Decision model for durable clinical benefit from front- or late-line immunotherapy alone or with chemotherapy in non-small cell lung cancer.

BACKGROUND: Predictive biomarkers and models of immune checkpoint inhibitors (ICIs) have been extensively studied in non-small cell lung cancer (NSCLC). However, evidence for many biomarkers remains inconclusive, and the opaqueness of machine learning models hinders practicality. We aimed to provide compelling evidence for biomarkers and develop a transparent decision tree model. METHODS: We consolidated data from 3,288 ICI-treated patients with NSCLC across real-world multicenter, public cohorts and the Choice-01 trial (ClinicalTrials.gov: NCT03856411). Over 50 features were examined for predicting durable clinical benefits (DCBs) from ICIs. Noteworthy biomarkers were identified to establish a decision tree model. Additionally, we explored the tumor microenvironment and peripheral CD8+ programmed death-1 (PD-1)+ T cell receptor (TCR) profiles. FINDINGS: Multivariate logistic regression analysis identified tumor histology, PD-ligand 1 (PD-L1) expression, tumor mutational burden, line, and regimen of ICI treatment as significant factors. Mutation subtypes of EGFR, KRAS, KEAP1, STK11, and disruptive TP53 mutations were associated with DCB. The decision tree (DT10) model, using the ten clinicopathological and genomic markers, showed superior performance in predicting DCB in the training set (area under the curve [AUC] = 0.82) and consistently outperformed other models in test sets. DT10-predicted-DCB patients manifested longer survival, an enriched inflamed tumor immune phenotype (67%), and higher peripheral TCR diversity, whereas the DT10-predicted-NDB (non-durable benefit) group showed an enriched desert immune phenotype (86%) and higher peripheral TCR clonality. CONCLUSIONS: The model effectively predicted DCB after front-/subsequent-line ICI treatment, with or without chemotherapy, for squamous and non-squamous lung cancer, offering clinicians valuable insights into efficacy prediction using cost-effective variables. FUNDING: This study was supported by the National Key R&D Program of China.

Image-based assessment of natural killer cell activity against glioblastoma stem cells.

Glioblastoma (GBM) poses a significant challenge in oncology and stands as the most aggressive form of brain cancer. A primary contributor to its relentless nature is the stem-like cancer cells, called glioblastoma stem cells (GSCs). GSCs have the capacity for self-renewal and tumorigenesis, leading to frequent GBM recurrences and complicating treatment modalities. While natural killer (NK) cells exhibit potential in targeting and eliminating stem-like cancer cells, their efficacy within the GBM microenvironment is limited due to constrained infiltration and function. To address this limitation, novel investigations focusing on boosting NK cell activity against GSCs are imperative. This study presents two streamlined image-based assays assessing NK cell migration and cytotoxicity towards GSCs. It details protocols and explores the strengths and limitations of these methods. These assays could aid in identifying novel targets to enhance NK cell activity towards GSCs, facilitating the development of NK cell-based immunotherapy for improved GBM treatment.

In-vitro assays for immuno-oncology drug efficacy assessment and screening for personalized cancer therapy: scopes and challenges.

INTRODUCTION: Immunotherapies have revolutionized cancer treatment, but often fail to produce desirable therapeutic outcomes in all patients. Due to the inter-patient heterogeneity and complexity of the tumor microenvironment, personalized treatment approaches are gaining demand. Researchers have long been using a range of in-vitro assays including 2D models, organoid co-cultures, and cancer-on-a-chip platforms for cancer drug screening. A comparative analysis of these assays with their suitability, high-throughput capacity, and clinical translatability is required for optimal translational use. AREAS COVERED: The review summarized in-vitro platforms with their comparative advantages and limitations including construction strategies, and translational potential for immuno-oncology drug efficacy assessment. We also discussed end-point analysis strategies so that researchers can contextualize their usefulness and optimally design experiments for personalized immunotherapy efficacy prediction. EXPERT OPINION: Researchers developed several in-vitro platforms that can provide information on personalized immunotherapy efficacy from different angles. Image-based assays are undoubtedly more suitable to gather a wide range of information including cellular morphology and phenotypical behaviors but need significant improvement to overcome issues including background noise, sample preparation difficulty, and long duration of experiment. More studies and clinical trials are needed to resolve these issues and validate the assays before they can be used in real-life scenarios.

