A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer
<h3>Background</h3><p dir="ltr">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 initiativ...
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2024
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| _version_ | 1864513509304303616 |
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| author | Mike Mason (18007491) |
| author2 | Óscar Lapuente-Santana (17676542) Anni S. Halkola (14774369) Wenyu Wang (800903) Raghvendra Mall (581171) Xu Xiao (314389) Jacob Kaufman (15140509) Jingxin Fu (8049782) Jacob Pfeil (6211715) Jineta Banerjee (3692488) Verena Chung (18007494) Han Chang (101808) Scott D. Chasalow (4281811) Hung Ying Lin (18007497) Rongrong Chai (18007500) Thomas Yu (7370072) Francesca Finotello (5803820) Tuomas Mirtti (155123) Mikko I. Mäyränpää (9751322) Jie Bao (404268) Emmy W. Verschuren (15075414) Eiman I. Ahmed (6595514) Michele Ceccarelli (184154) Lance D. Miller (8444226) Gianni Monaco (3411857) Wouter R. L. Hendrickx (17449924) Shimaa Sherif (12862990) Lin Yang (45852) Ming Tang (110136) Shengqing Stan Gu (9526939) Wubing Zhang (3233007) Yi Zhang (9093) Zexian Zeng (3539879) Avinash Das Sahu (144977) Yang Liu (4829) Wenxian Yang (471369) Davide Bedognetti (2632474) Jing Tang (33607) Federica Eduati (361702) Teemu D. Laajala (546439) William J. Geese (14994408) Justin Guinney (215130) Joseph D. Szustakowski (18007503) Benjamin G. Vincent (9143348) David P. Carbone (8809427) |
| author2_role | author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author |
| author_facet | Mike Mason (18007491) Óscar Lapuente-Santana (17676542) Anni S. Halkola (14774369) Wenyu Wang (800903) Raghvendra Mall (581171) Xu Xiao (314389) Jacob Kaufman (15140509) Jingxin Fu (8049782) Jacob Pfeil (6211715) Jineta Banerjee (3692488) Verena Chung (18007494) Han Chang (101808) Scott D. Chasalow (4281811) Hung Ying Lin (18007497) Rongrong Chai (18007500) Thomas Yu (7370072) Francesca Finotello (5803820) Tuomas Mirtti (155123) Mikko I. Mäyränpää (9751322) Jie Bao (404268) Emmy W. Verschuren (15075414) Eiman I. Ahmed (6595514) Michele Ceccarelli (184154) Lance D. Miller (8444226) Gianni Monaco (3411857) Wouter R. L. Hendrickx (17449924) Shimaa Sherif (12862990) Lin Yang (45852) Ming Tang (110136) Shengqing Stan Gu (9526939) Wubing Zhang (3233007) Yi Zhang (9093) Zexian Zeng (3539879) Avinash Das Sahu (144977) Yang Liu (4829) Wenxian Yang (471369) Davide Bedognetti (2632474) Jing Tang (33607) Federica Eduati (361702) Teemu D. Laajala (546439) William J. Geese (14994408) Justin Guinney (215130) Joseph D. Szustakowski (18007503) Benjamin G. Vincent (9143348) David P. Carbone (8809427) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mike Mason (18007491) Óscar Lapuente-Santana (17676542) Anni S. Halkola (14774369) Wenyu Wang (800903) Raghvendra Mall (581171) Xu Xiao (314389) Jacob Kaufman (15140509) Jingxin Fu (8049782) Jacob Pfeil (6211715) Jineta Banerjee (3692488) Verena Chung (18007494) Han Chang (101808) Scott D. Chasalow (4281811) Hung Ying Lin (18007497) Rongrong Chai (18007500) Thomas Yu (7370072) Francesca Finotello (5803820) Tuomas Mirtti (155123) Mikko I. Mäyränpää (9751322) Jie Bao (404268) Emmy W. Verschuren (15075414) Eiman I. Ahmed (6595514) Michele Ceccarelli (184154) Lance D. Miller (8444226) Gianni Monaco (3411857) Wouter R. L. Hendrickx (17449924) Shimaa Sherif (12862990) Lin Yang (45852) Ming Tang (110136) Shengqing Stan Gu (9526939) Wubing Zhang (3233007) Yi Zhang (9093) Zexian Zeng (3539879) Avinash Das Sahu (144977) Yang Liu (4829) Wenxian Yang (471369) Davide Bedognetti (2632474) Jing Tang (33607) Federica Eduati (361702) Teemu D. Laajala (546439) William J. Geese (14994408) Justin Guinney (215130) Joseph D. Szustakowski (18007503) Benjamin G. Vincent (9143348) David P. Carbone (8809427) |
| dc.date.none.fl_str_mv | 2024-02-21T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1186/s12967-023-04705-3 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_community_challenge_to_predict_clinical_outcomes_after_immune_checkpoint_blockade_in_non-small_cell_lung_cancer/26508322 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Oncology and carcinogenesis Pharmacology and pharmaceutical sciences Health sciences Health services and systems Non-small cell lung cancer Immune checkpoint inhibitor Programmed death-1 Programmed death ligand 1 Predictive model Biomarkers Crowdsource |
