ABSTRACT
Introduction
Pembrolizumab is a standard first-line therapy for advanced/metastatic non-small cell lung cancer (a/mNSCLC) lacking actionable mutations. Data from lower-middle-income countries (LMICs) remain scarce.
Methods
From January 2019 to June 2024, we prospectively analyzed 78 a/mNSCLC patients receiving pembrolizumab-based first-line therapy. Endpoints included overall survival (OS), progression-free survival (PFS), disease control rate (DCR), and conditional survival probabilities.
Results
With a median follow-up of 27 months, median OS was 21 months (95% CI: 12.2–30.8) and median PFS 6.3 months (95% CI: 5.5–10.1). At first response evaluation (2 months), partial response was seen in 47.4% (37/78), stable disease in 16.7% (13/78). Next-generation sequencing (85% tested) revealed non-actionable mutations in 70%; notably, 4 of 6 long-term survivors harbored KRAS mutations. PD-L1 TPS ≥ 50% significantly lowered progression and mortality risk. Age, performance status (ECOG), and disease response significantly influenced the OS. The conditional survival probability for an additional 6 months after surviving the first 6 months was 78.1% (90% in patients with controlled disease).
Conclusion
Real-world LMIC data demonstrated comparable effectiveness of pembrolizumab-based therapy in a/mNSCLC despite a higher proportion of adverse prognostic factors. More studies in diverse clinical settings are needed to provide a reliable estimate of benefit.
KEYWORDS: Comparative effectiveness, pembrolizumab, immunotherapy, non-small cell lung cancer, real-world data
Plain Language Summary
Why did we perform this study?
Pembrolizumab is an immunotherapy drug used to treat people with advanced lung cancer. Clinical trials have demonstrated its effectiveness in patients from richer countries. Whether this drug works outside clinical trials, especially in patients from poorer countries with fewer resources, is not known.
What did we do and find?
Dr Batra’s team treated 78 patients with pembrolizumab who were diagnosed with advanced lung cancer. They received treatment at a cancer center in a lower-middle-income country. We recorded how long they lived, how long their disease remained controlled, and any side effects they experienced. Half of all patients survived 21 months or longer, which matches clinical trial results. Nearly half of the patients had a good initial response to treatment. Patients whose tumors expressed higher levels of a marker called Programmed-Death Ligand 1 (PD-L1) experienced better survival outcomes. Older patients and those with poorer health at treatment initiation had worse outcomes.
What do these results mean?
Our results show that pembrolizumab can be just as effective in routine clinical care as in clinical trials, even when patients are older and have poorer health. Notably, some patients continue to live without receiving any treatment after receiving this drug.
How will this study influence the wider field?
Dr Batra’s team provides evidence supporting the effectiveness of pembrolizumab in countries with limited resources. Going forward, clinical trials should include a broader range of patients to accurately reflect real-world medical practice.
Graphical abstract
1. Introduction
Immune checkpoint inhibitors (ICI) (pembrolizumab, nivolumab, atezolizumab) alone or with chemotherapy improve survival outcomes in advanced/metastatic non – small cell lung cancer (a/mNSCLC) in proportion to PDL1 expression [1,2]. Pembrolizumab is a standard option for patients with a/mNSCLC with any PDL1 tumor proportion score (TPS) when combined with chemotherapy or as monotherapy (TPS ≥ 50%), irrespective of histology. While trials effectively minimize unknown confounding with unmatched internal validity, their strict design itself creates an efficacy-effectiveness gap when applied to routine clinical practice, i.e., reduced external validity [3].
This gap exists because trial-eligible patients represent a narrow sample of the possible real-world characteristics of patients (age, comorbidities, performance status, geographical distribution) and benefit from mandated adherence to trial protocols [4,5]. Structural barriers to trial enrollment rather than patient refusal to participate, results in a systematically low trial participation rate (USA; 2013–2017 rate = 7.1%), and consequently, researchers in high-income countries have reported a substantial difference (~6–12 months) in overall survival with pembrolizumab, even after matching real-world patients to the trial eligibility criteria [6–9].
