
==== Front
Chin Med J (Engl)
Chin Med J (Engl)
CM9
Chinese Medical Journal
0366-6999
2542-5641
Lippincott Williams & Wilkins Hagerstown, MD

39164816
CMJ-2023-2341
10.1097/CM9.0000000000003094
00008
3
Original Article
Genomic correlates of the response to first-line PD-1 blockade plus chemotherapy in patients with advanced non-small-cell lung cancer
Jiang Tao 1
Chen Jian 2
Wang Haowei 1
Wu Fengying 1
Chen Xiaoxia 1
Su Chunxia 1
Zhang Haiping 1
Zhou Fei 1
Yang Ying 3
Zhang Jiao 3
Sun Huaibo 3
Zhang Henghui 3 4
Zhou Caicun 1
Ren Shengxiang 1
Wei Peifang
1 Department of Medical Oncology, Shanghai Pulmonary Hospital & Thoracic Cancer Institute, Tongji University School of Medicine, Shanghai 200433, China
2 Department of Thoracic Surgery, Shanghai Pulmonary Hospital & Thoracic Cancer Institute, Tongji University School of Medicine, Shanghai 200433, China
3 Genecast Biotechnology Co., Ltd, Wuxi, Jiangsu 214104, China
4 Biomedical Innovation Center, Beijing Shijitan Hospital, Capital Medical University, Beijing, China; School of Oncology, Capital Medical University, Beijing 100038, China
Correspondence to: Caicun Zhou, Department of Medical Oncology, Shanghai Pulmonary Hospital & Thoracic Cancer Institute, Tongji University School of Medicine, Shanghai 200433, China E-Mail: caicunzhou_dr@163.com
21 8 2024
20 9 2024
137 18 22132222
30 9 2023
Copyright © 2024 The Chinese Medical Association, produced by Wolters Kluwer, Inc. under the CC-BY-NC-ND license.
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0

Abstract

Background:

Programmed death 1 (PD-1) blockade plus chemotherapy has become the new first-line standard of care for patients with advanced non-small-cell lung cancer (NSCLC). Yet not all NSCLC patients benefit from this regimen. This study aimed to investigate the predictors of PD-1 blockade plus chemotherapy in untreated advanced NSCLC.

Methods:

We integrated clinical, genomic, and survival data from 287 patients with untreated advanced NSCLC who were enrolled in one of five registered phase 3 trials and received PD-1 blockade plus chemotherapy or chemotherapy alone. We randomly assigned these patients into a discovery cohort (n = 125), a validation cohort (n = 82), and a control cohort (n = 80). The candidate genes that could predict the response to PD-1 blockade plus chemotherapy were identified using data from the discovery cohort and their predictive values were then evaluated in the three cohorts. Immune deconvolution was conducted using transcriptome data of 1014 NSCLC patients from The Cancer Genome Atlas dataset.

Results:

A genomic variation signature, in which one or more of the 15 candidate genes were altered, was correlated with significantly inferior response rates and survival outcomes in patients treated with first-line PD-1 blockade plus chemotherapy in both discovery and validation cohorts. Its predictive value held in multivariate analyses when adjusted for baseline parameters, programmed cell death ligand 1 (PD-L1) expression level, and tumor mutation burden. Moreover, applying both the 15-gene panel and PD-L1 expression level produced better performance than either alone in predicting benefit from this treatment combination. Immune landscape analyses revealed that tumors with one or more variation in the 15-gene panel were associated with few immune infiltrates, indicating an immune-desert tumor microenvironment.

Conclusion:

These findings indicate that a 15-gene panel can serve as a negative prediction biomarker for first-line PD-1 blockade plus chemotherapy in patients with advanced NSCLC.

Keywords:

Non-small-cell lung cancer (NSCLC)
Programmed death 1 (PD-1) blockade
Gene panel
Prediction
Biomarker
OPEN-ACCESSTRUE
SDCT
==== Body
pmcIntroduction

Blockade of programmed death 1 (PD-1) and its ligand (PD-L1) (PD-1 blockade) plus chemotherapy has become the new standard of care for patients with untreated advanced non-small-cell lung cancer (NSCLC) without driver gene variations.[123] A series of phase 3 randomized clinical trials have demonstrated that first-line PD-1 blockade plus chemotherapy can significantly improve the objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) compared with chemotherapy alone in patients with advanced NSCLC.[456789101112] Nevertheless, not all NSCLC patients benefit from this combination regimen and there are no officially approved predictive biomarkers to guide its clinical application. Hence, there is an urgent need to identify reliable biomarkers to predict the response to PD-1 blockade plus chemotherapy in untreated advanced NSCLC.

Genome variation in cancer cells can shape local and systemic immune phenotypes through various mechanisms, resulting in distinct anti-tumor immune responses.[13141516] For example, we previously showed that patients with ARID1A or TERT variations are associated with markedly abundant immune infiltrates and therefore showed better response to immunotherapy than wild-type populations,[17,18] whereas patients with KEAP1 variations correlated with significantly fewer immune infiltrates, especially CD8+ T cells, and showed limited response to PD-1 blockade.[19] These results indicate that genomic features of cancer cells can serve as biomarkers to guide the use of immunotherapy. Unlike PD-1 blockade monotherapy, the increased anti-tumor effect of PD-1 blockade plus chemotherapy is mainly based on the hypothesis that several cytotoxic drugs may induce tumor cell death to increase antigen release and activate T-cell-mediated immunity.[2021222324] Under these circumstances, whether the genomic landscape of tumor cells still plays a key role in orchestrating the response to PD-1 blockade plus chemotherapy remains undetermined.

Previous studies have investigated positive predictive biomarkers for first-line PD-1 blockade plus chemotherapy in NSCLC; however, it is also important to identify patients who cannot benefit from this combination regimen. In this study, we integrated clinical, genomic, and survival data of 287 patients with advanced NSCLC who received first-line PD-1 blockade plus chemotherapy or chemotherapy alone as part of several registered clinical trials in our center, and investigated the negatively predictive value of variations in certain genes to PD-1 blockade plus chemotherapy. To delineate the immune profiles of tumors with distinct mutational landscape, we also analyzed the genomic and transcriptomic data of 1014 NSCLC patients from The Cancer Genome Atlas (TCGA) database.

