Abstract
BackgroundPrecision oncology seeks to integrate multiple layers of data from a patient’s cancer to effectively tailor therapy. Conventional chemotherapies are sometimes effective but accompanied by adverse events, warranting the identification of a biomarker of chemosensitivity.ObjectiveIdentify an mRNA biomarker that predicts chemosensitivity across solid tumor subtypes.MethodsWe performed a pan-solid tumor analysis integrating gene expression and drug sensitivity profiles from 3 cancer cell line datasets to identify transcripts correlated with sensitivity to a panel of chemotherapeutics. We then tested the ability of an mRNA biomarker to predictive clinical outcomes in cohorts of patients with breast, lung, or ovarian cancer.ResultsExpression levels of several mRNA transcripts were significantly correlated with sensitivity or resistance chemotherapeutics in cancer cell line datasets. The only mRNA transcript significantly correlated with sensitization to multiple classes of DNA-damaging chemotherapeutics in all 3 cell line datasets was encoded by Schlafen Family Member 11 (SLFN11). Analyses of multiple breast, lung, and ovarian cancer patient cohorts treated with chemotherapy confirmed SLFN11 mRNA expression as a predictive biomarker of longer overall survival and improved tumor response.ConclusionsTumor SLFN11 mRNA expression is a biomarker of sensitivity to an array of DNA-damaging chemotherapeutics across solid tumor subtypes.
Highlights
In the emerging era of precision oncology, the molecular features of a tumor are being used to guide treatment decisions for each individual patient
Expression levels of several mRNA transcripts were significantly correlated with sensitivity or resistance chemotherapeutics in cancer cell line datasets
The only mRNA transcript significantly correlated with sensitization to multiple classes of DNA-damaging chemotherapeutics in all 3 cell line datasets was encoded by Schlafen Family Member 11 (SLFN11)
Summary
In the emerging era of precision oncology, the molecular features of a tumor are being used to guide treatment decisions for each individual patient. While the development of such tumor-targeted therapies is conceptually straightforward (i.e., drug targets aberrant protein), approaches to leverage molecular features of tumors to refine the use of “non-targeted therapies” (i.e., conventional chemotherapies) remain underdeveloped. This is especially important in the current clinical environment where chemotherapy, which we broadly define as a small molecule that is not targeted to an oncoprotein or prescribed due to a specific genetic aberration or cancer cell lineage characteristic, remains the standard-of-care treatment for most cancer subtypes. Conventional chemotherapies are sometimes effective but accompanied by adverse events, warranting the identification of a biomarker of chemosensitivity
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