You are here

  • Home
  • Archive
  • Volume 11, Issue 7
  • Efficacy of PD-(L)1 blockade monotherapy compared with PD-(L)1 blockade plus chemotherapy in first-line PD-L1-positive advanced lung adenocarcinomas: a cohort study

Article Text

Article menu
Clinical/translational cancer immunotherapy
Original research
Efficacy of PD-(L)1 blockade monotherapy compared with PD-(L)1 blockade plus chemotherapy in first-line PD-L1-positive advanced lung adenocarcinomas: a cohort study
  1. Arielle Elkrief1,2,3,
  2. Joao M Victor Alessi4,
  3. Biagio Ricciuti4,
  4. Samantha Brown5,
  5. Hira Rizvi1,
  6. Isabel R Preeshagul1,
  7. Xinan Wang6,
  8. Federica Pecci4,
  9. Alessandro Di Federico4,
  10. Giuseppe Lamberti4,
  11. Jacklynn V Egger1,
  12. Jamie E Chaft1,7,
  13. Charles M Rudin1,7,
  14. Gregory J Riely1,7,
  15. Mark G Kris1,7,
  16. Marc Ladanyi2,3,
  17. Yuan Chen5,
  18. Matthew D Hellmann1,7,
  19. Ronglai Shen5,
  20. Mark M Awad4 and
  21. Adam J Schoenfeld1,7
  1. 1Department of Medicine, Thoracic Oncology Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
  2. 2Human Oncology & Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA
  3. 3Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
  4. 4Lowe Center for Thoracic Oncology, Dana Farber Cancer Institute, Boston, Massachusetts, USA
  5. 5Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
  6. 6Environmental Health, Harvard University, Boston, Massachusetts, USA
  7. 7Weill Cornell Medical College, New York, New York, USA
  1. Correspondence to Dr Adam J Schoenfeld; schoenfa{at}mskcc.org

  • AE, JMVA and BR are joint first authors.

  • RS, MMA and AJS are joint senior authors.

Abstract

Background Single-agent PD-(L)1 blockade (IO) alone or in combination with chemotherapy (Chemotherapy-IO) is approved first-line therapies in patients with advanced lung adenocarcinomas (LUADs) with PD-L1 expression ≥1%. These regimens have not been compared prospectively. The primary objective was to compare first-line efficacies of single-agent IO to Chemotherapy-IO in patients with advanced LUADs. Secondary objectives were to explore if clinical, pathological, and genomic features were associated with differential response to Chemotherapy-IO versus IO.

Methods This was a multicenter retrospective cohort study. Inclusion criteria were patients with advanced LUADs with tumor PD-L1 ≥1% treated with first-line Chemotherapy-IO or IO. To compare the first-line efficacies of single-agent IO to Chemotherapy-IO, we conducted inverse probability weighted Cox proportional hazards models using estimated propensity scores.

Results The cohort analyzed included 866 patients. Relative to IO, Chemotherapy-IO was associated with improved objective response rate (ORR) (44% vs 35%, p=0.007) and progression-free survival (PFS) in patients with tumor PD-L1≥1% (HR 0.84, 95% CI 0.72 to 0.97, p=0.021) or PD-L1≥50% (ORR 55% vs 38%, p<0.001; PFS HR 0.68, 95% CI 0.53 to 0.87, p=0.002). Using propensity-adjusted analyses, only never-smokers in the PD-L1≥50% subgroup derived a differential survival benefit from Chemotherapy-IO vs IO (p=0.013). Among patients with very high tumor PD-L1 expression (≥90%), there were no differences in outcome between treatment groups. No genomic factors conferred differential survival benefit to Chemotherapy-IO versus IO.

Conclusions While the addition of chemotherapy to PD-(L)1 blockade increases the probability of initial response, never-smokers with tumor PD-L1≥50% comprise the only population identified that derived an apparent survival benefit with treatment intensification.

  • Immune Checkpoint Inhibitors
  • Combined Modality Therapy
  • Non-Small Cell Lung Cancer
  • Genetic Markers

Data availability statement

Data are available on reasonable request.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/jitc-2023-006994

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    WHAT IS ALREADY KNOWN ON THIS TOPIC

    • Single-agent PD-(L)1 blockade (IO) or in combination with platinum-doublet chemotherapy (Chemotherapy-IO) has each demonstrated superior survival compared with chemotherapy alone in patients with advanced non-small cell lung cancer with PD-L1≥1%, however, these regimens have never been compared prospectively, and limited studies are available to guide treatment selection.

    WHAT THIS STUDY ADDS

    • Relative to IO alone, Chemotherapy-IO increased the probability of initial response in patients with PD-L1≥1% and ≥50%, however, overall survival was similar. In subgroup analysis, never-smokers derived a survival benefit from Chemotherapy-IO compared with IO alone. No genomic factors conferred differential survival benefit to Chemotherapy-IO or IO.

    HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

    • Our study sheds light on the comparative effectiveness of Chemotherapy-IO and IO in the first-line setting across all relevant PD-L1 subgroups and integrating genomic analyses, which may help guide treatment selection and future clinical trial design.

