Checkpoint Inhibitor Response Research Articles

Integrated analysis of single-cell and bulk RNA sequencing data reveals a myeloid cell-related regulon predicting neoadjuvant immunotherapy response across cancers

BackgroundImmunotherapy has brought about a paradigm shift in the treatment of cancer. However, the majority of patients exhibit resistance or become refractory to immunotherapy, and the underlying mechanisms remain to be explored.MethodsSing-cell RNA sequencing (scRNA‑seq) datasets derived from 1 pretreatment and 1 posttreatment achieving pathological complete response (pCR) patient with lung adenocarcinoma (LUAD) who received neoadjuvant immunotherapy were collected, and pySCENIC was used to find the gene regulatory network (GRN) between cell types and immune checkpoint inhibitor (ICI) response. A regulon predicting ICI response was identified and validated using large‑scale pan-cancer data, including a colorectal cancer scRNA‑seq dataset, a breast cancer scRNA‑seq dataset, The Cancer Genome Atlas (TCGA) pan-cancer cohort, and 5 ICI transcriptomic cohorts. Symphony reference mapping was performed to construct the myeloid cell map.ResultsThirteen major cluster cell types were identified by comparing pretreatment and posttreatment patients, and the fraction of myeloid cells was higher in the posttreatment group (19.0% vs. 11.8%). A PPARG regulon (containing 23 target genes) was associated with ICI response, and its function was validated by a colorectal cancer scRNA‑seq dataset, a breast cancer scRNA‑seq dataset, TCGA pan-cancer cohort, and 5 ICI transcriptomic cohorts. Additionally, a myeloid cell map was developed, and cluster I, II, and III myeloid cells with high expression of PPARG were identified. Moreover, we constructed a website called PPARG (https://pparg.online/PPARG/ or http://43.134.20.130:3838/PPARG/), which provides a powerful discovery tool and resource value for researchers.ConclusionsThe PPARG regulon is a predictor of ICI response. The myeloid cell map enables the identification of PPARG subclusters in public scRNA-seq datasets and provides a powerful discovery tool and resource value.

The correlation between immune-related adverse events and efficacy of immune checkpoint inhibitors.

Immune checkpoint inhibitors have revolutionized cancer treatment by targeting the cytotoxic T lymphocyte antigen-4 and programmed death-1/ligand-1. Although immune checkpoint inhibitors show promising therapeutic efficacy, they often cause immune-related adverse events. Immune-related adverse events differ from the side effects of conventional chemotherapy and require vigilant monitoring. These events predominantly affect organs, such as the colon, liver, lungs, pituitary gland, thyroid and skin, with rare cases affecting the heart, nervous system and other tissues. As immune-related adverse events result from immune activation, indicating the reinvigoration of exhausted immune cells that attack both tumors and normal tissues, it is theoretically possible that immune-related adverse events may signal a better response to immune checkpoint inhibitor therapy. Recent retrospective studies have explored the link between immune-related adverse event development and clinical efficacy; however, the predictive value of immune-related adverse events in the immune checkpoint inhibitor response remains unclear. Additionally, studies have focused on immune-related adverse events, timing of onset and immunosuppressive treatments. This review focuses on pivotal studies of the association between immune-related adverse events and outcomes in patients treated with immune checkpoint inhibitors.

Association between pretreatment emotional distress and immune checkpoint inhibitor response in non-small-cell lung cancer.

Emotional distress (ED), commonly characterized by symptoms of depression and/or anxiety, is prevalent in patients with cancer. Preclinical studies suggest that ED can impair antitumor immune responses, but few clinical studies have explored its relationship with response to immune checkpoint inhibitors (ICIs). Here we report results from cohort 1 of the prospective observational STRESS-LUNG study, which investigated the association between ED and clinical efficacy of first-line treatment of ICIs in patients with advanced non-small-cell lung cancer. ED was assessed by Patient Health Questionnaire-9 and Generalized Anxiety Disorder 7-item scale. The study included 227 patients with 111 (48.9%) exhibiting ED who presented depression (Patient Health Questionnaire-9 score ≥5) and/or anxiety (Generalized Anxiety Disorder 7-item score ≥5) symptoms at baseline. On the primary endpoint analysis, patients with baseline ED exhibited a significantly shorter median progression-free survival compared with those without ED (7.9 months versus 15.5 months, hazard ratio 1.73, 95% confidence interval 1.23 to 2.43, P = 0.002). On the secondary endpoint analysis, ED was associated with lower objective response rate (46.8% versus 62.1%, odds ratio 0.54, P = 0.022), reduced 2-year overall survival rate of 46.5% versus 64.9% (hazard ratio for death 1.82, 95% confidence interval 1.12 to 2.97, P = 0.016) and detriments in quality of life. The exploratory analysis indicated that the ED group showed elevated blood cortisol levels, which was associated with adverse survival outcomes. This study suggests that there is an association between ED and worse clinical outcomes in patients with advanced non-small-cell lung cancer treated with ICIs, highlighting the potential significance of addressing ED in cancer management. ClinicalTrials.gov registration: NCT05477979 .

