Efficacy and clinicogenomic correlates of response to immune checkpoint inhibitors alone or with chemotherapy in non-small cell lung cancer

The real-world treatment characteristic and efficacy of immune checkpoint inhibitors in non-small cell lung cancer: Data from a retrospective cohort study

BACKGROUND

The efficacy and prognosis of immune checkpoint inhibitors (ICIs) remain unresolved issues. Here, we assessed the treatment characteristics and efficacy of ICIs in non-small cell lung cancer (NSCLC) using real-world data and evaluated the predictive value of factors, including programmed death-ligand 1 (PD-L1) expression, for the clinical outcome of ICIs in NSCLC.

METHODS

Analyzed data was collected from hospitalized patients in the West China Hospital of Sichuan University between January 2017 and March 2023. The Kaplan-Meier method was utilized for analyzing real-world progression-free survival (rwPFS), while Cox regression models was employed to access the correlation between the efficacy of immunotherapy and sociodemographic characteristics, disease information, and characteristics of ICI treatment.

RESULTS

A total of 545 patients were included in the retrospective study and characteristics of immunotherapy varied significantly among PD-L1 expression groups. The median rwPFS for the entire population was 9.76 months. Subgroup analyses revealed that patients with high PD-L1 expression, early TNM stage, first-line immunotherapy, EGFR wild-type and those who have not received radiotherapy and targeted therapy previously were more likely to have better rwPFS. Furthermore, multivariate Cox regression analyses identified PD-L1 expression, EGFR mutation status and previous radiotherapy as the most influential predictors of the response to ICI treatment.

CONCLUSIONS

This study presents the real-world experience of Chinese NSCLC patients undergoing ICI treatment, offering guidance for clinical decision-making based on various patient conditions, preferences, and indications for ICIs, through the evaluation of immunotherapy efficacy and predictors in NSCLC patients.

Decision model for durable clinical benefit from front- or late-line immunotherapy alone or with chemotherapy in non-small cell lung cancer

BACKGROUND

Predictive biomarkers and models of immune checkpoint inhibitors (ICIs) have been extensively studied in non-small cell lung cancer (NSCLC). However, evidence for many biomarkers remains inconclusive, and the opaqueness of machine learning models hinders practicality. We aimed to provide compelling evidence for biomarkers and develop a transparent decision tree model.

METHODS

We consolidated data from 3,288 ICI-treated patients with NSCLC across real-world multicenter, public cohorts and the Choice-01 trial (ClinicalTrials.gov: NCT03856411). Over 50 features were examined for predicting durable clinical benefits (DCBs) from ICIs. Noteworthy biomarkers were identified to establish a decision tree model. Additionally, we explored the tumor microenvironment and peripheral CD8+ programmed death-1 (PD-1)+ T cell receptor (TCR) profiles.

FINDINGS

Multivariate logistic regression analysis identified tumor histology, PD-ligand 1 (PD-L1) expression, tumor mutational burden, line, and regimen of ICI treatment as significant factors. Mutation subtypes of EGFR, KRAS, KEAP1, STK11, and disruptive TP53 mutations were associated with DCB. The decision tree (DT10) model, using the ten clinicopathological and genomic markers, showed superior performance in predicting DCB in the training set (area under the curve [AUC] = 0.82) and consistently outperformed other models in test sets. DT10-predicted-DCB patients manifested longer survival, an enriched inflamed tumor immune phenotype (67%), and higher peripheral TCR diversity, whereas the DT10-predicted-NDB (non-durable benefit) group showed an enriched desert immune phenotype (86%) and higher peripheral TCR clonality.

CONCLUSIONS

The model effectively predicted DCB after front-/subsequent-line ICI treatment, with or without chemotherapy, for squamous and non-squamous lung cancer, offering clinicians valuable insights into efficacy prediction using cost-effective variables.

FUNDING

This study was supported by the National Key R&D Program of China.

