Evidence map›Paper›PMID 40186692›Full record

ArticleMolecular diagnosis & therapy2025

Effective Utilization of a Customized Targeted Hybrid Capture RNA Sequencing in the Routine Molecular Categorization of Adolescent and Adult B-Lineage Acute Lymphoblastic Leukemia: A Real-World Experience.

Sreejesh Sreedharanunni, Venus Thakur, Anand Balakrishnan, Man Updesh Singh Sachdeva, Prabhjot Kaur, Sudhanshi Raina, Manu Jamwal, Charanpreet Singh, Praveen Sharma, Nabhajit Mallik and 7 more

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Article in Molecular diagnosis & therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Sreejesh Sreedharanunni *Department of Hematology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India. dr.s.sreejesh@gmail.com.ORCID 0000-0003-2626-4154
Venus Thakur *Department of Hematology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Anand BalakrishnanDepartment of Hematology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Man Updesh Singh SachdevaDepartment of Hematology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Prabhjot KaurDepartment of Hematology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Sudhanshi RainaDepartment of Hematology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Manu JamwalDepartment of Hematology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Charanpreet SinghDepartment of Clinical Hematology and Medical Oncology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Praveen SharmaDepartment of Hematology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Nabhajit MallikDepartment of Hematology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Shano NaseemDepartment of Hematology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Pulkit RastogiDepartment of Hematology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Arihant JainDepartment of Clinical Hematology and Medical Oncology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Gaurav PrakashDepartment of Clinical Hematology and Medical Oncology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Alka KhadwalDepartment of Clinical Hematology and Medical Oncology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Pankaj MalhotraDepartment of Clinical Hematology and Medical Oncology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Reena DasDepartment of Hematology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India.

Funding

MERC PGIMER IM/135/23-09-21-0114
6 · The paper itself

Abstract

introductionRecent World Health Organization (WHO) and International Consensus Classifications have introduced numerous molecular entities in B-lineage acute lymphoblastic leukemia (B-ALL), necessitating comprehensive genomic characterization by detecting gene fusions, expression, mutations, and exon deletions. While whole-genome plus transcriptome sequencing is the ideal strategy, it remains cost-prohibitive for routine use. This study reports a cost-effective and reasonably efficient alternate approach integrating a customized targeted hybrid capture RNA sequencing (RNAseq) into the routine workup. METHODOLOGY: A total of 95 consecutive adolescent/adult B-ALL cases negative for common chimeric gene fusions (CGF) (BCR::ABL1, KMT2A::AFF1, TCF3::PBX1, and ETV6::RUNX1) were analyzed using a customized 69-gene targeted RNAseq panel. In total, three fusion detection pipelines, the Trinity Cancer Transcriptome Analysis Toolkit (CTAT) Mutations pipeline, and the Toblerone alignment tool were employed, and the results were compared with fluorescence in situ hybridization (FISH)/multiplex ligation-dependent probe amplification (MLPA) testing.

resultsRNAseq identified fusions in 43% of cases (including BCR::ABL1-like: 15.8% and IGH::DUX4: 10.5%), demonstrating superior detection of cryptic intrachromosomal rearrangements. Somatic variants were detected in 30% of cases (including rat sarcoma (RAS) pathway and Janus kinase (JAK)-signal transducers and activators of transcription (STAT) variants in 18% and 5.3% respectively), and IKZF1 deletions were detected in 25% (77% concordance with MLPA). The integration of targeted RNAseq and comprehensive bioinformatic analysis with flow-cytometry-based ploidy analysis and FISH-based IGH rearrangements helped categorize 79% of common CGF-negative B-ALL. The BCR::ABL1/BCR::ABL1-like group showed a higher frequency of pathogenic IKZF1 deletions (50% versus 21.7%; p = 0.011), measurable residual disease (92% versus 51%; p = 0.009), and poorer overall survival (8.6 versus 22.8 months; p = 0.07). DISCUSSION AND

conclusionsEffective utilization of RNAseq data by comprehensive bioinformatic analysis to test fusions, mutations, and deletions, supported by only minimal supplementary FISH testing, provides a practical, cost-effective solution for the molecular characterization of B-ALL in real-world scenarios until a single alternative and cost-effective test is available.

Indexed as

Precursor B-Cell Lymphoblastic Leukemia-LymphomaPrecursor Cell Lymphoblastic Leukemia-LymphomaSequence Analysis, RNAAdolescentAdultBiomarkers, TumorFemaleGene Expression ProfilingHumansIn Situ Hybridization, FluorescenceMaleMiddle AgedMutationOncogene Proteins, FusionYoung AdultBiomarkers, TumorOncogene Proteins, Fusion

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.