Methods

Patient cohorts

Eligible patients diagnosed with advanced NSCLC who received first-line PD-1 blockade plus chemotherapy or chemotherapy alone were consecutively enrolled from five registered phase 3, randomized clinical trials in our center from May 1, 2016 to May 1, 2022.[789101112] Briefly, patients with the following criteria were eligible: aged 18–70 years, histologically or cytologically confirmed stage IIIB–IV NSCLC (as per the International Association for the Study of Lung Cancer Staging Handbook in Thoracic Oncology, 8th Edition), Eastern Cooperative Oncology Group performance status (ECOG PS) of 0 or 1, no previous systemic anti-tumor treatments, and at least one measurable lesion per Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST v1.1). Patients were excluded if they had severe central nervous system symptoms including uncontrollable intracranial hypertension or intracranial hemorrhage, corticosteroid use within 2 weeks before study treatment, history or presence of autoimmune disease or interstitial pneumonia, and use of immunosuppressants within 2 weeks of study treatment. The dose of each type of anti-PD-1/PD-L1 antibody and other anti-tumor drugs was determined according to the recommended dose of each drug or the predefined protocols of the phase 3 trials. The study protocol was approved by the Ethics Committee and Institutional Review Board of Shanghai Pulmonary Hospital (No. K21-057Z) and conducted according to the Declaration of Helsinki, Guidelines for Good Clinical Practice, and local laws and regulations of China. All patients provided written informed consent.

Data collection

After gaining permission, we collected the data of eligible patients from electronic medical records or the latest report of each clinical trial using the same criteria as for clinical data collection, including response to different treatments and clinical outcomes. The baseline parameters, including age, gender, smoking history, ECOG PS, lung cancer histology (World Health Organization classification), PD-L1 expression level, sites of metastasis, driver gene variation status, and therapeutic regimens, were collected. Age, smoking status, and ECOG PS were recorded at initial diagnosis. A never smoker was defined as a person who had smoked <100 cigarettes during his/her lifetime. PD-L1 expression level, as measured by the DAKO 22C3 immunohistochemical staining assay (DAKO 22C3) , was defined as the percentage of viable tumor cells showing partial or complete membrane staining at any intensity (positive was defined as ≥1%). Tests were conducted according to the manufacturer’s recommendations. Tumor response was assessed one month after the initiation of therapy and then every two months based on the RECIST v1.1. Treatment responses included complete response (CR), partial response (PR), stable disease (SD) or progressive disease (PD). Last follow-up was conducted on November 1, 2022.

Sample collection

According to the predefined protocol, we collected fresh samples at baseline or archival tumor tissues within one month before the protocol-defined treatments. Pretreatment blood samples (8–10 mL) were also collected in ethylenediaminetetraacetic acid (EDTA)-coated tubes (BD Biosciences, Franklin Lakes, NJ, USA) and centrifuged at 1800 g for 10 minutes within two hours of collection to separate white blood cells.

DNA sequencing

Formalin-fixed paraffin-embedded tumor samples with ≥20% tumor cells were qualified and included. White blood cell sediments were used for genomic DNA extraction as germline controls. DNA sequencing details, including DNA extraction, library preparation, sequence alignment, data processing, data filtering, and variant calling, are summarized in Supplementary Materials, http://links.lww.com/CM9/B970.

Selection of variant signatures

To identify candidate genes, we applied the following criteria: (1) the gene has to be altered in 2% of the study population; (2) gene variation enrichment in responders (CR+PR) versus non-responders (SD+PD) to PD-1 blockade plus chemotherapy; (3) gene variation enrichment in patients with a PFS ≥6 months vs. <6 months in the PD-1 blockade plus chemotherapy cohort; (4) the gene is reported to be correlated with efficacy of PD-1 blockade monotherapy or PD-1 blockade plus chemotherapy in previous publications; (5) the gene is involved in known anti-tumor immune response pathways, including antigen processing or presentation, priming, activation, trafficking or infiltration of T cells, and T cell-mediated cytotoxicity. Genes that met two or more criteria were selected as candidate genes in the discovery cohort. Nonsynonymous variations, gene fusions and amplifications were included. Variations of unknown significance were filtered out. Patients with variations in one or more of the candidate genes were defined as a “high-risk” group and those without any variations in the candidate genes were defined as a “low-risk” group. Treatment outcomes, including ORR, PFS and OS, for patients from high-risk versus low-risk groups were compared in the discovery cohort. The predictive value of the candidate gene variations was then evaluated in the validation and control cohorts.

Immune landscape analysis

To investigate whether tumors with different mutational landscapes had specific features of the tumor immune microenvironment, we conducted immune cell composition analysis using raw DNA and RNA sequencing data of NSCLC samples from TCGA database. A published online tool, CIBERSORT,[25] was used to perform immune cell subset analysis of the tumor samples with different mutational signatures. Differential gene expression analysis was performed using the DESseq2 package in R software (version 3.6.3, https://www.r-project.org/), with adjusted P <0.05 and |log2 (fold change)| >1.0. Output data were normalized using a negative binomial distribution statistical method. For each gene, the expression score was calculated as log2 (transcript per million + 1).

