on APL cells in a dose-dependent manner in vitro and vivo studies. A higher concentration of As 2 O 3 (0.5–2.0 pmol/L) led to apoptosis which was associated with mitochondrial pathway and the degradation of PML-RARα oncoprotein, while a lower concentration of As 2 O 3 (0.1–0.5 pmol/L) induced partial differentiation related to granulocytic pathway to some extent. We observed the leukocytes reduction and blast cells differentiation after chemotherapy. Additionally, Chinese investigators reported that all-trans retinoic acid could induce differentiation in t(8;21) AML leukemic cells. [16] But the underlying mechanisms of Homoharringtonine and As 2 O 3 are still needed to be elucidated in AML1-ETO positive cell lines.

5 Conclusion

To conclude, the regimen of Homoharringtonine coupled with As 2 O 3 may bring substantial effects on elderly AML-M2 patients, which must rely on randomized controlled trials on many more patients to confirm. Besides, more experiments on AML1-ETO – expressing cell lines should be carried out to understand the potential mechanisms.

Author contributions

Conceptualization: Zhipeng He.

Data curation: Meiling Chen, Lili Chen, Bixin Wang.

Investigation: Yiping Huang, Huixian Wang, Mengting Yang, Jiaying Chen.

Writing – original draft: Zhipeng He.

Writing – review and editing: Zhipeng He, Xueting Xiao, Yanhong Lu, Yong Wu.

• Cited Here |
• Google Scholar

acute myeloid leukemia; AML1/ETO; Arsenic Trioxide; Homoharringtonine

• + Favorites
• View in Gallery

• ASH Foundation
• Log in or create an account
• Publications
• Diversity Equity and Inclusion
• Global Initiatives
• Resources for Hematology Fellows
• American Society of Hematology
• Hematopoiesis Case Studies

Case Study: New Therapies for Acute Myeloid Leukemia

• Agenda for Nematology Research
• Precision Medicine
• Genome Editing and Gene Therapy
• Immunologic Treatment
• Research Support and Funding

A 76-year-old woman presents to the emergency department following two weeks of progressive dyspnea and fatigue, and a new rash. Her medical history is significant for stage 2 chronic kidney disease, coronary artery disease, and diabetes.

Physical examination results are within normal limits, except for skin pallor and a petechial rash on the lower extremities bilaterally. She has an Eastern Cooperative Oncology Group (ECOG) performance status score of 1. Complete blood count with differential is significant for a white blood cell count of 18 × 10 9 /L with 40 percent circulating blasts, hemoglobin 6.7 g/dL, and platelet count of 20 × 10 9 /L. A bone marrow biopsy reveals a hypercellular marrow with 22 percent blasts, consistent with a diagnosis of acute myeloid leukemia (AML). Flow cytometry demonstrates CD33 negativity, and classic cytogenetic analysis revealed a normal karyotype. Molecular markers are pending for FLT3, IDH1, IDH2, and NPM1.

A pre-treatment echocardiogram is performed and is notable for mild global systolic dysfunction and a left-ventricular ejection fraction of 45 percent.

Which of the following is the most appropriate therapy?

• Gilternitinib
• Azacitidine and venetoclax
• Liposomal daunorubicin and cytarabine
• Gemtuzumab ozogamicin

Explanation

The best treatment option for this patient is azacitidine and venetoclax. Recently, the U.S. Food and Drug Administration (FDA) approved the BCL-2 inhibitor venetoclax in combination with a hypomethylating agent for patients with newly diagnosed AML who are 75 years or older, or those with comorbidities that preclude the use of intensive induction chemotherapy. 1 Approval was based on preliminary data published in February 2018 from a phase Ib study of 57 patients to evaluate the safety and efficacy of either azacitidine or decitabine in combination with venetoclax. 2

Eligibility criteria included previously untreated patients aged 65 years and older with AML who were ineligible for standard induction therapy, ECOG performance status of 0 to 2, and intermediate-risk or poor-risk cytogenetics. During dose escalation, oral venetoclax was administered daily in combination with either decitabine (days 1-5) or azacitidine (days 1-7). Results from this study population showed a complete remission (CR) or CR with incomplete marrow recovery (CRi) in 61 percent of patients. 2 A follow-up of the same clinical trial was recently published in January 2019 evaluating 145 patients. 3 This study demonstrated a CR + CRi rate at all doses of 67 percent, with notable responses in those with poor-risk cytogenetics and those who were at least 75 years old. The median duration of CR + CRi was 11.3 months, with a median overall survival of 17.5 months. 3

While this patient has newly diagnosed AML, her age and comorbidities, including CKD, borderline heart function, and diabetes, likely preclude her from being able to tolerate a standard induction chemotherapy regimen. 4 Gilteritinib (answer A) is an oral kinase inhibitor that was recently approved for treatment of relapsed or refractory AML, with a FLT3 mutation based on interim analysis of 138 patients in the ADMIRAL trial, showing CR or CRh in 21 percent of patients. 5 Answer D is incorrect because the liposomal form of daunorubicin and cytarabine is approved for indications of newly diagnosed therapy-related AML (t-AML) or AML with myelodysplasia-related changes, 6 which is not the case with this patient. Additionally, the left ventricular dysfunction is a relative contraindication to the danunorubicin. Ivosidenib is an IDH-1 inhibitor approved for patients with relapsed or refractory AML with a mutation in the IDH-1 gene. 7 While gemtuzumab ozogamicin (GO), an anti-CD33 monoclonal antibody, is well tolerated in older patients with newly diagnosed or relapsed AML, its approval is for treatment of CD33+ disease. 8 It would be an inappropriate choice for this patient because her flow cytometry demonstrated CD33 negativity. It is also not used as a single agent for initial induction therapy.

In summary, for older patients with newly diagnosed AML, a hypomethylating agent in combination with venetoclax should be considered when comorbidities preclude the use of standard induction chemotherapy.

Case study submitted by Nicole Held, DO, and Talha Badar, MD, of Medical College of Wisconsin, Milwaukee, WI.

Resources  

• U.S. Food and Drug Administration FDA approves venetoclax in combination for AML in adults. . 2018.
• DiNardo CD, Pratz KW, Letai A, et al Safety and preliminary efficacy of venetoclax with decitabine or azacitidine in elderly patients with previously untreated acute myeloid luekaemia: a non-randomised, open-label, phase 1b study . Lancet Oncol. 2018 19:216-228.
• DiNardo CD, Pratz K, Pullarkat V, et al Venetoclax combined with decitabine or azacitidine in treatment-naïve, elderly patients with acute myeloid leukemia . Blood. 2019 133:7-17.
• Kantarjian H, O’brien S, Cortes J, et al Results of intensive chemotherapy in 998 patients age 65 years or older with acute myeloid leukemia or high-risk myelodysplastic syndrome: predictive prognostic models for outcome . Cancer. 2006 106:1090-1098.
• U.S. Food and Drug Administration FDA approves gilteritinib for relapsed or refractory acute myeloid leukemia (AML) with a FLT3 mutation . 2018.
• Vyxeos (daunorubicin and cytarabine) package insert . Jazz Pharmaceuticals. 2017.
• U.S. Food and Drug Administration FDA approves first targeted treatment for patients with relapsed or refractory acute myeloid leukemia who have a certain genetic mutation . 2018.
• Sievers EL, Larson RA, Stadtmauer EA, et al Efficacy and safety of gemtuzumab ozogamicin in patients with CD33-positive acute myeloid leukemia in first relapse . J Clin Oncol. 2001 19:3244-3254.

American Society of Hematology. (1). Case Study: New Therapies for Acute Myeloid Leukemia. Retrieved from https://www.hematology.org/education/trainees/fellows/case-studies/new-therapies-for-acute-myeloid-leukemia .

American Society of Hematology. "Case Study: New Therapies for Acute Myeloid Leukemia." Hematology.org. https://www.hematology.org/education/trainees/fellows/case-studies/new-therapies-for-acute-myeloid-leukemia (label-accessed September 03, 2024).

"American Society of Hematology." Case Study: New Therapies for Acute Myeloid Leukemia, 03 Sep. 2024 , https://www.hematology.org/education/trainees/fellows/case-studies/new-therapies-for-acute-myeloid-leukemia .

Citation Manager Formats

• Biomarker-Driven Lung Cancer
• HER2 Breast Cancer
• Chronic Lymphocytic Leukemia
• Small Cell Lung Cancer
• Renal Cell Carcinoma

• CONFERENCES
• PUBLICATIONS
• CASE-BASED ROUNDTABLE

Case-Based Overview: Newly Diagnosed Acute Myeloid Leukemia

Naval G. Daver, MD: This is the case of a 64-year-old patient with newly diagnosed acute myeloid leukemia. The patient has significant comorbidities, including an elevated BMI [body mass index], signifying a significant obesity, as well as underlying pneumonia and prior history of high blood pressure and diabetes. These are all comorbidities that make us start thinking about the optimal therapeutic choice for this patient. It&rsquo;s also very important to confirm the diagnosis for this patient, and the bone marrow that has been done does show a diagnosis of acute myeloid leukemia with more than 20% blast, which meets the WHO [World Health Organization] criteria.

The main things that I&rsquo;m thinking about at this time for this patient are, number 1, stabilizing him. The CT [computed tomography] scan does show that he has an active infiltrate concerning for an infectious pneumonia. So, I would probably start the patient on some oral or IV [intravenous] antibiotics, depending on his clinical signs and symptoms. And then most importantly, try to rush the molecular and chromosome testing to try and identify the optimal frontline therapy.

At this time, I&rsquo;m probably not inclined to go with intensive induction therapy for this patient. This is not just because of his age—he is 64, which is still a patient that could be eligible for intensive induction with cytarabine, anthracycline&mdash;but more so because of his significant comorbidities: the elevated BMI, the ongoing pneumonia, the blood pressure and diabetes issues. I&rsquo;m thinking of going with lower intensity therapies such as hypomethylating agent or low-dose cytarabine with venetoclax, or potentially targeted therapy-based combinations if we find a targetable mutation such as FLT3 or IDH1 , IDH2 mutations.

Historically for such a patient we would have considered standard induction therapy, which is a combination of cytarabine anthracycline. There are many different ways this induction therapy can be given. The most common is what we call &ldquo;3 plus 7,&rdquo; which is 3 days of the anthracycline—either idarubicin or daunorubicin&mdash;and 7 days of the cytarabine, either 100 or 200 per meter square. There are variations, some of which have shown higher response rates, such as FLAG-IDA [fludarabine, cytarabine, idarubicin and filgrastim], CLIA [idarubicin plus high‐dose cytarabine&thinsp;+&thinsp;clofarabine/cladribine], and others. So that would probably be the standard therapy that we&rsquo;ve used for acute myeloid leukemia for about 40 years now.

If this patient was diagnosed 4 or 5 years ago, for example, that would be the most likely treatment approach. We also did have lower intensity therapies such as Vidaza [azacitidine], decitabine. These have been available now for the last 12 or 13 years. The problem was that the response rates with Vidaza or decitabine as single-agent were only about 20% to 28%, which is much lower than the 65% to 75% you could get with 7 + 3. In most patients we would try to push to give them 7 + 3 unless, of course, they were extremely sick in the ICU [intensive care unit] or had really severe comorbidities.

But now with the advent of the new combinations such as azacitidine or decitabine with venetoclax added, we&rsquo;re seeing the response rates are going up to about 70% to 75%. Now, we actually don&rsquo;t necessarily push for the traditional 7 + 3 induction if we feel that the patient may not tolerate it but go for the azacitidine with venetoclax type of approach instead.

Transcript edited for clarity.

Case: A Male With Rapidly Progressing Acute Myeloid Leukemia

A 64-year-old male presented with a 2-week history of subjective fever, fatigue, shortness of breath, dizziness, and cough

• PE: Temperature 99.1 o F, pallor of the conjunctiva, multiple ecchymosis on upper and lower extremities
• PMH: DM controlled on metformin, hypertension, BMI >35, recent history of pneumonia treated with oral antibiotics

Diagnostic Work- Up

• WBC: 2.3 x 10 3 /&micro;L, RBC: 3.1212 x 10 6 /&micro;L, Hb: 9.3 g/dL, Ht: 23.1%, Plt: 83 x 10 3 /&micro;L, LDH: 275 U/L, blasts: 36%, absolute neutrophil count: 320 cells/&micro;L, PT: 16.1s,
• Few auer rods noted on bone marrow aspiration
• Diagnosed with AML with 43% blasts on pathology evaluation, flow-cytometry confirms AML
• Molecular panel and cytogenic testing pending and RUSH requested
• Chest CT revealed patchy consolidation in the left lower lung lobe with ill-defined nodules
• EKG and Echocardiogram unremarkable
• Started on prophylactic voriconazole, cefpodoxime, and valacyclovir
• Patient was started at this time on azacitidine and venetoclax; Azacitidine 75mg/m 2 Days 1-7 and Venetoclax Days 1-28. Venetoclax dose was 100mg with voriconazole.
• Was admitted for tumor lysis monitoring and hydration. Tolerated cycle 1 well. continue until disease progression or unacceptable toxicity
• Day 28 post-treatment bone marrow aspirate revealed low percent residual blasts (3% blasts by flow) with hypocellular BM (5-10% cellularity) and ANC 0.3, platelets 23K
• Venetoclax was interrupted at this time. Labs checked 2-3 times per week outpatient. Within 12 days after venetoclax interruption ANC>0.5 and platelets>50K.
• Cycle 2 started outpatient with standard dose azacitidine and venetoclax reduced to 14-21 days
• Patient subsequently developed pneumonia, treated with oral antibiotics
• Patient will continue routine bone marrow biopsies after cycle 4, and every 6 months thereafter or if disease progression is suspected

Luspatercept in MDS Promotes Transfusion Independence and Anemia Response

During a Case-Based Roundtable® event, Azra Raza, MD, discussed a patient case and outcomes of the COMMANDS trial of luspatercept in myelodysplastic syndrome in the first article of a 2-part series.

SELECT-AML-1 Trial of Tamibarotene Combo in AML Discontinues Enrollment

Following a futility analysis, the phase 2 SELECT-AML-1 trial of tamibarotene combined with venetoclax and azacitidine in newly diagnosed RARA-overexpressed acute myeloid leukemia will discontinue enrollment.

Frontline Acalabrutinib/BR Regimen Improves PFS in MCL

Acalabrutinib plus bendamustine and rituximab led to a 27% reduction in the risk of disease progression or death in the frontline setting for older patients with mantle cell lymphoma.

DeZern Discusses Incorporating Current Options for Managing Anemia in Low-Risk MDS

During a Case-Based Roundtable® event, Amy DeZern, MD, MHS, discussed dosing approaches for managing anemia with luspatercept in patients with low-risk myelodysplastic syndrome.

