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

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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:
Open Neural Network Exchange
Related Publication
  • Improved prediction of immune checkpoint blockade efficacy across multiple cancer types.
  • Chowell D, Yoo SK, Valero C, Pastore A, Krishna C, Lee M, Hoen D, Shi H, Kelly DW, Patel N, Makarov V, Ma X, Vuong L, Sabio EY, Weiss K, Kuo F, Lenz TL, Samstein RM, Riaz N, Adusumilli PS, Balachandran VP, Plitas G, Ari Hakimi A, Abdel-Wahab O, Shoushtari AN, Postow MA, Motzer RJ, Ladanyi M, Zehir A, Berger MF, Gönen M, Morris LGT, Weinhold N, Chan TA
  • Nature biotechnology , 4/ 2022 , Volume 40 , Issue 4 , pages: 499-506 , PubMed ID: 34725502
  • Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • 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 specificity in predicting clinical response to immunotherapy and predicted both overall survival and progression-free survival in the test data across different cancer types. Our model significantly outperformed predictions based on tumor mutational burden, which was recently approved by the U.S. Food and Drug Administration for this purpose. Additionally, the model provides quantitative assessments of the model features that are most salient for the predictions. We anticipate that this approach will substantially improve clinical decision-making in immunotherapy and inform future interventions.
Submitter of the first revision: Divyang Deep Tiwari
Submitter of this revision: Divyang Deep Tiwari
Curators: Divyang Deep Tiwari

Metadata information

BioModels Database BIOMD0000001066
BioModels Database MODEL2304250001
PubMed 34725502

Curation status

Modelling approach(es)

Connected external resources

Name Description Size Actions

Model files

RF16.onnx ONNX file of the Random Forest (RF16) trained model.
 1.80 MB Preview | Download

Additional files

ICB_annotation.csv Model annotation in csv format. 6.88 KB Preview | Download
Reproduced Figures.pdf Comparison between original figures (Fig. 1c, 1d and 2a from manuscript) and reproduced figures.
 727.33 KB Preview | Download
Reproduced Metrics.pdf Comparison between original confusion matrix (Fig. 2d-g from manuscript) and reproduced confusion matrix.
 64.12 KB Preview | Download Comparison between original confusion matrix (Fig. 2d-g from manuscript) and reproduced confusion matrix.
 529.05 KB Preview | Download Docker file and dependencies to train the RF16 model using script.
 304.04 KB Preview | Download Python script which loads the dataset to train the RF16 and RF11 model and create text files with prediction probabilities.
 3.36 KB Preview | Download

  • Model originally submitted by : Divyang Deep Tiwari
  • Submitted: May 11, 2023 10:28:52 AM
  • Last Modified: May 11, 2023 10:28:52 AM
  • Version: 1 public model Download this version
    • Submitted on: May 11, 2023 10:28:52 AM
    • Submitted by: Divyang Deep Tiwari
    • With comment: First submission of the Immune Checkpoint Blockade (ICB) machine learning model.