Biology subjects

Animesh, S.

Publications and source records attributed to Animesh, S..

3 recordsLinked to original sources

bioRxiv2022

Exploring the Neoantigen burden in Breast Carcinoma Patients

In this study we performed a multi-omics analysis comprising whole-exome sequencing (WES) and RNA sequencing (RNA-Seq) on seven breast cancer patients, consisting of three Estrogen receptor (ER) positive and four Triple negative breast cancer (TNBC) subtypes to understand the neoantigen burden in breast cancer tumor samples. We predicted both class-I and class-II human leukocyte antigen (HLA) bound neoantigens by analyzing matched tumor-normal pair of exomes. Across all the patients, we predicted 434 unique neoantigens (NeoFil) in total, affecting 237 different genes and 87% of them (n = 378) are expressed at RNA level (Neoexp). The missense mutations (87%) are the major contributor in neoantigen (Neoexp) generation, followed by frameshift (11%) and indels (2%). The neoantigens (NeoFil) were found to be positively correlated with the somatic mutations (R2 = 0.89). We also noted that the vast majority (99.98%) of the predicted neoantigens are patient specific. Overall, the current study offers significant insight into the neoantigen profile in tumor types with intermediate/low mutation burdens like breast cancer.

cancer biology↗

bioRxiv2021

Biop-C: A Method for Chromatin Interactome Analysis of Solid Cancer Needle Biopsy Samples

A major challenge in understanding the 3D genome organization of cancer samples is the lack of a method adapted to solid cancer needle biopsy samples. Here we developed Biop-C, a modified in situ Hi-C method, and applied it to characterize three nasopharyngeal cancer patient samples. We identified Topologically-Associated Domains (TADs), chromatin interaction loops, and Frequently Interacting regions (FIREs) at key oncogenes in nasopharyngeal cancer from Biop-C heat maps. Our results demonstrate the utility of our Biop-C method in investigating the 3D genome organization in solid cancers, and the importance of 3D genome organization in regulating oncogenes in nasopharyngeal cancer.

genomics↗

bioRxiv2020

Chromatin Interaction Neural Network (ChINN): A machine learning-based method for predicting chromatin interactions from DNA sequences

Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. Various computational methods have been developed to predict chromatin interactions. Most of these methods rely on large collections of ChIP-Seq/RNA-Seq/DNase-Seq datasets and predict only enhancer-promoter interactions. Some of the state-of-the-art methods have poor experimental designs, leading to over-exaggerated performances and misleading conclusions. Here we developed a computational method, Chromatin Interaction Neural Network (ChINN), to predict chromatin interactions between open chromatin regions by using only DNA sequences of the interacting open chromatin regions. ChINN is able to predict CTCF-, RNA polymerase II- and HiC-associated chromatin interactions between open chromatin regions. ChINN also shows good across-sample performances and captures various sequence features that are predictive of chromatin interactions. To apply our results to clinical patient data, we applied CHINN to predict chromatin interactions in 6 chronic lymphocytic leukemia (CLL) patient samples and a cohort of open chromatin data from 84 CLL samples that was previously published. Our results demonstrated extensive heterogeneity in chromatin interactions in patient samples, and one of the sources of this heterogeneity were the different subtypes of CLL.

bioinformatics↗