Odeh Couvertier, Valerie

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Clustering highly correlated predictors to extract early predictive signatures of CAR-T cell quality
(2020-07-03) Odeh Couvertier, Valerie; Torres García, Wandaliz; College of Engineering; Dávila-Padilla, Saylisse; González Barreto, David; Department of Industrial Engineering; Vásquez Urbano, Pedro
Improving accessibility to innovative cancer immunotherapies such as CAR-T cell therapy is imperative to allow treatment for patients in need. The establishment of critical quality attributes and parameters is crucial for ensuring the potency, safety, and consistency needed to guarantee a successful large-scale manufacturing transition, which in turn will lower costs and increase accessibility. Hence, this work aims to molecularly characterize T cells and to extract predictive features of quality at early stages of its manufacturing using machine learning techniques that mitigate the impact of multicollinearity. To this end, this work proposes a multi-step semi-supervised approach that incorporates consensus clustering of features before model fitting process and a median position metric that allows an unbiased importance ranking of both clusters and variables inside each cluster. Results demonstrated that this approach was able to mitigate the impact of highly correlated omics features and extract putative driving variables.