NARASIMHA: Novel Assay based on Targeted RNA Sequencing to Identify ChiMeric Gene Fusions in Hematological Malignancies

Fifth Edition of the World Health Organization Classification of Tumors of the Hematopoietic and Lymphoid Tissues: Acute Lymphoblastic Leukemias, Mixed-Phenotype Acute Leukemias, Myeloid/Lymphoid Neoplasms With Eosinophilia, Dendritic/Histiocytic Neoplasms, and Genetic Tumor Syndromes

This manuscript represents a review of lymphoblastic leukemia/lymphoma (acute lymphoblastic leukemia/lymphoblastic lymphoma), acute leukemias of ambiguous lineage, mixed-phenotype acute leukemias, myeloid/lymphoid neoplasms with eosinophilia and defining gene rearrangements, histiocytic and dendritic neoplasms, and genetic tumor syndromes of the 5th edition of the World Health Organization Classification of Tumors of the Hematopoietic and Lymphoid Tissues. The diagnostic, clinicopathologic, cytogenetic, and molecular genetic features are discussed. The differences in comparison to the 4th revised edition of the World Health Organization classification of hematolymphoid neoplasms are highlighted.

'Evaluation of adverse prognostic gene alterations & MRD positivity in BCR::ABL1-like B-lineage acute lymphoblastic leukaemia patients, in a resource-constrained setting

BACKGROUND

Early detection of BCR::ABL1-like ALL could impact treatment management and improve the overall survival/outcome. BCR::ABL1-like ALL cases are characterised by diverse genetic alterations activating cytokine receptors and kinase signalling. Its detection is still an unmet need in low-middle-income countries due to the unavailability of a patented TLDA assay.

METHODS

This study's rationale is to identify BCR::ABL1-like ALLs using the PHi-RACE classifier, followed by the characterisation of underlying adverse genetic alterations in recurrent gene abnormalities negative (RGAneg) B-ALLs (n = 108).

RESULTS

We identified 34.25% (37/108) BCR::ABL1-like ALLs using PHi-RACE classifier, characterised by TSLPR/CRLF2 expression (11.58%), IKZF1 (Δ4-7) deletion (18.9%) and chimeric gene fusions (34.61%). In overexpressed TSLPR/CRLF2 BCR::ABL1-like ALLs, we identified 33.33% (1/3) CRLF2::IGH and 33.33% (1/3) EPOR::IGH rearrangements with concomitant JAK2 mutation R683S (50%). We identified 18.91% CD13 (P = 0.02) and 27.02% CD33 (P = 0.05) aberrant myeloid markers positivity, which was significantly higher in BCR::ABL1-like ALLs compared to non-BCR::ABL1-like ALLs. MRD positivity was considerably higher (40% in BCR::ABL1-like vs. 19.29% in non-BCR::ABL1-like ALLs).

CONCLUSIONS

With this practical approach, we reported a high incidence of BCR::ABL1-like ALLs, and a lower frequency of CRLF2 alteration & associated CGFs. Recognising this entity, early at diagnosis is crucial to optimise personalised treatment strategies.

Emerging molecular subtypes and therapies in acute lymphoblastic leukemia

Tremendous strides have been made in the molecular and cytogenetic classification of acute lymphoblastic leukemia based on gene expression profiling data, leading to an expansion of entities in the recent International Consensus Classification (ICC) of myeloid neoplasms and acute leukemias and 2022 WHO Classification of Tumours: Haematolymphoid Tumors, 5th edition. This increased diagnostic and therapeutic complexity can be overwhelming, and this review compares nomenclature differences between the ICC and WHO 5th edition publications, compiles key features of each entity, and provides a diagnostic algorithmic approach. In covering B-lymphoblastic leukemia (B-ALL), we divided the entities into established (those present in the revised 4th edition WHO) and novel (those added to either the ICC or WHO 5th edition) groups. The established B-ALL entities include B-ALL with BCR::ABL1 fusion, BCR::ABL1-like features, KMT2A rearrangement, ETV6::RUNX1 rearrangement, high hyperdiploidy, hypodiploidy (focusing on near haploid and low hypodiploid), IGH::IL3 rearrangement, TCF3::PBX1 rearrangement, and iAMP21. The novel B-ALL entities include B-ALL with MYC rearrangement; DUX4 rearrangement; MEF2D rearrangement; ZNF384 or ZNF362 rearrangement, NUTM1 rearrangement; HLF rearrangement; UBTF::ATXN7L3/PAN3,CDX2; mutated IKZF1 N159Y; mutated PAX5 P80R; ETV6::RUNX1-like features; PAX5 alteration; mutated ZEB2 (p.H1038R)/IGH::CEBPE; ZNF384 rearranged-like; KMT2A-rearranged-like; and CRLF2 rearrangement (non-Ph-like). Classification of T-ALL is complex with some variability in how the subtypes are defined in recent literature. It was classified as early T-precursor lymphoblastic leukemia/lymphoma and T-ALL, NOS in the WHO revised 4th edition and WHO 5th edition. The ICC added an entity into early T-cell precursor ALL, BCL11B-activated, and also added provisional entities subclassified based on transcription factor families that are aberrantly activated.

