Biomarker For Immunotherapy Response Research Articles

Biomarkers of immunotherapy in glioblastoma

Abstract Glioblastoma (GBM) is the most common primary brain cancer, comprising half of all malignant brain tumors. Patients with GBM have a poor prognosis, with a median survival of 14–15 months. Current therapies for GBM, including chemotherapy, radiotherapy, and surgical resection, remain inadequate. Novel therapies are required to extend patient survival. Although immunotherapy has shown promise in other cancers, including melanoma and non-small lung cancer, its efficacy in GBM has been limited to subsets of patients. Identifying biomarkers of immunotherapy response in GBM could help stratify patients, identify new therapeutic targets, and develop more effective treatments. This article reviews existing and emerging biomarkers of clinical response to immunotherapy in GBM. The scope of this review includes immune checkpoint inhibitor and antitumoral vaccination approaches, summarizing the variety of molecular, cellular, and computational methodologies that have been explored in the setting of anti-GBM immunotherapies.

Immunotherapy for patients with advanced non-small cell lung cancer harboring oncogenic driver alterations other than EGFR: a multicenter real-world analysis.

The administration of immune checkpoint inhibitors (ICIs) in advanced non-small cell lung cancer (NSCLC) with oncogenic driver alterations other than epidermal growth factor receptor (EGFR) aroused a heated discussion. We thus aimed to evaluate ICI treatment in these patients in real-world routine clinical practice. A multicenter, retrospective study was conducted for NSCLC patients with at least one gene alteration (KRAS, HER2, BRAF, MET, RET, ALK, ROS1) receiving ICI monotherapy or combination treatment. The data regarding clinicopathologic characteristics, clinical efficacy, and safety were investigated. A total of 216 patients were included, the median age was 60 years, 72.7% of patients were male, and 46.8% had a smoking history. The molecular alterations involved KRAS (n=95), HER2 (n=42), BRAF (n=22), MET (n=21), RET (n=14), ALK (n=14), and ROS1 (n=8); 56.5% of patients received immunotherapy in the first-line, and the rest 43.5% were treated as a second-line and above. For the entire cohort who received immunotherapy-based regimens in the first-line, the median progression-free survival (PFS) was 7.5 months and the median overall survival (OS) was 24.8 months. For the entire cohort who received immunotherapy-based regimens in the second-line and above, the median PFS was 4.7 months and median OS was 17.1 months. KRAS mutated NSCLC treated with immunotherapy-based regimens in the first-line setting had a median PFS and OS were 7.8 and 26.1 months, respectively. Moreover, the median PFS and OS of immunotherapy-based regimens for KRAS-mutant NSCLC that progressed after chemotherapy were 5.9 and 17.1 months. Programmed death ligand 1 (PD-L1) expression level was not consistently associated with response to immunotherapy across different gene alteration subsets. In the KRAS group, PD-L1 positivity [tumor proportion score (TPS) ≥1%] was associated with better PFS and OS according to the multivariate Cox analysis. No statistically significant association was found for smoking status, age, or gender with clinical efficacy in any gene group analyses. KRAS-mutant NSCLC could obtain clinical benefits from ICIs either for treatment-naive patients or those who have experienced progression after chemotherapy, and PD-L1 positive expression (TPS >1%) may be a potential positive predictor. For NSCLC with ALK, RET and ROS1 rearrangement, MET exon 14 skipping mutation, or BRAF V600E mutation, effectiveness of single or combined ICI therapy remains limited, therefore, targeted therapies should be considered prior to immunotherapy regimens. Future studies should address the investigation of better predictive biomarkers for immunotherapy response in oncogene-driven NSCLC.

BAP1 Deficiency Inflames the Tumor Immune Microenvironment and Is a Candidate Biomarker for Immunotherapy Response in Malignant Pleural Mesothelioma

IntroductionMalignant pleural mesothelioma (MPM) is a rare and universally lethal malignancy with limited treatment options. Immunotherapy with immune checkpoint inhibitors (ICIs) has recently been approved for unresectable MPM, but response to ICIs is heterogeneous, and reliable biomarkers for prospective selection of appropriate subpopulations likely to benefit from ICIs remain elusive. MethodsWe performed multiscale integrative analyses of published primary tumor data set from The Cancer Genome Atlas (TCGA) and the French cohort E-MTAB-1719 to unravel the tumor immune microenvironment of MPM deficient in BAP1, one of the most frequently mutated tumor suppressor genes (TSGs) in the disease. The molecular profiling results were validated in independent cohorts of patients with MPM using immunohistochemistry and multiplex immunohistochemistry. ResultsWe revealed that BAP1 deficiency enriches immune-associated pathways in MPM, leading to increased mRNA signatures of interferon alfa/gamma response, activating dendritic cells, immune checkpoint receptors, and T-cell inflammation. This finding was confirmed in independent patient cohorts, where MPM tumors with low BAP1 levels are associated with an inflammatory tumor immune microenvironment characterized by increased exhausted precursor T-cells and macrophages but decreased myeloid-derived suppressor cells (MDSCs). In addition, BAP1low MPM cells are in close proximity to T cells and therefore can potentially be targeted with ICIs. Finally, we revealed that BAP1-proficient MPM is associated with a hyperactive mitogen-activated protein kinase (MAPK) pathway and may benefit from treatment with MEK inhibitors (MEKis). ConclusionOur results suggest that BAP1 plays an immunomodulatory role in MPM and that BAP1-deficient MPM may benefit from immunotherapy, which merits further clinical investigation.

