Low Platelet Counts at Diagnosis Predict Better Survival for Patients with Intermediate-Risk Acute Myeloid Leukemia

Molecular precision medicine: Multi-omics-based stratification model for acute myeloid leukemia

Acute myeloid leukemia (AML), as the most common malignancy of the hematopoietic system, poses challenges in treatment efficacy, relapse, and drug resistance. In this study, we have utilized 151 RNA sequencing datasets, 194 DNA methylation datasets, and 200 somatic mutation datasets from the AML cohort in the TCGA database to develop a multi-omics stratification model. This model enables comparison of prognosis, clinical features, gene mutations, immune microenvironment and drug sensitivity across subgroups. External validation datasets have been sourced from the GEO database, which includes 562 mRNA datasets and 136 miRNA datasets from 984 adult AML patients. Through multi-omics-based stratification model, we classified 126 AML patients into 4 clusters (CS). CS4 had the best prognosis, with the youngest age, highest M3 subtype proportion, fewest copy number alterations, and common mutations in WT1, FLT3, and KIT genes. It showed sensitivity to HDAC inhibitors and BCL-2 inhibitors. Both the M3 subtype and CS4 were identified as independent protective factors for survival. Conversely, CS3 had the worst prognosis due to older age, high copy number alterations, and frequent mutations in RUNX1, DNMT3A, and TP53 genes. Additionally, it showed higher proportions of cytotoxic cells and Tregs, suggesting potential sensitivity to mTOR inhibitors. CS1 had a better prognosis than CS2, with more copy number alterations, while CS2 had higher monocyte proportions. CS1 showed good sensitivity to cytarabine, while CS2 was sensitive to RXR agonists. Both CS1 and CS2, which predominantly featured mutations in FLT3, NPM1, and DNMT3A genes, benefited from FLT3 inhibitors. Using the Kappa test, our stratification model underwent robust validation in the miRNA and mRNA external validation datasets. With advancements in sequencing technology and machine learning algorithms, AML is poised to transition towards multi-omics precision medicine in the future. We aspire for our study to offer new perspectives on multi-drug combination clinical trials and multi-targeted precision medicine for AML.

Fatigue in newly diagnosed acute myeloid leukaemia: general population comparison and predictive factors

OBJECTIVES

This study compared the burden of fatigue between treatment-naïve patients with newly diagnosed acute myeloid leukaemia (AML) and the general population and investigated patient factors associated with fatigue severity.

METHODS

Pretreatment patient-reported fatigue was assessed with the Functional Assessment of Chronic Illness Therapy-Fatigue questionnaire in a sample of 463 newly diagnosed patients with AML who were enrolled in a clinical trial. Multivariable linear regression models were used to estimate the adjusted mean differences in fatigue between patients with AML and adults from the general population (n=847) by AML disease risk categories. A clinically meaningful difference in fatigue was defined as ≥3 points. Univariable and multivariable linear regression models were used to identify sociodemographic, clinical and molecular correlates of worse fatigue in patients with AML.

RESULTS

Patients with AML reported adjusted mean fatigue scores that were 7.5 points worse than the general population (95% CI -8.6 to -6.4, p<0.001). Across AML disease risk categories, adjusted mean differences in fatigue compared with the general population ranged from 6.7 points worse (patients with favourable risk: 95% CI -8.6 to -4.8, p<0.001) to 8.9 points worse (patients with poor risk, 95% CI -10.5 to -7.2, p<0.001). Overall, 91% of patients with AML reported fatigue that was equal to or worse than the general population's median fatigue score. Higher pretreatment fatigue was independently associated with female sex, WHO performance status ≥1 and lower platelet levels.

CONCLUSIONS

Patients with newly diagnosed AML reported worse fatigue than the general population, and mean differences exceeded twice the threshold for clinical significance. Our findings may help to identify patients with AML most likely to benefit from supportive care interventions to reduce fatigue.

