Transcriptional and Mutational Profiling of B-Other Acute Lymphoblastic Leukemia for Improved Diagnostics

An artificial intelligence-assisted clinical framework to facilitate diagnostics and translational discovery in hematologic neoplasia

BACKGROUND

The increasing volume and intricacy of sequencing data, along with other clinical and diagnostic data, like drug responses and measurable residual disease, creates challenges for efficient clinical comprehension and interpretation. Using paediatric B-cell precursor acute lymphoblastic leukaemia (BCP-ALL) as a use case, we present an artificial intelligence (AI)-assisted clinical framework clinALL that integrates genomic and clinical data into a user-friendly interface to support routine diagnostics and reveal translational insights for hematologic neoplasia.

METHODS

We performed targeted RNA sequencing in 1365 cases with haematological neoplasms, primarily paediatric B-cell precursor acute lymphoblastic leukaemia (BCP-ALL) from the AIEOP-BFM ALL study. We carried out fluorescence in situ hybridization (FISH), karyotyping and arrayCGH as part of the routine diagnostics. The analysis results of these assays as well as additional clinical information were integrated into an interactive web interface using Bokeh, where the main graph is based on Uniform Manifold Approximation and Projection (UMAP) analysis of the gene expression data. At the backend of the clinALL, we built both shallow machine learning models and a deep neural network using Scikit-learn and PyTorch respectively.

FINDINGS

By applying clinALL, 78% of undetermined patients under the current diagnostic protocol were stratified, and ambiguous cases were investigated. Translational insights were discovered, including IKZF1plus status dependent subpopulations of BCR::ABL1 positive patients, and a subpopulation within ETV6::RUNX1 positive patients that has a high relapse frequency. Our best machine learning models, LDA and PASNET-like neural network models, achieve F1 scores above 97% in predicting patients' subgroups.

INTERPRETATION

An AI-assisted clinical framework that integrates both genomic and clinical data can take full advantage of the available data, improve point-of-care decision-making and reveal clinically relevant insights promptly. Such a lightweight and easily transferable framework works for both whole transcriptome data as well as the cost-effective targeted RNA-seq, enabling efficient and equitable delivery of personalized medicine in small clinics in developing countries.

FUNDING

German Ministry of Education and Research (BMBF), German Research Foundation (DFG) and Foundation for Polish Science.

The landscape of cytogenetic and molecular genetic methods in diagnostics for hematologic neoplasia

Improvements made during the last decades in the management of patients with hematologic neoplasia have resulted in increase of overall survival. These advancements have become possible through progress in our understanding of genetic basis of different hematologic malignancies and their role in the current risk-adapted treatment protocols. In this review, we provide an overview of current cytogenetic and molecular genetic methods, commonly used in the genetic characterization of hematologic malignancies, describe the current developments in the cytogenetic and molecular diagnostics, and give an outlook into their future development. Furthermore, we give a brief overview of the most important public databases and guidelines for sequence variant interpretation.

Transcriptome Sequencing Allows Comprehensive Genomic Characterization of Pediatric B-Acute Lymphoblastic Leukemia in an Academic Clinical Laboratory

Studies have shown the power of transcriptome sequencing [RNA sequencing (RNA-Seq)] in identifying known and novel oncogenic drivers and molecular subtypes of B-acute lymphoblastic leukemia (B-ALL). The current study investigated whether the clinically validated RNA-Seq assay, coupled with a custom analysis pipeline, could be used for a comprehensive B-ALL classification. Following comprehensive clinical testing, RNA-Seq was performed on 76 retrospective B-ALL cases, 28 of which had known and 48 had undetermined subtype. Subtypes were accurately identified in all 28 known cases, and in 38 of 48 unknown cases (79%). The subtypes of the unknown cases included the following: PAX5alt (n = 12), DUX4-rearranged (n = 6), Philadelphia chromosome-like (n = 5), low hyperdiploid (n = 4), ETV6::RUNX1-like (n = 3), MEF2D-rearranged (n = 2), PAX5 P80R (n = 2), ZEB2/CEBP (n = 1), NUTM1-rearranged (n = 1), ZNF384-rearranged (n = 1), and TCF3::PBX1 (n = 1). In 15 of 38 cases (39%), classification based on expression profile was corroborated by detection of subtype-defining oncogenic drivers missed by clinical testing. RNA-Seq analysis also detected large copy number abnormalities, oncogenic hot-spot sequence variants, and intragenic IKZF1 deletions. This pilot study confirms the feasibility of implementing an RNA-Seq workflow for clinical diagnosis of molecular subtypes in pediatric B-ALL, reinforcing that RNA-Seq represents a promising global genomic assay for this heterogeneous leukemia.

