Publications by authors named "Sumanth Vasista"
Article Synopsis
- This study investigates how different molecular subgroups of non-small-cell lung cancer (NSCLC), identified through tumor genomic data, may respond differently to PD-(L)1 immunotherapy using computational biological modeling (CBM).
- Researchers analyzed data from 776 NSCLC patients, identifying eight molecular subgroups and evaluating their sensitivity to PD-(L)1 immunotherapy based on various genomic and immunological markers.
- The findings indicate that CBM can predict immunotherapy responses and provide insights into the underlying mechanisms, with a strong correlation found between modeled survival data and actual patient outcomes in a small clinical cohort.
Further characterization of thymic epithelial tumors (TETs) is needed. Genomic information from 102 evaluable TETs from The Cancer Genome Atlas (TCGA) dataset and from the IU-TAB-1 cell line (type AB thymoma) underwent clustering analysis to identify molecular subtypes of TETs. Six novel molecular subtypes (TH1-TH6) of TETs from the TCGA were identified, and there was no association with WHO histologic subtype.
View Article and Find Full Text PDFEarly T-cell precursor acute lymphoblastic leukemia (ETP-ALL) is an aggressive hematological malignancy for which optimal therapeutic approaches are poorly characterized. Using computational biology modeling (CBM) in conjunction with genomic data from cell lines and individual patients, we generated disease-specific protein network maps that were used to identify unique characteristics associated with the mutational profiles of ETP-ALL compared to non-ETP-ALL (T-ALL) cases and simulated cellular responses to a digital library of FDA-approved and investigational agents. Genomics-based classification of ETP-ALL patients using CBM had a prediction sensitivity and specificity of 93% and 87%, respectively.
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