    Introduction

    Drugs that inhibit PD-1 or PD-L1 (PD-(L)1 blockade) have transformed the treatment landscape for patients with advanced non-small cell lung cancer (NSCLC) without targetable driver mutations. In 2023, there are several US Food and Drug Administration-approved first-line PD-(L)-1 blockade regimens alone (IO) or in combination with platinum-doublet chemotherapy (Chemotherapy-IO), all of which demonstrated clinical benefit compared with chemotherapy.1–7 The increased breadth of treatment options in newly diagnosed advanced lung cancer and lack of published head-to-head trials makes regimen selection challenging. It is unclear if certain populations might derive similar or greater clinical benefit from IO monotherapy than Chemotherapy-IO and might thereby avoid the potential toxicities of chemotherapy.

    Prior studies have identified clinical and genomic factors associated with efficacy of PD-(L)1 blockade. Eastern Cooperative Oncology Group (ECOG) performance status, smoking history, PD-L1 expression, and tumor mutational burden (TMB) influence clinical activity and outcomes.8–10 NSCLCs harboring mutations in STK11 and/or KEAP1 have worse outcomes to PD-(L)1 blockade, particularly among KRAS-mutant NSCLCs.11 In these cases, addition of chemotherapy might overcome IO resistance.11 Conversely, other factors such as poor performance status may limit tolerability of chemotherapy and negate potential additive anti-cancer activity of combination therapy.12

    We posited that clinical, pathologic, and molecular analyses of a real-world patient population could identify patient subpopulations that might differentially benefit from IO versus Chemotherapy-IO in the first-line setting. In order to address biases and differences in baseline characteristics between the IO and Chemotherapy-IO groups, we performed propensity-adjusted analyses in 866 patient cases of lung adenocarcinoma (LUAD) without EGFR or ALK-sensitizing alterations treated with IO or Chemotherapy-IO at two institutions.

    Methods

    Patients

    After institutional review board approval, patients with advanced LUAD from Memorial Sloan Kettering Cancer Center (MSK) and Dana-Farber Cancer Institute (DFCI) between 2011 and 2020 were assessed in this retrospective analysis. Only patients with advanced LUAD treated in the first-line with IO or Chemotherapy-IO were eligible for analysis (online supplemental figure S1).

    Supplemental material

    PD-L1 expression (tumor proportion score) was evaluated in all patients included in the study and reported as the percentage of tumor cells with membranous staining as previously described.13 For genomic analyses, only patients with genomic sequencing by MSK-IMPACT (MSK) or OncoPanel (DFCI) NGS panels were included14 15 (online supplemental methods) TMB was harmonized for comparison as previously published (online supplemental methods).16 Patients with tumors with PD-L1<1% or harboring sensitizing EGFR or ALK alterations were excluded (defined by EGFR exon 19 deletions or exon 21 L858R mutations). Pre-planned analyses in PD-L1 1%–49% and ≥50% subgroups were conducted to align with current first-line treatment guidelines.17 The primary outcomes were objective response rate (ORR), progression-free survival (PFS), and overall survival (OS).

    Supplemental material

    Statistical analysis

    Descriptive statistics were used to describe the analysis population by treatment group (Chemotherapy-IO vs IO). Differences in baseline characteristics by group were evaluated using the Wilcoxon rank sum test, Fisher’s exact test, or Pearson’s χ2 test as appropriate.

    Patients who did not experience progression or death by the data lock date (October 21, 2021) were censored at date of last assessment. Investigator-assessed ORR was defined as the rate of partial response+complete response. Real-world investigator-assessed PFS was assessed from the date the patient began therapy to the date of progression as previously described.18–20 OS was calculated from treatment start date until date of death or last assessment. Kaplan-Meier curves and log-rank test statistics were computed to compare PFS and OS between groups.

    We conducted propensity score modeling for treatment group assignment using a logistic regression model with the following potential prognostic factors as categorical variables: age (≥65 vs <65), ECOG performance status (scores 2–3 vs scores 0–1), smoking status (current/former smokers vs never-smokers), PD-L1 tumor proportion score percentage (PD-L1≥50% vs PD-L1 1%–49%), and presence of liver or brain metastases at baseline. To account for the potential difference in treatment assignment, we conducted inverse probability weighted (IPW) Cox proportional hazards models using the estimated propensity scores. Three sets of IPW Cox models were explored. The first model (main effects model) contained treatment group along with clinical categorical variables (age, ECOG, smoking status, PD-L1 %, baseline liver metastases and baseline brain metastases) to examine the overall treatment effects on PFS and OS. The second model (treatment interaction model) further included interactions between treatment group and clinical variables to examine treatment effect modifications, to identify subgroups for differential benefit of Chemotherapy-IO versus IO. The third model was restricted to patients with genomic data available, building off our second model (treatment interaction model, genomics analysis cohort) with the additions of the following categorical variables: harmonized TMB score (see online supplemental methods) and five gene mutations (KRAS, TP53, KEAP1, STK11, and SMARCA4), as well as their interactions with treatment group. The five gene mutations were selected as they have previously been associated with IO outcomes in lung cancer.21–23 For each clinical subgroup of interest, we calculated the HR for treatment group (Chemotherapy-IO vs IO) adjusting for all clinical covariates included in the propensity score IPW Cox proportional hazards model and visualized these results using forest plots.