Cytogenetics and genomics in pediatric acute lymphoblastic leukaemia

The last five decades have witnessed significant improvement in diagnostics, treatment and management of children with acute lymphoblastic leukaemia (ALL). These advancements have become possible through progress in our understanding of the genetic and biological background of ALL, resulting in the introduction of risk-adapted treatment and novel therapeutic targets, e.g., tyrosine kinase inhibitors for BCR::ABL1-positive ALL. Further advances in the taxonomy of ALL and the discovery of new genetic biomarkers and therapeutic targets, as well as the introduction of targeted and immunotherapies into the frontline treatment protocols, may improve management and outcome of children with ALL. In this review we describe the current developments in the (cyto)genetic diagnostics and management of children with ALL, and provide an overview of the most important advances in the genetic classification of ALL. Furthermore, we discuss perspectives resulting from the development of new techniques, including artificial intelligence (AI).

The Gene Expression Classifier ALLCatchR Identifies B-cell Precursor ALL Subtypes and Underlying Developmental Trajectories Across Age

Current classifications (World Health Organization-HAEM5/ICC) define up to 26 molecular B-cell precursor acute lymphoblastic leukemia (BCP-ALL) disease subtypes by genomic driver aberrations and corresponding gene expression signatures. Identification of driver aberrations by transcriptome sequencing (RNA-Seq) is well established, while systematic approaches for gene expression analysis are less advanced. Therefore, we developed ALLCatchR, a machine learning-based classifier using RNA-Seq gene expression data to allocate BCP-ALL samples to all 21 gene expression-defined molecular subtypes. Trained on n = 1869 transcriptome profiles with established subtype definitions (4 cohorts; 55% pediatric / 45% adult), ALLCatchR allowed subtype allocation in 3 independent hold-out cohorts (n = 1018; 75% pediatric / 25% adult) with 95.7% accuracy (averaged sensitivity across subtypes: 91.1% / specificity: 99.8%). High-confidence predictions were achieved in 83.7% of samples with 98.9% accuracy. Only 1.2% of samples remained unclassified. ALLCatchR outperformed existing tools and identified novel driver candidates in previously unassigned samples. Additional modules provided predictions of samples blast counts, patient's sex, and immunophenotype, allowing the imputation in cases where these information are missing. We established a novel RNA-Seq reference of human B-lymphopoiesis using 7 FACS-sorted progenitor stages from healthy bone marrow donors. Implementation in ALLCatchR enabled projection of BCP-ALL samples to this trajectory. This identified shared proximity patterns of BCP-ALL subtypes to normal lymphopoiesis stages, extending immunophenotypic classifications with a novel framework for developmental comparisons of BCP-ALL. ALLCatchR enables RNA-Seq routine application for BCP-ALL diagnostics with systematic gene expression analysis for accurate subtype allocation and novel insights into underlying developmental trajectories.

Centrosome Amplification Is a Potential Molecular Target in Paediatric Acute Lymphoblastic Leukemia

Acute lymphoblastic leukemia (ALL) is the most common form of cancer in children, with most cases arising from fetal B cell precursor, termed B-ALL. Here, we use immunofluorescence analysis of B-ALL cells to identify centrosome amplification events that require the centrosome clustering pathway to successfully complete mitosis. Our data reveals that primary human B-ALL cells and immortal B-ALL cell lines from both human and mouse sources show defective bipolar spindle formation, abnormal mitotic progression, and cell death following treatment with centrosome clustering inhibitors (CCI). We demonstrate that CCI-refractory B-ALL cells exhibit markers for increased genomic instability, including DNA damage and micronuclei, as well as activation of the cyclic GMP-AMP synthase (cGAS)-nuclear factor kappa B (NF-κB) signalling pathway. Our analysis of cGAS knock-down B-ALL clones implicates cGAS in the sensitivity of B-ALL cells to CCI treatment. Due to its integral function and specificity to cancer cells, the centrosome clustering pathway presents a powerful molecular target for cancer treatment while mitigating the risk to healthy cells.

Conjoined Genes as Common Events in Childhood Acute Lymphoblastic Leukemia

Simple Summary

Acute lymphoblastic leukemia (ALL) is the most frequent childhood cancer. In recent years, broad application of NGS technologies enabled the discovery of novel genomically defined ALL. In this study, as a proof-of-principle, we applied RNA-seq technology to comprehensively profile the transcriptional landscape of a collection of 10 childhood BCP-ALL cases, and performed a deep bioinformatics analysis including several publicly available datasets, in order to characterize their full spectrum of transcriptional events. The paired-end RNA sequencing of our BCP-ALL pediatric cohort revealed a total of 9001 raw fusion events, which, after filtering, resulted in 245 candidate fusions. Overall, 235 out of 245 events were intra-chromosomal fusions, among which 229 involved two contiguous or overlapping genes, also known as conjoined genes (CGs). Among them, we identified a subset of 14 CGs (6.1%) exclusively expressed in leukemic cases but neither in solid cancers nor in normal samples. These events could be suggestive of a novel mechanism of transcriptional regulation in childhood leukemia and may represent novel potential leukemia-specific biomarkers.

