---
title: "Source page lacked article content for immune checkpoint inhibitor strategies in lung cancer"
id: "frontiers-in-immunology-16-immune-checkpoint-inhibitor-based-combinatory-and-alternative-strategies-for"
canonical_url: "https://medichelpline.com/clinical-feed/frontiers-in-immunology-16-immune-checkpoint-inhibitor-based-combinatory-and-alternative-strategies-for"
content_type: "clinical_feed_article"
specialty: "Infectious Disease"
source_name: "Frontiers in Immunology"
source_url: "https://www.frontiersin.org/articles/10.3389/fimmu.2026.1878931"
published_at: "2026-08-10T00:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Source page lacked article content for immune checkpoint inhibitor strategies in lung cancer
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/frontiers-in-immunology-16-immune-checkpoint-inhibitor-based-combinatory-and-alternative-strategies-for
- **Specialty:** [Infectious Disease](https://medichelpline.com/clinical-feed/infectious-disease.md)
- **Primary Source:** Frontiers in Immunology
- **Source URL:** [Original Journal Publication](https://www.frontiersin.org/articles/10.3389/fimmu.2026.1878931)
- **Published At:** 2026-08-10T00:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- The provided source page did not contain the body of the article titled on **immune checkpoint inhibitor-based combinatory and alternative strategies for immune treatment of lung cancers**; instead the page consisted largely of Frontiers website navigation and journal information. - The page included repeated sections from the Frontiers site: links to journal and site home, About us content (mission, history, leadership), publishing model details, and services and partnerships. - The page listed the sections of the Frontiers in Immunology journal (for example, Cancer Immunity and Immunotherapy, T Cell Biology, Viral Immunology) and links for authors (author guidelines, article types, submission checklist) and editorial information. - Functional links present on the page included All journals, All articles, Submit manuscript, Submit data, Search, and Login, but no article text, abstract, figures, methods, results, or discussion for the named article were shown. - Because the actual article text, data, authorship, and conclusions were not present on the provided source, any specifics about combinatory strategies, alternative immune treatments, trial outcomes, mechanisms, or recommendations were not reported and therefore cannot be summarized or rewritten from this source. - To obtain a clinical summary or rewrite grounded in the article, the complete article content or an accessible full-text source must be provided; alternatively, the original Frontiers link should be accessed to retrieve the article's substantive sections.
## Clinical Analysis & Structured Key Points
Frontiers | Immune checkpoint inhibitor-based combinatory and alternative strategies for immune treatment of lung cancers REVIEW article Front. Immunol. , 10 August 2026 Sec. Cancer Immunity and Immunotherapy Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1878931 Published in Frontiers in Immunology Cancer Immunity and Immunotherapy 7 impact factor 11.3 citescore Editor & Reviewers Edited by M F Massimo Fantini Reviewed by M K Manal Kanaan K M Katia Mangano Outline Figures and Tables Figure 1 View in article Figure 2 View in article Figure 3 View in article Table 1 FDA-approved ICI. View in article Table 2 Clinical trials of PD-1, PD-L1, and CTLA-4–specific IC antibodies in cancer. View in article Table 3 Current clinical trials for EGFR mut forms. View in article Table 4 Evolved generation of EGFR-targeting anti-tumor agents in lung cancers. View in article REVIEW article Front. Immunol. , 10 August 2026 Sec. Cancer Immunity and Immunotherapy Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1878931 Immune checkpoint inhibitor-based combinatory and alternative strategies for immune treatment of lung cancers J P Jun-Young Park 1,2 † C K Choong-Hwan Kwak 1,3 Y C Yu-Chan Chang 4 * C K Cheorl-Ho Kim 1 † * 1. Molecular and Cellular Glycobiology Unit, Department of Biological Sciences, SungKyunKwan University, Suwon, Gyunggi-Do, Republic of Korea 2. Environmental Diseases Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, Republic of Korea 3. Department of Brewing, Dong-Eui Institute of Technology, Busan, Republic of Korea 4. Department of Biomedicine Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan See more Article metrics View details Abstract Immune checkpoint inhibitors (ICIs), particularly antibodies targeting PD-1/PD-L1 and CTLA-4, have reshaped the treatment landscape of lung cancers, most notably non-small cell lung cancer (NSCLC). Nevertheless, only a subset of patients achieve durable benefit due to primary and acquired resistance that arises from tumor-intrinsic factors (e.g., oncogenic drivers and adaptive signaling), tumor-extrinsic determinants in the tumor microenvironment (TME) (e.g., impaired T-cell infiltration and immunosuppressive myeloid populations), and the dynamic evolution of biomarkers. Accordingly, current clinical and translational efforts in lung cancer focus on rational combination strategies—ICIs with chemotherapy, radiotherapy, and targeted agents (including RTK- and KRAS-pathway inhibitors)—as well as alternative approaches that modulate antigen presentation, myeloid regulation, and cancer stemness. In this review, we define a lung cancer–centered scope and summarize (i) established and emerging ICI-based regimens in lung cancer, (ii) mechanisms of resistance relevant to NSCLC and SCLC, (iii) biomarker integration for patient selection and monitoring, and (iv) future directions to optimize efficacy and safety through combinatory and alternative immunotherapeutic strategies. 1 Introduction The immune system maintains homeostasis by surveying pathogenic invaders and transformed cells through continuous communication between host cells and their environments. Immune checkpoints are physiological regulators that limit excessive T-cell activation and maintain self-tolerance. In cancer, tumors frequently exploit these pathways—most prominently the PD-1/PD-L1 axis and CTLA-4—to suppress anti-tumor immunity within the tumor microenvironment (TME) ( 1 ). In cancer, immune evasion is promoted by genetic instability and tumor evolution, which generate heterogeneous subclones and neoantigen landscapes ( 2 ). Importantly for lung cancer, smoking-associated mutational processes and intratumoral heterogeneity can shape tumor mutation burden, antigenicity, and responsiveness to IC blockade. Mechanistically, tumors may reduce antigen presentation (e.g., via MHC-I alterations), remodel surface antigens, and establish an immunosuppressive microenvironment to escape immune surveillance ( 3 ). Clinically, monoclonal antibodies that block PD-1 (e.g., Nivolumab/Opdivo, Pembrolizumab/Keytruda), PD-L1 (e.g., Durvalumab/Imfinzi, Atezolizumab/Tecentriq), and CTLA-4 (e.g., Ipilimumab) have become core immunotherapeutic agents, either as monotherapy or in combination regimens; representative approvals are summarized in Table 1 . Building on this shared framework, the key questions addressed throughout this review are how to extend response durability, overcome resistance mechanisms, and rationally combine ICIs with other modalities. Table 1 Drug name Cancer type Target Approval [ref] Pembrolizumab Malignant melanoma/BRAF V600 mutation PD-1 2014 ( 46 ) Nivolumab/Pembrolizumab Malignant HNC and SCC PD-1 2016 ( 47 ) Nivolumab Melanoma PD-1 2014 ( 51 ) Pembrolizumab HNC PD-1 2016 ( 47 ) Melanoma PD-1 2015 ( 57 ) Pembrolizumab/Nivolumab HNC and SCC PD-1 2016 ( 47 ) Cemiplimab SCC PD-1 2018 ( 48 ) Ipilimumab Metastatic melanoma CTLA-4 2011 ( 48 ) Ipilimumab Melanoma CTLA-4 2015 ( 56 ) Ipilimumab+Nivolumab Metastatic melanoma CTLA-4+ PD1 2015 ( 50 ) Avelumab Metastatic Merkel cell carcinoma PD-L1 2015 ( 53 ) FDA-approved ICI. HNC, head and neck cancer; SCC, squamous cell carcinoma. Beyond these canonical checkpoints, additional inhibitory and stimulatory receptors (e.g., LAG-3, TIM-3, TIGIT, and others) also shape anti-tumor immunity and are being actively investigated as therapeutic targets. Accordingly, ICI-based strategies increasingly consider not only checkpoint selection but also T-cell priming, antigen presentation, and the balance of suppressive versus effector immune populations within the TME. Mechanistically, checkpoint blockade can reinvigorate exhausted T cells and restore cytotoxic activity; however, its clinical impact varies substantially because tumors differ in antigenicity, interferon signaling, stromal architecture, and immune composition. Accordingly, ICI responsiveness is increasingly viewed as an emergent property of both tumor-intrinsic determinants (e.g., oncogenic signaling, antigen presentation defects) and tumor-extrinsic constraints (e.g., suppressive myeloid populations and dysfunctional antigen-presenting cells) ( 3 ). This perspective motivates biomarker-driven treatment selection and the design of combinations that convert “cold” tumors into “hot” immune ecosystems. In lung cancer, these principles are particularly relevant because mutational processes, intratumoral heterogeneity, and therapy-induced evolution can dynamically reshape the immune landscape. Even when PD-L1 expression or tumor mutation burden suggests benefit, resistance may arise through adaptive pathway rewiring, impaired T-cell infiltration, or expansion of immunosuppressive myeloid-derived suppressor cells and tumor-associated macrophages. Therefore, effective lung cancer immunotherapy increasingly requires coordinated strategies that address both the cancer cell and the TME, rather than isolated inhibition of a single checkpoint. Beyond checkpoint blockade alone, precision-oncology approaches now integrate targeted therapy, radiotherapy, and chemotherapy with ICIs to amplify tumor antigen release, enhance immune priming, and reduce immune escape. In parallel, emerging work links epigenetic and stem-like tumor programs to immune resistance, providing additional therapeutic entry points. These considerations collectively support a modular view of ICI-based treatment in which the optimal regimen is selected based on actionable drivers, immune contexture, and longitudinal biomarkers. To operationalize this strategy, molecular profiling and liquid-biopsy technologies have expanded the ability to track tumor evolution and to match patients to rational combinations ( 4 , 5 ). In lung cancer, next-generation sequencing of tumor tissue and circulating tumor DNA can identify oncogenic drivers, resistance alterations, and immunotherapy-relevant features (e.g., pathway activation patterns and genomic correlates of immune exclusion). These data increasingly inform trial design and clinical decision making, particularly when integrating ICIs with receptor tyrosine kinase (RTK) inhibitors or downstream pathway modulators. With this background, the remainder of this review is organized to maintain a lung cancer–centered narrative: we first outline the clinical logic of ICI-based regimens, then discuss mechanistic categories of resistance, and finally summarize combination and alternative strategies that target oncogenic signaling, the TME, and therapy-resistant tumor states, with an emphasis on biomarker-guided translation. As another approach to target the tumor-specific alterations apart from ICI, TKIs specific to the active gene mutations and rearrangements have been designed and developed. Also, to improve the inherent efficacy and resistance issues of TKIs, the liquid biopsy NGS of exosomal blood ctDNA as a ctcDNA has been diagnostically approved by the Food and Drug Administration (FDA) to detect quantitative and qualitative extrachromosomal DNA (ecDNA) from NSCLC ( 5 , 6 ). It has been known that the ecDNAs of the NSCLC include ALK, BRAF, DDR1/2, EGFR, ERBB2, Kirsten rat sarcoma (KRAS), MNNG HOS transforming gene, NTRK, mesenchymal epithelial transition (MET), RET, ROS1, and SOS Ras/Rac guanine nucleotide exchange factor 1 (SOS1) as well as PD-L1 and tumor mutation burden expression, covering gene amplifications, gene rearrangements, point mutations, gene deletions, epigenetic modifications, gene methylation alterations, gene insertions, gene fusions, and copy number alterations in blood and tissues ( 7 , 8 ). To date, EGFR and BRAF mutations, ALK gene rearrangements, and ROS1 fusion genes have been identified ( 9 ). Tumor-targeting inhibitors have been combined to overcome their inherent limitations by several strategies adopted from conventional signaling inhibitors. For example, therapy-resistant EGFR mutations have been alternatively targeted by allosteric (ATP non-competitive) inhibitors, which differ from conventional EGFR TKIs. The EGFR allosteric TKIs recognize a specific, distinct EGFR site that is different from the existing ATP-competitive inhibitors. These EGFR allosteric TKIs call for next-generation allosteric inhibitors. Such next-generation allosteric inhibitors against the conventional therapy-resistant EGFR mutants have been combined for effective therapy in NSCLC. Cancer stem cells (CSC) have also been associated with tumor resistance aspects. Finally, there is a recently recognized issue why the above tumor treatments are not as efficient rather than expected. A difficult reason is explained by the CSC in terms of tumor resistance aspects. CSC tumor phenotypes are regulated by Sonic Hedgehog (Shh) or Notch-related signaling pathways. CSC can be targeted by the transmembrane (TM) protein smoothened (SMO) known as the