Optimizing immunotherapy for lung cancer: integrating genetic alterations and the tumor mutational burden to refine patient selection

Immune checkpoint inhibitors (ICIs) targeting the programmed cell death 1 (PD-1)/programmed cell death 1-ligand 1 (PD-L1) pathway have significantly advanced the treatment of non-small cell lung cancer (NSCLC). This type of therapy has clearly demonstrated long-term efficacy, with some patient subgroups achieving a five-year survival rate exceeding 25% (1). However, the proportion of patients benefiting from ICI therapy is still small, highlighting the growing need for biomarkers to identify suitable candidates for treatment, thereby minimizing avoidable adverse effects and treatment costs.

Several genetic alterations are associated with the outcomes of ICI therapy for NSCLC, but their individual predictive power is limited. For example, a previous, large-scale study found that STK11 mutations, which are present in 5–33% of patients with NSCLC (2-6), generally correlate with a poorer treatment response to ICI therapy although the clinically relevant overall response rate (ORR) was 17.8% (7). Similarly, KEAP1 alterations, which occur in about 20% of patients with NSCLC (4), have an inconsistent impact on the response to ICI therapy. EGFR mutations, which are common, major, actionable alterations in NSCLC, especially among patients of Asian descent and never-smokers, are associated with poor ICI treatment efficacy despite several, previous studies having reported an ORR of 10–12% to anti-PD-(L)1 monotherapy in patients harboring these mutations (8-10). Furthermore, the tumor mutational burden (TMB) has been identified as a strong predictor of the response to ICI therapy albeit in the low-TMB context. These biomarkers, while informative, lack specificity, underscoring the need for a more comprehensive approach to guide decisions related to selecting and administering the optimal immunotherapy for NSCLC.

van de Haar et al. conducted a study aimed at developing a more specific strategy using biomarkers to identify patients with NSCLC who were unlikely to respond to ICI therapy (11). They analyzed whole genome sequencing data from a discovery cohort consisting of 75 patients with advanced NSCLC receiving anti-PD-(L)1 monotherapy and verified their findings using an independent cohort of 169 patients. Notably, patients harboring STK11/KEAP1/EGFR alterations who responded to ICI consistently had a high TMB (TMB ≥10 mutations/Mb). Building on this observation, they developed a biomarker combination strategy to predict unresponsiveness to ICI therapy based on the presence of at least one STK11/KEAP1/EGFR alteration and a low TMB (TMB <10 mutations/Mb). Their strategy demonstrated high specificity in predicting non-responsiveness to ICI therapy among patients with low TMB and STK11/KEAP1/EGFR alterations, with 0% (0 of 15) and 3% (1 of 34) of the patients of the discovery and validation cohort, respectively, showing a durable clinical benefit (DCB). The strategy identified 32% and 29% of the respective cohort as non-responders, thus establishing the combination of STK11/KEAP1/EGFR mutation status with TMB as a specific method of identifying NSCLC cases that are likely to be unresponsive to ICI therapy.

The novelty and strength of the study by van de Haar et al. lie in its comprehensive approach to evaluating the value of STK11, KEAP1, EGFR mutations and TMB for predicting the response to ICI therapy. Patients with these mutations have various, aberrant, oncogenic signaling pathways which promote resistance to ICIs by altering the regulation of immunosurveillance (12). Their study proposed a more specific model for predicting resistance to ICIs by combining STK11, KEAP1 or EGFR mutations with low TMB. Patients with this combination in both the discovery and validation cohorts consistently responded poorly to ICIs.

The biomarker combination approach provides higher specificity than individual markers in predicting resistance to ICIs, thereby potentially reducing unnecessary treatments and the associated adverse effects and costs. Furthermore, the study underscored the fact that a high TMB may help overcome resistance to ICI therapy linked to STK11, KEAP1, and EGFR alterations. While previous studies have found an association between these genetic alterations and resistance to ICIs, van de Haar et al. demonstrated that patients with these mutations and a high TMB often responded favorably to ICI therapy. Thus, combining biomarkers with the TMB may be a more effective way of stratifying the NSCLC patient population (Table 1).

Despite the significant insights afforded by their study, several critical issues remain unanswered. First, the retrospective, observational design of their study involves potential confounding factors and biases, such as patient selection bias, unmeasured confounders, and variations in treatment protocols, which limit the ability to establish causality. Furthermore, the discovery cohort of 75 patients was relatively small, especially for a genetic mutation subgroup, weakening the statistical power and necessitating a cautious approach to interpreting the findings. Also, the study’s focus on ICI monotherapy limits the generalizability of the findings to combination therapies, such as chemo-immunotherapy; the mechanism(s) underlying resistance to ICI therapy may vary in the context of combination therapies. The generalizability of these findings is further limited by the insufficient use of comprehensive genomic profiling (CGP) in current clinical practice. Although the use of CGP to identify actionable, genomic alterations and to guide treatment decisions is indeed increasing, access to this technology is still limited. For instance, in the United States, only about 20% of patients with NSCLC receive CGP (13). In practice, the findings of van de Haar et al. can guide physicians in deciding on subsequent treatment options for patients with major EGFR mutations, such as EGFR exon 21 p.L858R point mutation and exon 19 deletions, who have experienced disease progression on EGFR tyrosine kinase inhibitors (TKIs). ICIs are thought to be less efficacious in these patients (8-10) and are thus administered as post-EGFR-TKI therapy. While the PD-L1 protein expression level should still be considered, van de Haar et al. suggested that ICI monotherapy may expand treatment options for patients with a high TMB.