Statistical analysis

Continuous variables with normal distribution are presented as mean ± standard deviation, and those with skewed distribution are expressed as median (Q1, Q3). Categorical variables are shown as numbers and percentages. The Chi-squared test, or Fisher’s exact test when needed, was used to compare categorical variables. Continuous variables with normal distribution were analyzed by analysis of variance (ANOVA) and/or Tukey’s multiple comparison tests. The Mann–Whitney U test was used to compare continuous variables with skewed distribution between two groups and Kruskal–Wallis rank-sum test was used to compare continuous variables with skewed distribution across multiple groups. The correlation analysis between different mutational signatures and immune cell subset expression level was conducted using Spearman’s correlation analysis. ORR and disease control rate (DCR) were analyzed, and the corresponding 95% confidence interval (CI) was estimated, using the Clopper-Pearson method. Kaplan–Meier curves were used to estimate the median PFS and OS, with 95% CIs estimated using the Brookmeyer and Crowley method. Between-group comparisons for PFS and OS were assessed using a stratified log-rank test. Hazard ratio (HR) and associated 95% CI were calculated based on a stratified Cox proportional hazards model. All statistical analyses were conducted using GraphPad PRISM 9.0 (GraphPad Software, San Diego, CA, USA) and SPSS statistical software version 22.0 (SPSS Inc., Chicago, IL, USA). Two-sided P <0.05 was considered statistically significant.

Results

Study cohort and candidate gene identification

In total, 175 patients with non-squamous NSCLC (89 received PD-1 blockade plus chemotherapy; 86 received chemotherapy only) and 253 patients with squamous NSCLC (118 received PD-1 blockade plus chemotherapy; 135 received chemotherapy only) were initially identified [Figure 1]. From these, 287 patients with intact clinical, genomic and survival data (207 received PD-1 blockade plus chemotherapy; 80 received chemotherapy only) were included in the final analysis [Figure 1 and Supplementary Table 1, http://links.lww.com/CM9/B970]. Patients that received PD-1 blockade plus chemotherapy had significantly longer PFS (10.0 vs. 5.6 months; HR = 0.36; P <0.001; Supplementary Figure 1A, http://links.lww.com/CM9/B970) and OS (24.1 vs. 16.1 months; HR = 0.57; P <0.001; Supplementary Figure 1B, http://links.lww.com/CM9/B970) compared with those that received chemotherapy only. To identify candidate gene variations that can predict the treatment outcomes of PD-1 blockade plus chemotherapy, we first randomly chose 125 patients treated with first-line PD-1 blockade plus chemotherapy and assigned them into the discovery cohort. Eighty-two patients treated with first-line PD-1 blockade plus chemotherapy were assigned to the validation cohort. Demographic and baseline parameters were generally analogous between these two cohorts [Table 1 and Supplementary Table 2, http://links.lww.com/CM9/B970]. Eighty patients that received first-line chemotherapy alone were assigned to the control cohort. According to the predefined criteria of candidate gene selection (see Methods), we were able to generate a 15-gene panel that included KEAP1, STK11, CDKN2A, KDM5C, EPHA7, GRIN2A, MYCL, NOTCH3, POLD1, PRKAR1A, RARA, SMARCA4, SOX9, TNFAIP3, and VHL to define high-risk versus low-risk groups using data from the discovery cohort. After applying this panel, 55 (44.0%), 36 (43.9%) and 24 (30.0%) of patients from the discovery, validation, and control cohorts, respectively, were in the high-risk group. The demographic and baseline parameters were generally balanced between high-risk and low-risk groups in control cohort [Table 1]. However, distribution of sex, smoking history, histological type and TMB level were not balanced between high-risk and low-risk groups in discovery cohort, and distribution of smoking history and histological type were not balanced between high-risk and low-risk groups in validation cohort [Table 1].

Figure 1 Study overview and mutational landscape of the included patients. (A) Flowchart of patients’ selection and key analyses objectives. (B) Mutational landscape of all included patients. Upper panel: the proportion of PD-L1 expression. Middle panel: the matrix of mutations in a selection of frequently mutated genes. Columns represent sample sizes. Below panel: the baseline parameter. Right panel: color annotations. Chemo: Chemotherapy; del: Deletion; ins: Insertion; mOS: Median overall survival; mPFS: Median progression-free survival; NSCLC: Non-small-cell lung cancer; ORR: Objective response rate; PD-1 + chemo: Programmed death 1 (PD-1) blockade plus chemotherapy; PD-L1: Programmed death ligand 1; sub: Substitution.

Table 1 Baseline characteristics of all included patients with advanced NSCLC who received first-line PD-1 blockade plus chemotherapy or chemotherapy alone from five registered phase 3, randomized clinical trials.

Items	Discovery cohort (n = 125)	Validation cohort (n = 82)	Control cohort (n = 80)	
High risk (n = 55)	Low risk (n = 70)	χ2	P	High risk (n = 36)	Low risk (n = 46)	χ2	P	High risk (n = 24)	Low risk (n = 56)	χ2	P	
Age													
<65 years	35 (63.6)	49 (70.0)	0.566	0.452	23 (63.9)	33 (71.7)	0.574	0.448	14 (58.3)	32 (57.1)	0.010	0.921	
≥65 years	20 (36.4)	21 (30.0)			13 (36.1)	13 (28.3)			10 (41.7)	24 (42.9)			
Sex													
Male	52 (94.5)	54 (77.1)	5.950	0.015	33 (91.7)	36 (78.3)	1.809	0.179	23 (95.8)	49 (87.5)	0.536	0.464	
Female	3 (5.5)	16 (22.9)			3 (8.3)	10 (21.7)			1 (4.2)	7 (12.5)			
ECOG PS													
0	8 (14.5)	15 (21.4)	0.972	0.324	8 (22.2)	15 (32.6)	1.080	0.299	7 (29.2)	9 (16.1)	1.801	0.180	
1	47 (85.5)	55 (78.6)			28 (77.8)	31 (67.4)			17 (70.8)	47 (83.9)			
Smoking history													
Ever or never	6 (10.9)	20 (28.6)	5.833	0.016	2 (5.6)	12 (26.1)	4.650	0.031	3 (12.5)	15 (26.8)	1.232	0.267	
Current	49 (89.1)	50 (71.4)			34 (94.4)	34 (73.9)			21 (87.5)	41 (73.2)			
Histopathological type													
Adenocarcinoma	15 (27.3)	39 (55.7)	10.154	0.001	10 (27.8)	25 (54.3)	5.828	0.016	6 (25.0)	26 (46.4)	3.214	0.073	
Squamous cell carcinoma	40 (72.7)	31 (44.3)			26 (72.2)	21 (45.7)			18 (75.0)	30 (53.6)			
Disease stage													
III	8 (14.5)	18 (25.7)	2.332	0.127	10 (27.8)	13 (28.3)	0.002	0.962	4 (16.7)	9 (16.1)	0.070	0.791	
IV	47 (85.5)	52 (74.3)			26 (72.2)	33 (71.7)			20 (83.3)	47 (83.9)			
PD-L1 TPS													
<1%	25 (45.5)	25 (35.7)	1.218	0.270	15 (41.7)	13 (28.3)	1.614	0.204	11 (45.8)	20 (35.7)	0.725	0.395	
≥1%	30 (54.5)	45 (64.3)			21 (58.3)	33 (71.7)			13 (54.2)	36 (64.3)			
TMB													
≥10 Muts/Mb	23 (41.8)	8 (11.4)	15.252	0.001	11 (30.6)	9 (19.6)	1.323	0.250	8 (33.3)	16 (28.6)	0.181	0.670	
<10 Muts/Mb	32 (58.2)	62 (88.6)			25 (69.4)	37 (80.4)			16 (66.7)	40 (71.4)			
Data are presented as n (%). ECOG PS: Eastern Cooperative Oncology Group performance status; NSCLC: Non-small-cell lung cancer; PD-1: Programmed death 1; PD-L1: Programmed cell death ligand 1; TPS: Tumor proportion score; TMB: Tumor mutational burden; Muts: Mutations.

