Varunika Savla
A5092083437- Cancer Immunotherapy and Biomarkers
- CAR-T cell therapy research
- Renal cell carcinoma treatment
- Immunotherapy and Immune Responses
- Pancreatic and Hepatic Oncology Research
- Bladder and Urothelial Cancer Treatments
- Lung Cancer Research Studies
- Ferroptosis and cancer prognosis
- Cancer-related Molecular Pathways
- Immune cells in cancer
- Single-cell and spatial transcriptomics
- Mathematical Biology Tumor Growth
- Advanced Breast Cancer Therapies
- Phagocytosis and Immune Regulation
Brigham and Women's Hospital
2023-2025
Harvard University
2024
Abstract Immune checkpoint inhibitors (ICI) targeting the PD-1 pathway have transformed treatment of advanced renal cell carcinoma (RCC), but mechanisms underlying therapeutic response remain largely unknown. Herein, we perform transcriptomic analysis on RCC biospecimens from 102 patients enrolled in a phase II clinical trial frontline nivolumab (NCT03117309) to investigate determinants anti-PD1 monotherapy. Through bulk analysis, identify an enrichment genes associated with tertiary...
Abstract Purpose: Programmed cell death protein 1 (PD-1) expression on CD8+TIM-3−LAG-3− tumor-infiltrating cells predicts positive response to PD-1 blockade in metastatic clear-cell renal carcinoma (mccRCC). Because inhibition of signaling regulatory T (Treg) augments their immunosuppressive function, we hypothesized that Tregs would predict resistance inhibitors. Experimental Design: PD-1+ were phenotyped using multiparametric immunofluorescence ccRCC tissues from the CheckMate-025 trial...
<p>RNA expression (normalized matrix), related to Figure 1.</p>
<p>Differential gene expression analysis, including differentially expressed genes for ZNF683+ SLAMF7+ CD8+ Exhausted T cells compared to all other cell clusters, related Figure 5. Positive log fold-change (per cluster) indicates upregulation in cells.</p>
<p>Differential gene expression analysis, including top 1,000 differentially expressed genes for each cluster (compared with all other clusters) graph-based clustering of myeloid cells, related to Figure 4. Positive log fold-change (per cluster) indicates upregulation in interest compared clusters.</p>
<p>TCR specificity in advanced renal cell carcinoma.</p>
<div>Abstract<p>Immune checkpoint inhibitors targeting the PD-1 pathway have transformed treatment of advanced renal cell carcinoma (RCC), but mechanisms underlying therapeutic response remain largely unknown. In this study, we perform transcriptomic analysis on RCC biospecimens from 102 patients enrolled in a phase II clinical trial first-line nivolumab (NCT03117309) to investigate determinants anti–PD-1 monotherapy. Through bulk analysis, identify an enrichment genes associated...
<p>Functional validation of SLAMF7 activation on T cell functions and flow cytometry gating strategies.</p>
<p>Clinical metadata for patients enrolled in HCRN GU16-260 trial, related to Figure 1.</p>
<p>ZNF683+ SLAMF7+ Exhausted CD8+ T cell GES validation in external clinical cohorts.</p>
<p>Differential gene expression analysis, including top 1,000 differentially expressed genes for each cluster (compared with all other clusters) graph-based clustering of nonimmune cells related to, to Figure 4. Positive log fold-change (per cluster) indicates upregulation in interest compared clusters.</p>
<p>Spatial transcriptomics of TLS and ZNF683+ SLAMF7+ exhausted CD8+ T cells.</p>
<p>Non-immune and myeloid populations identified through scRNA-seq.</p>
<p>T cell populations identified through scRNA-seq.</p>
<p>Association of known T effector signatures and clinical outcomes.</p>
<p>T cell cytokine secretion data, related to Figure 5.</p>
<p>Differential gene expression analysis, including top 1,000 differentially expressed genes for each cluster (compared with all other clusters) graph-based clustering of T cells, related to Figure 5. Positive log fold-change (per cluster) indicates upregulation in interest compared cell clusters.</p>
<p>Signatures used for TLS and Teff analysis, related to Figures 2 3.</p>
<p>Somatic mutations (MAF file) and significantly recurrent in the HCRN GU16-260 Cohort (Mutig2CV output), related to Figure 1.</p>
<p>Patient and sample characteristics of bulk RNA sequencing cohort, related to Figure 1.</p>
<p>Gene set enrichment analysis of differentially expressed genes in patients with complete or partial response using ontology gene sets from MSigDB, related to Figure 2.</p>
<p>ZNF683+ SLAMF7+ Exhausted CD8+ T cell and TLS GES integration.</p>
<p>Overview of translational analysis the HCRN GU16-260 trial, continued from Fig 1.</p>
<p>Spearman correlation coefficients, related to generation of ZNF683+ SLAMF7+ CD8+ Exhausted T cell gene expression signature, Figure 6.</p>