Harmonizing tumor mutational burden analysis: Insights from a multicenter study using in silico reference data sets in clinical whole-exome sequencing (WES).

Tumor mutational burden (TMB) is a significant biomarker for predicting immune checkpoint inhibitor response, but the clinical performance of whole-exome sequencing (WES)-based TMB estimation has received less attention compared to panel-based methods. This study aimed to assess the reliability and comparability of WES-based TMB analysis among laboratories under routine testing conditions. A multicenter study was conducted involving 24 laboratories in China using in silico reference data sets. The accuracy and comparability of TMB estimation were evaluated using matched tumor-normal data sets. Factors such as accuracy of variant calls, limit of detection (LOD) of WES test, size of regions of interest (ROIs) used for TMB calculation, and TMB cutoff points were analyzed. The laboratories consistently underestimated the expected TMB scores in matched tumor-normal samples, with only 50% falling within the ±30% TMB interval. Samples with low TMB score (<2.5) received the consensus interpretation. Accuracy of variant calls, LOD of the WES test, ROI, and TMB cutoff points were important factors causing interlaboratory deviations. This study highlights real-world challenges in WES-based TMB analysis that need to be improved and optimized. This research will aid in the selection of more reasonable analytical procedures to minimize potential methodologic biases in estimating TMB in clinical exome sequencing tests. Harmonizing TMB estimation in clinical testing conditions is crucial for accurately evaluating patients' response to immunotherapy.

Deep learning based digital pathology for predicting treatment response to first-line PD-1 blockade in advanced gastric cancer

BackgroundAdvanced unresectable gastric cancer (GC) patients were previously treated with chemotherapy alone as the first-line therapy. However, with the Food and Drug Administration’s (FDA) 2022 approval of programmed cell death protein 1 (PD-1) inhibitor combined with chemotherapy as the first-li ne treatment for advanced unresectable GC, patients have significantly benefited. However, the significant costs and potential adverse effects necessitate precise patient selection. In recent years, the advent of deep learning (DL) has revolutionized the medical field, particularly in predicting tumor treatment responses. Our study utilizes DL to analyze pathological images, aiming to predict first-line PD-1 combined chemotherapy response for advanced-stage GC.MethodsIn this multicenter retrospective analysis, Hematoxylin and Eosin (H&E)-stained slides were collected from advanced GC patients across four medical centers. Treatment response was evaluated according to iRECIST 1.1 criteria after a comprehensive first-line PD-1 immunotherapy combined with chemotherapy. Three DL models were employed in an ensemble approach to create the immune checkpoint inhibitors Response Score (ICIsRS) as a novel histopathological biomarker derived from Whole Slide Images (WSIs).ResultsAnalyzing 148,181 patches from 313 WSIs of 264 advanced GC patients, the ensemble model exhibited superior predictive accuracy, leading to the creation of ICIsNet. The model demonstrated robust performance across four testing datasets, achieving AUC values of 0.92, 0.95, 0.96, and 1 respectively. The boxplot, constructed from the ICIsRS, reveals statistically significant disparities between the well response and poor response (all p-values < = 0.001).ConclusionICIsRS, a DL-derived biomarker from WSIs, effectively predicts advanced GC patients’ responses to PD-1 combined chemotherapy, offering a novel approach for personalized treatment planning and allowing for more individualized and potentially effective treatment strategies based on a patient’s unique response situations.

Clinical Validation of Artificial Intelligence-Powered PD-L1 Tumor Proportion Score Interpretation for Immune Checkpoint Inhibitor Response Prediction in Non-Small Cell Lung Cancer.

Evaluation of PD-L1 tumor proportion score (TPS) by pathologists has been very impactful but is limited by factors such as intraobserver/interobserver bias and intratumor heterogeneity. We developed an artificial intelligence (AI)-powered analyzer to assess TPS for the prediction of immune checkpoint inhibitor (ICI) response in advanced non-small cell lung cancer (NSCLC). The AI analyzer was trained with 393,565 tumor cells annotated by board-certified pathologists for PD-L1 expression in 802 whole-slide images (WSIs) stained by 22C3 pharmDx immunohistochemistry. The clinical performance of the analyzer was validated in an external cohort of 430 WSIs from patients with NSCLC. Three pathologists performed annotations of this external cohort, and their consensus TPS was compared with AI-based TPS. In comparing PD-L1 TPS assessed by AI analyzer and by pathologists, a significant positive correlation was observed (Spearman coefficient = 0.925; P < .001). The concordance of TPS between AI analyzer and pathologists according to TPS ≥50%, 1%-49%, and <1% was 85.7%, 89.3%, and 52.4%, respectively. In median progression-free survival (PFS), AI-based TPS predicted prognosis in the TPS 1%-49% or TPS <1% group better than the pathologist's reading, with the TPS ≥50% group as a reference (hazard ratio [HR], 1.49 [95% CI, 1.19 to 1.86] v HR, 1.36 [95% CI, 1.08 to 1.71] for TPS 1%-49% group, and HR, 2.38 [95% CI, 1.69 to 3.35] v HR, 1.62 [95% CI, 1.23 to 2.13] for TPS <1% group). PD-L1 TPS assessed by AI analyzer correlates with that of pathologists, with clinical performance also being comparable when referenced to PFS. The AI model can accurately predict tumor response and PFS of ICI in advanced NSCLC via assessment of PD-L1 TPS.

