Actionable mutations and targeted therapy in non-small cell lung cancer among Latin American and Hispanic patients: a systematic literature review of prognosis and meta-analysis

Introduction

Lung cancer is the leading cause of cancer-related mortality, with approximately 2 million new cases and 1.8 million deaths annually in both sexes (1,2). In the Latin American (LA) population, lung cancer has an incidence rate of 15.8 cases per 100,000. Lung cancer represents the leading cause of cancer-related death for both sexes (3). Tobacco consumption is the main, accounting for more than 80% of lung cancer cases (1,4). However, ~20% of lung cancer occurs in non-smokers where sensitivity to air pollution may play a role. Exposure to environmental hazards—including tobacco asbestos, radon, arsenic, cannabis and air pollution—has been associated to the pathogenesis of lung cancer, as have chronic conditions such as chronic obstructive pulmonary disease and persistent lung infections (5-11).

Oncogenic driver alterations such as EGFR, KRAS, ALK and ROS1 play a pivotal role in lung cancer carcinogenesis. The presence or absence of actionable mutations allows the implementation or modification of targeted therapeutic strategies and provides valuable insight into patient prognosis (12). Differences in mutational profiles have been found between patients with lung cancer from different racial groups, including Europeans, Native Americans, Asians, African Americans and LAs (13). It has been suggested that this genetic heterogeneity may have an impact on clinical outcome and prognosis (14,15). Some studies found that Hispanics and Asians experienced overall higher survival [relative risk (RR) =0.93, 95% confidence interval (CI): 0.89–0.98 and RR =0.82, 95% CI: 0.79–0.86, respectively] compared to non-Hispanic white patients (16-19). An international study conducted exclusively on a European population reported a median overall survival (OS) of 1.5 years (95% CI: 1.2–1.8) for the general population diagnosed with non-small cell lung cancer (NSCLC), 1.3 years (95% CI: 1.0–1.6) for the KRAS mutation cohort, and 2.2 years for the EGFR mutation cohort (20). A recent meta-analysis in non-Latino population reported that mutations in the EGFR gene, specifically in exons 19 and 21 significantly prolong progression-free survival (PFS) and OS [hazard ratio (HR) =0.64, 95% CI: 0.51–0.81 and HR =0.79, 95% CI: 0.52–1.21, respectively], particularly in patients treated with EGFR-tyrosine kinase inhibitors (TKIs). KRAS mutations predict worse PFS (HR =1.83; 95% CI: 1.40–2.40) and OS (HR =2.07; 95% CI: 1.54–2.78) in patients treated with chemotherapy (21). Some longitudinal studies have characterized various outcomes, including OS and PFS, in the Latin population with lung cancer based on the presence of certain mutations, mainly EGFR. However, there is a lack of data synthesizing such evidence in the Hispanic or Latino population. Understanding the variability of survival in LA patients with lung cancer based on the presence of driver mutations could have a significant impact on promoting the development of new targeted therapies tailored to the LA population, thereby improving survival and quality of life for these patients. The aim of our study is to assess the prognostic relationship of genetic alterations and targeted therapies with OS and PFS in LA patients with NSCLC. We present this article in accordance with the PRISMA reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-99/rc).

Methods

The protocol was submitted to PROSPERO, the International Prospective Register of Systematic Reviews, under the number CRD42024602035.

Inclusion criteria

• Study design: prospective and observational studies, such as cohort and descriptive cohort studies. All types of clinical trials were considered.
• Population: LA patients diagnosed with NSCLC at any stage of the disease (I–IV), regardless of the country where the study is conducted.
• Prognostic factor: driver mutations described in NSCLC, including ALK, BRAF, EGFR, HER2, KRAS, MET, RET, ROS1, and NRG1; and non-driver mutations including TP53, STK11, and NTRK, and its respective targeted therapies.
• Outcomes: primary outcomes are OS and PFS. OS is defined as the number or proportion of patients who remain alive during the follow-up period of each study, from the time of diagnosis of NSCLC or from the completion of the indicated treatment and extends until the time of the patient death. PFS is defined as the number or proportion of patients who remain alive without metastases during the follow-up period of each study; it begins at the time of NSCLC diagnosis or from the completion of the indicated treatment and ends until the specific moment when oncological disease appears in a site previously classified as healthy outside of the primary lesion.