A tale of two pathways: Review of immune checkpoint inhibitors in DNA mismatch repair-deficient and microsatellite instability-high endometrial cancers.

The DNA mismatch repair (MMR) pathway is critical for correcting DNA mismatches generated during DNA replication. MMR-deficiency (MMR-D) leads to microsatellite instability (MSI) associated with an increased mutation rate, driving cancer development. This is particularly relevant in endometrial cancer (EC) as 25%-30% of tumors are of MMR-D/MSI-high (MSI-H) phenotype. Comprehensive assessment using immunohistochemistry (IHC) and sequencing-based techniques are necessary to fully evaluate ECs given the importance of molecular subtyping in staging and prognosis. This also influences treatment selection as clinical trials have demonstrated survival benefits for immune checkpoint inhibitors (ICIs) alone and in combination with chemotherapy for MMR-D/MSI-H EC patients in various treatment settings. As a portion of MMR-D/MSI-H ECs are driven by Lynch syndrome, an inherited cancer predisposition syndrome that is also associated with colorectal cancer, this molecular subtype also prompts germline assessment that can affect at-risk family members. Additionally, heterogeneity in the tumor immune microenvironment and tumor mutation burden (TMB) have been described by MMR mechanism, meaning MLH1 promoter hypermethylation versus germline/somatic MMR gene mutation, and this may affect response to ICI therapies. Variations by ancestry in prevalence and mechanism of MMR-D/MSI-H tumors have also been reported and may influence health disparities given observed differences in tumors of Black compared to White patients which may affect ICI eligibility. These observations highlight the need for additional prospective studies to evaluate the nuances regarding MMR-D heterogeneity as well as markers of resistance to inform future trials of combination therapies to further improve outcomes for patients with EC.

The Prognostic Assessment of CDC20 in Patients with Renal Clear Cell Carcinoma and Its Relationship with Body Immunity.

This article analyzes the relationship between cell division cycle (CDC20) molecules and oncology outcomes in patients with renal clear cell carcinoma (KIRC). CDC20 appears to act as a regulatory protein interacting with many other proteins at multiple points in the cycle. The RNA sequencing data and corresponding clinical information of CDC20 molecules were obtained from The Cancer Genome Atlas (TCGA) database. The expression of CDC20 in kidney renal clear cell carcinoma tissue and adjacent normal tissue was detected by immunohistochemical methods. Logistic analysis was performed to analyze the role of CDC20 in the clinicopathological characteristics and prognosis of KIRC. Gene Set Enrichment Analysis (GSEA) was used to identify the signal pathways which were related to CDC20. Independent prognostic factors were evaluated using univariate and multivariate Cox regression analysis. A nomogram involved in CDC20 expression and clinicopathological variables was conducted to predict overall survival (OS) in KIRC patients at 1, 3, and 5 years. Furthermore, the relation between CDC20 and immunity was also studied. Our results showed that CDC20 was upregulated in kidney renal clear cell carcinoma tissues, accompanying shorter OS (all P < 0.05). According to the results obtained by immunohistochemistry and TCGA database, CDC20 was significantly upregulated in kidney renal clear cell carcinoma tissues compared with neighboring normal kidney tissues. Univariate and multivariate Cox regression analysis showed that high expression of CDC20 was an independent prognostic factor of poor prognosis in kidney renal clear cell carcinoma patients (all P < 0.05). GSEA analysis suggested that the high expression of CDC20 was related to eight multiple signaling pathways. In addition, CDC20 was linked to tumour mutation burden (TMB), immune checkpoint molecules, tumour microenvironment, and immunological infiltration.

From Cellular Infiltration Assessment to a Functional Gene Set-Based Prognostic Model for Breast Cancer.

