Real-World Outcomes of Immunotherapy in Second- or Later-Line Non-Small Cell Lung Cancer with Actionable Genetic Alterations

Simple Summary This study focused on actionable genetic alterations (AGAs) subtypes and pathologic or genetic biomarkers influencing the efficacy of immune checkpoint inhibitor (ICI) therapy in a real-world setting. In advanced non-small cell lung cancer (NSCLC) patients, the response to ICI monotherapy varies among AGAs. In previous studies, while patients with KRAS, BRAF, and MET exhibited favorable efficacy, it did not appear in patients with EGFR, ALK, ROS1, or RET. In this study, ICI monotherapy benefits differed across AGA subtypes but reaffirmed that KRAS, MET, and BRAF patients experienced longer benefits in the second- or later-line therapy. PD-L1 was a positive predictive biomarker, but not TMB. Co-existing STK11 with KRAS and TP53 with MET mutation were negatively correlated with ICI responses. Despite the limitation of a small sample size for some rare mutations, this study can still provide valuable insights that may guide clinical decision making and further research to validate the findings. Abstract Introduction: While the efficacy of immune checkpoint inhibitors (ICIs) in treating non-small cell lung cancer (NSCLC) patients with actionable genetic alterations (AGAs) is modest, certain patients demonstrate improved survival. Thus, this study aimed to evaluate the benefits of ICIs in NSCLC patients with diverse AGAs and verify the predictive biomarkers of ICI efficacy. Methods: From January 2018 to July 2022, this study compared the progression-free survival (PFS) of NSCLC patients with different AGAs treated with ICI monotherapy as second- or later-line therapy at Samsung Medical Center. To ascertain the predictors of ICIs efficacy, we adjusted ICIs’ effects on PFS in terms of clinical and molecular biomarkers. Results: EGFR (46.0%) was the most prevalent mutation in 324 patients. In multivariate analysis, PD-L1 positivity (tumor proportion score (TPS) ≥ 1%) (HR = 0.41) and the use of steroids for immune-related adverse events (HR = 0.46) were positive factors for ICI therapy in the AGAs group. Co-existing mutation of STK11 with KRAS mutation (HR = 4.53) and TP53 with MET mutation (HR = 9.78) was negatively associated with survival. Conclusions: The efficacy of ICI treatment varied across AGA subtypes, but patients with KRAS, MET, and BRAF mutations demonstrated relatively long-duration benefits of ICI therapy. PD-L1 was a significant positive predictive biomarker in all AGA groups.


Introduction
Immune checkpoint inhibitors (ICIs) block the pathways through which cancer cells evade the immune system [1].While the utilization of ICIs has significantly improved survival in patients with advanced NSCLC, room remains for improvement.For instance, the overall response rate (ORR) is approximately 20% for monotherapy; not all patients benefit from ICIs [2].Thus, many studies have been undertaken to identify biomarkers to optimize predictive biomarkers for the efficacy of ICI.The positive predictive nature of programmed death-ligand 1 (PD-L1) and tumor mutational burden (TMB) for ICI therapy is well established [3][4][5][6].Additional molecular or clinical biomarkers, such as tumorinfiltrating lymphocytes (TILs), gene expression profiling (GEP), mismatch repair and microsatellite instability, neutrophil-to-leukocyte ratio, somatic mutations including actionable genetic alterations (AGA) or co-existing mutations of STK11, KEAP1, and TP53 with other driver mutations [7,8], smoking history, antibiotics, microbiome, and the occurrence of immune-related adverse event (irAE) further inform clinical decision-making [2,9].
While ICIs have demonstrated benefits in subsets of AGAs, their efficacy can be influenced by characteristics of the tumor microenvironment (TME), which consists of various components, including PD-L1 and neoantigens, and varies with different types of AGAs [9].Because ICIs show modest benefits in patients with AGAs, the NCCN guideline [10] recommends treating ICIs after tyrosine kinase inhibitor therapy failure, as a second-or later-line treatment [11].Furthermore, some prospective observational studies report a lower response rate to ICI treatment in patients with co-existing mutations of KRAS, STK11, and TP53 than patients with wild-type genes [7,8].However, previous observations of efficacy and biomarkers for various AGA variances in non-small cell lung cancer (NSCLC) were insufficient because patients with AGAs have been ineligible for clinical trials and only specific factors, such as the PD-L1 and TMB, were available rather than comprehensive molecular data [7,12].
Meanwhile, cancer incidence and outcomes exhibit substantial disparities among racial and ethnic groups, with differing levels of exposure to risk factors and impeding access to high-quality cancer prevention, early detection, and treatment [13,14].Since 2017, a genetic panel test utilizing next-generation sequencing (NGS) technology has been officially designated as part of the national health insurance coverage for lung cancer patients in South Korea, and this test expedites the identification of genetic mutations.Consequently, South Korea is a conducive environment for implementing precision medicine based on precise and abundant NGS data for patients in advanced NSCLC [15].As a result, we aimed to evaluate outcomes of immunotherapy in second-or later-line non-small cell lung cancer, segmented by AGA, using a large hospital registry data.

