Challenges of PD-L1 testing in non-small cell lung cancer and beyond

Prognostic Value of KRAS Mutations in Relation to PDL1 Expression and Immunotherapy Treatment in Adenocarcinoma and Squamous Cell Carcinoma Patients: A Greek Cohort Study

BACKGROUND

Factors that could predict which patients will benefit from Immune Checkpoint Inhibitors (ICIs) are not fully understood. This study aimed to investigate the prognostic value of KRAS biomarker in patients with advanced non-small cell lung cancer (NSCLC) in relation to clinical characteristics, treatment response and PDL1 expression.

PATIENTS AND METHODS

The study included 100 patients with NSCLC who received immunotherapy with or without chemotherapy as 1st line treatment. In biopsy samples, the PDL1 biomarker expression rate and somatic mutations of KRAS gene were determined.

RESULTS

The mean age of the patients was 67 ± 8 years. Patients were all male and 66% were found with adenocarcinoma whereas 34% with squamous cell carcinoma. The KRAS G12C mutation was found with the highest percentage (73%). In the Kaplan-Meier survival analysis, patients with PDL1 > 49% in combination with a negative KRAS result had a median overall survival of 40 months compared to patients with a positive KRAS result (9 months, p < 0.05). In addition, patients diagnosed with adenocarcinoma, PDL1 < 49% and negative KRAS result had a median overall survival of 39 months compared to patients with a positive result (28 months, p < 0.05).

CONCLUSIONS

Our study suggests that the presence of KRAS mutations in advanced NSCLC patients has a poor prognostic value, regardless of their PDL1 expression values, after receiving immunotherapy as first-line treatment.

A whirl of radiomics-based biomarkers in cancer immunotherapy, why is large scale validation still lacking?

The search for understanding immunotherapy response has sparked interest in diverse areas of oncology, with artificial intelligence (AI) and radiomics emerging as promising tools, capable of gathering large amounts of information to identify suitable patients for treatment. The application of AI in radiology has grown, driven by the hypothesis that radiology images capture tumor phenotypes and thus could provide valuable insights into immunotherapy response likelihood. However, despite the rapid growth of studies, no algorithms in the field have reached clinical implementation, mainly due to the lack of standardized methods, hampering study comparisons and reproducibility across different datasets. In this review, we performed a comprehensive assessment of published data to identify sources of variability in radiomics study design that hinder the comparison of the different model performance and, therefore, clinical implementation. Subsequently, we conducted a use-case meta-analysis using homogenous studies to assess the overall performance of radiomics in estimating programmed death-ligand 1 (PD-L1) expression. Our findings indicate that, despite numerous attempts to predict immunotherapy response, only a limited number of studies share comparable methodologies and report sufficient data about cohorts and methods to be suitable for meta-analysis. Nevertheless, although only a few studies meet these criteria, their promising results underscore the importance of ongoing standardization and benchmarking efforts. This review highlights the importance of uniformity in study design and reporting. Such standardization is crucial to enable meaningful comparisons and demonstrate the validity of biomarkers across diverse populations, facilitating their implementation into the immunotherapy patient selection process.

AI/ML advances in non-small cell lung cancer biomarker discovery

Lung cancer is the leading cause of cancer deaths among both men and women, representing approximately 25% of cancer fatalities each year. The treatment landscape for non-small cell lung cancer (NSCLC) is rapidly evolving due to the progress made in biomarker-driven targeted therapies. While advancements in targeted treatments have improved survival rates for NSCLC patients with actionable biomarkers, long-term survival remains low, with an overall 5-year relative survival rate below 20%. Artificial intelligence/machine learning (AI/ML) algorithms have shown promise in biomarker discovery, yet NSCLC-specific studies capturing the clinical challenges targeted and emerging patterns identified using AI/ML approaches are lacking. Here, we employed a text-mining approach and identified 215 studies that reported potential biomarkers of NSCLC using AI/ML algorithms. We catalogued these studies with respect to BEST (Biomarkers, EndpointS, and other Tools) biomarker sub-types and summarized emerging patterns and trends in AI/ML-driven NSCLC biomarker discovery. We anticipate that our comprehensive review will contribute to the current understanding of AI/ML advances in NSCLC biomarker research and provide an important catalogue that may facilitate clinical adoption of AI/ML-derived biomarkers.