FDA Approves Improved Denileukin Diftitox in Cutaneous T-Cell Lymphoma

Following voluntary withdrawal in 2014, denileukin diftitox is now available again for the treatment of patients with cutaneous T-cell lymphoma who have received at least 1 prior systemic therapy.

FDA Rejects Iomab-B BLA Application, Requests New Randomized Study

The FDA has rejected the SIERRA study data for Iomab-B’s biologics license application filing due to insufficient evidence of overall survival improvement.

2 Commerce Drive Cranbury, NJ 08512

609-716-7777

Faculty and Disclosures

Medscape, LLC requires every individual in a position to control educational content to disclose all financial relationships with ineligible companies that have occurred within the past 24 months. Ineligible companies are organizations whose primary business is producing, marketing, selling, re-selling, or distributing healthcare products used by or on patients.

All relevant financial relationships for anyone with the ability to control the content of this educational activity are listed below and have been mitigated according to Medscape policies. Others involved in the planning of this activity have no relevant financial relationships.

Courtney D. DiNardo, MD, MSCE

Associate Professor Department of Leukemia Division of Cancer Medicine University of Texas MD Anderson Cancer Center Houston, Texas

Disclosure: Courtney D. DiNardo, MD, MSCE, has the following relevant financial relationships: Served as an advisor or consultant for: AbbVie/Genentech; Agios/Servier; Celgene/Bristol Myers Squibb; Cleave; GlaxoSmithKline; ImmuneOnc; Novartis; Takeda Received grants for clinical research from: AbbVie/Genentech; Agios/Servier; Astex; Celgene/Bristol Myers Squibb; Cleave; Daiichi Sankyo; Foghorn; ImmuneOnc Owns stock, stock options, or bonds from: Notable Labs

Heather P. Friedman, MPH

Medical Education Director, Medscape, LLC

Disclosure: Heather P. Friedman, MPH, has no relevant financial relationships.

Compliance Reviewer

Amanda jett, pharmd, bcacp.

Associate Director, Accreditation and Compliance, Medscape, LLC

Disclosure: Amanda Jett, PharmD, BCACP, has no relevant financial relationships.

Peer Reviewer

This activity has been peer reviewed and the reviewer has no relevant financial relationships.

CME / ABIM MOC

Case challenges in acute myeloid leukemia.

• Authors: Courtney D. DiNardo, MD, MSCE

CME / ABIM MOC Released: 4/8/2022

• THIS ACTIVITY HAS EXPIRED FOR CREDIT

Valid for credit through: 4/8/2023 , 11:59 PM EST

Target Audience and Goal Statement

This activity is intended for hematologic oncologists in both community and academic settings.

The goal of this activity is that the learner will be better able to treat patients with AML, taking into consideration the most current treatment options and side effects, and to personalize therapy for patients with AML.

Upon completion of this activity, participants will:

• Clinical trial data of AML therapies
• Individualizing therapy for patients with AML based on molecular profile
• Selecting treatment for patients with AML based on their fitness level
• Personalize treatment for patients with AML

Disclosures

Accreditation statements.

For Physicians

Medscape, LLC designates this enduring material for a maximum of 0.50 AMA PRA Category 1 Credit(s)™ . Physicians should claim only the credit commensurate with the extent of their participation in the activity. Successful completion of this CME activity, which includes participation in the evaluation component, enables the participant to earn up to 0.50 MOC points in the American Board of Internal Medicine's (ABIM) Maintenance of Certification (MOC) program. Participants will earn MOC points equivalent to the amount of CME credits claimed for the activity. It is the CME activity provider's responsibility to submit participant completion information to ACCME for the purpose of granting ABIM MOC credit. Aggregate participant data will be shared with commercial supporters of this activity.

Contact This Provider

For questions regarding the content of this activity, contact the accredited provider for this CME/CE activity noted above. For technical assistance, contact [email protected]

Instructions for Participation and Credit

There are no fees for participating in or receiving credit for this online educational activity. For information on applicability and acceptance of continuing education credit for this activity, please consult your professional licensing board. This activity is designed to be completed within the time designated on the title page; physicians should claim only those credits that reflect the time actually spent in the activity. To successfully earn credit, participants must complete the activity online during the valid credit period that is noted on the title page. To receive AMA PRA Category 1 Credit™ , you must receive a minimum score of 70% on the post-test. Follow these steps to earn CME/CE credit*:

• Read about the target audience, learning objectives, and author disclosures.
• Study the educational content online or print it out.
• Online, choose the best answer to each test question. To receive a certificate, you must receive a passing score as designated at the top of the test. We encourage you to complete the Activity Evaluation to provide feedback for future programming.

You may now view or print the certificate from your CME/CE Tracker. You may print the certificate, but you cannot alter it. Credits will be tallied in your CME/CE Tracker and archived for 6 years; at any point within this time period, you can print out the tally as well as the certificates from the CME/CE Tracker. *The credit that you receive is based on your user profile.

Assessment Survey

Before you begin, please take this brief survey.

• Activity Transcript

The educational activity presented above may involve simulated, case-based scenarios. The patients depicted in these scenarios are fictitious and no association with any actual patient, whether living or deceased, is intended or should be inferred. The material presented here does not necessarily reflect the views of Medscape, LLC, or any individuals or commercial entities that support companies that support educational programming on medscape.org. These materials may include discussion of therapeutic products that have not been approved by the US Food and Drug Administration, off-label uses of approved products, or data that were presented in abstract form. These data should be considered preliminary until published in a peer-reviewed journal. Readers should verify all information and data before treating patients or employing any therapies described in this or any educational activity. A qualified healthcare professional should be consulted before using any therapeutic product discussed herein.

Medscape Education © 2022  Medscape, LLC

A case study about Acute Myeloid Leukemia

• University of Charmo

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations
• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 26 August 2024

A multidimensional analysis reveals distinct immune phenotypes and the composition of immune aggregates in pediatric acute myeloid leukemia

• Joost B. Koedijk   ORCID: orcid.org/0000-0001-6463-3307 1 , 2 ,
• Inge van der Werf 1 , 3 , 4 ,
• Livius Penter   ORCID: orcid.org/0000-0002-9060-0207 5 , 6 , 7 , 8 , 9 , 10 , 11 ,
• Marijn A. Vermeulen 1 ,
• Farnaz Barneh 1 ,
• Alicia Perzolli   ORCID: orcid.org/0000-0003-1302-0943 1 , 2 ,
• Joyce I. Meesters-Ensing   ORCID: orcid.org/0000-0003-0780-9227 1 ,
• Dennis S. Metselaar 1 , 12 , 13 ,
• Thanasis Margaritis 1 ,
• Marta Fiocco 1 , 14 , 15 ,
• Hester A. de Groot-Kruseman 1 ,
• Rubina Moeniralam 1 ,
• Kristina Bang Christensen 16 ,
• Billie Porter 17 ,
• Kathleen Pfaff 17 ,
• Jacqueline S. Garcia   ORCID: orcid.org/0000-0003-2118-6302 5 , 7 ,
• Scott J. Rodig 18 ,
• Catherine J. Wu   ORCID: orcid.org/0000-0002-3348-5054 5 , 6 , 7 , 19 ,
• Henrik Hasle   ORCID: orcid.org/0000-0003-3976-9231 20 ,
• Stefan Nierkens 1 , 21 ,
• Mirjam E. Belderbos   ORCID: orcid.org/0000-0002-6164-2918 1 ,
• C. Michel Zwaan   ORCID: orcid.org/0000-0001-6892-8268 1 , 2   na1 &
• Olaf Heidenreich   ORCID: orcid.org/0000-0001-5404-6483 1 , 22 , 23   na1  

Leukemia ( 2024 ) Cite this article

896 Accesses

9 Altmetric

Metrics details

• Cancer microenvironment
• Tumour immunology

Because of the low mutational burden and consequently, fewer potential neoantigens, children with acute myeloid leukemia (AML) are thought to have a T cell-depleted or ‘cold’ tumor microenvironment and may have a low likelihood of response to T cell-directed immunotherapies. Understanding the composition, phenotype, and spatial organization of T cells and other microenvironmental populations in the pediatric AML bone marrow (BM) is essential for informing future immunotherapeutic trials about targetable immune-evasion mechanisms specific to pediatric AML. Here, we conducted a multidimensional analysis of the tumor immune microenvironment in pediatric AML and non-leukemic controls. We demonstrated that nearly one-third of pediatric AML cases has an immune-infiltrated BM, which is characterized by a decreased ratio of M2- to M1-like macrophages. Furthermore, we detected the presence of large T cell networks, both with and without colocalizing B cells, in the BM and dissected the cellular composition of T- and B cell-rich aggregates using spatial transcriptomics. These analyses revealed that these aggregates are hotspots of CD8 + T cells, memory B cells, plasma cells and/or plasmablasts, and M1-like macrophages. Collectively, our study provides a multidimensional characterization of the BM immune microenvironment in pediatric AML and indicates starting points for further investigations into immunomodulatory mechanisms in this devastating disease.

Similar content being viewed by others

High-dimensional and single-cell transcriptome analysis of the tumor microenvironment in angioimmunoblastic T cell lymphoma (AITL)

Pediatric T-cell acute lymphoblastic leukemia blast signature and MRD associated immune environment changes defined by single cell transcriptomics analysis

T-cell dysfunction in the glioblastoma microenvironment is mediated by myeloid cells releasing interleukin-10

Introduction.

The abundance and phenotype of intratumoral T cells are crucial for the effectiveness of T cell-directed immunotherapies such as immune checkpoint inhibitors (ICIs) and bispecific T cell-engagers [ 1 , 2 , 3 , 4 ]. Because of the low mutational burden and consequently, fewer potential neoantigens, children with acute myeloid leukemia (AML) are thought to have a T cell-depleted or ‘cold’ tumor microenvironment and therefore may have a low likelihood of response to ICIs and bispecific T cell-engagers [ 5 , 6 , 7 , 8 ]. Although clinical trials in adult AML patients treated with ICIs and bispecific T cell-engagers, whether as mono- or combination therapy, have largely been unsuccessful, a small subset of patients showed exceptional responses [ 4 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. This suggests that there may be specific subgroups that could benefit from these T cell-directed immunotherapies. To pave the way for successful ICI- and bispecific T cell-engager immunotherapies in both adult and pediatric AML, a better understanding of the heterogenous landscape of bone marrow (BM)-infiltrating T cells and the surrounding tumor microenvironment is needed. While recent studies have provided insights on this matter in adult AML, such as the identification of a relatively low presence of exhausted CD8 + T cells in the tumor microenvironment compared to cancers that respond well to ICIs [ 16 , 17 ], there is a paucity of data on the BM immune microenvironment in pediatric AML [ 11 , 18 , 19 , 20 ]. In addition to the need for a quantitative and qualitative assessment of the pediatric AML BM immune microenvironment, emerging research in solid cancers has shown that the spatial organization of the immune response is highly relevant for ICI efficacy [ 21 ]. For instance, ‘excluded’ tumors, where the immune response cannot invade the tumor bed, show poor responses to ICI [ 22 ]. Furthermore, the presence of intratumoral immune aggregates, such as tertiary lymphoid structures (TLSs), is associated with improved ICI responses in many solid cancers [ 23 , 24 , 25 , 26 , 27 , 28 ]. Despite its potential importance, the spatial organization of the immune response in hematological cancers remains understudied. Therefore, we generated a multidimensional view of the tumor immune microenvironment in treatment-naïve de novo pediatric AML, to inform future immunotherapeutic trials about potentially targetable immune-evasion mechanisms specific to this patient population. We identified distinct BM immune phenotypes and dissected the composition of immune aggregates in the BM of pediatric AML, which encourage further investigations into immunomodulatory mechanisms in this devastating disease.

Materials/subjects and methods

Ethical regulation.

This study complies with all relevant ethical regulations and was approved by the Institutional Review Boards of the Princess Máxima Center for Pediatric Oncology (PMCLAB2021.207 & PMCLAB2021.238), the Scientific Committee of the Dutch Nationwide Pathology Databank (PALGA: lzv2021-82) [ 29 ] and at participating sites of the ETCTN/CTEP 10026 study [ 12 , 13 ]. All patients treated at the Princess Máxima Center, Aarhus University Hospital, and Dana-Farber Cancer Institute provided written consent for banking and research use of these specimens, according to the Declaration of Helsinki.

Human patient samples

Formalin-fixed and paraffin-embedded (FFPE) BM biopsies taken from the crista of children with treatment-naïve de novo AML and non-leukemic controls (Supplementary Methods) were obtained from the Princess Máxima Center Biobank ( n  = 15), biobanks of 10 other Dutch hospitals ( n  = 28; mediated by the Dutch National Tissue Portal) and the biobank of Aarhus University Hospital ( n  = 29). BM biopsies of adult AML cases treated on the ETCTN/CTEP 10026 study ( n  = 6) were collected at Dana-Farber Cancer Institute [ 13 , 17 ]. Details on other pediatric AML datasets, immunohistochemistry/immunofluorescence, digital image analysis, immune-related gene expression profiling, and spatial transcriptomics are provided in the Supplementary Methods.

Statistical analyses

Statistical analyses were performed with GraphPad Prism V.9.3.0 (GraphPad Software, LA Jolla, CA, USA). Details on the various statistical tests used are provided in the Supplementary Methods. For spatial transcriptomics data, P  < 0.01 was considered statistically significant to correct for measuring multiple regions from the same biopsy. For all other comparisons, P  < 0.05 was considered statistically significant.

A subset of pediatric AML patients has high T cell infiltration in the bone marrow

To determine whether the BM microenvironment in pediatric AML is characterized by high (‘hot’) or low (‘cold’) T cell infiltration, we performed immunohistochemistry (IHC) with antibodies against CD3 and CD8 on 82 FFPE BM biopsies from pediatric patients with treatment-naïve de novo AML ( n  = 72), and age- and sex-matched non-leukemic individuals ( n  = 10; Fig.  1A ; representative images shown in Fig.  1B, C ; collectively referred to as ‘primary study cohort’). Patient characteristics are depicted in Table S 1 . We found a trend towards a decreased abundance of the number of T cells and a significant decrease in CD8 + T cells in pediatric AML cases in comparison to non-leukemic controls ( P  = 0.11 and P  = 0.011, respectively; Fig.  1D, E ), similar to other observations in adult and pediatric AML [ 20 , 30 , 31 ]. The T- and CD8 + T cell infiltration ranged from 55 to 9832 cells/mm 2 between individual pediatric AML cases, with a subset showing high T- and CD8 + T cell infiltration in the BM (above median of non-leukemic controls; n  = 22 and n  = 18, respectively; Fig.  1D, E ).