Prediction performance comparison of biomarkers for response to immune checkpoint inhibitors in advanced non-small cell lung cancer.

The aim of the present study was to compare the predictive accuracy of PD-L1 immunohistochemistry (IHC), tissue or blood tumor mutation burden (tTMB, bTMB), gene expression profile (GEP), driver gene mutation, and combined biomarkers for immunotherapy response of advanced non-small cell lung cancer (NSCLC). In part 1, clinical trials involved with predictive biomarker exploration for immunotherapy in advanced NSCLC were included. The area under the curve (AUC) of the summary receiver operating characteristic (SROC), sensitivity, specificity, likelihood ratio and predictive value of the biomarkers were evaluated. In part 2, public datasets of immune checkpoint inhibitor (ICI)-treated NSCLC involved with biomarkers were curated (N = 871). Odds ratio (OR) of the positive versus negative biomarker group for objective response rate (ORR) was measured. In part 1, the AUC of combined biomarkers (0.75) was higher than PD-L1 (0.64), tTMB (0.64), bTMB (0.68), GEP (0.67), and driver gene mutation (0.51). Combined biomarkers also had higher specificity, positive likelihood ratio and positive predictive value than single biomarkers. In part 2, the OR of combined biomarkers of PD-L1 plus TMB (PD-L1 cutoff 1%, 0.14; cutoff 50% 0.13) was lower than that of PD-L1 (cutoff 1%, 0.33; cutoff 50% 0.24), tTMB (0.28), bTMB (0.48), EGFR mutation (0.17) and KRAS mutation (0.47), for distinguishing ORR of patients after immunotherapy. Furthermore, positive PD-L1, tTMB-high, wild-type EGFR, and positive PD-L1 plus TMB were associated with prolonged progression-free survival (PFS). Combined biomarkers have superior predictive accuracy than single biomarkers for immunotherapy response of NSCLC. Further investigation is warranted to select optimal biomarkers for various clinical settings.

Abstract 3514: A deep learning model for detection and characterization of tertiary lymphoid structures in H&E-stained images from pancreatic ductal adenocarcinoma

Abstract Tertiary lymphoid structures (TLSs) are ectopic aggregates of a number of immune cells in nonlymphoid tissues under chronically inflamed environments and cancer. Emergence evidence suggested that TLSs were found in diverse cancers and TLSs have been referred as an independently predictive biomarker for immunotherapy response, especially immune checkpoint inhibitors. Moreover, the density and mature status of TLSs often positively associated with the favorable outcomes in cancers. Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related mortality with low survival rate in the whole world. Here, we developed a multi-resolution machine learning model for detection of TLSs from hema-toxylin and eosin (H&E) stained digital pathology slides as a potential clinical biomarker in PDAC. We selected 120 H&E slides (patients) with a total of 1,896 TLSs manually annotated as immature or mature TLSs by two pathologists. We also used immunohistochemistry (IHC) for CD20 and CD23 markers as a reference standard for H&E-based manual annotations. Our model mainly included detection and segmentation of individual TLSs. First, we used a transformer convolutional neural network to build a classification task to detect the presence of immature or mature TLSs. Then the instance segmentation model was applied to quantify the number of lymphocytes per unit square to validate the presence and maturation of TLSs. F1-score, calculated by the harmonic mean of precision (positive predictive value) and recall (sensitivity), was finally used to quantitatively evaluate the performance of model for each H&E slide. In validation dataset with 250 H&E slides (patients), we selected 50 slides to calculate F1-score, suggesting that our machine learning model achieved high F1-scores for detection and classification of immature TLSs (mean = 0.87, range from 0.79 to 0.95), and mature TLSs (mean = 0.92, range from 0.86 to 0.97). We further stratified 370 PDAC patients into three groups with mature TLSs (23%), immature TLSs (16%), and no TLSs (61%), respectively. Combined the clinical information, survival analysis revealed that patients with mature TLS had better survival outcomes than those without TLS (HR = 0.63; 95% CI, 0.44 - 0.90; P = 0.010). We developed a machine learning model that can accurately detect and classify TLSs in PDAC, and also demonstrated the prognosis value of predictive TLSs in H&E slides by automatedly machine learning model. These data highlight the promise of machine learning model for automated identification and quantification of the TLS on H&E slides in PDAC pathology. Citation Format: Chaoxian Zhao, Jidong Jia, Mingxi Lv, Jiayi Feng, Yijun Wang, Yingbin Liu. A deep learning model for detection and characterization of tertiary lymphoid structures in H&E-stained images from pancreatic ductal adenocarcinoma [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 3514.