The 15-gene panel can predict worse outcomes of PD-1 blockade plus chemotherapy

We first excluded the 15-gene panel as a generally prognostic factor in NSCLC using TCGA datasets (P = 0.545; Supplementary Figure 2, http://links.lww.com/CM9/B970). We then investigated the predictive and prognostic value of the 15-gene panel in the three cohorts. In the discovery cohort, patients in the high-risk group had a significantly inferior ORR (47.9% vs. 77.9%; P = 0.001; Figure 2A, B), PFS (5.7 vs. 17.5 months; HR = 3.06, 95% CI: 1.89–4.94; P <0.001; Figure 2C) and OS (14.0 vs. 35.3 months; HR = 2.31, 95% CI: 1.44–3.71; P <0.001; Figure 2D) compared with those in the low-risk group when receiving first-line PD-1 blockade plus chemotherapy. Univariable analyses showed that age, male, current smoking history, lower PD-L1 expression level, and high-risk group were associated with shorter PFS and OS [Figure 3]. In multivariable analyses, low-risk group was independently associated with remarkably better PFS (HR = 0.24; P <0.001; Figure 3A) and OS (HR = 0.47; P = 0.002; Figure 3B). We next evaluated the predictive value of the 15-gene panel in the validation cohort. Among 82 patients treated with first-line PD-1 blockade plus chemotherapy, those in the high-risk group had a dramatically worse ORR (54.3% vs. 73.9%; P = 0.048; Supplementary Figure 3A–B, http://links.lww.com/CM9/B970), PFS (7.3 vs. 19.8 months; HR = 2.41, 95% CI: 1.39–4.18; P <0.001; Supplementary Figure 3C, http://links.lww.com/CM9/B970) and OS (20.4 vs. 33.5 months; HR = 2.20, 95% CI: 1.22–3.95; P = 0.005; Supplementary Figure 3D, http://links.lww.com/CM9/B970) compared with those in the low-risk group. Univariable and multivariable analyses also showed the significantly predictive performance of the 15-gene panel for PD-1 blockade plus chemotherapy in the validation cohort [Supplementary Figure 4, http://links.lww.com/CM9/B970]. In the control cohort, both PFS and OS were analogous between patients in high- and low-risk groups [Supplementary Figure 5, http://links.lww.com/CM9/B970]. Taken together, these data highlight the utility of the 15-gene panel and indicate it to be a potential negative biomarker to first-line PD-1 blockade plus chemotherapy, but not chemotherapy alone, in advanced NSCLC.

Figure 2 15-gene panel predicts worse outcomes of PD-1 blockade plus chemotherapy in the discovery cohort. (A) Response rate of patients in high-risk group. (B) Response rate of patients in low-risk group. (C) Comparison of PFS between patients in high-risk versus low-risk groups. (D) Comparison of OS between patients in high-risk versus low-risk groups. HR: Hazard ratio; mOS: Median overall survival; mPFS: Median progression-free survival; PD: Progressive disease; PD-1: Programmed death 1; PR: Partial response; SD: Stable disease.

Figure 3 Univariable and multivariable Cox regression models in the discovery cohort. Analysis was conducted in the discovery cohort (n = 125) for PFS (A) and OS (B). Univariable HRs with 95% CIs are represented for each prognostic factor. P values were estimated by means of the Cox proportional hazard regression model in the univariable and multivariable analysis. CI: Confidence interval; ECOG PS: Eastern Cooperative Oncology Group performance status; HR: Hazard ratio; OS: Overall survival; PD-L1: Programmed death ligand 1; PFS: Progression free survival; TMB: Tumor mutation burden.

Combining the 15-gene panel with PD-L1 expression level improves predictive performance

To survey the impact of the 15-gene panel combined with PD-L1 expression level on survival benefit and treatment response, we then stratified patients from the discovery and validation cohorts using the 15-gene panel and PD-L1 expression level. We first found that the PD-L1 expression level was similar between patients in high- and low-risk groups (P = 0.150; Figure 4A), while patients in the high-risk group had markedly higher tumor mutational burden (TMB) compared with those in the low-risk group (P <0.001; Figure 4B). When we combined the 15-gene panel with the PD-L1 expression level, we found that they were complementary, non-redundant correlates of treatment benefit; patients with low-risk and positive PD-L1 expression had the longest PFS and OS, while those with low-risk and negative PD-L1 expression or high-risk and positive PD-L1 expression had intermediate PFS and OS, and those with high-risk and negative PD-L1 expression had the worst PFS and OS in both discovery [Figure 4C–D] and validation cohorts [Figure 4E–F]. These findings indicate that combining the 15-gene panel and the PD-L1 expression level could improve survival prediction of first-line PD-1 blockade plus chemotherapy-treated advanced NSCLC patients [Figure 4C–F].