hedgehog (HH) pathway receptor (HHR). The HHR SMO inhibitors are used as therapeutic agents. Therefore, to overcome the CSC issues, future studies on these signal blocking and cancer cell death are required. Representative CSC-modulating drugs such as Vismodegib, Sonidegib, and Glasdegib, however, exhibit severe adverse effects but effective for only based cell carcinomas. For various tumor therapeutic targets, serine hydroxymethyltransferase 1 (SHMT1) siRNA can silence to block DNA synthesis in tumor cells. Anti-cancer antibiotics, carbonic anhydrase 9 (CAIX) targeting peptides, importin β- and claudin 3 (CLDN3)–targeting inhibitors enhance the therapeutic potential against tumors. Here, we review ICI-based combinatory and alternative strategies for lung cancers, with a focus on NSCLC, by integrating clinical regimens, resistance mechanisms, TME biology, and biomarker-guided translation across targeted and immunotherapeutic modalities. 2 Immunotherapy by the anti-cancer ICI or blocking drugs Checkpoint blockade is designed to release inhibitory “brakes” on anti-tumor T cells, thereby restoring immune-mediated tumor control. In practice, the best-characterized clinical targets are PD-1/PD-L1 and CTLA-4, and antibodies against these pathways form the backbone of ICI therapy ( 10 , 11 ). Rather than reiterating general definitions throughout this review, we refer to the introductory summary above and focus here on how these checkpoints are leveraged in therapeutic strategies and combinations relevant to lung cancer. CTLA-4 is expressed on activated and regulatory T cells and competes with CD28 for binding to CD80/CD86 on antigen-presenting cells, thereby dampening early T-cell priming ( 12 ) ( Figure 1 ). Ipilimumab was the first anti-CTLA-4 antibody to demonstrate an overall survival benefit in melanoma, establishing proof of concept for checkpoint blockade ( 13 , 14 ). Subsequently, PD-1/PD-L1–directed antibodies expanded the clinical footprint of ICIs and enabled combination regimens (including PD-1 plus CTLA-4 blockade) in multiple settings ( 15 , 16 ). Importantly, while these foundational observations were first made outside lung cancer, they provide the mechanistic basis for the lung cancer regimens discussed in later sections and tables. Key FDA-approved agents and indications are summarized in Table 1 , and representative clinical trials are listed in Table 2 . Figure 1 Various inhibitory and stimulatory immune checkpoints. Action mechanistic approaches of PD1/PD-L1– and CTLA-4–specific mAb immune checkpoints. Dendritic cells (DCs) express MHC, B7, or PD-L1, while T cells express CD28, CTLA4, or PD-1 on their surface. Binding and blocking of CTLA-4 by anti-CTLA-4 mAbs such as Ipilimumab or Tremelimumab induces inhibitory signaling of T cell’s cytotoxicity. 4-1BB and 4-1BB ligand: inducible costimulatory molecules. 4-1BB [CD137 or TNF receptor superfamily member 9 (TNFRSF9)]. 4-1BB is expressed in mouse B/T cells, DC cells, macrophages and NK cells, while 4-1BB is expressed in the B cells, activated CD4+ T and CD8+ T cells, DCs, monocytes, and DNK cells. Table 2 CT ID Sponsor Condition Drug+combination Phase NCT04620200 Netherland Cancer Institute SCC Nivolumab or Nivolumab+Ipilimumab 2 NCT04154943 Regerneron Pharmaceuticals Partner: Sanofi CSCC Cemiplimab 2 NCT04710498 Stanford University, Partner: Genentech Advanced CSCC Atezolizumab 2 NCT03969004 Regerneron Pharmaceuticals (+Sanofi) CSCC Cemiplimab 3 NCT03834233 Instituto do Cancer do Estado de São Paulo Advanced CSCC Nivolumab 2 NCT05025813 Queensland Health (+Merck Sharp, Dohme Corp.) CSCC Pembrolizumab 2 NCT02760498 Regeneron Pharmaceuticals CSCC Nivolumab 2 NCT04242173 Regeneron Pharmaceuticals (+the H. Lee Moffitt Tumor Centre and Research Institute) Malignant CSCC Cemiplimab 2 NCT04050436 Replimune Inc. (+Regeneron Pharmaceuticals) Malignant CSCC Biological RP1+Cemiplimab 2 NCT04632433 Fondazione Melanoma Onlus Skin SCC Cemiplimab 2 NCT03684785 Exicure Inc. MCC, CSCC, Melanoma, HNSCC, Solid Tumors Cavrotolimod, Pembrolizumab, Cemiplimab 2 NCT05110781 Arnaud Bewley (+NCI, Genentech Inc.) HNCSCC, HNSCC, Stage III CSCC of Neck AJCC Atezolizumab 2 NCT04204837 Salzburger Landeskliniken (+BMS) SCC Nivolumab 2 NCT04808999 Diwakar Davar (+Merck Sharp, Dohme Corp.) SCC Pembrolizumab 2 NCT03944941 NCI (+Alliance for clinical trials in oncology) Skin cancer Avelumab, Cetuximab 2 NCT03284424 Merck Sharp & Dohme Corp. SCC Pembrolizumab 2 NCT03737721 EMD Serono, Alberta Cancer Foundation (+AHS Cancer control alberta) Skin SCC