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SLFN11 captures cancer-immunity interactions associated with platinum sensitivity in high-grade serous ovarian cancer
Claudia Winkler, … , Elisabetta Leo, Gabriele Zoppoli
Claudia Winkler, … , Elisabetta Leo, Gabriele Zoppoli
Published September 22, 2021
Citation Information: JCI Insight. 2021;6(18):e146098. https://doi.org/10.1172/jci.insight.146098.
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Research Article Cell biology Oncology

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

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Abstract

Large independent analyses on cancer cell lines followed by functional studies have identified Schlafen 11 (SLFN11), a putative helicase, as the strongest predictor of sensitivity to DNA-damaging agents (DDAs), including platinum. 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 (HGSOC) specimens before platinum-based chemotherapy treatment, we show, for the first time to our knowledge, that SLFN11 density in both the neoplastic and microenvironmental components was independently associated with favorable outcome. We observed SLFN11 expression in both infiltrating innate and adaptive immune cells, and analyses in a second, independent, cohort revealed that SLFN11 was associated with immune activation in HGSOC. We found that platinum treatments activated immune-related pathways in ovarian cancer cells in an SLFN11-dependent manner, representative of tumor-immune transactivation. Moreover, SLFN11 expression was induced in activated, isolated immune cell subpopulations, hinting that SLFN11 in the immune compartment may be an indicator of immune transactivation. In summary, we propose SLFN11 is a dual biomarker capturing simultaneously interconnected immunological and cancer cell–intrinsic functional dispositions associated with sensitivity to DDA treatment.

Authors

Claudia Winkler, Matthew King, Julie Berthe, Domenico Ferraioli, Anna Garuti, Federica Grillo, Jaime Rodriguez-Canales, Lorenzo Ferrando, Nicolas Chopin, Isabelle Ray-Coquard, Oona Delpuech, Darawan Rinchai, Davide Bedognetti, Alberto Ballestrero, Elisabetta Leo, Gabriele Zoppoli

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Figure 1

SLFN11 transcript and protein levels in HGSOC.

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SLFN11 transcript and protein levels in HGSOC.
(A) Scatterplot represent...
(A) Scatterplot representing SLFN11 transcript by qRT-PCR as –ΔΔCt (y axis) as a function of its protein assessment by IHC as H-score (x axis) in the nucleus of noncancer and cancer cells from HGSOC specimens; ρ is the Spearman’s correlation coefficient; the least squares regression is represented by the red line; and dots are measurements of SLFN11 by qRT-PCR and IHC in individual samples. (B) Scatterplot representing SLFN11 protein levels in HGSOC cancer cells. X axis: pathologist’s assessment; y axis: H-score measured by HALO Digital Pathology (DP) software. (C) Dot plot illustrating cancer cell H-scores in individual samples (y axis), ordered by increasing DP-assigned values (x axis), highlighting the excellent consistency of intraclass correlation coefficients (ICCs) between the 2 methods. Each dot represents a score assigned by either the DP software (HALO) or the pathologist performing the assessment. (D) Bland-Altman plot displaying the difference between HALO’s and pathologist’s H-scores for cancer cells (y axis) by the increasing mean of value couples for individual samples (x axis). All points lie within 1.96 SDs (dotted green horizontal lines) from the mean difference (dashed horizontal black line), indicating no relevant bias between raters, and an insignificant trend toward higher H-scores for HALO as the mean values increase. The red line represents a smoothed regression (loess) fit of the actual mean scores.

Copyright © 2023 American Society for Clinical Investigation
ISSN 2379-3708

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