Exclusion criteria

The following exclusion criteria were applied: (I) studies with discrepancies on the results described in text and tables and (II) studies that did not include molecular alteration from Latino patients.

Information sources and search strategy

A search was conducted in accordance with the guidelines outlined by the PRISMA-S strategy and the CHARMS checklist (22,23), which are specifically applicable to systematic reviews of prognostic literature. Furthermore, the TRIPOD reporting tool was employed to ensure proper documentation of the prognostic models utilized in each of the reviewed articles (23-25). The search was not restricted by language or publication year. We conducted the search in the following databases: Ovid (which provides access to MEDLINE and the Cochrane Central Register of Controlled Trials – CENTRAL), EMBASE, and LILACS. Additionally, local registries and World Health Organization (WHO) clinical trial records (apps.who.int/trialsearch) were examined. Grey literature was also retrieved using Google Scholar. The final search date for all databases was June 2024. Beyond the initial search, a hand-extended search was carried out by screening the reference lists of the selected articles. Search terms and truncations were employed and tailored to each database (Tables S1,S2). The terms considered included: carcinoma, non-small cell lung, lung neoplasms, pulmonary neoplasms, Hispanic or Latino, Spanish origin, Latin America, EGFR, KRAS, ALK, RET, MET, ROS1, HER2, BRAF, NTRK.

Study selection

After duplicate articles were removed, three reviewers (J.P.C.G., M.P.G.G., and D.C.C.) independently and blindly assessed the titles and abstracts of the articles retrieved from the databases. Any discrepancies regarding the inclusion of studies were resolved by consulting a third expert reviewer (R.P.M.). The Rayyan tool was used for this process. Once the initial screening of titles and abstracts was completed, J.P.C.G. and M.P.V. reviewed the full texts in the Mendeley reference manager to ensure the appropriate application of the selection criteria.

Data collection process and data extraction

Three reviewers (J.P.C.G., M.P.G.G., and D.C.C.) conducted data extraction using the REDCap digital form. The variables included in the extraction were: author, year, reported molecular alteration, number of patients, mean/median age, gender, smoking status, histology, tumor stage, Eastern Cooperative Oncology Group (ECOG) performance status, metastasis, treatment, functional limitation, OS and PFS. For OS and PFS, the median (in months), percentages (for each year of follow-up), or HR were extracted when cohort comparisons were available. Notably, OS and PFS were calculated across all reported tumor stages, as most primary studies did not stratify survival outcomes by clinical stage or other prognostic variables. Any discrepancies were resolved through discussion, and the corresponding authors of each manuscript were contacted when necessary.

Assessment of risk of bias, applicability, and critical appraisal of evidence

The Quality in Prognosis Studies–risk of bias tool (QUIPS) tool for assessing the risk of bias in prognostic studies was used, as recommended by the Cochrane Prognosis Methods Group (26). This tool includes six domains that consists of specific questions answered dichotomously, either “yes” or “no”. Based on the responses, studies were classified as having low, intermediate, or high risk of bias. In cases where missing data were identified, the corresponding authors were contacted. Following methodological recommendations, no imputations were performed (27). An assessment of the certainty of the evidence was conducted using the GRADEpro framework, specific to prognostic studies (28). This was applied to the most clinically relevant meta-analysis, which evaluated the prognostic performance of EGFR-TKI mutations for the outcomes of OS and PFS. The results indicated moderate certainty for OS and low certainty for PFS. In Table S3, we describe in detail how this certainty was obtained.

Statistical analysis

Summary measures: for the time-to-event outcomes, including OS and PFS, HR was extracted directly from each study if available. In studies where the HR was not reported, the adjustment methods proposed by Parmar et al. (29) and Tierney et al. (30) were used to indirectly calculate the HR measures based on the number of patients in each cohort, the number of patients at risk of experiencing the event of interest (death or progression) at each specified time interval in the study, and the number of events occurred. For the subgroups of patients with EGFR and TP53 co-mutations, data was not available to calculate a PFS meta-analysis. This approach allowed for the calculation of survival probabilities over the follow-up period for each cohort, facilitating comparisons of survival curves through the indirect calculation of the HR, thereby determining the existence of statistically significant differences and their respective quantification.