Abstract 2543: Evaluating biomarkers of immunotherapy response in a real-world metastatic NSCLC cohort

Abstract Background: Non-small cell lung cancer (NSCLC) is the leading cause of global cancer deaths, since a large proportion of cases are initially diagnosed at advanced stages. Therapeutic developments have shifted first-line (1L) therapy in the metastatic setting to immunotherapy (IO)-based regimens, albeit with heterogeneity in response. Patients harboring KEAP1 and STK11 mutations have been reported to exhibit both poorer outcomes and IO treatment resistance. Recent work suggests gene expression signatures which characterize mutant phenotypes may better identify those with compromised IO response. Leveraging a molecular real-world dataset of metastatic NSCLC patients treated with 1L IO, we aimed to evaluate gene expression signatures representing KEAP1 and STK11-mutated phenotypes and their impact on real-world outcomes. Methods: Metastatic NSCLC patients who received 1L anti-PD(L)1 as a monotherapy or in combination were identified from the multimodal real-world database of Tempus Labs, Inc. All patients in the analytical cohort had tissue biopsies sequenced on Tempus targeted DNA and whole transcriptome RNA assays within 90 days prior to the start of the 1L IO regimen. Patients were negative for actionable mutations with record duration of at least 30 days from 1L initiation. KEAP1 and STK11 mutants were identified with likely pathogenic and/or pathogenic mutations as annotated by Tempus bioinformatic pipelines. Gene expression signatures related to these mutations were obtained from recently published literature. Evaluation of mutations or signatures as genomic markers for IO non-response were conducted using real-world progression-free survival (rwPFS) and overall survival (rwOS). Results: A total of 332 patients were included in the study (45% IO monotherapy, 55% IO combination therapy). Previously validated biomarkers, including tumor mutation burden, T-cell inflamed GEP signature, and PD(L)1 expression, were associated with prolonged rwPFS. KEAP1 mutations were observed in 9% of analytical cohort with a higher proportion in non-squamous (11%) compared with squamous (4%) samples. For STK11, 10% were identified as mutants with a similar histology distribution (12% vs. 3%). Neither KEAP1 mutations nor related gene signatures evaluated in this study were significantly associated with real-world survival outcomes. Patients harboring STK11 mutations trended towards poor outcomes; furthermore, those with higher STK11 inactivation signature score were associated with shorter rwPFS and rwOS. Conclusions: STK11 inactivation phenotype described by a gene expression signature was able to distinguish patients with significantly poorer survival outcomes. This finding would need validation in independent cohorts. This real-world cohort study adds supporting evidence for the utility of gene expression signatures encompassing mutant phenotypes for patient stratification. Citation Format: Katherina C. Chua, Assieh Saadatpour, Ariel Chen, Aisha Hasan, Bevan Emma Huang, Stewart Bates. Evaluating biomarkers of immunotherapy response in a real-world metastatic NSCLC cohort [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 2543.

Abstract 2488: The penile cancer tumor microenvironment and immunotherapy response: Results from the PERICLES trial

Abstract Introduction: The phase II PERICLES trial investigated the efficacy of atezolizumab (anti-PD-L1) for advanced penile cancer, with or without radiotherapy. As previously published, durable responses (progression-free at 12 months) were observed in 4 out of 32 enrolled patients. Here, we present the final overall survival (OS) results and an analysis of potential biomarkers for immunotherapy response. Methods: Preplanned analysis projected that at 27/32 events, the study would have 80% power (2-sided α=0.05) to assess whether the OS for the entire cohort significantly differed from the hypothesized historical median for stage IV patients. Baseline tumor tissue samples were subjected to bulk-RNA sequencing (n=31), fluorescent (n=28) and chromogenic (n=30) multiplex immunohistochemistry. We quantified the densities of CD3+CD8+ cytotoxic T-cells, CD3+CD8−FoxP3− T-helper cells, CD3+CD8−FoxP3+ regulatory T-cells, CD68+ macrophages and CD20+ B-cells in both the tumor and stromal compartments and assessed spatial relationships. We also quantified CD8+PD1+ (potentially enriched for tumor reactivity) and CD8+PD1− T-cells. Differential expression and gene set enrichment analysis (GSEA) were performed using the hallmarks gene sets. Results: The median OS for the entire study cohort was 11.3 months (95% CI, 5.5-16.7), which is an improvement compared to the hypothesized historical median of 7.8 months for stage IV patients (one sample log-rank p=0.033). Biomarker analyses showed an increased density of stromal macrophages (p<0.001) and intratumoral cytotoxic T-cells (p=0.035) in durable responders compared to non-responders. Additionally, we observed an increased density of intratumoral CD8+PD1+ in durable responders (p<0.001). In contrast, intratumoral CD8+PD1− densities were comparable between durable and non-responders (p=0.057). Patients with a high density of intratumoral CD8+PD1+ also had an improved progression-free survival (PFS) (log-rank, p<0.001) and OS (log-rank, p=0.002). Analysis of the spatial relationships in the tumor microenvironment (TME) showed that durable responders had larger distances between cytotoxic T-cells and T-helper cells (p=0.034). GSEA revealed significant (FDR≤5%) up-regulation of IFN-α/γ responses in durable responders. In contrast, non-responders showed up-regulation of epithelial-mesenchymal transition, KRAS signaling and angiogenesis, suggesting potential barriers to an anti-tumor response. Conclusion: In the final clinical analysis, OS was improved with atezolizumab compared to the historical population of stage IV patients. CD8+PD1+ T-cells, a common proxy for tumor-reactive T-cells, and spatial relationships in the TME might serve as biomarkers of durable response to immunotherapy in advanced penile cancer. Citation Format: Tynisha S. Rafael, Alberto Gil-Jimenez, Hielke M. de Vries, Iris M. Seignette, Elise Bekers, Marta Lopez-Yurda, Dennis Peters, Erik Hooijberg, Annegien Broeks, Oscar R. Brouwer, Eva Schaake, Daniel J. Vis, Tanja D. de Gruijl, Lodewyk F.A. Wessels, Michiel S. van der Heijden. The penile cancer tumor microenvironment and immunotherapy response: Results from the PERICLES trial [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 2488.