Study Design and Study Population
This retrospective cohort study was conducted using a oncology data registry at Samsung Medical Center (SMC), referred to as Real-time autOmatically updated data warehOuse in healThcare (ROOT) [16].We obtained the data from January 2018 to July 2022 for this study.Inclusion criteria were advanced or metastatic NSCLC patients who received ICI therapy and had tested for AGAs.Patients who were treated with ICI as first-line treatment or combination therapy, and those who had a follow-up duration of less than 1 month for PFS, were excluded from the study.
The study was conducted pursuant to the Declaration of Helsinki.The Institutional Review Board of SMC granted approval for this study (IRB no.2022-08-013).The IRB waived the requirement for informed consent due to the retrospective nature of this study.

Outcomes
The primary endpoint was progress free survival (PFS).Overall survival (OS), ORR, and the 12-month PFS rate were the secondary endpoints.PFS was defined as the time from the start of ICI treatment to the documentation of disease progression or death from Cancers 2023, 15, 5450 3 of 13 any cause.OS was calculated as the time from ICI treatment initiation to death from any cause.ORR was a measure of how many patients achieved either a complete response (CR) or a partial response (PR) to ICI therapy.The response was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1.

Identification of Actionable Genetic Alteration, PD-L1, and TMB
AGAs were identified using companion diagnostics for each genetic alteration, such as NGS, polymerase chain reaction, immunohistochemical stain, and fluorescence in situ hybridization.The number and rate of each driver mutation tested using NGS are shown in Supplementary Figure S1.
PD-L1 expression was examined by using anti-PD-L1 antibodies (22C3, SP263, and SP142) and PD-L1 high was defined as ≥1%.TMB was defined as the inclusion of all non-synonymous and synonymous mutations, excluding germline variants and driver mutations.TMB-high was determined as ≥10 mutations/mega base (mut/Mb).Additionally, co-existing mutations of TP53, STK11, and KEAP1 were examined.
In addition, clinical data (sex; age at diagnosis; smoking history; Eastern Cooperative Oncology Group (ECOG) performance status; histologic finding; underlying co-morbidity, such as hypertension (HTN) and diabetes mellitus (DM); and concomitant medications during ICI treatment, such as radiotherapy, steroids, and antibiotics) were obtained from the ROOT.

Statistical Analyses
For quantitative variables, the summary is provided in terms of medians and 95% confidence intervals (CIs).Categorical variables, on the other hand, are summarized using numbers and percentages.Between-group comparisons for categorical variables were conducted using either the chi-squared test or Fisher's exact test to analyze the differences.All p values were two-sided and CIs were 95%, with statistical significance set at p < 0.05.The Kaplan-Meier method was applied to estimate median PFS (mPFS) and median OS (mOS), and the log-rank test was used to compare differences in event-time distributions among the oncogenic driver subgroups.Patients who did not report any event were censored at the start date of a new therapy or at the last follow-up date for PFS and at the last follow-up date for OS.Cox proportional hazards regression models were used to calculate hazard ratios (HRs).The multivariate analysis considered several important clinical and molecular factors as significant variables.The Cox proportional hazards model assumption was inspected using Schoenfeld residuals against the transformed time.R version 4.2.2 was utilized for conducting all statistical analyses.
In terms of co-existing mutations, TP53 was the most prevalent (63.1%) mutation and with a frequency of occurrence with EGFR (75.5%) and MET (73.9%) subtypes.Frequency of occurrence of STK11 was highest with KRAS (16.7%), followed by BRAF (14.3%).The occurrence of KEAP1 mutation was most common with HER2 (12.1%), while none of the patients in the ALK, BRAF, ROS1, or RET subgroups experienced occurrence of co-existing mutations (Table 2 and Figure 1C).

