CEWG Seminar Series
upcoming seminar series
November
Thursday, November 2, 2023
11 a.m.-12:30 p.m. ET
General Attendee Zoom Link: https://aacr.zoom.us/j/82376707114
Please click on the link above at 11am ET on the day of the seminar.
Theme: Deciphering Treatment Resistance to Predict Cancer Evolution
Moderator:
Alberto Bardelli, PhD
Director, Laboratory Molecular Oncology, Candiolo Cancer Institute IRCCS; Associate Professor, Deparment of Oncology, University of Turin, Candiolo, Torino, Italy
Mariangela Russo, PhD
Assistant Professor, Department of Oncology University of Turin; Molecular Biotechnology Center-MBC2, Torino, Italy
Title: Leveraging DNA Damage Response to Prevent Evolution of Drug Resistance
Abstract: The emergence of drug resistance limits the efficacy and prolonged clinical benefits of targeted therapies. However, after a period of clinical response and tumor shrinkage, the disease inevitably recurs. When cancer cells are challenged with targeted therapy, a sub-fraction of cells switch to a drug-tolerant persister state and survives, without genetic mechanisms of resistance. Persister cells significantly contribute to the development of secondary resistance and represent a major obstacle to the effectiveness of cancer therapies.
We recently reported that colorectal cancer persisters which survive exposure to lethal doses of anticancer drugs display a shift from high to low-fidelity DNA replication process, downregulate DNA repair processes and adaptively increase their mutation rate, a stress response known as adaptive mutability, thus favoring the acquisition of mutations conferring resistance. Knowledge that cancer cells alter DNA repair machineries under therapeutic stress exposes a vulnerability that could be exploited to design novel therapeutic approaches to interfere with clonal evolution, thus preventing the recurrence of the disease and prolonging the clinical benefit for cancer patients.
Andrea Sotorriva, PhD
Head of the Centre for Computational Biology; Professor, Cancer Genomics and Evolution, Human Technopole, Milan, Italy
Title: Measuring Cancer Evolution from (Epi)Genomic Data
Abstract: Cancers evolve following Darwinian rules. Predicting and potentially controlling disease evolution requires appropriate data modelling to measure evolutionary processes from human samples and model systems. This can only be done by combining appropriate data collection with computational and mathematical modelling of evolutionary dynamics. Moreover, cancer evolution is not only driven by genetic mutations, but also by epigenetic alterations that, when heritable, can fuel Darwinian selection. I will present our approach, which combines new data collection and computational methods to extract evolutionary biology from cancer genomic and epigenomic data. I will show how we deconvolute Darwinian genetic and non-genetic mechanisms from non-Darwinian cellular plasticity from human data. Finally I will introduce the huge challenges we currently face in modelling cancer evolution in light of the emerging complexity of multifactorial evolutionary processes.
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