Biomedical subjects

Shalabh Suman

Publications and source records attributed to Shalabh Suman.

2 recordsLinked to original sources

PubMed2026

The Genomic Landscape of MYC-, MYCL-, and MYCN-Amplified Solid Tumors.

PURPOSE: MYC, MYCN, and MYCL amplifications are recurrent oncogenic events across solid tumors. Currently, no standardized selection biomarker is available to identify patients with MYC-dependent tumors. EXPERIMENTAL DESIGN: We analyzed copy-number alterations of MYC family genes and their features in more than 68,000 tumor-normal paired samples from pediatric and adult patients sequenced with MSK-IMPACT (Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets) and annotated with FACETS (Fraction and Allele-Specific Copy Number Estimates from Tumor Sequencing). The relationship between amplification features and MYC mRNA expression levels were evaluated in more than 10,000 samples from The Cancer Genome Atlas (TCGA). RESULTS: Across MSK Cancer Center samples, MYC amplifications were most common, found in 2,949 samples compared with 310 in MYCL and 217 in MYCN. Although MYCN and MYCL amplifications were predominantly focal (<10 Mb, 79% and 93%, respectively), MYC amplifications were frequently broader (>10 Mb, 62%). Although most tumor types showed similar features between broad and focal amplifications of MYC, in select cancer types, we identified differing co-occurrence and mutual exclusivity patterns with other disease-specific drivers. Furthermore, although MYC-amplified TCGA samples showed higher mRNA expression than wild-type ones, the focality of MYC amplification was seen to have limited influence on expression levels. CONCLUSIONS: Our results suggest that MYC dependency likely depends on many factors, including, but not limited to, total copy number of the detected amplification, lineage-specific factors, concomitant presence or absence of additional oncogenic alterations, and in some cases amplification focality.

Humans↗

PubMed2024

Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data.

UNLABELLED: Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor-type classifiers trained on genomic features have been explored, but the most accurate methods are not clinically feasible, relying on features derived from whole-genome sequencing (WGS), or predicting across limited cancer types. We use genomic features from a data set of 39,787 solid tumors sequenced using a clinically targeted cancer gene panel to develop Genome-Derived-Diagnosis Ensemble (GDD-ENS): a hyperparameter ensemble for classifying tumor type using deep neural networks. GDD-ENS achieves 93% accuracy for high-confidence predictions across 38 cancer types, rivaling the performance of WGS-based methods. GDD-ENS can also guide diagnoses of rare type and cancers of unknown primary and incorporate patient-specific clinical information for improved predictions. Overall, integrating GDD-ENS into prospective clinical sequencing workflows could provide clinically relevant tumor-type predictions to guide treatment decisions in real time. SIGNIFICANCE: We describe a highly accurate tumor-type prediction model, designed specifically for clinical implementation. Our model relies only on widely used cancer gene panel sequencing data, predicts across 38 distinct cancer types, and supports integration of patient-specific nongenomic information for enhanced decision support in challenging diagnostic situations. See related commentary by Garg, p. 906. This article is featured in Selected Articles from This Issue, p. 897.

Humans↗