Chopin, N. | Search bioRxiv

bioRxiv2020

SLFN11 captures cancer-immunity interactions associated with platinum sensitivity in ovarian cancer

Large independent analyses on cancer cell lines followed by functional studies have identified Schlafen 11 (SLFN11), a putative DNA/RNA helicase, as the strongest predictor of sensitivity to DNA-damaging agents. However, its role as a prognostic biomarker is undefined, partially due to the lack of validated methods to score SLFN11 in human tissues. Here, we implemented a pipeline to quantify SLFN11 in human cancer samples. By analyzing a cohort of high-grade serous ovarian carcinoma specimens prior platinum-based chemotherapy treatment, we demonstrate that SLFN11 is expressed by infiltrating innate and adaptive immune cells. We show, for the first time, that SLFN11 density in both the neoplastic and microenvironmental components was independently associated with favorable outcome. Transcriptomic analyses suggested the presence of a hitherto modulation of the cancer-immunity cycle orchestrated by SLFN11. We propose SLFN11 as a dual biomarker capturing simultaneously interconnected immunological and cancercell-intrinsic functional dispositions associated with sensitivity to DNA damaging agents.

cancer biology↗

bioRxiv2019

Imprecise neural computations as source of human adaptive behavior in volatile environments

Everyday life features uncertain and ever-changing situations. In such environments, optimal adaptive behavior requires higher-order inferential capabilities to grasp the volatility of external contingencies. These capabilities however involve complex and rapidly intractable computations, so that we poorly understand how humans develop efficient adaptive behaviors in such environments. Here we demonstrate this counterintuitive result: simple, low-level inferential processes involving imprecise computations conforming to the psychophysical Weber Law actually lead to near-optimal adaptive behavior, regardless of the environment volatility. Using volatile experimental settings, we further show that such imprecise, low-level inferential processes accounted for observed human adaptive performances, unlike optimal adaptive models involving higher-order inferential capabilities, their biologically more plausible, algorithmic approximations and non-inferential adaptive models like reinforcement learning. Thus, minimal inferential capabilities may have evolved along with imprecise neural computations as contributing to near-optimal adaptive behavior in real-life environments, while leading humans to make suboptimal choices in canonical decision-making tasks.

neuroscience↗