Biology subjects

Chatila, W. K.

Publications and source records attributed to Chatila, W. K..

6 recordsLinked to original sources

bioRxiv2026

POLQ-driven repair scars shape the immunogenic landscape of homologous recombination-deficient pancreatic cancer

Pancreatic cancer (PC) is broadly resistant to immune checkpoint blockade, although a subset of homologous recombination-deficient (HRD) tumors exhibits durable immune engagement. The genomic features that distinguish these immune-responsive tumors from immune-inert HRD tumors remain poorly understood. Here we identify a microhomology-mediated end joining (MMEJ) repair scar, the MMEJ Deletion Footprint (MDF), as a genomic readout of POLQ-associated error-prone repair that enriches for frameshift indels. Across the multi-omic discovery cohort integrating tumor genomics, single-nucleus transcriptomics and spatial immune profiling, MDF-high HRD PC exhibited increased frameshift-indel-derived neoantigens and interferon programs. MDF was further associated with remodeling of the myeloid compartment toward MHC II-high dendritic cell-like antigen-presenting macrophage states and the immune synapse architecture marked by increased spatial interaction between APC-like macrophages and cytotoxic CD8+ T cells. These tissue-level features aligned with a functional trajectory shift of CD8+ T cells, consistent with effective anti-tumor immunity and was associated with favorable clinical outcomes of patients. Together, our findings position MMEJ-linked repair scarring as actionable biology that connects an HRD genotype to immune organization and suggests rational immunotherapy combinations that may enhance antigen presentation and myeloid activation to extend durable benefit in HRD-lineage cancers.

cancer biology↗

bioRxiv2025

Integrated histopathologic modeling of detailed tumor subtypes and actionable biomarkers

Accurate cancer subtyping with accompanying molecular characterization is critical for precision oncology. While machine learning approaches have been applied to both digital pathology and cancer genomics, previous work has been limited in sample size and has typically aggregated granular cancer subtypes into coarse groupings, likely obfuscating informative molecular and prognostic associations and phenotypic variation of more detailed tumor subtypes. Accordingly, we collated 378,123 hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) with matched targeted DNA clinical sequencing results and OncoTree detailed cancer subtypes from a real-world cohort of 71,142 patients. Using this scaled, granular dataset and a cancer subtype knowledge graph, we developed Mosaic: a family of calibrated machine learning models using H&E WSI embeddings to classify tumors and identify molecular phenotypes across 163 detailed subtypes. The cancer subtyping module (Aeon) achieved an area under the receiver operating characteristic curve (AUROC) of 0.992 overall, with 161/163 subtypes reaching an AUROC [≥] 0.90 and improved performance over a state-of-the-art genomics-based classifier. The genomic inference module (Paladin) achieved an AUROC [≥] 0.80 for 167 pairs of detailed subtypes and genomic targets. We further used the learned histopathologic representations to i) identify key associations of the histopathologic embeddings with clinical biomarkers; ii) identify unsupervised sub-clusters of tumors with genomic determinants of tumor phenotype; iii) specify granular diagnoses for cancers of unknown primary, evaluated by genomic associations and expected clinical outcome distributions; iv) annotate functional significance for variants of uncertain significance (VUS); and v) identify cases that mimic the phenotypic effect of known DNA variants on H&E in the absence of detectable DNA alterations. Taken together, this work advances our understanding of phenotypic variation of granular tumor subtypes, their relevance to enhanced diagnostics, and their potential utility in risk stratification with multimodal machine learning in cancer.

cancer biology↗

bioRxiv2025

Paired primary-metastasis patient-derived organoids and mouse models identify phenotypic evolution and druggable dependencies of peritoneal metastasis from appendiceal cancer

Peritoneal carcinomatosis is a common yet deadly manifestation of gastrointestinal cancers, with few effective treatments. To identify targetable determinants of peritoneal metastasis, we focused on appendiceal adenocarcinoma (AC), a gastrointestinal cancer that metastasizes almost exclusively to the peritoneum. Current treatments are extrapolated from colorectal cancer (CRC), yet AC has distinct genomic alterations, mucinous morphology and peritoneum restricted metastatic pattern. Further, no stable preclinical models of AC exist, limiting drug discovery and representing an unmet clinical need. We establish a first-in-class stable biobank of 16 long-term cultured AC patient-derived organoids (PDOs), including 3 matched, simultaneously resected primary AC-peritoneal carcinomatosis (AC-PC) pairs. By enriching for cancer cells, AC PDOs enable accurate genomic characterization relative to paucicellular AC tissue. We establish an organoid orthotopic intraperitoneal xenograft model that recapitulates diffuse peritoneal carcinomatosis and show that PC-organoids retain increased metastatic capacity, decreased growth factor dependency and sensitivity to standard of care chemotherapy relative to matched primary AC organoids. Single cell profiling of AC-PC pairs reveals dedifferentiation from mucinous differentiated states in primary AC into intestinal stem cell and fetal progenitor states in AC-PC, with upregulation of oncogenic signaling pathways. Through hypothesis-driven drug testing, we identify KRASMULTI-ON inhibitor RMC-7977 and Wnt-targeting tyrosine kinase inhibitor WNTinib as novel, clinically actionable strategies to target AC-PC more effectively.

