Biomarkers For Immune Checkpoint Inhibitors Research Articles

Unraveling the potential biomarkers of immune checkpoint inhibitors in advanced ovarian cancer: a comprehensive review.

The ongoing research on the role of immunotherapy in advanced ovarian cancer (OC) and current clinical trials indicate that patients shown limited response to immune checkpoint inhibitor (ICI) monotherapy. When combined with other treatments or drugs, the efficacy of immunotherapy will be significantly improved. Biomarkers can be used to identify patients with better responses, thereby improving the precision and efficacy of immunotherapy. Key biomarkers for advanced OC include homologous repair deficiency, programmed death-ligand (PD-L) 1 expression, chemokines, and tumor infiltrating lymphocytes. These biomarkers could be applied in the future to select the most suitable patient populations. This review comprehensively examines the research and development of biomarkers in OC immunotherapy from three omics perspectives: genomics, transcriptomics, and proteomics, which may provide guidance for the effectiveness of OC immunotherapy strategies.

Establishing a predictive model for tumor mutation burden status based on 18F-FDG PET/CT and clinical features of non-small cell lung cancer patients.

Tumor mutation burden (TMB) has emerged as a promising biomarker for immune checkpoint inhibitors (ICI) response, but its detection through whole exome sequencing (WES) is costly and invasive. This study aims to establish a predictive model for TMB using baseline metabolic parameters (MPs) of 18F-fluorodeoxyglucose (FDG) uptake on positron emission tomography/computed tomography (PET/CT) and clinical features in non-small cell lung cancer (NSCLC) patients, potentially offering a non-invasive and cost-effective method to predict TMB status. A total of 223 NSCLC patients with baseline 18F-FDG PET/CT scans and TMB detection results were retrospectively enrolled from January 2019 to September 2023, and were divided into two groups: TMB-high (≥4 mutations/Mb, 96 patients) and TMB-low (<4 mutations/Mb, 127 patients). Twelve clinical features and five PET parameters were assessed. Univariate analysis was conducted in all patients to reveal the preliminary associations between variables and TMB status. All patients were randomly divided into a training set (n=135) and a validation set (n=88). Feature selection was performed using lasso regression and logistic regression analyses. A predictive model and nomogram were established with the features selected above. Decision curve analysis (DCA) was performed to assess the clinical utility of the developed model. Two clinical features and two PET parameters were identified through lasso regression and logistic regression analysis including pathology type, cancer antigen 125 (CA125) level, maximum standardized uptake value (SUVmax), and metabolic tumor volume (MTV). The predictive model exhibited an area under the curve (AUC) of 0.822 [95% confidence interval (CI), 0.751-0.894], and internal validation yielded an AUC of 0.822 (95% CI, 0.731-0.912). The model was well-calibrated. The developed nomogram, incorporating the four selected variables, showed promising potential in evaluating TMB status in NSCLC patients. In this study, a predictive model combining 18F-FDG PET/CT and clinical features of NSCLC patients effectively distinguished between TMB-high and TMB-low status. The nomogram generated from this model holds significant promise for predicting TMB status, offering valuable insights for clinical decision-making.

Serum Inflammatory Dynamics as Novel Biomarkers for Immune Checkpoint Inhibitors in Non-small-cell Lung Cancer With Bone Metastases.

The aim of the study was to develop a novel predictive scoring system based on the dynamics of serum inflammatory indicators in immune checkpoint inhibitor (ICI) treatment on non-small-cell lung cancer (NSCLC) with bone metastases. Sixty patients with NSCLC and bone metastases treated with ICIs between January 2016 and March 2021 were included in the development cohort. Serum neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein (CRP) levels were assessed before (pre-value) and 6 weeks after (post-value) ICI treatment, and a novel predictive score was developed: pre-value ≥ post-value, 0 points; pre-value < post-value, 1 point; total score: 0-2 points. The associations of these dynamics and the score with clinical outcomes, including overall survival (OS), progression-free survival (PFS), response rate (RR) of bone metastases, and disease control rate (DCR), were evaluated. Furthermore, cross-validation was performed with 23 patients after April 2021 using the same inclusion criteria. The patients with decreased serum inflammation levels had significantly better OS, PFS, and RR than those with increased levels. Applying the developed score to the development cohort, the patients with 0 points had significantly better OS, PFS, and RR than others. In multivariable analysis, the score independently predicted treatment response to ICI for bone metastasis and prognosis. Cross-validation showed that OS, PFS, and RR were significantly better in the patients in the 0-point group. The early NLR and CRP dynamics were associated with therapeutic responses to ICIs in NSCLC with bone metastases. Our novel scoring system based on these dynamics is simple and has a high predictive accuracy.