A Schematic overview of the study population, used techniques, and the digital image analysis pipeline. AML cases are categorized in immune-infiltrated (red) and immune-depleted (blue) groups according to their T cell infiltration levels (above or below median of non-leukemic controls). Representative bone marrow biopsy images from a treatment-naïve pediatric AML case ( B ) and a non-leukemic control ( C ) showing H&E staining, CD3 + T cells, and CD8 + T cells. White lobules indicate adipocytes. Comparison of the normalized abundance of CD3 + T cells ( D ) and CD8 + T cells ( E ) in the bone marrow between pediatric AML cases and non-leukemic controls using the Mann–Whitney test. The dashed lines indicate the median CD3 + ( D ) and CD8 + ( E ) T cell abundance in non-leukemic controls, respectively. Normalized abundance of CD3 + ( F ) and CD8 + T cells ( G ) per cytogenetic subgroup. ‘Normal’ indicates normal karyotype, while ‘Others’ is a merge of cytogenetic abnormalities different from the five defined cytogenetic subgroups. See Table S 1 . ‘Complex’ indicates cases with complex karyotype AML (≥3 chromosomal abnormalities). The dashed lines shown in Fig. 1D, E are also shown in Fig. 1F-G. H Schematic overview of the TARGET-AML cohort, the additional non-leukemic control group, the performed analysis (CIBERSORTx), and the subsequent categorization of patients into the immune-infiltrated or immune-depleted groups (based on median T- and CD8 + T cell abundance in non-leukemic controls). Estimated absolute (ABS) abundance of T- ( I ) and CD8 + T ( J ) cells in the bone marrow of treatment-naïve pediatric AML cases in the TARGET-AML cohort. The dashed lines shown in Fig. 1I, J indicate the estimated median bone marrow T- and CD8 + T cell abundance in four non-leukemic controls.

We next explored whether this heterogeneity in T cell infiltration reflected inherent differences in disease biology. Notably, eleven out of nineteen patients with KMT2A -rearranged AML (58%) and five out of eight patients with complex karyotype AML (63%) had high BM T cell infiltration (Fig.  1F ). However, substantial heterogeneity in T cell infiltration was also present within both the KMT2A -rearranged and complex karyotype AML groups (Fig.  1F, G ). Thus, both high and low levels of BM T cell infiltration were noted among KMT2A -rearranged and complex karyotype AML patients. Most cases in the other cytogenetic subgroups had low overall T- and CD8 + T cell infiltration (Fig.  1F, G ). Specifically, twelve out of thirteen cases (92%) with normal karyotype AML had low total T- and CD8 + T cell infiltration. Among these thirteen cases, nine (69%) had both low T cell infiltration and a FLT3 -ITD and/or NPM1 mutation (Table S 1 ), which have been associated with low T- and NK cell infiltration in adult AML [ 32 ]. Using diagnostic flow cytometry data of an independent cohort of 20 pediatric cases with normal karyotype AML ( n  = 12 wildtype, n  = 8 FLT3 -ITD and/or NPM1 mutation), we confirmed this association ( P  = 0.013; Fig. S 1A ).

Given the relatively small number of samples in some cytogenetic subgroups, we also reanalyzed a large bulk RNA-sequencing dataset of treatment-naïve pediatric AML BM aspirates (TARGET-AML cohort, n  = 159 patients; Fig.  1H ) [ 33 ]. Using the immune deconvolution tool CIBERSORTx [ 17 , 34 , 35 ], we estimated the absolute abundance scores of T- and CD8 + T cells, which ranged from 0.15 to 5.15 and 0 to 1.43, respectively (Fig.  1H-J ). We categorized patients into overall T- and CD8 + T cell-high and -low groups based on the median estimated BM T- and CD8 + T cell abundance in four non-leukemic pediatric controls (BM bulk RNA-sequencing data [ 36 ]). The TARGET-AML cohort showed substantial heterogeneity in overall T- and CD8 + T cell levels across all cytogenetic subgroups, with only nine out of 27 KMT2A -rearranged AML cases (33%) having high T cell infiltration (Fig.  1I, J ). Consequently, we hypothesized that specific KMT2A -rearrangements may be associated with different levels of T cell infiltration in the BM. We therefore compared the levels of T- and CD8 + T cells among cases with KMT2A -rearrangements with at least three samples per group (TARGET-AML cohort). This analysis suggested a lower abundance of CD8 + T cells in KMT2A :: ELL AML compared to KMT2A :: MLLT3 AML ( P  = 0.044; Fig. S 1B, C ), although the small sample size precluded firm conclusions. Among cases with complex karyotype AML, thirteen out of 24 cases (54%) had high T cell infiltration (Fig.  1I ). However, we were not able to identify any commonalities between those with high or low T cell infiltration. Other clinical factors including the abundance of leukemic blasts and AML differentiation stage showed no or only marginal correlation with T- and CD8 + T cell infiltration (Fig. S 1D–K ).

To further characterize the immune profiles of pediatric AML cases with a high abundance of T cells in the BM, we categorized AML cases in the primary study cohort into two groups using the non-leukemic controls’ median CD3 + T cell abundance as the cut-off value (1133 cells/mm 2 ; Fig.  2A ). Focused IHC analyses of CD20 expression showed that patients in the CD3 + T cell-high group also had a significantly higher abundance of CD20 + B cells in comparison to the CD3 + T cell-low group ( P  < 0.001; Fig.  2B ). Accordingly, we termed the two groups ‘immune-infiltrated’ and ‘immune-depleted’ ( n  = 22 and n  = 50, respectively). We subsequently investigated whether these groups demonstrated differences in clinical outcomes upon standard chemotherapy treatment (Supplementary Methods), as seen in many other cancer types [ 1 ]. In our cohort, we did not detect differences in overall survival (OS) between the immune-infiltrated and immune-depleted group (Fig. S 2A ). To increase statistical power, we repeated the analysis in the TARGET-AML cohort. Again, we did not detect differences in OS between the immune-infiltrated (CD3 + T cell-high, n  = 66) and immune-depleted (CD3 + T cell-low, n  = 93) groups (Fig. S 2B ), together suggesting that the extent of T cell infiltration in the BM at diagnosis is not critical for patient survival in pediatric AML cases treated with standard chemotherapy regimens. Taken together, we identified wide heterogeneity in T cell infiltration in the BM of pediatric AML with nearly one-third of cases (31%) showing notably high T cell infiltration. In addition, our findings suggest that specific genetic alterations may be linked to the extent of T cell infiltration in the BM of pediatric AML, although the exact underlying mechanisms remain to be unraveled [ 37 , 38 ].

A Schematic overview of the study population, used techniques, and analyses performed in Fig. 2B–D. B Comparison of the normalized abundance of CD20 + B cells in the bone marrow of pediatric AML (CD20 stains available for 69 cases) with CD3 + T cell levels above or below the median of non-leukemic controls (later referred to as immune-infiltrated and immune-depleted, respectively; Mann–Whitney test). C Volcano plot of differentially expressed genes between immune-infiltrated ( n  = 6) and immune-depleted ( n  = 17) pediatric AML bone marrow biopsies, identified using DEseq2 with an FDR cut-off of 0.05 and minimum fold change (FC) of 2. D Single-sample gene set enrichment analysis of differentially expressed genes between immune-infiltrated and immune-depleted cases using the GO Biological Processes gene set with an FDR cut-off of 0.05. WikiPathways-related results are shown in Table S 2 . E Schematic overview of the study populations for which gene-expression data was available (primary study cohort and TARGET-AML cohort), and the associated analysis. F Correlation plot of the negative correlation between the M2-predominance score and the normalized number of CD3 + T cells, calculated using Spearman correlation. G Comparison of the M2-predominance score between immune-infiltrated ( n  = 6) and immune-depleted ( n  = 17) cases using the Mann–Whitney test. H Correlation plot of the negative correlation between the M2-predominance score and the estimated abundance of T cells in the bone marrow of TARGET-AML cases, calculated using Spearman correlation. I Comparison of the M2-predominance score between immune-infiltrated ( n  = 80) and immune-depleted ( n  = 79) cases using the Mann–Whitney test.

The ratio of M2- to M1-like macrophages is linked to the extent of T cell infiltration in the pediatric AML bone marrow

To elucidate mechanisms driving high- and low immune infiltration in the BM of pediatric AML, we examined the expression of immune-related genes in a cytogenetically representative subset of immune-infiltrated ( n  = 6) and immune-depleted cases ( n  = 17) using the NanoString PanCancer IO 360 TM panel (Fig.  2A ). Using differential gene expression analysis, we identified genes related to T- and/or NK cells to be significantly upregulated in immune-infiltrated compared to immune-depleted pediatric AML (Fig.  2C ), confirming our categorization of cases into these two groups. Regarding factors that either promote or restrict T cell infiltration, we found that immune-infiltrated cases demonstrated significantly higher expression of genes related to T cell-attracting chemokines ( CXCL9 , CXCL10 ), and their corresponding receptor ( CXCR3 ; Fig.  2C ). In line with this, pathway analysis using the GO biological processes and WikiPathways gene sets indicated that immune-infiltrated cases were enriched in T cell migration and chemokine signaling, suggesting that immune-depleted cases lack signals that attract T cells to the BM (Fig.  2D ; full list of pathways in Table S 2 ). In solid cancers, macrophages have been described as key players in attracting T cells [ 39 ]. Specifically, pro-inflammatory M1-like macrophages are known to be a primary source of T cell-attracting chemokines, while anti-inflammatory M2-like macrophages restrict T cell infiltration into the tumor [ 39 , 40 ]. Previously, a gene expression-based score that reflects the ratio between M2- and M1-like macrophages (a score > 0 indicates M2-predominance, while a score < 0 indicates M1-predominance; collectively termed M2-predominance score) has been developed for five cancer types including AML [ 41 ]. We applied this score to our cohort and found that the M2-predominance score was negatively correlated with the BM T- and CD8 + T cell abundance ( r  = –0.75 [95% CI: –0.89 to –0.48], P  < 0.001; r  = –0.50 [95% CI: –0.76 to –0.12], P  = 0.014, respectively; Fig.  2E, F and Fig. S 2C ). Concordantly, we identified a significantly decreased M2-predominance score in immune-infiltrated compared to immune-depleted cases ( P  = 0.001; Fig.  2G ). Likewise, in the TARGET-AML cohort, we detected a (subtle) negative correlation between the M2-predominance score and the T- and CD8 + T cell abundance in the BM ( r  = –0.25 [95% CI: –0.39 to –0.10], P  = 0.002; r  = –0.20 [95% CI: –0.35 to –0.05], P  = 0.011, respectively; Fig.  2H and Fig. S 2D ), and a significantly decreased M2-predominance score in the immune-infiltrated compared to the immune-depleted group ( P  < 0.001.; Fig.  2I ). Altogether, our data suggest that the M2-:M1-like macrophage ratio is linked to the extent of T cell infiltration in the BM of pediatric AML, in line with data from (preclinical) studies in other cancers [ 39 , 41 ].

T cells cluster into aggregates in the bone marrow of pediatric AML

Several studies in solid cancers have identified the presence of immune aggregates in the tumor microenvironment, such as T- and B cell-rich structures resembling secondary lymphoid organs (TLSs) and T cell-rich structures lacking B cells [ 23 , 24 , 25 , 42 , 43 ]. These aggregates may represent sites of priming or re-activation of anti-tumor immune responses and have been associated with better responses to ICIs in many cancer types [ 23 , 24 , 25 , 26 , 27 , 28 , 44 , 45 ]. As TLSs and other immune aggregates often develop at sites of chronic inflammation [ 23 , 24 , 25 ], we next asked whether similar structures were present in the BM of immune-infiltrated pediatric AML cases. Accordingly, we first investigated the number of T cell networks in pediatric immune-infiltrated ( n  = 22), immune-depleted ( n  = 50), and non-leukemic BM biopsies ( n  = 10). In this analysis, T cell networks were defined as at least ten directly interacting T cells (≤10 μm between adjacent nuclei), to avoid classifying randomly dispersed T cells as networks (Fig.  3A ) [ 43 ]. We found that T cell networks were present in both AML groups and controls, although they were significantly more frequent in the BM of immune-infiltrated compared to immune-depleted cases ( P  < 0.001; Fig.  3B and Table S 1 ; representative image of a T cell network shown in Fig.  3C ). Moreover, controls had significantly more T cell networks in comparison to immune-depleted cases ( P  = 0.027; Fig.  3B ; representative images of immune-depleted biopsies shown in Fig. S 3A, B ).

A Illustration of the identification of directly interacting T cells (above) and T cell networks (below) using Delaunay Triangulation. B Comparison of the normalized abundance of T cell networks between immune-infiltrated ( n  = 22), immune-depleted ( n  = 50), and non-leukemic control biopsies ( n  = 10; Kruskal-Wallis followed by Dunn’s multiple comparisons test). C Representative image of a T cell network in a treatment-naïve pediatric AML patient. D Schematic of the definition of a large T cell network. E Comparison of the number of T cell networks with at least 100 T cells/network between immune-infiltrated ( n  = 22), immune-depleted ( n  = 50), and non-leukemic control biopsies ( n  = 10; Kruskal-Wallis followed by Dunn’s multiple comparisons test). F Representative image of large T cell networks in a treatment-naïve pediatric AML patient. G Schematic of the definition of a lymphoid aggregate. H , I Representative images of lymphoid aggregates.

In addition, we identified large T cell networks, defined as at least 100 directly interacting T cells (Fig.  3D ), to be more abundant in the BM of immune-infiltrated compared to immune-depleted cases ( P  < 0.001; Fig.  3E ; representative image shown in Fig.  3F ). KMT2A -rearranged (9/17 cases; 53%) and complex karyotype AML (3/17 cases; 18%) were the most common cytogenetic subgroups among cases with large T cell networks ( n  = 17 in total; Table S 1 ). These large networks often colocalized with a dense network of B cells in immune-infiltrated cases (termed ‘lymphoid aggregates’; LAs; Fig.  3G ; 9/15 cases; 60%); representative images shown in Fig.  3H–I , Fig. S 3C, D ; 0/2 immune-depleted cases. However, the LAs identified in pediatric AML patients were not organized in a fashion seen in mature TLSs with an inner zone of B cells surrounded by T cells [ 23 , 24 , 25 ]. Instead, T- and B cells appeared to be mixed throughout the aggregate, as seen in immature TLSs and other immune aggregates (representative images shown in Fig.  3H–I and Fig. S 3C, D ) [ 25 , 42 ]. In the BM of non-leukemic control biopsies, we did not identify LAs or large T cell-dominant networks with no or sparse B cells (representative image shown in Fig. S 3E ). Taken together, these data show that large networks of T cells, both with and without colocalizing B cells, are frequent in the BM of treatment-naïve immune-infiltrated pediatric AML cases.