Adeno-to-squamous transition drives resistance to KRAS inhibition in LKB1 mutant lung cancer

KRASG12C inhibitors (adagrasib and sotorasib) have shown clinical promise in targeting KRASG12C-mutated lung cancers; however, most patients eventually develop resistance. In lung patients with adenocarcinoma with KRASG12C and STK11/LKB1 co-mutations, we find an enrichment of the squamous cell carcinoma gene signature in pre-treatment biopsies correlates with a poor response to adagrasib. Studies of Lkb1-deficient KRASG12C and KrasG12D lung cancer mouse models and organoids treated with KRAS inhibitors reveal tumors invoke a lineage plasticity program, adeno-to-squamous transition (AST), that enables resistance to KRAS inhibition. Transcriptomic and epigenomic analyses reveal ΔNp63 drives AST and modulates response to KRAS inhibition. We identify an intermediate high-plastic cell state marked by expression of an AST plasticity signature and Krt6a. Notably, expression of the AST plasticity signature and KRT6A at baseline correlates with poor adagrasib responses. These data indicate the role of AST in KRAS inhibitor resistance and provide predictive biomarkers for KRAS-targeted therapies in lung cancer.

Immunotherapy for non-small cell lung cancer

Immune checkpoint inhibitors (ICI) bind to programmed cell death-1 (PD-1)/PD-1 ligand-1 (PD-L1) and Cytotoxic T-lymphocyte antigen-4 (CTLA-4), which suppress T-cell function and inhibit their inhibitory function, resulting in T-cell activation. ICI have been approved for a wide range of cancers, including malignant melanoma, renal cell carcinoma, non-small cell lung cancer, head and neck cancer, Hodgkin's disease, small-cell lung cancer, malignant pleural mesothelioma, gastric cancer, esophageal cancer, breast cancer, uterine cancer, and hepatocellular carcinoma, and the number of indications continues to grow. In addition to the treatment of advanced disease, the anti-tumor effect has been demonstrated across disease stages, from locally advanced disease to early-stage operative disease. The treatment of lung cancer is at the forefront of this trend and long-term durable responses and survival benefits in lung cancer have been exhibited that were unimaginable when cytotoxic anticancer agents were the only treatment options. However, treatment efficacy varies greatly from case to case, and no biomarkers have been developed to accurately predict efficacy. In this article, we discuss the past and future of ICI therapy for lung cancer, based on clinical and basic evidence accumulated to-date.

Clinical Benefit from Docetaxel +/- Ramucirumab Is Not Associated with Mutation Status in Metastatic Non-Small-Cell Lung Cancer Patients Who Progressed on Platinum Doublets and Immunotherapy

Docetaxel +/- ramucirumab remains the standard-of-care therapy for patients with metastatic non-small-cell lung cancer (NSCLC) after progression on platinum doublets and immune checkpoint inhibitors (ICIs). The aim of our study was to investigate whether the cancer gene mutation status was associated with clinical benefits from docetaxel +/- ramucirumab. We also investigated whether platinum/taxane-based regimens offered a better clinical benefit in this patient population. A total of 454 patients were analyzed (docetaxel +/- ramucirumab n=381; platinum/taxane-based regimens n=73). Progression-free survival (PFS) and overall survival (OS) were compared among different subpopulations with different cancer gene mutations and between patients who received docetaxel +/- ramucirumab versus platinum/taxane-based regimens. Among patients who received docetaxel +/- ramucirumab, the top mutated cancer genes included TP53 (n=167), KRAS (n=127), EGFR (n=65), STK11 (n=32), ERBB2 (HER2) (n=26), etc. None of these cancer gene mutations or PD-L1 expression was associated with PFS or OS. Platinum/taxane-based regimens were associated with a significantly longer mQS (13.00 m, 95% Cl: 11.20-14.80 m versus 8.40 m, 95% Cl: 7.12-9.68 m, LogRank P=0.019) than docetaxel +/- ramcirumab. Key prognostic factors including age, histology, and performance status were not different between these two groups. In conclusion, in patients with metastatic NSCLC who have progressed on platinum doublets and ICIs, the clinical benefit from docetaxel +/- ramucirumab is not associated with the cancer gene mutation status. Platinum/taxane-based regimens may offer a superior clinical benefit over docetaxel +/- ramucirumab in this patient population.

Role of Epiregulin in Lung Tumorigenesis and Therapeutic Resistance

Epidermal growth factor (EGF) signaling regulates multiple cellular processes and plays an essential role in tumorigenesis. Epiregulin (EREG), a member of the EGF family, binds to the epidermal growth factor receptor (EGFR) and ErbB4, and it stimulates EGFR-related downstream pathways. Increasing evidence indicates that both the aberrant expression and oncogenic function of EREG play pivotal roles in tumor development in many human cancers, including non-small cell lung cancer (NSCLC). EREG overexpression is induced by activating mutations in the EGFR, KRAS, and BRAF and contributes to the aggressive phenotypes of NSCLC with oncogenic drivers. Recent studies have elucidated the roles of EREG in a tumor microenvironment, including the epithelial-mesenchymal transition, angiogenesis, immune evasion, and resistance to anticancer therapy. In this review, we summarized the current understanding of EREG as an oncogene and discussed its oncogenic role in lung tumorigenesis and therapeutic resistance.