Data synthesis and meta-analysis: before conducting the meta-analysis, an assessment was performed to identify potential sources of clinical and methodological heterogeneity, ensuring that the comparisons were appropriate, consistent, and that the groups were comparable. A random effect model meta-analysis was performed based on the reporting of prognostic factors, where at least two studies reported the HR directly or the data to calculate it indirectly for the outcomes of OS and PFS, including their standard error or variance and the respective CIs for the outcomes of interest. The pooled HR were calculated using the generic inverse variance method with a 95% CI. Heterogeneity was assessed using the I² statistic, considering high heterogeneity for values greater than 75% with a significance level of 0.05. Subgroup analyses were planned for each treatment generation if possible. When comparing the survival medians (in months) among more than two groups, a Kruskal-Wallis test was performed, and a Dwass-Steel post hoc test was then used to identify the differences indicated by the initial test. All quantitative analyses of the included studies were performed using the R programming language, R Studio software V. 4.3.2 and RevMan V. 5.4.1.

Results

General information of included articles

A total of 503 original articles were identified, of which 86 were removed as duplicates. After the title and abstract screening, 333 articles were excluded. Finally, 47 articles including 20 abstracts (31-50) and 27 full-text studies met the selection criteria, comprising two sets: those identified through database searches (18,51-60) and those from manual reference screening (61-76). Data from 15 manuscripts (18,53-55,57,59,60,63,64,66,67,70,73-75) were used for comparisons between different molecular factors and were included in a quantitative synthesis for HR calculation; the remaining articles reported data related to unadjusted median OS and PFS in the cohorts. Figure 1 shows the PRISMA flowchart, detailing the screening and selection process. All included studies were observational and longitudinal cohort studies. Of these 41 (87.2%) were retrospective or historical cohorts based on existing medical records. Table 1 provides a detailed description of the design and recruitment period of each study, indicating whether the information was extracted from an abstract or full text.

Figure 1 PRISMA flowchart.

Clinical and demographic data of the population included

Clinical data were collected from 21,046 patients diagnosed with NSCLC in Latin America. The mean age of patients was 60.5 [standard deviation (SD) =9.17] years and 52.6% (n=6,684/12,710) of the cases were women. Regarding the smoking status, 57.3% (n=8,155/14,240) have a story of tobacco exposure. Eleven thousand one hundred and forty-four cases reported the histopathological subtype and 91.8% (n=10,234) of them were adenocarcinomas, 5.9% (n=654) were squamous cell carcinoma, 1.2% (n=130) were carcinoma not otherwise specified (NOS), 0.1% (n=6) were adenosquamous and 1.1% (n=120) corresponded to other histology. At diagnosis, 83.8% (n=4,802/5,729) were stage IV disease, followed by stage IIA in 5.7% (n=324/5,729). In patients with metastatic disease, it was possible to obtain information in 2,539 patients. The most common site of metastatic disease was the central nervous system in 46.1% (n=1,171/2,539), followed by the lung in 25.4% (n=644/2,539) and the bone in 18.9% (n=481/2,539). Functional status data were collected for 4,048 patients, with most classified as ECOG 0 (4.8%, n=193) or ECOG 1 (69.7%, n=2,820). Treatment information was available for 2,144 patients: 745 (34.7%) received chemotherapy, and 1,228 (57.3%) were treated with EGFR-TKIs. Of those receiving EGFR-TKIs, 35.4% (n=758) were given to first-generation, 8.6% (n=184) second-generation, and 13.3% (n=286) third-generation EGFR-TKIs. Additionally, 105 patients (4.9%) received first-generation ALK-TKIs, and 66 (3.1%) underwent surgical treatment. Table 1 includes all the demographic and clinical information from each of the included articles. Figure 2 provides a detailed description of clinical data in LA patients with NSCLC driven by somatic mutations. Information regarding the methodology used to detect molecular alterations was available in 24. Most studies used targeted polymerase chain reaction (PCR) techniques, primarily focused on the EGFR and KRAS genes (Table 1). Six manuscripts reported molecular alterations identified through next-generation sequencing (NGS)(46,54,55,57,60,69). In five articles, the methodology for detecting ALK rearrangements was reported, with the D5F3 antibody used in three studies (56,65,76). The remaining studies employed combined strategies, including targeted PCR, NGS, and other additional techniques.