| dc.title.none.fl_str_mv | A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background</h3><p dir="ltr">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.</p><h3>Methods</h3><p dir="ltr">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.</p><h3>Results</h3><p dir="ltr">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.</p><h3>Conclusions</h3><p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Translational Medicine<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1186/s12967-023-04705-3" target="_blank">https://dx.doi.org/10.1186/s12967-023-04705-3</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_fd0f55040a3938d8a3900b614ea44f92 |
| identifier_str_mv | 10.1186/s12967-023-04705-3 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26508322 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancerMike Mason (18007491)Óscar Lapuente-Santana (17676542)Anni S. Halkola (14774369)Wenyu Wang (800903)Raghvendra Mall (581171)Xu Xiao (314389)Jacob Kaufman (15140509)Jingxin Fu (8049782)Jacob Pfeil (6211715)Jineta Banerjee (3692488)Verena Chung (18007494)Han Chang (101808)Scott D. Chasalow (4281811)Hung Ying Lin (18007497)Rongrong Chai (18007500)Thomas Yu (7370072)Francesca Finotello (5803820)Tuomas Mirtti (155123)Mikko I. Mäyränpää (9751322)Jie Bao (404268)Emmy W. Verschuren (15075414)Eiman I. Ahmed (6595514)Michele Ceccarelli (184154)Lance D. Miller (8444226)Gianni Monaco (3411857)Wouter R. L. Hendrickx (17449924)Shimaa Sherif (12862990)Lin Yang (45852)Ming Tang (110136)Shengqing Stan Gu (9526939)Wubing Zhang (3233007)Yi Zhang (9093)Zexian Zeng (3539879)Avinash Das Sahu (144977)Yang Liu (4829)Wenxian Yang (471369)Davide Bedognetti (2632474)Jing Tang (33607)Federica Eduati (361702)Teemu D. Laajala (546439)William J. Geese (14994408)Justin Guinney (215130)Joseph D. Szustakowski (18007503)Benjamin G. Vincent (9143348)David P. Carbone (8809427)Biomedical and clinical sciencesOncology and carcinogenesisPharmacology and pharmaceutical sciencesHealth sciencesHealth services and systemsNon-small cell lung cancerImmune checkpoint inhibitorProgrammed death-1Programmed death ligand 1Predictive modelBiomarkers Crowdsource<h3>Background</h3><p dir="ltr">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.</p><h3>Methods</h3><p dir="ltr">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.</p><h3>Results</h3><p dir="ltr">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.</p><h3>Conclusions</h3><p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Translational Medicine<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1186/s12967-023-04705-3" target="_blank">https://dx.doi.org/10.1186/s12967-023-04705-3</a></p>2024-02-21T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1186/s12967-023-04705-3https://figshare.com/articles/journal_contribution/A_community_challenge_to_predict_clinical_outcomes_after_immune_checkpoint_blockade_in_non-small_cell_lung_cancer/26508322CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/265083222024-02-21T12:00:00Z |
| spellingShingle | A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer Mike Mason (18007491) Biomedical and clinical sciences Oncology and carcinogenesis Pharmacology and pharmaceutical sciences Health sciences Health services and systems Non-small cell lung cancer Immune checkpoint inhibitor Programmed death-1 Programmed death ligand 1 Predictive model Biomarkers Crowdsource |
| status_str | publishedVersion |
| title | A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer |
| title_full | A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer |
| title_fullStr | A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer |
| title_full_unstemmed | A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer |
| title_short | A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer |
| title_sort | A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer |
| topic | Biomedical and clinical sciences Oncology and carcinogenesis Pharmacology and pharmaceutical sciences Health sciences Health services and systems Non-small cell lung cancer Immune checkpoint inhibitor Programmed death-1 Programmed death ligand 1 Predictive model Biomarkers Crowdsource |