Given that TLSs and other immune aggregates may function as sites of intratumoral immune priming that can lead to successful anti-tumor immunity in solid cancers [ 23 , 24 , 25 , 26 , 27 , 28 , 44 , 45 , 46 , 47 ], they may also serve as markers of leukemia-specific immune responses in hematological malignancies. Consequently, we performed pilot work to explore whether responses to immune checkpoint inhibitors were associated with the presence of LAs and/or large T cell-dominant networks in the AML BM. As data from pediatric AML cases treated with such therapies were not available, we applied multiplex immunofluorescence to pre- and post-treatment BM biopsies of transplant-naïve and post-transplant adult AML cases (three responders and three non-responders) treated with both ipilimumab and decitabine in the context of a clinical trial (NCT02890329 [ 13 , 17 ]; Fig. S 4A ). Accordingly, BM biopsies collected at baseline, time of best response, and end of treatment were stained with antibodies against CD3, CD20, and CD34 (n = 17 in total; representative images of LAs and large T cell-dominant networks are shown in Fig. S 4B–D ; patient characteristics are depicted in Table S 3 ). LAs were only observed in responders (response definitions in Supplementary Methods), although their time of appearance differed (Fig. S 4E–J ). Large T cell-dominant networks were present in two responders at baseline and at time of best response (AML1002 + AML1006; Fig. S 4E,G ). Among non-responders, one patient had large T cell-dominant networks at baseline (AML1010; Fig. S 4I ). Like pediatric AML, T- and B cells in lymphoid aggregates were not organized in distinct zones (Fig. S 4B, C ). Thus, in this exploratory analysis, we found that large T cell-dominant networks were present both at baseline and at time of best response in two out of three responders, whereas LAs were present in all three responders but at different time points. Although the patient heterogeneity and small cohort size preclude conclusions regarding the predictive utility of LAs and T cell-dominant networks, our preliminary findings encourage future investigations into the association between immune aggregates and ICI response in both adult and pediatric AML, as done in solid cancers [ 26 , 27 , 28 , 44 ].

Spatial transcriptomics unravels the composition of lymphoid aggregates in the bone marrow of pediatric AML

The identification of LAs prompted us to explore whether the profiles of these aggregates shared similarities with TLSs in solid cancers (i.e. several T cell subsets, germinal center B cells, plasma cells, dendritic cells) [ 23 , 24 , 25 ]. To accurately identify TLSs, several transcriptomic signatures have been proposed [ 25 ]: a 12-gene chemokine signature [ 48 ] (‘12chem’; reflective of TLSs independent of their maturation stage), an 8-gene follicular helper T cell signature [ 49 ] (‘Tfh’; reflective of mature TLSs), and a 29-gene TLS imprint signature [ 27 ] (reflective of mature TLSs). We aimed to examine the expression of these signatures in both LA and BM regions without such aggregates. Towards this aim, we performed spatial transcriptomics using the GeoMx spatial transcriptomics platform [ 50 ] on eight BM biopsies: four immune-infiltrated AML biopsies with LAs, and four reference biopsies (two immune-infiltrated AML biopsies without LAs and two non-leukemic control biopsies; patient characteristics are depicted in Table S 1 ). Regions of interest (ROIs) included areas with LAs, ‘mixed’ areas containing leukemic cells and various other populations, and ‘control’ areas in the non-leukemic BM (region types are illustrated in Fig.  4A–C ; representative images of ROI selection are shown in Fig. S 5A ). In total, we successfully sequenced 143 regions with an average of 335 cells per region (range: 126 to 826 cells): 35 LA regions, 92 mixed regions, and 16 control regions. Our analysis revealed two main clusters of regions in the UMAP: cluster 1 contained both regions with LAs and mixed regions from biopsies with LAs, while cluster 2 consisted of mixed regions from biopsies without these aggregates, and control regions (Fig.  4D ). Consequently, we divided mixed regions in two distinct groups: areas from biopsies with LAs (MIXED1, N  = 48), and those from biopsies without such aggregates (MIXED2, N  = 44). All three TLS-specific gene signatures were significantly enriched in LA regions in comparison to both mixed- and control regions (Fig.  4E, F and S 5B ). Thus, while the identified LAs lacked the typical organization of mature TLS, we did identify enrichment of the Tfh- and TLS imprint signatures representative of mature TLSs.

Overview of the different types of regions profiled using spatial transcriptomics in immune-infiltrated biopsies with lymphoid aggregates ( A ), immune-infiltrated biopsies without lymphoid aggregates ( B ), and non-leukemic biopsies ( C ). Green cells indicate T cells, blue cells indicate B cells, and pink/purple cells indicate AML blasts or normal myeloid cells. These examples do not necessarily reflect the actual abundance of these subsets. LA: lymphoid aggregate. D UMAP of transcriptomic profiles of various region types, organized into two separate clusters. Comparison of the expression of the ‘Tfh’ ( E ), and ‘TLS imprint’ ( F ) signatures across different region types (Kruskal-Wallis followed by Dunn’s multiple comparisons test). G – L Deconvoluted abundance of various cell subsets and the M2-predominance score compared across different region types (Kruskal-Wallis followed by Dunn’s multiple comparisons test). In case of two p-values, the upper one is associated with the Kruskal-Wallis test, while the lower one reflects the result of Dunn’s multiple comparison test.

To gain insight into differences in immune and stromal cell types between LA and other BM regions, we performed immune deconvolution by integrating spatial transcriptomic data with single-cell (sc) and flow-sorted bulk RNA-sequencing data of microenvironmental cell populations [ 51 ]. This analysis revealed that LA regions had an increased abundance of CD4 + T cells, CD8 + T cells, non-plasma B cells, plasma cells, and macrophages (Fig.  4G–K ). In both LA and MIXED1-regions, the CD4 + :CD8 + T cell ratio was lower compared to MIXED2- and control regions (approximately 1:1 in both vs. 2:1 and 3:1, respectively; Fig. S 5C ), indicating a relatively higher abundance of CD8 + T cells in the former regions. When considering macrophage polarization, we noted a significantly lower M2-predominance score in LA regions compared to both mixed regions, consistent with the negative correlation between M2-predominance and BM T cell infiltration observed above (Figs.  4L , 2F ). Moreover, endothelial cells and fibroblasts were enriched in LA regions compared to MIXED1-regions (Fig. S 5D, E ). Additionally, NK- and dendritic cells were distributed evenly across AML biopsies, while MIXED2 regions had a significantly higher abundance of neutrophils compared to both LA and MIXED1-regions (Fig. S 5F–H ). To confirm our deconvolution results, we performed differential gene expression analysis between LA, mixed, and control regions. In LA regions compared to non-aggregate regions within the same biopsies (MIXED1), we found upregulation of many T- and B cell genes (e.g., CD3D, CD8B, CD79A ), as well as genes associated with TLS formation in solid cancers ( LTB, CCL19) and B cell recruitment (CXCL13 ; Fig. S 6A ). Furthermore, immunoglobulin-related genes ( IGHG1 , IGHG3 , IGHG4 , IGKC ) were significantly enriched in LA compared to MIXED1-regions, consistent with an increased abundance of plasma and/or memory B cells (Fig. S 6A ). Similar results were observed when comparing LA regions with MIXED2- and control regions (Table S 4 ).

Given the combination of a lack of distinct T- and B cell zones and an increased estimated abundance of plasma cells in these LAs, we next sought to perform a more in-depth characterization of the B cells in these regions. To do so, we again applied immune deconvolution by integrating our spatial transcriptomic data with another scRNA-sequencing reference dataset (Fig.  5A ). In this case, we used a comprehensive scRNA-sequencing dataset of pediatric tonsillar B cells [ 52 ], which included naïve B cells, germinal center B cells, plasmablasts, and memory B cells, as a reference. In line with the lack of a follicle-like morphology of LAs, we did not identify germinal center B cells in these regions (Fig.  5B ). Similarly, naïve B cells were absent. The most abundant B cells were memory B cells, while a smaller fraction consisted of plasmablasts, together indicating the presence of differentiated B cell subsets in these aggregates (Fig.  5B ). Since LA regions showed increased expression of the cytotoxicity-related genes GZMA and GZMK compared to neighboring regions (Fig. S 6A ), we also aimed to investigate the cytotoxic capacity of CD8 + T cells in these regions. Accordingly, we performed immune deconvolution of our spatial transcriptomic dataset using a scRNA-sequencing dataset of BM CD8 + T cells from adult AML patients [ 16 ]. This reference dataset included naïve-, memory-, cytotoxic- (CTL), mucosal-associated invariant T- (MAIT), and ‘dysfunctional’ CD8 + T cells. The CD8 + T cells classified as dysfunctional were characterized by increased expression of multiple immune checkpoint receptor (IR) genes, such as PDCD1 , LAG3 , and HAVCR2 , and reduced expression of cytotoxicity-related genes such as GZMB , GNLY , and PRF1 . Notably, deconvolution revealed that these potentially dysfunctional CD8 + T cells were enriched in LA regions compared to mixed- and control regions, at the expense of both naïve- and cytotoxic CD8 + T cells (Fig.  5C, D ). Moreover, memory CD8 + T cells were not identified in any of the biopsies. The increased proportion of potentially dysfunctional CD8 + T cells in LA regions prompted us to investigate whether these T cells were truly dysfunctional (i.e. no cytotoxic potential). Accordingly, we examined the expression of the cytotoxicity marker granzyme B (GZMB) in CD8 + T cells that expressed at least two immune checkpoint receptor markers (IR ++ / +++ ; PD-1, LAG3, and/or TIM-3) in a subset of the LA-rich biopsies that had been characterized using spatial transcriptomics ( n  = 2 biopsies with six LAs in total). Using multiplex immunofluorescence with antibodies against CD3, CD8, PD-1, LAG3, TIM-3, and GZMB, in combination with standard IHC for CD20, we identified that 58% (range: 30–100%) of CD3 + CD8 + IR ++/+++ T cells in LAs expressed GZMB (representative images shown in Fig.  5E ; Fig.  5F ), suggesting that more than half of the potentially dysfunctional CD8 + T cells still had cytotoxic capacity. In addition, since TLS-associated regulatory T cells (Tregs) have been found to attenuate the positive prognostic effect of TLS-associated CD8 + T cells in human non-small cell lung cancer, colorectal cancer, and soft tissue sarcoma [ 44 , 53 , 54 ], we assessed the presence of putative Tregs (CD3 + FOXP3 + ) in LAs in the same subset of biopsies ( n  = 2). In the six examined LAs, we observed very few Tregs (<1%), suggesting that Tregs are hardly present in LAs in pediatric AML (representative image shown in Fig. S 6B ; Fig. S 6C ). Taken together, our integrative spatial analyses demonstrate that LAs in the BM of pediatric AML are specifically enriched for CD8 + T cells, memory B cells, plasma cells and/or plasmablasts, and M1-like macrophages. Despite the lack of germinal center B cells and separate T- and B cell zones, the presence of memory B cells, plasma cells and/or plasmablasts, and immunoglobulin gene expression suggests that LAs in pediatric AML are sites of B cell differentiation. Furthermore, the presence of CD8 + T cells expressing multiple immune checkpoint markers, yet not fully exhausted, within LAs, encourages future investigations into the potential of leveraging these LA-associated T cells for immunotherapeutic efficacy.

A Schematic overview of the analysis approach applied to the spatial transcriptomics dataset and the subsequent multiplex immunofluorescence (IF). scRNA-seq: single-cell RNA-sequencing. B Proportions of memory B cells, plasmablasts, and unassigned B cells in lymphoid aggregates (LA). C Proportions of naïve-, cytotoxic (CTL), mucosal-associated invariant T (MAIT)-, and potentially dysfunctional CD8 + T cells in lymphoid aggregate, mixed, and control regions. D Comparison of the deconvoluted proportions of potentially dysfunctional CD8 + T cells in lymphoid aggregate, mixed, and control regions (Kruskal-Wallis followed by Dunn’s multiple comparisons test). In case of multiple p -values, the upper one is associated with the Kruskal-Wallis test, while the lower ones reflects the result of Dunn’s multiple comparison test. E Representative image of the multiplex immunofluorescence analysis of a lymphoid aggregate. The names below each image indicate which antibodies are shown. The green boxes on the lower row are zoomed in on the part of the biopsy in the green box in the upper left image. F The proportion of CD3 + CD8 + T cells that expressed two or three inhibitor receptors (IR ++ / +++ ) positive (or not) for granzyme B (GZMB).

In this study, we performed a multidimensional assessment of the tumor immune microenvironment in pediatric AML. We demonstrated that nearly one-third of pediatric AML cases has an immune-infiltrated BM, which is characterized by a decreased M2-:M1-like macrophage ratio. Furthermore, we detected the presence of large T cell networks, both with and without colocalizing B cells, in the BM and dissected the cellular composition of T- and B cell-rich aggregates using spatial transcriptomics. These analyses revealed that these LAs are hotspots of CD8 + T cells, memory B cells, plasma cells and/or plasmablasts, and M1-like macrophages. Collectively, our study provides a multidimensional characterization of the BM immune microenvironment in pediatric AML and indicates starting points for further investigations into immunomodulatory mechanisms in this devastating disease.

Understanding the prevalence of distinct immune phenotypes and associated targetable immune-evasion mechanisms in the BM of pediatric AML is key for better prospective selection of patients in future immunotherapy trials [ 32 , 44 ]. Although we identified a subset with high abundance of T cells, many pediatric AML cases had an immune-depleted BM microenvironment. We observed that FLT3 -ITD and/or NPM1 mutations were associated with low T cell infiltration in the BM of pediatric AML, consistent with data in adult AML [ 32 ]. Although we did not investigate the direct consequences of these molecular alterations, such as alterations in the expression of immunomodulatory surface molecules [ 37 , 38 ], we identified M2-like macrophage predominance in immune-depleted cases. The observed negative correlation between the M2-:M1-like macrophage ratio and the extent of T cell infiltration is consistent with data from (preclinical) studies in other cancers, suggesting a role for M2-like macrophages (or a lack of M1-like macrophages) in limiting T cell infiltration to the leukemic BM [ 39 , 40 ]. Consequently, targeting M2-like macrophages (e.g., using checkpoint blockade) to overcome their immunosuppressive functions could be an attractive therapeutic strategy for immune-depleted AML [ 55 , 56 ].