    Analyses were conducted using R V.4.1.1 with the tidyverse (V.1.3.1),24 gtsummary (V.1.6.0),25 survival (V.3.3.1) and survminer (V.0.4.9) packages.26

    Results

    Patient characteristics

    Among all patients with advanced LUAD treated with Chemotherapy-IO or IO without a targetable driver alteration in EGFR or ALK, 866 patients met criteria for inclusion (online supplemental figure S1). Median follow-up for the clinical analysis cohort was 23 months (IQR: 12–38); 395 (45%) patients were treated with Chemotherapy-IO and 471 (55%) were treated with IO (table 1). Relative to the Chemotherapy-IO group, patients in the IO group were slightly older (median age 69 vs 67, p<0.001) with a more frequent and heavier smoking history (median pack years 30 vs 25 p=0.016) (table 1). PD-L1 high expression (≥50%) was enriched within the IO group relative to the Chemotherapy-IO group (85% vs 30% with PD-L1 high, p<0.001) (online supplemental figure S2A).17 Median harmonized TMB score (0.09 vs −0.05, p=0.013) was also significantly higher in the IO group (online supplemental figure S2B). Other baseline clinical factors such as ECOG performance status and sex were similarly distributed between the Chemotherapy-IO and IO groups (table 1). According to the propensity model, in the overall population (PD-L1≥1%), current/former smokers (OR 0.43, 95% CI 0.25 to 0.72, p=0.001), and those with tumor PD-L1≥50% (OR 0.07, 95% CI 0.05 to 0.10, p<0.001) were more likely to receive IO than Chemotherapy-IO (online supplemental table S1). Similar analysis for the PD-L1≥50% and 1%–49% subgroups are presented in online supplemental tables S2,3.

    Supplemental material

    View this table:
    Table 1

    Baseline characteristics

    Clinical outcomes

    Among 866 patients with PD-L1≥1%, Chemotherapy-IO was associated with improved ORR (44% vs 35%, p=0.007) (figure 1A) and improved PFS (median PFS 6.9 (95% CI 6.0 to 8.6) vs 4.9 months (95% CI 4.1 to 6.0), p=0.021) (figure 1B), consistent with the results from the main effects model (online supplemental table S4) and treatment interaction model (online supplemental table S5) for PFS. There was no difference in OS between the Chemotherapy-IO versus IO groups (median OS 17 months, (95% CI 15 to 22) vs 20 months (95% CI 17 to 24), p=0.50) (figure 1C), consistent with results from the main effects model (online supplemental table S6) and the treatment interaction model (online supplemental table S7). While some factors such as age<65 (HR 0.59, 95% CI 0.43 to 0.82), and never smoking status (HR 0.42, 95% CI 0.20 to 0.92) were associated with improved PFS in the Chemotherapy-IO group (figure 1D), these were not significant for OS (figure 1E). Consistent with this, these factors were no longer significant in the treatment interaction model for both PFS (online supplemental table S5) and OS (online supplemental table S7).

    Figure 1
    Figure 1

    Comparative effectiveness of chemotherapy plus PD-(L)1 blockade (Chemotherapy-IO) versus single-agent PD-(L)1 blockade (IO) in patients with PD-L1≥1%. (A) Objective response rate, (B) progression-free survival (PFS), and (C) overall survival (OS) among patients with tumor PD-L1≥1% who received first-line Chemotherapy-IO (N=395) vs IO (N=471). (D.) pfs and E.) OS analysis for Chemotherapy-IO versus IO adjusting for covariates of interest among different subgroups. Median survival times presented with CIs in brackets. Error bars in ORR plots represent 95% CI. ECOG, Eastern Cooperative Oncology Group; IO, single-agen anti-PD(L)-1 blockade.

    In sum, among patients with PD-L1≥1%, Chemotherapy-IO demonstrated a PFS benefit in a propensity score-adjusted analysis; however, there was no OS benefit associated with Chemotherapy-IO compared with IO monotherapy.

    Clinical outcomes by PD-L1 subgroup

    In the PD-L1≥50% subgroup in 515 patients, Chemotherapy-IO was associated with improved ORR (55% vs 38%, p<0.001) (figure 2A) and PFS (median PFS 10.0 (95% CI 6.9 to 16.0) vs 4.9 months (95% CI 3.9 to 6.5), p=0.002) (figure 2B), consistent with the main effects model (p=0.037) (online supplemental table S8) and treatment interaction model (HR 0.22, 95% CI 0.1 to 0.51, p<0.001) (online supplemental table S9). This PFS benefit favoring Chemotherapy-IO was most pronounced in never-smokers (HR 0.31, 95% CI 0.16, 0.57) and patients <65 years of age (HR 0.48, 95% CI 0.31 to 0.72) (figure 2D), consistent with the treatment interaction model for smoking (p=0.013), but not for age (p=0.7) (online supplemental table S9). There was no significant difference in OS between the Chemotherapy-IO versus IO groups (median OS 32 (95% CI: 20 to not reached (NR)) vs 20 months (95% CI 17 to 26), p=0.40) (figure 2C), consistent in the main effects model (online supplemental table S10) and treatment interaction model (online supplemental table S11). Never-smokers derived an OS benefit to Chemotherapy-IO (HR 2.81, 95% CI 1.04 to 7.6, p=0.042) (online supplemental table S11). Examining clinical outcomes in these subgroups in greater detail, ORR in never-smokers was higher with Chemotherapy-IO versus IO group (76% vs 21%, p<0.001, respectively) (figure 3A). Median PFS among never-smokers in the Chemotherapy-IO versus IO group was 10.0 months vs 2.5 months, respectively (figure 3B). ORR among patients <65 years of age in the Chemotherapy-IO versus IO groups was higher (63% vs 40%, respectively; p=0.004) (figure 3D). Median PFS among <65 years of age in the Chemotherapy-IO versus IO group was 17.0 months vs 4.7 months, respectively (figure 3E). Taken together, these results suggest that in the PD-L1≥50% subgroup, there was a PFS benefit in never-smokers and in younger adults, and OS benefit in never-smokers favoring Chemotherapy-IO compared with IO.