It is reasonable to anticipate that inequitable trial participation and disparity in drug availability would result in a wider gap in lower-middle countries (LMICs) – the existence and quantification of which is of importance to policy makers in advocating for value-based tiered pricing and patient assistance programs [10–12]. We therefore report outcomes associated with first line pembrolizumab in patients with a/mNSCLC from an LMIC setting.
2. Materials and methods
This IRB-approved analysis used a prospectively maintained institutional database of Rajiv Gandhi Cancer Institute & Research Centre, so the need for separate consent was waived. We included (1) a/mNSCLC without EGFR, ALK, or ROS1 mutations; (2) first line ICI or ICI+Chemo treatment; (3) known PD-L1 expression, and (4) all interventions (diagnostic, therapeutic, and ancillary) delivered within our institution. We excluded (1) large cell neuroendocrine carcinoma; (2) synchronous dual-primary malignancies; (3) co-existing SCLC/NSCLC.
Between January 2019 and June 2024, 81 consecutive eligible patients treated with pembrolizumab-based therapy were included. Three excluded patients lacked outcome and treatment data. Medical records of 78 patients were reviewed, and the dataset cutoff for analysis was set as 15 October 2024. We studied OS, PFS after ICI, and DCR on ICI after immune partial response (iPR), or immune stable disease (iSD). Two pathologists reported biopsies based on the 2015 WHO criteria. PD-L1 expression and scoring on tumor blocks used SP263 Ventana assays (Roche Diagnostics, Switzerland). Two pathologists independently assessed the Tumour Proportion Score (TPS) using a cutoff of >1% as “positive.” Sixty-six underwent next-generation sequencing (Ion Torrent S5 platform, Thermo Fisher Scientific, USA). Nine patients received pembrolizumab alone (200 mg q3 weekly), while 69 received ICI+Chemo. Treatment continued until disease progression with imaging (iRECIST criteria evaluation) every 2 months [13].
2.1. Statistical analysis
Continuous variables were summarized as medians with ranges and categorical variables as frequencies and percentages. OS was calculated from diagnosis date to death (or censoring) and PFS from systemic therapy initiation date to progression, therapy change, or death (or censoring). Survival curves used the Kaplan–Meier method, with subgroup comparison using two-sided Mantel-Cox log-rank tests.
Clinically meaningful variables were assessed for multicollinearity, then identified using the conditional backward elimination method before inclusion in the multivariable Cox proportional hazards model for OS and PFS. The proportional hazards assumption was tested using Schoenfeld residuals, and all variables satisfied this assumption. Logistic regression analysis using the same methodology identified variables influencing DCR.
Conditional survival probability (CS) assessed the likelihood of additional survival for specified periods, given predefined survival. Due to the limited sample size and median follow-up duration, we assessed the CS probability for 6 additional months after patients in our cohort survived 6, 12, and 18 months. Selection bias was minimized by screening all a/mNSCLC records for inclusion as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) cohort reporting guidelines (checklist in supplement) [14]. All statistical tests were two-sided, with significance set at p < 0.05. Analyses were performed using R v4.3.2 (R Core Team, 2023, Austria) with the survival, survminer and muhaz packages, while figures were created using Prism v10 (Dotmatics, USA), ggplot2 and swimplot packages.
3. Results
The study’s demographic and treatment details for 78 patients are provided (Table 1). The median follow-up, OS, and PFS for the entire cohort was 27.3 months, 21 months (95% CI: 12.2–30.8), and 6.3 months (95% CI: 5.5–10.1), respectively (Figure 1, panel A). In the ICI+Chemo group (n = 69), median OS and PFS were 21.6 months (95% CI: 12.1–30.8) and 6.3 months (95% CI: 5.5–10.1), respectively.
Table 1.
