Tumor Immune Signature Research Articles

GUCA2A dysregulation as a promising biomarker for accurate diagnosis and prognosis of colorectal cancer

Colorectal cancer is a leading cause of global mortality and presents a significant barrier to improving life expectancy. The primary objective of this study was to discern a unique differentially expressed gene (DEG) that exhibits a strong association with colorectal cancer. By achieving this goal, the research aims to contribute valuable insights to the field of translational medicine. We performed analysis of colorectal cancer microarray and the TCGA colon adenoma carcinoma (COAD) datasets to identify DEGs associated with COAD and common DEGs were selected. Furthermore, a pan-cancer analysis encompassing 33 different cancer types was performed to identify differential genes significantly expressed only in COAD. Then, comprehensively in-silico analysis including gene set enrichment analysis, constructing Protein–Protein interaction, co-expression, and competing endogenous RNA (ceRNA) networks, investigating the correlation between tumor-immune signatures in distinct tumor microenvironment and also the potential interactions between the identified gene and various drugs was executed. Further, the candidate gene was experimentally validated in tumoral colorectal tissues and colorectal adenomatous polyps by qRael-Time PCR. GUCA2A emerged as a significant DEG specific to colorectal cancer (|log2FC|> 1 and adjusted q-value < 0.05). Importantly, GUCA2A exhibited excellent diagnostic performance for COAD, with a 99.6% and 78% area under the curve (AUC) based on TCGA-COAD and colon cancer patients. In addition, GUCA2A expression in adenomatous polyps equal to or larger than 5 mm was significantly lower compared to smaller than 5 mm. Moreover, low expression of GUCA2A significantly impacted overall patient survival. Significant correlations were observed between tumor-immune signatures and GUCA2A expression. The ceRNA constructed included GUCA2A, 8 shared miRNAs, and 61 circRNAs. This study identifies GUCA2A as a promising prognostic and diagnostic biomarker for colorectal cancer. Further investigations are warranted to explore the potential of GUCA2A as a therapeutic biomarker.

Prognostic and Potential Therapeutic Roles of PRKDC Expression in Lung Cancer.

PRKDC is a key factor involved in the ligation step of the non-homologous end joining pathway. Its dysfunction has proven to be a biomarker for radiosensitivity of cancer cells. However, the prognostic value of PRKDC and its underlying mechanisms have not been clarified yet. In this study, we found that PRKDC overexpressed in lung adenocarcinoma (LUAD) and is significantly related to unfavorable survival, while downregulation of PRKDC is link to inflamed tumor immune signature. Our further in vitro results also showed a potent antitumor efficacy of PRKDC inhibitors alone or combined with cisplatin in human lung cancer cells. This study demonstrated that PRKDC is a potential prognostic biomarker, immunotherapy target, and promising combination candidate for chemotherapy for lung cancer, and highlighted the potential of PRKDC-targeted inhibitors for the treatment of lung cancer.

Tumor-Immune Signatures of Treatment Resistance to Brentuximab Vedotin with Ipilimumab and/or Nivolumab in Hodgkin Lymphoma.

Identification of multi-omic immune markers from peripheral blood may help elucidate resistance mechanisms to checkpoint inhibitor and antibody-drug conjugate combinations with potential implications for treatment decisions in relapsed HL.

Biomarkers predicting response to 5 immunotherapy arms in the neoadjuvant I-SPY2 trial for early-stage breast cancer (BC): Evaluation of immune subtyping in the response predictive subtypes (RPS).

