Biology subjects

Paris, C.

Publications and source records attributed to Paris, C..

2 recordsLinked to original sources

bioRxiv2019

Targeting DNA damage response and Replication Stress in Pancreatic Cancer

Continuing recalcitrance to therapy cements pancreatic cancer (PC) as the most lethal malignancy, which is set to become the second leading cause of cancer death in our society. We interrogated the transcriptome, genome, proteome and functional characteristics of 61 novel PC patient-derived cell lines to define novel therapeutic strategies targeting the DNA damage response (DDR) and replication stress. We show that patient-derived cell lines faithfully recapitulate the epithelial component of pancreatic tumors including previously described molecular subtypes. Biomarkers of DDR deficiency, including a novel signature of homologous recombination deficiency, co-segregates with response to platinum and PARP inhibitor therapy in vitro and in vivo. We generated a novel signature of replication stress with potential clinical utility in predicting response to ATR and WEE1 inhibitor treatment. Replication stress and DDR deficiency are independent of each other, creating opportunities for therapy in DDR proficient PC, and post-platinum therapy.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=145 SRC=\"FIGDIR/small/713545v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (47K):\norg.highwire.dtl.DTLVardef@106f2b8org.highwire.dtl.DTLVardef@1294d22org.highwire.dtl.DTLVardef@86f5d3org.highwire.dtl.DTLVardef@751e13_HPS_FORMAT_FIGEXP M_FIG C_FIG STATEMENT OF SIGNIFICANCEWe define therapeutic strategies that target subgroups of PC using novel signatures of DNA damage response deficiency and replication stress. This potentially offers patients with DNA repair defects therapeutic options outside standard of care platinum chemotherapy and is being tested in clinical trials on the Precision-Panc platform.

cancer biology↗

bioRxiv2019

Inference of selection from genetic time series using various parametric approximations to the Wright-Fisher model

Detecting genomic regions under selection is an important objective of population genetics. Typical analyses for this goal are based on exploiting genetic diversity patterns in present time data but rapid advances in DNA sequencing have increased the availability of time series genomic data. A common approach to analyze such data is to model the temporal evolution of an allele frequency as a Markov chain. Based on this principle, several methods have been proposed to infer selection intensity. One of their differences lies in how they model the transition probabilities of the Markoiv chain. Using the Wright-Fisher model is a natural choice but its computational cost is prohibitive for large population sizes so approximations to this model based on parametric distributions have been proposed. Here, we compared the performance of some of these approximations with respect to their power to detect selection and estimation of the selection coefficient. We developped a new generic Hidden Markov Model likelihood calculator and applied it on genetic time series simulated under various evolutionary scenarios. The Beta-with-Spikes approximation, which combines discrete fixation probabilities with a continuous Beta distribution, was found to perform consistently better than the others. This distribution provides an almost perfect fit to the Wright-Fisher model in terms of selection inference, for a computational cost that does not increase with population size. We further evaluate this model for population sizes not accessible to the Wright-Fisher model and illustrate its performance on a dataset of two divergently selected chicken populations.

genetics↗