Baseline characteristics (n = 78) of the study population.
| n | % | |
|---|---|---|
| Median Age – years (IQR) | 65 (58–71) | – |
| Gender (Male/Female) | 61/17 | 78%/22% |
| Histology – Squamous/Adenocarcinoma/NOS | 28/47/3 | 36%/60%/4% |
| Never smoker/Smoker/Reformed smoker | 26/45/7 | 33%/58%/9% |
| ECOG PS 0–1/ECOG PS 2–3 | 46/32 | 59%/41% |
| Median number of comorbidities (Range) | 1 (0–6) | – |
| Pleural effusion present at diagnosis/absent | 22/56 | 28%/72% |
| Extra-Thoracic Metastases present at diagnosis/absent | 47/31 | 60%/40% |
| Median number of extra-thoracic sites involved (Range) | 1 (0–3) | – |
| Brain metastases at diagnosis/absent | 16/31 | 34%/66% |
| PDL1 status − 0%/1–49%/50–100% | 35/17/26 | 45%/22%/33% |
| Treatment delivered – IO with chemotherapy/IO alone | 69/9 | 88%/12% |
| Median time from diagnosis to initiating treatment – days (IQR) | ||
| IO with chemotherapy group | 15 (8–30) | – |
| IO alone group | 24 (21–36) | – |
| Median delay between chemotherapy to initiating IO – days (IQR) | 21 (0–41) | – |
| Median time from diagnosis to initiating IO – days (IQR) | ||
| IO with chemotherapy group | 36 (26–55) | – |
| IO alone group | 24 (21–36) | – |
| Next Generation Sequencing performed | 66 | 85% |
| Mutations Detected | ||
| None | 20 | 30% |
| TP53 | 18 | 27% |
| KRAS | 12 | 18% |
| MET | 6 | 9% |
| ERBB2 | 2 | 3% |
| BRAF | 1 | – |
| STK11 | 1 | – |
| RET | 1 | – |
| RAF1 | 1 | – |
| KEAP1 | 1 | – |
| Other rare | 3 | 4% |
Abbreviations: ECOG, Eastern Co-operative Oncology Group; IO, Immunotherapy; IQR, Inter-Quartile Range; PDL1, Programmed Death Ligand-1; PS, Performance Status.
Figure 1.
Kaplan-Meir survival curves of: (A) OS and PFS for the entire cohort, and; (B) OS stratified by post-progression treatment status. In panel A, survival rates with 95% confidence intervals are labeled at key time points for OS (purple curve) and PFS (blue curve). In panel B, patients are grouped by progression status and subsequent treatment: no progression (green), progression without further treatment (red), and progression with further treatment (orange). Six patients who survived more than 2 years without any further treatment after IO are indicated by circular labels. The specific mutation in four patients whose NGS data was available is also shown.
When limited to those patients with a PDL1 ≥1% receiving ICI+Chemo (n = 34), median OS and PFS was 26.2 months (95% CI: 8.6–39) and 9.7 months (95% CI: 5.5–10.8), respectively. Remarkably, six patients (7.7%) were alive beyond 2 years (median, range: 36.7 months, 27.3–48.4), without receiving any further treatment after immunotherapy (Figure 1, panel B); of these four had KRAS mutations, with three harboring KRASG12C. All patients with KRAS mutations were treated with ICI+Chemo.
NGS was performed in 85%, and 30% had no detectable mutations, while the rest harbored non-actionable mutations (OS and PFS stratified by mutations are shown in Figure 2). First response evaluation showed a 48% (37/78) partial response rate and 17% (13/78) stable disease rate. About 51% (40/78) received further systemic treatment after experiencing progression. Non-parametric hazard functions were estimated for both progression and mortality to assess their respective temporal risks (Supplemental Figure S1). The hazard for progression rose sharply within the first 3 months, and this elevated hazard of the entire dataset during this period (due to the inclusion of patients who received ICI alone) suggests that ICI alone may not prevent early progression as effectively as ICI+Chemo. The hazard for death was comparable and remained relatively stable which suggests that despite progression, patients continue to benefit from subsequent lines of therapy.
Figure 2.
Swimmer plots for overall survival and progression-free survival (stratified by response at first evaluation) for patients with mutations detected on NGS.