PD-L1 Expression by RNA-Sequencing in Non-Small Cell Lung Cancer: Concordance with Immunohistochemistry and Associations with Pembrolizumab Treatment Outcomes

Programmed cell death ligand (PD-L1) expression by immunohistochemistry (IHC) lacks sensitivity for pembrolizumab immunotherapy selection in non-small cell lung cancer (NSCLC), particularly for tumors with low expression. We retrospectively evaluated transcriptomic PD-L1 by mRNA next-generation sequencing (RNA-seq). In an unselected NSCLC patient cohort (n = 3168) tested during standard care (2017-2021), PD-L1 IHC and RNA-seq demonstrated moderate concordance, with 80% agreement overall. Most discordant cases were either low or negative for PD-L1 expression by IHC but high by RNA-seq. RNA-seq accurately discriminated PD-L1 IHC high from low tumors by receiver operator curve (ROC) analysis but could not distinguish PD-L1 IHC low from negative tumors. In a separate pembrolizumab monotherapy cohort (n = 102), NSCLC tumors classified as PD-L1 high versus not high by RNA-seq had significantly improved response, progression-free survival, and overall survival as an individual measure and in combination with IHC high or low status. PD-L1 IHC status (high or low) trended toward but had no significant associations with improved outcomes. Conventional PD-L1 IHC testing has inherent limitations, making it an imperfect reference standard for evaluating novel testing technologies. RNA-seq offers an objective PD-L1 measure that could represent a complementary method to IHC to improve NSCLC patient selection for immunotherapy.

Biomarkers for Immune Checkpoint Inhibitors in Renal Cell Carcinoma

Immune checkpoint inhibitor (ICI) therapy has revolutionized renal cell carcinoma treatment. Patients previously thought to be palliative now occasionally achieve complete cures from ICI. However, since immunotherapies stimulate the immune system to induce anti-tumor immunity, they often lead to adverse autoimmunity. Furthermore, some patients receive no benefit from ICI, thereby unnecessarily risking adverse events. In many tumor types, PD-L1 expression levels, immune infiltration, and tumor mutation burden predict the response to ICI and help inform clinical decision making to better target ICI to patients most likely to experience benefits. Unfortunately, renal cell carcinoma is an outlier, as these biomarkers fail to discriminate between positive and negative responses to ICI therapy. Emerging biomarkers such as gene expression profiles and the loss of pro-angiogenic proteins VHL and PBRM-1 show promise for identifying renal cell carcinoma cases likely to respond to ICI. This review provides an overview of the mechanistic underpinnings of different biomarkers and describes the theoretical rationale for their use. We discuss the effectiveness of each biomarker in renal cell carcinoma and other cancer types, and we introduce novel biomarkers that have demonstrated some promise in clinical trials.

Functional binding of PD1 ligands predicts response to anti-PD1 treatment in patients with cancer

Accurate predictive biomarkers of response to immune checkpoint inhibitors (ICIs) are required for better stratifying patients with cancer to ICI treatments. Here, we present a new concept for a bioassay to predict the response to anti-PD1 therapies, which is based on measuring the binding functionality of PDL1 and PDL2 to their receptor, PD1. In detail, we developed a cell-based reporting system, called the immuno-checkpoint artificial reporter with overexpression of PD1 (IcAR-PD1) and evaluated the functionality of PDL1 and PDL2 binding in tumor cell lines, patient-derived xenografts, and fixed-tissue tumor samples obtained from patients with cancer. In a retrospective clinical study, we found that the functionality of PDL1 and PDL2 predicts response to anti-PD1 and that the functionality of PDL1 binding is a more effective predictor than PDL1 protein expression alone. Our findings suggest that assessing the functionality of ligand binding is superior to staining of protein expression for predicting response to ICIs.

