A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer

Mike Mason; Óscar Lapuente-Santana; Anni S. Halkola; Wenyu Wang; Raghvendra Mall; Xu Xiao; Jacob Kaufman; Jingxin Fu; Jacob Pfeil; Jineta Banerjee; Verena Chung; Han Chang; Scott D. Chasalow; Hung Ying Lin; Rongrong Chai; Thomas Yu; Francesca Finotello; Tuomas Mirtti; Mikko I. Mäyränpää; Jie Bao; Emmy W. Verschuren; Eiman I. Ahmed; Michele Ceccarelli; Lance D. Miller; Gianni Monaco; Wouter R.L. Hendrickx; Shimaa Sherif; Lin Yang; Ming Tang; Shengqing Stan Gu; Wubing Zhang; Yi Zhang; Zexian Zeng; Avinash Das Sahu; Yang Liu; Wenxian Yang; Davide Bedognetti; Jing Tang; Federica Eduati; Teemu D. Laajala; William J. Geese; Justin Guinney; Joseph D. Szustakowski; Benjamin G. Vincent; David P. Carbone

doi:10.1186/s12967-023-04705-3

A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer

Mike Mason, Óscar Lapuente-Santana, Anni S. Halkola, Wenyu Wang, Raghvendra Mall, Xu Xiao, Jacob Kaufman, Jingxin Fu, Jacob Pfeil, Jineta Banerjee, Verena Chung, Han Chang, Scott D. Chasalow, Hung Ying Lin, Rongrong Chai, Thomas Yu, Francesca Finotello, Tuomas Mirtti, Mikko I. Mäyränpää, Jie BaoEmmy W. Verschuren, Eiman I. Ahmed, Michele Ceccarelli, Lance D. Miller, Gianni Monaco, Wouter R.L. Hendrickx, Shimaa Sherif, Lin Yang, Ming Tang, Shengqing Stan Gu, Wubing Zhang, Yi Zhang, Zexian Zeng, Avinash Das Sahu, Yang Liu, Wenxian Yang, Davide Bedognetti, Jing Tang, Federica Eduati, Teemu D. Laajala, William J. Geese, Justin Guinney, Joseph D. Szustakowski, Benjamin G. Vincent, David P. Carbone (Corresponding author)

Onderzoeksoutput: Bijdrage aan tijdschrift › Tijdschriftartikel › Academic › peer review

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BACKGROUND: Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC.

METHODS: Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials.

RESULTS: A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1.

CONCLUSIONS: This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy.

TRIAL REGISTRATION: CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.

Originele taal-2	Engels
Artikelnummer	190
Aantal pagina's	16
Tijdschrift	Journal of Translational Medicine
Volume	22
Nummer van het tijdschrift	1
DOI's	https://doi.org/10.1186/s12967-023-04705-3
Status	Gepubliceerd - 21 feb. 2024