3.1. Univariable analyses of OS and PFS (supplemental table S1)
Increasing age (HR = 1.04, p = 0.02), poor performance status (PS 2–3 vs PS 1–2; HR = 3.05, p < 0.001), and progressive disease at first evaluation (vs patients with PR; HR = 4.06, p < 0.001) reduced OS. No variables influenced PFS. The time from diagnosis to pembrolizumab initiation did not significantly affect OS (HR = 0.96, p = 0.62) or PFS (HR = 0.92, p = 0.31).
3.2. Multivariable analyses for OS and PFS (Figure 3)
Figure 3.
Multivariable Cox proportional hazards regression analysis for OS and PFS.
Abbreviations: BM, Brain Metastases; CI, Confidence Interval; C-Index, Concordance Index; ECOG PS, Eastern Cooperative Oncology Group Performance Status; HR, Hazard Ratio; OS, Overall Survival; PD, Progressive Disease; PD-L1, Programmed Death-Ligand 1; PFS, Progression-Free Survival; PH, Proportional Hazards; PR, Partial Response; SD, Stable Disease.
On multivariable Cox proportional hazard analysis, higher PD-L1 (50–100%) was significantly associated with reduced risk of progression (HR = 0.42, p = 0.013) and mortality (HR = 0.35, p = 0.015). Increasing age (HR = 1.07, p = 0.009) significantly reduced OS, while better PS (HR = 0.32, p = 0.001), and stable (HR = 0.16, p = 0.01) or partial response (HR = 0.14, p < 0.001) at first evaluation increased OS. Both models satisfied the global proportional hazards assumption (p > 0.05for both) and had good discriminative performance (C-index > 0.7 for both).
3.3. Univariable and multivariable analysis for DCR (supplemental tables S2 and S3)
None of the variables were significantly associated with DCR on univariable and multivariable logistic regression analysis, though the presence of extra-thoracic metastases showed a trend (OR = 0.37, p = 0.067).
3.4. Conditional survival (CS) probability (Figure 4)
Figure 4.
Conditional survival (CS) analysis curves. The Kaplan-Meier (KM) survival curves illustrate conditional survival probabilities for patients who have already survived 0, 6, 12, and 18 months after diagnosis. The table below the curves displays overall conditional survival probabilities for an additional 6 months after having survived for 6, 12, and 18 months. The conditional survival probabilities in the subgroup where disease control was achieved are also shown.
Overall, the probability of surviving another 6 months after surviving the initial 6 months was 78.1%, which increased further as patients survived longer. Similarly, a third period had a higher survival probability after the survival of the second. In the subgroup of patients with controlled disease, CS probabilities were higher compared to the overall cohort.
3.5. Toxicity
Immune-related adverse events (irAEs) were recorded in 18 of 78 patients (23%); almost all were grade 1–2, and pembrolizumab could be continued with supportive care in 15 of these cases, while three patients required permanent discontinuation. Chemotherapy-induced toxicity was less frequent but more severe: one patient receiving chemo-immunotherapy died of febrile neutropenia clearly attributable to the chemotherapy component, and three additional patients developed grade ≥3 febrile neutropenia which prompted dose reduction and/or cessation of chemotherapy. Pemetrexed was discontinued in seven patients because of nephrotoxicity, after which pembrolizumab monotherapy continued further uneventfully.
4. Discussion
Our study supports the use of first-line pembrolizumab-based therapy in LMICs as it demonstrates equivalent oncological benefits without an effectiveness-efficacy gap (Table 2). The prognostic variables identified in our analysis are concordant with previous studies, affirming the veracity of our findings and, to the best of our knowledge, is also the largest experience from our country.
Table 2.