Figure 2 Summary of relevant clinical information of the patients. (A) Histopathological subtype. (B) Sex. (C) Smoking status. (D) Clinical stage. (E) Metastasis sites. (F) Performance status. (G) Treatment trends. ECOG, Eastern Cooperative Oncology Group; NOS, not otherwise specified; TKI, tyrosine kinase inhibitor.

Meta-analysis of HR for actionable mutations in the LA population with NSCLC

Information was extracted from 15 articles (n=13,925) containing data used to calculate the probability of survival at various follow-up months (18,53-55,57,59,60,63,64,66,67,70,73-75). We identified 11,973 patients in whom the EGFR mutation was compared in those treated with TKI+/− chemotherapy versus wild-type (WT) patients treated with chemotherapy, with an HR of 0.67 (95% CI: 0.57–0.78) for OS and an HR 0.75 (95% CI: 0.61–0.91) for PFS (Figure 3A). Patients with EGFR gene mutations treated with targeted therapies have not only a better OS, but also a lower risk of disease progression compared to patients without these mutations treated with conventional systemic chemotherapy. In the subgroup of patients with EGFR and TP53 co-mutations, there was no survival benefit in terms of OS (n=649, HR =1.21; 95% CI: 0.46–3.20) (Figure 3B). We conducted an independent analysis for those cohorts that compared exon 19 deletion versus the L858R mutation (n=126), finding no difference in terms of OS (HR =0.61; 95% CI: 0.32–1.17) and PFS (HR =0.59; 95% CI: 0.21–1.67) (Figure 3C).

Figure 3 Meta-analysis of HR. (A) OS and PFS comparing EGFR-TKI and WT-CT cohorts. (B) OS comparing EGFR with/without TP53 co-mutation. (C) OS and PFS comparing EGFR-exon 19 deletion and EGFR-L858R cohorts. (D) OS and PFS comparing EGFR-TKI and WT-CT cohorts according to TKI generation. (E) OS and PFS comparing common and uncommon EGFR mutations. (F) OS and PFS comparing EGFR-T790M mutation and other EGFR mutation cohorts. Each red box represents the point estimate of the effect size (HR) for an individual study, with its size proportional to the weight assigned in the meta-analysis using the IV method. Horizontal black lines indicate the 95% CI of each estimate. The diamond at the bottom represents the pooled HR and its corresponding 95% CI. The x-axis displays HR values, with the vertical line at HR =1 indicating no difference in risk. Statistical measures include CI, df, Chi² (Chi-squared test for heterogeneity), I² (Higgins’ heterogeneity index), and IV (inverse variance method used for pooling). * and ^† represent different cohorts from the same study. CI, confidence interval; df, degrees of freedom; CT, chemotherapy; HR, hazard ratio; IV, inverse variance; OS, overall survival; PFS, progression-free survival; SE, standard error; TKI, tyrosine kinase inhibitor; WT, wild-type.

Data from five cohorts (n=11,376) with actionable mutations treated with first- or second-generation TKIs were compared to cohorts without these alterations who received systemic chemotherapy-based treatment. We found that patients treated with TKIs targeting EGFR mutations had better OS compared to those treated with chemotherapy (HR =0.62; 95% CI: 0.54–0.72). In a subgroup analysis of patients treated with first-generation TKIs, we observed a reduced risk of progression (HR =0.75; 95% CI: 0.61–0.91) but no significant OS benefit (HR =0.69; 95% CI: 0.38–1.27). However, among patients treated with second-generation TKIs (afatinib), a reduction in mortality was evident compared to those treated with conventional chemotherapy (HR =0.64; 95% CI: 0.54–0.76) (Figure 3D).

When analyzing cohorts of patients with common EGFR mutations (exon 19 deletions and L858R) compared to those with rare mutations (G719S, L861Q, and T854A) (n=183), we found that the former group had a lower risk of mortality (HR =0.12; 95% CI: 0.05–0.30); however, no differences were found in terms of disease progression (HR =0.44; 95% CI: 0.14–1.38) (Figure 3E).

We analyzed the prognostic impact of the T790M mutation (n=423); however, we did not find differences in OS (HR =1.11; 95% CI: 0.30–4.03) or PFS (HR =1.26; 95% CI: 0.78–2.02) in the included studies (Figure 3F).

