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. 2023 Feb 8;14(1):695.
doi: 10.1038/s41467-023-36328-z.

Efficacy and clinicogenomic correlates of response to immune checkpoint inhibitors alone or with chemotherapy in non-small cell lung cancer

Lingzhi Hong #  1   2 , Muhammad Aminu #  2 , Shenduo Li #  3 , Xuetao Lu  4 , Milena Petranovic  5 , Maliazurina B Saad  2 , Pingjun Chen  2 , Kang Qin  1 , Susan Varghese  1 , Waree Rinsurongkawong  4 , Vadeerat Rinsurongkawong  4 , Amy Spelman  1 , Yasir Y Elamin  1 , Marcelo V Negrao  1 , Ferdinandos Skoulidis  1 , Carl M Gay  1 , Tina Cascone  1 , Saumil J Gandhi  6 , Steven H Lin  6 , Percy P Lee  6 , Brett W Carter  7 , Carol C Wu  7 , Mara B Antonoff  8 , Boris Sepesi  8 , Jeff Lewis  4 , Don L Gibbons  1 , Ara A Vaporciyan  8 , Xiuning Le  1 , J Jack Lee  4 , Sinchita Roy-Chowdhuri  9 , Mark J Routbort  9 , Justin F Gainor  10 , John V Heymach  1 , Yanyan Lou  3 , Jia Wu  1   2 , Jianjun Zhang  11   12 , Natalie I Vokes  13   14
Affiliations

Efficacy and clinicogenomic correlates of response to immune checkpoint inhibitors alone or with chemotherapy in non-small cell lung cancer

Lingzhi Hong et al. Nat Commun. .

Abstract

The role of combination chemotherapy with immune checkpoint inhibitors (ICI) (ICI-chemo) over ICI monotherapy (ICI-mono) in non-small cell lung cancer (NSCLC) remains underexplored. In this retrospective study of 1133 NSCLC patients, treatment with ICI-mono vs ICI-chemo associate with higher rates of early progression, but similar long-term progression-free and overall survival. Sequential vs concurrent ICI and chemotherapy have similar long-term survival, suggesting no synergism from combination therapy. Integrative modeling identified PD-L1, disease burden (Stage IVb; liver metastases), and STK11 and JAK2 alterations as features associate with a higher likelihood of early progression on ICI-mono. CDKN2A alterations associate with worse long-term outcomes in ICI-chemo patients. These results are validated in independent external (n = 89) and internal (n = 393) cohorts. This real-world study suggests that ICI-chemo may protect against early progression but does not influence overall survival, and nominates features that identify those patients at risk for early progression who may maximally benefit from ICI-chemo.

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Conflict of interest statement

