Shain, K. H. | Search bioRxiv

bioRxiv2022

Replicative Instability Drives Cancer Progression

In the past decade, defective DNA repair has been increasingly linked with cancer progression. Human tumors with markers of defective DNA repair and increased replication stress have been shown to exhibit genomic instability and poor survival rates across tumor types. Here we utilize-omics data from two independent consortia to identify the genetic underpinnings of replication stress, therapy resistance, and primary carcinoma to brain metastasis in BRCA wildtype tumors. In doing so, we have defined a new pan-cancer class of tumors characterized by replicative instability (RIN). RIN is defined by genomic evolution secondary to replicative challenge. Our data supports a model whereby defective single-strand break repair, translesion synthesis, and non-homologous end joining effectors drive RIN. Collectively, we find that RIN accelerates cancer progression by driving copy number alterations and transcriptional program rewiring that promote tumor evolution. Statement of SignificanceDefining the genetic basis of genomic instability with wildtype BRCA repair effectors is a significant unmet need in cancer research. Here we identify and characterize a pan-cancer cohort of tumors driven by replicative instability (RIN). We find that RIN drives therapy resistance and distant metastases across multiple tumor types.

cancer biology↗

bioRxiv2019

Multiple Myeloma DREAM Challenge Reveals Epigenetic Regulator PHF19 As Marker of Aggressive Disease

While the past decade has seen meaningful improvements in clinical outcomes for multiple myeloma patients, a subset of patients do not benefit from current therapeutics for unclear reasons. Many gene expression-based models of risk have been developed, but each model uses a different combination of genes and often involve assaying many genes making them difficult to implement. We organized the Multiple Myeloma DREAM Challenge, a crowdsourced effort to develop models of rapid progression in newly diagnosed myeloma patients and to benchmark these against previously published models. This effort lead to more robust predictors and found that incorporating specific demographic and clinical features improved gene expression-based models of high risk. Furthermore, post challenge analysis identified a novel expression-based risk marker and histone modifier, PHF19, which featured prominently in several independent models. Lastly, we show that a simple four feature predictor composed of age, International Staging System stage (ISS), and expression of PHF19 and MMSET performs similarly to more complex models with many more gene expression features included.\n\nKey pointsO_LIMost comprehensive and unbiased assessment of prognostic biomarkers in MM resulting in a robust and parsimonious model.\nC_LIO_LIIdentification of PHF19 as the expression based biomarker most strongly associated with rapid progression in MM patients.\nC_LI

cancer biology↗