Pan-cancer analysis reveals CCL5/CSF2 as potential predictive biomarkers for immune checkpoint inhibitors

BackgroundCurrently, there are no optimal biomarkers available for distinguishing patients who will respond to immune checkpoint inhibitors (ICIs) therapies. Consequently, the exploration of novel biomarkers that can predict responsiveness to ICIs is crucial in the field of immunotherapy.MethodsWe estimated the proportions of 22 immune cell components in 10 cancer types (6,128 tumors) using the CIBERSORT algorithm, and further classified patients based on their tumor immune cell proportions in a pan-cancer setting using k-means clustering. Differentially expressed immune genes between the patient subgroups were identified, and potential predictive biomarkers for ICIs were explored. Finally, the predictive value of the identified biomarkers was verified in patients with urothelial carcinoma (UC) and esophageal squamous cell carcinoma (ESCC) who received ICIs.ResultsOur study identified two subgroups of patients with distinct immune infiltrating phenotypes and differing clinical outcomes. The patient subgroup with improved outcomes displayed tumors enriched with genes related to immune response regulation and pathway activation. Furthermore, CCL5 and CSF2 were identified as immune-related hub-genes and were found to be prognostic in a pan-cancer setting. Importantly, UC and ESCC patients with high expression of CCL5 and low expression of CSF2 responded better to ICIs.ConclusionWe demonstrated CCL5 and CSF2 as potential novel biomarkers for predicting the response to ICIs in patients with UC and ESCC. The predictive value of these biomarkers in other cancer types warrants further evaluation in future studies.

Artificial Intelligence-Powered Spatial Analysis of Tumor-Infiltrating Lymphocytes as a Potential Biomarker for Immune Checkpoint Inhibitors in Patients with Biliary Tract Cancer.

Recently, anti-programmed cell death-1/anti-programmed cell death ligand-1 (anti-PD1/L1) immunotherapy has been demonstrated for its efficacy when combined with cytotoxic chemotherapy in randomized phase 3 trials for advanced biliary tract cancer (BTC). However, no biomarker predictive of benefit has been established for anti-PD1/L1 in BTC. Here, we evaluated tumor-infiltrating lymphocytes (TIL) using artificial intelligence-powered immune phenotype (AI-IP) analysis in advanced BTC treated with anti-PD1. Pretreatment hematoxylin and eosin (H&E)-stained whole-slide images from 339 patients with advanced BTC who received anti-PD1 as second-line treatment or beyond, were employed for AI-IP analysis and correlative analysis between AI-IP and efficacy outcomes with anti-PD1. Next, data and images of the BTC cohort from The Cancer Genome Atlas (TCGA) were additionally analyzed to evaluate the transcriptomic and mutational characteristics of various AI-IP in BTC. Overall, AI-IP were classified as inflamed [high intratumoral TIL (iTIL)] in 40 patients (11.8%), immune-excluded (low iTIL and high stromal TIL) in 167 patients (49.3%), and immune-desert (low TIL overall) in 132 patients (38.9%). The inflamed IP group showed a substantially higher overall response rate compared with the noninflamed IP groups (27.5% vs. 7.7%, P < 0.001). Median overall survival and progression-free survival were significantly longer in the inflamed IP group than in the noninflamed IP group (OS, 12.6 vs. 5.1 months; P = 0.002; PFS, 4.5 vs. 1.9 months; P < 0.001). In the TCGA cohort analysis, the inflamed IP showed increased cytolytic activity scores and IFNγ signature compared with the noninflamed IP. AI-IP based on spatial TIL analysis was effective in predicting the efficacy outcomes in patients with BTC treated with anti-PD1 therapy. Further validation is necessary in the context of anti-PD1/L1 plus gemcitabine-cisplatin.

Predictive Biomarkers and Resistance Mechanisms of Checkpoint Inhibitors in Malignant Solid Tumors.