Discussion
This study demonstrated that outcomes of ICIs treatment varied according to the type of AGA.Similarly to a previous study, our study showed that KRAS, MET, and BRAF subgroups benefited more from ICIs treatment compared to EGFR and ALK subgroups [17].In the IMMUNOTARGET study, ICI treatment had favorable effects in patients with

Discussion
This study demonstrated that outcomes of ICIs treatment varied according to the type of AGA.Similarly to a previous study, our study showed that KRAS, MET, and BRAF subgroups benefited more from ICIs treatment compared to EGFR and ALK subgroups [17].In the IMMUNOTARGET study, ICI treatment had favorable effects in patients with KRAS, BRAF, and MET mutations but not in those with EGFR, ALK, ROS1, or RET mutations.Specifically, the 12-month PFS indicated that long-term responders were more prevalent in KRAS (25.6%),MET (23.4%), and BRAF (18.0%) subgroups than in RET (7.0%), EGFR (6.4%), and ALK (5.9%) subgroups [17].
The immunogenicity of BRAF and KRAS mutations has been studied in vivo and in vitro [18,19].By integrating the examination of immune-related scores (such as GEP scores, T cell markers, IFN-γ signatures, and chemokines) into our findings, we could deduce that BRAF mutation might lead to equilibrated immunomodulatory effects [18].The presence of a KRAS mutation exhibited a correlation with an inflammatory TME and tumor immunogenicity.Indeed, KRAS mutation is associated with a higher proportion of PD-L1+/TIL + cells, indicating KRAS-mutant tumors exhibit an inflammatory phenotype characterized through adaptive immune resistance.Additionally, KRAS mutation promotes elevated TMB and enhanced immunogenicity [19].
The existence of different ICIs treatment outcomes for each KRAS subtype (G12C vs. non-G12C) is controversial.Our study and Jeanson et al.'s study showed that there was no difference between the group with G12C and the group with non-G12C subtype [20].On the contrary, in Taiwan Wu et al. reported that the G12C subtype was favorably associated with ICI effectiveness compared to G12V [21].
A meta-analysis reported that patients with EGFR mutations did not benefit from ICI monotherapy as a second-line therapy compared to docetaxel [9].Consistent with prior findings [22][23][24], the EGFR subtype in the present study did not benefit from ICI treatment in terms of mPFS across all subgroups, even for the high-TPS group (≥50%).
Consistent with prior findings [17,25], we demonstrated that high PD-L1 expression predicts prolonged mPFS in the KRAS and MET-mutant subgroups, as well as the whole AGA group treated with ICI.In NSCLC, smoking is associated with higher TMB, which potentiates ICI therapy [11].In our study, however, both high TMB and smoking status did not influence the survival of any AGA group.Notably, the variability of the median TMB per AGA subgroup in this study was consistent with the systematic review by Sha et al. [26].
The frequency of co-existing mutation with STK11 also varied across AGAs in our study.Additionally, the prevalence rate of 16.7% for STK11 and KRAS co-existing mutation in our study was less than the 35% reported by a retrospective study (STRIKE registry-CLICaP) among Hispanics (n = 13) [7].Based on prospective VISION and MAGIC I studies by Pavan et al., STK11 with KRAS mutations was associated with poor ICI outcomes [8,27].In line with the result, the STK11-KRAS co-existing mutation could be a potential biomarker associated with severe ICI outcomes.Furthermore, the presence of the TP53 mutation was found to correlate with declining PFS with ICI treatment in the MET subgroup, which is consistent with the trial reported by Pavan et al. [8].The potential predictive role of TP53 in NSCLC is still controversial [28] as both MET and TP53 directly contribute to regulating PD-L1 [25].Nevertheless, further clinical trials are necessary to investigator this factor in the future.
Additional findings of the present study include the revelation that steroid use to treat irAE during ICI therapy is associated with prolonged mPFS in the AGA group, meaning steroid use to treat irAE does not negatively impact PFS.This result is consistent with a meta-analysis [29] that concluded that irAEs are beneficial for survival and response in advanced NSCLC.Moreover, if steroids were introduced during the initial eight weeks of ICI therapy in patients with NSCLC without any indication of cancer, there was no adverse effect on the prognosis [30].
Several limitations were encountered in this study.First, it relied on a retrospective analysis of an observational study.AGAs were detected using diverse testing methods, and TMB data were only available for 28.4% of the total AGA patient.Second, analyses regarding rare alterations such as BRAF and ROS1 were limited.Finally, these data were extracted from a single center, making it difficult to generalize the findings.Despite these limitations, our study reflects real-world clinical practice and provides valuable insights regarding the utilization of ICI monotherapy in patients with advanced or recurrent NSCLC harboring AGAs and suggests the need for personalized treatment strategies based on genomic profiling.Further large-scale prospective trials are required to validate these biomarkers and to explore new predictive biomarkers for effectiveness of ICI treatment.