Additional research is necessary to address unresolved, clinical questions before the implications of these findings can be understood fully. The role of PD-L1 expression in the response to treatment, especially in relation to other biomarkers like STK11/KEAP1/EGFR mutations, as well as the TMB, requires further investigation. Moreover, this approach should be tested with combination therapies, such as chemo-immunotherapy or therapies incorporating anti-CTLA-4 antibodies, as the current study focuses solely on anti-PD-1/PD-L1 antibody monotherapy. It has been reported that chemotherapy increases TMB, and in patients with these genetic alterations and low TMB (14), combining ICI with chemotherapy or administering ICI therapy after chemotherapy may improve outcomes. Such studies will hopefully lend impetus to the development of a predictive model incorporating the evolving realities of clinical practice with the end, ultimately, of optimizing biomarker-driven treatment strategies for patients with NSCLC.

Acknowledgments

We thank Mr. James R. Valera for his assistance with editing this manuscript.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Translational Cancer Research. The article has undergone external peer review.

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1734/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1734/coif). T.H. received payment for speaker’s bureaus from Chugai Pharmaceutical, Ono Pharmaceutical, and Eisai outside the submitted work. M.S. has received grants from Taiho Pharmaceutical, Chugai Pharmaceutical, Eli Lilly, Nippon Kayaku, Kyowa Hakko Kirin and personal fees from AstraZeneca, MSD K.K, Chugai Pharmaceutical, Taiho Pharmaceutical, Eli Lilly, Ono Pharmaceutical, Bristol Myers Squibb, Nippon Boehringer Ingelheim, Pfizer, Novartis, Takeda Pharmaceutical, Kyowa Hakko Kirin, Nippon Kayaku, Daiichi-Sankyo Company, Merck Biopharma, and Amgen Inc. outside the submitted work. Y.H. has received personal fees from AstraZeneca, Eli Lilly, Taiho pharmaceutical, Chugai Pharmaceutical, Ono Pharmaceutical, Bristol Myers Squibb, Kyowa Kirin, Nippon Kayaku, Takeda, Eisai, Novartis, MSD, Daiichi-Sankyo and Pfizer outside the submitted work. The other author has 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/.

References

1. Garon EB, Hellmann MD, Rizvi NA, et al. Five-Year Overall Survival for Patients With Advanced Non-Small-Cell Lung Cancer Treated With Pembrolizumab: Results From the Phase I KEYNOTE-001 Study. J Clin Oncol 2019;37:2518-27. [Crossref] [PubMed]
2. Hellmann MD, Nathanson T, Rizvi H, et al. Genomic Features of Response to Combination Immunotherapy in Patients with Advanced Non-Small-Cell Lung Cancer. Cancer Cell 2018;33:843-852.e4. [Crossref] [PubMed]
3. Sanchez-Cespedes M, Parrella P, Esteller M, et al. Inactivation of LKB1/STK11 is a common event in adenocarcinomas of the lung. Cancer Res 2002;62:3659-62. [PubMed]
4. Comprehensive molecular profiling of lung adenocarcinoma. Nature 2014;511:543-50. [Crossref] [PubMed]
5. Imielinski M, Berger AH, Hammerman PS, et al. Mapping the hallmarks of lung adenocarcinoma with massively parallel sequencing. Cell 2012;150:1107-20. [Crossref] [PubMed]
6. Facchinetti F, Bluthgen MV, Tergemina-Clain G, et al. LKB1/STK11 mutations in non-small cell lung cancer patients: Descriptive analysis and prognostic value. Lung Cancer 2017;112:62-8. [Crossref] [PubMed]
7. Vanguri RS, Luo J, Aukerman AT, et al. Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer. Nat Cancer 2022;3:1151-64. [Crossref] [PubMed]
8. Garassino MC, Cho BC, Kim JH, et al. Durvalumab as third-line or later treatment for advanced non-small-cell lung cancer (ATLANTIC): an open-label, single-arm, phase 2 study. Lancet Oncol 2018;19:521-36. [Crossref] [PubMed]
9. Mazieres J, Drilon A, Lusque A, et al. Immune checkpoint inhibitors for patients with advanced lung cancer and oncogenic driver alterations: results from the IMMUNOTARGET registry. Ann Oncol 2019;30:1321-8. [Crossref] [PubMed]
10. Hastings K, Yu HA, Wei W, et al. EGFR mutation subtypes and response to immune checkpoint blockade treatment in non-small-cell lung cancer. Ann Oncol 2019;30:1311-20. [Crossref] [PubMed]
11. van de Haar J, Mankor JM, Hummelink K, et al. Combining Genomic Biomarkers to Guide Immunotherapy in Non-Small Cell Lung Cancer. Clin Cancer Res 2024;30:1307-18. [Crossref] [PubMed]
12. Kobayashi Y, Lim SO, Yamaguchi H. Oncogenic signaling pathways associated with immune evasion and resistance to immune checkpoint inhibitors in cancer. Semin Cancer Biol 2020;65:51-64. [Crossref] [PubMed]
13. Wallenta Law J, Bapat B, Sweetnam C, et al. Real-World Impact of Comprehensive Genomic Profiling on Biomarker Detection, Receipt of Therapy, and Clinical Outcomes in Advanced Non-Small Cell Lung Cancer. JCO Precis Oncol 2024;8:e2400075. [Crossref] [PubMed]
14. Huang RJ, Huang YS, An N, et al. Pan-cancer analysis of heterogeneity of tumor mutational burden and genomic mutation under treatment pressure. ESMO Open 2024;9:103494. [Crossref] [PubMed]