Harnessing genetic interactions for prediction of immune checkpoint inhibitors response signature in cancer cells

Genetic interactions (GIs) refer to two altered genes having a combined effect that is not seen individually. They play a crucial role in influencing drug efficacy. We utilized CGIdb 2.0 (http://www.medsysbio.org/CGIdb2/), an updated database of comprehensively published GIs information, encompassing synthetic lethality (SL), synthetic viability (SV), and chemical-genetic interactions. CGIdb 2.0 elucidates GIs relationships between or within protein complex models by integrating protein-protein physical interactions. Additionally, we introduced GENIUS (GENetic Interactions mediated drUg Signature) to leverage GIs for identifying the response signature of immune checkpoint inhibitors (ICIs). GENIUS identified high MAP4K4 expression as a resistance signature and high HERC4 expression as a sensitivity signature for ICIs treatment. Melanoma patients with high expression of MAP4K4 were associated with decreased efficacy and poorer survival following ICIs treatment. Conversely, overexpression of HERC4 in melanoma patients correlated with a positive response to ICIs. Notably, HERC4 enhances sensitivity to immunotherapy by facilitating antigen presentation. Analyses of immune cell infiltration and single-cell data revealed that B cells expressing MAP4K4 may contribute to resistance to ICIs in melanoma. Overall, CGIdb 2.0, provides integrated GIs data, thus serving as a crucial tool for exploring drug effects.

Abstract LB091: A multigene signature for immunotherapy in patients with high risk non-muscle-invasive bladder cancer

Abstract Introduction: Approximately 80% of bladder cancer patients are initially diagnosed with non-muscle invasive bladder cancer (NMIBC). However, most NMIBC patients have a high recurrence rates, and up to 45% progress to muscle-invasive bladder cancer (MIBC) and metastatic disease. Treatment for NMIBC includes transurethral resection and, depending on the risk of recurrence or progression, intravesical chemotherapy or immunotherapy, such as Bacillus Calmette-Guérin(BCG). Our aim is to use a multigene signature in patients with high-risk NMIBC to determine whether patients may benefit from improved ICIs compared to conventional PD-L1 expression and to evaluate the clinical utility. Methods: A multi-gene signature obtained from the various NMIBC data was applied to stratify the UROMOL2021 cohort (n=535) using consensus clustering. Each subtype was distinguished by biological pathway analysis. Validation analysis was performed in three independent cohorts including the IMvigor210 (n=298), IMvigor010 (n=377) and ABACUS (n=148) clinical trials treated with PD-L1 inhibitors. The log-rank test, chi-square test, Fisher’s exact test, and cox regression analysis were used to evaluate clinical utility. Results: Based on consensus cluster analysis, NMIBC patients in the UROMOL2021 cohort were classified into five subgroups exhibiting unique characteristics, including DNA damage repair (DDR). Cross-validation was performed using two independent NMIBC cohorts and one MIBC cohort. Survival analysis showed that the subgroup associated with DDR (DDR and DDR+Tcell) had the highest rates of relapse and disease progression (progression-free survival, p&amp;lt;0.001) and benefited from Immune checkpoint inhibitors (ICIs) (response, p=0.01; overall survival, p=0.004). Conclusions: This study suggests that the multi-gene signature may play a role to enhance the responsiveness of ICIs for high-risk NMIBC. Additionally, it is important to help improve ICI treatment approaches that currently focus on PD-L1 expression on cancer cells, allowing more patients to receive priority consideration for ICI therapy. Citation Format: Seung-Woo Baek, Seon-Kyu Kim, Sun-Hee Leem, In-Sun Chu. A multigene signature for immunotherapy in patients with high risk non-muscle-invasive bladder cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB091.

Myeloid-T cell interplay and cell state transitions associated with checkpoint inhibitor response in melanoma.