Conclusions
This study explored the differences in the efficacy of ICI therapy for NSCLC patients with AGAs who received ICI monotherapy as a second-or later-line treatment and the potential predictive biomarkers that influence these responses.PD-L1 was a significant positive predictive biomarker in the AGA group.The overall response and long-term efficacy of ICI treatment varied across AGA subtypes; however, patients with KRAS and MET mutations experienced greater benefits from ICIs compared to those with other mutations.Steroid treatment for irAE does not interfere with the ORR or ICI response.Co-existing mutations of STK11 with KRAS and TP53 with MET mutations were negatively correlated with ICI response.

Supplementary Materials:
The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/cancers15225450/s1, Figure S1 S1.Clinical characteristics of patients receiving ICI monotherapy as second-line or later-line treatment.Table S2.Univariate analysis of the predictive factors of PFS in patients with AGA mutations.Table S3.Multivariate analyses of the predictive factors of PFS in patients with AGA mutations (EGFR, KRAS, HER2, and MET subgroups).Table S4.PFS analysis of PD-L1 expression in patients with AGA mutations (EGFR, KRAS, HER2, and MET subgroups).
Funding: This study was supported by grants from the Korean Society of Medical Oncology (KSMO) 2021 and the National Research Foundation of Korea (NRF) funded by the Ministry of Science and Information and Communication Technology (ICT) (No.NRF-2021R1F1A1054782).The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Institutional Review Board Statement:
The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013) and approved by the IRB of Samsung Medical Center (NO: SMC 2022-08-013).
Informed Consent Statement: Patient consent was waived for this retrospective analysis by the IRB of Samsung Medical Center.
0.03) were the positive predictive factors associated with longer PFS in the AGA group (Figure3A).

Figure 4 .
Figure 4. Algorithm for positive and negative predictive biomarkers associated with effective immunotherapy.Green: positive predictive marker.Red: negative predictive marker.Gray: Name of AGA sub group.The size of the circles represents the difference in HR.

Figure 4 .
Figure 4. Algorithm for positive and negative predictive biomarkers associated with effective immunotherapy.Green: positive predictive marker.Red: negative predictive marker.Gray: Name of AGA sub group.The size of the circles represents the difference in HR.

Table 1 .
Clinical characteristics of patients with AGAs.
Note: Values are shown as number (%) unless indicated otherwise.Abbreviations: AGA, actionable genetic alterations; CI, confidence interval; TMB, tumor mutation burden.Wildtype used as the control.

Table 3 .
ICI response in receiving ICI monotherapy as second-line or later-line treatment.
Note: Values are shown as number (%) unless indicated otherwise, immune checkpoint inhibitor; AGA, actionable genetic alterations; CR, complete response; PR, partial response; SD, stable disease; PD, progressive disease; NE, not evaluable; ORR, overall response rate; PFS, progression-free survival; OS, overall survival; CI: confidence interval; NR: not reached; NA: not applicable.Wildtype as the control.