In addition, our integrative spatial analyses showed that T cells in the BM can be organized into multicellular aggregates. Although T- and B cell-rich LAs did not contain distinct T- and B cell zones and germinal center B cells typically seen in mature TLSs in solid cancers, the localized enrichment of memory B cells, plasma cells and/or plasmablasts, and immunoglobulin gene expression suggests that these aggregates are sites of B cell maturation. Moreover, the 1:1 ratio of CD4 + and CD8 + T cells observed in LAs has also been associated with mature TLSs [ 57 ]. Notably, CD8 + T cells in LAs were found to be in different cell states, including a subset that expressed multiple immune checkpoint markers. GZMB-expression was identified in more than half of these cells, suggesting that these CD8 + T cells are not fully exhausted and may be susceptible to ICI therapy [ 58 ]. In addition to T- and B cell-rich immune aggregates, we detected large T cell networks with sparse B cells. Since these networks have also been associated with ICI response in solid cancers, further investigations into the different types of immune aggregates in AML and their relevance for immunotherapy response are needed [ 42 , 45 , 59 , 60 ]. For instance, do large T cell-dominant networks relate to LAs, and do they (differentially) associate with ICI efficacy in AML? Since TLS are considered to be sites where tumor-specific T- and B cell immune responses may be generated [ 23 , 24 , 25 ], future research also needs to investigate whether immune aggregates in AML are tumor-directed.

Altogether, our analyses deepen the understanding of the BM immune microenvironment in AML and provides an impetus to explore how intratumoral immune aggregates could be exploited for improving immunotherapy outcomes. Further, our work provides a framework for leveraging spatial transcriptomics to interrogate the spatial organization of the leukemic BM. Advances in spatial transcriptomic techniques now allow for investigating the spatial dimension of hematological malignancies at subcellular resolution, opening an exciting path towards new discoveries in the field of AML [ 61 , 62 ].

Data availability

Normalized sequencing data can be accessed from the Gene Expression Omnibus (nCounter data: GSE228481; GeoMx data: GSE248597). Requests for raw sequencing data should be addressed to and will be fulfilled by the corresponding author (OH).

Bruni D, Angell HK, Galon J. The immune contexture and Immunoscore in cancer prognosis and therapeutic efficacy. Nat Rev Cancer. 2020;20:662–80.

Article   PubMed   CAS   Google Scholar  

Havel JJ, Chowell D, Chan TA. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat Rev Cancer. 2019;19:133–50.

Article   PubMed   PubMed Central   CAS   Google Scholar  

Belmontes B, Sawant DV, Zhong W, Tan H, Kaul A, Aeffner F, et al. Immunotherapy combinations overcome resistance to bispecific T cell engager treatment in T cell-cold solid tumors. Sci Transl Med. 2021;13:eabd1524.

Vadakekolathu J, Rutella S. Escape from T-cell-targeting immunotherapies in acute myeloid leukemia. Blood. 2024;143:2689–700.

Gröbner SN, Worst BC, Weischenfeldt J, Buchhalter I, Kleinheinz K, Rudneva VA, et al. The landscape of genomic alterations across childhood cancers. Nature. 2018;555:321–7.

Article   PubMed   Google Scholar  

Majzner RG, Heitzeneder S, Mackall CL. Harnessing the immunotherapy revolution for the treatment of childhood cancers. Cancer Cell. 2017;31:476–85.

Pfister SM, Reyes-Múgica M, Chan JKC, Hasle H, Lazar AJ, Rossi S, et al. A summary of the inaugural WHO classification of pediatric tumors: transitioning from the optical into the molecular era. Cancer Discov. 2022;12:331–55.

Koedijk JB, van der Werf I, Calkoen FG, Nierkens S, Kaspers GJL, Zwaan CM, et al. Paving the way for immunotherapy in pediatric acute myeloid leukemia: current knowledge and the way forward. Cancers (Basel). 2021;13:4364.

Daver N, Garcia-Manero G, Basu S, Boddu PC, Alfayez M, Cortes JE, et al. Efficacy, safety, and biomarkers of response to azacitidine and nivolumab in relapsed/refractory acute myeloid leukemia: a nonrandomized, open-label, phase II study. Cancer Discov. 2019;9:370–83.

Uy GL, Aldoss I, Foster MC, Sayre PH, Wieduwilt MJ, Advani AS, et al. Flotetuzumab as salvage immunotherapy for refractory acute myeloid leukemia. Blood. 2021;137:751–62.

Vadakekolathu J, Minden MD, Hood T, Church SE, Reeder S, Altmann H, et al. Immune landscapes predict chemotherapy resistance and immunotherapy response in acute myeloid leukemia. Sci Transl Med. 2020;12:eaaz0463.

Davids MS, Kim HT, Bachireddy P, Costello C, Liguori R, Savell A, et al. Ipilimumab for patients with relapse after allogeneic transplantation. N Engl J Med. 2016;375:143–53.

Garcia JS, Flamand Y, Penter L, Keng M, Tomlinson BK, Mendez LM, et al. Ipilimumab plus decitabine for patients with MDS or AML in posttransplant or transplant-naïve settings. Blood. 2023;141:1884–8.

Ravandi F, Subklewe M, Walter RB, Vachhani P, Ossenkoppele G, Buecklein V, et al. Safety and tolerability of AMG 330 in adults with relapsed/refractory AML: a phase 1a dose-escalation study. Leuk Lymphoma. 2024:1-11. https://doi.org/10.1080/10428194.2024.2346755 Epub ahead of print.

Zeidner JF, Vincent BG, Ivanova A, Moore D, McKinnon KP, Wilkinson AD, et al. Phase II trial of pembrolizumab after high-dose cytarabine in relapsed/refractory acute myeloid leukemia. Blood Cancer Discov. 2021;2:616–29.

Desai PN, Wang B, Fonseca A, Borges P, Jelloul FZ, Reville PK, et al. Single-cell profiling of CD8+ T cells in acute myeloid leukemia reveals a continuous spectrum of differentiation and clonal hyperexpansion. Cancer Immunol Res. 2023:CIR-22-0961. https://doi.org/10.1158/2326-6066.CIR-22-0961 . Epub ahead of print.

Penter L, Liu Y, Wolff JO, Yang L, Taing L, Jhaveri A, et al. Mechanisms of response and resistance to combined decitabine and ipilimumab for advanced myeloid disease. Blood. 2023;141:1817–30.

Bailur JK, McCachren SS, Pendleton K, Vasquez JC, Lim HS, Duffy A, et al. Risk-associated alterations in marrow T cells in pediatric leukemia. JCI Insight. 2020;5:e140179.

Article   PubMed   PubMed Central   Google Scholar  

Rutella S, Vadakekolathu J, Mazziotta F, Reeder S, Yau TO, Mukhopadhyay R, et al. Immune dysfunction signatures predict outcomes and define checkpoint blockade-unresponsive microenvironments in acute myeloid leukemia. J Clin Invest. 2022;132:e159579.

Lasry A, Nadorp B, Fornerod M, Nicolet D, Wu H, Walker CJ, et al. An inflammatory state remodels the immune microenvironment and improves risk stratification in acute myeloid leukemia. Nat Cancer. 2023;4:27–42.

PubMed   CAS   Google Scholar  

Galon J, Bruni D. Approaches to treat immune hot, altered and cold tumours with combination immunotherapies. Nat Rev Drug Discov. 2019;18:197–218.

Tumeh PC, Harview CL, Yearley JH, Shintaku IP, Taylor EJ, Robert L, et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature. 2014;515:568–71.

Schumacher TN, Thommen DS. Tertiary lymphoid structures in cancer. Science. 2022;375:eabf9419.

Fridman WH, Meylan M, Petitprez F, Sun CM, Italiano A, Sautès-Fridman C. B cells and tertiary lymphoid structures as determinants of tumour immune contexture and clinical outcome. Nat Rev Clin Oncol. 2022;19:441–57.

Fridman WH, Meylan M, Pupier G, Calvez A, Hernandez I, Sautès-Fridman C. Tertiary lymphoid structures and B cells: An intratumoral immunity cycle. Immunity. 2023;56:2254–69.

Helmink BA, Reddy SM, Gao J, Zhang S, Basar R, Thakur R, et al. B cells and tertiary lymphoid structures promote immunotherapy response. Nature. 2020;577:549–55.

Meylan M, Petitprez F, Becht E, Bougoüin A, Pupier G, Calvez A, et al. Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer. Immunity. 2022;55:527–41.e5.

Vanhersecke L, Brunet M, Guégan JP, Rey C, Bougouin A, Cousin S, et al. Mature tertiary lymphoid structures predict immune checkpoint inhibitor efficacy in solid tumors independently of PD-L1 expression. Nat Cancer. 2021;2:794–802.

Casparie M, Tiebosch AT, Burger G, Blauwgeers H, van de Pol A, van Krieken JH, et al. Pathology databanking and biobanking in The Netherlands, a central role for PALGA, the nationwide histopathology and cytopathology data network and archive. Cell Oncol. 2007;29:19–24.

PubMed   PubMed Central   CAS   Google Scholar  

van Galen P, Hovestadt V, Wadsworth Ii MH, Hughes TK, Griffin GK, Battaglia S, et al. Single-cell RNA-Seq reveals AML hierarchies relevant to disease progression and immunity. Cell. 2019;176:1265–81.e24.

Brück O, Dufva O, Hohtari H, Blom S, Turkki R, Ilander M, et al. Immune profiles in acute myeloid leukemia bone marrow associate with patient age, T-cell receptor clonality, and survival. Blood Adv. 2020;4:274–86.

Dufva O, Pölönen P, Brück O, Keränen MAI, Klievink J, Mehtonen J, et al. Immunogenomic landscape of hematological malignancies. Cancer Cell. 2020;38:380–99.e13.

Bolouri H, Farrar JE, Triche T Jr, Ries RE, Lim EL, Alonzo TA, et al. The molecular landscape of pediatric acute myeloid leukemia reveals recurrent structural alterations and age-specific mutational interactions. Nat Med. 2018;24:103–12.

Newman AM, Steen CB, Liu CL, Gentles AJ, Chaudhuri AA, Scherer F, et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol. 2019;37:773–82.

Wang Y, Cai YY, Herold T, Nie RC, Zhang Y, Gale RP, et al. An immune risk score predicts survival of patients with acute myeloid leukemia receiving chemotherapy. Clin Cancer Res. 2021;27:255–66.

Koedijk JB, van Beek TB, Vermeulen MA, Kester LA, Schweighart EK, Nierkens S, et al. Case report: immune dysregulation associated with long-lasting regression of a (pre)leukemic clone. Front Immunol. 2023;14:1280885.

Austin RJ, Straube J, Halder R, Janardhanan Y, Bruedigam C, Witkowski M, et al. Oncogenic drivers dictate immune control of acute myeloid leukemia. Nat Commun. 2023;14:2155.

Straube J, Janardhanan Y, Haldar R, Bywater MJ. Immune control in acute myeloid leukemia. Exp Hematol. 2024:104256. https://doi.org/10.1016/j.exphem.2024.104256 Epub ahead of print.

House IG, Savas P, Lai J, Chen AXY, Oliver AJ, Teo ZL, et al. Macrophage-Derived CXCL9 and CXCL10 Are Required for Antitumor Immune Responses Following Immune Checkpoint Blockade. Clin Cancer Res. 2020;26:487–504.

Italiani P, Boraschi D. From monocytes to M1/M2 macrophages: phenotypical vs. functional differentiation. Front Immunol. 2014;5:514.

Jiang P, Gu S, Pan D, Fu J, Sahu A, Hu X, et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med. 2018;24:1550–8.

Laumont CM, Nelson BH. B cells in the tumor microenvironment: Multi-faceted organizers, regulators, and effectors of anti-tumor immunity. Cancer Cell. 2023;41:466–89.

Gaglia G, Burger ML, Ritch CC, Rammos D, Dai Y, Crossland GE, et al. Lymphocyte networks are dynamic cellular communities in the immunoregulatory landscape of lung adenocarcinoma. Cancer Cell. 2023;41:871–86.e10.

Italiano A, Bessede A, Pulido M, Bompas E, Piperno-Neumann S, Chevreau C, et al. Pembrolizumab in soft-tissue sarcomas with tertiary lymphoid structures: a phase 2 PEMBROSARC trial cohort. Nat Med. 2022;28:1199–206.

Siddiqui I, Schaeuble K, Chennupati V, Fuertes Marraco SA, Calderon-Copete S, Pais Ferreira D, et al. Intratumoral Tcf1(+)PD-1(+)CD8(+) T cells with stem-like properties promote tumor control in response to vaccination and checkpoint blockade immunotherapy. Immunity. 2019;50:195–211.e10.

Cottrell TR, Thompson ED, Forde PM, Stein JE, Duffield AS, Anagnostou V, et al. Pathologic features of response to neoadjuvant anti-PD-1 in resected non-small-cell lung carcinoma: a proposal for quantitative immune-related pathologic response criteria (irPRC). Ann Oncol. 2018;29:1853–60.

van Dijk N, Gil-Jimenez A, Silina K, Hendricksen K, Smit LA, de Feijter JM, et al. Preoperative ipilimumab plus nivolumab in locoregionally advanced urothelial cancer: the NABUCCO trial. Nat Med. 2020;26:1839–44.

Coppola D, Nebozhyn M, Khalil F, Dai H, Yeatman T, Loboda A, et al. Unique ectopic lymph node-like structures present in human primary colorectal carcinoma are identified by immune gene array profiling. Am J Pathol. 2011;179:37–45.

Gu-Trantien C, Loi S, Garaud S, Equeter C, Libin M, de Wind A, et al. CD4 + follicular helper T cell infiltration predicts breast cancer survival. J Clin Invest. 2013;123:2873–92.

Merritt CR, Ong GT, Church SE, Barker K, Danaher P, Geiss G, et al. Multiplex digital spatial profiling of proteins and RNA in fixed tissue. Nat Biotechnol. 2020;38:586–99.

Danaher P, Kim Y, Nelson B, Griswold M, Yang Z, Piazza E, et al. Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data. Nat Commun. 2022;13:385.

King HW, Orban N, Riches JC, Clear AJ, Warnes G, Teichmann SA, et al. Single-cell analysis of human B cell maturation predicts how antibody class switching shapes selection dynamics. Sci Immunol. 2021;6:eabe6291.

Devi-Marulkar P, Fastenackels S, Karapentiantz P, Goc J, Germain C, Kaplon H, et al. Regulatory T cells infiltrate the tumor-induced tertiary lymphoïd structures and are associated with poor clinical outcome in NSCLC. Commun Biol. 2022;5:1416.

Schweiger T, Berghoff AS, Glogner C, Glueck O, Rajky O, Traxler D, et al. Tumor-infiltrating lymphocyte subsets and tertiary lymphoid structures in pulmonary metastases from colorectal cancer. Clin Exp Metastasis. 2016;33:727–39.

