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An Ensemble Learning Approach for Cancer Drug Prediction

Darsh Mandera, View ORCID ProfileAnna Ritz
doi: https://doi.org/10.1101/2020.08.10.245142
Darsh Mandera
1Jesuit High School, Portland, Oregon, USA;
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  • For correspondence: dsmandera@gmail.com dsmandera@gmail.com
Anna Ritz
2Reed College, Portland, Oregon, USA;
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  • For correspondence: aritz@reed.edu
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Abstract

Predicting the response to a particular drug for specific cancer, despite known genetic mutations, still remains a huge challenge in modern oncology and precision medicine. Today, prescribing a drug for a cancer patient is based on a doctor’s analysis of various articles and previous clinical trials; it is an extremely time-consuming process. We developed a machine learning classifier to automatically predict a drug given a carcinogenic gene mutation profile. Using the Breast Invasive Carcinoma Dataset from The Cancer Genome Atlas (TCGA), the method first selects features from mutated genes and then applies K-Fold, Decision Tree, Random Forest and Ensemble Learning classifiers to predict best drugs. Ensemble Learning yielded prediction accuracy of 66% on the test set in predicting the correct drug. To validate that the model is general-purpose, Lung Adenocarcinoma (LUAD) data and Colorectal Adenocarcinoma (COADREAD) data from TCGA was trained and tested, yielding prediction accuracies 50% and 66% respectively. The resulting accuracy indicates a direct correlation between prediction accuracy and cancer data size. More importantly, the results of LUAD and COADREAD show that the implemented model is general purpose as it is able to achieve similar results across multiple cancer types. We further verified the validity of the model by implementing it on patients with unclear recovery status from the COADREAD dataset. In every case, the model predicted a drug that was administered to each patient. This method will offer oncologists significant time-saving compared to their current approach of extensive background research, and offers personalized patient care for cancer patients.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/annaritz/cancer-drug-pred

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted August 11, 2020.
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An Ensemble Learning Approach for Cancer Drug Prediction
Darsh Mandera, Anna Ritz
bioRxiv 2020.08.10.245142; doi: https://doi.org/10.1101/2020.08.10.245142
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An Ensemble Learning Approach for Cancer Drug Prediction
Darsh Mandera, Anna Ritz
bioRxiv 2020.08.10.245142; doi: https://doi.org/10.1101/2020.08.10.245142

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