Comparison of benchmark results from clinical trials with the reported literature on efficacy-effectiveness gap.
| Author(s), trial/cohort, publication year(s) [ref] | Design | n | Income region* | Trial/study arm | Histology | PDL1 cutoff | ECOG PS ≥ 2 | mPFS (95% CI)(mo) | mOS (95% CI)(mo) | Absolute mPFS gap (mo) | Absolute mOS gap (mo) |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Benchmark results from trial arms of pembrolizumab delivered as monotherapy or combination therapy in first-line setting | |||||||||||
| Reck et al.; KEYNOTE-024; 2016, 2019 & 2021 [20–22] | Open-label, Phase III | 154 | Only HICs | Pembro | Any | ≥50% | 0% | 7.7 (6.1–10.2) | 26.3 (18.3–40.4) | – | – |
| Gandhi et al., Gadjeel et al., Rodriguez-Abreu et al.; KEYNOTE-189;2018, 2020 & 2021 [15–17] | Double-blind, Phase III | 410 | Only HICs | Pembro+Chemo | Non-SqCC | None | 0% | ITT: 9.0 (8.1–10.4); PDL1 ≥ 50%: 11.1 (9.2–16.5); PDL1 1–49%: 9.4 (8.1–13.8) |
ITT: 22 (19.5–24.5); PDL1 ≥ 50%: 27.7 (20.4 – Not Reached); PDL1 1–49%: 21.8 (17.7–25.6) |
– | – |
| Pas-Ares et al.; KEYNOTE-407; 2018 & 2020 [18,19] | Double-blind, Phase III | 278 | HICs & UMICs | Pembro+Chemo | SqCC | None | 0% | ITT: 8.0 (6.3–8.4); PDL1 ≥ 1%: 8.2 (6.3–10.2) |
ITT: 17.1 (14.4–19.9) PDL1 ≥ 1%: 18.9 (14–22.2) |
– | – |
| Mok et al. & de Castro et al.; KEYNOTE-042; 2019 & 2023 [23,24] | Open-label, Phase III | 637 | Mostly HICs & UMICs. Two LMICs. |
Pembro | Any | ≥1% | 0% | PDL1 ≥ 50%: 6.5 (5.9–8.6); PDL1 ≥ 20%: 6.2 (5.4–7.8); PDL1 ≥ 1%: 5.6 (4.3–6.2) |
PDL1 ≥ 50%: 20 (15.4–24.9); PDL1 ≥ 20%: 18 (15.5–21.5); PDL1 ≥ 1%: 16.4 (14–19.6) |
– | – |
| Middleton et al.; PePS2; 2020 [33] | Single arm, Phase II | 24 | Only HICs | Pembro | Any | None | 100% | 4.3 (1.9–13.1) | 7.9 (2.6 – Not Reached) | ||
| Selected multicentre retrospective studies reporting results of pembrolizumab delivered as monotherapy or combination therapy in first-line setting | |||||||||||
| Hektoen et al.; Norwegian cohort; 2025 [8] | Retrospective, Multicentre | 1179 | HIC | Pembro | Any | None | 27% | NR | Overall cohort: 13.8; KN024-match cohort: 18.9 |
– | ** Overall cohort: 16.2; ** KN024-match cohort: 11.1 |
| 1074 | HIC | Pembro+Chemo | Any | None | 20% | NR | Overall cohort: 12.8; KN189-match cohort: 15.2 |
– | *** Overall cohort: 9.2; *** KN189-match cohort: 6.8 |
||
| Cortellini et al.; PEMBRO-REAL global cohort; 2025 [26] | Retrospective, Multicentre | 1050 | Mostly HICs & UMICs. One LMIC. |
Pembro | Any | ≥50% | 15.3% | Overall cohort: 10.4 (8.5–11.8); KN024-match cohort: 11.4 (9.7–13.3) |
Overall cohort: 21.8 (19.1–25.7); KN024-match cohort: 27.5 (22.8–31.3) |
^ Overall cohort: None; ^ KN024-match cohort: None |
^ Overall cohort: 4.5; ^ KN024-match cohort: None |
| Cafaro et al.; PEMBRO-REAL Italian cohort; 2024 [27,28] | Retrospective, Multicentre | 880 | HIC | Pembro | Any | ≥50% | 8.1% | 8.6 (7.6–10.0) | 25.5 (21.8–31.6) | ^ None; ❖ None | ^ 0.8; ❖ None |