102 Background: Previously, we showed that in our first PD1-inhibitor (PD1-inh) arm of I-SPY2, pCR associates with high STAT1/chemokine/dendritic signatures in TN and with high B-cell/low mast cell in HR+. From these results, we defined a research-grade Immune classifier incorporated into the RPS (PMID: 35623341), a schema designed to increase pCR if used to prioritize treatment. A clinical-grade version of the Immune (ImPrint) and other RPS biomarkers are now used in I-SPY2.2. Here we evaluate immune markers in 5 Immune-Oncology (IO) therapy arms (2 PD1-inh, 2 PD1-inh combinations, and 1 PDL1-inh combination). Methods: 343 patients with HER2-negative BC with information on pCR and mRNA in 5 IO arms (n 60-72 pts) plus controls (Ctr: 343) were considered. 32 continuous markers including 30 immune (7 checkpoint genes, 14 immune cell, 3 T/B-cell prognostic, 1 TGFB and 5 tumor-immune) and ESR1/PGR and proliferation signatures, were assessed for association with pCR using logistic regression. p-values were adjusted using the Benjamini-Hochberg method (BH p&lt;0.05). Correlations to multiplex immunofluorescence (mIF) data from our initial arm (immune cell and spatial proximity markers) were calculated. Performance of ImPrint, developed with Agendia Inc, was characterized overall and within HR subsets. Results: A larger number of the research-grade immune markers predict response to IO in HR+ than in TN, with the most for HR+ in combination-IO arms (27/32 biomarkers). Tumor-immune signatures dominated by chemokines/cytokines were most consistently associated with pCR across IO arms and across receptor status. Moreover, we found that these markers correlate to mIF spatial proximity measures reflecting high spatial co-localization of PD1+ immune and PDL1+ tumor cells, in TN especially (r=0.59; p=0.003). The ImPrint classifier was evaluated in the IO arms. In HR+, 28% were ImPrint+; and pCR rates were 76% in ImPrint+ vs. 16% in ImPrint-. In TN, 46% were ImPrint+; and pCR rates were 75% in ImPrint+ and 37% in ImPrint-. Overall (HR+ and TN, in all IO arms), pCR rates were 75% in ImPrint+ and 23% in ImPrint-. Performance varied by arm, with the highest pCR rates for HR+/ImPrint+ &gt;90%; and for TN/ImPrint+ &gt;81%. In contrast, pCR rates in the control arm were 34% for ImPrint+ (HR+:33%; TN: 34%) and 13% for ImPrint- (HR+: 21%; TN:8%). Conclusions: Tumor-immune signaling signatures predict response for IO drug class in both TN and HR+HER2-. The ImPrint single-sample classifier predicts response to a variety of IO regimens in both subsets and may inform prioritization of IO vs other treatments and best balance likely benefit vs risk of serious immune-related adverse events. Clinical trial information: NCT01042379 .

TCGA RNA-Seq and Tumor-Infiltrating Lymphocyte Imaging Data Reveal Cold Tumor Signatures of Invasive Ductal Carcinomas and Estrogen Receptor-Positive Human Breast Tumors.

Comparative studies of immune-active hot and immune-deserted cold tumors are critical for identifying therapeutic targets and strategies to improve immunotherapy outcomes in cancer patients. Tumors with high tumor-infiltrating lymphocytes (TILs) are likely to respond to immunotherapy. We used the human breast cancer RNA-seq data from the cancer genome atlas (TCGA) and classified them into hot and cold tumors based on their lymphocyte infiltration scores. We compared the immune profiles of hot and cold tumors, their corresponding normal tissue adjacent to the tumor (NAT), and normal breast tissues from healthy individuals from the Genotype-Tissue Expression (GTEx) database. Cold tumors showed a significantly lower effector T cells, lower levels of antigen presentation, higher pro-tumorigenic M2 macrophages, and higher expression of extracellular matrix (ECM) stiffness-associated genes. Hot/cold dichotomy was further tested using TIL maps and H&E whole-slide pathology images from the cancer imaging archive (TCIA). Analysis of both datasets revealed that infiltrating ductal carcinoma and estrogen receptor ER-positive tumors were significantly associated with cold features. However, only TIL map analysis indicated lobular carcinomas as cold tumors and triple-negative breast cancers (TNBC) as hot tumors. Thus, RNA-seq data may be clinically relevant to tumor immune signatures when the results are supported by pathological evidence.

Immune desert in MMR-deficient tumors predicts poor responsiveness of immune checkpoint inhibition.