The Fundamental Role of Oxime and Oxime Ether Moieties in Improving the Physicochemical and Anticancer Properties of Structurally Diverse Scaffolds

The present review explores the critical role of oxime and oxime ether moieties in enhancing the physicochemical and anticancer properties of structurally diverse molecular frameworks. Specific examples are carefully selected to illustrate the distinct contributions of these functional groups to general strategies for molecular design, modulation of biological activities, computational modeling, and structure-activity relationship studies. An extensive literature search was conducted across three databases, including PubMed, Google Scholar, and Scifinder, enabling us to create one of the most comprehensive overviews of how oximes and oxime ethers impact antitumor activities within a wide range of structural frameworks. This search focused on various combinations of keywords or their synonyms, related to the anticancer activity of oximes and oxime ethers, structure-activity relationships, mechanism of action, as well as molecular dynamics and docking studies. Each article was evaluated based on its scientific merit and the depth of the study, resulting in 268 cited references and more than 336 illustrative chemical structures carefully selected to support this analysis. As many previous reviews focus on one subclass of this extensive family of compounds, this report represents one of the rare and fully comprehensive assessments of the anticancer potential of this group of molecules across diverse molecular scaffolds.

First-Line Chemoimmunotherapy versus Sequential Platinum-Based Chemotherapy Followed by Immunotherapy in Patients with Non-Small Cell Lung Cancer with ≤49% Programmed Death-Ligand 1 Expression: A Real-World Multicenter Retrospective Study

BACKGROUND

The long overall survival (OS) observed among patients with non-small cell lung cancer (NSCLC) with high programmed death-ligand 1 (PD-L1) expression in chemoimmunotherapy (CIT) groups in previous phase III trials suggests the limited efficacy of CIT among the subgroup with ≤49% PD-L1 expression on tumor cells. Hence, sequential treatment with first-line platinum-based chemotherapy followed by second-line immune checkpoint inhibitor treatment (SEQ) is an option. This study examined whether first-line CIT would provide better outcomes than SEQ in patients with advanced NSCLC with ≤49% PD-L1 expression.

METHODS

This retrospective study evaluated patients with untreated NSCLC who received first-line CIT or SEQ at nine hospitals in Japan. OS, progression-free survival (PFS), PFS-2 (the time from first-line treatment to progression to second-line treatment or death), and other related outcomes were evaluated between the CIT and SEQ groups.

RESULTS

Among the 305 enrolled patients, 234 eligible patients were analyzed: 165 in the CIT group and 69 in the SEQ group. The COX proportional hazards model suggested a significant interaction between PD-L1 expression and OS (p = 0.006). OS in the CIT group was significantly longer than that in the SEQ group in the 1-49% PD-L1 expression subgroup but not in the <1% PD-L1 expression subgroup. Among the subgroup with 1-49% PD-L1 expression, the CIT group exhibited longer median PFS than the SEQ group (CIT: 9.3 months (95% CI: 6.7-14.8) vs. SEQ:5.5 months (95% CI: 4.5-6.1); p < 0.001), while the median PFS in the CIT group was not statistically longer than the median PFS-2 in the SEQ group (p = 0.586). There was no significant difference between the median PFS in the CIT and SEQ groups among the <1% PD-L1 expression subgroup (p = 0.883); the median PFS-2 in the SEQ group was significantly longer than the median PFS in the CIT group (10.5 months (95% CI: 5.9-15.3) vs. 6.4 months (95% CI: 4.9-7.5); p = 0.024).

CONCLUSIONS

CIT is recommended for patients with NSCLC with 1-49% PD-L1 expression because it significantly improved OS and PFS compared to SEQ. CIT had limited benefits in patients with <1% PD-L1 expression, and the median PFS-2 in the SEQ group was significantly longer than the median PFS in the CIT group. These findings will help physicians select the most suitable treatment option for patients with NSCLC, considering PD-L1 expressions.