| Retrospective, Multicentre | 279 | HIC | Pembro+Chemo | Non-SqCC | < 50% | 5.9% | 8.0 (6.5–9.2) | NR | ¶ 1.0 | – | |
| Rousseau et al.; ATHENA French cohort; 2024 [29] | Retrospective, Multicentre | 13148 | HIC | Pembro | Any | Assumed ≥50% | NR | NR | # 22.4 [21.5–23.1] | – | – |
| 23925 | Pembro+Chemo | Any | None | NR | NR | # 19.9 [19.5–20.5] | – | – | |||
| Descourt et al.; ESCKEYP GFPC French cohort; 2022 [30] | Retrospective, Multicentre | 845 | HIC | Pembro | Any | ≥50% | 20.2% | 8.2 (6.9–9.5) | 22.6 (18.5–27.4) | ^ None; ❖ None | ^ 3.7; ❖ None |
| Waterhouse et al. & Velcheti et al.; Flatiron-Health US cohort; 2021 & 2025 [9,25] | Retrospective, Multicentre | 2166 | HIC | Pembro | Non-SqCC | None | 23% | 4.6 (4.1–5.2) | 14.1 (12.4–15.8) | ^ 3.1–3.5 | ^ 12.2–15 |
| 875 | SqCC | None | 25% | 4.2 (3.6–5.2) | 11.3 (9.8–12.8) | ||||||
| 3457 | Pembro+Chemo | Non-SqCC | None | 15% | 5.5 (5.2–5.7) | 12.0 (11.3–12.8) | ¶ 3.5 | ¶ 10 | |||
| 814 | SqCC | None | 18% | 5.9 (5.4–6.4) | 10.6 (9.3–11.8) | ‡ 2.1 | ‡ 6.5 | ||||
| 804 | Pembro | Any | ≥50% | 0% | NR | 19.2 (16.6–21.4) | – | ^ 10.8; ❖ 0.8 | |||
| Tamiya et al.; HOPE-001 Japanese cohort; 2019 [31] | Retrospective, Multicentre | 213 | HIC | Pembro | Any | ≥50% | 20% | 8.3 (6.0–10.7) | 17.8 (17.8 – NR) | ^ None; ❖ None | ^ 8.5; ❖ 2.2 |
*World Bank country classifications by income level for 2024–2025 (available at: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups; last accessed 12th April 2025).
^Compared to KEYNOTE-024; ¶ Compared to KEYNOTE-189; ‡ Compared to KEYNOTE-407; ❖ Compared to KEYNOTE-042; # Results landmarked at 2 months, therefore efficacy-effectiveness gap not calculated.
Note on gap calculation methodology: Hektoen et al. restricted realworld patients to KEYNOTE024/189 eligibility criteria, then applied raked (iterative proportionalfitting) weights to align age and sex with the trial population [8]. Cortellini et al. created a “lookalike” cohort solely by excluding patients who failed KEYNOTE024 criteria with no additional propensity or weighting adjustment [26]. For the rest of the referenced real-world studies, the authors of this study calculated unadjusted difference in median OS/PFS versus the corresponding KEYNOTE trial.
Abbreviations: CI, Confidence Interval; Chemo, Chemotherapy; ECOG PS, Eastern Cooperative Oncology Group Performance Status; HICs, High-Income Countries; ICI, Immune Checkpoint Inhibitor; ITT, Intention-to-Treat; LMICs, Lower-Middle-Income Countries; mo, Months; mOS, Median Overall Survival; mPFS, Median Progression-Free Survival; Non-SqCC, Non-Squamous Cell Carcinoma; NR, Not Reported; PDL1, Programmed Death Ligand-1; Pembro, Pembrolizumab; SqCC, Squamous Cell Carcinoma; UMICs, Upper-Middle-Income Countries.