cancer biology↗

bioRxiv2022

Hematopoietic-specific heterozygous loss of Dnmt3a exacerbates colitis-associated colon cancer

Clonal hematopoiesis (CH) is defined as clonal expansion of mutant hematopoietic stem cells absent diagnosis of a hematologic malignancy. Presence of CH in solid tumor patients, including colon cancer, correlates with shorter survival. We hypothesized that bone marrow-derived cells with heterozygous loss-of-function mutations of DNMT3A, the most common genetic alteration in CH, contribute to the pathogenesis of colon cancer. In a mouse model that combines colitis-associated colon cancer with experimental CH driven by Dnmt3a+/{Delta}, we found higher tumor penetrance and increased tumor burden compared to controls. Histopathological analysis revealed accentuated colonic epithelium injury, dysplasia and adenocarcinoma formation. Transcriptome profiling of colon tumors identified enrichment of gene signatures associated with carcinogenesis, including angiogenesis. Treatment with the angiogenesis inhibitor axitinib eliminated the colon tumor-promoting effect of experimental CH driven by Dnmt3a haploinsufficiency. This study provides conceptually novel insights into non-tumor-cell-autonomous effect of hematopoietic alterations on colon carcinogenesis and identifies potential therapeutic strategies. SUMMARYA pre-clinical mouse model demonstrates that genetic alterations in the blood system characteristic of clonal hematopoiesis (CH) contribute to an aggressive solid tumor phenotype. It further identifies cancer angiogenesis as a potential therapeutic target to mitigate adverse CH effects.

cancer biology↗

bioRxiv2021

Genomic characterization of metastatic patterns from prospective clinical sequencing of 25,000 patients

Progression to metastatic disease remains the main cause of cancer death. Yet, the underlying genomic mechanisms driving metastasis remain largely unknown. Here, we present MSK-MET, an integrated pan-cancer cohort of tumor genomic and clinical outcome data from more than 25,000 patients. We analyzed this dataset to identify associations between tumor genomic alterations and patterns of metastatic dissemination across 50 tumor types. We found that chromosomal instability is strongly correlated with metastatic burden in some tumor types, including prostate adenocarcinoma, lung adenocarcinoma and HR-positive breast ductal carcinoma, but not in others, such as colorectal adenocarcinoma, pancreatic adenocarcinoma and high-grade serous ovarian cancer. We also identified specific somatic alterations associated with increased metastatic burden and specific routes of metastatic spread. Our data offer a unique resource for the investigation of the biological basis for metastatic spread and highlight the crucial role of chromosomal instability in cancer progression.

cancer biology↗

bioRxiv2020

Respiratory complex and tissue lineage drive mutational patterns in the tumor mitochondrial genome

Mitochondrial DNA (mtDNA) encodes essential protein subunits and translational machinery for four distinct complexes of oxidative phosphorylation (OXPHOS). Using repurposed whole-exome sequencing data, we demonstrate that pathogenic mtDNA mutations arise in tumors at a rate comparable to the most common cancer driver genes. We identify OXPHOS complexes as critical determinants shaping somatic mtDNA mutation patterns across tumor lineages. Loss-of-function mutations accumulate at an elevated rate specifically in Complex I, and often arise at specific homopolymeric hotspots. In contrast, Complex V is depleted of all non-synonymous mutations, suggesting that mutations directly impacting ATP synthesis are under negative selection. Both common truncating mutations and rarer missense alleles are associated with a pan-lineage transcriptional program, even in cancer types where mtDNA mutations are comparatively rare. Pathogenic mutations of mtDNA are associated with substantial increases in overall survival of colorectal adenocarcinoma patients, demonstrating a clear functional relationship between genotype and phenotype. The mitochondrial genome is therefore frequently and functionally disrupted across many cancers, with significant implications for patient stratification, prognosis and therapeutic development.

genomics↗