Abstract 2689: Mass cytometry analysis of CXCR3 expression in tumor-infiltrated myeloid cells as a predictive biomarker For immunotherapy response

Abstract Monocytes have emerged as important regulators of cancer progression. We have recently found that CXCR3+ circulating monocytes in humans are associated with better response to immunotherapy. Here, we aimed to study the functions and heterogeneity of CXCR3+ monocytes in two different triple negative breast cancer (TNBC) tumors in mice, using either AT3 or EO771 tumor cell lines. AT-3 tumors exhibit higher resistance to anti-PD-1 and anti-CTLA-4 therapy in C57BL/6 breast cancer mouse models compared to EO771 tumors. We evaluated 36 surface protein markers on tumor-infiltrated myeloid cells in tumors isolated from each model using CyTOF mass cytometry. Analysis of monocytes, macrophages and dendritic cells in the tumors using unsupervised clustering identified 13 cell subsets. Significant differences were discovered between the two tumor types in frequencies of nine of 13 identified subsets. However, 3 of these subsets displayed relatively high expression of CXCR3 in EO771 tumors when compared to AT-3 tumors. The significant CXCR3 positive subsets were identified as CXCR3+ early macrophages, CXCR3+ Ly6C+CD11blo classical monocytes and Trem2+ CXCR3+ dendritic cells. We observed higher expression of CXCR4, Trem2, and CCR1 in CXCR3+ myeloid cells when compared to their CXCR3− counterparts in both tumor models. To elucidate the role of these CXCR3+ cells, we performed low-input bulk RNA sequencing of CD115+/CD11b+ cells from blood and spleen of EO771 tumor bearing mice with a wild type or CXCR3KO genotype. Ingenuity Pathway Analysis (IPA) of the differentially expressed genes suggests a significant inhibition of the IL-10 signaling pathway of CXCR3KO cells. These differentially expressed markers and genes suggest a functional importance in this subset in migration to the tumor and antigen presentation, both of which are anti-tumoral functions. Ongoing studies are aimed towards understanding the role of CXCR3+ myeloid cells in regulating response to immunotherapies. Citation Format: Gabriel A. Valentin-Guillama, Nandini Chatterjee, Ahmad Alimadadi, Catherine C. Hedrick. Mass cytometry analysis of CXCR3 expression in tumor-infiltrated myeloid cells as a predictive biomarker For immunotherapy response [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 2689.

Repolarization of immunosuppressive macrophages by targeting SLAMF7-regulated CCL2 signaling sensitizes hepatocellular carcinoma to immunotherapy.

Immune checkpoint inhibitors have limited efficacy in hepatocellular carcinoma (HCC). Macrophages are the most abundant immune cells in HCC, suggesting that a better understanding of the intrinsic processes by which tumor cells regulate macrophages could help identify strategies to improve response to immunotherapy. As signaling lymphocytic activation molecule (SLAM) family members regulate various immune functions, we investigated the role of specific SLAM receptors in the immunobiology of HCC. Comparison of the transcriptomic landscapes of immunotherapy-responsive and non-responsive advanced HCC patients identified SLAMF7 upregulation in immunotherapy-responsive HCC, and HCC patients who responded to immunotherapy also displayed higher serum levels of SLAMF7. Loss of Slamf7 in liver-specific knockout mice led to increased hepatocarcinogenesis and metastasis, elevated immunosuppressive macrophage infiltration, and upregulated PD-1 expression in CD8+ T cells. HCC cell-intrinsic SLAMF7 suppressed MAPK/ATF2-mediated CCL2 expression to regulate macrophage migration and polarization in vitro. Mechanistically, SLAMF7 associated with SH2 domain-containing adaptor protein B (SHB) through its cytoplasmic 304 tyrosine site to facilitate the recruitment of SHIP1 to SLAMF7 and inhibit the ubiquitination of TRAF6, thereby attenuating MAPK pathway activation and CCL2 transcription. Pharmacological antagonism of the CCL2/CCR2 axis potentiated the therapeutic effect of anti-PD-1 antibody in orthotopic HCC mouse models with low SLAMF7 expression. In conclusion, this study highlights SLAMF7 as a regulator of macrophage function and a potential predictive biomarker of immunotherapy response in HCC. Strategies targeting CCL2 signaling to induce macrophage repolarization in HCC with low SLAMF7 might enhance the efficacy of immunotherapy.