The treatment of melanoma, the deadliest form of skin cancer, has greatly benefited from immunotherapy. However, many patients do not show a durable response, which is only partially explained by known resistance mechanisms. We performed single-cell RNA sequencing of tumor immune infiltrates and matched peripheral blood mononuclear cells of 22 checkpoint inhibitor (CPI)-naive stage III-IV metastatic melanoma patients. After sample collection, the same patients received CPI treatment, and their response was assessed. CPI responders showed high levels of classical monocytes in peripheral blood, which preferentially transitioned toward CXCL9-expressingmacrophages in tumors. Trajectories of tumor-infiltrating CD8+ Tcells diverged at the level of effector memory/stem-like Tcells, with non-responder cells progressing into a state characterized by cellular stress and apoptosis-related gene expression. Consistently, predicted non-responder-enriched myeloid-T/natural killer cell interactions were primarily immunosuppressive, while responder-enriched interactions were supportive of Tcell priming and effector function. Our study illustrates that the tumor immune microenvironment prior to CPI treatment can be indicative of response. In perspective, modulating the myeloid and/or effector cell compartment by altering the described cell interactions and transitions could improve immunotherapy response. This research was funded by Roche Pharma Research and Early Development.

Dual‐Sensing Nanoreporter for Dynamic and High‐Throughput Monitoring of Immune Checkpoint Inhibitor Responses in Tumor‐Derived Organoids

AbstractHeterogenous immune responses to checkpoint blockade therapy remain a major challenge to early prediction of treatment efficacy. Current methods of evaluating treatment efficacy in tumors are ineffective in accurately monitoring the disease status after immunotherapy early on. This study reports an immune response monitoring strategy that longitudinally reports on two crucial protease activities involved in T cell‐mediated target cell death, granzyme B (GrzB) and downstream caspase 3 (Casp3), during and after immune checkpoint antibody treatment. Specifically, activatable nanoreporters are engineered to sensitively and selectively capture the activity of GrzB and Casp3 enzymes in tumor cells triggered by cytotoxic T cells in real‐time, providing a direct readout of the tumor response to immune checkpoint inhibitors (ICIs). Furthermore, incorporating 3D tumor‐derived organoids and ex vivo cultures on microfluidic devices with nanoreporters enables high‐throughput screening of various ICIs and their combinations in immunocompetent tumor models. These findings suggest that efficient monitoring of dynamic immunological processes in the tumor microenvironment could help uncover mechanisms associated with ICI response or resistance. It is further anticipated that combining the dual‐sensing nanoreporter with tumor‐derived organoid‐on‐chip technology could lead to an early prediction of treatment outcomes with high fidelity and accelerate the development of optimal treatment in immuno‐oncology.

Targeting CD73 with flavonoids inhibits cancer stem cells and increases lymphocyte infiltration in a triple-negative breast cancer mouse model.

Chemotherapy remains the mainstay treatment for triple-negative breast cancer (TNBC) due to the lack of specific targets. Given a modest response of immune checkpoint inhibitors in TNBC patients, improving immunotherapy is an urgent and crucial task in this field. CD73 has emerged as a novel immunotherapeutic target, given its elevated expression on tumor, stromal, and specific immune cells, and its established role in inhibiting anti-cancer immunity. CD73-generated adenosine suppresses immunity by attenuating tumor-infiltrating T- and NK-cell activation, while amplifying regulatory T cell activation. Chemotherapy often leads to increased CD73 expression and activity, further suppressing anti-tumor immunity. While debulking the tumor mass, chemotherapy also enriches heterogenous cancer stem cells (CSC), potentially leading to tumor relapse. Therefore, drugs targeting both CD73, and CSCs hold promise for enhancing chemotherapy efficacy, overcoming treatment resistance, and improving clinical outcomes. However, safe and effective inhibitors of CD73 have not been developed as of now. We used in silico docking to screen compounds that may be repurposed for inhibiting CD73. The efficacy of these compounds was investigated through flow cytometry, RT-qPCR, CD73 activity, cell viability, tumorsphere formation, and other in vitro functional assays. For assessment of clinical translatability, TNBC patient-derived xenograft organotypic cultures were utilized. We also employed the ovalbumin-expressing AT3 TNBC mouse model to evaluate tumor-specific lymphocyte responses. We identified quercetin and luteolin, currently used as over-the-counter supplements, to have high in silico complementarity with CD73. When quercetin and luteolin were combined with the chemotherapeutic paclitaxel in a triple-drug regimen, we found an effective downregulation in paclitaxel-enhanced CD73 and CSC-promoting pathways YAP and Wnt. We found that CD73 expression was required for the maintenance of CD44highCD24low CSCs, and co-targeting CD73, YAP, and Wnt effectively suppressed the growth of human TNBC cell lines and patient-derived xenograft organotypic cultures. Furthermore, triple-drug combination inhibited paclitaxel-enriched CSCs and simultaneously improved lymphocyte infiltration in syngeneic TNBC mouse tumors. Conclusively, our findings elucidate the significance of CSCs in impairing anti-tumor immunity. The high efficacy of our triple-drug regimen in clinically relevant platforms not only underscores the importance for further mechanistic investigations but also paves the way for potential development of new, safe, and cost-effective therapeutic strategies for TNBC.