In the analysis of other actionable mutations, limited data were found to evaluate KRAS as a factor showing a trend toward a negative impact on OS (HR =1.49; 95% CI: 0.75–2.96) without reaching statistical significance (Figure S1).

Impact of driver mutation on OS and PFS in Latino patients with NSCLC

The medians with their respective measures of dispersion for OS and PFS are described in Table 2. We found that the median OS for EGFR, ALK, and KRAS was 31.95 (range, 20.50–37.90) months, 28.45 (range, 20.30–50.10) months, and 15.70 (range, 8.34–22.40) months, while the median PFS for these genes was 15.15 (range, 9.70–19.90) months, 10.20 (range, 7.07–17.70) months, and 6.47 (range, 4.30–9.60) months, respectively. Statistically significant differences were found only between the EGFR and KRAS mutation cohorts for both OS (P<0.001) and PFS (P<0.001) (Figure 4).

Figure 4 Boxplots of medians of OS and PFS for ALK, EGFR and KRAS mutated patients. OS, overall survival; PFS, progression-free survival.

Risk of bias and critical appraisal

We found that over 75% of the full-text articles had a low risk of bias for the domains of bias due to enrolment, bias due to measurement of prognostic factors, bias due to measurement of outcomes, and bias in statistical analysis and reporting. For bias due to attrition (data loss), six of the 27 full-text studies (22.2%) were judged at high risk (18,51,59,60,67,71), as shown in Figure S2. Most studies showed a moderate risk of bias in this domain, making it the worst performing domain. The domain that received the most negative ratings and highlighted a significant area for improvement in future cohort studies was the domain of confounding factors; approximately 70% of the articles received a high risk of bias rating in this domain. Only the studies by Arrieta et al. 2015 (70), Cardona et al. 2016 (71), Hernández-Pedro et al. 2019 (54) and Rodríguez-Lara et al. 2019 (53) were consistently given a low-risk rating in this bias domain. The weighted risk of bias rating for all articles indicated that 55.6% of the studies had an intermediate risk of bias. The remaining percentage was evenly distributed between articles with low risk and high risk of bias (22.2%, each). Confounding and data loss were significant sources of bias in the studies included in this review. In Figures S2,S3, the risk of bias identified for each article is described according to each domain in our review. An assessment of the certainty of the evidence was conducted using the GRADEpro framework, specific to prognostic studies. This was applied to the most clinically relevant meta-analysis, which evaluated the prognostic performance of EGFR-TKI mutations for the outcomes of OS and PFS. The results indicated moderate certainty for OS and low certainty for PFS. In Table S2, we describe in detail how this certainty was obtained.

Discussion

Our review includes a synthesis of evidence from cohort studies conducted in LA populations diagnosed with NSCLC, with most data coming from retrospective records. According to our analysis, LA patients with NSCLC treated with EGFR TKIs have a 33% reduction in the risk of death and a 25% reduction in the risk of progression compared to patients without the mutation treated with non-targeted regimens. Consistent with our findings, Zhao et al. (77) reported differences in PFS outcomes for EGFR-TKIs compared to those treated with chemotherapy (HR =0.50; 95% CI: 0.30–0.84), and also found a significant difference in PFS for the subgroup of EGFR-TKIs and chemotherapy compared to those receiving chemotherapy alone (HR =0.37; 95% CI: 0.16–0.85). These findings are consistent in early-stage NSCLC, where targeted therapy improves both OS (HR =0.72; 95% CI: 0.54–0.96) and PFS (HR =0.43; 95% CI: 0.30–0.63) compared to chemotherapy or placebo. The benefits are observed across stages IB (HR =0.37; 95% CI: 0.23–0.61), II (HR =0.33; 95% CI: 0.23–0.48), and III (HR =0.30; 95% CI: 0.21–0.45) (78).

The subgroup analysis presented in this review showed that first-generation EGFR TKIs improved PFS compared to chemotherapy (Figure 3D). Similarly, two meta-analyses reported better PFS outcomes in patients treated with first-generation TKIs compared to the control group (HR =0.54, 95% CI: 0.37–0.79 and HR 0.44, 95% CI: 0.28–0.70, respectively) (77,78). As reported in our results, other studies have also found no differences in OS outcomes in this group (77-79). Second-generation TKIs are generally effective but often fail to overcome therapeutic resistance in lung cancer, particularly in the presence of resistance mutation (80). populations highlight an increasing resistance to second and third-generation TKIs, driven by various mechanisms (64). Our results showed that patients treated with second-generation EGFR TKIs had improved OS compared to those receiving chemotherapy, consistent with the findings of the study by Yang et al. (81). LUX-Lung7 clinical trial demonstrated no significant difference in OS between NSCLC patients treated with the first-generation EGFR TKI (gefitinib) and those treated with the second-generation EGFR TKI (afatinib) (HR =0.86; 95% CI: 0.66–1.12) (82).

