Publications

CONFERENCE PAPERS

Exploring Genetic-histologic Relationships in Breast Cancer

Ruchi Chauhan, PK Vinod, CV Jawahar [paper]

International Symposium of Biomedical Imaging 2021

The advent of Digital Pathology presents opportunities for computer vision for fast, accurate, and objective solutions for histopathological images and aid in knowledge discovery. This work uses deep learning to predict genomic biomarkers - TP53 mutation, PIK3CA mutation, ER status, PR status, HER2 status, and intrinsic subtypes, from breast cancer histopathology images. Furthermore, we attempt to understand the underlying morphology as to how these genomic biomarkers manifest in images. Since gene sequencing is expensive, not always available, or even feasible, predicting these biomarkers from images would help in diagnosis, prognosis, and effective treatment planning. We outperform the existing works with a minimum improvement of 0.02 and a maximum of 0.13 AUROC scores across all tasks. We also gain insights that can serve as hypotheses for further experimentation, including the presence of lymphocytes and karyorrhexis. Moreover, our fully automated workflow can be extended to other tasks across other cancer subtypes.

Image Segmentation using Hybrid Representations

Alakh Desai, Ruchi Chauhan, Jayanthi Sivaswamy [paper] [video]

International Symposium of Biomedical Imaging (ISBI) 2020

This work explores a hybrid approach to segmentation as an alternative to a purely data-driven approach. We introduce an end-to-end U-Net based network called DU-Net, which uses additional frequency preserving features, namely the Scattering Coefficients (SC), for medical image segmentation. SC are translation invariant and Lipschitz continuous to deformations which help DU-Net outperform other conventional CNN counterparts on four datasets and two segmentation tasks: Optic Disc and Optic Cup in color fundus images and fetal Head in ultrasound images. The proposed method shows remarkable improvement over the basic U-Net with performance competitive to state-of-the-art methods. The results indicate that it is possible to use a lighter network trained with fewer images (without any augmentation) to attain good segmentation results.

JOURNAL ARTICLES

Host metabolic reprogramming in response to SARS-CoV-2 infection: A systems biology approach

STR Moolamalla, Rami B, Ruchi Chauhan, UD Priyakumar, PK Vinod [paper]

Journal of Microbial Pathogenesis 2021

Understanding the pathogenesis of SARS-CoV-2 is important for developing effective treatment strategies. Viruses hijack the host metabolism to redirect the resources for their replication and survival. How SARS-CoV-2 influences the host metabolism is still unclear. In this study, we analyzed transcriptomic data obtained from different human respiratory cell lines and patient samples (Swab, PBMC, lung biopsy, BALF) to understand the metabolic alterations in response to SARS-CoV-2 infection. For this purpose, the expression pattern of metabolic genes in the human genome-scale metabolic network model Recon3D was explored. We identified metabolic genes and pathways and reporter metabolites under each SARS-CoV-2-infected condition and compared them to identify common and unique changes in the metabolism. Our analysis revealed host-dependent dysregulation of glycolysis, mitochondrial metabolism, amino acid metabolism, glutathione metabolism, polyamine synthesis, and lipid metabolism. We observed different metabolic changes that are pro- and antiviral in nature. We generated hypotheses on how antiviral metabolism can be targeted/enhanced for reducing viral titers. These warrant further exploration with more samples and in vitro studies to test predictions.

Clinico-Genomic Analysis Reveals Mutations Associated with COVID-19 Disease Severity: Possible Modulation by RNA Structure

Mehta P, Alle S, Chaturvedi A, Swaminathan A, Saifi S, Maurya R, Chattopadhyay P, Devi P, Chauhan R, Kanakan A, Vasudevan JS, Sethuraman R, Chidambaram S, Srivastava M, Chakravarthi A, Jacob J, Namagiri M, Konala V, Jha S, Priyakumar UD, Vinod PK, Pandey R [paper]

Pathogens 2021

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) manifests a broad spectrum of clinical presentations, varying in severity from asymptomatic to mortality. As the viral infection spread, it evolved and developed into many variants of concern. Understanding the impact of mutations in the SARS-CoV-2 genome on the clinical phenotype and associated co-morbidities is important for treatment and prevention as the pandemic progresses. Based on the mild, moderate, and severe clinical phenotypes, we analyzed the possible association between both, the clinical sub-phenotypes and genomic mutations with respect to the severity and outcome of the patients. We found a significant association between the requirement of respiratory support and co-morbidities. We also identified six SARS-CoV-2 genome mutations that were significantly correlated with severity and mortality in our cohort. We examined structural alterations at the RNA and protein levels as a result of three of these mutations: A26194T, T28854T, and C25611A, present in the Orf3a and N protein. The RNA secondary structure change due to the above mutations can be one of the modulators of the disease outcome. Our findings highlight the importance of integrative analysis in which clinical and genetic components of the disease are co-analyzed. In combination with genomic surveillance, the clinical outcome-associated mutations could help identify individuals for priority medical support

BOOK CHAPTERS

Bytes, Pixels, and Bases: Machine Learning in Imaging-omics for Renal Carcinoma

Ruchi Chauhan, CV Jawahar, PK Vinod

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