Figure 4 Combination of 15-gene panel and PD-L1 expression showed better predictive performance. (A) Comparison of PD-L1 expression level between patients in high-risk versus low-risk groups. (B) Comparison of TMB between patients in high-risk versus low-risk groups. (C) Comparisons of PFS among patients with low-risk and positive PD-L1 expression, low-risk and negative PD-L1 expression, high-risk and positive PD-L1 expression, and high-risk and negative PD-L1 expression in the discovery cohort. (D) Comparisons of OS among patients with low-risk and positive PD-L1 expression, low-risk and negative PD-L1 expression, high-risk and positive PD-L1 expression, and high-risk and negative PD-L1 expression in the discovery cohort. (E) Comparisons of PFS among patients with low-risk and positive PD-L1 expression, low-risk and negative PD-L1 expression, high-risk and positive PD-L1 expression, and high-risk and negative PD-L1 expression in the validation cohort. (F) Comparisons of OS among patients with low-risk and positive PD-L1 expression, low-risk and negative PD-L1 expression, high-risk and positive PD-L1 expression, and high-risk and negative PD-L1 expression in the validation cohort. mOS: Median overall survival; mPFS: Median progression-free survival; NR: Not reached; OS: Overall survival; PD-L1: Programmed death ligand 1; PFS: Progression free survival; TMB: Tumor mutation burden.

Genomic and immune landscapes of tumors in high-risk versus low-risk groups

To determine the immune profiles of tumors in high-risk versus low-risk groups, we analyzed genomic and transcriptomic data of 1014 patients with NSCLC from the TCGA database (440 patients in the high-risk group and 574 in the low-risk group). The mutational frequency of each gene from the 15-gene panel in our cohort was similar to that in the TCGA cohort [Figure 1B and Figure 5A] and some genes showed a significant co-occurrence (such as KEAP1 and STK11, KEAP1 and SMARCA4, SOX9 and GRIN2A) [Figure 5B], which was consistent with previous publications.[262728] We then analyzed transcriptomic data of all included samples and found that 652 genes were significantly down-regulated and 897 were up-regulated in tumors from the high-risk group compared with tumors from the low-risk group [Supplementary Figure 6, http://links.lww.com/CM9/B970]. We performed gene set enrichment analysis to identify markedly changed pathways and observed no specific immune-related pathways [Supplementary Figure 7, http://links.lww.com/CM9/B970]. When we conducted correlation analysis between each gene and immune checkpoint expression, we observed that most of the genes in the 15-gene panel, were associated with lower immune checkpoint expression [Supplementary Figure 8, http://links.lww.com/CM9/B970]. CIBERSORT revealed that most genes in the 15-gene panel were associated with lower immune cell abundance [Figure 5C] and that tumors in the high-risk group had significantly lower proportions of immune infiltrates [Figure 5D], indicating that variations in the 15-gene panel predict an immune-desert tumor microenvironment in NSCLC.

Figure 5 Genomic and immune landscape of tumors in high-risk versus low-risk groups. (A) Mutational landscape of each gene in the 15-gene panel in 1014 NSCLC patients from TCGA dataset. Upper panel: the matrix of mutations in a selection of frequently mutated genes. Columns represent samples. Below panel: the baseline parameter. Right panel: mutational frequency of each gene. (B) Genetic co-occurrence among the 15 genes. (C) Correlation of each gene mutations and immune cell abundance. (D) Comparison of immune infiltrates between patients in high-risk versus low-risk groups. MDSC: Myeloid-derived suppressor cells; NSCLC: Non-small-cell lung cancer; TCGA: The Cancer Genome Atlas.

Discussion

Despite the obvious survival benefit of PD-1 blockade plus chemotherapy in some patients with untreated advanced NSCLC, nearly half showed no response. This disparity in treatment benefit emphasizes an unmet need to investigate predictive biomarkers.[29] Considering the potential impact of genomic variations in tumor cells on the tumor immune microenvironment, this study aimed to evaluate the predictive significance of variations in certain genes to PD-1 blockade plus chemotherapy in patients with untreated advanced NSCLC from several registered phase 3 trials.[789101112] Our findings revealed that one or more variations in the 15-gene panel were associated with significantly worse treatment response and survival outcomes in NSCLC patients treated with first-line PD-1 blockade plus chemotherapy, but not chemotherapy alone. We also found that a combination of the 15-gene panel and PD-L1 expression level showed improved predictive performance. Using genomic and transcriptomic data of 1014 NSCLC patients, we found that tumors in the high-risk group were associated with decreased immune infiltrates and anti-tumor immunity, further supporting it as a negatively predictive biomarker in this setting.

Genomic variations in cancer cells play a critical role in orchestrating the spatial distribution, composition, and activation state of immune cells via direct or indirect effects, which subsequently impact the anti-tumor immune response and treatment benefit of immunotherapy.[13303132] Several studies have attempted to use these variations to predict the response to PD-1 blockade. One successful example is the application of TMB.[33,34] Despite several caveats, TMB has been approved as a companion diagnostic for pembrolizumab treatment of adult and pediatric solid tumors. Additionally, variation in at least two of 52 candidate genes was superior to TMB in predicting the clinical benefits for PD-1 blockade in patients with advanced NSCLC.[35] Bai et al[36] developed a genomic variation signature consisting of eight genes that could predict the response to anti-PD-(L)1 therapy in non-squamous NSCLC. These results indicate that variations in select genes may be able to predict the response of NSCLC to immunotherapy. Accordingly, we integrated clinical, genomic, and survival data of 287 patients with advanced NSCLC who received first-line PD-1 blockade plus chemotherapy or chemotherapy alone from five registered trials and found that the 15-gene panel could negatively predict the response to PD-1 blockade plus chemotherapy in advanced NSCLC. However, the robustness of its predictive value requires further investigation.