Bibliografische nota

Financiering

Xiaole Shirley Liu contributed to the development of the TIDE model. Josue Samayoa contributed to the development of the @jacob.pfeil model. Abraham Apfel contributed data analysis advice. Medical writing and editorial support were provided by Thierry Deltheil, PhD, and Matthew Weddig of Spark Medica Inc., funded by Bristol Myers Squibb. Results from this study have been presented, in part, at the 14th annual RECOMB/ISCB Conference on Regulatory and Systems Genomics with DREAM Challenges (RSGDREAM 2022), November 8–9, 2022, Las Vegas, Nevada, USA. MM, HC, and HYL are employees of Bristol Myers Squibb. MIM has received lecture and/or advisory board fees from Boehringer Ingelheim, Bristol Myers Squibb, MSD, Takeda, Bayer, Amgen, Roche, and Aiforia Technologies Oy. WY is a shareholder of Aginome Scientific. JG is an employee of Tempus Labs. SSG is the recipient of a fellowship from the Sara Elizabeth O’Brien Trust. JS, SC, and WJG are employees and shareholders of Bristol Myers Squibb. BGV received consulting fees from GeneCentric Therapeutics. DPC has received advisory board fees, consulting fees, presentation fees, or consulting fees from Regeneron, Novartis, AbbVie, AstraZeneca, Bristol Myers Squibb, Roche, Arcus Biosciences, Mirati, Iovance Biotherapeutics, Pfizer, Onc Live, InThought, Glaxo-Smith Kline, Intellisphere, Sanofi, Merck KGgA, Merck/EMD Serono, Johnson & Johnson, Jazz, Janssen, Curio Science, PDD development, G1 Therapeutics, OncoHost, Eisai, Flame Biosciences, Novocure, Merck, Daiichi Sankyo, and Boehringer Ingelheim. Other authors declare that they do not have competing interests. TDL was funded by the Finnish Cancer Institute and the Finnish Cultural foundation as a FICAN Cancer Researcher. ASH received funding from the University of Turku Graduate School (MATTI), the Academy of Finland (grants 310507, 313267, and 326238), the Cancer Society of Finland, and the Sigrid Jusélius Foundation. MIM received funding from the Finnish Medical Foundation. TM received funding from the Academy of Finland. FF was supported by the Austrian Science Fund (FWF) [T 974-B30] and the Oesterreichische Nationalbank (OeNB) [18496]. OL-S was supported by the Department of Biomedical Engineering, Eindhoven University of Technology. WW, JB, and JT were supported by an ERC Starting Grant (DrugComb, No. 716063), the Academy of Finland (No. 317680), and the Sigrid Jusélius Foundation. WW was funded by the FIMM-EMBL International PhD programme, Doctoral Programme of Biomedicine at the University of Helsinki, Cancer Foundation Finland, K. Albin Johanssons stiftelse, Ida Montinin Säätiö, Orion Research Foundation sr, and Biomedicum Helsinki Foundation. EV was supported by the Academy of Finland (No. 328437), the iCAN Digital Precision Cancer Medicine Flagship (No. 320185 Academy of Finland), and the CAN-PRO Translational Cancer Medicine Research Program Unit. Data analysis resources were provided by the CSC – IT Center for Science, Finland. JK received funding from the Department of Defense (Lung Cancer Research Program Concept Award LC180633) and was the recipient of a SITC-AstraZeneca Lung Cancer Clinical Fellowship (SPS256666). LY and YL received PACT funding through FNIH. MT received funding from the NIH. SSG was the recipient of the Sara Elizabeth O’Brien Trust Fellowship. ADS received funding from the NCI (K99CA248953) and the Human Immunome Project (MP19-02–190). DB received a grant from the Sidra Medicine Internal Funds (SDR400123). MC received the following grant from AIRC: IG 2018 ID 21846. The study was supported by Bristol Myers Squibb. Xiaole Shirley Liu contributed to the development of the TIDE model. Josue Samayoa contributed to the development of the @jacob.pfeil model. Abraham Apfel contributed data analysis advice. Medical writing and editorial support were provided by Thierry Deltheil, PhD, and Matthew Weddig of Spark Medica Inc., funded by Bristol Myers Squibb.

Financiers	Financiernummer
Sidra Medicine Internal Funds	SDR400123
National Institutes of Health, NIH
U.S. Department of Defense	LC180633, SPS256666
National Cancer Institute	K99CA248953
Bristol Myers Squibb	2022
Sara Elizabeth O'Brien Trust
Helsingin Yliopisto
Suomen Lääketieteen Säätiö
Engineering Research Centers	317680, 716063
Finnish Cancer Institute
Human Immunome Project	MP19-02–190
Case Western Reserve University
Academy of Finland	326238, 310507, 313267
Austrian Science Fund	T 974-B30
Technische Universiteit Eindhoven
Suomen Kulttuurirahasto
Biomedicum Helsinki-säätiö	320185, 328437
Oesterreichische Nationalbank	18496
Syöpäjärjestöt
Orionin Tutkimussäätiö
Syöpäsäätiö
Turun yliopiston tutkijakoulu

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Mason, M., Lapuente-Santana, Ó., Halkola, A. S., Wang, W., Mall, R., Xiao, X., Kaufman, J., Fu, J., Pfeil, J., Banerjee, J., Chung, V., Chang, H., Chasalow, S. D., Lin, H. Y., Chai, R., Yu, T., Finotello, F., Mirtti, T., Mäyränpää, M. I., ... Carbone, D. P. (2024). A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer. Journal of Translational Medicine, 22(1), Artikel 190. https://doi.org/10.1186/s12967-023-04705-3

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title = "A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer",

abstract = "BACKGROUND: Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC.METHODS: Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials.RESULTS: A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1.CONCLUSIONS: This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy.TRIAL REGISTRATION: CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.",

keywords = "Humans, Carcinoma, Non-Small-Cell Lung/drug therapy, Immune Checkpoint Inhibitors/therapeutic use, Lung Neoplasms/pathology, B7-H1 Antigen, Biomarkers, Tumor, Programmed death ligand 1, Immune checkpoint inhibitor, Predictive model, Crowdsource, Non-small cell lung cancer, Biomarkers, Programmed death-1",