    Figure 2
    Figure 2

    Comparative effectiveness of chemotherapy plus PD-(L)1 blockade (Chemotherapy-IO) versus single-agent PD-(L)1 blockade (IO) in patients with PD-L1≥50%. (A) Objective response rate (ORR), (B) progression-free survival (PFS) and (C) overall survival (OS) among patients with tumor PD-L1≥50% who received first-line Chemotherapy-IO (N=117) vs IO (N=398). (D) PFS and (E) OS analysis for Chemotherapy-IO versus IO adjusting for covariates of interest among different subgroups. Median survival times presented with CIs in brackets. Error bars in ORR plots represent 95% CI. ECOG, Eastern Cooperative Oncology Group; IO, single-agen anti-PD(L)-1 blockade.

    Figure 3
    Figure 3

    Comparative effectiveness of chemotherapy plus PD-(L)1 blockade (Chemotherapy-IO) versus single-agent PD-(L)1 blockade (IO) in patients with PD-L1≥50% in subgroups of interest. (A) Objective response rate, (B) progression-free survival (PFS), and (C) overall survival (OS) between Chemotherapy-IO versus IO groups in never-smokers with tumor PD-L1≥50%. (D) Objective response rate, (E) PFS, and (F) OS between Chemotherapy-IO versus IO groups in patients <65 years of age with PD-L1≥50%. Median survival times presented with CIs in brackets. Error bars in ORR plots represent 95% CI. IO, single-agen anti-PD(L)-1 blockade.

    Next, since very high PD-L1% (≥90%) has previously been associated with improved outcomes to IO,27 we explored this group in our dataset. Among patients with very high tumor PD-L1 expression (≥90%, N=212) there was no difference in ORR (p=0.2) (online supplemental figure S3A), PFS (p=0.2) (online supplemental figure S3B) or OS (p=0.5) (online supplemental figure S3B) between the Chemotherapy-IO or IO groups.

    Supplemental material

    Lastly, in the PD-L1 1%–49% subgroup (N=351 patients), while Chemotherapy-IO was associated with improved ORR (39% vs 19%, p<0.001) (online supplemental figure S4A) and PFS (median PFS 6.2 (95% CI 5.7 to 7.5) vs 4.4 months (95% CI 3.4 to 6.7), p=0.02) (online supplemental figure S4B) compared with IO, this was not significant for OS (median OS 15 (95% CI 13 to 19) vs 17 months (95% CI 13 to 24), p=0.8) (online supplemental figure S4C), or in the adjusted analyses (online supplemental tables S12-S14) in any subgroup examined.

    Supplemental material

    Differential genomic biomarkers of response to Chemotherapy-IO versus IO

    There were 572 patients with tumor PD-L1≥1% who were treated with Chemotherapy-IO (N=262) or IO (N=310) and underwent genomic sequencing (online supplemental figure S1, genomic analysis cohort). Median follow-up for the genomic analysis cohort was 24 months (IQR: 13–40). Patient baseline characteristics for the genomic analysis cohort were similar to the clinical analysis cohort (online supplemental table S15).

    ORR in Chemotherapy-IO and IO within different mutation subgroups of interest in KRAS, TP53, SMARCA4, KEAP1, STK11, or TMB are shown in figure 4A. Notably, in patients with SMARCA4 mutations, ORR was higher for those treated with IO alone (48% vs 29%), with a non-significant trend for improved PFS (7.6 vs 4.6 months, p=0.3) (figure 4B) as well as a non-significant trend for improved OS (25 vs 12 months, p=0.06) (figure 4C) favoring IO alone in patients with SMARCA4 mutations. In adjusted analyses, for patients with SMARCA4 mutations, the PFS benefit favoring IO alone was significant in the treatment interaction model (p=0.011) (online supplemental table S16), but not for OS (online supplemental table S17). No other specific mutations were significant in the treatment interaction model for PFS (online supplemental table S16) or OS (online supplemental table S17). For TMB, we observed a non-significant trend for improved PFS among patients with high median TMB favoring Chemotherapy-IO (figure 4D), also consistent with the treatment interaction model for PFS (p=0.003) (online supplemental table S16), however, this was not significant for the unadjusted OS analysis (figure 4E) or in the treatment interaction model for OS (online supplemental table S17).

    Figure 4
    Figure 4

    Comparative effectiveness of chemotherapy plus PD-(L)1 blockade (Chemotherapy-IO) versus single-agent PD-(L)1 blockade (IO) in the genomic analysis cohort (PD-L1≥1%). (A) Objective response rate (ORR) among genomic subgroups of interest (KRAS, TP53, SMARCA4, KEAP1, STK11, TMB). (B) Progression-free survival (PFS) and (C) overall survival (OS) of chemotherapy-IO versus IO in patients with SMARCA4 mutations. (D) PFS and (E) OS for the TMB high groups. Median survival times presented with CIs in brackets. Median survival times presented with CIs in brackets. Error bars in ORR plots represent 95% CI. Mut; mutated; IO, single-agen anti-PD(L)-1 blockade; TMB, tumor mutational burden; WT, wild type.