Rational combination of several potential biomarkers for immunotherapy can improve predictive performance.[363738] Here, we explored the combined effect of our 15-gene panel and PD-L1 expression level for predicting the response to PD-1 blockade plus chemotherapy. Using both the 15-gene panel and PD-L1 expression level showed better performance than either alone in predicting treatment benefit from this combination. Patients with low-risk and positive PD-L1 expression are most likely to benefit from PD-1 blockade plus chemotherapy, underscoring the rationale of using the 15-gene panel combined with PD-L1 expression level in advanced NSCLC patients treated with first-line PD-1 blockade plus chemotherapy. Both the 15-gene panel and PD-L1 expression level should be taken into consideration in future prospective clinical trials.

Given the potential predictive value of the 15-gene panel, we were eager to know the immune profiles of tumors in high-risk versus low-risk groups. We deconvoluted transcriptomic data of 1014 NSCLC patients and found that tumors in the high-risk group had significantly lower immune infiltrates compared with those in the low-risk group, indicating that these tumors possess an immunosuppressive microenvironment phenotype. These results support the use of immune infiltrates as a potential predictor for PD-1 blockade plus chemotherapy in NSCLC. However, detailed mechanistic understanding of how the 15-gene panel shapes tumor response to immunotherapy is not known. Although variations in some of the genes, such as KEAP1,[39]STK11,[40]KDM5C,[41]EPHA7,[42] and SMARCA4,[43] are associated with the inferior response to PD-1 blockade, the biological underpinning of the 15-gene panel requires further translational research.

Several limitations of this study should be acknowledged. First, although a large cohort of NSCLC patients treated with first-line PD-1 blockade plus chemotherapy from clinical trials was analyzed, this study was a retrospective analysis and lacked an independent validation cohort. These findings should therefore be cautiously interpreted. Second, all of the included patients were Asian. Whether the 15-gene panel can be applied to predict response to PD-1 blockade plus chemotherapy in patients of non-Asian ancestry remains unknown. Third, we only focused on genomic features to predict treatment benefit, and did not evaluate the tumor immune microenvironment features. The integration of multi-dimensional data would be valuable for the identification of robust biomarkers. Fourth, the genomic landscape and important variations were extensively distinct between non-squamous and squamous NSCLC. This impacts the robustness of the final gene panel and results should be interpreted with caution. Finally, while we analyzed the transcriptomic data of 1014 tumors to depict the immune profiles of tumors in high-risk versus low-risk groups, we did not uncover the underlying biological mechanism. Future clinical and translational research will provide mechanistic insights into how one or more variations in the 15-gene panel can predict survival benefit of NSCLC patients receiving first-line PD-1 blockade plus chemotherapy.

In summary, we suggest that one or more variations in the 15-gene panel represents a potential negative biomarker to predict the treatment response and survival outcome of patients with untreated advanced NSCLC receiving PD-1 blockade plus chemotherapy. Further investigation by prospective studies will help to optimize PD-1 blockade plus chemotherapy in the populations that are more likely to derive treatment benefit.

Acknowledgments

We are grateful to all patients and their families and all clinical study teams who participated in the study. We also thank the study sponsors and Jeremy Allen, PhD, from Liwen Bianji (Edanz) (https://www.liwenbianji.cn) for editing the English text of a draft of this manuscript.

Funding

This study was supported by grants from the National Natural Science Foundation of China (Nos. 82102859, 82172869, 82141101, 82272875, and 12126605), the Shanghai Rising-Star Program (Nos. 23QA1408000 and 22QA1407800), the Shanghai “Rising Stars of Medical Talent” Youth Development Program Youth Medical Talents—Specialist Program, the Original Exploration Project of Shanghai Natural Science Foundation (No. 23ZR1480600) and the Health and Family Planning Commission of Shanghai Municipality (No. 20224Y0067).

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request. All requests for raw data will be reviewed by the leading clinical site to check whether the request is subject to any intellectual property or confidentiality obligations. A signed data access agreement with the sponsor is required before accessing shared data. Source data are provided with this paper.

Conflicts of interest

Caicun Zhou has received honoraria as a speaker from Roche, Lilly China, Boehringer Ingelheim, Merck, Hengrui, Qilu, Sanofi, Merck Sharp & Dohme, Innovent Biologics, C-Stone, Luye Pharma, TopAlliance Biosciences, and Amoy Diagnostics, and advisor fees from Innovent Biologics, Hengrui, Qilu, and TopAlliance Biosciences. Shengxiang Ren has received honoraria as a speaker from Boehringer Ingelheim, Lilly, Merck Sharp & Dohme, Roche, Hengrui, and Junshi, advisor fees from Roche, Merck Sharp & Dohme, and Boehringer Ingelheim, and research funding from Hengrui. Ying Yang, Jiao Zhang, Huaibo Sun, and Henghui Zhang were employees of Genecast Biotechnology Co., Ltd. The authors report no other disclosures.

Supplementary Material

Tao Jiang and Jian Chen contributed equally to this work.

How to cite this article: Jiang T, Chen J, Wang HW, Wu FY, Chen XX, Su CX, Zhang HP, Zhou F, Yang Y, Zhang J, Sun HB, Zhang HH, Zhou CC, Ren SX. Genomic correlates of the response to first-line PD-1 blockade plus chemotherapy in patients with advanced non-small-cell lung cancer. Chin Med J 2024;137:2213–2222. doi: 10.1097/CM9.0000000000003094
==== Refs
References