Predictive biomarkers for immune checkpoint inhibitors (ICIs) in solid tumors such as melanoma, hepatocellular carcinoma (HCC), colorectal cancer (CRC), non-small cell lung cancer (NSCLC), endometrial carcinoma, renal cell carcinoma (RCC), or urothelial carcinoma (UC) include programmed cell death ligand 1 (PD-L1) expression, tumor mutational burden (TMB), defective deoxyribonucleic acid (DNA) mismatch repair (dMMR), microsatellite instability (MSI), and the tumor microenvironment (TME). Over the past decade, several types of ICIs, including cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) inhibitors, anti-programmed cell death 1 (PD-1) antibodies, anti-programmed cell death ligand 1 (PD-L1) antibodies, and anti-lymphocyte activation gene-3 (LAG-3) antibodies have been studied and approved by the Food and Drug Administration (FDA), with ongoing research on others. Recent studies highlight the critical role of the gut microbiome in influencing a positive therapeutic response to ICIs, emphasizing the importance of modeling factors that can maintain a healthy microbiome. However, resistance mechanisms can emerge, such as increased expression of alternative immune checkpoints, T-cell immunoglobulin (Ig), mucin domain-containing protein 3 (TIM-3), LAG-3, impaired antigen presentation, and alterations in the TME. This review aims to synthesize the data regarding the interactions between microbiota and immunotherapy (IT). Understanding these mechanisms is essential for optimizing ICI therapy and developing effective combination strategies.

Role of gut microbiome in the outcome of lymphoma patients treated with checkpoint inhibitors-The MicroLinf Study.

Biomarkers for immune checkpoint inhibitors (ICIs) response and resistance include PD-L1 expression and other environmental factors, among which the gut microbiome (GM) is gaining increasing interest especially in lymphomas. To explore the potential role of GM in this clinical issue, feces of 30 relapsed/refractory lymphoma (Hodgkin and primary mediastinal B-cell lymphoma) patients undergoing ICIs were collected from start to end of treatment (EoT). GM was profiled through Illumina, that is, 16S rRNA sequencing, and subsequently processed through a bioinformatics pipeline. The overall response rate to ICIs was 30.5%, with no association between patients clinical characteristics and response/survival outcomes. Regarding GM, responder patients showed a peculiar significant enrichment of Lachnospira, while non-responder ones showed higher presence of Enterobacteriaceae (at baseline and maintained till EoT). Recognizing patient-related factors that may influence response to ICIs is becoming critical to optimize the treatment pathway of heavily pretreated, young patients with a potentially long-life expectancy. These preliminary results indicate potential early GM signatures of ICIs response in lymphoma, which could pave the way for future research to improve patients prognosis with new adjuvant strategies.

Identification of DNA Repair-related Genes as Biomarkers Reflecting MSI, TMB, and TIL in Colorectal Cancer.

There are no established biomarkers for immune checkpoint inhibitors (ICI) in colorectal cancer (CRC) with microsatellite stability (MSS) or proficient mismatch repair (pMMR). Therefore, this study aimed to identify biomarkers for ICI benefit in patients with pMMR by analyzing the down-regulated DNA repair-related genes involved in highly immunogenic and immune responses, and comparing their expression levels and clinical features. Mismatch repair (MMR), tumor-infiltrating lymphocytes (TIL), and tumor mutation burden (TMB) were evaluated in 13 CRC cases and mRNA expression levels of 95 DNA repair-related genes were measured. DNA repair-related genes with reduced mRNA expression in the high immunogenicity and high immune response groups were identified. Then, the mRNA expression levels of the identified DNA repair-related genes were measured in 135 patients with CRC. Hierarchical cluster analysis was performed using the mRNA expression levels to compare the clinicopathological characteristics of each cluster. ATR, LIG4, and RAD52 mRNA levels were significantly down-regulated in the high immunogenicity group. GADD45B, SMUG1, and XRCC6 mRNA levels were significantly down-regulated in the high immune response group. Cases in the cluster with reduced mRNA expression of the six genes were pMMR cases. CD8 mRNA expression level was higher in this cluster than in the other clusters. Decreased mRNA expression levels of ATR, LIG4, RAD52, GADD45B, SMUG1, and XRCC6 genes were associated with high cytotoxic T cell and TMB levels, suggesting that these genes could serve as biomarkers for ICI efficacy in pMMR cases.