PHi-RACE: PGIMER in-house rapid & cost effective classifier for the detection of BCR-ABL1-like acute lymphoblastic leukaemia in Indian patients

For the detection of BCR-ABL1-like ALL cases, two methodologies, specifically Gene expression profiling (GEP) or Next-generation targeted sequencing (NGS) and TaqMan based low-density (TLDA) card, are being used. NGS is very costly and TLDA is not widely commercially available. In this study, we quantified the expression of 8 selected overexpressed genes in 536 B-ALL cases. We identified 26.67% (143/536) BCR-ABL1-like ALLs using hierarchical clustering and principal component analysis. BCR-ABL1-like ALL cases were significantly older at presentation (p = 0.036) and had male preponderance (p = 0.047) compared to BCR-ABL1-negative ALL cases. MRD-positivity and induction failure were more commonest in BCR-ABL1-like ALL cases (30.55 vs.19.35% in BCR-ABL1-negative ALL cases). Lastly, we built a PHi-RACE classifier (sensitivity = 95.2%, specificity= 83.7%, AUC= 0.927) using logistic regression to detect BCR-ABL1-like ALL cases promptly at diagnosis. This classifier is beneficial for hematologists in quick decision making at baseline to start tailored treatment regimes.

Clinical impact of panel-based error-corrected next generation sequencing versus flow cytometry to detect measurable residual disease (MRD) in acute myeloid leukemia (AML)

We accrued 201 patients of adult AML treated with conventional therapy, in morphological remission, and evaluated MRD using sensitive error-corrected next generation sequencing (NGS-MRD) and multiparameter flow cytometry (FCM-MRD) at the end of induction (PI) and consolidation (PC). Nearly 71% of patients were PI NGS-MRD⁺ and 40.9% PC NGS-MRD⁺ (median VAF 0.76%). NGS-MRD⁺ patients had a significantly higher cumulative incidence of relapse (p = 0.003), inferior overall survival (p = 0.001) and relapse free survival (p < 0.001) as compared to NGS-MRD^- patients. NGS-MRD was predictive of inferior outcome in intermediate cytogenetic risk and demonstrated potential in favorable cytogenetic risk AML. PI NGS-MRD^- patients had a significantly improved survival as compared to patients who became NGS-MRD^- subsequently indicating that kinetics of NGS-MRD clearance was of paramount importance. NGS-MRD identified over 80% of cases identified by flow cytometry at PI time point whereas FCM identified 49.3% identified by NGS. Only a fraction of cases were NGS-MRD^- but FCM-MRD⁺. NGS-MRD provided additional information of the risk of relapse when compared to FCM-MRD. We demonstrate a widely applicable, scalable NGS-MRD approach that is clinically informative and synergistic to FCM-MRD in AML treated with conventional therapies. Maximum clinical utility may be leveraged by combining FCM and NGS-MRD modalities.

Targeted RNA-Sequencing Enables Detection of Relevant Translocations and Single Nucleotide Variants and Provides a Method for Classification of Hematological Malignancies-RANKING

BACKGROUND

Patients with hematological malignancies (HMs) carry a wide range of chromosomal and molecular abnormalities that impact their prognosis and treatment. Since no current technique can detect all relevant abnormalities, technique(s) are chosen depending on the reason for referral, and abnormalities can be missed. We tested targeted transcriptome sequencing as a single platform to detect all relevant abnormalities and compared it to current techniques.

MATERIAL AND METHODS

We performed RNA-sequencing of 1385 genes (TruSight RNA Pan-Cancer, Illumina) in bone marrow from 136 patients with a primary diagnosis of HM. We then applied machine learning to expression profile data to perform leukemia classification, a method we named RANKING. Gene fusions for all the genes in the panel were detected, and overexpression of the genes EVI1, CCND1, and BCL2 was quantified. Single nucleotide variants/indels were analyzed in acute myeloid leukemia (AML), myelodysplastic syndrome and patients with acute lymphoblastic leukemia (ALL) using a virtual myeloid (54 genes) or lymphoid panel (72 genes).

RESULTS

RANKING correctly predicted the leukemia classification of all AML and ALL samples and improved classification in 3 patients. Compared to current methods, only one variant was missed, c.2447A>T in KIT (RT-PCR at 10-4), and BCL2 overexpression was not seen due to a t(14; 18)(q32; q21) in 2% of the cells. Our RNA-sequencing method also identified 6 additional fusion genes and overexpression of CCND1 due to a t(11; 14)(q13; q32) in 2 samples.

CONCLUSIONS

Our combination of targeted RNA-sequencing and data analysis workflow can improve the detection of relevant variants, and expression patterns can assist in establishing HM classification.