Abstract

Acute lymphoblastic leukemia (ALL) is the most frequent childhood cancer. For the last three decades, conventional cytogenetic and molecular approaches allowed the identification of genetic abnormalities having prognostic and therapeutic relevance. Although the current cure rate in pediatric B cell acute leukemia is approximately 90%, it remains one of the leading causes of mortality in childhood. Furthermore, in the contemporary protocols, chemotherapy intensity was raised to the maximal levels of tolerability, and further improvements in the outcome will depend on the characterization and reclassification of the disease, as well as on the development of new targeted drugs. The recent technological advances in genome-wide profiling techniques have allowed the exploration of the molecular heterogeneity of this disease, even though some potentially interesting biomarkers such as conjoined genes have not been deeply investigated yet. In the present study, we performed the transcriptome sequencing (RNA-seq) of 10 pediatric B cell precursor (BCP)-ALL cases with different risk (four standard- and six high-risk patients) enrolled in the Italian AIEOP-BFM ALL2000 protocol, in order to characterize the full spectrum of transcriptional events and to identify novel potential genetic mechanisms sustaining their different early response to therapy. Total RNA was extracted from primary leukemic blasts and RNA-seq was performed by Illumina technology. Bioinformatics analysis focused on fusion transcripts, originated from either inter- or intra-chromosomal structural rearrangements. Starting from a raw list of 9001 candidate events, by employing a custom-made bioinformatics pipeline, we obtained a short list of 245 candidate fusions. Among them, 10 events were compatible with chromosomal translocations. Strikingly, 235/245 events were intra-chromosomal fusions, 229 of which involved two contiguous or overlapping genes, resulting in the so-called conjoined genes (CGs). To explore the specificity of these events in leukemia, we performed an extensive bioinformatics meta-analysis and evaluated the presence of the fusions identified in our 10 BCP-ALL cohort in several other publicly available RNA-seq datasets, including leukemic, solid tumor and normal sample collections. Overall, 14/229 (6.1%) CGs were found to be exclusively expressed in leukemic cases, suggesting an association between CGs and leukemia. Moreover, CGs were found to be common events both in standard- and high-risk BCP-ALL patients and it might be suggestive of a novel potential transcriptional regulation mechanism active in leukemic cells.

A Multicenter Cohort Study on Children Suffering from Acute Lymphoblastic Leukemia: Effects of Obesity on Mortality

Background

Overweight and obesity have been reported in specific patients and disease survivors compared to other types of childhood cancer. This study is aimed at determining the effect of children's obesity on the mortality of acute lymphoblastic leukemia.

Method

Children admitted to Inner Mongolia International Mongolian Hospital from 1 January 2017 to 31 December 2020 participated in this study. 1070 children were analyzed. A multi-middle-class poll was conducted. All children under the age of 15 were followed up within 24 months of diagnosis. Overweight and obesity are identified according to the World Health Organization and the Centers for Disease Control and Prevention. Premature death and reoccurrence of emergencies are the main consequences.

Results

The initial ethical rate for the first 24 months of testing was 19.9% (NS 213). The lowest cancer survival rate (DFS) was childhood obesity (73%) (24 months), compared with average weight (81%). Diagnosis of overweight/obesity is a predictor of early death (WHO: HR = 1.4, 95% CI: 1.0-2.0; CDC: HR = 1.6, 95% CI: 1.1-2.3). However, there was no association between overweight and obesity (WHO: HR = 1.5, 95% effective interval: 0.9-2.5; CDC: human resources = 1.0, 95% effective interval: 0.6-1.6) and obesity (WHO: HR = 1.5, 95% effective interval: 0.7-3.2; CDC: HR = 1.4, 95% effective interval: 0.9-2.3). Early recurrence was observed.

Conclusion

Overweight and obese people belong to the subclass with a high risk of death in the treatment of leukemia.

Increasing Complexity of Molecular Landscapes in Human Hematopoietic Stem and Progenitor Cells during Development and Aging

The past five decades have seen significant progress in our understanding of human hematopoiesis. This has in part been due to the unprecedented development of advanced technologies, which have allowed the identification and characterization of rare subsets of human hematopoietic stem and progenitor cells and their lineage trajectories from embryonic through to adult life. Additionally, surrogate in vitro and in vivo models, although not fully recapitulating human hematopoiesis, have spurred on these scientific advances. These approaches have heightened our knowledge of hematological disorders and diseases and have led to their improved diagnosis and therapies. Here, we review human hematopoiesis at each end of the age spectrum, during embryonic and fetal development and on aging, providing exemplars of recent progress in deciphering the increasingly complex cellular and molecular hematopoietic landscapes in health and disease. This review concludes by highlighting links between chronic inflammation and metabolic and epigenetic changes associated with aging and in the development of clonal hematopoiesis.