Comparative bioinformatic analysis of KRAS, STK11 and KEAP1 (co-)mutations in non-small cell lung cancer with a special focus on KRAS G12C

OBJECTIVES

Mutations in STK11 (STK11MUT) and KEAP1 (KEAP1MUT) occur frequently in non-small cell lung cancer (NSCLC) and are often co-mutated with KRAS. Several studies linked the co-occurrence of KRASMUT + STK11MUT, as well as KRASMUT + KEAP1MUT to reduced response to immune checkpoint inhibitors (ICI) and even a negative impact on survival. Data focusing STK11 + KEAP1 co-mutations or the triple mutation (KRAS + STK11 + KEAP1) are scarce. The recent availability of KRAS-G12C inhibitors increases the clinical relevance of those co-mutations in KRAS-mutated NSCLC.

MATERIALS AND METHODS

We present a comprehensive bioinformatic analysis encompassing six datasets retrieved from cBioPortal.

RESULTS

Independent of the treatment, triple mutations and STK11MUT + KEAP1MUT were significantly associated with a reduced overall survival (OS). Across treatments, OS of patients with a KRAS G12C triple mutation was significantly reduced compared to patients with KRAS G12C-only. Under ICI-therapy, there was no significant difference in OS between patients harboring the KRAS G12C-only and patients with the KRAS G12C triple mutation, but a significant difference between patients harboring KRAS non-G12C and KRAS non-G12C triple mutations. Triple mutated primary tumors showed a significantly increased frequency of distant metastases to bone and adrenal glands compared to KRAS-only mutated tumors. Additionally, our drug response analysis in cancer cell lines harboring the triple mutations revealed the WNT pathway inhibitor XAV-939 as a potential future drug candidate for this mutational situation.

CONCLUSION

The triple mutation status may serve as a negative prognostic and predictive factor across treatments compared to KRASMUT-only. KRAS G12C generally seems to be a negative predictive marker for ICI-therapy.

Enhancing personalized immune checkpoint therapy by immune archetyping and pharmacological targeting

Immune checkpoint inhibitors (ICIs) are an expanding class of immunotherapeutic agents with the potential to cure cancer. Despite the outstanding clinical response in patient subsets, most individuals become refractory or develop resistance. Patient stratification and personalized immunotherapies are limited by the absence of predictive response markers. Recent findings show that dominant patterns of immune cell composition, T-cell status and heterogeneity, and spatiotemporal distribution of immune cells within the tumor microenvironment (TME) are becoming essential determinants of prognosis and therapeutic response. In this context, ICIs also function as investigational tools and proof of concept, allowing the validation of the identified mechanisms. After reviewing the current state of ICIs, this article will explore new comprehensive predictive markers for ICIs based on recent discoveries. We will discuss the recent establishment of a classification of TMEs into immune archetypes as a tool for personalized immune profiling, allowing patient stratification before ICI treatment. We will discuss the developing comprehension of T-cell diversity and its role in shaping the immune profile of patients. We describe the potential of strategies that score the mutual spatiotemporal modulation between T-cells and other cellular components of the TME. Additionally, we will provide an overview of a range of synthetic and naturally occurring or derived small molecules. We will compare compounds that were recently identified by in silico prediction to wet lab-validated drug candidates with the potential to function as ICIs and/or modulators of the cellular components of the TME.

Advancing Understanding of Non-Small Cell Lung Cancer with Multiplexed Antibody-Based Spatial Imaging Technologies

Non-small cell lung cancer (NSCLC) remains a cause of significant morbidity and mortality, despite significant advances made in its treatment using immune checkpoint inhibitors (ICIs) over the last decade; while a minority experience prolonged responses with ICIs, benefit is limited for most patients. The development of multiplexed antibody-based (MAB) spatial tissue imaging technologies has revolutionised analysis of the tumour microenvironment (TME), enabling identification of a wide range of cell types and subtypes, and analysis of the spatial relationships and interactions between them. Such study has the potential to translate into a greater understanding of treatment susceptibility and resistance, factors influencing prognosis and recurrence risk, and identification of novel therapeutic approaches and rational treatment combinations to improve patient outcomes in the clinic. Herein we review studies that have leveraged MAB technologies to deliver novel insights into the TME of NSCLC.