Pan-cancer evaluation of regulated cell death to predict overall survival and immune checkpoint inhibitor response

Regulated cell death (RCD) plays a pivotal role in various biological processes, including development, tissue homeostasis, and immune response. However, a comprehensive assessment of RCD status and its associated features at the pan-cancer level remains unexplored. Furthermore, despite significant advancements in immune checkpoint inhibitors (ICI), only a fraction of cancer patients currently benefit from treatments. Given the emerging evidence linking RCD and ICI efficacy, we hypothesize that the RCD status could serve as a promising biomarker for predicting the ICI response and overall survival (OS) in patients with malignant tumors. We defined the RCD levels as the RCD score, allowing us to delineate the RCD landscape across 30 cancer types, 29 normal tissues in bulk, and 2,573,921 cells from 82 scRNA-Seq datasets. By leveraging large-scale datasets, we aimed to establish the positive association of RCD with immunity and identify the RCD signature. Utilizing 7 machine-learning algorithms and 18 ICI cohorts, we developed an RCD signature (RCD.Sig) for predicting ICI response. Additionally, we employed 101 combinations of 10 machine-learning algorithms to construct a novel RCD survival-related signature (RCD.Sur.Sig) for predicting OS. Furthermore, we obtained CRISPR data to identify potential therapeutic targets. Our study presents an integrative framework for assessing RCD status and reveals a strong connection between RCD status and ICI effectiveness. Moreover, we establish two clinically applicable signatures and identify promising potential therapeutic targets for patients with tumors.

Abstract 5132: Multi-omics analysis uncovers predictive biomarkers for the efficacy and outcomes of immune checkpoint inhibitor in combination with chemotherapy in advanced unresectable biliary tract cancers

Abstract Background: Biliary tract cancer, typically diagnosed at advanced stages with a poor prognosis, has witnessed a glimmer of hope in the progress of immune checkpoint inhibitors (ICIs). Yet, their limited response rates necessitate the search for effective biomarkers to refine patient selection. Methods: A total of 125 patients with a confirmed histological diagnosis of unresectable advanced or metastatic biliary tract cancers (BTC) who received first-line ICIs in combination with chemotherapy (chemoimmunotherapy) were prospectively enrolled. All baseline samples from 125 patients underwent targeted DNA sequencing, with an additional 62 patients undergoing RNA sequencing, and 85 patients had accessible mIHC data. The associations between molecular characteristics and the response to chemoimmunotherapy, progression-free survival (PFS) and overall survival (OS) were evaluated. Results: The cohort had a median age of 63 years (range from 34 to 82) and 52.8% (66/125) were male, including 54 with gallbladder cancer, 57 with intrahepatic cholangiocarcinoma, and 14 with extrahepatic cholangiocarcinoma. The median duration of follow-up was 14.8 months for the entire cohort, with the median PFS and OS of 6.9 months (95%CI: 6.2-7.9) and 11.8 months (95%CI: 10.3-14.8), respectively. The most mutated genes were TP53 (64/125, 51.2%), KRAS (34/125, 27.2%), ERBB2 (18/125, 14.4%), and ARID1A (17/125, 13.6%). Mutations of TP53 (51.2%, p = 0.042), BRCA2 (4.8%, p = 0.002), cytokine genes (6.4%, p = 0.004), and high tumor mutation burden (p = 0.072) demonstrated significant correlation with chemoimmunotherapy response. KRAS G12D mutations (PFS: P &amp;lt; 0.001; OS: P = 0.034) and ARID1A loss-of-function mutations (PFS: P = 0.009; OS: P = 0.012) were adverse survival factors, while high CXCL9 or CTLA4 expression was associated with response (CXCL9, P = 0.014; CTLA4, P = 0.067), improved PFS (CXCL9, P = 0.018; CTLA4, P = 0.008), and longer OS (CXCL9, P = 0.010; CTLA4, P = 0.008) under chemoimmunotherapy. Patients were classified into three subtypes using the identified survival biomarkers. Among them, Type I patients, characterized by the absence of KRAS G12D or ARID1A mutations but expressing high levels of CTLA4 or CXCL9, demonstrated the best outcomes under chemoimmunotherapy. Interestingly, further RNA analysis suggested that elevated CXCL9 expression correlated with heightened immune checkpoint expressions, including CTLA4, PD-L1, and PD-1 (all P &amp;lt; 0.001), as well as increased tumor microenvironment immune activity, findings validated in two additional independent patient cohorts both internal and external. Conclusions: Our study revealed predictive biomarkers relevant to the response and efficacy of immune checkpoint inhibitors in combination with chemotherapy in advanced biliary tract cancer. Citation Format: Jieer Ying, Qi Xu, Jiaojiao Ni, Hanlin Chen, Chaoqun Li, Yanru Xie, Qinhong Zheng, Jianying Jin, Junrong Yan, Xiaoying Wu, Qiuxiang Ou, Li Yuan, Wei Zhuo, Haimeng Tang. Multi-omics analysis uncovers predictive biomarkers for the efficacy and outcomes of immune checkpoint inhibitor in combination with chemotherapy in advanced unresectable biliary tract cancers [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 5132.

