Multi-omics analysis defines highly refractory RAS burdened immature subgroup of infant acute lymphoblastic leukemia

Concepts in B cell acute lymphoblastic leukemia pathogenesis

B cell acute lymphoblastic leukemia (B-ALL) arises from genetic alterations impacting B cell progenitors, ultimately leading to clinically overt disease. Extensive collaborative efforts in basic and clinical research have significantly improved patient prognoses. Nevertheless, a subset of patients demonstrate resistance to conventional chemotherapeutic approaches and emerging immunotherapeutic interventions. This review highlights the mechanistic underpinnings governing B-ALL transformation. Beginning with exploring normative B cell lymphopoiesis, we delineate the influence of recurrent germline and somatic genetic aberrations on the perturbation of B cell progenitor differentiation and protumorigenic signaling, thereby facilitating the neoplastic transformation underlying B-ALL progression. Additionally, we highlight recent advances in the multifaceted landscape of B-ALL, encompassing metabolic reprogramming, microbiome influences, inflammation, and the discernible impact of socioeconomic and racial disparities on B-ALL transformation and patient survival.

A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology

Multi-omics data play a crucial role in precision medicine, mainly to understand the diverse biological interaction between different omics. Machine learning approaches have been extensively employed in this context over the years. This review aims to comprehensively summarize and categorize these advancements, focusing on the integration of multi-omics data, which includes genomics, transcriptomics, proteomics and metabolomics, alongside clinical data. We discuss various machine learning techniques and computational methodologies used for integrating distinct omics datasets and provide valuable insights into their application. The review emphasizes both the challenges and opportunities present in multi-omics data integration, precision medicine and patient stratification, offering practical recommendations for method selection in various scenarios. Recent advances in deep learning and network-based approaches are also explored, highlighting their potential to harmonize diverse biological information layers. Additionally, we present a roadmap for the integration of multi-omics data in precision oncology, outlining the advantages, challenges and implementation difficulties. Hence this review offers a thorough overview of current literature, providing researchers with insights into machine learning techniques for patient stratification, particularly in precision oncology. Contact:  anirban@klyuniv.ac.in.

Aberrant stem cell and developmental programs in pediatric leukemia

Hematopoiesis is a finely orchestrated process, whereby hematopoietic stem cells give rise to all mature blood cells. Crucially, they maintain the ability to self-renew and/or differentiate to replenish downstream progeny. This process starts at an embryonic stage and continues throughout the human lifespan. Blood cancers such as leukemia occur when normal hematopoiesis is disrupted, leading to uncontrolled proliferation and a block in differentiation of progenitors of a particular lineage (myeloid or lymphoid). Although normal stem cell programs are crucial for tissue homeostasis, these can be co-opted in many cancers, including leukemia. Myeloid or lymphoid leukemias often display stem cell-like properties that not only allow proliferation and survival of leukemic blasts but also enable them to escape treatments currently employed to treat patients. In addition, some leukemias, especially in children, have a fetal stem cell profile, which may reflect the developmental origins of the disease. Aberrant fetal stem cell programs necessary for leukemia maintenance are particularly attractive therapeutic targets. Understanding how hijacked stem cell programs lead to aberrant gene expression in place and time, and drive the biology of leukemia, will help us develop the best treatment strategies for patients.

Metabolic Fingerprint in Childhood Acute Lymphoblastic Leukemia

INTRODUCTION

Acute lymphoblastic leukemia (ALL) is the most prevalent childhood malignancy. Despite high cure rates, several questions remain regarding predisposition, response to treatment, and prognosis of the disease. The role of intermediary metabolism in the individualized mechanistic pathways of the disease is unclear. We have hypothesized that children with any (sub)type of ALL have a distinct metabolomic fingerprint at diagnosis when compared: (i) to a control group; (ii) to children with a different (sub)type of ALL; (iii) to the end of the induction treatment.

MATERIALS AND METHODS

In this prospective case-control study (NCT03035344), plasma and urinary metabolites were analyzed in 34 children with ALL before the beginning (D0) and at the end of the induction treatment (D33). Their metabolic fingerprint was defined by targeted analysis of 106 metabolites and compared to that of an equal number of matched controls. Multivariate and univariate statistical analyses were performed using SIMCAP and scripts under the R programming language.