A new prognostic risk model for acute myeloid leukemia patients based on telomere-related genes

Telomere maintenance is critical to ensure unlimited cancer cell proliferation, but the role of telomere-related genes in acute myeloid leukemia (AML) has not yet been thoroughly discussed. This study aims to develop a new prognostic risk model based on telomere-related genes and analyze potential mechanisms and targets. Cox regression analyses were used to build the prognostic risk model. Kaplan-Meier (KM) survival analysis and receiver operating characteristic (ROC) curve were used to assess the model performance. At the same time, we analyzed the relationship between the risk score and chemotherapy and immunotherapy and preliminarily explored possible mechanisms of immune resistance. The real-time polymerase chain reaction (PCR) was used to detect the prognosis gene expression levels. Finally, a prognostic signature of six telomere-related genes (TGPS6) including ALDH2, CDK18, DNMT3B, FRAT2, LGALSL, and RBL2 was constructed. The TGPS6 score was confirmed as an independent prognostic factor (HR 2.74, CI [2.13-3.53], p < 0.001) in AML and the five-year area under the ROC curve (AUC) value of the score in the training and validation set reached 0.74, 0.81 respectively. In addition, the TGPS6 perfected the European LeukemiaNet (ELN) 2017 prognosis risk stratification and performed well in both AML and cytogenetically normal AML (CN-AML) cohorts. The TGPS6 score also provided a reference for chemotherapy and immunotherapy in patients with AML.

Influence on therapeutic outcome of platelet count at diagnosis in patients with de novo non-APL acute myeloid leukemia

BACKGROUND

Platelet (PLT) count at diagnosis plays an important role in cancer development and progression in solid tumors. However, it remains controversial whether PLT count at diagnosis influences therapeutic outcome in patients with non-acute promyelocytic leukemia (APL) acute myeloid leukemia (AML).

METHODS

This study analyzed the relationship between PLT count at diagnosis and genetic mutations in a cohort of 330 newly diagnosed non-APL AML patients. The impact of PLT count on complete remission, minimal residual disease status and relapse-free survival (RFS) were evaluated after chemotherapy or allogeneic hematopoietic stem cell transplantation (allo-HSCT).

RESULTS

Our studies showed that patients with DNMT3A mutations have a higher PLT count at diagnosis, while patients with CEBPA biallelic mutations or t(8;21)(q22; q22) translocation had lower PLT count at diagnosis. Furthermore, non-APL AML patients with high platelet count (> 65 × 109/L) at diagnosis had worse response to induction chemotherapy and RFS than those with low PLT count. In addition, allo-HSCT could not absolutely attenuated the negative impact of high PLT count on the survival of non-APL AML patients.

CONCLUSION

PLT count at diagnosis has a predictive value for therapeutic outcome for non-APL AML patients.

Evaluation and validation of the prognostic value of platelet indices in patients with leukemia

Platelets (PLTs) are believed to play a role in the process by which tumors can accelerate their growth rate, as well as offer the physical and mechanical support necessary to evade the immunological system and metastasis. There is, however, no literature available if PLTs have a role in leukemia. It is significant for PLTs to play a part in hematological malignancies from a therapeutic standpoint and to have the capacity to serve as a prognostic marker in the evolution of leukemia. This is because PLTs play a crucial role in the development of cancer and tumors. In this study, it will be shown that PLT count can be used to predict long-term prognosis after chemotherapy especially in the case of acute myeloid leukemia patients. Furthermore, low PLT-to-lymphocyte ratio and mean PLT volume, as well as high PLT distribution width, are associated with poor prognosis and may represent a novel independent prognostic factor.

Defining Current Patterns of Blood Product Use during Intensive Induction Chemotherapy in Newly Diagnosed Acute Myeloid Leukemia Patients

Introduction

Blood product transfusion retains a critical role in the supportive care of patients with acute myeloid leukemia (AML). Whereas previous studies have shown increased transfusion dependency to portend inferior outcome, predictive factors of an increased transfusion burden and the prognostic impact of transfusion support have not been assessed recently.

Methods/Patients

We performed a retrospective analysis on a recent cohort of patients given intensive induction chemotherapy in 2014-2022.