Avelumab 2 NCT01129154 Dr. Joseph Kerger, Partner: the Universitaires UCL de Mont-Godinne, Dr Lionel Duck, Cliniques Saint-Pierre Ottignies Clinics universitaires, Saint-Luc-Université Catholique de Louvain SCC Panitumumab 2 NCT03833167 Merck Sharp, Dohme Corp. SCC Pembrolizumab 3 NCT01979211 University of Cincinnat SCC Cetuximab 2 NCT02964559 Emory University (+Merck Sharp & Dohme Corp.) Recurrent SCC Pembrolizumab 2 NCT03836105 Regeneron Pharmaceuticals BCC, CSCC Cemiplimab 2 NCT02978625 NCI MCC, Skin SCC Nivolumab, Talimogene, Laherparepvec 2 NCT00240682 France Chartres Hospital France SCC Cetuximab 2 NCT03082534 Merck Sharp & Dohme Corp., (+Assuntina G. Sacco, MD) CSCC, HNSCC Pembrolizumab, Cetuximab 2 Clinical trials of PD-1, PD-L1, and CTLA-4–specific IC antibodies in cancer. SCC, squamous cell carcinoma; CSCC, cutaneous SCC; MCC, Merkel cell carcinoma; NCI, National Cancer Institute; BMS, Bristol-Myers Squibb. Although ICIs can generate durable responses, their use is constrained by immune-related adverse events, cost, and heterogeneous efficacy across patients. These limitations have accelerated combination approaches that aim to increase immunogenicity and remodel the TME, including partnerships with chemotherapy, radiotherapy, targeted therapies, and selected immunomodulatory agents ( 17 , 18 ). The goal is to improve both response rates and durability without unacceptably increasing toxicity. 3 Key oncogenic driver, EGFR, and its gene mutation-specific TKIs in NSCLC Lung cancer has largely been classified into the two major groups of NSCLC and SCLC ( Figure 2 ). Lung cancer, as bronchogenic carcinoma, develops in the bronchi, bronchioles, and alveoli of the respiratory epithelia. Lung cancer has been known as a leading cancer death worldwide, as in the USA, lung cancer is a greater cause of death than colon cancer, prostate cancer, and stomach cancer. Epidemic growth factor (EGF), as a target for treatment, is abnormally activated in cancers, including NSCLC. Therefore, several anti-tumor agents targeting EGFR have been developed mainly in lung cancer ( Figure 3 ). Figure 2 Classification of lung cancer. Figure 3 EGFR-targeted antitumor agents in lung cancer. EGFR-TKIs target EGFR, and a classical Gefitinib is effective for lung adenocarcinoma carrying sensitizing EGFR mutations. EGFR-TKIs drugs inhibit EGFR tyrosine kinase activity and are currently the preferred regimen for NSCLC. EGFR mutations (EGFRmut) in NSCLC are common. General EGFR mutations in NSCLC are sensitive to EGFR TKIs, but incurable EGFRs are not recognized by EGFR TKIs. These issues are an affordable resistance mechanism in EGFRmut. Micro-RNA–related mechanisms are not fully explained to support the resistance mechanisms. As the known first-generation inhibitors, reversible EGFR-TKIs are Erlotinib, Gefitinib, and Icotinib ( 19 ). Irreversible second-generation agents, including Afatinib and Neratinib, have been developed. Third-generation agents include Olmutinib and Osimertinib ( 20 ). Compared to conventional platinum-based chemotherapeutic agents, these agents have low cytotoxicity and high targeting advantages. They also show a high objective remission rate (ORR) and progression-free survival (PFS). Currently, resistance issue against EGFR-TKIs causes therapeutic difficulty in cancer patients within one year, although initial treatment with EGFR-TKIs to NSCLC patients yields a high efficacy of up to 80% ( 21 ). New EGFR-TKI drug classes, such as Mobocertinib and Tepotinib, also caused resistance within one year. Therefore, the resistant mechanism affordable for EGFR-TKIs must be elucidated. One EGFR-TKI resistant mechanism is the so-called drug efflux by drug transport proteins. Others are transcription factors in drug-resistant cancer cells that cause resistance via epithelial transformation. Potentially, autophagosomes in tumor cells degrade the EGFR-TKIs, and non-coding microRNAs may confer tumor cell resistance to EGFR TKIs. 3.1 Mutation issue in EGFR gene and EGFR mutant TKIs in NSCLC Mutations in the EGFR gene are frequently observed and are particularly prevalent in adenocarcinoma patients from Asian populations of non-smokers. EGFR mutant forms promote cancer cell division. Caucasian NSCLC patients show a 15% EGFR gene mutation, and Asian NSCLC patients show a 30%–50% EGFR mutation. Asian female non-smokers and adenocarcinoma patients have higher EGFR mutation rates than others. EGFR mut in NSCLC represents a cause of unsatisfactory
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