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Nat. Med., Jan 06 2025, | https://doi.org/10.1038/s41591-024-03398-5
Prediction of checkpoint inhibitor immunotherapy efficacy for cancer using routine blood tests and clinical data
Seong-Keun Yoo1,2,3,4,5,6,22, Conall W. Fitzgerald7,8,22, Byuri Angela Cho1,2,3,4,5,6,22, Bailey G. Fitzgerald9,22, Catherine Han7,8, Elizabeth S. Koh7,8, Abhinav Pandey7,8, Hannah Sfreddo7,8, Fionnuala Crowley10,11, Michelle Rudshteyn Korostin10, Neha Debnath12, Yan Leyfman13, Cristina Valero7,8, Mark Lee7,8, Joris L. Vos7,8, Andrew Sangho Lee7,8, Karena Zhao7,8, Stanley Lam7,8, Ezekiel Olumuyide1,2, Fengshen Kuo14,15, Eric A. Wilson1,2,3,4,5,6, Pauline Hamon1,2,6, Clotilde Hennequin1,2,6, Miriam Saffern1,2, Lynda Vuong14,15, A. Ari Hakimi14,15, Brian Brown1,2,5,16, Miriam Merad1,2,6,10,17, Sacha Gnjatic 1,2,3,10,17, Nina Bhardwaj1,10,18, Matthew D. Galsky10, Eric E. Schadt19, Robert M. Samstein1,2, Thomas U. Marron1,2,10,20, Mithat Gönen21, Luc G. T. Morris7,8 & Diego Chowell1,2,3,4,5,6
1Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
2Department of Immunology and Immunotherapy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
3Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
4Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
5Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
6Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
7Head and Neck Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
8Laboratory of Experimental Cancer Immunogenomics, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
9Department of Medicine, Thoracic Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.
10Division of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
11Brookdale Department of Geriatrics and Palliative Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
12Internal Medicine, Icahn School of Medicine, Mount Sinai Morningside and West, New York, NY, USA.
13Internal Medicine, Icahn School of Medicine at Mount Sinai South Nassau, Rockville Centre, NY, USA.
14Department of Surgery, Urology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
15Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
16Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
17Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
18Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
19Pathos, New York, NY, USA.
20Early Phase Trials Unit, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
21Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
22These authors contributed equally: Seong-Keun Yoo, Conall W. Fitzgerald, Byuri Angela Cho, Bailey G. Fitzgerald.
Corresponding authors
Correspondence to Luc G. T. Morris or Diego Chowell.
1Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
2Department of Immunology and Immunotherapy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
3Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
4Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
5Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
6Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
7Head and Neck Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
8Laboratory of Experimental Cancer Immunogenomics, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
9Department of Medicine, Thoracic Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.
10Division of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
11Brookdale Department of Geriatrics and Palliative Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
12Internal Medicine, Icahn School of Medicine, Mount Sinai Morningside and West, New York, NY, USA.
13Internal Medicine, Icahn School of Medicine at Mount Sinai South Nassau, Rockville Centre, NY, USA.
14Department of Surgery, Urology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
15Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
16Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
17Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
18Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
19Pathos, New York, NY, USA.
20Early Phase Trials Unit, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
21Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
22These authors contributed equally: Seong-Keun Yoo, Conall W. Fitzgerald, Byuri Angela Cho, Bailey G. Fitzgerald.
Corresponding authors
Correspondence to Luc G. T. Morris or Diego Chowell.
Abstract
Predicting whether a patient with cancer will benefit from immune checkpoint inhibitors (ICIs) without resorting to advanced genomic or immunologic assays is an important clinical need. To address this, we developed and evaluated SCORPIO, a machine learning system that utilizes routine blood tests (complete blood count and comprehensive metabolic profile) alongside clinical characteristics from 9,745 ICI-treated patients across 21 cancer types. SCORPIO was trained on data from 1,628 patients across 17 cancer types from Memorial Sloan Kettering Cancer Center. In two internal test sets comprising 2,511 patients across 19 cancer types, SCORPIO achieved median time-dependent area under the receiver operating characteristic curve (AUC(t)) values of 0.763 and 0.759 for predicting overall survival at 6, 12, 18, 24 and 30 months, outperforming tumor mutational burden (TMB), which showed median AUC(t) values of 0.503 and 0.543. Additionally, SCORPIO demonstrated superior predictive performance for predicting clinical benefit (tumor response or prolonged stability), with AUC values of 0.714 and 0.641, compared to TMB (AUC = 0.546 and 0.573). External validation was performed using 10 global phase 3 trials (4,447 patients across 6 cancer types) and a real-world cohort from the Mount Sinai Health System (1,159 patients across 18 cancer types). In these external cohorts, SCORPIO maintained robust performance in predicting ICI outcomes, surpassing programmed death-ligand 1 immunostaining. These findings underscore SCORPIO's reliability and adaptability, highlighting its potential to predict patient outcomes with ICI therapy across diverse cancer types and healthcare settings.
논문정보
- Research article
- 2025년 01월 (BRIC 등록일 2025-01-09)
- 국외 연구진
- 의약학 > 종양의학
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