    Discussion and conclusion

    We examined the efficacy of IO compared with treatment intensification with Chemotherapy-IO, in the context of key clinical and molecular features of LUAD. Within the PD-L1≥50% subgroup, we found the addition of chemotherapy to PD-(L)1 blockade led to improvements in ORR and PFS, but this only translated into an OS benefit in never-smokers. The only population that did not derive any initial benefit to Chemotherapy-IO over IO was the PD-L1 very high (≥90%) subgroup.

    Our analyses found that the addition of chemotherapy increases the probability of initial response in a heterogenous patient population with differential sensitivity to chemotherapy and immunotherapy, but long-term benefit appears largely driven by whether PD-(L)1 blockade generates durable antitumor immunity. The lack of OS benefit with Chemotherapy-IO compared with IO in our overall study population is also supported by recent clinical analyses of real-world and randomized controlled trials in patients with PD-L1≥50% non-squamous NSCLC.18 28

    We initially hypothesized that distinct clinical and molecular characteristics previously associated with IO resistance (eg, baseline liver metastases, STK11, and/or KEAP1 mutations) might distinguish patient populations that would benefit from treatment intensification with Chemotherapy-IO.11 Contrary to our hypothesis, we found that while factors such as liver metastases and STK11 mutations were associated with poor response overall, they did not confer superior long-term benefit from Chemotherapy-IO. Thus, treatment escalation with chemotherapy may not be sufficient or indicated solely based on these factors. Conversely, predictors of benefit from IO, such as very high PD-L1% (≥90%), may be a useful indicator of IO monotherapy appropriateness.29 For this select group of patients, we found no difference in initial response between the Chemotherapy-IO or IO groups. Interestingly, our study also found that patients with SMARCA4 mutations experienced better response rates and PFS with IO compared with Chemotherapy-IO. The clinical implication of this observation is supported by prior studies, which have identified SMARCA4 mutation as a poor prognostic factor, potentially associated with resistance to chemotherapy30 31 but improved response to IO.17 However, only 12% of patients had SMARCA4 mutations. More extensive molecular and gene expression analyses may be helpful in determining if specific genomic signatures benefit differentially from Chemotherapy-IO versus IO, but these analyses require much larger sample size for adequate power in the face of multiple hypothesis testing.

    Never smoking status emerged as the primary factor associated with survival benefit favoring Chemotherapy-IO within the PD-L1≥50% subgroup. Prior studies have found a correlation between smoking history and increased TMB and that smoking history could be a potential clinical surrogate for TMB.32 33 However, here, we found that even when accounting for TMB in a propensity adjusted model, smoking status remained a distinct predictive factor. Smoking history is a readily available biomarker that could be of value in determining regimen selection. Our analysis suggests that Chemotherapy-IO should be strongly considered for never-smokers even in the presence of high TMB and/or PD-L1 expression. Further work is needed to identify the underlying biological basis for this pattern. It is possible that the advantage observed for Chemotherapy-IO in the never-smoker population (PD-L1≥50%), despite adjustment for TMB, might represent a subset of LUAD which although it tests genomically negative for drivers such as EGFR or ALK, may in fact, be a group of patients whose cancer has yet unidentified drivers, for which existing data suggests inferior IO response. For example, several studies have identified oncogenic fusions using RNA sequencing for patients without a driver alteration identified through targeted NGS methods.

    The increased response rate and PFS benefit generally observed with Chemotherapy-IO suggests that therapeutic escalation could have an important role in aggressive or extensive disease. Here, the addition of chemotherapy could help provide more immediate symptom relief. Further, these patients may not otherwise have the opportunity to receive salvage chemotherapy after progression on immunotherapy. In this scenario, initial disease control with chemotherapy could create a window of disease control to enable the more gradual effects of IO to become operant. Notably, however, in the setting of brain or liver metastases, we did not find any clinical benefit from the addition of chemotherapy to IO, consistent with prior analysis.18 In this setting, it is unclear if the added toxicity that comes with chemotherapy may abrogate the potential clinical benefits. Further investigation is also needed to understand the precise impact of volume of disease on patient outcomes.

    Our study is a retrospective analysis with the associated limitations, but we sought to minimize inherent bias by including two large institutions and by applying robust propensity-matching methodology. The modest sample size of the PD-L1 1%–49% subgroup, especially within the patients who received single-agent IO limited the power of the analysis for this subpopulation. Despite these limitations, our study is the first to comprehensively study the comparative effectiveness of Chemotherapy-IO across all relevant PD-L1 subgroups, integrating TMB and genomic analyses for the first time.

    In conclusion, we observed improvements in ORR and PFS associated with Chemotherapy-IO compared with IO, particularly in young, never-smokers. Never smoking status in the PD-L1≥50% subgroup was the only characteristic in which ORR and PFS improvement translated into a survival benefit. No genomic alterations favored OS with Chemotherapy-IO compared with IO. Our findings demonstrate that the addition of chemotherapy to PD-(L)1 blockade generally increases the probability of initial response but leads to improved survival only among never-smokers in the PD-L1≥50% subgroup. Our study highlights the critical nature of ongoing clinical trials prospectively evaluating the comparative effectiveness of anti-PD-(L)1 with and without chemotherapy (NCT03793179, NCT04547504).

    Data availability statement

    Data are available on reasonable request.