M.V.N. reports Research funding to institution from Mirati, Novartis, Alaunos, Checkmate, AstraZeneca, Pfizer, Genentech; and Consultant/Advisory Board from Mirati, Novartis, Genentech, and Merck/MSD, outside the submitted work. Y.Y.E. reports research support from Spectrum, AstraZeneca, Takeda, Eli Lilly, Xcovery, and Tuning Point Therapeutics; and advisory role for AstraZeneca, Eli Lilly, Sanofi, BMS, Spectrum and Turning Point; and accommodation expenses from Eli Lilly. F.S. reports consulting fees and advisory roles from Amgen Inc., AstraZeneca Pharmaceuticals, Novartis, BeiGene, Tango Therapeutics, Calithera Biosciences, Navire Pharma, Medscape LLC, Intellisphere LLC, Guardant Health, and BergenBio; speaker fees from BMS, RV MaisPromocao Eventos LTDS, the Visiting Speakers Program in Oncology at McGill University and the Universite´ de Montre´al, AIM Group International, and ESMO; fees for travel, food, and beverage from Tango Therapeutics, AstraZeneca Pharmaceuticals, Amgen Inc., Guardant Health, and Dava Oncology; stock or stock options in BioNTech SE and Moderna Inc.; research grants (to institution) from Amgen Inc., Mirati Therapeutics, Boehringer Ingelheim, Merck & Co, and Novartis; Study Chair funds (to institution) from Pfizer; and research grants (spouse, to institution) from Almmune. C.M.G. reports fees for advisory committees from AstraZeneca, Bristol Myers Squibb, Jazz Pharmaceuticals, G1 therapeutics, and Monte Rosa Therapeutics, research support from AstraZeneca, and speaker’s fees from AstraZeneca and Beigene. T.C. reports speaker fees/honoraria from the Society for Immunotherapy of Cancer (SITC), Bristol Myers Squibb, Roche, Medscape, IDEOlogy Health, Physicians’ Education Resource®, LLC (PER®), OncLive and PeerView; travel, food and beverage expenses from Physicians' Education Resource®, LLC (PER®), Dava Oncology, SITC, International Association for the Study of Lung Cancer, IDEOlogy Health and Bristol Myers Squibb; advisory role/consulting fees from MedImmune/AstraZeneca, Bristol Myers Squibb, EMD Serono, Merck, Genentech, Arrowhead Pharmaceuticals and Regeneron; and institutional research funding from MedImmune/AstraZeneca, Bristol Myers Squibb, Boehringer Ingelheim and EMD Serono, all outside of the submitted work. S.J.G. reports research support from AstraZeneca, BMS, and Millenium Pharmaceuticals, all outside of the submitted work. P.P.L. reports personal fees from Viewray, Inc., AstraZeneca, Inc., personal fees and non-financial support from Varian, Inc., personal fees from Genentech, Inc., outside the submitted work. D.L.G. reports honoraria for scientific advisory boards from AstraZeneca, Sanofi, Alethia Biotherapeutics, Menarini, Eli Lilly, 4D Pharma and Onconova, and research support from Janssen, Takeda, Astellas, Ribon Therapeutics, NGM Biopharmaceuticals, Boehringer Ingelheim, Mirati Therapeutics and AstraZeneca, all outside of the submitted work. X.L. reports receiving consultant and advisory fee from Eli Lilly, AstraZeneca, EMD Serono, Daiishi Sanko, Spectrum Therapeutics, Boehringer Ingelheim, Hengrui Therapeutics, Novartis, and research funding from Eli Lilly, Boehringer Ingelheim, all outside of the submitted work. J.F.G. has served as a compensated consultant or received honoraria from Bristol-Myers Squibb, Genentech/Roche, Takeda, Loxo/Lilly, Blueprint, AstraZeneca, Gilead, Moderna, AstraZeneca, Curie Therapeutics, Mirati, Nuvalent, Pfizer, Novartis, Merck, iTeos, Karyopharm, Silverback Therapeutics, and GlydeBio; research support from Novartis, Genentech/Roche, and Takeda; institutional research support from Bristol-Myers Squibb, Tesaro, Moderna, Blueprint, Jounce, Array Biopharma, Merck, Adaptimmune, Novartis, and Alexo; and has an immediate family member who is an employee with equity at Ironwood Pharmaceuticals. J.V.H. reports receiving advisory/consulting fees from AstraZeneca, Boehringer-Ingeheim, Catalyst, Genentech, GlaxoSmithKline, Guardant Health, Foundation Medicine, Hengrui Therapeutics, Eli Lilly, Novartis, Spectrum, Sanofi, Takeda Pharmaceuticals, Mirati Therapeutics, Bristiol-Myers Squibb, BrightPath Biotherapeutics, Janssen Global Services, Nexus Health Systems, EMD Serono, Pneuma Respiratory, Kairos Venture Investments, Leads Biolabs, RefleXion, and research funding from GlaxoSmithKline, AstraZeneca, Spectrum, all outside of the submitted work. Y.L. reports research funding from Merck, MacroGenics, Tolero Pharmaceuticals, AstraZeneca, Vaccinex, Blueprint Medicines, Harpoon Therapeutics, Sun Pharma Advanced Research, Bristol-Myers Squibb, Kyowa Pharmaceuticals, Tesaro, Bayer HealthCare, Mirati Therapeutics, Daiichi Sankyo. Scientific Advisory boards for AstraZeneca Pharmaceuticals, Janssen Pharmaceutical, Lilly Oncology, Turning point therapeutics. Consultation fee from AstraZeneca. Honorarium from Clarion Health Care. J.Z. reports grants from Merck, Novartis, Johnson and Johnson, personal fees from BMS, AZ, Novartis, Johnson and Johnson, GenePlus, Hengrui, Innovent, outside the submitted work. N.I.V. receives consulting fees from Sanofi, Regeneron, Oncocyte, and Eli Lilly, and research funding from Mirati, outside the submitted work. The other authors declare no competing interests in the submitted work.