Abstract 1040: Development of a novel RNA-based microsatellite stable predictive response signature (MSS-PRS) to identify MSS colorectal cancer (CRC) patients with a microsatellite instability-high (MSI-H) molecular phenotype

Abstract Introduction: MSI-H and mismatch repair deficient (dMMR) tumors are associated with favorable immune checkpoint inhibitor (ICI) responses (PMID: 3326454). However, up to 95% of CRC tumors are MSS/proficient MMR (pMMR) leading to poorer prognosis and treatment outcomes than MSI-H/dMMR patients (PMID: 12867608, 26028255). In MSS/pMMR metastatic CRC, multiple combination therapies are being investigated despite a lack of biomarkers to guide therapy. Therefore, we developed an RNA-based MSS-PRS with a primary objective to identify MSS tumors that have MSI-H/dMMR molecular features indicative of ICI response. Experimental Procedures: The MSS-PRS was developed using the TCGA CRC cohort (COAD; n=268). Training labels were assigned using the MSI Mantis score and mutation status from 8 genes with reported MSI association. Samples called “activated” had both a gene mutation and an MSI Mantis score &amp;gt; 0.4 (i.e., MSI-H), and samples called “non-activated” were wild type for all genes and had MSI Mantis score &amp;lt; 0.4. All other samples were designated “ambiguous” and excluded from training. Two thirds of the non-ambiguous samples were assigned to the classifier training set and all remaining samples were assigned to the test set. Using ClaNC software (PMID: 16269418) and cross-validation in the training set, a nearest centroid classifier was developed from a set of high mean, high variance candidate genes to select an optimal gene set to separate activated and non-activated groups. The classifier performance was evaluated in the test set. Results: The MSS-PRS contained 112-genes enriched for mismatched DNA binding, DNA damage repair, PD-L1, innate and adaptive immune response, cellular immunity, and cytokines. In the test set, the classifier called 44 of 46 MSI-H samples activated; of the remaining 77 samples that were MSS tumors, 38 were called activated and 39 were called not activated. Visual inspection of heat maps of tumor by MSI/MSS-PRS status and their association with TMB and CRC-related mutations suggest greater differences between MSS tumors called activated and MSI-H tumors than between MSS tumors called activated and MSS tumors called not activated. In contrast, immune marker expression profiles in MSS tumors called activated were markedly more similar to MSI-H tumors compared to MSS tumors called non-activated. Summary and Conclusions: Herein we described the development of a novel MSS-PRS that captures an MSI-H/dMMR-like molecular phenotype in a subset of patients with MSS tumors despite their lack of high TMB or overt MSI defects. Based upon these initial findings, further development of the MSS-PRS and its clinical validation as a tool to select patients with MSS tumors who may benefit from ICI-containing treatment regimens is warranted. Citation Format: Kirk Pappan, Gregory Mayhew, Jonathan Shepherd, Yuelong Guo, Kirk Beebe, Joel Eisner, Michael Milburn. Development of a novel RNA-based microsatellite stable predictive response signature (MSS-PRS) to identify MSS colorectal cancer (CRC) patients with a microsatellite instability-high (MSI-H) molecular phenotype [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 1040.

Abstract 5149: Extracellular matrix remodeling in metastatic mismatch repair deficient endometrial cancer:Implications for immune checkpoint inhibitor response prediction