Somatic CpG hypermutation is associated with mismatch repair deficiency in cancer

Somatic hypermutation in cancer has gained momentum with the increased use of tumour mutation burden as a biomarker for immune checkpoint inhibitors. Spontaneous deamination of 5-methylcytosine to thymine at CpG dinucleotides is one of the most ubiquitous endogenous mutational processes in normal and cancer cells. Here, we performed a systematic investigation of somatic CpG hypermutation at a pan-cancer level. We studied 30,191 cancer patients and 103 cancer types and developed an algorithm to identify somatic CpG hypermutation. Across cancer types, we observed the highest prevalence in paediatric leukaemia (3.5%), paediatric high-grade glioma (1.7%), and colorectal cancer (1%). We discovered germline variants and somatic mutations in the mismatch repair complex MutSα (MSH2-MSH6) as genetic drivers of somatic CpG hypermutation in cancer, which frequently converged on CpG sites and TP53 driver mutations. We further observe an association between somatic CpG hypermutation and response to immune checkpoint inhibitors. Overall, our study identified novel cancer types that display somatic CpG hypermutation, strong association with MutSα-deficiency, and potential utility in cancer immunotherapy.

Immune profiling of mouse lung adenocarcinoma paraffin tissues using multiplex immunofluorescence panel: a pilot study

BackgroundImmune profiling has become an important tool for identifying predictive, prognostic and response biomarkers for immune checkpoint inhibitors from tumor microenvironment (TME). We aimed to build a multiplex immunofluorescence (mIF) panel to apply to formalin-fixed and paraffin-embedded tissues in mice tumors and to explore the programmed cell death protein 1/ programmed cell death 1 ligand 1 (PD-1/PD-L1) axis.ResultsAn automated eight-color mIF panel was evaluated to study the TME using seven antibodies, including cytokeratin 19, CD3e, CD8a, CD4, PD-1, PD-L1, F4-80 and DAPI, then was applied in six mice lung adenocarcinoma samples. Cell phenotypes were quantified by software to explore the co-localization and spatial distribution between immune cells within the TME. This mice panel was successfully optimized and applied to a small cohort of mice lung adenocarcinoma cases. Image analysis showed a sparse degree of immune cell expression pattern in this cohort. From the spatial analysis we found that T cells and macrophages expressing PD-L1 were close to the malignant cells and other immune cells.ConclusionsComprehensive immune profiling using mIF in translational studies improves our ability to correlate the PD-1/PD-L1 axis and spatial distribution of lymphocytes and macrophages in mouse lung cancer cells to provide new cues for immunotherapy, that can be translated to human tumors for cancer intervention.

Defining clinically useful biomarkers of immune checkpoint inhibitors in solid tumours.

Although more than a decade has passed since the approval of immune checkpoint inhibitors (ICIs) for the treatment of melanoma and non-small-cell lung, breast and gastrointestinal cancers, many patients still show limited response. US Food and Drug Administration (FDA)-approved biomarkers include programmed cell death 1 ligand 1 (PDL1) expression, microsatellite status (that is, microsatellite instability-high (MSI-H)) and tumour mutational burden (TMB), but these have limited utility and/or lack standardized testing approaches for pan-cancer applications. Tissue-based analytes (such as tumour gene signatures, tumour antigen presentation or tumour microenvironment profiles) show a correlation with immune response, but equally, these demonstrate limited efficacy, as they represent a single time point and a single spatial assessment. Patient heterogeneity as well as inter- and intra-tumoural differences across different tissue sites and time points represent substantial challenges for static biomarkers. However, dynamic biomarkers such as longitudinal biopsies or novel, less-invasive markers such as blood-based biomarkers, radiomics and the gut microbiome show increasing potential for the dynamic identification of ICI response, and patient-tailored predictors identified through neoadjuvant trials or novel ex vivo tumour models can help to personalize treatment. In this Perspective, we critically assess the multiple new static, dynamic and patient-specific biomarkers, highlight the newest consortia and trial efforts, and provide recommendations for future clinical trials to make meaningful steps forwards in the field.

Serum albumin and derived neutrophil-to-lymphocyte ratio are potential predictive biomarkers for immune checkpoint inhibitors in small cell lung cancer.

