gabrielaltay/chowell-2021

Improved prediction of immune checkpoint blockade efficacy across multiple cancer types

Description

This dataset contains genomic, molecular, demographic, and clinical data from 1,479 patients treated with immune checkpoint blockade (ICB) across 16 different cancer types. The data was curated from the MSK-IMPACT cohort and used to develop a machine learning model for predicting ICB treatment response.

The dataset is provided as the supplementary data (Supplementary Table 3) from:

Chowell, D., Yoo, S.K., Valero, C. et al. Improved prediction of immune checkpoint blockade efficacy across multiple cancer types. Nat Biotechnol 40, 499--506 (2022). https://doi.org/10.1038/s41587-021-01070-8

The original XLSX file (41587_2021_1070_MOESM3_ESM.xlsx) is included in this repository and is also available directly from the publisher at Supplementary Table 3. The parquet files in data/ were converted from the "Training" and "Test" sheets in the XLSX file.

Abstract

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.

Dataset Structure

The dataset is split into training (1,184 samples) and test (295 samples) sets, preserving the original splits from the publication.

Features

Feature	Type	Description
SAMPLE_ID	int	Unique sample identifier
Cancer_type_grouped_2	string	Cancer type (16 categories: Bladder, Breast, Colorectal, Endometrial, Esophageal, Gastric, Head & Neck, Hepatobiliary, Melanoma, Mesothelioma, NSCLC, Ovarian, Pancreatic, Renal, Sarcoma, SCLC)
Cancer_Type2	int	Encoded cancer type (0, 1, 2)
Cancer_Type	string	Grouped cancer type (Melanoma, NSCLC, Others)
Chemo_before_IO (1:Yes; 0:No)	int	Whether chemotherapy was administered before immunotherapy
Age	float	Patient age
Sex (1:Male; 0:Female)	int	Patient sex
BMI	float	Body mass index
Stage (1:IV; 0:I-III)	int	Cancer stage
Stage at IO start	string	Detailed stage at immunotherapy start
NLR	float	Neutrophil-to-lymphocyte ratio
Platelets	int	Platelet count
HGB	float	Hemoglobin level
Albumin	float	Albumin level
Drug (1:Combo; 0:PD1/PDL1orCTLA4)	int	Drug type (combination vs. single agent)
Drug_class	string	Drug class description
TMB	float	Tumor mutational burden
FCNA	float	Fraction of copy number alterations
HED	float	HLA evolutionary divergence
HLA_LOH	int	HLA loss of heterozygosity
MSI (1:Unstable; 0:Stable_Indeterminate)	int	Microsatellite instability status
MSI_SCORE	float	Microsatellite instability score
Response (1:Responder; 0:Non-responder)	int	Clinical response to immunotherapy
OS_Event	int	Overall survival event indicator
OS_Months	float	Overall survival in months
PFS_Event	int	Progression-free survival event indicator
PFS_Months	float	Progression-free survival in months
RF16_prob	float	Random forest model predicted probability

Source

• Publication: Nature Biotechnology
• Supplementary Data: Supplementary Table 3 (XLSX)
• PubMed: 34725502
• PMC: PMC9363980

Citation

@article{chowell2022improved,
  title={Improved prediction of immune checkpoint blockade efficacy across multiple cancer types},
  author={Chowell, Diego and Yoo, Seong-Keun and Valero, Cristina and Pastore, Alessandro and Krishna, Chirag and Lee, Mark and Hoen, Douglas and Shi, Hongyu and Kelly, Daniel W and Patel, Neal and Makarov, Vladimir and Ma, Xiaoxiao and Vuong, Lynda and Sabio, Erich Y and Weiss, Kate and Kuo, Fengshen and Lenz, Tobias L and Samstein, Robert M and Riaz, Nadeem and Adusumilli, Prasad S and Balachandran, Vinod P and Plitas, George and Hakimi, A Ari and Abdel-Wahab, Omar and Shoushtari, Alexander N and Postow, Michael A and Motzer, Robert J and Ladanyi, Marc and Zehir, Ahmet and Berger, Michael F and G{\"o}nen, Mithat and Morris, Luc G T and Weinhold, Nils and Chan, Timothy A},
  journal={Nature Biotechnology},
  volume={40},
  number={4},
  pages={499--506},
  year={2022},
  publisher={Nature Publishing Group},
  doi={10.1038/s41587-021-01070-8}
}