Results

The analysis comprised 180 patients with a median age of 57 years with 80% designated as de novo AML. Fifty-four patients (31%) were FLT3-ITD mutated, and 73 patients (42%) harbored NPM1. Favorable risk and intermediate risk ELN 2017 patients accounted for 43% and 34% of patients, respectively. The median number of red blood cell (RBC) and platelet units given during induction were 9 and 7 units, respectively. Seventeen patients (9%) received cryoprecipitate, and fresh frozen plasma (FFP) was given to 12 patients (7%). Lower initial hemoglobin and platelet levels were predictive of increased use of RBC (p < 0.0001) and platelet transfusions (p < 0.0001). FFP was significantly associated with induction related mortality (42% vs. 5%; p < 0.0001) and with FLT3-ITD (72% vs. 28%; p = 0.004). Blood group AB experienced improved mean overall survival compared to blood group O patients (4.1 years vs. 2.8 years; p = 0.025). In multivariate analysis, increased number of FFP (hazard ratio [HR], 4.23; 95% confidence interval [CI], 2.1-8.6; p < 0.001) and RBC units (HR, 1.8; 95% CI, 1.2-2.8; p = 0.008) given was associated with inferior survival.

Conclusion

Transfusion needs during induction crucially impact the clinical trajectory of AML patients.

Platelet is an unfavorable prognostic biomarker and associated with leukemia stem cells and immunomodulatory factors in acute myeloid leukemia

Many clinical features, besides cytogenetic and molecular abnormalities, can affect the prognosis of the patients with acute myeloid leukemia (AML). Within this context it remains unclear if and how platelet counts affect the outcome of AML patients. In the present study, we examined the platelet counts at diagnosis in 633 newly diagnosed adult patients with AML from January 2010 to April 2021, and divided the cases into the group with low level of platelet counts (≤30×109/L, n=316) and high level of platelet counts (>30×109/L, n=317) according to the median platelet counts. We then validated the prognostic significance and potential mechanism of platelet counts on the relevance of spectral features for diagnostic risk stratification, initial induction therapy response, treatment effect maintenance, long-term survival, leukemia stem cells (LSCs) proportion, immunomodulatory cytokines level and immune cell subsets proportion. The results suggested that AML patients with a high level of platelet counts at diagnosis were associated with a high-risk molecular cytogenetic stratification, low complete remission (CR) rate, poor leukemia free survival (LFS), high proportion of LSCs, high level of transforming growth factor-β (TGF-β) and interleukin-1β (IL-1β), high proportion of regulatory T cells (Tregs) and monocytic myeloid-derived suppressor cells (M-MDSCs). It was demonstrated that platelet might be an unfavorable prognostic biomarker and was associated with LSCs and immunomodulatory cytokines as well as immune cell subsets in AML.

Re-induction therapy in patients with acute myeloid leukemia not in complete remission after the first course of treatment

A standard salvage regimen for patients with acute myeloid leukemia (AML) who are not in complete remission (CR) after initial induction therapy does not exist. We retrospectively investigated re-induction therapy for 151 patients with AML who did not achieve CR after the initial course between January 2014 and March 2021. The re-induction regimen did not correlate with the CR rate after the second course, whereas patients had similar 5-year overall survival (OS) and event-free survival (EFS) based on different re-induction regimens. Multivariable analysis revealed that International European Leukaemia Net (ELN) risk stratification independently predicted both OS and EFS among patients not in CR after the first course, although the re-induction regimen did not predict prognosis. Urgent salvage alloHSCT may improve the prognosis of patients with refractory AML. In summary, our study showed that the re-induction regimen did not significantly predict the prognosis of patients with AML not in CR after the first course of treatment. The development and selection of an efficient treatment algorithm for the treatment of AML remains a pressing research challenge.

Disseminated intravascular coagulation in a cohort of adult acute leukemia patients: a single center experience

OBJECTIVES

We aimed to detect the incidence of disseminated intravascular coagulation (DIC) in patients with acute leukemia (AL) and find out its association with types of AL and patients' clinical and pathological parameters.