Although many efforts have been devoted to identify biomarkers to predict the responsiveness of immune checkpoint inhibitors, including expression of programmed death-ligand 1 (PD-L1) and major histocompatibility complex (MHC) I, microsatellite instability (MSI), mismatch repair (MMR) defect, tumor mutation burden (TMB), tertiary lymphoid structures (TLSs), and several transcriptional signatures, the sensitivity of these indicators remains to be further improved. Here, we integrated T-cell spatial distribution and intratumor transcriptional signals in predicting the response to immune checkpoint therapy in MMR-deficient tumors including tumors of Lynch syndrome (LS). In both cohorts, MMR-deficient tumors displayed personalized tumor immune signatures, including inflamed, immune excluded, and immune desert, which were not only individual-specific but also organ-specific. Furthermore, the immune desert tumor exhibited a more malignant phenotype characterized by low differentiation adenocarcinoma, larger tumor sizes, and higher metastasis rate. Moreover, the tumor immune signatures associated with distinct populations of infiltrating immune cells were comparable to TLSs and more sensitive than transcriptional signature gene expression profiles (GEPs) in immunotherapy prediction. Surprisingly, the tumor immune signatures might arise from the somatic mutations. Notably, patients with MMR deficiency had benefited from the typing of immune signatures and later immune checkpoint inhibition. Our findings suggest that compared to PD-L1 expression, MMR, TMB, and GEPs, characterization of the tumor immune signatures in MMR-deficient tumors improves the efficiency of predicting the responsiveness of immune checkpoint inhibition.

Losartan controls immune checkpoint blocker-induced edema and improves survival in glioblastoma mouse models

Immune checkpoint blockers (ICBs) have failed in all phase III glioblastoma trials. Here, we found that ICBs induce cerebral edema in some patients and mice with glioblastoma. Through single-cell RNA sequencing, intravital imaging, and CD8+ T cell blocking studies in mice, we demonstrated that this edema results from an inflammatory response following antiprogrammed death 1 (PD1) antibody treatment that disrupts the blood-tumor barrier. Used in lieu of immunosuppressive corticosteroids, the angiotensin receptor blocker losartan prevented this ICB-induced edema and reprogrammed the tumor microenvironment, curing 20% of mice which increased to 40% in combination with standard of care treatment. Using a bihemispheric tumor model, we identified a "hot" tumor immune signature prior to losartan+anti-PD1 therapy that predicted long-term survival. Our findings provide the rationale and associated biomarkers to test losartan with ICBs in glioblastoma patients.

Genetic mutation and tumor microbiota determine heterogenicity of tumor immune signature: Evidence from gastric and colorectal synchronous cancers.

Both colorectal and gastric cancer are lethal solid-tumor malignancies, leading to the majority of cancer-associated deaths worldwide. Although colorectal cancer (CRC) and gastric cancer (GC) share many similarities, the prognosis and drug response of CRC and GC are different. However, determinants for such differences have not been elucidated. To avoid genetic background variance, we performed multi-omics analysis, including single-cell RNA sequencing, whole-exome sequencing, and microbiome sequencing, to dissect the tumor immune signature of synchronous primary tumors of GC and CRC. We found that cellular components of juxta-tumoral sites were quite similar, while tumoral cellular components were specific to the tumoral sites. In addition, the mutational landscape and microbiome contributed to the distinct TME cellular components. Overall, we found that different prognoses and drug responses of GC and CRC were mainly due to the distinct TME determined by mutational landscape and microbiome components.

The development of a novel signature based on the m6A RNA methylation regulator-related ceRNA network to predict prognosis and therapy response in sarcomas.

