Dataset Information

Chowell2022 - Random Forest model to predict efficacy of immune checkpoint blockade across multiple cancer patient cohorts

ABSTRACT: This is a Random Forest algorithm-based machine learning model called RF16, which incorporates a total of 16 genomic, molecular, demographic, and clinical features to predict the immunotherapy response for a patient. The model assigns a value of 0 for NonResponder and 1 for Responder. Please be aware that the column names in the GitHub code and the downloaded dataset from the publication may vary. Users are advised to make minor adjustments to either the code or the dataset to ensure compatibility. The curated version of the model has modified the column names in the training code to align with the dataset. GitHub repository: https://github.com/CCF-ChanLab/MSK-IMPACT-IO

SUBMITTER: Divyang Deep Tiwari

PROVIDER: BIOMD0000001066 | BioModels | 2023-05-09

REPOSITORIES: BioModels

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Dataset's files

Source:

			Action	DRS
	BIOMD0000001066?filename=ICB_annotation.csv	Csv
	BIOMD0000001066?filename=RF16.onnx	Other
	BIOMD0000001066?filename=Reproduced%20Figures.pdf	Pdf
	BIOMD0000001066?filename=Reproduced%20Metrics.pdf	Pdf
	BIOMD0000001066?filename=dockerICB_predict.zip	Other

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Publications

Improved prediction of immune checkpoint blockade efficacy across multiple cancer types.

Chowell Diego D Yoo Seong-Keun SK Valero Cristina C Pastore Alessandro A Krishna Chirag C Lee Mark M Hoen Douglas D Shi Hongyu H Kelly Daniel W DW Patel Neal N Makarov Vladimir V Ma Xiaoxiao X Vuong Lynda L Sabio Erich Y EY Weiss Kate K Kuo Fengshen F Lenz Tobias L TL Samstein Robert M RM Riaz Nadeem N Adusumilli Prasad S PS Balachandran Vinod P VP Plitas George G Ari Hakimi A A Abdel-Wahab Omar O Shoushtari Alexander N AN Postow Michael A MA Motzer Robert J RJ Ladanyi Marc M Zehir Ahmet A Berger Michael F MF Gönen Mithat M Morris Luc G T LGT Weinhold Nils N Chan Timothy A TA

Nature biotechnology 20211101 4

Only a fraction of patients with cancer respond to immune checkpoint blockade (ICB) treatment, but current decision-making procedures have limited accuracy. In this study, we developed a machine learning model to predict ICB response by integrating genomic, molecular, demographic and clinical data from a comprehensively curated cohort (MSK-IMPACT) with 1,479 patients treated with ICB across 16 different cancer types. In a retrospective analysis, the model achieved high sensitivity and specificit ...[more]

PMID: 34725502

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