Brauneck F, Fischer B, Witt M, Muschhammer J, Oelrich J, da Costa Avelar PH, et al. TIGIT blockade repolarizes AML-associated TIGIT(+) M2 macrophages to an M1 phenotype and increases CD47-mediated phagocytosis. J Immunother Cancer. 2022;10:e004794.

Perzolli A, Koedijk JB, Zwaan CM, Heidenreich O. Targeting the innate immune system in pediatric and adult AML. Leukemia. 2024;38:1191–201.

Vaccaro A, Yang F, van de Walle T, Franke S, Lugano R, Dénes A, et al. Cellular and molecular events organizing the assembly of tertiary lymphoid structures in glioblastoma. bioRxiv. 2024:2024.07.04.601824. https://www.biorxiv.org/content/10.1101/2024.07.04.601824v1 .

Philip M, Schietinger A. CD8(+) T cell differentiation and dysfunction in cancer. Nat Rev Immunol. 2022;22:209–23.

Chen JH, Nieman LT, Spurrell M, Jorgji V, Richieri P, Xu KH, et al. Human lung cancer harbors spatially organized stem-immunity hubs associated with response to immunotherapy. Nat Immunol. 2024;25:644–58.

Jansen CS, Prokhnevska N, Master VA, Sanda MG, Carlisle JW, Bilen MA, et al. An intra-tumoral niche maintains and differentiates stem-like CD8 T cells. Nature. 2019;576:465–70.

He S, Bhatt R, Brown C, Brown EA, Buhr DL, Chantranuvatana K, et al. High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging. Nat Biotechnol. 2022;40:1794–806.

Janesick A, Shelansky R, Gottscho AD, Wagner F, Williams SR, Rouault M, et al. High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis. Nat Commun. 2023;14:8353.

Download references

Acknowledgements

We would like to thank all patients and/or their families for their generous consent for the research use of these samples; the staff of the University Medical Center Utrecht Tissue Facility for their excellent immunohistochemistry service (Domenico Castigliego, Petra van der Weide, Petra van der Kraak, Erica Siera, Karina Timmer, Sven van Kempen), and the team at Utrecht Sequencing Facility for performing the NanoString experiments (nCounter and GeoMx) and for providing assistance with data-analysis (dr. Ies Nijman, Robin Geene, Pim Kloosterman); dr. Thierry van den Bosch, dr. Ravian van Ineveld, Ella de Boed, Nienke van Herk, Thijs van den Broek, Maurice de Haan, and Susanne Gamas Vis provided help with imaging experiments and/or analysis; Dr. Matthew S. Davids for clinical data of patients treated on ETCTN/CTEP 9204; Dr. Ivette Deckers and dr. Annette Gijsbers (PALGA) performed essential work for the acquisition of bone biopsies from other hospitals; prof. dr. Gertjan Kaspers and dr. Bianca Goemans aided with identifying potential patients, the biobank staff (Jantien Woudstra, Marion Koopmans, dr. Edwin Sonneveld) helped to identify patient material, and Arie Maat aided with the sectioning of bone biopsy sections; dr. Caroline Lindemans, members from the Heidenreich group (dr. Katarzyna Szoltysek, dr. Farnaz Barneh, dr. Mauricio Ferrao-Blanco, Elizabeth Schweighart, Nina van der Wilt), the Van Heesch group (dr. Ana Pinheiro-Lopes), and the single-cell sequencing facility (dr. Lindy Visser) at the Princess Máxima Center for Pediatric Oncology for carefully reading the manuscript and/or fruitful discussions. Figures have been created using BioRender.com.

This work has been funded in part by a KIKA (329) program grant to OH. LP was a Scholar of the American Society of Hematology, is participant in the BIH Charité Digital Clinician Scientist Program funded by the DFG, the Charité—Universitätsmedizin Berlin, and the Berlin Institute of Health at Charité (BIH) and is supported by the Max-Eder program of the German Cancer Aid (Deutsche Krebshilfe), by the Else Kröner-Fresenius-Stiftung (2023_EKEA.102), and the DKMS John Hansen Research Grant. JSG is supported by the Conquer Cancer Foundation Career Development Award, Leukemia and Lymphoma Society Translational Research Program Award, and NIH K08CA245209. NCI CTEP provided study drug (Ipilimumab) support. This work was supported by National Institutes of Health, National Cancer Institute grant P01CA229092 (CJW), UM1CA186709 (Principal Investigator: Geoffrey Shapiro), National Cancer Institute Cancer Therapy Evaluation Program, Bristol-Myers Squibb, and LLS Therapy Accelerator Program. This work was further supported by KIKA (TM) and the CIMAC-CIDC Network. Scientific and financial support for the CIMAC-CIDC Network is provided through National Institutes of Health, National Cancer Institute Cooperative Agreements U24CA224319 (to the Icahn School of Medicine at Mount Sinai CIMAC), U24CA224331 (to the Dana-Farber Cancer Institute CIMAC), U24CA224285 (to the MD Anderson Cancer Center CIMAC), U24CA224309 (to the Stanford University CIMAC), and U24CA224316 (to the CIDC at Dana-Farber Cancer Institute). The CIMAC-CIDC website is found at https://cimac-network.org/ .

Author information

These authors contributed equally: C. Michel Zwaan, Olaf Heidenreich.

Authors and Affiliations

Princess Máxima Center for Pediatric Oncology, 3584 CS, Utrecht, The Netherlands

Joost B. Koedijk, Inge van der Werf, Marijn A. Vermeulen, Farnaz Barneh, Alicia Perzolli, Joyce I. Meesters-Ensing, Dennis S. Metselaar, Thanasis Margaritis, Marta Fiocco, Hester A. de Groot-Kruseman, Rubina Moeniralam, Stefan Nierkens, Mirjam E. Belderbos, C. Michel Zwaan & Olaf Heidenreich

Department of Pediatric Oncology, Erasmus MC/Sophia Children’s Hospital, 3015 GD, Rotterdam, The Netherlands

Joost B. Koedijk, Alicia Perzolli & C. Michel Zwaan

Oncode Institute, 3521 AL, Utrecht, The Netherlands

Inge van der Werf

Sanford Stem Cell Institute, Division of Regenerative Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, USA

Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

Livius Penter, Jacqueline S. Garcia & Catherine J. Wu

Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA

Livius Penter & Catherine J. Wu

Harvard Medical School, Boston, MA, USA

Department of Hematology, Oncology, and Cancer Immunology, Campus Virchow Klinikum, Berlin, Germany

Livius Penter

Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany

German Cancer Consortium (DKTK), partner site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany

Berlin Institute of Health at Charité – Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, BIH Charité Digital Clinician Scientist Program, Charitéplatz 1, 10117, Berlin, Germany

Hopp Children’s Cancer Center Heidelberg (KiTZ), Heidelberg, Germany

Dennis S. Metselaar

Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ) and German Consortium (DKTK), Im Neuenheimer Feld 280, Heidelberg, Germany

Mathematical Institute, Leiden University, Leiden, The Netherlands

Marta Fiocco

Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands

Department of Pathology, Aarhus University Hospital, Aarhus, Denmark

Kristina Bang Christensen

Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

Billie Porter & Kathleen Pfaff

Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA

Scott J. Rodig

Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

Catherine J. Wu

Pediatrics and Adolescent Medicine, Aarhus University Hospital, Aarhus, Denmark

Henrik Hasle

Center for Translational Immunology, University Medical Center Utrecht, 3584 CX, Utrecht, The Netherlands

Stefan Nierkens

University Medical Center Utrecht, 3584 CX, Utrecht, The Netherlands

Olaf Heidenreich

Wolfson Childhood Cancer Research Centre, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization: JBK, IW, MAV, SN, MB, CMZ, OH. Methodology: JBK, IW, MAV, MF, SN, MB, CMZ, OH. Data acquisition: JBK, MAV, FB, JIM-E, DEM, TM, HG-K, RM, KBC, BP, KP, HH. Data analysis and interpretation: JBK, IW, LP, FB, AP, JIM-E, DSM, TM, JSG, SJR, CJW, HH. Writing—Original Draft: JBK; Writing—Reviewing & Editing: all authors; Supervision: SN, MB, CMZ, OH.

Corresponding author

Correspondence to Olaf Heidenreich .

Ethics declarations

Competing interests.

JSG reports serving on steering committee and receiving personal fees from AbbVie, Astellas Pharma, Genentech, and Servier and institutional research funds from AbbVie, Genentech, Pfizer, and AstraZeneca. SJR receives research support from Affimed, Merck, Bristol-Myers-Squibb, and is a member of the Scientific Advisory Board for Immunitas Therapeutics. CJW is an equity holder of BioNtech, Inc, receives research support from Pharmacyclics, and is a Scientific Advisory Board member of Repertoire, Aethon Therapeutics, and Adventris. CMZ receives institutional research support from Pfizer, Abbvie, Takeda, Jazz, Kura Oncology, Gilead, and Daiichi Sankyo, provides consultancy services for Kura Oncology, Bristol-Myers-Squibb, Novartis, Gilead, Incyte, Beigene, and Syndax, and serves on advisory committees for Novartis, Sanofi, and Incyte. O.H. receives institutional research support from Syndax and Roche. The remaining authors declare no competing financial interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information including methods, figures and legends, supplementary tables, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Koedijk, J.B., van der Werf, I., Penter, L. et al. A multidimensional analysis reveals distinct immune phenotypes and the composition of immune aggregates in pediatric acute myeloid leukemia. Leukemia (2024). https://doi.org/10.1038/s41375-024-02381-w

Download citation

Received : 28 February 2024

Revised : 02 August 2024

Accepted : 13 August 2024

Published : 26 August 2024

DOI : https://doi.org/10.1038/s41375-024-02381-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

A novel molecular classification based on efferocytosis-related genes for predicting clinical outcome and treatment response in acute myeloid leukemia

• Original Research Paper
• Published: 02 September 2024

Cite this article

• Fangmin Zhong 1 ,
• Fangyi Yao 1 ,
• Qin Bai 1 ,
• Jing Liu 1 ,
• Xiaolin Li 1 ,
• Bo Huang 1 &
• Xiaozhong Wang 1  

Previous studies have shown that macrophage-mediated efferocytosis is involved in immunosuppression in acute myeloid leukemia (AML). However, the regulatory role of efferocytosis in AML remains unclear and needs further elucidation.

We first identified the key efferocytosis-related genes (ERGs) based on the expression matrix. Efferocytosis-related molecular subtypes were obtained by consensus clustering algorithm. Differences in immune landscape and biological processes among molecular subtypes were further evaluated. The efferocytosis score model was constructed to quantify molecular subtypes and evaluate its value in prognosis prediction and treatment decision-making in AML.

Three distinct efferocytosis-related molecular subtypes were identified and divided into immune activation, immune desert, and immunosuppression subtypes based on the characteristics of the immune landscape. We evaluated the differences in clinical and biological features among different molecular subtypes, and the construction of an efferocytosis score model can effectively quantify the subtypes. A low efferocytosis score is associated with immune activation and reduced mutation frequency, and patients have a better prognosis. A high efferocytosis score reflects immune exhaustion, increased activity of tumor marker pathways, and poor prognosis. The prognostic predictive value of the efferocytosis score model was confirmed in six AML cohorts. Patients exhibiting high efferocytosis scores may derive therapeutic benefits from anti-PD-1 immunotherapy, whereas those with low efferocytosis scores tend to exhibit greater sensitivity towards chemotherapy. Analysis of treatment data in ex vivo AML cells revealed a group of drugs with significant differences in sensitivity between different efferocytosis score groups. Finally, we validated model gene expression in a clinical cohort.

Conclusions

This study reveals that efferocytosis plays a non-negligible role in shaping the diversity and complexity of the AML immune microenvironment. Assessing the individual efferocytosis-related molecular subtype in individuals will help to enhance our understanding of the characterization of the AML immune landscape and guide the establishment of more effective clinical treatment strategies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Data availability

No datasets were generated or analysed during the current study.

Newell L, Cook R. Advances in acute myeloid leukemia. BMJ (Clinical research ed.) 375, n2026, https://doi.org/10.1136/bmj.n2026 (2021).

Shimony S, Stahl M, Stone RM. Acute myeloid leukemia: 2023 update on diagnosis, risk-stratification, and management. Am J Hematol. 2023;98:502–26. https://doi.org/10.1002/ajh.26822 .

Article   PubMed   Google Scholar  

DiNardo CD, Perl AE. Advances in patient care through increasingly individualized therapy. Nat Rev Clin Oncol. 2019;16:73–4. https://doi.org/10.1038/s41571-018-0156-2 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Kantarjian H, et al. Acute myeloid leukemia: current progress and future directions. Blood cancer J. 2021;11. https://doi.org/10.1038/s41408-021-00425-3 .

Lindsley RC, et al. Acute myeloid leukemia ontogeny is defined by distinct somatic mutations. Blood. 2015;125:1367–76. https://doi.org/10.1182/blood-2014-11-610543 .

Kumar S, Calianese D, Birge RB. Efferocytosis of dying cells differentially modulate immunological outcomes in tumor microenvironment. Immunol Rev. 2017;280:149–64. https://doi.org/10.1111/imr.12587 .

Werfel TA, Cook RS. Efferocytosis in the tumor microenvironment. Semin Immunopathol. 2018;40:545–54. https://doi.org/10.1007/s00281-018-0698-5 .

Article   PubMed   PubMed Central   Google Scholar  

Sachet M, Liang YY, Oehler R. The immune response to secondary necrotic cells. Apoptosis: Int J Program cell Death. 2017;22:1189–204. https://doi.org/10.1007/s10495-017-1413-z .

Article   CAS   Google Scholar  

Gardai SJ, et al. Cell-surface calreticulin initiates clearance of viable or apoptotic cells through trans-activation of LRP on the phagocyte. Cell. 2005;123:321–34. https://doi.org/10.1016/j.cell.2005.08.032 .

Article   CAS   PubMed   Google Scholar  

Vandivier RW, Henson PM, Douglas IS. Burying the dead: the impact of failed apoptotic cell removal (efferocytosis) on chronic inflammatory lung disease. Chest. 2006;129:1673–82. https://doi.org/10.1378/chest.129.6.1673 .

Kojima Y, Weissman IL, Leeper NJ. The role of efferocytosis in atherosclerosis. Circulation. 2017;135:476–89. https://doi.org/10.1161/circulationaha.116.025684 .

Zhou Y, Yao Y, Deng Y, Shao A. Regulation of efferocytosis as a novel cancer therapy. Cell Communication Signaling: CCS. 2020;18:71. https://doi.org/10.1186/s12964-020-00542-9 .