Background: N6 methyladenosine (m6A)-related noncoding RNAs (including lncRNAs and miRNAs) are closely related to the development of cancer. However, the gene signature and prognostic value of m6A regulators and m6A-associated RNAs in regulating sarcoma (SARC) development and progression remain largely unexplored. Therefore, further research is required. Methods: We obtained expression data for RNA sequencing (RNA-seq) and miRNAs of SARC from The Cancer Genome Atlas (TCGA) datasets. Correlation analysis and two target gene prediction databases (miRTarBase and LncBase v.2) were used to deduce m6A-related miRNAs and lncRNAs, and Cytoscape software was used to construct ceRNA-regulating networks. Based on univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression analyses, an m6A-associated RNA risk signature (m6Ascore) model was established. Prognostic differences between subgroups were explored using Kaplan–Meier (KM) analysis. Risk score-related biological phenotypes were analyzed in terms of functional enrichment, tumor immune signature, and tumor mutation signature. Finally, potential immunotherapy features and drug sensitivity predictions for this model were also discussed. Results: A total of 16 miRNAs, 104 lncRNAs, and 11 mRNAs were incorporated into the ceRNA network. The risk score was obtained based on RP11-283I3.6, hsa-miR-455-3p, and CBLL1. Patients were divided into two risk groups using the risk score, with patients in the low-risk group having longer overall survival (OS) than those in the high-risk group. The receiver operating characteristic (ROC) curves indicated that risk characteristic performed well in predicting the prognosis of patients with SARC. In addition, lower m6Ascore was also positively correlated with the abundance of immune cells such as monocytes and mast cells activated, and several immune checkpoint genes were highly expressed in the low-m6Ascore group. According to our analysis, lower m6Ascore may lead to better immunotherapy response and OS outcomes. The risk signature was significantly associated with the chemosensitivity of SARC. Finally, a nomogram was constructed to predict the OS in patients with SARC. The concordance index (C-index) for the nomogram was 0.744 (95% CI: 0.707–0.784). The decision curve analysis (DCA), calibration plot, and ROC curve all showed that this nomogram had good predictive performance. Conclusion: This m6Ascore risk model based on m6A RNA methylation regulator-related RNAs may be promising for clinical prediction of prognosis and might contain potential biomarkers for treatment response prediction for SARC patients.

Immune Profiling in Gastric Cancer Reveals the Dynamic Landscape of Immune Signature Underlying Tumor Progression.

The profiling of the tumor immune microenvironment (TIME) is critical for guiding immunotherapy strategies. However, how the composition of the immune landscape affects the tumor progression of gastric cancer (GC) is ill-defined. Here, we used mass cytometry to perform simultaneous in-depth immune profiling of the tumor, adjacent tissues, and blood cells from GC patients and revealed a unique GC tumor-immune signature, where CD8+ T cells were present at a lower frequency in tumor tissues compared to adjacent tissues, whereas regulatory T cells and tumor-associated macrophages (TAMs) were significantly increased, indicating strong suppressive TIME in GC. Incorporated with oncogenic genomic traits, we found that the unique immunophenotype was interactively shaped by a specific GC gene signature across tumor progression. Earlier-stage GC lesions with IFN signaling enrichment harbored significantly altered T-cell compartments while advanced GC featured by metabolism signaling activation was accumulated by TAMs. Interestingly, PD-1 expression on CD8+ T cells was relatively higher in earlier-stage GC patients, indicating that these patients may derive more benefits from PD-1 inhibitors. The dynamic properties of diverse immune cell types revealed by our study provide new dimensions to the immune landscape of GC and facilitate the development of novel immunotherapy strategies for GC patients.

Comprehensive Molecular Analyses of a Macrophage-Related Gene Signature With Regard to Prognosis, Immune Features, and Biomarkers for Immunotherapy in Hepatocellular Carcinoma Based on WGCNA and the LASSO Algorithm.

Macrophages have been reported to exert a crucial role in hepatocellular carcinoma (HCC). This study aimed to explore the macrophage-related genes and establish a macrophage-related signature (MRS) model to predict the overall survival (OS) of patients with HCC based on these genes’ expression. We screened the macrophage-related gene module by weighted gene coexpression network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO) Cox regression analysis was utilized for further selection, and the selected genes were entered into stepwise regression to develop the MRS model, which was further validated in the Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC) datasets. We analyzed the biological phenotypes associated with macrophages in terms of functional enrichment, tumor immune signature, and tumor mutational signature. The patient’s response to immunotherapy was inferred by the tumor immune dysfunction and exclusion (TIDE) score, the immunophenotype score (IPS), and the IMvigor210 dataset. A novel MRS model was established based on the LASSO regression coefficients of the genes PON1, IL15RA, NEIL3, HILPDA, PFN2, HAVCR1, ANXA10, CDCA8, EPO, S100A9, TTK, KLRB1, SPP1, STC2, CYP26B1, GPC1, G6PD, and CBX2. In either dataset, MRS was identified as an independent risk factor for OS in HCC patients. Additionally, our research indicated that a high-risk score in the MRS model was significantly correlated with tumor staging, pathological grade, tumor–node–metastasis (TNM) stage, and survival. Several genes of the human leukocyte antigen (HLA) family and immune checkpoints were highly expressed in the high-risk group. In addition, the frequency of tumor mutations was also higher in the high-risk group. According to our analyses, a higher risk score in the MRS model may predict a better response to immunotherapy.