RESULTS

Metabolomic analysis showed distinct changes in patients with ALL compared to controls on both D0 and D33. The metabolomic fingerprint within the patient group differed significantly between common B-ALL and pre-B ALL and between D0 and D33, reflecting the effect of treatment. We have further identified the major components of this metabolic dysregulation, indicating shifts in fatty acid synthesis, transfer and oxidation, in amino acid and glycerophospholipid metabolism, and in the glutaminolysis/TCA cycle.

CONCLUSIONS

The disease type and time point-specific metabolic alterations observed in pediatric ALL are of particular interest as they may offer potential for the discovery of new prognostic biomarkers and therapeutic targets.

Functional relevance of circRNA aberrant expression in pediatric acute leukemia with KMT2A::AFF1 fusion

ABSTRACT

Circular RNAs (circRNAs) are emerging molecular players in leukemogenesis and promising therapeutic targets. In KMT2A::AFF1 (MLL::AF4)-rearranged leukemia, an aggressive disease compared with other pediatric B-cell precursor (BCP) acute lymphoblastic leukemia (ALL), data about circRNAs are limited. Here, we disclose the circRNA landscape of infant patients with KMT2A::AFF1 translocated BCP-ALL showing dysregulated, mostly ectopically expressed, circRNAs in leukemia cells. Most of these circRNAs, apart from circHIPK3 and circZNF609, previously associated with oncogenic behavior in ALL, are still uncharacterized. An in vitro loss-of-function screening identified an oncogenic role of circFKBP5, circKLHL2, circNR3C1, and circPAN3 in KMT2A::AFF1 ALL, whose silencing affected cell proliferation and apoptosis. Further study in an extended cohort disclosed a significantly correlated expression of these oncogenic circRNAs and their putative involvement in common regulatory networks. Moreover, it showed that circAFF1 upregulation occurs in a subset of cases with HOXA KMT2A::AFF1 ALL. Collectively, functional analyses and patient data reveal oncogenic circRNA upregulation as a relevant mechanism that sustains the malignant cell phenotype in KMT2A::AFF1 ALL.

KMT2A oncoproteins induce epigenetic resistance to targeted therapies

Chromosomal translocations involving the Lysine-Methyl-Tansferase-2A ( KMT2A ) locus generate potent oncogenes that cause highly aggressive acute leukemias 1 . KMT2A and the most frequent translocation partners encode proteins that interact with DNA to regulate developmental gene expression 2 . KMT2A-oncogenic fusion proteins (oncoproteins) contribute to the epigenetic mechanisms that allow KMT2A -rearranged leukemias to evade targeted therapies. By profiling the oncoprotein-target sites of 34 KMT2A -rearranged leukemia samples, we find that the genomic enrichment of oncoprotein binding is highly variable between samples. At high levels of expression, the oncoproteins preferentially activate either the lymphoid or myeloid lineage program depending on the fusion partner. These fusion-partner-dependent binding sites correspond to the frequencies of each mutation in acute lymphoid leukemia versus acute myeloid leukemia. By profiling a sample that underwent a lymphoid-to-myeloid lineage switching event in response to lymphoid-directed treatment, we find the global oncoprotein levels are reduced and the oncoprotein-target gene network changes. At lower levels of expression, the oncoprotein shifts to a non-canonical regulatory program that favors the myeloid lineage, and in a subset of resistant patients, the Menin inhibitor Revumenib induces a similar response. The dynamic shifts in KMT2A oncoproteins we describe likely contribute to epigenetic resistance of KMT2A -rearranged leukemias to targeted therapies.

