Akansha Srivastava supervised by Dr. Vinod Palakkad Krishnanunni received  her doctorate in Biology (Bio). Here’s a  summary of her research work on: Uncovering the Molecular Landscape of Gynecologic and Breast Cancers from Bulk Omics to Single Cell and Spatial Transcriptomics Gynecologic and breast cancers are among the leading causes of morbidity and mortality in women worldwide, posing a significant threat to women’s health. It is estimated that roughly one in five women will be diagnosed with cancer during their lifetime, with approximately one in twelve succumbing to the disease. Their molecular and cellular heterogeneity significantly contributes to therapeutic resistance and variability in clinical outcomes. Deciphering these complexities at various resolution is essential for advancing precision oncology. This thesis aims to unravel the molecular landscape of these cancers by leveraging bulk omics, single-cell RNA-seq (scRNA-seq), and spatial transcriptomics datasets. Metabolic reprogramming is a hallmark of cancer and is characterized by significant heterogeneity, presenting challenges for treatment efficacy and patient outcomes. Understanding metabolic adaptation and its heterogeneity in tumor tissues may provide new insights and help in developing personalized therapeutic strategies. We investigated the metabolic alterations of endometrial cancer (EC) to understand the variations in metabolism within tumor samples. Integration of transcriptomics data of EC (RNA-Seq) and the human genome-scale metabolic network was performed to identify the metabolic subtypes of EC and uncover the underlying dysregulated metabolic pathways and reporter metabolites in each subtype. The relationship between metabolic subtypes and clinical variables was explored. Further, we correlated the metabolic changes occurring at the transcriptome level with the genomic alterations. Based on metabolic profile, EC patients were stratified into two subtypes (metabolic subtype-1 and subtype-2) that significantly correlated to patient survival, tumor stages, mutation, and copy number variations. We observed the co-activation of the pentose phosphate pathway, one-carbon metabolism, and genes involved in controlling estrogen levels in metabolic subtype-2, which is linked to poor survival. PNMT and ERBB2 are also upregulated in metabolic subtype-2 samples and present on the same chromosome locus 17q12, which is amplified. PTEN and TP53 mutations show mutually exclusive behavior between subtypes and display a difference in survival. This work identifies metabolic subtypes with distinct characteristics at the transcriptome and genome levels, highlighting the metabolic heterogeneity within EC. Expanding this work to single-cell resolution, we proposed a single-cell network approach to characterize malignant heterogeneity in gynecologic and breast cancers, focusing on the transcriptional regulatory mechanisms driving metabolic alterations. By leveraging scRNAseq data, we assessed the metabolic pathway activities and inferred cancer-specific protein-protein interactomes (PPI) and gene regulatory networks (GRNs). We explored the crosstalk between these networks to identify key alterations in metabolic regulation. Clustering cells by metabolic pathways revealed tumor heterogeneity across cancers, highlighting variations in oxidative phosphorylation, glycolysis, cholesterol, fatty acid, hormone, amino acid, and redox metabolism. Our analysis identified metabolic modules associated with these pathways, along with their key transcriptional regulators. These findings provide insights into the complex interplay between metabolic rewiring and transcriptional regulation in gynecologic and breast cancers, paving the way for potential targeted therapeutic strategies in precision oncology. Furthermore, this pipeline for dissecting coregulatory metabolic networks can be broadly applied to decipher metabolic regulation in any disease at single-cell resolution. While scRNA-seq provides a detailed view of cellular heterogeneity by resolving individual cell states and transcriptional programs, it lacks spatial context. Spatial transcriptomics bridges this gap by mapping gene expression profiles to their precise locations within the tissue, enabling the study of cellular interactions within the tumor microenvironment (TME). In this study, we present a systems-level analysis of spatial transcriptomics data to dissect the tumor microenvironment in chemotherapy-treated high-grade serous ovarian cancer (HGSOC) patients. By integrating gene expression profiles with spatial localization and histological context, we quantified hallmark pathway activities across tissue regions. We also constructed a gene co-expression network within tumor-enriched regions. Finally, we examined the association of these molecular features with treatment response. Clustering based on pathway activity scores revealed spatially distinct regions enriched for different hallmark pathways, uncovering functionally diverse cellular subpopulations within the tumor microenvironment. Tumor cell-enriched clusters show elevated activity in pathways related to proliferation, metabolism, and stress response, while fibroblast-rich regions exhibit upregulation of epithelial–mesenchymal transition (EMT). Unsupervised co-expression analysis further revealed gene modules associated with both biological processes and clinical phenotypes. Poor responders exhibit higher expression of gene modules involved in stress response, ribosomal function, lipid metabolism, oxidative phosphorylation, mTORC1 signaling, and cell-cycle regulation. In contrast, good responders show elevated activity in modules enriched for immune activation, extracellular matrix (ECM) remodeling, and inflammatory signaling. Our findings provide insights into spatially resolved functional states, transcriptional heterogeneity, and molecular features associated with treatment response, offering a foundation for precision oncology approaches in ovarian cancer. scRNA-seq and spatial transcriptomics provide invaluable insights into the complex biology of tumors; however, bulk omics datasets remain essential in clinical settings due to their feasibility and scalability. Stratification of cancer patients is essential for precision oncology but remains challenging due to the heterogeneous nature of tumors and the sparsity of somatic mutation data. In this study, we present an extended version of our previously developed DeepGraphMut (DGM) framework, integrating multi-omics data, specifically somatic mutations and gene expression, to identify and characterize molecular subtypes in breast cancer. Unsupervised clustering identified two subtypes of breast cancer with distinct survival outcomes. A Cox proportional hazards model trained on the learned embeddings achieved a C-index of 0.74. Characterization of these subtypes revealed genomic and transcriptomic differences, with Subtype-1 exhibiting a higher mutation burden, distinct expression signatures, and association with poor prognosis. This work demonstrates the utility of integrating multiomics data within graph-based frameworks for clinically meaningful patient stratification in cancer. This thesis offers a multi-resolution characterization of the molecular landscape in gynecologic and breast cancers, contributing novel insights and methodological advancements to the field of cancer biology. Our findings lay the groundwork for future research into personalized treatment strategies and biomarker discovery in gynecologic and breast cancers.

  November 2025