Abstract Mismatch repair deficient (MMRd) status is a predictive biomarker for Immune checkpoint inhibitors (ICI) in endometrial cancer (EC), however, half of these patients (pts) do not respond. Most studies exploring biomarkers of response to ICI have focused on tumor or immune cell factors. We aimed to describe the extracellular matrix components of the tumor microenvironment (TME) in ICI-Responder (R) versus Non-Responder (NR) MMRd EC pts to identify new predictive biomarkers of response. Clinical data and outcomes of metastatic MMRd EC pts, treated with ICI (2016-2021), were retrospectively collected. Pts were classified as Rs (CR, PR, or SD ≥12 months) or NRs (PD or SD &amp;lt;12 months). Pre-ICI FFPE tumor samples were subjected to Biognosys UltraDeep TrueDiscovery™ Mass Spectrometry (MS) for identification of differentially regulated protein expression between R and NR. Criteria for protein candidate selection were p-value &amp;lt; 0.01 and average absolute log2 fold change (AVG Log ratio) &amp;gt; 0.58. Collagen was visualized on whole slides using Masson’s trichrome and quantified by Qupath. Collagen density was expressed as % positive surface in the intratumoral area. Second-harmonic generation (SHG) microscopy plus polarimetry (p-SHG) was used to estimate collagen content and describe the spatial orientation of fibrillar collagen in the intratumoral area. intraepithelial (ie) and stromal (s) CD3+ and CD8+ cells were quantified by Qupath. Non-parametric t-tests and Spearman’s rank coefficient (r) for linear correlation were used. Twenty-five tumor samples were included in the analysis: 17 from Rs and 8 from NRs. MS identified collagen type V alpha-2 (AVR Log ratio 2.9, p 0.008) and type XXII alpha-1 chains (AVR Log ratio 1.14, p 0.006) as significantly upregulated in NRs. Using spatial imaging, collagen density in the intratumoral area was significantly higher in NRs (median: 14.2% vs 4.6%, p 0.01). Intratumoral collagen density was positively correlated with collagen type V expression (r 0,54, p 0.016), and negatively correlated with ie and s CD3+ cell (r -0.39, p 0.054; r -0.4, p 0.048, respectively) and ie and s CD8+ cell (r -0.39, p 0.053; and r -0.29, p 0.166) infiltration. SHG confirmed that fibrillar collagen content in the tumor area was significantly greater in NRs (p 0.02). p-SHG revealed that NR tumors tended to show a highly disorganized collagen matrix. In contrast, a linear collagen pattern was associated with response to ICI: 89% of tumors displaying this pattern were Rs. Both MS and two different spatial approaches consistently demonstrated that increased collagen expression or density may serve as a potential predictive biomarker of resistance to ICI in MMRd metastatic EC, contributing to create an immunosuppressive TME. The spatial orientation pattern of collagen fibers may also be crucial in shaping immune TME and influencing the response to ICI. Citation Format: Juan Francisco Grau, Carole Aimé, Martin Mehnert, Elisa Yaniz-Galende, Catherine Genestie, Audrey Le Formal, Elodie Edmond, Antoine Amaury Lachaud, Patricia Pautier, Judith Michels, Sébastien Gouy, Amandine Maulard, Emeline Colomba-Blameble, Felix Blanc-Durand, Marie-Claire Schanne-Klein, Alexandra Leary. Extracellular matrix remodeling in metastatic mismatch repair deficient endometrial cancer:Implications for immune checkpoint inhibitor response prediction [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 5149.

Construction of an immune-related gene signature for overall survival prediction and immune infiltration in gastric cancer.

Treatment options for patients with gastric cancer (GC) continue to improve, but the overall prognosis is poor. The use of PD-1 inhibitors has also brought benefits to patients with advanced GC and has gradually become the new standard treatment option at present, and there is an urgent need to identify valuable biomarkers to classify patients with different characteristics into subgroups. To determined the effects of differentially expressed immune-related genes (DEIRGs) on the development, prognosis, tumor microenvironment (TME), and treatment response among GC patients with the expectation of providing new biomarkers for personalized treatment of GC populations. Gene expression data and clinical pathologic information were downloaded from The Cancer Genome Atlas (TCGA), and immune-related genes (IRGs) were searched from ImmPort. DEIRGs were extracted from the intersection of the differentially-expressed genes (DEGs) and IRGs lists. The enrichment pathways of key genes were obtained by analyzing the Kyoto Encyclopedia of Genes and Genomes (KEGGs) and Gene Ontology (GO) databases. To identify genes associated with prognosis, a tumor risk score model based on DEIRGs was constructed using Least Absolute Shrinkage and Selection Operator and multivariate Cox regression. The tumor risk score was divided into high- and low-risk groups. The entire cohort was randomly divided into a 2:1 training cohort and a test cohort for internal validation to assess the feasibility of the risk model. The infiltration of immune cells was obtained using 'CIBERSORT,' and the infiltration of immune subgroups in high- and low-risk groups was analyzed. The GC immune score data were obtained and the difference in immune scores between the two groups was analyzed. We collected 412 GC and 36 adjacent tissue samples, and identified 3627 DEGs and 1311 IRGs. A total of 482 DEIRGs were obtained. GO analysis showed that DEIRGs were mainly distributed in immunoglobulin complexes, receptor ligand activity, and signaling receptor activators. KEGG pathway analysis showed that the top three DEIRGs enrichment types were cytokine-cytokine receptors, neuroactive ligand receptor interactions, and viral protein interactions. We ultimately obtained an immune-related signature based on 10 genes, including 9 risk genes (LCN1, LEAP2, TMSB15A mRNA, DEFB126, PI15, IGHD3-16, IGLV3-22, CGB5, and GLP2R) and 1 protective gene (LGR6). Kaplan-Meier survival analysis, receiver operating characteristic curve analysis, and risk curves confirmed that the risk model had good predictive ability. Multivariate COX analysis showed that age, stage, and risk score were independent prognostic factors for patients with GC. Meanwhile, patients in the low-risk group had higher tumor mutation burden and immunophenotype, which can be used to predict the immune checkpoint inhibitor response. Both cytotoxic T lymphocyte antigen4+ and programmed death 1+ patients with lower risk scores were more sensitive to immunotherapy. In this study a new prognostic model consisting of 10 DEIRGs was constructed based on the TME. By providing risk factor analysis and prognostic information, our risk model can provide new directions for immunotherapy in GC patients.

