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Abstract LB-397: Pembrolizumab plus pemetrexed and platinum vs placebo plus pemetrexed and platinum as first-line therapy for metastatic nonsquamous NSCLC: analysis of KEYNOTE-189 by <i>STK11</i> and <i>KEAP1</i> status

2020· article· en· W3083563724 on OpenAlexaff
Shirish M. Gadgeel, Delvys Rodríguez‐Abreu, Enriqueta Felip, Emilio Esteban, Giovanna Speranza, Martin Reck, Rina Hui, Michael Boyer, Edward B. Garon, Hidehito Horinouchi, Răzvan Cristescu, Deepti Aurora-Garg, Andrey Loboda, Jared Lunceford, Julie Kobie, Mark Ayers, Bilal Piperdi, M. Catherine Pietanza, Marina Chiara Garassino

Bibliographic record

VenueCancer Research · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsPembrolizumabPemetrexedSTK11MedicineInternal medicineOncologyGemcitabineChemotherapyCancerImmunotherapyKRAS

Abstract

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Abstract Background: Mutations in the tumor suppressor genes STK11 (also known as LKB1) and KEAP1 have been associated with poorer clinical outcomes in patients (pts) with NSCLC. In an exploratory analysis, we assessed the prevalence of STK11 and KEAP1 mutations and their association with efficacy in KEYNOTE-189 (NCT02578680). Methods: STK11 and KEAP1 status and tumor mutational burden (TMB) were assessed by whole-exome sequencing (WES) in pts who had available tumor and matched-normal tissue. PD-L1 was assessed by the PD-L1 IHC 22C3 pharmDx assay. The association of STK11 and KEAP1 status with efficacy and their correlation with TMB and PD-L1 expression distributions were evaluated descriptively. Results: WES data from both tumor and normal DNA were evaluable for 289 (47%) of 616 pts, of whom 54 (19%) had an STK11 mutation and 68 (24%) had a KEAP1 mutation; 29 (10%) had both STK11 and KEAP1 mutations. PD-L1 TPS tended to be lower in pts with vs without STK11 mutation (median [IQR] 0% [0-16] vs 15% [0-75]), whereas TMB score tended to be higher in pts with mutation (209 [132-265] vs 146 [89-264]). Similar patterns were seen for pts with vs without KEAP1 mutation (PD-L1 TPS: 1% [0-13] vs 20% [0-75]; TMB: 173 [124-267] vs 147 [89-263]). Although ORR of pembrolizumab plus chemotherapy was lower and PFS and OS shorter in pts with vs without STK11 and KEAP1 mutation, pembrolizumab plus chemotherapy was associated with numerically better outcomes than placebo plus chemotherapy regardless of mutation status (Table). 95% CIs were wide given the modest mutation frequency and the 2:1 randomization in favor of pembrolizumab plus chemotherapy. Conclusions: Data from this exploratory analysis support use of pembrolizumab plus pemetrexed and platinum as standard first-line therapy for pts with metastatic nonsquamous NSCLC regardless of STK11 or KEAP1 status. STK11KEAP1With MutationWithout MutationWith MutationWithout MutationPembro + ChemoPlacebo + ChemoPembro + ChemoPlacebo + ChemoPembro + ChemoPlacebo + ChemoPembro + ChemoPlacebo + Chemo(n = 36)(n = 18)(n = 168)(n = 67)(n = 45)(n = 23)(n = 159)(n = 62)ORR, % (95% CI)31 (16-48)17 (4-41)49 (41-57)16 (8-27)36 (22-51)17 (5-39)48 (40-56)16 (8-28)PFS, median, mo (95% CI)6 (4-9)5 (5-9)10 (8-14)5 (5-5)5 (4-11)5 (5-9)10 (8-14)5 (5-5)PFS, HR (95% CI)0.81 (0.44-1.47)0.38 (0.27-0.52)0.65 (0.38-1.12)0.38 (0.28-0.53)OS, median, mo (95% CI)17 (5-NR)8 (7-NR)23 (20-NR)12 (8-25)13 (7-NR)9 (7-NR)24 (20-NR)12 (8-NR)OS, HR (95% CI)0.75 (0.37-1.50)0.59 (0.41-0.85)0.81 (0.44-1.49)0.57 (0.39-0.84) Citation Format: Shirish M. Gadgeel, Delvys Rodriguez-Abreu, Enriqueta Felip, Emilio Esteban, Giovanna Speranza, Martin Reck, Rina Hui, Michael Boyer, Edward B. Garon, Hidehito Horinouchi, Razvan Cristescu, Deepti Aurora-Garg, Andrey Loboda, Jared Lunceford, Julie Kobie, Mark Ayers, Bilal Piperdi, M. Catherine Pietanza, Marina C. Garassino. Pembrolizumab plus pemetrexed and platinum vs placebo plus pemetrexed and platinum as first-line therapy for metastatic nonsquamous NSCLC: analysis of KEYNOTE-189 by STK11 and KEAP1 status [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-397.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.046
GPT teacher head0.351
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations17
Published2020
Admission routes1
Has abstractyes

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