Biomarkers of Immunotherapy Response in Patients with Non-Small-Cell Lung Cancer: Microbiota Composition, Short-Chain Fatty Acids, and Intestinal Permeability.

Immune checkpoint inhibitors have been proposed as the standard treatment for different stages of non-small-cell lung cancer in multiple indications. Not all patients benefit from these treatments, however, and certain patients develop immune-related adverse events. Although the search for predictors of response to these drugs is a major field of research, these issues have yet to be resolved. It has been postulated that microbiota could play a relevant role in conditioning the response to cancer treatments; however, the human factor of intestinal permeability also needs to be considered as it is closely related to the regulation of host-microbiota interaction. In this article, we analyzed the possible relationship between the response to immune checkpoint inhibitors and the onset of immune-related adverse events, gut microbiota status, and intestinal membrane permeability. In a pioneering step, we also measured short-chain fatty acid content in feces. Although the correlation analyses failed to identify predictive biomarkers, even when all variables were integrated, our patients' microbial gut ecosystems were rich and diverse, and the intestinal barrier's integrity was preserved. These results add new knowledge on the composition of microbiota and its correlation with barrier permeability and short-chain fatty acids and suggest that more studies are required before these potential biomarkers can be incorporated into the clinical management of patients via immune checkpoint inhibitor treatment.

Role of CD38 in anti-tumor immunity of small cell lung cancer.

Immune checkpoint blockade (ICB) with or without chemotherapy has a very modest benefit in patients with small cell lung cancer (SCLC). SCLC tumors are characterized by high tumor mutation burden (TMB) and low PD-L1 expression. Therefore, TMB and PD-L1 do not serve as biomarkers of ICB response in SCLC. CD38, a transmembrane glycoprotein, mediates immunosuppression in non-small cell lung cancer (NSCLC). In this brief report, we highlight the potential role of CD38 as a probable biomarker of immunotherapy response in SCLC. We evaluated the role of CD38 as a determinant of tumor immune microenvironment in SCLC with bulk and single-cell transcriptomic analyses and protein assessments of clinical samples and preclinical models, including CD38 in vivo blockade. In SCLC clinical samples, CD38 levels were significantly correlated with the gene expression of the immunosuppressive markers FOXP3, PD-1 and CTLA-4. CD38 expression was significantly enhanced after chemotherapy and ICB treatment in SCLC preclinical models and clinical samples. A combination of cisplatin/etoposide, ICB, and CD38 blockade delayed tumor growth compared to cisplatin/etoposide. Our study provides a preliminary but important direction toward exploring CD38 as a potential biomarker of ICB response and CD38 blockade as a combination strategy for chemo-immunotherapy in SCLC.

Abstract B023: The role of ribosomal protein L22 in regulating the immune landscape in mismatch repair deficient endometrial cancers

Abstract The purpose of this study is to identify novel mechanisms of immune evasion in mismatch repair deficient endometrial cancers. Despite having a hypermutated phenotype that is proposed to lead to the accumulation of many neoantigens for immune recognition, our group has found that mismatch repair deficient endometrial cancers characterized by MLH1 protein loss display large variations in the number of tumors infiltrating CD8+ T cells (TILs) across different tumors. To better understand how variations in TILs arise in mismatch repair deficient endometrial cancers, we utilized human endometrial cancer samples from the UNCseq cohort. Immunohistochemistry (IHC) was used to identify expression patterns in proteins of interest across mismatch repair deficient endometrial cancers. To determine how expression patterns of proteins of interest correlate with TILs in our patient samples, Definiens Tissue Studio was utilized to quantify immunofluorescent CD8+ TILs in our mismatch repair deficient endometrial cancer cohort. Although upregulation of programmed death ligand 1 (PD-L1) expression is often cited as an explanation for low TILs, analysis of human data by our group shows that PD-L1 expression does not correlate with TIL numbers in mismatch repair deficient endometrial cancers. Interestingly, The Cancer Genome Atlas (TCGA) data reported that ribosomal protein L22 (RPL22) is mutated in ~50% of mismatch repair deficient endometrial cancers. We analyzed RPL22 by IHC and found that it was lost in 52% of our human mismatch repair deficient endometrial cancer cohort. Loss of various ribosomal proteins can alter translation efficiency of proteins critical for promoting immunosurveillance and the immune response in cancer. In our cohort, mismatch repair deficient endometrial cancers that have lost expression of RPL22 have lower numbers of CD8+ TILs. Preliminary data also show that human endometrial cancer cell lines that do not express RPL22 also do not express beta-2-microglobulin (β2M), a component of the major histocompatibility complex I (MHC-I) molecules that is necessary for neoantigen presentation to CD8+ T cells. Thus, we hypothesize that RPL22 loss in mismatch repair deficient endometrial cancers results in decreased translation of β2M and CD8+ T cell evasion. Moving forward, we speculate that RPL22 could be utilized as a predictive biomarker for immunotherapy response in mismatch repair deficient endometrial cancers. Citation Format: Macy L. Osborne, Hannah Atkins, Andrew B. Gladden, Russell R. Broaddus. The role of ribosomal protein L22 in regulating the immune landscape in mismatch repair deficient endometrial cancers [abstract]. In: Proceedings of the AACR Special Conference on Endometrial Cancer: Transforming Care through Science; 2023 Nov 16-18; Boston, Massachusetts. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(5_Suppl):Abstract nr B023.

