Abstract: Provided is a computer-implemented method, including inputting to a trained machine learning classifier genomic information of a non-training subject that includes features from a tumor sample, wherein the trained machine learning classifier trained on features of tumor samples obtained from training subjects and their a responsiveness to checkpoint inhibition treatment and the machine-learning classifier is trained to predict responsiveness to the treatment, and generating a checkpoint inhibition responsiveness classification predictive of the subject's responding to the checkpoint inhibition with the trained machine-learning classifier, and reporting the checkpoint inhibition responsiveness classification using a graphical user interface. Also provided are a computer system for performing the method and a machine learning classifier trained by the method.

Abstract: Provided is a computer-implemented method, including inputting to a trained machine learning classifier genomic information of a non-training subject that includes features from a tumor sample, wherein the trained machine learning classifier trained on features of tumor samples obtained from training subjects and their a responsiveness to checkpoint inhibition treatment and the machine-learning classifier is trained to predict responsiveness to the treatment, and generating a checkpoint inhibition responsiveness classification predictive of the subject's responding to the checkpoint inhibition with the trained machine-learning classifier, and reporting the checkpoint inhibition responsiveness classification using a graphical user interface. Also provided are a computer system for performing the method and a machine learning classifier trained by the method.

Abstract: Computer implemented methods and computer systems are provided for estimating cancer cell fractions indicating proportions of cancer cells carrying one or more mutations of interest using one or more nucleic acid samples from a subject. The methods and systems provided herein implement processes that use a variational Bayesian mixture model to cluster initial cancer cell fractions and obtain the one or more final cancer cell fractions, the initial cancer cell fractions accounting for cancer purity and copy numbers. The disclosed methods and systems improve accuracy, validity, and reliability of tests for cancer clonality, and save time, materials, cost, and computer resources required for the tests, which can help design more affective cancer treatments.