When compared to the ITT population of trials which placed no restriction on PDL1 status (KEYNOTE-189 & KEYNOTE-407), the mOS and mPFS of our ICI+Chemo group (which included both SqCC & non-SqCC) was 21.6 months and 6.3 months, respectively [15–19]. While mPFS was shorter in our cohort compared to both trials – the reason for which we could not identify – it is better than other larger retrospective multi-center cohort analyses from high-income countries, despite the inclusion of the highest proportion of patients with ECOG PS ≥ 2 (Table 2). The number of patients in our cohort who received ICI alone was low, limiting any meaningful statistical analysis and comparison with pembrolizumab alone trials [20–24].
In patients who received ICI+Chemo with PDL1 ≥1%, median OS and PFS were 26.2 months and 9.7 months, respectively, which is in close agreement with not just subgroup analyses of KEYNOTE-189 and KEYNOTE-407, but also larger multi-center cohorts who restricted their analysis to patients with PDL1 ≥50% [8,9,25–31]. A recent meta-analysis of 26 real-world studies (24 of 26 studies from HICs) reported pooled 1-year OS and PFS rates of 43% (95% CI: 32–51) and 27% (95% CI: 21–33), respectively, which also compares favorably with our results (Figure 1A) [32]. This could be explained by our practice of routinely performing NGS testing in-house. With a turnaround time of 2–3 weeks to receive the NGS report, our protocol of initiating chemotherapy first and adding ICI in the second cycle of chemotherapy (median delay = 21 days) did not prove deleterious to our outcomes. This approach was intentionally designed to minimize the exposure of patients harboring driver mutations to ICI, which is known to be detrimental.
Our analysis verified prognostic survival factors in a/mNSCLC receiving ICI, namely older age, poor performance status, and higher PD-L1 expression. Age and PDL1 expression have been consistently reported as significant in clinical trials, meta-analyses, and multi-center retrospective analyses. An illustration of the performance-status effect is the PePS2 trial, uniquely designed to enroll only patients with ECOG PS 2 and who were subsequently treated with pembrolizumab. While treatment was safe (no grade-5 toxicities or early deaths), mOS and mPFS were substantially shorter (Table 2) [33].
Age-related selection bias is equally pronounced, and a distinction needs to be made between “fit” elderly patients (age ≥65 years and ECOG PS 0–1) versus phenotypically “frail” elderly patients (age ≥65 years and ECOG ≥ 2) [34]. This selection of “fit” elderly patients explains why reported meta-analyses of these trials comparing older to younger patients do not demonstrate a difference in efficacy, a finding that is divergent from real-world results [35,36]. Since half of the older patients in our cohort (19 of 40 aged ≥65 years) had ECOG PS 2–3 and fared worse, our findings and those reported by other researchers help to further quantify their combined effect on patient outcomes.
ur analysis also identified a brief period of high risk after initiating pembrolizumab, where the hazard of progression rises rapidly, and a subset of patients succumb early. Conditional survival probability also demonstrated that patients who get past the initial high-risk period can do exceptionally well, akin to trial patients. Our limited sample size precludes a meaningful analysis of these subsets, and we advocate for more research on their reliable identification.
Our report has several limitations, most importantly, the modest sample size, which has consequent implications for statistical confidence. It is worth noting that among the four benchmark trials (Table 2), KEYNOTE-024 is the only one that reports per-center enrollment [20]. Its busiest site accrued 14 patients in 21 months – approximately one patient every 1.5 months (trial dates 25 August 2014–9 May 2016; data available at clinicaltrials.gov). Extrapolated to our 66-month study window, that rate would yield approximately 44 patients; an optimistic doubling of throughput (one patient every 3 weeks) after regulatory approval would yield approximately 88 patients. In comparison, we enrolled 78 patients, a figure that compares favorably with trial-era per-center performance despite a substantial portion of our accrual occurring during the COVID-19 pandemic. Another contributor to our restricted sample size is the cost associated with pembrolizumab, which is disproportionately skewed against widespread adoption across LMICs.