The histological stromal inflammatory subtypes and its transcriptional immune signature defined in oral squamous cell carcinoma with the soluble Semaphorin 4D immune biomarker

Oral squamous cell carcinoma (OSCC) is a disfiguring malignancy that occurs in close proximity to vital structures, sometimes with limited treatment options. Thus, the recent immunotherapy treatment is a promising alternative. The peri-tumoral stromal inflammatory profile (PTSIP) has shown to be an important factor in determining patient's response to immunotherapy. Soluble biomarkers are a promising tool that can monitor the status of PTSIP and serve as a diagnostic method of the histological inflammatory stromal (HIS) subtypes. Semaphorin 4D (Sema4D) is a soluble immune biomarker that we previously detected in OSCC patient's plasma. <i>The objective</i> of this work is to define the HIS subtypes in OSCC according to the status of the PTSIP and assess the diagnostic potential of the soluble Sema4D (sSema4D) in peripheral blood, to identify OSCC HIS subtypes. <h3>Findings</h3> According to University of Maryland institutional review board approval, in a cohort of eighty excisional OSCC tumor tissue biopsies, we described the three HIS subtypes; inflamed (hot), the immune excluded and the de- serted (cold). Immune focused gene expression analysis of the IFN-γ signature using representative tumor samples of the three HIS subtypes was described. We also analyzed sSema4D in plasma of the same eighty OSCC patients, using healthy donors and chronic inflammation/ autoimmune/ allergy patients as controls. High sSema4D levels (HsS4D) in plasma of OSCC correlated directly with the immune excluded HIS subtype (p=0.0001). HsS4D in blood clustered with low IFN-γ tumor immune signature (TIS). <h3>Conclusions</h3> Our data demonstrates the histological inflammatory subtypes and the corresponding transcriptional immune signature in OSCC. It highlights sSema4D as an immune biomarker that can be diagnostic of the tumor stromal inflammation. These findings open new avenues for advancing personalized immunotherapy in OSCC.

A risk model developed based on tumor microenvironment predicts overall survival and associates with tumor immunity of patients with lung adenocarcinoma.

Tumor microenvironment (TME) has been reported to exhibit a crucial effect in lung cancer. Therefore, this study was aimed to investigate the genes associated with TME and develop a risk score to predict the overall survival (OS) of patients with lung adenocarcinoma (LUAD) based on these genes. The immune and stromal scores were generated by the ESTIMATE algorithm for LUAD patients in The Cancer Genome Atlas (TCGA) database. Differentially expressed gene and weighted gene co-expression network analyses were used to derive immune- and stromal-related genes. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression was applied for further selection and the selected genes were inputted into stepwise regression to develop TME-related risk score (TMErisk) which was further validated in Gene Expression Omnibus (GEO) datasets. TMErisk-related biological phenotypes were analyzed in function enrichment, tumor immune signature, and tumor mutation signature. The patient's response to immunotherapy was inferred by the tumor immune dysfunction and exclusion (TIDE) score and immunophenoscore (IPS). According to our results, TMErisk was developed based on SERPINE1, CX3CR1, CD200R1, GBP1, IRF1, STAP1, LOX, and OR7E47P. Furthermore, high TMErisk was identified as a poor factor for OS in TCGA and GEO datasets, as well as in subgroup analysis with different gender, smoking status, age, race, anatomic site, therapies, and tumor-node-metastasis (TNM) stages. Higher TMErisk is also associated negatively with the abundance of B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and other stromal or immune cells. Several genes of the human leukocyte antigen (HLA) family and immune checkpoints were less expressed in the high-TMErisk group. Mutations of 19 genes occurred more frequently in the high-TMErisk group. These mutations may be associated with TME change and indicate patients' response to immunotherapy. According to our analyses, a lower TMErisk score may indicate better response and OS outcome of immunotherapy.

Immune signatures to affect overall survival (OS) and response to bevacizumab (Bev) or cetuximab (Cet) in patients (pts) with metastatic colorectal cancer (mCRC) of CALGB/SWOG 80405 (Alliance).