    Ethics statements

    Patient consent for publication

    Acknowledgments

    The authors wish to acknowledge Clare Wilhelm for editorial support. The authors wish to acknowledge the support of the Center Core grant (P30 CA008748), the Molecular Diagnostics Service in the Department of Pathology, and the Marie-Josee and Henry R. Kravis Center for Molecular Oncology.

    References

    1. ↵
      1. Gandhi L,
      2. Rodríguez-Abreu D,
      3. Gadgeel S, et al
      . Pembrolizumab plus chemotherapy in metastatic non–small-cell lung cancer. N Engl J Med 2018;378:2078–92. doi:10.1056/NEJMoa1801005
      OpenUrlCrossRefPubMed
    2. ↵
      1. Hellmann MD,
      2. Paz-Ares L,
      3. Bernabe Caro R, et al
      . Nivolumab plus Ipilimumab in advanced non-small-cell lung cancer. N Engl J Med 2019;381:2020–31. doi:10.1056/NEJMoa1910231
      OpenUrlCrossRefPubMed
    3. ↵
      1. Paz-Ares L,
      2. Ciuleanu T-E,
      3. Cobo M, et al
      . First-line Nivolumab plus Ipilimumab combined with two cycles of chemotherapy in patients with non-small-cell lung cancer (Checkmate 9La): an international, randomised, open-label, phase 3 trial. Lancet Oncol 2021;22:198–211. doi:10.1016/S1470-2045(20)30641-0
      OpenUrlCrossRefPubMed
    4. ↵
      1. Herbst RS,
      2. Giaccone G,
      3. de Marinis F, et al
      . Atezolizumab for first-line treatment of PD-L1–selected patients with NSCLC. N Engl J Med 2020;383:1328–39. doi:10.1056/NEJMoa1917346
      OpenUrlCrossRefPubMed
    5. ↵
      1. Socinski MA,
      2. Jotte RM,
      3. Cappuzzo F, et al
      . Atezolizumab for first-line treatment of metastatic Nonsquamous NSCLC. N Engl J Med 2018;378:2288–301. doi:10.1056/NEJMoa1716948
      OpenUrlCrossRefPubMed
    6. ↵
      1. Gogishvili M,
      2. Melkadze T,
      3. Makharadze T, et al
      . Cemiplimab plus chemotherapy versus chemotherapy alone in non-small cell lung cancer: a randomized, controlled, double-blind phase 3 trial. Nat Med 2022;28:2374–80. doi:10.1038/s41591-022-01977-y
      OpenUrl
    7. ↵
      1. Reck M,
      2. Rodríguez-Abreu D,
      3. Robinson AG, et al
      . Updated analysis of KEYNOTE-024: Pembrolizumab versus platinum-based chemotherapy for advanced non–small-cell lung cancer with PD-L1 tumor proportion score of 50% or greater. J Clin Oncol 2019;37:537–46. doi:10.1200/JCO.18.00149
      OpenUrlCrossRefPubMed
    8. ↵
      1. Hellmann MD,
      2. Nathanson T,
      3. Rizvi H, et al
      . Genomic features of response to combination Immunotherapy in patients with advanced non-small-cell lung cancer. Cancer Cell 2018;33:843–52. doi:10.1016/j.ccell.2018.03.018
      OpenUrlCrossRefPubMed
    9. ↵
      1. Sehgal K,
      2. Gill RR,
      3. Widick P, et al
      . Association of performance status with survival in patients with advanced non-small cell lung cancer treated with Pembrolizumab monotherapy. JAMA Netw Open 2021;4:e2037120. doi:10.1001/jamanetworkopen.2020.37120
    10. ↵
      1. Aguilar EJ,
      2. Ricciuti B,
      3. Gainor JF, et al
      . Outcomes to first-line Pembrolizumab in patients with non-small-cell lung cancer and very high PD-L1 expression. Ann Oncol 2019;30:1653–9. doi:10.1093/annonc/mdz288
      OpenUrlCrossRefPubMed
    11. ↵
      1. Ricciuti B,
      2. Arbour KC,
      3. Lin JJ, et al
      . Diminished efficacy of programmed Death-(Ligand)1 inhibition in Stk11- and Keap1-mutant lung adenocarcinoma is affected by KRAS Mutation status. J Thorac Oncol 2022;17:399–410. doi:10.1016/j.jtho.2021.10.013
      OpenUrlCrossRef
    12. ↵
      1. Khaki AR,
      2. Glisch C,
      3. Petrillo LA
      . Immunotherapy in patients with poor performance status: the jury is still out on this special population. JCO Oncol Pract 2021;17:583–6. doi:10.1200/OP.21.00397
      OpenUrl
    13. ↵
      1. Schoenfeld AJ,
      2. Rizvi H,
      3. Bandlamudi C, et al
      . Clinical and molecular correlates of PD-L1 expression in patients with lung adenocarcinomas. Ann Oncol 2020;31:599–608. doi:10.1016/j.annonc.2020.01.065
      OpenUrlCrossRefPubMed
    14. ↵
      1. Cheng DT,
      2. Mitchell TN,
      3. Zehir A, et al
      . Memorial Sloan Kettering-integrated Mutation profiling of actionable cancer targets (MSK-IMPACT): A hybridization capture-based next-generation sequencing clinical assay for solid tumor molecular oncology. J Mol Diagn 2015;17:251–64. doi:10.1016/j.jmoldx.2014.12.006
      OpenUrlCrossRefPubMed
    15. ↵
      1. Garcia EP,
      2. Minkovsky A,
      3. Jia Y, et al
      . Validation of Oncopanel: A targeted next-generation sequencing assay for the detection of somatic variants in cancer. Arch Pathol Lab Med 2017;141:751–8. doi:10.5858/arpa.2016-0527-OA
      OpenUrlCrossRefPubMed
    16. ↵
      1. Vokes NI,
      2. Liu D,
      3. Ricciuti B, et al
