Ariaki Dandawate

The Cistrome Data Browser is a resource of ChIP-seq, ATAC-seq and DNase-seq data from humans and mice. It provides maps of the genome-wide locations of transcription factors, cofactors, chromatin remodelers, histone post-translational modifications and regions of chromatin accessible to endonuclease activity. Cistrome DB v3.0 contains approximately 45 000 human and 44 000 mouse samples with about 32 000 newly collected datasets compared to the previous release. The Cistrome DB v3.0 user interface is implemented as a single page application that unifies menu driven and data driven search functions and provides an embedded genome browser, which allows users to find and visualize data more effectively. Users can find informative chromatin profiles through keyword, menu, and data-driven search tools. Browser search functions can predict the regulators of query genes as well as the cell type and factor dependent functionality of potential cis-regulatory elements. Cistrome DB v3.0 expands the display of quality control statistics, incorporates sequence logos into motif enrichment displays and includes more expansive sample metadata. Cistrome DB v3.0 is available at http://db3.cistrome.org/browser.

SUMMARY: The regulation of genes by cis-regulatory elements (CREs) is complex and differs between cell types. Visual analysis of large collections of chromatin profiles across diverse cell types, integrated with computational methods, can reveal meaningful biological insights. We developed Cistrome Explorer, a web-based interactive visual analytics tool for exploring thousands of chromatin profiles in diverse cell types. Integrated with the Cistrome Data Browser database which contains thousands of ChIP-seq, DNase-seq and ATAC-seq samples, Cistrome Explorer enables the discovery of patterns of CREs across cell types and the identification of transcription factor binding underlying these patterns. AVAILABILITY AND IMPLEMENTATION: Cistrome Explorer and its source code are available at http://cisvis.gehlenborglab.org/ and released under the MIT License. Documentation can be accessed via http://cisvis.gehlenborglab.org/docs/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The regulation of genes by cis-regulatory elements is complex and differs between cell types. Visual analysis of large collections of chromatin profiles across diverse cell types, integrated with computational methods, can reveal meaningful biological insights.We developed Cistrome Explorer, a web-based interactive visual analytics tool for exploring thousands of chromatin profiles in diverse cell types. Integrated with the Cistrome Data Browser database which contains thousands of ChIP-seq, DNase-seq, and ATAC-seq samples, Cistrome Explorer enables the discovery of patterns of cis-regulatory elements across cell types and the identification of transcription factor binding underlying these patterns.Cistrome Explorer and its source code are available at http://cisvis.gehlenborglab.org/ and released under the MIT License. Documentation can be accessed via http://cisvis.gehlenborglab.org/docs/.

Abstract By leveraging a sequence-based deep learning framework, we seek to uncover mechanisms by which mutations arise in noncoding regulatory regions, potentially leading to the discovery of novel targets in metastatic breast cancer. Research around metastatic breast cancer has largely focused on analyzing coding mutations to characterize progression. Though noncoding mutations are known to affect transcription factor binding and regulation of gene expression, few noncoding mutation drivers have been identified. Previously published work has shown that metastatic mutation rate correlates with open chromatin in the cells-of-origin. However, this work has mostly been done at a coarse, region-level scale, identifying mutational hotspot regions. In this work, we aim to uncover genomic positions in regulatory regions of metastatic breast cancer with elevated mutation rates, and identify their potential mutation mechanism. We propose a novel deep learning model that uncovers the sequence context-based covariates of per-base mutation rate in regulatory regions of metastatic breast cancer. With access to over 500,000 mutations from the Hartwig Medical Foundation cohort, the neural network is trained on sequences from regulatory regions in normal breast epithelium, and predicts per-base mutation rate profiles for the region. As a result, the model learns how sequence features change mutation likelihood at particular genomic positions. Analysis of the saliency map of the model allows for identification of specific sites with higher-than-expected mutation rates, which is potentially indicative of increased transcription factor binding that extends beyond selection by pro-metastatic regulatory programs. These findings provide a method to connect noncoding mutation patterns to mutation mechanism and regulatory effects in metastatic genomes. Future work aims at expanding this model to a pan-cancer level, revealing shared and cancer-specific noncoding mutations that have potential to reveal patterns of metastasis. This scalable sequence model framework provides an advantage over existing methods particularly due to its base-pair level resolution modeling of the metastatic cancer genome. Thus, its implications for modeling and uncovering regulatory mechanisms of cancer is key. Citation Format: Ariaki Dandawate, Christina Leslie, Ekta Khurana. Modeling base-pair level mutation rate in metastatic breast cancer using a sequence-based deep learning model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1500.