Chemokine CCL21 determines immunotherapy response in hepatocellular carcinoma by affecting neutrophil polarization

BackgroundThe efficacy of immune checkpoint inhibitors (ICIs) in hepatocellular carcinoma (HCC) is poor and great heterogeneity among individuals. Chemokines are highly correlated with tumor immune response. Here, we aimed to identify an effective chemokine for predicting the efficacy of immunotherapy in HCC.MethodsChemokine C‐C motif ligand 21 (CCL21) was screened by transcriptomic analysis in tumor tissues from HCC patients with different responses to ICIs. The least absolute shrinkage and selection operator (LASSO) regression analysis was conducted to construct a predictive nomogram. Neutrophils in vitro and HCC subcutaneous tumor model in vivo were applied to explore the role of CCL21 on the tumor microenvironment (TME) of HCC.ResultsTranscriptome analysis showed that CCL21 level was much higher in HCC patients with response to immunotherapy. The predictive nomogram was constructed and validated as a classifier. CCL21 could inhibit N2 neutrophil polarization by suppressing the activation of nuclear factor kappa B (NF-κB) pathway. In addition, CCL21 enhanced the therapeutic efficacy of ICIs.ConclusionCCL21 may serve as a predictive biomarker for immunotherapy response in HCC patients. High levels of CCL21 in TME inhibit immunosuppressive polarization of neutrophils. CCL21 in combination with ICIs may offer a novel therapeutic strategy for HCC.

Tumor Copy Number Alteration Burden as a Predictor for Resistance to Immune Checkpoint Blockade across Different Cancer Types.

Immune checkpoint blockade (ICB) benefits only a subset of advanced cancer patients, and predictive biomarkers for immunotherapy response are needed. Recently, copy number alteration (CNA) burden has been proposed to predict ICB resistance. We assessed this finding using the publicly accessible data for 1661 ICB-treated patients whose tumors were profiled by MSK-IMPACT, an approved targeted assay in clinical care. We tested the hypothesis that the continuous increase in CNA burden is associated with poor overall survival following ICB. In addition, we hypothesized that the combinatorial biomarkers of tumor mutational burden (TMB) and CNA burden would better stratify patients for immune status and ICB response. Of the 1661 cases, 79% (n = 1307) were treated with anti PD-1/PD-L1 and the remaining 21% (n = 354) with anti CTLA-4 or the combination of both. In a multivariate analysis, increase in CNA burden was associated with poor overall survival [HR = 1.52, 95% CI (1.01-2.30), p = 0.04]. The combination of biomarkers TMB and CNA burden stratified patients into four clinically distinct subsets among which "LowTMB/HighCNA" showed the worst survival (p < 0.0001). The four patient subsets had unique CNA profiles and enriched pathways, which could predict transcriptional and phenotypic effects related to immune signaling and CD8+ T-cell abundance in the tumor microenvironment. CNA burden was associated with poor overall survival in patients receiving ICB and could improve patient stratification when incorporated with TMB. These findings may guide patient selection for immunotherapy or alternative strategies.

Identification and validation of an individualized metabolic prognostic signature for predicting the biochemical recurrence of prostate cancer based on the immune microenvironment