3515 Background: CALGB/SWOG 80405 was a randomized phase III trial in first-line mCRC patients treated with Bev, Cet, or both, plus chemotherapy. No difference in OS was found between Bev and Cet. We tested the effect of immune signatures on OS in all the three arms of the study and analyzed differences in OS between the Cet and Bev arms. Methods: 578 primary tumors were profiled by RNAseq. Immune signatures of TGF-β, cytotoxic T cells, wound healing, macrophages, lymphocytes, and INF-γ, as well as relative frequencies of CD8+ T-cells, memory resting CD4+ T cells, memory activated CD4+ T cells, macrophages M1 and M2, and activated mast cells were measured. Multivariate Cox proportional hazard models were applied using elastic-net penalization with covariates (age, race, gender, all RAS and BRAF V600E mutations). For relevant signatures, optimal cut-offs for OS were calculated. Results: In all the three arms of the study, high expression of macrophages M2 (HR 6.81, 95% CI 3.56-30.16) and TGF-β (HR 1.37, 95% CI 1.03-2.10) conferred reduced OS compared to low expression; high expression of plasma cells (HR 0.52, 95% CI 0.27-0.83) and memory-activated CD4+ T cells (HR 0.34, 95% CI 0.10-0.65) conferred increased OS compared to low expression. Using optimal cut-offs from these 4 signatures, pts have been categorized as to whether they had either 4, 3, 2, 1, or 0 beneficial signatures associated with increased OS. In all arms of the study (N = 469, after accounting for covariates), the median (95% CI) OS decreased from 42.5 (35.8-47.8; N = 79), to 31.0 (28.8-34.4; N = 177), 25.2 (20.6-27.9; N = 144), and 17.0 (13.5-20.4; N = 69) months when the number of beneficial signatures decreased from 4, to 3, 2, and 0-1 (combined due to a low number of pts), respectively (p = 3.48e-11). In the Bev arm (N = 205), high expression of macrophages M2 conferred reduced OS compared to low expression (HR 6.6, 95% CI 2.7-67.1). In the Cet arm (N = 165), high expression of macrophages M2 conferred reduced OS compared to low expression (HR 4.3, 95% CI 2.1-79.8); high expression of plasma cells (HR 0.36, 95% CI 0.06-0.55) and memory activated CD4+ T cells (HR 0.37, 95% CI 0.03-0.98) conferred increased OS compared to low expression of either signatures. The plasma cell signature interacted with Bev and Cet on the OS of pts (interaction p = 0.009). Conclusions: Tumor immune signatures in mCRC pts are determinants of survival. In pts treated with Bev- and Cet-combination therapies that are standard of care, immune signatures affect response to therapy. These results, provide new markers for treatment selection and for the development of novel active combinations including immune checkpoint inhibitors. Support: U10CA180821, U10CA180882, U24CA196171; https://acknowledgments.alliancefound.org

Identification of molecular heterogeneity of hepatocellular carcinoma based on immune gene expression signatures.

Although various molecular subtypes of hepatocellular carcinoma (HCC) have been investigated, most of these studies identify HCC subtype based on genomic profiling. Few studies have investigated the classification based on immune signatures, and none have classified HCC based on Immune activation and immunosuppressive. We performed immune gene expression of tumor tissue in 374 HCC patients from The Cancer Genome Atlas (TCGA) database and used unsupervised consensus clustering to stratify tumors. We then used HCC patients from the International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) as replication datasets. Based on the expression of 782 immune-related genes, HCC was stratified into four distinct immune subtypes. Tumors in one cluster (high immune activation; high-IA) indicate a higher level of Immune activation, which was characterized by higher anti-tumor immunity, higher pro-tumor immune-suppressive cell types, higher fractions of CD8+ T cells and M0 Macrophages compared with other subtypes. The high-IA also presents higher cancer-related hallmark signatures, such as epithelial-mesenchymal transition (EMT), angiogenesis, and apoptosis. We also found subpopulations of regulatory and exhaustion T lymphocyte were characterized by an opposite trend in high-IA, though samples in high-IA response to immunotherapy with better survival. The comparison of the immune profile in tumor and normal tissue indicates the activation of immune responses which only occurred in high-IA patients, while we conducted comparison of cirrhosis and non-cirrhosis tumor immune signatures, immune response activation was almost occurred in high-IA, but some of immune responses occurred in low-IA (low immune activation).