Figures

Fig. 1
Fig. 1. Clinical outcomes in first-line patients treated with immune checkpoint inhibitors (ICI) as monotherapy (ICI-mono, n = 300) or with chemotherapy (ICI-chemo, n = 375) in the MDACC primary cohort.
Comparison of a, progression-free survival (PFS) and b, overall survival (OS) between ICI-mono vs ICI-chemo. c, PFS in ICI-mono stratified by PD-L1. d PFS in ICI-chemo stratified by PD-L1. Hazard ratio (HR) and p values were calculated using unadjusted cox proportional hazards regression models. e Aalen’s additive hazard model on PFS and f on OS. Coefficient <0 favors ICI-chemo. Dashed gray lines indicate 95% confidence interval. Source data are provided as a Source Data file.
Fig. 2
Fig. 2. Subgroup analyses of overall survival (OS) in MDACC-primary cohort first-line patients (n = 675) stratified by chemotherapy administration in the 2nd line.
a Analysis schema. b Kaplan–meier plot of OS of all first-line patients stratified by sequential vs combination chemotherapy; c OS in patients with PD-L1 intermediate (1%–49%) or low (0 or <1%); and d OS in patients with PD-L1 high (≥50%). P values were calculated using log-rank analysis. Source data are provided as a Source Data file. ICI-mono only: immune checkpoint inhibitor (ICI) monotherapy; ICI-chemo: concurrent ICI and chemotherapy; ICI-mono then chemo: sequential ICI monotherapy followed by chemotherapy.
Fig. 3
Fig. 3. Clinicopathological predictors of outcome by treatment strategy in the MDACC primary cohort (n = 1133).
Forest plot of clinicopathologic variables and association with a, progression-free survival (PFS) and b, overall survival (OS) on univariate (black) and multivariate (pink) analysis, stratified by ICI-monotherapy (ICI-mono, left panel) and ICI-chemotherapy (ICI-chemo, right panel). Data are presented as the hazard ratio with error bars showing 95% confidence interval. Cox proportional hazards regression models were applied to calculate the hazard ratio. c, d Forest plot of difference in treatment effect between ICI-mono and ICI-chemo on c PFS and d OS. Data are presented as the treatment effect estimates with error bars showing the 95% confidence interval; subtee R package was used to generate treatment effect estimates. e Kaplan–meier plot comparing PFS (left panel) and OS (right panel) in patients treated with ICI-mono vs ICI-chemo, stratified by smoking status. Source data are provided as a Source Data file.
Fig. 4
Fig. 4. Association between gene alterations and outcomes in the MDACC primary cohort (n = 735 with genomic data).
a, b Volcano plot from univariate logistic regression depicting odds ratio (x-axis) versus −log 10 (P-value) (y-axis) for 3-month progression in patients treated immune checkpoint inhibitors (ICI) as a monotherapy (ICI-mono) or b with chemotherapy (ICI-chemo); logistic regression models with unadjusted effect were applied to calculate the odds ratio and p values. c Treatment effect analyses of gene subgroups on 3-month progression. Data are presented as the treatment effect estimates with error bars showing the lower and upper bounds of the 95% confidence interval. d, e Volcano plot from univariate cox regression depicting hazard ratio (x-axis) versus −log 10 (P value) (y-axis) for overall progression-free survival (PFS) in patients treated with d ICI-mono or e ICI-chemo; cox proportional hazards regression models with unadjusted effects were applied to calculate the hazard ratio and p values. f Treatment effect analyses of gene subgroups on PFS. Treatment effect estimates and the lower and upper bounds of the 95% confidence interval are shown as dots and whiskers. Source data are provided as a Source Data file.
Fig. 5
Fig. 5. Association between CDKN2A and progression-free survival (PFS).
a, b Kaplan–Meier plot of PFS in patients treated with immune checkpoint inhibitor (ICI) monotherapy (ICI-mono) vs concurrent ICI-chemotherapy (ICI-chemo) stratified by CDKN2A in a, MDACC-primary (n = 735) and b, Mayo cohorts (n = 89); hazard ratio (HR) with 95% confident interval and p values within the tables were calculated using unadjusted cox proportional hazards regression models; p values in the survival plot were calculated using log-rank analysis. c Co-mutation plot of genes significantly associated with outcome, MDACC-primary cohort. d Distribution of CDKN2A alterations (mutation and deletion) in MDACC-primary, MDACC-validation, and Mayo cohorts. Source data are provided as a Source Data file.
Fig. 6
Fig. 6. Predictive models of 3-months progression on MDACC-primary cohort (training cohort) by logistic regression (LR), support vector machines (SVM), random forest (RF), and generalized additive model (GAM) for clinicopathological and genomic variables in immune checkpoint inhibitor (ICI) monotherapy (ICI-mono; a–c) and ICI with concurrent chemotherapy (ICI-chemo; d–f) treated patients.
a Bar chart showing the contribution of features with significant p-value from chi-squared feature selection; positive association with 3-month progression (worse effect) shown in purple, negative association (better effect) shown in pink. b Area under the curve (AUC) values generated by different model structures and number of included variables and c overall model performance relative to PD-L1 as benchmark in ICI-mono by receiver operator characteristic (ROC) curve. d Features ranked by significance in ICI-chemo cohort. e AUCs with increasing features and different model structures and f ROC for best performing LR model in ICI-chemo cohort. g ROC curves for the best performing LR models in ICI-mono vs ICI-chemo. Source data are provided as a Source Data file.
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