Genetic alterations and MRD refine risk assessment for KMT2A-rearranged B-cell precursor ALL in adults: a GRAALL study

KMT2A-rearranged (KMT2A-r) B-cell precursor acute lymphoblastic leukemia (BCP-ALL) is widely recognized as a high-risk leukemia in both children and adults. However, there is a paucity of data on adults treated in recent protocols, and the optimal treatment strategy for these patients is still a matter of debate. In this study, we set out to refine the prognosis of adult KMT2A-r BCP-ALL treated with modern chemotherapy regimen and investigate the prognostic impact of comutations and minimal residual disease (MRD). Of 1091 adult patients with Philadelphia-negative BCP-ALL enrolled in 3 consecutive trials from the Group for Research on Adult Acute Lymphoblastic Leukemia (GRAALL), 141 (12.9%) had KMT2A-r, with 5-year cumulative incidence of relapse (CIR) and overall survival (OS) rates of 40.7% and 53.3%, respectively. Molecular profiling highlighted a low mutational burden in this subtype, reminiscent of infant BCP-ALL. However, the presence of TP53 and/or IKZF1 alterations defined a subset of patients with significantly poorer CIR (69.3% vs 36.2%; P = .001) and OS (28.1% vs 60.7%; P = .006) rates. Next, we analyzed the prognostic implication of MRD measured after induction and first consolidation, using both immunoglobulin (IG) or T-cell receptor (TR) gene rearrangements and KMT2A genomic fusion as markers. In approximately one-third of patients, IG/TR rearrangements were absent or displayed clonal evolution during the disease course, compromising MRD monitoring. In contrast, KMT2A-based MRD was highly reliable and strongly associated with outcome, with early good responders having an excellent outcome (3-year CIR, 7.1%; OS, 92.9%). Altogether, our study reveals striking heterogeneity in outcomes within adults with KMT2A-r BCP-ALL and provides new biomarkers to guide risk-based therapeutic stratification.

Integrative models of histopathological images and multi-omics data predict prognosis in endometrial carcinoma

Objective

This study aimed to predict the molecular features of endometrial carcinoma (EC) and the overall survival (OS) of EC patients using histopathological imaging.

Methods

The patients from The Cancer Genome Atlas (TCGA) were separated into the training set (n = 215) and test set (n = 214) in proportion of 1:1. By analyzing quantitative histological image features and setting up random forest model verified by cross-validation, we constructed prognostic models for OS. The model performance is evaluated with the time-dependent receiver operating characteristics (AUC) over the test set.

Results

Prognostic models based on histopathological imaging features (HIF) predicted OS in the test set (5-year AUC = 0.803). The performance of combining histopathology and omics transcends that of genomics, transcriptomics, or proteomics alone. Additionally, multi-dimensional omics data, including HIF, genomics, transcriptomics, and proteomics, attained the largest AUCs of 0.866, 0.869, and 0.856 at years 1, 3, and 5, respectively, showcasing the highest discrepancy in survival (HR = 18.347, 95% CI [11.09-25.65], p < 0.001).

Conclusions

The results of this experiment indicated that the complementary features of HIF could improve the prognostic performance of EC patients. Moreover, the integration of HIF and multi-dimensional omics data might ameliorate survival prediction and risk stratification in clinical practice.

Multi-Omic Approaches to Classify, Predict, and Treat Acute Leukemias

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer, in which nearly 5% of the cases are diagnosed before the first year of age [...].

Evolution and optimization of therapies for acute lymphoblastic leukemia in infants

Acute lymphoblastic leukemia (ALL) in infants accounts for less than 5% of pediatric ALL and is biologically and clinically unique. Approximately 70% to 80% of cases present as an aggressive leukemia with KMT2A gene rearrangement (KMT2A-r), which is one of the most difficult-to-cure forms of pediatric leukemia. Owing to continuing global efforts through multicenter clinical trials since the mid-1990s, a standard of care for infant KMT2A-r ALL, including minimal residual disease-based risk stratifications, "hybrid chemotherapy" incorporating myeloid leukemia-like drugs (e.g., cytarabine) into the ALL chemotherapy backbone, and selective use of allogeneic hematopoietic stem cell transplantation, has now been established. However, there are still many concerns regarding treatment of infants with KMT2A-r ALL, including insufficient efficacy of the current standard therapies, limited pharmacokinetic/pharmacodynamic data on drugs in infants, and management of both acute and late toxicities. Refinements in risk stratification based on leukemia biology, as well as the introduction of emerging novel immunotherapies and molecular-targeted drugs to contemporary therapy, through international collaboration would provide key solutions for further improvement in outcomes.