BackgroundProstate cancer (PCa) is the most prevalent genitourinary malignancy in men, with a significant proportion of patients developing biochemical recurrence (BCR) after treatment. The immune microenvironment and metabolic alterations have crucial implications for the tumorigenesis and progression of PCa. Therefore, identifying metabolic genes associated with the immune microenvironment holds promise for predicting BCR and improving PCa prognosis.MethodsIn this study, ssGSEA and hierarchical clustering analysis were first conducted to evaluate and group PCa samples, followed by the use of the ESTIMATE and CIBERSORT algorithms to characterize the immunophenotypes and tumor microenvironment. The differential metabolic genes (MTGs) between groups were utilized to develop a prognostic-related signature. The predictive performance of the signature was assessed by principal component analysis (PCA), receiver operating characteristic (ROC) curve analysis, survival analysis, and the TIDE algorithm. A miRNA-MTGs regulatory network and predictive nomogram were constructed. Moreover, the expression of prognostic MTGs in PCa was detected by RT‒qPCR.ResultsPCa samples from the TCGA cohort were separated into two groups: the immune-low group and immune-high group. Forty-eight differentially expressed MTGs between the groups were identified, including 37 up-regulated and 11 down-regulated MTGs. Subsequently, CEL, CYP3A4, and PDE6G were identified as the genes most strongly associated with the BCR of PCa patients and these genes were utilized to establish the MTGs-based prognostic signatures. PCA, ROC curves analysis, Kaplan–Meier survival analysis, and the nomogram all showed the good predictive ability of the signature regardless of clinical variables. Furthermore, the MTGs-based signature was indicated as a potential predictive biomarker for immunotherapy response. Nine miRNAs involved in the regulation of prognostic MTGs were determined. In addition to the CEL gene, the PDE6G and CYP3A4 genes were expressed at higher levels in PCa samples.ConclusionsThe MTGs-based signature represents a novel approach with promising potential for predicting BCR in PCa patients.

The Role of SPEN Mutations as Predictive Biomarkers for Immunotherapy Response in Colorectal Cancer: Insights from a Retrospective Cohort Analysis.

Colorectal cancer (CRC) is the leading cause of cancer deaths, and treatment, especially in the metastatic stage, is challenging. Immune checkpoint inhibitors (ICIs) have revolutionized CRC treatment, but response varies, emphasizing the need for effective biomarkers. This study explores SPEN mutations as potential biomarkers. Using data from the Memorial Sloan Kettering Cancer Center (MSKCC) and The Cancer Genome Atlas (TCGA)-Colorectal Cancer, this research applied bioinformatics tools and statistical analysis to SPEN (Split Ends) mutant and wild-type CRC patients treated with ICIs. Focus areas included mutation rates, immune cell infiltration, and DNA damage response pathways. The SPEN mutation rate was found to be 13.8% (15/109 patients) in the MSKCC cohort and 6.65% (35/526 patients) in the TCGA cohort. Our findings indicate that CRC patients with SPEN mutations had a longer median overall survival (OS) than the wild-type group. These patients also had higher tumor mutational burden (TMB), microsatellite instability (MSI) scores, and programmed death-ligand 1 (PD-L1) expression. SPEN mutants also exhibited increased DNA damage response (DDR) pathway mutations and a greater presence of activated immune cells, like M1 macrophages and CD8+ T cells, while wild-type patients had more resting/suppressive immune cells. Furthermore, distinct mutation patterns, notably with TP53, indicated a unique molecular subtype in SPEN-mutated CRC. We conclude that SPEN mutations might improve ICI efficacy in CRC due to increased immunogenicity and an inflammatory tumor microenvironment. SPEN mutations could be predictive biomarkers for ICI responsiveness, underscoring their value in personalized therapy and highlighting the importance of genomic data in clinical decisions. This research lays the groundwork for future precision oncology studies.

A novel sub-regional radiomics model to predict immunotherapy response in non-small cell lung carcinoma.

Identifying precise biomarkers of immunotherapy response for non-small cell lung carcinoma (NSCLC) before treatment is challenging. This study aimed to construct and investigate the potential performance of a sub-regional radiomics model (SRRM) as a novel tumor biomarker in predicting the response of patients with NSCLC treated with immune checkpoint inhibitors, and test whether its predictive performance is superior to that of conventional radiomics, tumor mutational burden (TMB) score and programmed death ligand-1 (PD-L1) expression. We categorized 264 patients from retrospective databases of two centers into training (n = 159) and validation (n = 105) cohorts. Radiomic features were extracted from three sub-regions of the tumor region of interest using the K-means method. We extracted 1,896 features from each sub-region, resulting in 5688 features per sample. The least absolute shrinkage and selection operator regression method was used to select sub-regional radiomic features. The SRRM was constructed and validated using the support vector machine algorithm. We used next-generation sequencing to classify patients from the two cohorts into high TMB (≥ 10 muts/Mb) and low TMB (< 10 muts/Mb) groups; immunohistochemistry was performed to assess PD-L1 expression in formalin-fixed, paraffin-embedded tumor sections, with high expression defined as ≥ 50% of tumor cells being positive. Associations between the SRRM and progression-free survival (PFS) and variant genes were assessed. Eleven sub-regional radiomic features were employed to develop the SRRM. The areas under the receiver operating characteristic curve (AUCs) of the proposed SRRM were 0.90 (95% confidence interval [CI] 0.84-0.96) and 0.86 (95% CI 0.76-0.95) in the training and validation cohorts, respectively. The SRRM (low vs. high; cutoff value = 0.936) was significantly associated with PFS in the training (hazard ratio [HR] = 0.35 [0.24-0.50], P < 0.001) and validation (HR = 0.42 [0.26-0.67], P = 0.001) cohorts. A significant correlation between the SRRM and three variant genes (H3C4, PAX5, and EGFR) was observed. In the validation cohort, the SRRM demonstrated a higher AUC (0.86, P < 0.001) than that for PD-L1 expression (0.66, P = 0.034) and TMB score (0.54, P = 0.552). The SRRM had better predictive performance and was superior to conventional radiomics, PD-L1 expression, and TMB score. The SRRM effectively stratified the progression-free survival (PFS) risk among patients with NSCLC receiving immunotherapy.