Self-Renewing CD8+ T-cell Abundance in Blood Associates with Response to Immunotherapy

Treatment with immune checkpoint blockade (ICB) often fails to elicit durable antitumor immunity. Recent studies suggest that ICB does not restore potency to terminally dysfunctional T cells, but instead drives proliferation and differentiation of self-renewing progenitor T cells into fresh, effector-like T cells. Antitumor immunity catalyzed by ICB is characterized by mobilization of antitumor T cells in systemic circulation and tumor. To address whether abundance of self-renewing T cells in blood is associated with immunotherapy response, we used flow cytometry of peripheral blood from a cohort of patients with metastatic non-small cell lung cancer (NSCLC) treated with ICB. At baseline, expression of T-cell factor 1 (TCF1), a marker of self-renewing T cells, was detected at higher frequency in effector-memory (CCR7-) CD8+ T cells from patients who experienced durable clinical benefit compared to those with primary resistance to ICB. On-treatment blood samples from patients benefiting from ICB also exhibited a greater frequency of TCF1+CCR7-CD8+ T cells and higher proportions of TCF1 expression in treatment-expanded PD-1+CCR7-CD8+ T cells. The observed correlation of TCF1 frequency in CCR7-CD8+ T cells and response to ICB suggests that broader examination of self-renewing T-cell abundance in blood will determine its potential as a noninvasive, predictive biomarker of response and resistance to immunotherapy.

Baseline immune signature score of Tregs × HLA-DR+CD4+ T cells × PD1+CD8+ T cells predicts outcome to immunotherapy in cancer patients

Background

The use of immunotherapy (IT) is rapidly increasing across different tumor entities. PD-L1 expression is primarily used for therapy evaluation. The disadvantages of PD-L1 status are spatial and temporal heterogeneity as well as tumor type-dependent variation of predictive value. To optimize patient selection for IT, new prediction markers for therapy success are needed. Based on the systemic efficacy of IT, we dissected the immune signature of peripheral blood as an easily accessible predictive biomarker for therapeutic success.

Methods

We conducted a retrospective clinical study of 62 cancer patients treated with IT. We assessed peripheral immune cell counts before the start of IT via flow cytometry. The predictive value for therapy response of developed immune signature scores was tested by ROC curve analyses and scores were correlated with time to progression (TTP).

Results

High score values of "Tregs ÷ (CD4⁺/CD8⁺ ratio)" (Score A) and high score values of "Tregs × HLA-DR⁺CD4⁺ T cells × PD1⁺CD8⁺ T cells" (Score B) significantly correlated with response at first staging (p = 0.001; p < 0.001). At the optimal cutoff point, Score A correctly predicted 79.1% and Score B correctly predicted 89.3% of the staging results (sensitivity: 86.2%, 90.0%; specificity: 64.3%, 87.5%). A high Score A and Score B statistically correlated with prolonged median TTP (6.13 vs. 2.17 months, p = 0.025; 6.43 vs. 1.83 months, p = 0.016). Cox regression analyses for TTP showed a risk reduction of 55.7% (HR = 0.44, p = 0.029) for Score A and an adjusted risk reduction of 73.2% (HR = 0.27, p = 0.016) for Score B.

Conclusion

The two identified immune signature scores showed high predictive value for therapy response as well as for prolonged TTP in a pan-cancer patient population. Our scores are easy to determine by using peripheral blood and flow cytometry, apply to different cancer entities, and allow an outcome prediction before the start of IT.

The Role of Genomics and Proteomics in Lung Cancer Early Detection and Treatment

Lung cancer is the leading cause of cancer-related death worldwide, with non-small-cell lung cancer (NSCLC) being the primary type. Unfortunately, it is often diagnosed at advanced stages, when therapy leaves patients with a dismal prognosis. Despite the advances in genomics and proteomics in the past decade, leading to progress in developing tools for early diagnosis, targeted therapies have shown promising results; however, the 5-year survival of NSCLC patients is only about 15%. Low-dose computed tomography or chest X-ray are the main types of screening tools. Lung cancer patients without specific, actionable mutations are currently treated with conventional therapies, such as platinum-based chemotherapy; however, resistances and relapses often occur in these patients. More noninvasive, inexpensive, and safer diagnostic methods based on novel biomarkers for NSCLC are of paramount importance. In the current review, we summarize genomic and proteomic biomarkers utilized for the early detection and treatment of NSCLC. We further discuss future opportunities to improve biomarkers for early detection and the effective treatment of NSCLC.

