Mapping the in vivo fitness landscape of lung adenocarcinoma tumor suppression in mice

Abstract

The functional impact of most genomic alterations found in cancer, alone or in combination, remains largely unknown. Here we integrate tumor barcoding, CRISPR/Cas9-mediated genome editing and ultra-deep barcode sequencing to interrogate pairwise combinations of tumor suppressor alterations in autochthonous mouse models of human lung adenocarcinoma. We map the tumor suppressive effects of 31 common lung adenocarcinoma genotypes and identify a landscape of context dependence and differential effect strengths.

Measuring the distribution of fitness effects in somatic evolution by combining clonal dynamics with dN/dS ratios

“…Extensive experimental effort is ongoing to determine the fitness effects of mutations. Most prominently is lineage tracing of mutations in mouse models 4,5 , but these methods are not sufficiently high-throughput to produce the DFE for all somatic mutations. Other studies have estimated the selective coefficient of somatic mutations by measuring the frequency of such mutations over time in the same individual using longitudinal sampling 6,7 however this method is broadly limited to somatic evolution in the blood (where it is feasible to take samples from healthy individuals over time) and in rare cases of patients under active surveillance.…”

Quantifying local malignant adaptation in tissue‐specific evolutionary trajectories by harnessing cancer’s repeatability at the genetic level

“…It also ignores the contribution of epigenetic alterations as potential drivers; the inclusion of which will require a more general understanding on the recurrent epigenetic alterations functionally linked to tumour formation (Timp & Feinberg, 2013 (Ortmann et al, 2015). As occurrence-driven definition of epistatic interactions requires large numbers of observations, animal models in which driver combinations can be induced and followed over time could provide valuable controls for pre-identified targets (Rogers et al, 2018). Aside from genetic alterations, our model does not include the interactive adaptation relationship between a preinvasive cell and its environment, the interplay between both being very likely to modify selective pressures as potential tumour-initiating cells develop (Bissell & Radisky, 2001;Rozhok, Salstrom, & DeGregori, 2016;.…”

Quantifying local malignant adaptation in tissue-specific evolutionary trajectories by harnessing cancer’s repeatability at the genetic level

“…By extension, reconstructing fitness/adaptation landscapes of already invasive tumours based on different therapeutic options can help tailor ad-hoc therapeutic regimens: based on the fitness and frequency of all (sub)clones composing each tumour, these landscapes can allow to optimise sequential drug schedules so as to dynamically reduce overall fitness (Nichol et al, 2017). Occurrence-driven definition of epistatic interactions requires large numbers of observations but can be completed by animal models in which driver combinations can be induced and followed over time (Rogers et al, 2018). The reconstruction of therapy-specific human fitness landscapes thus critically requires efforts to generate large, centralised public datasets, with accurate clinical annotation for each treatment type, ideally with samples before and after treatment.…”