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Boroevich, K.

Publications and source records attributed to Boroevich, K..

2 recordsLinked to original sources

Quantification of multicellular colonization in tumor metastasis using exome sequencing data

Metastasis is a major cause of cancer-related mortality, and it is essential to understand how metastasis occurs in order to overcome it. One relevant question is the origin of a metastatic tumor cell population. Although the hypothesis of a single-cell origin for metastasis from a primary tumor has long been prevalent, several recent studies using mouse models have supported a multi-cellular origin of metastasis. Human bulk whole-exome sequencing (WES) studies also have demonstrated a multiple clonal origin of metastasis, with different mutational compositions. Specifically, there has not yet been strong research to determine how many founder cells colonize a metastatic tumor. To address this question, we developed a method to quantify the founder cell population size in a metastasis using paired WES data from primary and metachronous metastatic tumors. Simulation studies demonstrated the proposed method gives unbiased results with sufficient accuracy in the range of realistic settings. Applying the proposed method to real WES data from four colorectal cancer patients, all samples supported a multi-cellular origin of metastasis and the founder size was quantified, ranging from 3 to 15 cells. Such a wide-ranging founder sizes estimated by the proposed method suggests that there are large variations in genetic similarity between primary and metastatic tumors in the same subjects, which might be involved in (dis)similarity of drug responses between tumors.

cancer biology

An integrative machine learning approach for prediction of toxicity-related drug safety

Recent trends in drug development have been marked by diminishing returns of escalating costs and falling rate of new drug approval. Unacceptable drug toxicity is a substantial cause of drug failure during clinical trials as well as the leading cause of drug withdraws after release to market. Computational methods capable of predicting these failures can reduce waste of resources and time devoted to the investigation of compounds that ultimately fail. We propose an original machine learning method that leverages identity of drug targets and off-targets, functional impact score computed from Gene Ontology annotations, and biological network data to predict drug toxicity. We demonstrate that our method (TargeTox) can distinguish potentially idiosyncratically toxic drugs from safe drugs and is also suitable for speculative evaluation of different target sets to support the design of optimal low-toxicity combinations.\n\nSummary blurbPrediction of toxicity-related drug clinical trial failures, withdrawals from market and idiosyncratic toxicity risk by combining biological network analysis with machine learning.

systems biology