Abstract Background: Mutant allele copy number dosage gains are widely observed in oncogene-driven cancers. Nearly 1 in 5 KRAS-mutated (KRAS MUT) pancreatic cancers harbor KRAS MUT dosage gains that are associated with worse outcomes and aggressive disease. It is unclear if this dosage-dependent prognostic effect, likely mediated by oncogene addiction, extends to other KRAS MUT cancers or to other oncogenes. Herein, we characterize the clinicogenomic effects of mutant dosage gains in KRAS and 10 other oncogenes across cancer types and evaluate the role of KRAS mutant dosage gains in mediating response to RAS-directed therapies. Methods: Our initial study cohort comprised 33,473 patients across 12 common cancer types with tumors profiled by MSK-IMPACT. Analysis was restricted to 10,129 patients with diploid tumors harboring an OncoKB-annotated oncogenic mutation in one of the 11 oncogenes. Allele-specific copy number was inferred using FACETS. Normalized data from The Cancer Genome Atlas was used for gene expression analysis. Cell line drug screening data for sotorasib was obtained from the PRISM DepMap study. Results: Among tumors with allelic imbalances, selection for gain or retention of the mutant allele versus wild-type was ubiquitous. Mutant dosage gains were observed in 18% (1,819 of 10,129) of all oncogene-mutated tumors. Notably, a majority (87%) of these were 1- or 2-copy gains, whereas only 9% were focal amplifications (ranging from 0% in KRAS MUT endometrial cancer (UEC) to 30% in EGFR MUT LUAD). The prevalence of KRAS MUT gains in LUAD (18%) was comparable to that of colon (23%), rectal (18%), and pancreatic (14%) cancers (PAAD), despite varying distributions of specific KRAS variants. Mutant dosage gains were more common in metastatic tumors (27%) compared to primary tumors (13%, p=1.3×10-50) pan-cancer. TP53 mutations co-occurred with mutant dosage gains in KRAS MUT and EGFR MUT lung cancers and PIK3CA MUT breast cancers. Interestingly, dosage gains were depleted in microsatellite instability (MSI)-high tumors pan-cancer (8%, vs. 18% in tumors without MSI, p=0.002) and mutually exclusive with MSI-associated mutations in KRAS MUT UEC. Gene expression analysis revealed that KRAS MUT dosage gains were associated with higher KRAS expression in a dose-dependent manner (p=4.4×10-13 in LUAD, p=5.6×10-4 in PAAD). KRAS G12C mutant cell lines with gene-level copy number gains exhibited markedly enhanced sensitivity to sotorasib compared to those without these gains. Additionally, among patients with KRAS G12C lung cancer treated with sotorasib, the rate of partial response (PR) was significantly higher in those with tumors with KRAS MUT gains (57%, 8/14) compared to those without dosage gains (17%, 6/36, p=0.011). Conclusions: We establish that mutant dosage gains are widespread in oncogenes and represent important on-target biomarkers that may predict sensitivity to targeted therapies. Our findings underscore the need to standardize clinical reporting of mutant dosage gains and elucidate their functional role in therapeutic sensitivity. Citation Format: Maria A. Perry, Allison L. Richards, Adam Price, Sam E. Tischfield, Henry Walch, Helen Xie, Ariaki Dandawate, Karl Pichotta, Christopher Fong, Justin Jee, Nikolaus Schultz, Rohit Thummalapalli, Helena A. Yu, Ping Chi, Anupriya Singhal, Wungki Park, Eileen M. O'Reilly, Yonina Murciano-Goroff, Mark Awad, Rona Yaeger, Kathryn C. Arbour, Mark T. A. Donoghue, Michael F. Berger, Chaitanya Bandlamudi. Mutant allele copy number gains define distinct molecular subtypes of oncogene-driven cancers and are associated with response to allele-specific KRASG12C inhibitor sotorasib in NSCLC [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: RAS Oncogenesis and Therapeutics; 2026 Mar 5-8; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(5_Suppl_1):Abstract nr A050.