METHODS

In this prospective study, 59 newly diagnosed adults with AL were clinically examined and screened for DIC presentation time. Coagulation tests, including prothrombin time, activated partial thromboplastin time, fibrinogen level, D-dimer, antithrombin, and protein C and protein S levels were all assessed. The International Society for Thrombosis and Hemostasis scoring system was adopted to diagnose overt DIC.

RESULTS

The age of the studied patients ranged from 15 to 81 years with a median of 41 years; male to female ratio was 1.1:1. acute myeloid leukemia (AML) constituted 64.4% of the total cases (38 patients). DIC was detected in 28 patients (47.5%); its incidence was higher in AML than in acute lymphoblastic leukemia (ALL) (52.6% vs. 38.1%). Overt DIC was significantly associated with bleeding manifestations, duration of symptoms, and leukocytosis ( P -values = 0.050, 0.044, and 0.003, respectively). Bleeding events were encountered in 50.8% of patients (25 AML and 5 ALL patients). Bleeding was associated significantly with leukocytosis, thrombocytopenia, and low fibrinogen level. Thrombosis was found in two patients (3.4%) at presentation.

CONCLUSIONS

Overt DIC was common in patients with AL at presentation, mostly in AML. Routine testing for coagulopathy in newly diagnosed AL patients will possibly aid in improving the overall patients' survival.

Prognostic Role of Cell Blood Count in Chronic Myeloid Neoplasm and Acute Myeloid Leukemia and Its Possible Implications in Hematopoietic Stem Cell Transplantation

Numerous prognostic indexes have been developed in hematological diseases based on patient characteristics and genetic or molecular assessment. However, less attention was paid to more accessible parameters, such as neutrophils, lymphocytes, monocytes, and platelet counts. Although many studies have defined the role of neutrophil-to-lymphocyte or platelet-to-lymphocyte in lymphoid malignancies, few applications exist for myeloid neoplasm or hematopoietic stem cell transplantation procedures. In this review, we synthesized literature data on the prognostic value of count blood cells in myeloid malignancies and hematopoietic stem cell transplantation in the context of classical prognostic factors and clinical outcomes.

Identification of Survival-Related Genes in Acute Myeloid Leukemia (AML) Based on Cytogenetically Normal AML Samples Using Weighted Gene Coexpression Network Analysis

The prognosis of acute myeloid leukemia (AML) remains a challenge. In this study, we applied the weighted gene coexpression network analysis (WGCNA) to find survival-specific genes in AML based on 42 adult CN-AML samples from The Cancer Genome Atlas (TCGA) database. Eighteen hub genes (ABCA13, ANXA3, ARG1, BTNL8, C11orf42, CEACAM1, CEACAM3, CHI3L1, CRISP2, CYP4F3, GPR84, HP, LTF, MMP8, OLR1, PADI2, RGL4, and RILPL1) were found to be related to AML patient survival time. We then compared the hub gene expression levels between AML peripheral blood (PB) samples ( $n=162$ ) and control healthy whole blood samples ( $n=337$ ). Seventeen of the hub genes showed lower expression levels in AML PB samples. The gene expression analysis was also done among AML BM (bone marrow) samples of different stages: diagnosis ( $n=142$ ), posttreatment ( $n=42$ ), and recurrent ( $n=12$ ) stages. The results showed a significant increase of ANXA3, CEACM1, RGL4, RILPL1, and HP in posttreatment samples compared to diagnosis and/or recurrent samples. Transcription factor (TF) prediction of the hub genes suggested LTF as the top hit, overlapping 10 hub genes, while LTF itself is just one of the hub genes. Also, 3671 correlation links were shown between 128 mRNAs and 209 lncRNAs found in survival time-related modules. Generally, we identified candidate mRNA biomarkers based on CN-AML data which can be extensively used in AML prognosis. In addition, we mapped their potential regulatory mechanisms with correlated lncRNAs, providing new insights into potential targets for therapies in AML.