Soluble Sema4D in Plasma of Head and Neck Squamous Cell Carcinoma Patients Is Associated With Underlying Non-Inflamed Tumor Profile

Semaphorin 4D (Sema4D) is a glycoprotein that is expressed by several tumors and immune cells. It can function as a membrane bound protein or as a cleaved soluble protein (sSema4D). We sought to investigate the translational potential of plasma sSema4D as an immune marker in plasma of patients with head and neck squamous cell carcinoma (HNSCC). Paired peripheral blood and tumor tissue samples of 104 patients with HNSCC were collected at the same time point to allow for real time analysis. Scoring of the histological inflammatory subtype (HIS) was carried out using Sema4D immunohistochemistry on the tumor tissue. sSema4D was detected in plasma using direct ELISA assay. Defining elevated sSema4D as values above the 95th percentile in healthy controls, our data showed that sSema4D levels in plasma were elevated in 25.0% (95% CI, 16.7–34.9%) of the patients with HNSCC and showed significant association with HIS immune excluded (HIS-IE) (p = 0.007), Sema4D+ve tumor cells (TCs) (p = 0.018) and PD-L1+ve immune cells (ICs) (p = 0.038). A multi-variable logistic regression analysis showed that HIS was significantly (P = 0.004) associated with elevated sSema4D, an association not explained by available patient-level factors. Using the IO-360 nanoString platform, differential gene expression (DGE) analysis of 10 HNSCC tumor tissues showed that patients with high sSema4D in plasma (HsS4D) clustered as IFN-γ negative tumor immune signature and were mostly HIS-IE. The IC type in the HsS4D paired tumor tissue was predominantly myeloid, while the lymphoid compartment was higher in the low sSema4D (LsS4D). The Wnt signaling pathway was upregulated in the HsS4D group. Further analysis using the IO-360, 770 gene set, showed significant non-inflamed profile of the HsS4D tumors compared to the LsS4D. In conclusion, our data reveals an association between sSema4D and the histological inflammatory subtype.

Basal and Luminal Molecular Subtypes in Naturally-Occurring Canine Urothelial Carcinoma are Associated with Tumor Immune Signatures and Dog Breed.

Improved therapies are needed for patients with invasive urothelial carcinoma (InvUC). Tailoring treatment to molecular subtypes holds promise, but requires further study, including studies in pre-clinical animal models. Naturally-occurring canine InvUC harbors luminal and basal subtypes, mimicking those observed in humans, and could offer a relevant model for the disease in people. To further validate the canine InvUC model, clinical and tumor characteristics associated with luminal and basal subtypes in dogs were determined, with comparison to findings from humans. RNA sequencing (RNA-seq) analyses were performed on 56 canine InvUC tissues and bladder mucosa from four normal dogs. Data were aligned to CanFam 3.1, and differentially expressed genes identified. Data were interrogated with panels of genes defining luminal and basal subtypes, immune signatures, and other tumor features. Subject and tumor characteristics, and outcome data were obtained from medical records. Twenty-nine tumors were classified as luminal and 27 tumors as basal subtype. Basal tumors were strongly associated with immune infiltration (OR 52.22, 95%CI 4.68-582.38, P = 0.001) and cancer progression signatures in RNA-seq analyses, more advanced clinical stage, and earlier onset of distant metastases in exploratory analyses (P = 0.0113). Luminal tumors were strongly associated with breeds at high risk for InvUC (OR 0.06, 95%CI 0.01 -0.37, P = 0.002), non-immune infiltrative signatures, and less advanced clinical stage. Dogs with InvUC could provide a valuable model for testing new treatment strategies in the context of molecular subtype and immune status, and the search for germline variants impacting InvUC onset and subtype.

Integrating Machine Learning and Tumor Immune Signature to Predict Oncologic Outcomes in Resected Biliary Tract Cancer.