The EGFR exon 19 deletion and the EGFR exon 21 L858R account for 90% of EGFR mutations and are generally associated with a good response to TKI treatment. The study by Zhuo et al. (83) did not find differences in PFS and OS in NSCLC Asian between the EGFR exon 19 deletion and L858R mutation groups (P=0.072). These findings are consistent with our results (Figure 3C) and other meta-analysis performed in Asian population receiving first-generation EGFR-TKIs (HR =0.88; 95% CI: 0.67–1.16) (84). Interestingly, it has been reported that EGFR TKIs provided greater benefit to the sub-population with the EGFR exon 19 deletion compared to those with the EGFR L858R mutation in terms of PFS (HR =0.69; 95% CI: 0.57–0.82) and OS (HR =0.61; 95% CI: 0.43–0.86) (85) which is consistent with other registries (86).

One of the most common resistance mutations is T790M, which accounts for nearly half of the mechanisms by which lung tumor cells develop resistance to first- and second-generation EGFR TKIs. This mutation increases the receptor’s affinity for adenosine triphosphate, contributing to resistance. T790M has been associated with tumor persistence in patients exposed to EGFR TKIs and is also implicated in de novo oncogenic processes (87,88). Additionally, T790M has been reported to be more frequent in the exon 19 deletion [odds ratio (OR) =1.65; 95% CI: 1.17–2.32] (89). In our review, only two studies evaluated the presence of the EGFR T790M mutation as a prognostic factor in LA patients and showed no differences in terms of OS and PFS (Figure 3F) (66,74). Consistent with Liu et al. (90), acquired T790M was associated with better PFS and OS versus primary T790M; this pattern is consistent with other studies (91).

In 2017, the Food and Drug Administration (FDA) approved osimertinib for the treatment of patients with metastatic EGFR T790M mutation-positive NSCLC (92), and since then, many studies have evaluated its effectiveness. A recent meta-analysis showed that the presence of T790M mutation confers a benefit of OS (HR =0.57; 95% CI: 0.37–0.90) and PFS (HR =0.58; 95% CI: 0.36–0.91) in Asian patients treated with osimertinib (93). Chamorro et al. (68) performed a study including only the LA population, demonstrating that among patients who progress versus those who respond to osimertinib treatment, underlying genomic differences—including AXL mRNA, BIM mRNA, EGFR L858R, and high TMB—may have an additional prognostic impact beyond the T790M mutation.

We found that common mutations were associated with a reduction in mortality compared to uncommon mutations (Figure 3E). It has been reported that the median OS and PFS for lung cancer patients harboring uncommon EGFR mutations were 25.6 (range, 18.2–33.0) and 11.1 (range, 7.2–15) months, respectively, with better survival outcomes when treated with third-generation EGFR TKIs (94,95). Other studies have shown that the L861Q EGFR mutation has the highest response rate (75.0%; 95% CI: 56.6–88.5%) and the longest PFS (15.1; 95% CI: 10.6–24.9 months) to third-generation EGFR TKIs compared to other uncommon mutations (95,96). These findings highlight the importance of incorporating intensive treatment strategies in this subpopulation to substantially improve survival and progression outcomes.

In our review, the presence of a TP53 co-mutation did not show significant changes in the prognosis of the disease, which is consistent with other studies that found shorter OS (97-99) and PFS (97,99), regardless of the generation of EGFR-TKI used in the patients. Regarding KRAS mutations, we found that patients with this molecular alteration had a significant reduction in OS and PFS compared to those with EGFR mutations. This has been previously reported by Ying et al. (100) who found that KRAS mutations were associated with shorter OS (HR =2.09; 95% CI: 1.56–2.80) and PFS (HR =1.82; 95% CI: 1.50–2.20) compared to EGFR-mutated patients. Other studies have shown that patients with KRAS G12V and KRAS codon 13 mutations have even lower OS (101,102) and PFS (103). Additionally, immune checkpoint inhibitors (ICIs) were found to improve OS in patients with previously treated KRAS-mutant NSCLC (HR =0.64; 95% CI: 0.43–0.96) (104).