      . Harmonization of tumor mutational burden Quantification and association with response to immune Checkpoint blockade in non-small-cell lung cancer. JCO Precis Oncol 2019;3:PO.19.00171. doi:10.1200/PO.19.00171
    17. ↵
      1. Ettinger DS,
      2. Wood DE,
      3. Aisner DL, et al
      . Non-small cell lung cancer, version 3.2022, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw 2022;20:497–530. doi:10.6004/jnccn.2022.0025
      OpenUrlCrossRefPubMed
    18. ↵
      1. Pérol M,
      2. Felip E,
      3. Dafni U, et al
      . Effectiveness of PD-(L)1 inhibitors alone or in combination with platinum-doublet chemotherapy in first-line (1L) non-squamous non-small-cell lung cancer (Nsq-NSCLC) with PD-L1-high expression using real-world data. Ann Oncol 2022;33:511–21. doi:10.1016/j.annonc.2022.02.008
      OpenUrlCrossRefPubMed
    19. ↵
      1. Curtis MD,
      2. Griffith SD,
      3. Tucker M, et al
      . Development and validation of a high-quality composite real-world mortality Endpoint. Health Serv Res 2018;53:4460–76. doi:10.1111/1475-6773.12872
      OpenUrlPubMed
    20. ↵
      1. Griffith SD,
      2. Tucker M,
      3. Bowser B, et al
      . Generating real-world tumor burden endpoints from electronic health record data: comparison of RECIST, Radiology-anchored, and clinician-anchored approaches for abstracting real-world progression in non-small cell lung cancer. Adv Ther 2019;36:2122–36. doi:10.1007/s12325-019-00970-1
      OpenUrlCrossRef
    21. ↵
      1. Arbour KC,
      2. Rizvi H,
      3. Plodkowski AJ, et al
      . Treatment outcomes and clinical characteristics of patients with KRAS-G12C-mutant non-small cell lung cancer. Clin Cancer Res 2021;27:2209–15. doi:10.1158/1078-0432.CCR-20-4023
      OpenUrlAbstract/FREE Full Text
    22. ↵
      1. Lin X,
      2. Wang L,
      3. Xie X, et al
      . Prognostic biomarker Tp53 mutations for immune Checkpoint blockade therapy and its association with tumor Microenvironment of lung adenocarcinoma. Front Mol Biosci 2020;7:602328. doi:10.3389/fmolb.2020.602328
    23. ↵
      1. Schoenfeld AJ,
      2. Bandlamudi C,
      3. Lavery JA, et al
      . The Genomic landscape of Smarca4 alterations and associations with outcomes in patients with lung cancer. Clin Cancer Res 2020;26:5701–8. doi:10.1158/1078-0432.CCR-20-1825
      OpenUrlAbstract/FREE Full Text
    24. ↵
      1. Wickham H,
      2. Averick M,
      3. Bryan J, et al
      . Welcome to the Tidyverse. JOSS 2019;4:1686. doi:10.21105/joss.01686
      OpenUrl
    25. ↵
      1. Sjoberg D,
      2. Whiting K,
      3. Curry M, et al
      . Reproducable summary Tabes with the Gtsummary package. The R Journal 2021;13:570. doi:10.32614/RJ-2021-053
    26. ↵
      Modeling survival data: extending the Cox model. 2000.
    27. ↵
      1. Sezer A,
      2. Kilickap S,
      3. Gümüş M, et al
      . Cemiplimab monotherapy for first-line treatment of advanced non-small-cell lung cancer with PD-L1 of at least 50%: a Multicentre, open-label, global, phase 3, randomised, controlled trial. Lancet 2021;397:592–604. doi:10.1016/S0140-6736(21)00228-2
      OpenUrlCrossRefPubMed
    28. ↵
      1. Akinboro O,
      2. Vallejo JJ,
      3. Mishra-Kalyani PS, et al
      . Outcomes of anti-PD-(L1) therapy in combination with chemotherapy versus Immunotherapy (IO) alone for first-line (1L) treatment of advanced non-small cell lung cancer (NSCLC) with PD-L1 score 1-49%: FDA pooled analysis. JCO 2021;39:9001. doi:10.1200/JCO.2021.39.15_suppl.9001
    29. ↵
      1. Ricciuti B,
      2. Arbour KC,
      3. Alessi JVM, et al
      . Association of a very high tumor mutational load with increased Cd8+ and PD-1+ T-cell infiltration and improved clinical outcomes to PD-(L)1 blockade across different PD-L1 expression levels in non-small cell lung cancer. JCO 2021;39:9018. doi:10.1200/JCO.2021.39.15_suppl.9018
    30. ↵
      1. Song S,
      2. Nguyen V,
      3. Schrank T, et al
      . Loss of SWI/SNF Chromatin remodeling alters Nrf2 signaling in non-small cell lung carcinoma. Mol Cancer Res 2020;18:1777–88. doi:10.1158/1541-7786.MCR-20-0082
      OpenUrlAbstract/FREE Full Text
    31. ↵
      1. Rekhtman N,
      2. Montecalvo J,
      3. Chang JC, et al
      . Smarca4-deficient Thoracic Sarcomatoid tumors represent primarily smoking-related undifferentiated Carcinomas rather than primary Thoracic Sarcomas. J Thorac Oncol 2020;15:231–47. doi:10.1016/j.jtho.2019.10.023
      OpenUrl
    32. ↵
      1. Alexandrov LB,
      2. Ju YS,
      3. Haase K, et al
      . Mutational signatures associated with tobacco smoking in human cancer. Science 2016;354:618–22. doi:10.1126/science.aag0299
      OpenUrlAbstract/FREE Full Text
    33. ↵
      1. Wang X,
      2. Ricciuti B,
      3. Nguyen T, et al
      . Association between smoking history and tumor Mutation burden in advanced non-small cell lung cancer. Cancer Res 2021;81:2566–73. doi:10.1158/0008-5472.CAN-20-3991
      OpenUrlAbstract/FREE Full Text