Receptor-interacting protein kinase 2 is associated with tumor immune infiltration, immunotherapy-related biomarkers, and affects gastric cancer cells growth in vivo.

Background: The objective of this study was to analyze the research trend of four RIPK genes (RIPK1, RIPK2, RIPK3, and RIPK4), their expression variations in tumors, and the correlation between RIPK2 expression and immune-related biomarkers in gastric cancer (GC). Methods: The PubMed database was utilized to investigate the research trend surrounding four RIPKs genes in tumors. The ULCAN database was employed to analyze the differential expression of these four RIPKs genes. TCGA data were utilized to examine the association between RIPK2 expression and various factors including tumor immune infiltration and immune-related biomarkers. Lastly, the impact of targeting RIPK2 on the growth of GC cells was confirmed through tumor formation assay, immunohistochemistry, and Tunnel assays. Results: In the field of tumor biology, there has been a sustained increase in research focused on the four RIPKs genes over the past decade. Four RIPKs genes are differentially expressed in a majority of tumors. Furthermore, this investigation has unveiled a connection between the expression of RIPK2 and the infiltration of four immune cells, as well as the presence of RNA methylation modifying enzymes, specifically m1A, m6A, and m5C, in GC. Additionally, RIPK2 expression was associated with the genes related to immune checkpoint regulation, as well as genes associated with immunoinhibitors and immunostimulators. It was also revealed that RIPK2 expression was correlated to immunotherapy response biomarkers, namely MSI and TMB, and tumor stemness. Ultimately, it was demonstrated that targeting the RIPK2 effectively regulated GC cells growth through the suppression of PCNA expression and the induction of apoptosis. Conclusion: The expression of RIPK2 is correlated with immune cell infiltration, RNA methyltransferase activity, tumor stemness, checkpoint-related genes, and immunotherapy-related biomarkers. Suppression of RIPK2 impedes the growth of GC cells in vivo. Consequently, RIPK2 holds promise as a viable immunotherapy target for various types of cancer.

Tumor Mutational Burden as a Biomarker of Immunotherapy Response: An Immunogram Approach in Onco-immunology

Abstract: Immune checkpoint inhibitors have revolutionized cancer treatment by allowing T cells to reactivate. Tumor mutational burden (TMB) is a biomarker that has emerged as a viable diagnostic for locating patients who would benefit from immunotherapy in particular cancer types. Greater neo-antigens mean more opportunities for T cell identification, and TMB is clinically linked to better immune checkpoint inhibitors. Tumor foreignness is a cancer immunogram, and TMB can be used as a substitute for foreignness. The role of TMB analysis as an independent predictor of immunotherapy response in the context of immune checkpoint inhibitor medications is the subject of this mini-review.

Identifying biomarkers associated with immunotherapy response in melanoma by multi-omics analysis

Despite immune checkpoint inhibitors (ICIs) have shown the greatest success in melanoma treatment, only a subset of melanoma patients responds well to ICIs. Thus, identifying predictive biomarkers for immunotherapy response is crucial. In this study, we took complementary advantages of immunotherapy data and The Cancer Genome Atlas (TCGA) multi-omics data to explore the predictive biomarkers for the response to immunotherapy in melanoma. We first predicted responsive and non-responsive melanomas in the TCGA skin cutaneous melanoma (SKCM) cohort based on both somatic mutation and transcriptome datasets which involved immunotherapy data for melanoma. This method identified 170 responsive and 56 non-responsive melanomas in TCGA-SKCM. Based on the TCGA-SKCM data, we performed a comprehensive comparison of multi-omics molecular features between responsive and non-responsive melanomas. We identified the molecular features significantly associated with immunotherapy response in melanoma at the genome, transcriptome, epigenome, and proteome levels, respectively. Our analysis confirmed certain immunotherapy response-associated biomarkers, such as tumor mutation burden (TMB), copy number alteration (CNA), intratumor heterogeneity (ITH), PD-L1 expression, and tumor immunity. Moreover, we identified some novel molecular features associated with immunotherapy response: (1) the activation of mast cells and dendritic cells correlating negatively with immunotherapy response; (2) the enrichment of many oncogenic pathways correlating positively with immunotherapy response, such as JAK-STAT, RAS, MAPK, HIF-1, PI3K-Akt, and VEGF pathways; and (3) a number of microRNAs and proteins whose expression correlates with immunotherapy response. In addition, the mTOR signaling pathway has a negative association with immunotherapy response. The novel biomarkers have potential predictive values in immunotherapy response and warrant further investigation.