Although second-generation EGFR-TKIs offer broader and irreversible inhibition of the ErbB family, this does not always lead to better OS. A likely reason is that resistance mechanisms, such as T790M mutations, occur with both first- and second-generation drugs. In addition, the greater toxicity of second-generation TKIs often leads to dose reductions or early treatment discontinuation, limiting their potential effectiveness. Beyond drug-related factors, OS can also be influenced by contextual issues (105). In Latin America, for instance, limited access to targeted therapies due to socioeconomic barriers may reduce the real-world benefit of these treatments. Differences in access to subsequent therapies and supportive care, especially in multi-center or international studies, can also have a stronger effect on OS than the choice of initial EGFR-TKI.

Finally, our review found a prognostic benefit for the population with ALK gene alterations in terms of OS and PFS compared to EGFR and KRAS mutations. This trend was also observed by other authors, who reported a clear survival benefit in populations with ALK rearrangements compared to other mutations (HR =0.50, 95% CI: 0.25–0.97 and HR =0.20, 95% CI: 0.05–0.88, respectively) (106,107). A network meta-analyses have found that third-generation ALK-TKI (lorlatinib) presents the highest overall response rate (ORR) and PFS rates compared to other inhibitors (108,109).

The low representation of LA individuals in clinical trials makes it difficult to apply results from other studies, as genetic and environmental factors differ. Less than 4% of clinical trial participants are from Latin groups (110). The limited evidence on their response to emerging health technologies, combined with substantial ancestry-related heterogeneity, contributes to the underreporting of population-level variability (111). Since pharmacological responses and follow-up strategies in adverse socioeconomic conditions differ, many results from international clinical trials are likely not generalizable to Latin communities (112).

Our review has some limitations: first, it relies mostly on observational studies, most of them with a retrospective design. Second, third-generation TKIs were not included in the meta-analysis, as these drugs were recently incorporated into LA healthcare systems. Third, many studies did not report survival probabilities at each follow-up, requiring indirect data modeling from Kaplan-Meier curves. Fourth, confounding variables (age, sex, cancer stage, functional status, comorbidities, environmental pollutants) were not available for meta-regression or subgroup analysis, which may have influenced the results. Fifth, it was not possible to perform a sensitivity analysis to explore the robustness of the results due to the limited amount of evidence. Additionally, OS and PFS outcomes were extracted without stratification by tumor stage or prognostic variables, as most primary studies reported aggregated data. The lack of detailed information on molecular alterations and treatment rationale limited our ability to subclassify chemotherapy use. Although estimates were feasible for EGFR-TKI generations and ALK-targeted therapies, significant heterogeneity in reporting made it methodologically unfeasible to extract consistent treatment-related data. These limitations should be considered when interpreting our findings.

Conclusions

In the Latino population, patients with EGFR-mutated NSCLC treated with targeted therapy have better OS along with a lower risk of progression, compared to WT patients treated with chemotherapy, particularly those treated with second-generation EGFR-TKIs. Statistically significant differences in both OS and PFS were found only between EGFR and KRAS mutation cohorts, with KRAS-mutant NSCLC showing the worst outcomes. Common mutations had a lower risk of death but no difference in disease progression. The significant underrepresentation of Latinos in randomized and blinded lung cancer trials underscores the need to invest resources in this population to address the knowledge and evidence gap.

Acknowledgments

J.P.C-G. is grateful for funding support from the National Cancer Institute (Nos. R56CA280636, D43CA260689, U54CA233306, U54CA283766, and U54CA280811) of the National Institutes of Health (NIH). J.P.C-G. received funding for a research training travel award from the UC Davis Multi-Disciplinary Cancer Research Training Program to Advance Precision Cancer Prevention and Care in Latin America (No. D43CA260869; PIs: Luis G. Carvajal-Carmona and Laura Fejerman).

Footnote

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-99/rc

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-99/prf

Funding: This work was supported by the Fundación Universitaria de Ciencias de la Salud -FUCS, Bogotá, Colombia.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-99/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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