Immune checkpoint inhibitors (ICIs) have reshaped the treatment landscape of small cell lung cancer (SCLC), but only a minority of patients benefit from this therapy. Therefore, it is critical to identify potential risk factors that could predict the efficacy of ICI treatment in SCLC patients and identify patient subgroups who may benefit the most from ICI therapy. Our study included a total of 183 SCLC patients who had received at least one dose of ICI treatment. We utilized both logistic regression and Cox proportional hazard regression to evaluate whether various patient clinical factors and serum biomarkers could serve as predictors of patient response to treatment and overall survival (OS) during ICI therapy. Logistic regression showed that patients with a history of surgery (p=0.003, OR 9.06, 95% CI: (2.17, 37.9)) and no metastasis (p=0.008, OR 7.82, 95% CI: (1.73, 35.4)) exhibited a higher odds of response to ICI treatment. Cox regression analyses demonstrated that pretreatment blood albumin (p=0.003, HR 1.72, 95% CI: (1.21, 2.45)) and derived neutrophil to lymphocyte ratio (dNLR) (p=0.003, HR 1.71, 95% CI: (1.20-2.44)) were independent predictors for OS in SCLC patients. By establishing a pre-treatment prognostic scoring system based on baseline albumin and dNLR, we found that patients with high albumin and low dNLR exhibited a significantly better prognosis than those with low albumin and high dNLR in both the full (P<.0001, HR 0.33, 95% CI: 0.20-0.55) and the metastatic cohort (P<.0001, HR 0.28, 95% CI: 0.15-0.51). The better prognostic group also had younger age, higher BMI and lower systemic inflammatory biomarker values than the unfavorable group (P<.0001). Our data reveals the significant role of metastasis status and treatment history in predicting the initial response of SCLC patients to ICI treatment. However, baseline serum albumin and dNLR provide a more precise prognostic prediction for patient OS. The scoring system based on albumin and dNLR enhances the ability to stratify patient prognosis and holds the potential to guide clinical decision-making for SCLC patients undergoing ICI therapy.

Intrapatient variation in PD-L1 expression and tumor mutational burden and the impact on outcomes to immune checkpoint inhibitor therapy in patients with non-small-cell lung cancer

Programmed death receptor ligand 1 (PD-L1) tumor proportion score (TPS) and tumor mutational burden (TMB) are key predictive biomarkers for immune checkpoint inhibitor (ICI) efficacy in non-small-cell lung cancer (NSCLC). Data on their variation across multiple samples are limited. Patients with NSCLC and multiple PD-L1 TPS and/or TMB assessments were included. Clinicopathologic and genomic data were analyzed according to PD-L1 and TMB variation. In total, 402 PD-L1 sample pairs and 413 TMB sample pairs were included. Concordance between pairs was moderate for PD-L1 (ρ= 0.53, P < 0.0001) and high for TMB (ρ= 0.80, P < 0.0001). Shorter time between biopsies correlated with higher concordance in PD-L1, but not in TMB. Major increases (ΔTPS ≥+50%) and decreases (ΔTPS ≤-50%) in PD-L1 were observed in 9.7% and 8.0% of cases, respectively. PD-L1, but not TMB, decreased with intervening ICI (P= 0.02). Acquired copy number loss of CD274, PDCD1LG2, and JAK2 were associated with major decrease in PD-L1 (q < 0.05). Among patients with multiple PD-L1 assessments before ICI, cases where all samples had a PD-L1 ≥1%, compared to cases with at least one sample with PD-L1 <1% and another with PD-L1 ≥1%, achieved improved objective response rate and progression-free survival (PFS). Among patients with at least one PD-L1 <1% and one ≥1% before ICI, cases where the most proximal sample was PD-L1 ≥1% had longer median PFS compared to cases where the most proximal PD-L1 was <1%. Among patients with multiple TMB assessments before ICI, patients with a TMB ≥10 mut/Mb based on the most recent assessment, as compared to those with a TMB <10 mut/Mb, achieved improved PFS and overall survival to ICI; instead, no differences were observed when patients were categorized using the oldest TMB assessment. Despite intrapatient concordance in PD-L1 and TMB, variation in these biomarkers can influence ICI outcomes, warranting consideration for reassessment before ICI initiation when feasible.

Concordance of PD-L1 combined positive score (CPS) analysis between primary gastric cancer and matched peritoneal metastasis.

e16068 Background: Peritoneum represents one of the most common metastatic sites of gastroesophageal adenocarcinoma (GEA) and is associated with poor prognosis. Programmed death-ligand 1 (PD-L1) expression is a recognized predictive biomarker of response to immune checkpoint inhibitors (ICIs) in the first-line setting. However, data from pivotal trials showed poorer clinical efficacy of ICIs in the sub-set of patients with peritoneal involvement, regardless of PD-L1 score magnitude. Here, we investigated the temporal and spatial heterogeneity of PD-L1 expression between primary tumor and matched peritoneal metastasis. Methods: PD-L1 expression according to CPS was determined by immunohistochemistry using VENTANA PD-L1 (SP263) assays on formalin-fixed paraffin-embedded (FFPE) tumor tissues derived from 22 patients treated at our institution for advanced GEA from September 2015 to August 2023. Concordance rate of PD-L1 expression between primary tumor and matched peritoneal metastasis using PD-L1 CPS cut-off of 1 and 5 was reported (spatial heterogeneity); additionally, concordance between PD-L1 value before and after the administration of systemic treatments was analyzed (temporal heterogeneity). Results: 12(54.5%)/5(22.7%) primary tumor samples and 12(54.5%)/1(4.5%) peritoneal metastases showed PD-L1 CPS ≥ 1/ ≥ 5, respectively. Concordance of PD-L1 CPS between primary and peritoneal specimens was 54.5% and 72.7% according to CPS cuts-off of 1 and 5, respectively, highlighting marked spatial heterogeneity. High concordance rate (94%) was reported between negative PD-L1 CPS primary tumor samples and matched peritoneal metastases, against the cut-off of CPS ≥ 5; in contrast, none of the positive primary tumor samples retained PD-L1 expression in the peritoneum. Evident temporal heterogeneity of PD-L1 expression was also observed, with 56% concordance against CPS ≥ 5 in the pre- versus post-treatment comparison. Conclusions: PD-L1 expression in GEA is characterized by spatial and temporal heterogeneity which is more prominent for lower CPS cut-offs values. This evidence could hamper its role as predictive biomarker for ICIs. The high intra-patient discordance rate of PD-L1 CPS expression between positive primary tumor samples and matched peritoneal metastases suggests that peritoneal specimens should not be recommended as the preferred source for PD-L1 assessment.

Heme-oxygenase-1: A potential biomarker for ICIs in patients with advanced lung cancer.

e20505 Background: Immune checkpoint inhibitors (ICIs) significantly improved the outcome in patients with lung cancer. However, heterogeneity in clinical benefit and absence of a reliable biomarker still represent an unmet need. Metabolic reprogramming plays a critical role in cancer behaviour. In particular, heme-ossigenase-1 (HO-1) expression on monocytes may have a role in treatment resistance. Methods: “ImmunoEGA” is a prospective, observational study in patients with advanced tumors, treated with ICIs at the ‘Maggiore della Carità’ Hospital in Novara, IT. Overall, 150 patients have been recruited from 2018 to 2023; of these, 122 were affected by lung cancer. In the present study we analysed monocytes subpopulations and HO-1 expression on blood samples collected from 34 patients at the following time points: baseline, at time of disease evaluation and at disease progression (PD). Monocytes were classified into three different phenotypes based on CD14 and CD16 expression: classical (CD14+CD16–), non-classical (CD14-CD16+) and intermediate (CD14+CD16+). The primary endpoint was treatment outcome in terms of progression free survival (PFS) and/or overall survival (OS). Results: At baseline, the frequency of classical monocytes was higher as compared to non-classical and intermediate subgroups, independently from type of response (mean percentage: 48,4% vs 7,8 vs 23,9%; p &lt; 0.0001). Compared with baseline, we observed a relative increase in the frequency of classical (54,8 vs. 56,9) and intermediate (19,5 vs. 21,1) monocytes in patients with stable disease (SD) at the time of best response. Conversely, patients with PD showed an increase in non-classical (6,3 vs.14,1) and intermediate monocytes (26,3 vs. 30,9). In patients with partial response (PR) all monocyte subgroups increased proportionally (classical: 40,6 vs. 45,5; intermediate 24,2 vs. 25,9; non-classical 7,8 vs. 13,6). A higher baseline frequency of classical monocyte positively correlated with PFS (14 vs 6.5 m, p = 0,0171); these data were not observed for OS. No differences were detectable in HO-1 expression across monocytes subtypes at baseline and at the time of best response (SD or PR). Conversely, HO-1 expression showed an increasing trend across monocytes subpopulations at PD. Lower on classical (mPFS 15 vs 7 m, p = 0,0186) and intermediate monocytes (mPFS 15.5 vs 6.5 m p = 0,0057) at baseline correlated with better PFS. Patients with lower HO-1 expression on intermediate monocytes at baseline had a better outcome (OS 18 vs 11.5 m, p = 0.0378). Conclusions: In patients with lung cancer, higher frequency of classical monocytes and lower HO-1 expression in intermediate and classical monocytes predict better outcomes with ICI. The expression of HO-1 in classical and intermediate monocytes significantly increases at PD, suggesting its implication in promoting cancer progression. HO-1 expression could be used as a biomarker of immunotherapy benefit.

Immunotherapy in thymic epithelial tumors: tissue predictive biomarkers for immune checkpoint inhibitors.

Thymic epithelial tumors (TETs) are rare malignant neoplasms arising in the thymus gland. Nevertheless, TETs, including thymomas (TMs), thymic carcinomas (TCs), and thymic neuroendocrine neoplasms (TNENs), are the most common mediastinal malignancies overall. A multidisciplinary approach is required for the appropriate diagnostic and therapeutic management of TETs. To date, the main therapeutic strategies are largely depended on the stage of the tumor and they include surgery with or without neoadjuvant or adjuvant therapy, represented by platinum-based chemotherapy, radiotherapy or chemoradiotherapy. Immune checkpoint inhibitors (ICIs) are ongoing under evaluation in the advanced or metastatic diseases despite the challenges related to the very low tumor mutation burden (TMB) and the high incidence of immune-related adverse events in TETs. In this regard, predictive impact of tissue biomarkers expression such as programmed cell death ligand-1 (PD-L1), and other emerging biomarkers, as well as their optimal and shared interpretation are currently under evaluation in order to predict response rates to ICIs in TETs.

Epithelial−mesenchymal transition induced by tumor cell-intrinsic PD-L1 signaling predicts a poor response to immune checkpoint inhibitors in PD-L1-high lung cancer

BackgroundWe investigated the role of tumor cell-intrinsic PD-L1 signaling in the epithelial−mesenchymal transition (EMT) in non-small-cell lung cancer (NSCLC) and the role of EMT as a predictive biomarker for immune checkpoint inhibitor (ICI) therapy.MethodsPD-L1-overexpressing or PD-L1-knockdown NSCLC cells underwent RNA-seq and EMT phenotype assessment. Mouse lung cancer LLC cells were injected into nude mice. Two cohorts of patients with NSCLC undergoing ICI therapy were analyzed.ResultsRNA-seq showed that EMT pathways were enriched in PD-L1-high NSCLC cells. EMT was enhanced by PD-L1 in NSCLC cells, which was mediated by transforming growth factor-β (TGFβ). PD-L1 promoted the activation of p38-MAPK by binding to and inhibiting the protein phosphatase PPM1B, thereby increasing the TGFβ production. Tumor growth and metastasis increased in nude mice injected with PD-L1-overexpressing LLC cells. In the ICI cohort, EMT signature was higher in patients with progressive disease than in those with responses, and EMT was significantly associated with poor survival in PD-L1-high NSCLC. In PD-L1-high NSCLC, EMT was associated with increased M2-macrophage and regulatory T-cell infiltrations and decreased cytotoxic T-cell infiltration.ConclusionsTumor cell-intrinsic PD-L1 function contributes to NSCLC progression by promoting EMT. EMT may predict an unfavorable outcome after ICI therapy in PD-L1-high NSCLC.

Spatial Profiles in Triple-negative Breast Cancer: Unraveling the Tumor Microenvironment and Biomarkers for Immune Checkpoint Inhibitors

Abstract Objective: Immune checkpoint inhibitors (ICIs) have become an important treatment option for cancer. However, the predictive power of current biomarkers is limited for treatment response, especially in triple-negative breast cancer (TNBC). Investigation of the tumor microenvironment (TME) may provide biological insights into the response to ICIs by uncovering the interactions among tumor and immune cells. Emerging technologies of spatial transcriptomics (ST) and proteomics allow clinical researchers to better understand the TME. Data Sources and Study Selection: We reviewed the results of articles published in the past 10 years worldwide. Results: Emerging spatial profiling technologies can be classified into image-based and sequencing-based methods, both of which preserve information on tissue architecture with gene expression and/or protein abundance profiles. Here, we reviewed articles studying TNBC using spatial profiling techniques. By integrating spatial profiles, recent studies showed the relevance of gene and protein expression profiles in the TME of different subgroups. These ST and proteomic characteristics were shown to be associated with patients’ survival. Conclusion: The application of spatial profiling techniques to cancer research has significantly advanced our understanding of breast cancer biology, particularly in the context of TNBC. We are confident that the technology has the potential to revolutionize the prediction of treatment outcomes in the near future. By elucidating the nuances within the TME, spatial profiling opens up new possibilities for personalized strategies for immunotherapy.

Tissue-specific thresholds of mutation burden associated with anti-PD-1/L1 therapy benefit and prognosis in microsatellite-stable cancers.

Immune checkpoint inhibitors (ICIs) targeting programmed cell death protein 1 or its ligand (PD-1/L1) have expanded the treatment landscape against cancers but are effective in only a subset of patients. Tumor mutation burden (TMB) is postulated to be a generic determinant of ICI-dependent tumor rejection. Here we describe the association between TMB and survival outcomes among microsatellite-stable cancers in a real-world clinicogenomic cohort consisting of 70,698 patients distributed across 27 histologies. TMB was associated with survival benefit or detriment depending on tissue and treatment context, with eight cancer types demonstrating a specific association between TMB and improved outcomes upon treatment with anti-PD-1/L1 therapies. Survival benefits were noted over a broad range of TMB cutoffs across cancer types, and a dose-dependent relationship between TMB and outcomes was observed in a subset of cancers. These results have implications for the use of cancer-agnostic and universal TMB cutoffs to guide the use of anti-PD-1/L1 therapies, and they underline the importance of tissue context in the development of ICI biomarkers.

Abstract 7621: Serum albumin and derived neutrophil-to-lymphocyte ratio are potential predictive biomarkers for immune checkpoint inhibitors in small cell lung cancer

Abstract Background: Immune checkpoint inhibitors (ICIs) have reshaped the treatment landscape of small cell lung cancer (SCLC), but only a minority of patients benefit from this therapy. Therefore, it is critical to identify potential risk factors that could predict the efficacy of ICI treatment in SCLC patients and identify patient subgroups who may benefit the most from ICI therapy. Methods: Our study included a total of 183 SCLC patients who had received at least one dose of ICI treatment. We utilized both logistic regression and Cox proportional hazard regression to evaluate whether various patient clinical factors and serum biomarkers, could serve as predictors of patient response to treatment and overall survival (OS) during ICI therapy. Results: Logistic regression showed that patients with a history of surgery (p=0.003, OR 9.06, 95% CI: (2.17, 37.9)) and no metastasis (p=0.008, OR 7.82, 95% CI: (1.73, 35.4)) exhibited a higher odds of response to ICI treatment. Cox regression analyses demonstrated that pretreatment blood albumin (p=0.003, HR 1.72, 95% CI: (1.21, 2.45)) and derived neutrophil to lymphocyte ratio (dNLR) (p=0.003, HR 1.71, 95% CI: (1.20-2.44)) were independent predictors for OS in SCLC patients. By establishing a pre-treatment prognostic scoring system based on baseline albumin and dNLR, we found that patients with high albumin and low dNLR exhibited a significantly better prognosis than those with low albumin and high dNLR in both the full (P&amp;lt;.0001, HR 0.33, 95% CI: 0.20-0.55) and the metastatic cohort (P&amp;lt;.0001, HR 0.28, 95% CI: 0.15-0.51). The better prognostic group also had younger age, higher BMI and lower systemic inflammatory biomarker values than the unfavorable group (P&amp;lt;.0001). Conclusion: Our data reveals the significant role of metastasis status and treatment history in predicting the initial response of SCLC patients to ICI treatment. However, baseline serum albumin and dNLR provide a more precise prognostic prediction for patient OS. The scoring system based on albumin and dNLR enhances the ability to stratify patient prognosis and holds the potential to guide clinical decision-making for SCLC patients undergoing ICI therapy. Citation Format: Zhanpeng Kuang, Jessica Miao, Xiaoli Zhang. Serum albumin and derived neutrophil-to-lymphocyte ratio are potential predictive biomarkers for immune checkpoint inhibitors in small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7621.