Background: Cancer heterogeneity is a major challenge in clinical practice, and to some extent, the varying combinations of different cell types and their cross-talk with tumor cells that modulate the tumor microenvironment (TME) are thought to be responsible. Despite recent methodological advances in cancer, a reliable and robust model that could effectively investigate heterogeneity with direct prognostic/diagnostic clinical application remained elusive. Results: To investigate cancer heterogeneity, we took advantage of single-cell transcriptome data and constructed the first indication- and cell type-specific reference gene expression profile (RGEP) for breast cancer (BC) that can accurately predict the cellular infiltration. By utilizing the BC-specific RGEP combined with a proven deconvolution model (LinDeconSeq), we were able to determine the intrinsic gene expression of 15 cell types in BC tissues. Besides identifying significant differences in cellular proportions between molecular subtypes, we also evaluated the varying degree of immune cell infiltration (basal-like subtype: highest; Her2 subtype: lowest) across all available TCGA-BRCA cohorts. By converting the cellular proportions into functional gene sets, we further developed a 24 functional gene set-based prognostic model that can effectively discriminate the overall survival (P = 5.9 × 10-33, n = 1091, TCGA-BRCA cohort) and therapeutic response (chemotherapy and immunotherapy) (P = 6.5 × 10-3, n = 348, IMvigor210 cohort) in the tumor patients. Conclusions: Herein, we have developed a highly reliable BC-RGEP that adequately annotates different cell types and estimates the cellular infiltration. Of importance, the functional gene set-based prognostic model that we have introduced here showed a great ability to screen patients based on their therapeutic response. On a broader perspective, we provide a perspective to generate similar models in other cancer types to identify shared factors that drives cancer heterogeneity.

Determining the immune environment of cutaneous T-cell lymphoma lesions through the assessment of lesional blood drops.

Detailed analysis of the cells that infiltrate lesional skin cannot be performed in skin biopsy specimens using immunohistochemistry or cell separation techniques because enzyme treatments applied during the isolation step can destroy small amounts of protein and minor cell populations in the biopsy specimen. Here, we describe a method for isolating T cells from drops of whole blood obtained from lesions during skin biopsy in patients with cutaneous T-cell lymphoma. Lesional blood is assumed to contain lesional resident cells, cells from capillary vessels, and blood overflowing from capillary vessels into the lesion area. The lesional blood showed substantial increases in distinct cell populations, chemokines, and the expression of various genes. The proportion of CD8+CD45RO+ T cells in the lesional blood negatively correlated with the modified severity-weighted assessment tool scores. CD4+CD45RO+ T cells in the lesional blood expressed genes associated with the development of cancer and progression of cutaneous T-cell lymphoma. In addition, CD8+CD45RO+ T cells in lesional blood had unique T-cell receptor repertoires in lesions of each stage. Assessment of lesional blood drops might provide new insight into the pathogenesis of mycosis fungoides and facilitate evaluation of the treatment efficacy for mycosis fungoides as well as other skin inflammatory diseases.

Assessment of Immune Cell Populations in Tumor Tissue and Peripheral Blood Samples from Head and Neck Squamous Cell Carcinoma Patients.

Head and neck squamous cell carcinoma (HNSCC) is a common type of cancer worldwide. Strong connections have been revealed between immune cells and the pathogenesis of HNSCC. Important differences regarding the levels of immune cell subpopulations in both peripheral circulation and tumor microenvironment were emphasized, with some of them having prognostic significance. In our study, we performed an analysis of immune changes in the tumor tissue and the peripheral blood of untreated HNSCC patients, investigating the proportions of different immune cell populations in these two compartments. The local infiltrating lymphocytes were mainly cytotoxic T cells (CD8+). We have also revealed an increased level of B lymphocytes (CD19+) in the tumor microenvironment. In peripheral blood, the most important lymphocyte subtype was represented by the helper T lymphocytes (CD4+). We also found an increased proportion of circulating NK cells (CD56+). Our results showed significant differences between all investigated lymphocyte subtypes in the peripheral blood and the tumor tissue of untreated HNSCC patients, suggesting that the local and systemic expressions of antitumor immune responses are different and that investigation of immune cell proportions in peripheral circulation has different cues that do not reflect the immune infiltrate pattern within the tumor microenvironment. Further studies are necessary to unveil the complex interplay involving local and systemic events in the immune system's fight against cancer.