author = "Mike Mason and {\'O}scar Lapuente-Santana and Halkola, {Anni S.} and Wenyu Wang and Raghvendra Mall and Xu Xiao and Jacob Kaufman and Jingxin Fu and Jacob Pfeil and Jineta Banerjee and Verena Chung and Han Chang and Chasalow, {Scott D.} and Lin, {Hung Ying} and Rongrong Chai and Thomas Yu and Francesca Finotello and Tuomas Mirtti and M{\"a}yr{\"a}np{\"a}{\"a}, {Mikko I.} and Jie Bao and Verschuren, {Emmy W.} and Ahmed, {Eiman I.} and Michele Ceccarelli and Miller, {Lance D.} and Gianni Monaco and Hendrickx, {Wouter R.L.} and Shimaa Sherif and Lin Yang and Ming Tang and Gu, {Shengqing Stan} and Wubing Zhang and Yi Zhang and Zexian Zeng and {Das Sahu}, Avinash and Yang Liu and Wenxian Yang and Davide Bedognetti and Jing Tang and Federica Eduati and Laajala, {Teemu D.} and Geese, {William J.} and Justin Guinney and Szustakowski, {Joseph D.} and Vincent, {Benjamin G.} and Carbone, {David P.}",

note = "{\textcopyright} 2024. The Author(s).",

year = "2024",

month = feb,

day = "21",

doi = "10.1186/s12967-023-04705-3",

language = "English",

volume = "22",

journal = "Journal of Translational Medicine",

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Mason, M, Lapuente-Santana, Ó, Halkola, AS, Wang, W, Mall, R, Xiao, X, Kaufman, J, Fu, J, Pfeil, J, Banerjee, J, Chung, V, Chang, H, Chasalow, SD, Lin, HY, Chai, R, Yu, T, Finotello, F, Mirtti, T, Mäyränpää, MI, Bao, J, Verschuren, EW, Ahmed, EI, Ceccarelli, M, Miller, LD, Monaco, G, Hendrickx, WRL, Sherif, S, Yang, L, Tang, M, Gu, SS, Zhang, W, Zhang, Y, Zeng, Z, Das Sahu, A, Liu, Y, Yang, W, Bedognetti, D, Tang, J, Eduati, F, Laajala, TD, Geese, WJ, Guinney, J, Szustakowski, JD, Vincent, BG & Carbone, DP 2024, 'A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer', Journal of Translational Medicine, vol. 22, nr. 1, 190. https://doi.org/10.1186/s12967-023-04705-3

TY - JOUR

T1 - A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer

AU - Mason, Mike

AU - Lapuente-Santana, Óscar

AU - Halkola, Anni S.

AU - Wang, Wenyu

AU - Mall, Raghvendra

AU - Xiao, Xu

AU - Kaufman, Jacob

AU - Fu, Jingxin

AU - Pfeil, Jacob

AU - Banerjee, Jineta

AU - Chung, Verena

AU - Chang, Han

AU - Chasalow, Scott D.

AU - Lin, Hung Ying

AU - Chai, Rongrong

AU - Yu, Thomas

AU - Finotello, Francesca

AU - Mirtti, Tuomas

AU - Mäyränpää, Mikko I.

AU - Bao, Jie

AU - Verschuren, Emmy W.

AU - Ahmed, Eiman I.

AU - Ceccarelli, Michele

AU - Miller, Lance D.

AU - Monaco, Gianni

AU - Hendrickx, Wouter R.L.

AU - Sherif, Shimaa

AU - Yang, Lin

AU - Tang, Ming

AU - Gu, Shengqing Stan

AU - Zhang, Wubing

AU - Zhang, Yi

AU - Zeng, Zexian

AU - Das Sahu, Avinash

AU - Liu, Yang

AU - Yang, Wenxian

AU - Bedognetti, Davide

AU - Tang, Jing

AU - Eduati, Federica

AU - Laajala, Teemu D.

AU - Geese, William J.

AU - Guinney, Justin

AU - Szustakowski, Joseph D.

AU - Vincent, Benjamin G.

AU - Carbone, David P.

PY - 2024/2/21

Y1 - 2024/2/21

N2 - BACKGROUND: Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC.METHODS: Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials.RESULTS: A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1.CONCLUSIONS: This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy.TRIAL REGISTRATION: CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.

AB - BACKGROUND: Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC.METHODS: Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials.RESULTS: A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1.CONCLUSIONS: This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy.TRIAL REGISTRATION: CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.

KW - Humans

KW - Carcinoma, Non-Small-Cell Lung/drug therapy

KW - Immune Checkpoint Inhibitors/therapeutic use

KW - Lung Neoplasms/pathology

KW - B7-H1 Antigen

KW - Biomarkers, Tumor

KW - Programmed death ligand 1

KW - Immune checkpoint inhibitor

KW - Predictive model

KW - Crowdsource

KW - Non-small cell lung cancer

KW - Biomarkers

KW - Programmed death-1

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U2 - 10.1186/s12967-023-04705-3

DO - 10.1186/s12967-023-04705-3

M3 - Article

C2 - 38383458

SN - 1479-5876

VL - 22

JO - Journal of Translational Medicine

JF - Journal of Translational Medicine

IS - 1

M1 - 190

ER -

A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer

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