Platelet-Acute Leukemia Interactions

Acute leukemia (AL) is a hematological malignancy with high morbidity and mortality that is caused by abnormal hematopoietic stem cells. AL can change the parameters, quality, and function of platelets through numerous mechanisms, resulting in bleeding and even death in AL patients. Hence, AL patients are often clinically treated using normal platelet transfusion. However, studies have found that platelets can also affect AL cells. This review discusses the changes occurring in platelet count, mean platelet volume, platelet distribution width, reticulated platelets, platelet membrane glycoprotein, platelet aggregation, and activation in AL patients, the causes of these changes, and the possible significance of these changes for patient prognosis. The effects of platelets on the proliferation and drug resistance of AL cells are also discussed.

Investigation of Biomarkers Associated with Low Platelet Counts in Normal Karyotype Acute Myeloid Leukemia

Acute myeloid leukemia (AML) patients are at risk of bleeding due to disease-related lack of platelets and systemic coagulopathy. Platelets play a role in hemostasis. Leukemic blasts have been shown to alter platelet activation in vitro. Here we investigated biomarkers associated with thrombocytopenia in normal karyotype AML (NK-AML). From The Cancer Genome Atlas database, case-control study was performed between normal karyotype (NK) platelet-decreased AML (PD-AML, platelet count < 100 × 109/L, n = 24) and NK platelet-not-decreased AML (PND-AML, with platelet count ≥ 100 × 109/L, n = 13). Differentially expressed gene analysis, pathway analysis and modelling for predicting platelet decrease in AML were performed. DEG analysis and pathway analysis revealed 157 genes and eight pathways specific for PD-AML, respectively. Most of the eight pathways were significantly involved in G-protein-coupled receptor-related pathway, cytokine-related pathway, and bone remodeling pathway. Among the key genes involved in at least one pathway, three genes including CSF1R, TNFSF15 and CLEC10A were selected as promising biomarkers for predicting PD-AML (0.847 of AUC in support vector machine model). This is the first study that identified biomarkers using RNA expression data analysis and could help understand the pathophysiology in AML with low platelet count.

GATA2 rs2335052 and GATA2 rs78245253 single-nucleotide polymorphisms in Chinese patients with acute myelocytic leukemia

INTRODUCTION

GATA binding protein 2 (GATA2) gene, involved in progression of hematologic malignancies and various solid tumors, is a susceptibility gene for inherited acute myeloid leukemia (AML). However, the influence of its single-nucleotide polymorphisms (SNPs) on AML remains unknown.

METHODS

We used allele-specific PCR to genotype GATA2 rs2335052 and rs78245253 in 159 newly diagnosed AML (non-M3) patients and 300 healthy volunteers, and all of participants came from China. And 34 common hematological tumor gene mutations in 159 AML patients were detected by next-generation sequencing. Kaplan-Meier survival analysis and Cox proportional hazard regression were used to analyze the association between the two SNPs and the prognosis of AML.

RESULTS

We found GATA2 rs2335052 C/T genotype, rs2335052 T/T genotype and rs78245253 G/C genotype in 51.6%, 13.8% and 11.3% AML patients. Our results demonstrated that GATA2 rs2335052 and rs78245253 were associated with certain laboratory features in AML patients, which had no effect on the pathogeny, chemotherapy response and recurrence of patients. Nevertheless, Kaplan-Meier survival analysis showed that, compared with rs78245253 G/G genotype, rs78245253 G/C genotype was significantly related to a decrease in overall survival (OS) (P = .020). Additionally, multivariate cox regression analysis showed that GATA2 rs78245253 was an independent risk factor for OS of AML patients in China.

CONCLUSION

GATA2 rs78245253 was an independent predictor for prognosis of AML patients in China and may be used as a potential indicator to predict the survival of AML patients in China. Further studies are needed to validate these findings and clarify the underlying mechanism.

