Proceedings: AACR Annual Meeting 2021; April 10-15, 2021 and May 17-21, 2021; Philadelphia, PA
We report the advances of a cloud-based software utilizing advanced algorithms to identify the molecular cancer drivers and its signal transduction. To test, a classifier based on the NFκB signaling pathway gene expression profiles that predicts the clinical therapeutic response to chemo-radiation therapy of Squamous Cell Carcinomas of the Head and Neck (SCCHN) patients are described here. Expression of HPV16, p16 and 84 NFκB signaling pathway genes were determined using real time RT PCR in SCCHN patient samples and SCCHN cell lines. Gene expression was correlated to chemo-radiation response in cell lines and the model was blindly tested in SCCHN patients to predict their clinical outcome to chemo-radiation. This tool intends to fuse biological domain knowledge by way of integrating disease targets, cell line experiments towards creating a gene expression classifier for clinical outcome. The gene expression-based predicted clinical outcome compared with patients actual observed clinical outcome correctly identified the clinical response in 73% of the cases. Such work will allow design of tailored individualized clinical treatment strategies to target NFκB inhibition using biological modifying agents with or without chemo/radiation therapy. This will help to develop several targets to directly inhibit the NFκB activity or inhibit the downstream pivotal targets. The significance of the classifier would be the ability to use the biologic attributes of a tumor to tailor specific treatment protocols for each individual patient. This would not only spare the chemo-radiation resistant patients the ill effects of the therapy but will also decrease the delay in effective treatment among the resistant patients. The long-term objective is to use such pro-survival driver genes in other cancers; SMAD, TGFβ in pancreatic cancer, and AR in prostate cancer. This tool (MeghaOncomine) intends to fuse biological domain knowledge by way of integrating cancer targets, cell line experiments towards creating a gene expression classifier for clinical outcome.
Citation Format: Rao V. Papineni, Seema Gupta, Aravindan Natarajan, Ram Papineni, Chandra Kasinathan. Cloud-based biological domain knowledge derived gene expression classifier to predict the cancer clinical outcome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB018.
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