1. Grant MJ Herbst RS Goldberg SB . Selecting the optimal immunotherapy regimen in driver-negative metastatic NSCLC. Nat Rev Clin Oncol 2021;18 :625–644. doi: 10.1038/s41571-021-00520-1.34168333
2. Owen DH Singh N Ismaila N Blanchard E Celano P Florez N , . Therapy for Stage IV Non-Small-Cell Lung Cancer Without Driver Alterations: ASCO Living Guideline, Version 2022.2. J Clin Oncol 2023;41 :e1–e9. doi: 10.1200/JCO.22.02121.36534935
3. Hendriks LE Kerr KM Menis J Mok TS Nestle U Passaro A , . Non-oncogene-addicted metastatic non-small-cell lung cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up. Ann Oncol 2023;34 :358–376. doi: 10.1016/j.annonc.2022.12.013.36669645
4. Gandhi L Rodríguez-Abreu D Gadgeel S Esteban E Felip E De Angelis F , . Pembrolizumab plus Chemotherapy in Metastatic Non-Small-Cell Lung Cancer. N Engl J Med 2018;378 :2078–2092. doi: 10.1056/NEJMoa1801005.29658856
5. Paz-Ares L Luft A Vicente D Tafreshi A Gümüş M Mazières J , . Pembrolizumab plus Chemotherapy for Squamous Non-Small-Cell Lung Cancer. N Engl J Med 2018;379 :2040–2051. doi: 10.1056/NEJMoa1810865.30280635
6. Yang Y Wang Z Fang J Yu Q Han B Cang S , . Efficacy and Safety of Sintilimab Plus Pemetrexed and Platinum as First-Line Treatment for Locally Advanced or Metastatic Nonsquamous NSCLC: a Randomized, Double-Blind, Phase 3 Study (Oncology pRogram by InnovENT anti-PD-1-11). J Thorac Oncol 2020;15 :1636–1646. doi: 10.1016/j.jtho.2020.07.014.32781263
7. Zhou C Chen G Huang Y Zhou J Lin L Feng J , . Camrelizumab plus carboplatin and pemetrexed versus chemotherapy alone in chemotherapy-naive patients with advanced non-squamous non-small-cell lung cancer (CameL): a randomised, open-label, multicentre, phase 3 trial. Lancet Respir Med 2021;9 :305–314. doi: 10.1016/S2213-2600(20)30365-9.33347829
8. Ren S Chen J Xu X Jiang T Cheng Y Chen G , . Camrelizumab Plus Carboplatin and Paclitaxel as First-Line Treatment for Advanced Squamous NSCLC (CameL-Sq): A Phase 3 Trial. J Thorac Oncol 2022;17 :544–557. doi: 10.1016/j.jtho.2021.11.018.34923163
9. Zhou C Chen G Huang Y Zhou J Lin L Feng J , . Camrelizumab Plus Carboplatin and Pemetrexed as First-Line Treatment for Advanced Nonsquamous NSCLC: Extended Follow-Up of CameL Phase 3 Trial. J Thorac Oncol 2023;18 :628–639. doi: 10.1016/j.jtho.2022.12.017.36646210
10. Zhou C Wang Z Sun Y Cao L Ma Z Wu R , . Sugemalimab versus placebo, in combination with platinum-based chemotherapy, as first-line treatment of metastatic non-small-cell lung cancer (GEMSTONE-302): interim and final analyses of a double-blind, randomised, phase 3 clinical trial. Lancet Oncol 2022;23 :220–233. doi: 10.1016/S1470-2045(21)00650-1.35038432
11. Zhou C Wu L Fan Y Wang Z Liu L Chen G , . Sintilimab Plus Platinum and Gemcitabine as First-Line Treatment for Advanced or Metastatic Squamous NSCLC: Results From a Randomized, Double-Blind, Phase 3 Trial (ORIENT-12). J Thorac Oncol 2021;16 :1501–1511. doi: 10.1016/j.jtho.2021.04.011.34048947
12. Wang Z Wu L Li B Cheng Y Li X Wang X , . Toripalimab Plus Chemotherapy for Patients With Treatment-Naive Advanced Non-Small-Cell Lung Cancer: A Multicenter Randomized Phase III Trial (CHOICE-01). J Clin Oncol 2023;41 :651–663. doi: 10.1200/JCO.22.00727.36206498
13. van Weverwijk A de Visser KE . Mechanisms driving the immunoregulatory function of cancer cells. Nat Rev Cancer 2023;23 :193–215. doi: 10.1038/s41568-022-00544-4.36717668
14. Spranger S Gajewski TF . Impact of oncogenic pathways on evasion of antitumour immune responses. Nat Rev Cancer 2018;18 :139–147. doi: 10.1038/nrc.2017.117.29326431
15. Binnewies M Roberts EW Kersten K Chan V Fearon DF Merad M , . Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat Med 2018;24 :541–550. doi: 10.1038/s41591-018-0014-x.29686425
16. Kalbasi A Ribas A . Tumour-intrinsic resistance to immune checkpoint blockade. Nat Rev Immunol 2020;20 :25–39. doi: 10.1038/s41577-019-0218-4.31570880
17. Jiang T Chen X Su C Ren S Zhou C . Pan-cancer analysis of ARID1A Alterations as Biomarkers for Immunotherapy Outcomes. J Cancer 2020;11 :776–780. doi: 10.7150/jca.41296.31949479
18. Jiang T Jia Q Fang W Ren S Chen X Su C , . Pan-cancer analysis identifies TERT alterations as predictive biomarkers for immune checkpoint inhibitors treatment. Clin Transl Med 2020;10 :e109. doi: 10.1002/ctm2.109.32564494
19. Chen X Su C Ren S Zhou C Jiang T . Pan-cancer analysis of KEAP1 mutations as biomarkers for immunotherapy outcomes. Ann Transl Med 2020;8 :141. doi: 10.21037/atm.2019.11.52.32175433
20. Inoue H Tani K . Multimodal immunogenic cancer cell death as a consequence of anticancer cytotoxic treatments. Cell Death Differ 2014;21 :39–49. doi: 10.1038/cdd.2013.84.23832118
21. Wang Z Till B Gao Q . Chemotherapeutic agent-mediated elimination of myeloid-derived suppressor cells. Oncoimmunology 2017;6 :e1331807. doi: 10.1080/2162402X.2017.1331807.28811975