A clinical prediction model identifies a subgroup with inferior survival within intermediate risk acute myeloid leukemia

Intermediate risk acute myeloid leukemia (AML) comprises around 50% of AML patients and is featured with heterogeneous clinical outcomes. The study aimed to generate a prediction model to identify intermediate risk AML patients with an inferior survival. We performed targeted next generation sequencing analysis for 121 patients with 2017 European LeukemiaNet-defined intermediate risk AML, revealing 122 mutated genes, with 24 genes mutated in > 10% of patients. A prognostic nomogram characterized by white blood cell count ≥10×10⁹/L at diagnosis, mutated DNMT3A and genes involved in signaling pathways was developed for 110 patients who were with clinical outcomes. Two subgroups were identified: intermediate low risk (ILR; 43.6%, 48/110) and intermediate high risk (IHR; 56.4%, 62/110). The model was prognostic of overall survival (OS) and relapse-free survival (RFS) (OS: Concordance index [C-index]: 0.703, 95%CI: 0.643-0.763; RFS: C-index: 0.681, 95%CI 0.620-0.741), and was successfully validated with two independent cohorts. Allogeneic hematopoietic stem cell transplantation (alloHSCT) reduced the relapse risk of IHR patients (3-year RFS: alloHSCT: 40.0±12.8% vs. chemotherapy: 8.6±5.8%, P= 0.010). The prediction model can help identify patients with an unfavorable prognosis and refine risk-adapted therapy for intermediate risk AML patients.

Platelets: The Emerging Clinical Diagnostics and Therapy Selection of Cancer Liquid Biopsies

Due to the inherent molecular heterogeneity of metastatic tumours and the dynamic evolution ability of tumour genomes, tumour tissues obtained through biopsy and other methods cannot capture all of the features of tumour genomes. A new diagnostic concept called “liquid biopsy” has received widespread attention in recent years. Liquid biopsy has changed the clinical practice of oncology and is widely used to guide targeted drug utilization, monitor disease progression and track drug resistance. The latest research subject in liquid biopsy is platelets. Platelets originate from multifunctional haematopoietic stem cells in the bone marrow haematopoietic system. They are small cells from the cytoplasm of bone marrow megakaryocytes. Their main physiological functions are to participate in the processes of physiological haemostasis and coagulation. Tumour cells transfer biomolecules (such as RNA) to platelets through direct contact and release of exosomes, which changes the platelet precursor RNA. Under the stimulation of tumour cells and the tumour microenvironment, platelet precursor mRNA is spliced into mature RNA and converted into functional protein to respond to external stimuli, forming tumour-educated platelets (TEPs). The detection of TEPs in the peripheral blood of patients is expected to be used in clinical tumour diagnosis. This emerging liquid biopsy method can replace and supplement the current tumour detection methods. Further research on the role of platelets in tumour diagnosis will help provide a novel theoretical basis for clinical tumour diagnosis.

Dynamic prediction of relapse in patients with acute leukemias after allogeneic transplantation: Joint model for minimal residual disease

INTRODUCTION

Relapse remains the leading cause of treatment failure after allogeneic hematopoietic stem cell transplantation (alloHSCT) in leukemia. Numerous investigations have demonstrated that minimal residual disease (MRD) before or after alloHSCT is prognostic of relapse risk. These MRD data were collected at specific checkpoints and could not dynamically predict the relapse risk after alloHSCT, which needs serial monitoring.

METHODS

In the present study, we retrospectively analyzed MRD measured with multi-parameter flow cytometry in 207 acute myeloid leukemia (AML) patients (acute promyelocytic leukemia excluded), and 124 acute B lymphoblastic leukemia (ALL) patients. A three-step method based on joint model was used to build a relapse risk prediction model.

RESULTS

The 3-year overall survival and relapse-free survival rates of the entire cohort were 67.1% ± 2.8% and 61.6% ± 2.8%, respectively. The model included disease status before alloHSCT, acute and chronic graft-versus-host disease, and serial MRD data. The time-dependent receiver operating characteristics was used to evaluate the ability of the model. It fitted well with actual incidence of relapse. The serial MRD data collected after alloHSCT had better discrimination capabilities for recurrence prediction with the area under the curve from 0.67 to 0.91 (AML: 0.66-0.89; ALL: 0.70-0.96).

CONCLUSION

The joint model was able to dynamically predict relapse-free probability after alloHSCT, which would be a useful tool to provide important information to guide decision-making in the clinic and facilitate the individualized therapy.