Bingle L, Brown NJ, Lewis CE. The role of tumour-associated macrophages in tumour progression: implications for new anticancer therapies. J Pathol. 2002;196:254–65. https://doi.org/10.1002/path.1027 .

Kurahara H et al. Significance of M2-polarized tumor-associated macrophage in pancreatic cancer. The Journal of surgical research 167, e211-219, https://doi.org/10.1016/j.jss.2009.05.026 (2011).

Voll RE, et al. Immunosuppressive effects of apoptotic cells. Nature. 1997;390:350–1. https://doi.org/10.1038/37022 .

Joseph M, Enting D. Immune responses in bladder Cancer-role of Immune Cell populations, prognostic factors and therapeutic implications. Front Oncol. 2019;9:1270. https://doi.org/10.3389/fonc.2019.01270 .

Birge RB, et al. Phosphatidylserine is a global immunosuppressive signal in efferocytosis, infectious disease, and cancer. Cell Death Differ. 2016;23:962–78. https://doi.org/10.1038/cdd.2016.11 .

Cruz Cruz J, et al. Inhibiting efferocytosis reverses macrophage-mediated immunosuppression in the leukemia microenvironment. Front Immunol. 2023;14. https://doi.org/10.3389/fimmu.2023.1146721 .

Wilkerson M, Hayes D. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinf (Oxford England). 2010;26:1572–3. https://doi.org/10.1093/bioinformatics/btq170 .

Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16:284–7. https://doi.org/10.1089/omi.2011.0118 .

Newman A, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12:453–7. https://doi.org/10.1038/nmeth.3337 .

Geeleher P, Cox N, Huang R. pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS ONE. 2014;9:e107468. https://doi.org/10.1371/journal.pone.0107468 .

Li SQ, et al. Transcriptome profiling reveals the high incidence of hnRNPA1 exon 8 inclusion in chronic myeloid leukemia. J Adv Res. 2020;24:301–10. https://doi.org/10.1016/j.jare.2020.04.016 .

Lee-Sherick AB, et al. Aberrant mer receptor tyrosine kinase expression contributes to leukemogenesis in acute myeloid leukemia. Oncogene. 2013;32:5359–68. https://doi.org/10.1038/onc.2013.40 .

Komohara Y, Fujiwara Y, Ohnishi K, Takeya M. Tumor-associated macrophages: potential therapeutic targets for anti-cancer therapy. Adv Drug Deliv Rev. 2016;99:180–5. https://doi.org/10.1016/j.addr.2015.11.009 .

Zhang L, Gajewski TF, Kline J. PD-1/PD-L1 interactions inhibit antitumor immune responses in a murine acute myeloid leukemia model. Blood. 2009;114:1545–52. https://doi.org/10.1182/blood-2009-03-206672 .

Davids MS, et al. Ipilimumab for patients with relapse after allogeneic transplantation. N Engl J Med. 2016;375:143–53. https://doi.org/10.1056/NEJMoa1601202 .

Daver N, et al. Efficacy, Safety, and biomarkers of response to Azacitidine and Nivolumab in Relapsed/Refractory Acute myeloid leukemia: a nonrandomized, Open-Label, phase II study. Cancer Discov. 2019;9:370–83. https://doi.org/10.1158/2159-8290.Cd-18-0774 .

Abbas HA, et al. Single cell T cell landscape and T cell receptor repertoire profiling of AML in context of PD-1 blockade therapy. Nat Commun. 2021;12:6071. https://doi.org/10.1038/s41467-021-26282-z .

Milella M, et al. Beyond single pathway inhibition: MEK inhibitors as a platform for the development of pharmacological combinations with synergistic anti-leukemic effects. Curr Pharm Design. 2005;11:2779–95. https://doi.org/10.2174/1381612054546842 .

Milella M, et al. MEK blockade converts AML differentiating response to retinoids into extensive apoptosis. Blood. 2007;109:2121–9. https://doi.org/10.1182/blood-2006-05-024679 .

Wei CR, Ge XF, Wang Y, Li XR. MEK inhibitor CI-1040 induces apoptosis in acute myeloid leukemia cells in vitro. Eur Rev Med Pharmacol Sci. 2016;20:1961–8.

PubMed   Google Scholar  

Jain N, et al. Phase II study of the oral MEK inhibitor selumetinib in advanced acute myelogenous leukemia: a University of Chicago phase II consortium trial. Clin Cancer Res. 2014;20:490–8. https://doi.org/10.1158/1078-0432.Ccr-13-1311 .

Wang ES, et al. Crenolanib and intensive chemotherapy in adults with newly diagnosed FLT3-Mutated AML. J Clin Oncol. 2024;42:1776–87. https://doi.org/10.1200/jco.23.01061 .

Cao S, et al. Proteolysis-targeting chimera (PROTAC) modification of Dovitinib enhances the Antiproliferative Effect against FLT3-ITD-Positive Acute myeloid leukemia cells. J Med Chem. 2021;64:16497–511. https://doi.org/10.1021/acs.jmedchem.1c00996 .

Perl AE, et al. Gilteritinib or Chemotherapy for relapsed or refractory FLT3-Mutated AML. N Engl J Med. 2019;381:1728–40. https://doi.org/10.1056/NEJMoa1902688 .

Wang ES, et al. Phase 1 trial of linifanib (ABT-869) in patients with refractory or relapsed acute myeloid leukemia. Leuk Lymphoma. 2012;53:1543–51. https://doi.org/10.3109/10428194.2012.660631 .

Hernandez-Davies JE, et al. The multitargeted receptor tyrosine kinase inhibitor linifanib (ABT-869) induces apoptosis through an akt and glycogen synthase kinase 3β-dependent pathway. Mol Cancer Ther. 2011;10:949–59. https://doi.org/10.1158/1535-7163.Mct-10-0904 .

Download references

Acknowledgements

The study was funded by the National Natural Science Foundation of China (82160405, 82160038, 82260035, 82301578), the Natural Science Foundation of Jiangxi Province (20232BAB216037, 20232BAB216050, 20224BAB216037), and the Incubation Program of the Second Affiliated Hospital of Nanchang University (2022YNFY12007).

Author information

Authors and affiliations.

Jiangxi Province Key Laboratory of Immunology and Inflammation, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China

Fangmin Zhong, Fangyi Yao, Qin Bai, Jing Liu, Xiaolin Li, Bo Huang & Xiaozhong Wang

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization, Supervision, and Project administration: XL, BH and XW; Funding acquisition: FZ, FY, BH, XW, JL, XL; Resources: FZ and XW; Validation and Visualization: FZ, FY, JL, QB and BH; Methodology: FZ; Data Curation: FZ; Formal analysis: FZ; Software: FZ; Writing - original draft: FZ; Writing - review & editing: XL, BH and XW. All authors edited and approved the final version of the submitted manuscript.

Corresponding authors

Correspondence to Xiaolin Li , Bo Huang or Xiaozhong Wang .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Communicated by L Li.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions.

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Zhong, F., Yao, F., Bai, Q. et al. A novel molecular classification based on efferocytosis-related genes for predicting clinical outcome and treatment response in acute myeloid leukemia. Inflamm. Res. (2024). https://doi.org/10.1007/s00011-024-01938-w

Download citation

Received : 25 May 2024

Revised : 06 August 2024

Accepted : 21 August 2024

Published : 02 September 2024

DOI : https://doi.org/10.1007/s00011-024-01938-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Acute myeloid leukemia
• Efferocytosis
• Molecular subtypes
• Immune landscape
• Immunotherapy
• Find a journal
• Publish with us
• Track your research

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

Molecular Features and Treatment Paradigms of Acute Myeloid Leukemia

Affiliation.

• 1 Department of Hematology and Medical Oncology, NYU Langone Health, Perlmutter Cancer Center, New York, NY 10016, USA.
• PMID: 39200232
• PMCID: PMC11351617
• DOI: 10.3390/biomedicines12081768

Acute myeloid leukemia (AML) is a common hematologic malignancy that is considered to be a disease of aging, and traditionally has been treated with induction chemotherapy, followed by consolidation chemotherapy and/or allogenic hematopoietic stem cell transplantation. More recently, with the use of next-generation sequencing and access to molecular information, targeted molecular approaches to the treatment of AML have been adopted. Molecular targeting is gaining prominence, as AML mostly afflicts the elderly population, who often cannot tolerate traditional chemotherapy. Understanding molecular changes at the gene level is also important for accurate disease classification, risk stratification, and prognosis, allowing for more personalized medicine. Some mutations are well studied and have an established gene-specific therapy, including FLT3 and IDH1/2, while others are being investigated in clinical trials. However, data on most known mutations in AML are still minimal and therapeutic studies are in pre-clinical stages, highlighting the importance of further research and elucidation of the pathophysiology involving these genes. In this review, we aim to highlight the key molecular alterations and chromosomal changes that characterize AML, with a focus on pathophysiology, presently available treatment approaches, and future therapeutic options.

Keywords: acute myeloid leukemia; molecular; targeted therapy.

PubMed Disclaimer

Conflict of interest statement

Jun H. Choi: He does not have any conflict of interest or anything to declare. Mihir Shukla: He does not have any conflict of interest or anything to declare. Maher Abdul-Hay: Rigel and Daiichi advisory boards, Jazz, Takeda and Servier advisory boards and speaker bureau.

This figure demonstrates the effects…

This figure demonstrates the effects of key mutations and the effect on cellular…

• Kell J. Considerations and challenges for patients with refractory and relapsed acute myeloid leukaemia. Leuk. Res. 2016;47:149–160. doi: 10.1016/j.leukres.2016.05.025. - DOI - PubMed
• Bowman R.L., Busque L., Levine R.L. Clonal Hematopoiesis and Evolution to Hematopoietic Malignancies. Cell Stem Cell. 2018;22:157–170. doi: 10.1016/j.stem.2018.01.011. - DOI - PMC - PubMed
• Shimony S., Stahl M., Stone R.M. Acute myeloid leukemia: 2023 update on diagnosis, risk-stratification, and management. Am. J. Hematol. 2023;98:502–526. doi: 10.1002/ajh.26822. - DOI - PubMed
• Arber D.A., Hasserjian R.P., Orazi A., Mathews V., Roberts A.W., Schiffer C.A., Roug A.S., Cazzola M., Döhner H., Tefferi A. Classification of myeloid neoplasms/acute leukemia: Global perspectives and the international consensus classification approach. Am. J. Hematol. 2022;97:514–518. doi: 10.1002/ajh.26503. - DOI - PMC - PubMed
• Döhner H., Weisdorf D.J., Bloomfield C.D. Acute Myeloid Leukemia. N. Engl. J. Med. 2015;373:1136–1152. doi: 10.1056/NEJMra1406184. - DOI - PubMed

Publication types

• Search in MeSH

Grants and funding

• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Saf Health Work
• v.14(4); 2023 Dec
• PMC10770274

Case-Control Study of Occupational Acute Myeloid Leukemia in the Republic of Korea

Min young park.

1 Department of Occupational and Environmental Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea

Hyoung-Ryoul Kim

Jun-pyo myong, byung-sik cho.

2 Department of Hematology, Catholic Hematology Hospital, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea

3 Leukemia Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea

Mo-Yeol Kang

Associated data.

We conducted a case-control study to identify high-risk occupations and exposure to occupational hazards for acute myeloid leukemia (AML).

When patients with AML admitted to the Department of Hematology in the study hospital for the first time are referred to the Department of Occupational and Environmental Medicine, data on occupation are collected by investigators to evaluate work-relatedness. Community-based controls were recruited through an online survey agency, and four controls per case were matched. Occupational information was estimated using structured questionnaires covering 27 specific occupations and 32 exposure agents. Conditional logistic regression analysis was performed by pairing cases and controls.

In the analysis of the risk of AML according to occupational classification, a significant association was found in paint manufacturing or painting work (OR = 2.22, 95% CI: 1.03–4.81) and aircrew (OR = 6.00, 95% CI: 1.00–35.91) in males, and in pesticide industry (OR = 6.89, 95% CI: 1.69–28.07) and cokes and steel industry (OR = 2.00, 95% CI: 1.18–22.06) in ≥60 years old. Moreover, the risk of AML increased significantly as the cumulative exposure to thinners increased. In the analyses stratified by sex and age, the association between pesticide exposure and AML was significant in males (OR = 3.28, 95% CI: 1.10–9.77) and in ≥60 years old (OR = 6.22, 95% CI: 1.48–26.08).

This case-control study identified high-risk occupational groups in the Republic of Korea including paint manufacturers and painters, aircrew, and those who are occupationally exposed to pesticides or paint thinners.

1. Introduction

Cancer is the leading cause of death in developed countries, and there is a growing interest in understanding its causes. Although statistics vary, approximately 3–6% of all cancers worldwide are caused by exposure to carcinogens in the workplace [ 1 ], and, especially in the Republic of Korea, the incidence of cancer due to occupational hazards is estimated to be 5% of all cancer diagnoses [ 2 ].

Respiratory cancer is the most widely recognized occupational cancer, and additionally, bladder cancer, sinus cancer, leukemia, lymphoma, and skin cancer are well known as occupational cancers with a relatively high incidence. According to an analysis of industrial accident compensation decisions and precedents of occupational cancer by the Korea Labor Welfare Service [ 3 ], cancer of the lymphohematopoietic system is the second most frequent occupational cancer source of claims and approvals for industrial accident compensation, after lung cancer. In a previous study, the attributable risk of cancer of the lymphohematopoietic system in the Republic of Korea was estimated to be approximately 15% [ 2 ].

The number of occupational cancer compensation cases in the Republic of Korea is very small compared with that in European countries [ 4 ]. Therefore, compared with other countries, patients in the Republic of Korea with work-related cancer are more likely to suffer from the economic burden acquired during cancer treatment because they do not receive appropriate workers' compensation. In particular, lymphohematopoietic cancers are the second highest reported occupational cancer after lung cancer in the Republic of Korea, but there has been a lack of research and exploration. Therefore, it is urgent to identify those at risk for lymphohematopoietic cancers so that patients receive workers' compensation that allows them to receive appropriate treatment without financial difficulties.

Approximately 25% of all cases of adult leukemia are acute myeloid leukemia (AML) [ 5 ], and there have been just a few international attempts at epidemiologic studies of AML. Previous studies have identified a large number of potential risk factors for AML including personal and family medical histories; lifestyle; environmental exposures; occupations and industries; and exposure to chemical, physical, or biological agents [ 6 ]. In a case-control study conducted at 29 hospitals in Shanghai, China [ 7 ], benzene, diesel fuel, metals, pesticides, adhesives, paints, and coatings were associated with an increased risk of AML, and a study conducted at the Texas M.D. Anderson Cancer Center reported that occupational exposure to organic solvents above a moderate level significantly increased the risk of AML [ 8 ]. However, suspected factors were not found to significantly increase the risk of AML in a large-scale epidemiological study of occupational organic solvent exposure and AML conducted in Nordic countries [ 9 ]. As such, the potential relationships between multiple occupational hazard exposures and AML have been investigated in epidemiological studies, but different studies have had inconsistent results. Especially in the Republic of Korea, there has been little systematic approach to risk factors.