22. Lesterhuis WJ Punt CJ Hato SV Eleveld-Trancikova D Jansen BJ Nierkens S , . Platinum-based drugs disrupt STAT6-mediated suppression of immune responses against cancer in humans and mice. J Clin Invest 2011;121 :3100–3108. doi: 10.1172/JCI43656.21765211
23. Patel SA Minn AJ . Combination Cancer Therapy with Immune Checkpoint Blockade: Mechanisms and Strategies. Immunity 2018;48 :417–433. doi: 10.1016/j.immuni.2018.03.007.29562193
24. Salas-Benito D Pérez-Gracia JL Ponz-Sarvisé M Rodriguez-Ruiz ME Martínez-Forero I Castañón E , . Paradigms on Immunotherapy Combinations with Chemotherapy. Cancer Discov 2021;11 :1353–1367. doi: 10.1158/2159-8290.CD-20-1312.33712487
25. Newman AM Liu CL Green MR Gentles AJ Feng W Xu Y , . Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 2015;12 :453–457. doi: 10.1038/nmeth.3337.25822800
26. Boeschen M Kuhn CK Wirtz H Seyfarth HJ Frille A Lordick F , . Comparative bioinformatic analysis of KRAS, STK11 and KEAP1 (co-)mutations in non-small cell lung cancer with a special focus on KRAS G12C. Lung Cancer 2023;184 :107361. doi: 10.1016/j.lungcan.2023.107361.37699269
27. Pan M Jiang C Zhang Z Achacoso N Solorzano-Pinto AV Tse P , . Sex- and Co-Mutation-Dependent Prognosis in Patients with SMARCA4-Mutated Malignancies. Cancers (Basel) 2023;15 :2665. doi: 10.3390/cancers15102665.37345003
28. Cordeiro de Lima VC Corassa M Saldanha E Freitas H Arrieta O Raez L , . STK11 and KEAP1 mutations in non-small cell lung cancer patients: Descriptive analysis and prognostic value among Hispanics (STRIKE registry-CLICaP). Lung Cancer 2022;170 :114–121. doi: 10.1016/j.lungcan.2022.06.010.35753125
29. Sholl LM . Biomarkers of response to checkpoint inhibitors beyond PD-L1 in lung cancer. Mod Pathol 2022;35 :66–74. doi: 10.1038/s41379-021-00932-5.34608245
30. Gentles AJ Newman AM Liu CL Bratman SV Feng W Kim D , . The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat Med 2015;21 :938–945. doi: 10.1038/nm.3909.26193342
31. Ritterhouse LL Gogakos T . Molecular Biomarkers of Response to Cancer Immunotherapy. Clin Lab Med 2022;42 :469–484. doi: 10.1016/j.cll.2022.05.004.36150824
32. Braun DA Hou Y Bakouny Z Ficial M Sant’ Angelo M Forman J , . Interplay of somatic alterations and immune infiltration modulates response to PD-1 blockade in advanced clear cell renal cell carcinoma. Nat Med 2020;26 :909–918. doi: 10.1038/s41591-020-0839-y.32472114
33. Chan TA Yarchoan M Jaffee E Swanton C Quezada SA Stenzinger A , . Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. Ann Oncol 2019;30 :44–56. doi: 10.1093/annonc/mdy495.30395155
34. Meri-Abad M Moreno-Manuel A García SG Calabuig-Fariñas S Pérez RS Herrero CC , . Clinical and technical insights of tumour mutational burden in non-small cell lung cancer. Crit Rev Oncol Hematol 2023;182 :103891. doi: 10.1016/j.critrevonc.2022.103891.36565893
35. Pan D Hu AY Antonia SJ Li CY . A Gene Mutation Signature Predicting Immunotherapy Benefits in Patients With NSCLC. J Thorac Oncol 2021;16 :419–427. doi: 10.1016/j.jtho.2020.11.021.33307194
36. Bai X Wu DH Ma SC Wang J Tang XR Kang S , . Development and validation of a genomic mutation signature to predict response to PD-1 inhibitors in non-squamous NSCLC: a multicohort study. J Immunother Cancer 2020;8 :e000381. doi: 10.1136/jitc-2019-000381.32606052
37. Goodman AM Castro A Pyke RM Okamura R Kato S Riviere P , . MHC-I genotype and tumor mutational burden predict response to immunotherapy. Genome Med 2020;12 :45. doi: 10.1186/s13073-020-00743-4.32430031
38. Chowell D Krishna C Pierini F Makarov V Rizvi NA Kuo F , . Evolutionary divergence of HLA class I genotype impacts efficacy of cancer immunotherapy. Nat Med 2019;25 :1715–1720. doi: 10.1038/s41591-019-0639-4.31700181
39. Shaverdian N Offin M Shepherd AF Simone CB 2nd Gelblum DY Wu AJ , . The Impact of Durvalumab on Local-Regional Control in Stage III NSCLCs Treated With Chemoradiation and on KEAP1-NFE2L2-Mutant Tumors. J Thorac Oncol 2021;16 :1392–1402. doi: 10.1016/j.jtho.2021.04.019.33992811
40. Malhotra J Ryan B Patel M Chan N Guo Y Aisner J , . Clinical outcomes and immune phenotypes associated with STK11 co-occurring mutations in non-small cell lung cancer. J Thorac Dis 2022;14 :1772–1783. doi: 10.21037/jtd-21-1377.35813711
41. Liu D Benzaquen J Morris L Ilié M Hofman P . Mutations in KMT2C, BCOR and KDM5C Predict Response to Immune Checkpoint Blockade Therapy in Non-Small Cell Lung Cancer. Cancers (Basel) 2022;14 :2816. doi: 10.3390/cancers14112816.35681795
42. Zhang Z Wu HX Lin WH Wang ZX Yang LP Zeng ZL , . EPHA7 mutation as a predictive biomarker for immune checkpoint inhibitors in multiple cancers. BMC Med 2021;19 :26. doi: 10.1186/s12916-020-01899-x.33526018
43. Gantzer J Davidson G Vokshi B Weingertner N Bougoüin A Moreira M , . Immune-Desert Tumor Microenvironment in Thoracic SMARCA4-Deficient Undifferentiated Tumors with Limited Efficacy of Immune Checkpoint Inhibitors. Oncologist 2022;27 :501–511. doi: 10.1093/oncolo/oyac040.35278076
