Biology subjects

DiNatale, R. G.

Publications and source records attributed to DiNatale, R. G..

3 recordsLinked to original sources

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↗

bioRxiv2021

Resource-efficient pooled sequencing expands translational impact in solid tumors

Intratumoral genetic heterogeneity (ITH) poses a significant challenge to utilizing sequencing for decision making in the management of cancer. Although sequencing of multiple tumor regions can address the pitfalls of ITH, it does so at a significant increase in cost and resource utilization. We propose a pooled multiregional sequencing strategy, whereby DNA aliquots from multiple tumor regions are mixed prior to sequencing, as a cost-effective strategy to boost translational value by addressing ITH while preserving valuable residual tissue for secondary analysis. Focusing on kidney cancer, we demonstrate that DNA pooling from as few as two regions significantly increases mutation detection while reducing clonality misattribution. This leads to an increased fraction of patients identified with therapeutically actionable mutations, improved patient risk stratification, and improved inference of evolutionary trajectories with an accuracy comparable to bona fide multiregional sequencing. The same approach applied to non-small-cell lung cancer data substantially improves tumor mutational burden (TMB) detection. Our findings demonstrate that pooled DNA sequencing strategies are a cost-effective alternative to address intrinsic genetic heterogeneity in clinical settings.

genomics↗

bioRxiv2020

9p21 Loss Defines the Evolutionary Patterns of Aggressive Renal Cell Carcinomas

Dedifferentiation and acquisition of chromosomal instability in renal cell carcinoma portends dismal prognosis and aggressive clinical behavior. However, the absence of reliable experimental models dramatically impacts the understanding of mechanisms underlying malignant progression. Here we established an in vivo genetic platform to rapidly generate somatic mosaic genetically engineerd immune-competent mouse models of renal tumors, recapitulating the genomic and phenotypic features of these malignancies. Leveraging somatic chromosomal engineering, we demonstrated that ablation of the murine locus syntenic to human 9p21 drives the rapid expansion of aggressive mesenchymal clones with prominent metastatic behavior, characterized by early emergence of chromosomal instability, whole-genome duplication, and conserved patterns of aneuploidy. This model of punctuated equilibrium provides a remarkable example of cross-species convergent evolution. SignificanceTo better understand the role of 9p21 in malignant progression, we generated a somatic mosaic GEMM of renal cancer, capturing the histological, genomic and evolutionary features of human disease. With this technology we demonstrated a critica role of 9p21 loss in metastatic evolution of RCC and provide a unique tool for testing new therapeutic treatments.

cancer biology↗