The Seoul St. Mary's Hospital operates a cooperative program in collaboration with the Department of Occupational and Environmental Medicine (OEM) and Department of Hematology for patients with AML to identify work-related AML [ 10 ]. From June 1, 2018 to December 31, 2020, 376 patients with AML who were newly hospitalized in the Department of Hematology at the study hospital were asked about their occupation and exposure histories. Based on this information, we conducted a case-control study to identify high-risk occupations and exposure to occupational hazards for AML in the Republic of Korea.

2. Materials and methods

2.1. study design and data sources.

The Seoul St. Mary's Hospital has established an occupational AML surveillance system in cooperation with the Department of OEM and the Department of Hematology in 2018. When patients with AML admitted to the Department of Hematology for the first time are referred to the Department of OEM, data on occupation are collected by investigators to evaluate work-relatedness. A trained investigator conducts an exposure assessment using a structured questionnaire, followed by detailed interviews on occupational processes and exposure to substances [ 10 ].

The cases in this study were defined as all adults aged 19 years or older who were admitted to the Seoul St. Mary's Hospital with newly diagnosed AML and referred to the Department of OEM between June 1, 2018 and December 30, 2020. Patients who were initially diagnosed with AML but were subsequently diagnosed with other diseases through bone marrow examination and those who visited the Republic of Korea only for treatment were excluded. Patients who visited the Department of OEM as outpatients rather than as inpatients were excluded because they did not have an opportunity to complete the structured questionnaire. In addition, patients who had received chemotherapy or radiation therapy for other cancers before being diagnosed with AML were excluded from the study ( Fig. 1 ).

A schematic diagram of participants with acute myeloid leukemia. MDS, Myelodysplastic syndromes; OEM, Occupational and Environmental Medicine.

Community-based controls without a history of AML or other malignant hematological diseases were recruited through an online survey agency. Four controls per case were matched for sex and age. A difference in age of up to 5 years was allowed between the cases and controls. Cases that could not be successfully matched to four controls due to a lack of recruitment in the community were matched with two or three controls. The matching age of the cases and controls was defined as the age at diagnosis and the age at survey participation, respectively.

For each case and control, occupational information had been collected using the same structured questionnaires covering 27 specific occupations and 32 exposure agents, which were based on the International Agency for Research on Cancer monograph and other previous studies for acute myeloid leukemia [ 7 , 8 , 11 , 12 ]. The cumulative exposure grade for the agents, especially formaldehyde, adhesives, thinners, and radiation, was estimated by evaluating the following logic:

The exposure time grade was estimated by multiplying information on the period (years) of use of the agents, number of days of use per month, exposure time per day, and regular work status. The exposure level grade was evaluated by multiplying the exposure time grade by the subjective intensity level of exposure experienced by the workers. The subjective exposure intensity was chosen among “never exposed”, “almost not exposed”, “exposed but at a low level”, or “exposed at high concentration”. The participants could determine the intensity based on their individual experiences, such as their dirty coveralls after work, chemical splashes on their glasses, or smelling a strong odor. The cumulative exposure grade was evaluated by considering the exposure level grade, personal protective equipment, and ventilation status information. The cumulative exposure grade was classified as high, medium, or low. The items responded to as nominal variables (regularity of exposure, subjective exposure intensity, personal protective equipment, and ventilation status) and all grades (exposure time, exposure level, and cumulative exposure) were calculated using researcher-defined constants and weight values, respectively. All grade evaluation processes were derived through independent evaluations by two industrial hygiene experts.

2.2. Statistical analysis

A descriptive statistical analysis was performed on the basic demographic characteristics of the case and control groups. Conditional logistic regression analysis was performed by pairing cases and controls according to sex and age, and the odds ratio (OR) and 95% confidence interval (CI) were calculated according to the exposure variables (occupations and exposure agents). In addition, stratified analyses were conducted by sex and age group (≥60 and <60 years) for each occupational classification and exposure agent to explore potential age-specific and sex-specific effect modification with occupations and exposures. Regarding the exposure agents, we estimated the exposure-response relationship according to the cumulative exposure grade by performing a simple linear regression as a test for trend. SAS 9.4 (SAS Institute Inc., Cary, NC, USA) was used for the statistical analysis.

The demographic characteristics of the participants are shown in Table 1 . A total of 320 patients with newly diagnosed AML were admitted to the study hospital during the study period; however, 16 patients (5%) were excluded from the study because of insufficient recruitment of controls ( Fig. 1 ). Therefore, a total of 304 patients with AML and 1170 community controls were included in the study. The proportion of male participants was 56.58%, and the 60–69-year-old age group was the largest age group (26.32% of cases and 31.79% of controls) ( Table 1 ).

Table 1

Demographic characteristics of acute myeloid leukemia cases and controls

Of the 27 occupations surveyed, 4 occupations (incineration plant, textile industry, asphalt manufacturing, and nuclear power plants) had no cases at all, while significant associations with AML were observed in paint manufacturing or painting (OR = 2.10, 95% CI: 1.01–4.35) and aircrews (OR = 6.00, 95% CI: 1.00–35.91) ( Supplementary Table 1 ). The ORs of AML according to occupation stratified by sex and age are shown in Table 2 , which shows a total of 10 occupational categories with ≥3 cases or significant results. In the analyses stratified by sex, AML was significantly associated with paint manufacturing or painting (OR = 2.22, 95% CI: 1.03–4.81) and aircrew (OR = 6.00, 95% CI: 1.00–35.91) in men. In the analyses stratified by age group, AML was significantly associated with paint manufacturing and painting in those under 60 years (OR = 3.11, 95% CI: 1.16–8.35) and working in the pesticide industry (OR = 6.89, 95% CI: 1.69–28.07) and in the cokes and steel industry (OR = 2.00, 95% CI: 1.18–22.06) in those over 60 years. The results of all surveyed occupations are shown in Supplementary Tables 3 and 4 .

Table 2

Sex and age specific odds ratio (OR) and 95% confidence intervals (CI) of acute myeloid leukemia according to occupations

Among the 32 exposure agents investigated, 16 agents showed no exposure in the case group, and no agents were found to have a statistically significant association with AML ( Supplementary Table 2 ). AML was significantly associated with pesticide exposure in males (OR = 3.28, 95% CI: 1.10–9.77) and in those aged 60 years and older (OR = 6.22, 95% CI: 1.48–26.08) ( Table 3 ). Adhesives, formaldehyde, radiation, and thinners were identified as exposure agents for which the exposure-response relationship could be examined according to the cumulative exposure grade, as the exposure of both the patient and control groups could be classified as high, medium, or low. All these substances showed a tendency for the OR values to increase as the cumulative exposure grade increased from low to high, but the exposure dose-response relationship was statistically significant only for thinners (p for trend = 0.02) ( Table 4 ). The results of all surveyed exposure agents are shown in Supplementary Tables 5 and 6 .

Table 3

Sex and age specific odds ratio (OR) and 95% confidence intervals (CI) of acute myeloid leukemia according to exposure agents

Table 4

The odds ratio (OR) and 95% confidence intervals (CI) of acute myeloid leukemia according to exposure agents by cumulative exposure grade

4. Discussion

This case-control study attempted to identify high-risk occupations and exposure to occupational hazards associated with AML using data from a general hospital in the Republic of Korea. The case-control study design allowed us to systematically explore a large number of factors associated with AML. In the analysis of the risk of AML according to occupational classification, a significant association was found in paint manufacturing or painting work and aircrew. Although none of the results of the analysis were based on the presence or absence of occupational exposure to specific substances, according to the cumulative exposure level of occupational exposure to substances, the risk of AML was significantly higher in the group with a high (top) cumulative exposure level to thinners. Moreover, the risk of AML increased as the cumulative exposure to adhesives, formaldehyde, radiation, and thinners increased, although the test for trend was not statistically significant. In the analyses stratified by sex and age, the association between pesticide exposure and AML was significant in males and those aged 60 years or older.

Airline cabin crew are exposed to levels of ionizing radiation of 2 to 4 mSv annually [ 13 ]. This dose is almost twice the typical annual dose that people in the general population receive from natural and medicinal sources. Gamma and neutron radiation dominate the cosmic radiation at typical cruising altitudes (8,000–10,000 m), with a small number of heavy nuclei. In 1990, the International Commission on Radiological Protection suggested that exposure of aircrew to radiation during flights should be considered an occupational exposure [ 14 ]. However, protection against exposure to cosmic radiation is insufficient in the Republic of Korea. Previous research on the incidence of cancer in airline pilots found an elevated prevalence of skin, prostate, and brain cancers [ 15 ]. Other studies have reported a higher risk of leukemia in airline cabin crew [ 16 ]. Given its large relative excess risk and the lack of other risk factors, leukemia (apart from chronic lymphocytic leukemia) is a cancer that can serve as a good indicator of the health impact of ionizing radiation. There have also been reports of an increased prevalence of chromosomal abnormalities in flight attendants that could indicate cancer risk [ 17 ]. A population-based cohort study of Danish pilots revealed that increasing flight hours in jets increased the risk of AML [ 18 ].

Paint manufacturing and painting have previously been reported to increase the risk of AML [ 19 ]. A case-control study of patients with AML in Novi Sad (Yugoslavia) and London found that painters had a significantly higher risk of developing AML (OR 4.57) [ 20 ]. Exposure to organic solvents such as benzene, toluene, xylene, degreasers, adhesives, and glues containing organic solvents is common in these jobs, raising the possibility that one or more of these exposures leads to a higher risk of AML. We found a statistically significant increase in the risk of AML among those who had occupational exposure to paint thinners, and the risk of AML increased according to the cumulative exposure grade. Our findings are consistent with those of a previous meta-analysis of industry-based cohorts that found that exposure to benzene at work increased the incidence of AML in a dose-dependent manner [ 21 ]. The association between solvents and AML has been investigated in several previous case-control studies. In a case-control study of AML in Italy, there was no association between exposure to any solvent and AML [ 22 ]. However, a hospital-based case-control study of AML in Shanghai found that exposures associated with an increased risk of AML included benzene, diesel fuel, metals, insecticides, fertilizers, glues and adhesives, paints and other coatings, and inks and pigments [ 7 ]. As the most researched solvent in relation to leukemia risk, benzene, which is sometimes mixed as an impurity in thinner, is recognized to be hemotoxic even at low levels of exposure [ 23 ]. However, evidence supporting an elevated risk of AML due to exposure to organic solvents other than benzene is mixed [ 7 , 20 , 22 , 23 ].

Several epidemiologic studies have investigated the relationship between pesticide exposure and AML, but the results have been inconsistent. In a systematic review, 17 cohort studies yielded a meta-rate ratio estimate of 1.21 for occupational pesticide exposure, but there was significant heterogeneity among the studies, largely due to the different occupational groups evaluated [ 24 ]. A statistically significant increase in risk was observed in studies targeting manufacturing workers and pesticide applicators, but not among farmers or agricultural workers. In a recent meta-analysis of 14 case-control studies including 3,955 cases and 9,948 controls, occupational pesticide exposure was found to be associated with an increased risk of AML (OR 1.51), which was not affected by sensitivity analyses [ 25 ]. However, although age was adjusted or matched in most studies, the researchers did not conduct an analysis of the risk of AML stratified by age as in this study.

As a case-control study, our study has some inherent limitations. The first is the potential for misclassification of exposure caused by recall bias, which is inherent in all case-control studies. To reduce this effect, OEM experts conducted an occupational history interview using a structured questionnaire. The reliability and applicability of self-reported occupational histories in determining exposure history were reviewed by two independent occupational hygiene experts for both cases and controls. Second, owing to the large number of ORs estimated (for a large number of risk factors and AML combinations), some ORs may have been statistically significant by chance alone. Although the observed age-specific and sex-specific variation in risk estimates may be because of physiological changes caused by age and sex on toxicokinetics, it is also possible due to the potential of coincidental observations. Third, patients were recruited in our study from one Korean hospital. Therefore, the patients included in the study may not be representative of the entire population of patients with AML in the Republic of Korea. However, the study hospital was a large university hospital that treats patients with AML from throughout the country. Thus, the patient population may be somewhat representative of the Korean AML patient population. Fourth, we were unable to consider other known risk factors or potential confounders associated with AML such as income, educational level, smoking, and genetics [ 6 ]. Because the data of cases for this study were obtained from hospital care and treatment settings, there is a limitation in that additional demographic information was not investigated. It is also not possible to rule out the possibility of residual confounding due to measurement error or unknown confounding factors. Finally, many of the risk estimates were based on small sample sizes; thus, caution should be exercised when interpreting the results. We plan to perform an updated analysis once we have recruited more patients with AML.

On the other hand, this study also has several strengths. To our knowledge, this is the first case-control study in the Republic of Korea to investigate occupation and occupational exposures as risk factors for newly diagnosed AML. Moreover, we were able to estimate overall occupational exposure more accurately by collecting lifetime occupational history rather than limiting the analyses to the most recent job.

In conclusion, this case-control study identified high-risk occupational groups in the Republic of Korea including paint manufacturers and painters, aircrew, and those who are occupationally exposed to pesticides or paint thinners. The research methodology used in this study serves as a reference for follow-up studies including multicenter hospital. The methods of this study, using data from the cooperative system established at the study hospital, are expected to influence the establishment of surveillance systems at other hospitals in the Republic of Korea in the future.

Once knowledge based on real-world experience is established to identify high-risk occupations and occupational hazard exposure for AML, it will affect the awareness of medical staff in hospitals and the operation of surveillance systems for occupational health services. Thus, more patients with AML and occupational cancers are expected to receive adequate treatment opportunities through compensation for occupational diseases. In addition, this study could be used to establish a policy basis for worker health protection and play an expanded role in disease prevention by raising awareness regarding risk factors for occupational cancers among workers and the general public.

Ethical approval

This study was conducted in accordance with the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of the Seoul St. Mary's Hospital, the Catholic University of Korea (IRB number: KC21RISI0551).

The authors wish to acknowledge the financial support of the Catholic Medical Center Research Foundation made in the program year of 2021.

Conflicts of interest

The authors declare that they have no competing interests.

Acknowledgments

We would like to express our great gratitude to Soongjeon Lee and Gyeongchae Lim for contributing to the evaluation of occupational exposure as industrial hygiene experts.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.shaw.2023.10.010 .

Appendix A. Supplementary data

The following is the Supplementary data to this article: