Immune Checkpoint Inhibitors Response Research Articles

Differential outcomes and immune checkpoint inhibitor response among endometrial cancer patients with MLH1 hypermethylation versus MLH1 “Lynch-like” mismatch repair gene mutation

ObjectivesTo identify differential survival outcomes and immune checkpoint inhibitor (ICI) response in MLH1 hypermethylated versus MLH1 mutated (“Lynch-like”) endometrial tumors and determine whether their molecular profiles can elucidate the differential outcomes. Methods1673 mismatch repair deficient endometrial tumors were analyzed by next-generation sequencing and whole transcriptome sequencing (Caris Life Sciences, Phoenix, AZ). PD-L1, ER, and PR were tested by immunohistochemistry and immune cell infiltrates were calculated using MCP-counter. Significance was determined using Chi-square and Mann-Whitney U tests and adjusted for multiple comparisons. Overall survival (OS) was depicted using Kaplan-Meier survival curves. ResultsThe endometrial cancer cohort comprised 89.2% patients with MLH1 hypermethylated tumors and 10.8% with MLH1 mutated tumors, with median ages of 67 and 60 years, respectively (p < 0.01). Patients with MLH1 hypermethylated tumors had significantly worse OS and trended toward worse OS following ICI treatment than patients with MLH1 mutated tumors. The immune microenvironment of MLH1 hypermethylated relative to MLH1 mutated was characterized by decreased PD-L1 positivity, immune checkpoint gene expression, immune cell infiltration, T cell inflamed scores, and interferon gamma (IFNγ) scores. MLH1 hypermethylation was also associated with decreased mutation rates in TP53 and DNA damage repair genes, but increased rates of JAK1, FGFR2, CCND1, and PTEN mutations, as well as increased ER and PR positivity. ConclusionsEndometrial cancer patients with MLH1 hypermethylation display significantly decreased survival and discrepant immunotherapy responses compared to patients with MLH1 mutated tumors, which was associated with differential mutational profiles, a more immune cold phenotype, and increased ER/PR expression in MLH1 hypermethylated tumors. Providers may consider early transition from single agent ICI to a multi-agent regimen or hormonal therapy for patients with MLH1 hypermethylated tumors.

Integration of peripheral blood- and tissue-based biomarkers of response to immune checkpoint blockade in urothelial carcinoma.

As predictive biomarkers of response to immune checkpoint inhibitors (ICIs) remain a major unmet clinical need in patients with urothelial carcinoma (UC), we sought to identify tissue-based immune biomarkers of clinical benefit to ICIs using multiplex immunofluorescence and to integrate these findings with previously identified peripheral blood biomarkers of response. Fifty-five pretreatment and 12 paired on-treatment UC specimens were identified from patients treated with nivolumab with or without ipilimumab. Whole tissue sections were stained with a 12-plex mIF panel, including CD8, PD-1/CD279, PD-L1/CD274, CD68, CD3, CD4, FoxP3, TCF1/7, Ki67, LAG-3, MHC-II/HLA-DR, and pancytokeratin+SOX10 to identify over three million cells. Immune tissue densities were compared to progression-free survival (PFS) and best overall response (BOR) by RECIST version 1.1. Correlation coefficients were calculated between tissue-based and circulating immune populations. The frequency of intratumoral CD3+ LAG-3+ cells was higher in responders compared to nonresponders (p = 0.0001). LAG-3+ cellular aggregates were associated with response, including CD3+ LAG-3+ in proximity to CD3+ (p = 0.01). Exploratory multivariate modeling showed an association between intratumoral CD3+ LAG-3+ cells and improved PFS independent of prognostic clinical factors (log HR -7.0; 95% confidence interval [CI] -12.7 to -1.4), as well as established biomarkers predictive of ICI response (log HR -5.0; 95% CI -9.8 to -0.2). Intratumoral LAG-3+ immune cell populations warrant further study as a predictive biomarker of clinical benefit to ICIs. Differences in LAG-3+ lymphocyte populations across the intratumoral and peripheral compartments may provide complementary information that could inform the future development of multimodal composite biomarkers of ICI response. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Utility of Tumor Mutational Burden as a Biomarker for Response to Immune Checkpoint Inhibition in the VA Population.

Immune checkpoint inhibitors (ICIs) are used for an increasing number of indications across various tumor types, as well as several tumor-agnostic indications in patients with advanced cancer. Although many patients benefit from ICI therapy, others do not, highlighting a need for better predictive biomarkers. Tumor mutational burden (TMB) reflects the global number of mutations within a tumor and has been widely explored as a predictive biomarker of ICI response. The current tumor type-agnostic US Food and Drug Administration approval of pembrolizumab for metastatic solid tumors defines high TMB (TMB-H) as ≥10 mut/Mb as measured by FoundationOne CDx. This fixed cutoff may not be the ideal value across all solid tumors. We performed a retrospective analysis of the association of survival outcomes with TMB in patients treated with ICI for five major cancer types, using real-world data from the VA. Survival was measured from initiation of ICI, and Kaplan-Meier survival curves were compared by log-rank test. Overall survival (OS) was significantly longer for patients with TMB-H versus TMB low tumors in non-small-cell lung cancer (NSCLC; n = 1,593), head and neck (H&N) cancer (n = 222), and urothelial cancer (n = 332). OS was not significantly different based on TMB status in melanoma (n = 207) or esophageal/gastric cancer (n = 248). Consistent with previous studies, a predictive value of TMB ≥10 mut/Mb for ICI response was found in NSCLC and H&N, but not in esophageal/gastric cancer. Although inconclusive in the literature, significant association was found in urothelial cancer. The predictive value of TMB in melanoma was inconclusive. Our analysis does not support the use of a fixed threshold for TMB as a standalone predictive biomarker for ICI across all solid tumors.

Spatially aware deep learning reveals tumor heterogeneity patterns that encode distinct kidney cancer states

Clear cell renal cell carcinoma (ccRCC) is molecularly heterogeneous, immune infiltrated, and selectively sensitive to immune checkpoint inhibition (ICI). However, the joint tumor-immune states that mediate ICI response remain elusive. We develop spatially aware deep-learning models of tumor and immune features to learn representations of ccRCC tumors using diagnostic whole-slide images (WSIs) in untreated and treated contexts (n= 1,102 patients). We identify patterns of grade heterogeneity in WSIs not achievable through human pathologist analysis, and these graph-based "microheterogeneity" structures associate with PBRM1 loss of function and with patient outcomes. Joint analysis of tumor phenotypes and immune infiltration identifies a subpopulation of highly infiltrated, microheterogeneous tumors responsive to ICI. In paired multiplex immunofluorescence images of ccRCC, microheterogeneity associates with greater PD1 activation in CD8+ lymphocytes and increased tumor-immune interactions. Our work reveals spatially interacting tumor-immune structures underlying ccRCC biology that may also inform selective response to ICI.

Longitudinal Monitoring of Circulating Tumor DNA to Assess the Efficacy of Immune Checkpoint Inhibitors in Patients With Advanced Genitourinary Malignancies.

Circulating tumor DNA (ctDNA) detection in blood has emerged as a prognostic and predictive biomarker demonstrating improved assessment of treatment response in patients receiving immune checkpoint inhibitors (ICIs). Here, we performed a pilot study to support the role of ctDNA for longitudinal treatment response monitoring in patients with advanced genitourinary (GU) malignancies receiving ICIs. Patients with histologically confirmed advanced GU malignancies were prospectively enrolled. All eligible patients received ICI treatment for at least 12 weeks, followed by serial collection of blood samples every 6-8 weeks and conventional scans approximately every 12 weeks until disease progression. ctDNA analysis was performed using Signatera, a tumor-informed multiplex-polymerase chain reaction next-generation sequencing assay. Overall, the objective response rate (ORR) was reported and its association with ctDNA status was evaluated. Concordance rate between ctDNA dynamics and conventional imaging was also assessed. ctDNA analysis was performed on 98 banked plasma samples from 20 patients (15 renal, four urothelial, and one prostate). The median follow-up from the time of initiation of ICI to progressive disease (PD) or data cutoff was 67.7 weeks (range, 19.6-169.6). The ORR was 70% (14/20). Eight patients ultimately developed PD. The overall concordance between ctDNA dynamics and radiographic response was observed in 83% (15/18) of patients. Among the three patients with discordant results, two developed CNS metastases and one progressed with extracranial systemic disease while ctDNA remained undetectable. In this pilot study, longitudinal ctDNA analysis for monitoring response to ICI in patients with advanced GU tumors was feasible. Larger prospective studies are warranted to validate the utility of ctDNA as an ICI response monitoring tool in patients with advanced GU malignancies.

Combining bulk and single-cell RNA-sequencing data to develop an NK cell-related prognostic signature for hepatocellular carcinoma based on an integrated machine learning framework

BackgroundThe application of molecular targeting therapy and immunotherapy has notably prolonged the survival of patients with hepatocellular carcinoma (HCC). However, multidrug resistance and high molecular heterogeneity of HCC still prevent the further improvement of clinical benefits. Dysfunction of tumor-infiltrating natural killer (NK) cells was strongly related to HCC progression and survival benefits of HCC patients. Hence, an NK cell-related prognostic signature was built up to predict HCC patients’ prognosis and immunotherapeutic response.MethodsNK cell markers were selected from scRNA-Seq data obtained from GSE162616 data set. A consensus machine learning framework including a total of 77 algorithms was developed to establish the gene signature in TCGA–LIHC data set, GSE14520 data set, GSE76427 data set and ICGC–LIRI–JP data set. Moreover, the predictive efficacy on ICI response was externally validated by GSE91061 data set and PRJEB23709 data set.ResultsWith the highest C-index among 77 algorithms, a 11-gene signature was established by the combination of LASSO and CoxBoost algorithm, which classified patients into high- and low-risk group. The prognostic signature displayed a good predictive performance for overall survival rate, moderate to high predictive accuracy and was an independent risk factor for HCC patients’ prognosis in TCGA, GEO and ICGC cohorts. Compared with high-risk group, low-risk patients showed higher IPS–PD1 blocker, IPS–CTLA4 blocker, common immune checkpoints expression but lower TIDE score, which indicated low-risk patients might be prone to benefiting from ICI treatment. Moreover, a real-world cohort, PRJEB23709, also revealed better immunotherapeutic response in low-risk group.ConclusionsOverall, the present study developed a gene signature based on NK cell-related genes, which offered a novel platform for prognosis and immunotherapeutic response evaluation of HCC patients.

Association of ERCC family mutations with prognosis and immune checkpoint inhibitors response in multiple cancers

The proteins encoded by the excision repair cross-complementing (ERCC) family are pivotal in DNA damage repair and maintaining genome stability. However, the precise role of the ERCC family in tumor prognosis and the effectiveness of immune checkpoint inhibitors (ICI) therapy remain uncertain. This study aimed to explore the connection between ERCC mutations and prognosis as well as the response to ICI. We observed that patients with ERCC mutations exhibited enhanced progression-free survival (PFS) and overall survival (OS) in two independent pan-cancer cohorts. Furthermore, this mutant subgroup showed higher tumor mutation burden (TMB) compared to the wild-type subgroup. Notably, ERCC mutations were associated with better OS (HR 0.54, 95% CI 0.42–0.70; P < 0.001) in pan-cancer patients who underwent ICI therapy (N = 1661). These findings were validated in a separate cohort, where patients in the ERCC mutant subgroup demonstrated improved clinical outcomes (HR 0.56, 95% CI 0.37–0.84; P = 0.03) and higher response rates (51.9% vs. 26.8%) than the wild-type subgroup. Further analysis revealed that patients with ERCC mutations displayed elevated tumor neoantigen burden (TNB) levels and increased infiltration of immune-response cells. Our study suggests that ERCC mutations are linked to enhanced immunogenicity and improved ICI efficacy, thus potentially serving as a biomarker for ICI therapy.

Host immune signatures as predictors of response to immunotherapy-based regimens in patients with metastatic renal cell carcinoma (mRCC)

Abstract Background Treatment options for mRCC have evolved to include VEGF targeted therapies (VEGF-TT), immune checkpoint inhibitors (ICIs), or combinations of both. However, clinical responses to systemic therapies in mRCC remain largely unpredictable and robust biomarkers are still lacking. The interaction between the tumor and its immune microenvironment has been shown to influence clinical outcomes in patients treated with ICI-based regimens. The aim of this study was to characterize the T-cell and B-cell immune repertoires in patients with mRCC treated with VEGF-TT, ICI or a combination of both, and evaluate their associations with clinical outcomes. Methods We identified patients with mRCC at Dana-Farber Cancer Institute treated with VEGF-TT, ICI or both, and for whom tumor and/or blood samples were available. T-cell receptor sequencing (TCR-seq) was performed on peripheral blood mononuclear cells (PBMCs), collected before and during therapy. Bulk RNA-sequencing (RNA-seq) was performed on available pre-treatment PBMCs and primary tumor samples. Immunoglobulin heavy chain (IgH) isotypes were inferred from bulk RNA-seq data using TRUST4. Parameters of the T-cell and B-cell repertoires were evaluated in responders vs. non-responders to systemic therapies, and between pre- and on-treatment samples within patient subgroups. Results In total, blood (PBMC) samples from 386 patients were available across all treatment cohorts (186 VEGF-TT, 126 ICI and 74 ICI+VEGF-TT). Following quality-control, TCR-seq data were available for 367 patients (228 pre-treatment and 139 on-treatment), while RNA-seq data were available for 105 PBMC and 17 tumor-derived pre-treatment samples. In the TCR-seq analysis, responders to ICI-based regimens (ICI or VEGF-TT+ICI) presented a trend towards an increased baseline (pre-treatment) TCR clonality as compared to non-responders (p=0.06) (Fig. 1A), corresponding to a less polyclonal T cell repertoire in responders at baseline. No significant changes in clonality were seen between pre- and on-treatment samples among responders to ICI regimens (p=0.14), as opposed to non-responders where a significant increase was identified (p=0.001). Therefore, responders to ICI-based regimens seem to have a more oligoclonal TCR repertoire (increased clonality) at baseline with no treatment-induced changes, whereas non-responders to ICI-based regimens seem to evolve from a more polyclonal to a more oligoclonal TCR repertoire in response to treatment. The analysis of IgH isotypes in baseline blood samples showed higher fraction of IgG1 in responders (vs. non-responders) to ICI regimens (p=0.01) (Fig. 1B). This was further confirmed in the analysis of IgH isotypes inferred from tumor samples (p=0.04). No significant differences in IgH isotype fractions were identified between responders and non-responders to VEGF-TT. Furthermore, while shared clonotypes were detected between blood and tumor samples, there were no differences in the Jaccard similarity index between responders and non-responders to ICI-based or VEGF-TT regimens. Figure 1: (A) Evaluation of pre-treatment and post-treatment TCR clonality in responders vs. non-responders across treatment cohorts. (B) Differences in the fraction of IGH isotypes between responders and non-responders across treatment cohorts. Conclusions We were successfully able to characterize T-cell receptor and B-cell IgH repertoires in a large cohort of patients with mRCC, and evaluate their associations with ICI response. Our results show that baseline TCR clonality and IgG1 antibody fraction are associated with the response to ICI regimens, suggesting a potential role for immune biomarker development. CDMRP DOD Funding: yes

Elevated levels of neutrophil related chemokine citrullinated histone H3, interleukin-8 and C-reaction protein in patients with immune checkpoint inhibitor therapy: predictive biomarkers for response to treatment

BackgroundImmune checkpoint inhibitor (ICI) therapy has been used in various tumors. The biomarkers predictive of a response to ICI treatment remain unclear, and additional and combined biomarkers are urgently needed. Secreted factors related to the tumor microenvironment (TME) have been evaluated to identify novel noninvasive predictive biomarkers.MethodsWe analyzed 85 patients undergoing ICI therapy as the primary cohort. The associations between ICI response and all biomarkers were evaluated. A prediction model and a nomogram were developed and validated based on the above factors.ResultsSeventy-seven patients were enrolled in the validation cohort. In the primary cohort, the baseline serum levels of H3Cit, IL-8 and CRP were significantly higher in nonresponder patients. A model based on these three factors was developed, and the “risk score” of an ICI response was calculated with the formula: “risk score” = 3.4591×H3Cit + 2.5808×IL8 + 2.0045 ×CRP– 11.3844. The cutoff point of the “risk score” was 0.528, and patients with a “risk score” lower than 0.528 were more likely to benefit from ICI treatment (AUC: 0.937, 95% CI: 0.886–0.988, with sensitivity 80.60%, specificity 91.40%). The AUC was 0.719 (95% CI: 0.600-0.837, P = 0.001), with a sensitivity of 70.00% and specificity of 65.20% in the validation cohort.ConclusionsA model incorporating H3Cit, IL-8 and CRP has an excellent prediction ability for ICI response; thus, patients with a lower “risk score” selectively benefit from ICI treatment, which may have significant clinical implications for the early detection of an ICI response.

Antigenic targets in clear cell renal cell carcinoma.

Immune checkpoint inhibitors (ICIs) have transformed the management of advanced renal cell carcinoma (RCC), but most patients still do not receive a long-term benefit from these therapies, and many experience off-target, immune-related adverse effects. RCC is also different from many other ICI-responsive tumors, as it has only a modest mutation burden, and total neoantigen load does not correlate with ICI response. In order to improve the efficacy and safety of immunotherapies for RCC, it is therefore critical to identify the antigens that are targeted in effective anti-tumor immunity. In this review, we describe the potential classes of target antigens, and provide examples of previous and ongoing efforts to investigate and target antigens in RCC, with a focus on clear cell histology. Ultimately, we believe that a concerted antigen discovery effort in RCC will enable an improved understanding of response and resistance to current therapies, and lay a foundation for the future development of "precision" antigen-directed immunotherapies.

Immune Checkpoint Inhibitors in "Special" NSCLC Populations: A Viable Approach?

Over the last decade, the therapeutic scenario for advanced non-small-cell lung cancer (NSCLC) has undergone a major paradigm shift. Immune checkpoint inhibitors (ICIs) have shown a meaningful clinical and survival improvement in different settings of the disease. However, the real benefit of this therapeutic approach remains controversial in selected NSCLC subsets, such as those of the elderly with active brain metastases or oncogene-addicted mutations. This is mainly due to the exclusion or underrepresentation of these patient subpopulations in most pivotal phase III studies; this precludes the generalization of ICI efficacy in this context. Moreover, no predictive biomarkers of ICI response exist that can help with patient selection for this therapeutic approach. Here, we critically summarize the current state of ICI efficacy in the most common "special" NSCLC subpopulations.

SETBP1 mutation determines sensitivity to immune checkpoint inhibitors in melanoma and NSCLC.

SET binding protein 1 (SETBP1) plays crucial roles in various biological processes; however, its involvement in cancer immune checkpoint inhibitor (ICI) treatments has never been studied. In this study, we collected a total of 631 melanoma and 109 non-small cell lung cancer (NSCLC) samples treated with ICI agents (i.e., anti-CTLA-4, anti-PD-1/PD-L1, or combination therapy). Additionally, we obtained their corresponding somatic mutational profiles. We observed that SETBP1 mutated (SETBP1-MUT) melanoma patients exhibited significantly prolonged ICI survival outcomes compared to wild-type patients (HR: 0.56, 95% CI: 0.38-0.81, P = 0.002). Consistently, an elevated ICI response rate was also noticed in the SETBP1-MUT group (42.9% vs. 29.1%, P = 0.016). The Association of SETBP1 mutations with favorable immunotherapeutic prognosis and response was further supported by an independent NSCLC cohort (both P < 0.05). Additional immunological analyses revealed that favorable immune infiltration, tumor immunogenicity, and immune response circuits were enriched in SETBP1-MUT patients. Overall, our findings suggest that SETBP1 mutations may serve as a new biomarker for stratifying beneficiaries of ICI treatments in melanoma and NSCLC, which provides possible evidence for tailoring clinical immunotherapeutic strategies.

Association of Claudin-1 overexpression with immune exclusion in nasopharyngeal carcinoma and its potential as a novel therapeutic target.

151 Background: Claudin-1 (CLDN1) is a tight junction protein overexpressed in several cancers, where it is involved in the tumor microenvironment remodeling and stiffening of the tumor-stroma interface. This potentially prevents penetration of tumor infiltrating immune cells, in turn hindering the efficacy of immune checkpoint inhibitors (ICI). Given recent evidence supporting ICI efficacy in nasopharyngeal carcinoma (NPC), we investigated whether CLDN1 is overexpressed in NPC and its association with the tumor immune microenvironment (TME). Methods: 131 NPC patients treated at a single institution were prospectively recruited. TME profiling was performed using deconvolutional methods (CIBERSORT, quanTIseq) on tumor transcriptomes to estimate proportions of immune cell subsets, and expression of immune-related genes and pathways. Transcriptome profiling was performed on tumors with &gt;70% cellularity and normal controls using Illumina Truseq (CA). We stratified tumors by their median CLDN1 expression and compared the immune profile of CLDN1high versus CLDN1low tumors. Cox regression model was used to analyze disease-free survival (DFS) as the primary clinical endpoint. Results: We observed CLDN1 overexpression in NPC versus normal tissues ( P=0.0046) that was independent of known prognostic clinical parameters e.g., TNM-stage and EBV DNA levels. Consistent with our hypothesis, CLDN1 expression was negatively correlated with total immune cell fraction in the tumor ( P&lt;0.001). We observed reduced abundance of most immune subsets in CLDN1high tumors, notably CD8 T cells ( P&lt;0.001), activated CD4 memory T cells ( P&lt;0.001), and M1 and M2 macrophages ( P=0.048 and P&lt;0.001). There was an overall downregulation of both stimulatory and suppressive GO immune-related gene-sets and genes in CLDN1high versus CLDN1low tumors, thereby corroborating our preceding observation of reduced immune infiltration in the TME in CLDN1 overexpressing NPC. Our findings were validated in a published dataset of 113 NPC tumors (Zhang et al., 2017). Of clinical relevance, reduced immune infiltration in the TME was prognostic for DFS; HR of CLDN1high, CD8 T celllow (stratified by median) NPC was 2.1 (95% CI:0.9-5.0) when compared with CLDN1low, CD8 T cellhigh NPC. Conclusions: Here, we report the overexpression of CLDN1 in NPC that is ubiquitous across disease stages and associated with an immune-low TME. This influence on the TME is associated with an inferior outcome of NPC patients. Given the role of the tumor TME in ICI response, our findings provide a rationale for future trials investigating combination of CLDN1 targeting with ICI to reverse this immune exclusion effect, potentially leading to improved outcomes.

Identification of intratumoral and peripheral T-cell receptor (TCR) repertoire features associated with acquired (Ar) and primary (Pr) resistance to immune checkpoint inhibitors (ICI).

59 Background: Our understanding of the mechanisms of Pr vs Ar to ICI is limited. T-cells are the main driver of ICI response. Therefore, interrogation of intratumoral and peripheral TCR repertoire could expand our comprehension of T-cell mediated factors underlying Pr and Ar to ICI. Methods: The Immune Resistance Interrogation Study (NCT04243720) is a prospective study to comprehensively characterize cancers with Pr vs Ar to ICI (Genta et. al., ASCO 2021). We conducted TCRβ capture and sequencing (CapTCR-seq) on paired tumors and blood samples collected from solid tumor patients (pts) at the time of progression on ICI. Tumor and peripheral TCR diversities were calculated for each pt. Grouping Lymphocyte Interactions by Paratope Hotspots (GLIPHII) was used to investigate the potential antigen specificities of intratumoral T-cells. Pt-derived TCRs were pooled with TCRs from a public database with known antigen specificities (TCRdb). GLIPHII grouped pt-derived and external TCRs with similar CDR3s (hypervariable TCR domains) in clusters. The clusters were then converted into a network model representing unique TCRs as nodes. The number of connections between each pt-derived TCR and similar TCRs from TCRdb was defined as node degree. Comparisons between groups were done using Mann Whitney U Test. Results: As of February 2023, 75 pts (45 Pr/30 Ar) were enrolled. CapTCR-Seq in blood and tumor samples was completed in 13 pts (7Pr/6Ar) with the following characteristics: median age 57 years (26-77), 8 male (62%), 8 melanoma (62%), 4 HNSCC (31%), and 1 GE-junction (7%). 5 pts received PD-1/PD-L1 inhibitor monotherapy (38%), 8 pts ICl-based combinations (62%). No significant differences in intratumoral (10.3 [1.3-34.2] vs 15.2 [6.6-32.4] p=0.29) and peripheral (145.5 [65.3- 521.9] vs 136.0 [35.1-474.4] p=0.53) Shannon diversities were observed between Pr and Ar. Twelve of 13 (92%) pts had at least 1 intratumoral TCR clustered with TCRdb derived TCRs. A higher median of node degree was observed in non-melanoma tumors (5.5 [2-12] vs 2.0 [1-4] p=0.03). A trend towards a higher median of node degree was reported in Ar vs Pr (5.5 [1-12] vs 2.0 [1-4] p=0 .07 ). Some of the GLIPHll-identified clusters contained TCRs derived from multiple Ar pts. Conclusions: lntratumoral TCRs from non-melanoma and Ar pts had a higher node degree, indicating similarity with a larger number of external TCRs. The difference between Pr and Ar was not significant, potentially due to the small sample size. Similar TCRs belonging to the same GLIPHll-identified clusters were shared among multiple Ar pts. If confirmed in a larger dataset, these findings might suggest the existence of a set of intratumoral exhausted T-cells shared by multiple pts contributing to Ar but not to Pr. Pt accrual, sample collection and analysis are ongoing. Additional data will be presented.

Biomarkers for Immune Checkpoint Inhibitor Response in NSCLC: Current Developments and Applicability.

Lung cancer has the highest mortality rate among all cancer types, resulting in over 1.8 million deaths annually. Immunotherapy utilizing immune checkpoint inhibitors (ICIs) has revolutionized the treatment of non-small cell lung cancer (NSCLC). ICIs, predominantly monoclonal antibodies, modulate co-stimulatory and co-inhibitory signals crucial for maintaining immune tolerance. Despite significant therapeutic advancements in NSCLC, patients still face challenges such as disease progression, recurrence, and high mortality rates. Therefore, there is a need for predictive biomarkers that can guide lung cancer treatment strategies. Currently, programmed death-ligand 1 (PD-L1) expression is the only established biomarker for predicting ICI response. However, its accuracy and robustness are not consistently reliable. This review provides an overview of potential biomarkers currently under development or in the validation stage that hold promise in improving the classification of responders and non-responders to ICI therapy in the near future.

Biomarkers of immune checkpoint inhibitor response and toxicity: Challenges and opportunities.

Immune checkpoint inhibitors have transformed cancer therapy, but their optimal use is still constrained by lack of response and toxicity. Biomarkers of response may facilitate drug development by allowing appropriate therapy selection and focusing clinical trial enrollment. However, aside from PD-L1 staining in a subset of tumors and rarely mismatch repair deficiency, no biomarkers are routinely used in the clinic. In addition, severe toxicities may cause severe morbidity, therapy discontinuation, and even death. Here, we review the state of the field with a focus on our research in therapeutic biomarkers and toxicities from immune checkpoint inhibitors.

The Prognostic and Predictive Value of Human Gastrointestinal Microbiome and Exosomal mRNA Expression of PD-L1 and IFNγ for Immune Checkpoint Inhibitors Response in Metastatic Melanoma Patients: PROTOCOL TRIAL.

Immunotherapy has been successful in treating advanced melanoma, but a large proportion of patients do not respond to the treatment with immune checkpoint inhibitors (ICIs). Preclinical and small cohort studies suggest gastrointestinal microbiome composition and exosomal mRNA expression of PD-L1 and IFNγ from the primary tumor, stool and body fluids as potential biomarkers for response. Patients treated with immune checkpoint inhibitors as a first line treatment for metastatic melanoma are recruted to this prospective study. Stool samples are submitted before the start of treatment, at the 12th (+/-2) week and 28th (+/-2) week, and at the occurrence of event (suspected disease progression/hyperprogression, immune-related adverse event (irAE), deterioration). Peripheral venous blood samples are taken additionally at the same time points for cytologic and molecular tests. Histological material from the tumor tissue is obtained before the start of immunotherapy treatment. Primary objectives are to determine whether the human gastrointestinal microbiome (bacterial and viral) and the exosomal mRNA expression of PD-L1 and IFNγ and its dynamics predicts the response to treatment with PD-1 and CTLA-4 inhibitors and its association with the occurrence of irAE. The response is evaluated radiologically with imaging methods in accordance with the irRECIST criteria. This is the first study to combine and investigate multiple potential predictive and prognostic biomarkers and their dynamics in first line ICI in metastatic melanoma patients.

Comprehensive Review: Unveiling the Pro-Oncogenic Roles of IL-1ß and PD-1/PD-L1 in NSCLC Development and Targeting Their Pathways for Clinical Management.

In the past decade, targeted therapies for solid tumors, including non-small cell lung cancer (NSCLC), have advanced significantly, offering tailored treatment options for patients. However, individuals without targetable mutations pose a clinical challenge, as they may not respond to standard treatments like immune-checkpoint inhibitors (ICIs) and novel targeted therapies. While the mechanism of action of ICIs seems promising, the lack of a robust response limits their widespread use. Although the expression levels of programmed death ligand 1 (PD-L1) on tumor cells are used to predict ICI response, identifying new biomarkers, particularly those associated with the tumor microenvironment (TME), is crucial to address this unmet need. Recently, inflammatory cytokines such as interleukin-1 beta (IL-1β) have emerged as a key area of focus and hold significant potential implications for future clinical practice. Combinatorial approaches of IL-1β inhibitors and ICIs may provide a potential therapeutic modality for NSCLC patients without targetable mutations. Recent advancements in our understanding of the intricate relationship between inflammation and oncogenesis, particularly involving the IL-1β/PD-1/PD-L1 pathway, have shed light on their application in lung cancer development and clinical outcomes of patients. Targeting these pathways in cancers like NSCLC holds immense potential to revolutionize cancer treatment, particularly for patients lacking targetable genetic mutations. However, despite these promising prospects, there remain certain aspects of this pathway that require further investigation, particularly regarding treatment resistance. Therefore, the objective of this review is to delve into the role of IL-1β in NSCLC, its participation in inflammatory pathways, and its intricate crosstalk with the PD-1/PD-L1 pathway. Additionally, we aim to explore the potential of IL-1β as a therapeutic target for NSCLC treatment.

The Local Microbiome in Esophageal Cancer and Treatment Response: A Review of Emerging Data and Future Directions.

peer-reviewed, published studies describing the role of local, gastrointestinal-specific microbiota or the role of the gut microbiome in treatment responses were reviewed. PubMed was searched from 1 September 2022 to 1 November 2022, using the following terms in combination: "microbiome", "tumor microbiome", "esophageal cancer", "cancer", "cancer treatment", and "immunotherapy". Original research articles were considered, and other reviews or editorials were discarded. In total, approximately 250 articles were considered. over 70 studies describing microbiome research in either gastrointestinal carcinogenesis or the systemic treatment response were identified and reviewed. a growing body of evidence supports the role of the esophageal microbiome in both esophageal tumorigenesis and the immune checkpoint inhibitor response. More well-designed, comprehensive studies are required to collect the appropriate clinical, microbial, and immunophenotype data that are needed to clarify the precise role of the microbiome in esophageal carcinogenesis and treatment.

Construction and validation of a gene expression classifier to predict immunotherapy response in primary triple-negative breast cancer

BackgroundImmune checkpoint inhibitors (ICI) improve clinical outcomes in triple-negative breast cancer (TNBC) patients. However, a subset of patients does not respond to treatment. Biomarkers that show ICI predictive potential in other solid tumors, such as levels of PD-L1 and the tumor mutational burden, among others, show a modest predictive performance in patients with TNBC.MethodsWe built machine learning models based on pre-ICI treatment gene expression profiles to construct gene expression classifiers to identify primary TNBC ICI-responder patients. This study involved 188 ICI-naïve and 721 specimens treated with ICI plus chemotherapy, including TNBC tumors, HR+/HER2− breast tumors, and other solid non-breast tumors.ResultsThe 37-gene TNBC ICI predictive (TNBC-ICI) classifier performs well in predicting pathological complete response (pCR) to ICI plus chemotherapy on an independent TNBC validation cohort (AUC = 0.86). The TNBC-ICI classifier shows better performance than other molecular signatures, including PD-1 (PDCD1) and PD-L1 (CD274) gene expression (AUC = 0.67). Integrating TNBC-ICI with molecular signatures does not improve the efficiency of the classifier (AUC = 0.75). TNBC-ICI displays a modest accuracy in predicting ICI response in two different cohorts of patients with HR + /HER2- breast cancer (AUC = 0.72 to pembrolizumab and AUC = 0.75 to durvalumab). Evaluation of six cohorts of patients with non-breast solid tumors treated with ICI plus chemotherapy shows overall poor performance (median AUC = 0.67).ConclusionTNBC-ICI predicts pCR to ICI plus chemotherapy in patients with primary TNBC. The study provides a guide to implementing the TNBC-ICI classifier in clinical studies. Further validations will consolidate a novel predictive panel to improve the treatment decision-making for patients with TNBC.