    Supplementary materials

    Footnotes

    • Twitter @ArielleElkrief, @alessi_joao, @XinAnnWang, @charlesrudin, @DrMarkAwad, @AdamJSchoenfeld

    • Contributors Study conducts and design: AE, JVA, BR, SB, YC, RS, AS and MA; data acquisition: AE, JVA, BR, HR and JE; statistical analysis: AE, JVA, BR, SB, YC and RS; data interpretation: AE, JVA, BR, SB, YC, RS, AS and MA; drafting the manuscript or revising it critically: all authors; final approval of the manuscript: all authors. Guarantor: AJS.

    • Funding AE effort funded by the Canadian Institutes of Health Research, the Royal College of Surgeons and Physicians of Canada, and the Henry R. Shibata Fellowship. XW effort funded by: National Institutes of Health grant U01CA209414. MA effort was funded by: V Foundation, Elva J. and Clayton L. McLaughlin Fund for Lung Cancer Research. This study was funded by National Institute of Health grants (cancer center grant P30CA008748 to MSKCC) and philanthropy (grants from John and Georgia DallePezze to MSKCC, The Ning Zhao & Ge Li Family Initiative for Lung Cancer Research and New Therapies, and the James A. Fieber Lung Cancer Research Fund).

    • Competing interests JEC has consulted for: AstraZeneca, BMS, Genentech, Merck, Flame Biosciences, Regeneron-Sanofi, Guardant Health, Arcus Biosciences and also declared research funding to institution from: AstraZeneca, BMS, Genentech, Merck, Novartis. CMR has consulted with AbbVie, Amgen, Astra Zeneca, D2G, Daiichi Sankyo, Epizyme, Genentech/Roche, Ipsen, Jazz, Kowa, Lilly, Merck, and Syros. He serves on the scientific advisory boards of Bridge Medicines, Earli, and Harpoon Therapeutics. GJR reports institutional research support from Mirati, Lilly, Takeda, Merck, Roche, Pfizer, and Novartis. MGK reports honoraria from AstraZeneca, Pfizer, Merus, and BerGenBio. MDH reports grants from BMS; and personal fees from Achilles; Adagene; Adicet; Arcus; AstraZeneca; Blueprint; BMS; DaVolterra; Eli Lilly; Genentech/Roche; Genzyme/Sanofi; Janssen; Immunai; Instil Bio; Mana Therapeutics; Merck; Mirati; Natera; Pact Pharma; Shattuck Labs; and Regeneron; as well as equity options from Factorial, Immunai, Shattuck Labs, Arcus, and Avail Bio. A patent filed by Memorial Sloan Kettering related to the use of tumor mutational burden to predict response to immunotherapy (PCT/US2015/062208) is pending and licensed by PGDx. Subsequent to the completion of this work, MDH began as an employee (and equity holder) at AstraZeneca. MA reports that he was a consultant to: Bristol-Myers Squibb, Merck, Genentech, AstraZeneca, Mirati, Novartis, Blueprint Medicine, Abbvie, Gritstone, NextCure, EMD Serono and received research funding (to institute) from: Bristol-Myers Squibb, Eli Lilly, Genentech, AstraZeneca, Amgen. AS reports consulting/advising role to J&J, KSQ therapeutics, BMS, Merck, Enara Bio, Perceptive Advisors, Oppenheimer and Co, Umoja Biopharma, Legend Biotech, Iovance Biotherapeutics, Lyell Immunopharma and Heat Biologics, research funding from GSK (Inst), PACT pharma (Inst), Iovance Biotherapeutics (Inst), Achilles therapeutics (Inst), Merck (Inst), BMS (Inst), Harpoon Therapeutics (Inst) and Amgen (Inst). AS reports consulting/advising role to J&J, KSQ therapeutics, BMS, Merck, Enara Bio, Perceptive Advisors, Oppenheimer and Co, Umoja Biopharma, Legend Biotech, Iovance Biotherapeutics, Lyell Immunopharma and Heat Biologics. Research funding: GSK (Inst), PACT pharma (Inst), Iovance Biotherapeutics (Inst), Achilles therapeutics (Inst), Merck (Inst), BMS (Inst), Harpoon Therapeutics (Inst) and Amgen (Inst).

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.