SwarmDeepSurv: swarm intelligence advances deep survival network for prognostic radiomics signatures in four solid cancers

Survival models exist to study relationships between biomarkers and treatment effects. Deep learning-powered survival models supersede the classical Cox proportional hazards (CoxPH) model, but substantial performance drops were observed on high-dimensional features because of irrelevant/redundant information. To fill this gap, we proposed SwarmDeepSurv by integrating swarm intelligence algorithms with the deep survival model. Furthermore, four objective functions were designed to optimize prognostic prediction while regularizing selected feature numbers. When testing on multicenter sets (n = 1,058) of four different cancer types, SwarmDeepSurv was less prone to overfitting and achieved optimal patient risk stratification compared with popular survival modeling algorithms. Strikingly, SwarmDeepSurv selected different features compared with classical feature selection algorithms, including the least absolute shrinkage and selection operator (LASSO), with nearly no feature overlapping across these models. Taken together, SwarmDeepSurv offers an alternative approach to model relationships between radiomics features and survival endpoints, which can further extend to study other input data types including genomics.

Long Non-Coding RNAs as Emerging Targets in Lung Cancer

Long non-coding RNAs (LncRNAs) are mRNA-like molecules that do not encode for proteins and that are longer than 200 nucleotides. LncRNAs play important biological roles in normal cell physiology and organism development. Therefore, deregulation of their activities is involved in disease processes such as cancer. Lung cancer is the leading cause of cancer-related deaths due to late stage at diagnosis, distant metastasis, and high rates of therapeutic failure. LncRNAs are emerging as important molecules in lung cancer for their oncogenic or tumor-suppressive functions. LncRNAs are highly stable in circulation, presenting an opportunity for use as non-invasive and early-stage cancer diagnostic tools. Here, we summarize the latest works providing in vivo evidence available for lncRNAs role in cancer development, therapy-induced resistance, and their potential as biomarkers for diagnosis and prognosis, with a focus on lung cancer. Additionally, we discuss current therapeutic approaches to target lncRNAs. The evidence discussed here strongly suggests that investigation of lncRNAs in lung cancer in addition to protein-coding genes will provide a holistic view of molecular mechanisms of cancer initiation, development, and progression, and could open up a new avenue for cancer treatment.

Cancer Cell-Intrinsic Alterations Associated with an Immunosuppressive Tumor Microenvironment and Resistance to Immunotherapy in Lung Cancer

Despite the great clinical success of immunotherapy in lung cancer patients, only a small percentage of them (<40%) will benefit from this therapy alone or combined with other strategies. Cancer cell-intrinsic and cell-extrinsic mechanisms have been associated with a lack of response to immunotherapy. The present study is focused on cancer cell-intrinsic genetic, epigenetic, transcriptomic and metabolic alterations that reshape the tumor microenvironment (TME) and determine response or refractoriness to immune checkpoint inhibitors (ICIs). Mutations in KRAS, SKT11(LKB1), KEAP1 and TP53 and co-mutations of these genes are the main determinants of ICI response in non-small-cell lung cancer (NSCLC) patients. Recent insights into metabolic changes in cancer cells that impose restrictions on cytotoxic T cells and the efficacy of ICIs indicate that targeting such metabolic restrictions may favor therapeutic responses. Other emerging pathways for therapeutic interventions include epigenetic modulators and DNA damage repair (DDR) pathways, especially in small-cell lung cancer (SCLC). Therefore, the many potential pathways for enhancing the effect of ICIs suggest that, in a few years, we will have much more personalized medicine for lung cancer patients treated with immunotherapy. Such strategies could include vaccines and chimeric antigen receptor (CAR) cells.

Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review

The identification of biomarkers plays a crucial role in personalized medicine, both in the clinical and research settings. However, the contrast between predictive and prognostic biomarkers can be challenging due to the overlap between the two. A prognostic biomarker predicts the future outcome of cancer, regardless of treatment, and a predictive biomarker predicts the effectiveness of a therapeutic intervention. Misclassifying a prognostic biomarker as predictive (or vice versa) can have serious financial and personal consequences for patients. To address this issue, various statistical and machine learning approaches have been developed. The aim of this study is to present an in-depth analysis of recent advancements, trends, challenges, and future prospects in biomarker identification. A systematic search was conducted using PubMed to identify relevant studies published between 2017 and 2023. The selected studies were analyzed to better understand the concept of biomarker identification, evaluate machine learning methods, assess the level of research activity, and highlight the application of these methods in cancer research and treatment. Furthermore, existing obstacles and concerns are discussed to identify prospective research areas. We believe that this review will serve as a valuable resource for researchers, providing insights into the methods and approaches used in biomarker discovery and identifying future research opportunities.