Improved methods are needed to predict outcomes in biliary tract cancers (BTCs). We aimed to build an immune-related signature and establish holistic models using machine learning. Samples were from 305 BTC patients treated with curative-intent resection, divided into derivation and validation cohorts in a two-to-one ratio. Spatial resolution of T cell infiltration and PD-1/PD-L1 expression was assessed by immunohistochemistry. An immune signature was constructed using classification and regression tree. Machine learning was applied to develop prediction models for disease-specific survival (DSS) and recurrence-free survival (RFS). The immune signature composed of CD3+, CD8+, and PD-1+ cell densities and PD-L1 expression within tumor epithelium significantly stratified patients into three clusters, with median DSS varying from 11.7 to 80.8months and median RFS varying from 6.2 to 62.0months. Gradient boosting machines (GBM) outperformed rival machine-learning algorithms and selected the same 11 covariates for DSS and RFS prediction: immune signature, tumor site, age, bilirubin, albumin, carcinoembryonic antigen, cancer antigen 19-9, tumor size, tumor differentiation, resection margin, and nodal metastasis. The clinical-immune GBM models accurately predicted DSS and RFS, with respective concordance index of 0.776-0.816 and 0.741-0.781. GBM models showed significantly improved performance compared with tumor-node-metastasis staging system. The immune signature promises to stratify prognosis and allocate treatment in resected BTC. The clinical-immune GBM models accurately predict recurrence and death from BTC following surgery.

Immune profiling of uveal melanoma identifies a potential signature associated with response to immunotherapy

BackgroundTo date, no systemic therapy, including immunotherapy, exists to improve clinical outcomes in metastatic uveal melanoma (UM) patients. To understand the role of immune infiltrates in the genesis, metastasis, and...

Abstract A10: Logical modelling of tumor-immune crosstalk network to predict the mechanism of immune evasion in muscle-invasive bladder cancer (MIBC)

Abstract Muscle-invasive bladder cancer (MIBC) is an aggressive disease with limited treatment options. Checkpoint inhibitors, although approved as second-line therapy, have limited response rate that is indicative of intrinsic resistance to immune therapy. Efforts are being made to improve the response rate by designing novel drug combinations, one such being combination of chemotherapy and checkpoint inhibitors. Chemotherapy like the platinum-based therapy perturbs the tumor immune system, which contributes to the overall response to treatment. The tumor-immune system relationship is extremely dynamic, and the tumor post treatment constantly re-equilibrates the microenvironment to support its growth. The ability to capture the dynamic changes of the immune microenvironment will allow us to design treatments that can modulate the immune cells for a sustained antitumor effect. Here we have performed transcriptomic analysis of publicly available expression data provided by TCGA (The Cancer Genome Atlas) on MIBC. Using the deconvolution algorithm Cibersort, we generated tumor immune signatures for patients of MIBC. We observed that patients with high CD8+ T-cell expression signature are positively associated with improved survival. Moreover, we observed that patients with low CD8+ T-cell infiltration signature showed nonpolarized macrophage and resting CD4 T-cell expression signature, indicating that the patients' tumors have a “noninflamed” phenotype. To model the interplay between the tumor and the immune system, we constructed a network of bladder tumor-macrophage-CD4 T-cell (BMT network) crosstalk. The tumor expression data from the TCGA were used to build the tumor network, which was linked to the macrophage polarization network and CD4 T-cell differentiation network via “extracellular” cytokines. The change in the cytokine profile modulates the regulatory network of the macrophage polarization network and CD4 T-cell differentiation network. The BMT network is made dynamic by applying logic based Boolean formalism. The network structure of the BMT network was validated by comparing the network simulations to published experimental data. Network simulation with cisplatin treatment changed the immune profile of the BMT network depending on the initial condition of the network. The initial condition is described by the cytokine profile of the “extracellular” cytokine, and the immune profile is described by the macrophage polarized state and the CD4 T-cell differentiated cell. The BMT network generates a dynamic cytokine profile under different perturbation that allows us to predict the change in the tumor immune phenotypes in response to therapy and thus generate hypotheses on designing combination treatments to different immunotherapy. Citation Format: Shruti Shah, Barbara A. Foster, Donald E. Mager. Logical modelling of tumor-immune crosstalk network to predict the mechanism of immune evasion in muscle-invasive bladder cancer (MIBC) [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2019 May 18-21; Denver, CO. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(15_Suppl):Abstract nr A10.