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Anupma Nayak

Publications and source records attributed to Anupma Nayak.

2 recordsLinked to original sources
PubMed2026

Comparative analysis of distinct genomic landscapes in young-onset gBRCA1/2 breast cancer.

Carriers of germline BRCA1/2 pathogenic variants (gBRCA1/2 PVs) have elevated young-onset breast cancer risk. To define the pretreatment genomic landscapes of young-onset gBRCA-associated breast cancer, we evaluated 136 treatment-naive tumors diagnosed before age 50 in the prospective POSH study and 66 noncarriers from The Cancer Genome Atlas. Using whole-exome sequencing, we analyzed somatic variation, allele-specific loss of heterozygosity (asLOH), homologous recombination deficiency (HRD), and single-base substitution (SBS) signatures. gBRCA1 and gBRCA2 breast cancers had high rates of asLOH but differed significantly in average HRD scores and median SBS composition of signatures SBS1 (aging-associated), SBS18 (ROS-associated), and SBS3 (HRD-associated). Compared with gBRCA2 tumors, gBRCA1 tumors with asLOH were significantly enriched for alterations in hallmark ROS, DNA repair, and epithelial-mesenchymal transition pathways. In ER-positive, HER2-negative tumors from gBRCA1/2 carriers compared with noncarriers, we found significant enrichment of RB1, TP53, FAT1, and MYC single-nucleotide variants, indels, and copy number variants associated with CDK4/6 inhibitor (CDK4/6i) resistance. Together, these findings demonstrate significant differences between gBRCA1- and gBRCA2-associated breast cancers, and preexisting CDK4/6i resistance mechanisms, supporting prospective trials comparing individualized therapy for gBRCA1 versus gBRCA2 carriers and comparing poly(ADP-ribose) polymerase inhibitors versus CDK4/6i for ER-positive gBRCA1/2-associated breast cancer.

Humans↗
PubMed2025

Designing smart spatial omics experiments with S2Omics.

Spatial omics technologies have transformed biomedical research by enabling high-resolution molecular profiling while preserving the native tissue architecture. These advances provide unprecedented insights into tissue structure and function. However, the high cost and time-intensive nature of spatial omics experiments necessitate careful experimental design, particularly in selecting regions of interest (ROIs) from large tissue sections. Currently, ROI selection is performed manually, which introduces subjectivity, inconsistency, and a lack of reproducibility. Previous studies have shown strong correlations between spatial molecular patterns and histological features, suggesting that readily available and cost-effective histology images can be leveraged to guide spatial omics experiments. Here, we present S2Omics, an end-to-end workflow that automatically selects ROIs from histology images with the goal of maximizing molecular information content in the ROIs. Through comprehensive evaluations across multiple spatial omics platforms and tissue types, we demonstrate that S2Omics enables systematic and reproducible ROI selection and enhances the robustness and impact of downstream biological discovery.

digital pathology↗