Imaging immunity in patients with cancer using positron emission tomography

Immune checkpoint inhibitors and related molecules can achieve tumour regression, and even prolonged survival, for a subset of cancer patients with an otherwise dire prognosis. However, it remains unclear why some patients respond to immunotherapy and others do not. PET imaging has the potential to characterise the spatial and temporal heterogeneity of both immunotherapy target molecules and the tumor immune microenvironment, suggesting a tantalising vision of personally-adapted immunomodulatory treatment regimens. Personalised combinations of immunotherapy with local therapies and other systemic therapies, would be informed by immune imaging and subsequently modified in accordance with therapeutically induced immune environmental changes. An ideal PET imaging biomarker would facilitate the choice of initial therapy and would permit sequential imaging in time-frames that could provide actionable information to guide subsequent therapy. Such imaging should provide either prognostic or predictive measures of responsiveness relevant to key immunotherapy types but, most importantly, guide key decisions on initiation, continuation, change or cessation of treatment to reduce the cost and morbidity of treatment while enhancing survival outcomes. We survey the current literature, focusing on clinically relevant immune checkpoint immunotherapies, for which novel PET tracers are being developed, and discuss what steps are needed to make this vision a reality.

Program death ligand-1 immunocytochemistry in lung cancer cytological samples: A systematic review

In this era of personalized medicine, targeted immunotherapies like immune checkpoint inhibitors (ICI) blocking the programmed death‐1 (PD‐1)/program death ligand‐1 (PD‐L1) axis have become an integral part of treating advanced stage non‐small cell lung carcinoma (NSCLC) and many other cancer types. Multiple monoclonal antibodies are available commercially to detect PD‐L1 expression in tumor cells by immunohistochemistry (IHC). As most clinical trials initially required tumor biopsy for PD‐L1 detection by IHC, many of the currently available PD‐1/PD‐L1 assays have been developed and validated on formalin fixed tissue specimens. The majority (>50%) of lung cancer cases do not have a surgical biopsy or resection specimen available for ancillary testing and instead must rely primarily on fine needle aspiration biopsy specimens for diagnosis, staging and ancillary tests. Review of the literature shows multiple studies exploring the feasibility of PD‐L1 IHC on cytological samples. In addition, there are studies addressing various aspects of IHC validation on cytology preparations including pre‐analytical (e.g., different fixatives), analytical (e.g., antibody clone, staining platforms, inter and intra‐observer agreement, cytology‐histology concordance) and post‐analytical (e.g., clinical outcome) issues. Although promising results in this field have emerged utilizing cytology samples, many important questions still need to be addressed. This review summarizes the literature of PD‐L1 IHC in lung cytology specimens and provides practical tips for optimizing analysis.

Therapeutic strategies to remodel immunologically cold tumors

Immune checkpoint inhibitors (ICIs) induce a durable response in a wide range of tumor types, but only a minority of patients outside these ‘responsive’ tumor types respond, with some totally resistant. The primary predictor of intrinsic immune resistance to ICIs is the complete or near‐complete absence of lymphocytes from the tumor, so‐called immunologically cold tumors. Here, we propose two broad approaches to convert ‘cold’ tumors into ‘hot’ tumors. The first is to induce immunogenic tumor cell death, through the use of oncolytic viruses or bacteria, conventional cancer therapies (e.g. chemotherapy or radiation therapy) or small molecule drugs. The second approach is to target the tumor microenvironment, and covers diverse options such as depleting immune suppressive cells; inhibiting transforming growth factor‐beta; remodelling the tumor vasculature or hypoxic environment; strengthening the infiltration and activation of antigen‐presenting cells and/or effector T cells in the tumor microenvironment with immune modulators; and enhancing immunogenicity through personalised cancer vaccines. Strategies that successfully modify cold tumors to overcome their resistance to ICIs represent mechanistically driven approaches that will ultimately result in rational combination therapies to extend the clinical benefits of immunotherapy to a broader cancer cohort.