Abstract B012: Transcriptomic insights reveal role of BCL2 proteins in replication checkpoint inhibitor response

Abstract B012: Transcriptomic insights reveal role of BCL2 proteins in replication checkpoint inhibitor response

Mechanism exploration of Zoledronic acid combined with PD-1 in the treatment of hepatocellular carcinoma

How to increase the response of immune checkpoint inhibitors (ICIs) is a challenge. In clinical, we found that Zoledronic acid (ZA) may increase the anti-tumor effect of immunotherapy for hepatocellular carcinoma (HCC). To explore the underlying mechanism, we established a mouse model of HCC by subcutaneously injecting Hepa1-6 cell line. The result showed that the tumor volume in the ZA plus anti-PD-1 monocloning antibody (anti-PD-1 mAb) treatment groups was significantly smaller than that of control group, and the onset time of tumor inhibition was even shorter than that of the anti-PD-1 mAb group. Using flow cytometry (FC) to detect the proportion of major immune cell subsets in tumor tissues of each group of mice, we found that the synergistic anti-tumor effect of ZA and anti-PD-1 mAb may be related to ZA-induced polarization of macrophages toward the M1 phenotype. Next, we performed bulk RNA sequencing on tumor samples from different groups to obtain differentially expressed genes (DEGs), which were then input DEGs into pathway enrichment analysis. Data indicated that ZA participated in the M1-type polarization via ferroptosis-related pathways. Our results revealed how ZA involves in the anti-tumor effect of PD-1 monoclonal antibody and provided a potential therapeutic candidate for patients with HCC.

DNA Methylation Signatures Correlate with Response to Immune Checkpoint Inhibitors in Metastatic Melanoma.

DNA methylation profiles have emerged as potential predictors of therapeutic response in various solid tumors. This study aimed to analyze the DNA methylation profiles of patients with stage IV metastatic melanoma undergoing first-line immune checkpoint inhibitor treatment and evaluate their correlation with a radiological response according to immune-related Response Evaluation Criteria in Solid Tumors (iRECIST). A total of 81 tissue samples from 71 patients with metastatic melanoma (27 female, 44 male) were included in this study. We utilized Illumina Methylation EPIC Beadchips to retrieve their genome-wide methylation profile by interrogating >850,000 CpG sites. Clustering based on the 500 most differentially methylated genes was conducted to identify distinct methylation patterns associated with immune checkpoint inhibitor response. Results were further aligned with an independent, previously published data set. The median progression-free survival was 8.5 months (range: 0-104.1 months), and the median overall survival was 30.6 months (range: 0-104.1 months). Objective responses were observed in 29 patients (40.8%). DNA methylation profiling revealed specific signatures that correlated with radiological response to immune checkpoint inhibitors. Three distinct clusters were identified based on the methylation patterns of the 500 most differentially methylated genes. Cluster 1 (12/12) and cluster 2 (12/24) exhibited a higher proportion of responders, while cluster 3 (39/45) predominantly consisted of non-responders. In the validation data set, responders also showed more frequent hypomethylation although differences in the data sets limit the interpretation. These findings suggest that DNA methylation profiling of tumor tissues might serve as a predictive biomarker for immune checkpoint inhibitor response in patients with metastatic melanoma. Further validation studies are warranted to confirm the efficiency of DNA methylation profiling as a predictive tool in the context of immunotherapy for metastatic melanoma.

NF1 mutations as biomarker of response to immune checkpoint blockades for lung adenocarcinoma patients

Little is known about immune checkpoint inhibitors (ICI) response of NF1-mutated lung adenocarcinomas. 341/4,181 (8.2%) TCGA lung adenocarcinomas samples have a somatic NF1 mutation. NF1-mutated tumors have higher TMB (p < 0.0001), higher expression of immune genes (“hot phenotype”) and higher CD8 + T cell (p = 0.03) and macrophage (p = 0.02) infiltrations compared to NF1 wild-type tumors. NF1 mutation status appears as a candidate predictive biomarker for ICI response in lung adenocarcinoma patients.