Second, we report a single-center experience, though we contend that it actually facilitated the complete capture of variables from electronic health records. We acknowledge that additional factors could limit the generalizability of our results, namely, referral patterns (higher proportion of complex cases; urban versus rural patient backgrounds) and institutional infrastructure (efficiency of diagnostic workup and treatment delivery; imaging frequency; irAE management and on-site multidisciplinary support). Ideally, a multicentre retrospective analysis would provide additional validation of our results, and we are cautiously optimistic that the dissemination of our results would facilitate such an endeavor in the future. Third, while the clinical features of patients included in our analysis are a representative sample of our population at large, their average socio-economic strata may be higher. We also acknowledge that while comorbidities did not influence outcomes (supplemental table S1) and treatment adherence was not influenced by irAEs (all patients who experienced irAEs continued pembrolizumab after supportive care), residual confounding by unmeasured factors (severity of individual comorbidities) may persist. Finally, additional statistical analyses on patients who received pembrolizumab alone and those who developed toxicity could not be performed due to limited sample size.
5. Conclusions
This analysis from an LMIC demonstrates the concordant clinical efficacy of pembrolizumab in advanced/metastatic NSCLC, with evidence of sustained benefit for a few patients despite not receiving any treatment. Nevertheless, larger-scale studies and increased representation in future clinical trials from LMICs are needed to refine treatment strategies for these patients as a whole.
Supplementary Material
Acknowledgments
We acknowledge the contribution of Ms. Sangeeta Dhanuka in the development of this manuscript.
Funding Statement
This paper was not funded.
Article highlights
Pembrolizumab-based therapy demonstrates real-world effectiveness in advanced NSCLC comparable to pivotal clinical trials, even in lower-middle-income countries
Age, performance status and PD-L1 significantly influence treatment outcomes
Immunotherapy-related adverse events were manageable, and patients surviving initial treatment phases experienced substantially improved conditional survival probabilities
Our results support advocacy for policy changes to provide equitable access in resource-constrained settings
Future trials should be representative in terms of inclusion of patients from lower-middle-income countries to better influence global clinical practice
Author contributions
Conceptualization: UB; Data curation: UB, MS, AB, RS, SN; Formal Analysis: IA; Investigation: UB, IA; Methodology: IA; Project administration: UB; Resources: UB; Software: IA; Supervision: UB; Validation: KSC, IA; Visualization: IA; Writing – original draft: MS, IA, AD, SC, AB, PU, KB, PNF, KT, IMC, LVB, LC, KSS; Writing – review and editing: UB, MS, AAM, KSC, IA, AD, SC, AB, RS, VB, PU, KB, AM, PNF, KT, IMC, LVB, LC, KSS
Disclosure statement
The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.
No writing assistance was utilized in the production of this manuscript.
Reviewer disclosures
Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.
Ethical declaration
This study’s protocol was submitted to and reviewed by the Institutional Review Board and Ethics Committee (IRB/IEC) of the Rajiv Gandhi Cancer Institute & Research Center, New Delhi, India (RGCIRC). After protocol review, the IRB/IEC of RGCIRC waived the need for additional consent from patients, as the analysis was retrospective and all patients had consented to receive the prescribed treatment. The IRB/IEC of RGCIRC is registered with the Department of Health Research (EC/NEW/INST/2020/74). The composition and operating procedures of the Ethics Committee are as per ICH-GCP procedure, ICMR guidelines, Indian GCP and CT rules 2019.
Data availability statement
This study was performed at Rajiv Gandhi Cancer Institute & Research Centre and is stored in the institutional data repository. The authors do not own these data and hence are not permitted to share them in the original form (only in aggregate form). Reasonable requests for access to data will be considered individually by contacting the corresponding author. The members of the Data Monitoring & Ethics Committee will decide whether to share data.
Supplementary Information
Supplemental data for this article can be accessed online at https://doi.org/10.1080/1750743X.2025.2548754
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
This study was performed at Rajiv Gandhi Cancer Institute & Research Centre and is stored in the institutional data repository. The authors do not own these data and hence are not permitted to share them in the original form (only in aggregate form). Reasonable requests for access to data will be considered individually by contacting the corresponding author. The members of the Data Monitoring & Ethics Committee will decide whether to share data.