Assessment of the immune landscapes of advanced ovarian cancer in an optimized in vivo model.

BACKGROUND: Ovarian cancer (OC) is typically diagnosed late, associated with high rates of metastasis and the onset of ascites during late stage disease. Understanding the tumor microenvironment and how it impacts the efficacy of current treatments, including immunotherapies, needs effective in vivo models that are fully characterized. In particular, understanding the role of immune cells within the tumor and ascitic fluid could provide important insights into why OC fails to respond to immunotherapies. In this work, we comprehensively described the immune cell infiltrates in tumor nodules and the ascitic fluid within an optimized preclinical model of advanced ovarian cancer. METHODS: Green Fluorescent Protein (GFP)-ID8 OC cells were injected intraperitoneally into C57BL/6 mice and the development of advanced stage OC monitored. Nine weeks after tumor injection, mice were sacrificed and tumor nodules analyzed to identify specific immune infiltrates by immunohistochemistry. Ascites, developed in tumor bearing mice over a 10-week period, was characterized by mass cytometry (CyTOF) to qualitatively and quantitatively assess the distribution of the immune cell subsets, and their relationship to ascites from ovarian cancer patients. RESULTS: Tumor nodules in the peritoneal cavity proved to be enriched in T cells, antigen presenting cells and macrophages, demonstrating an active immune environment and cell-mediated immunity. Assessment of the immune landscape in the ascites showed the predominance of CD8+ , CD4+ , B- , and memory T cells, among others, and the coexistance of different immune cell types within the same tumor microenvironment. CONCLUSIONS: We performed, for the first time, a multiparametric analysis of the ascitic fluid and specifically identify immune cell populations in the peritoneal cavity of mice with advanced OC. Data obtained highlights the impact of CytOF as a diagnostic tool for this malignancy, with the opportunity to concomitantly identify novel targets, and define personalized therapeutic options.

Automated assessment of CD8+ T-lymphocytes and stroma fractions complement conventional staging of colorectal cancer.

BACKGROUND: Tumor development is critically dependent on the supporting stroma consisting of inflammatory cells and fibroblasts. This study intended to improve prognostic prediction for early colorectal cancer (CRC) by combined estimation of T-lymphocyte and stroma fractions with conventional markers. METHODS: In total 509 and 1041 stage II/ΙΙΙ CRC from the VICTOR and QUASAR 2 trials were included as a training set and a validation set, respectively. Intratumoral CD8+ T-lymphocytes and stroma were identified and quantified by machine-based learning on digital sections. The primary endpoint was to evaluate the prognostic value of the combined marker for time to recurrence (TTR). FINDINGS: For low-risk patients (n = 598; stage Ⅱ, and stage ΙΙΙ pT1-3 pN1 with neither lymphatic (L-) nor vascular (V-) invasion), low stroma fraction (n = 511) identified a good prognostic subgroup with 5-year TTR of 86% (95% CI 83-89), versus the high stroma subgroup TTR of 78% (HR = 1.75, 95% CI 1.05-2.92; P = 0.029). For high-risk patients (n = 394; stage ΙΙΙ pT3 pN1 L+/V+, pT4, or pN2), combined low CD8+ and high stroma fraction identified a poor prognostic subgroup (n = 34) with 5-year TTR of 29% (95% CI 17-50), versus the high CD8+ fraction and low stroma fraction subgroup (n = 138) of 64% (HR = 2.86, 95% CI 1.75-4.69; P < 0.001). INTERPRETATION: Quantification of intratumoral CD8+ T-lymphocyte and stroma fractions can be combined with conventional prognostic markers to improve patient stratification.

Tertiary lymphoid structures (TLS) identification and density assessment on H&E-stained digital slides of lung cancer.

Tertiary lymphoid structures (TLS) are ectopic aggregates of lymphoid cells in inflamed, infected, or tumoral tissues that are easily recognized on an H&E histology slide as discrete entities, distinct from lymphocytes. TLS are associated with improved cancer prognosis but there is no standardised method available to quantify their presence. Previous studies have used immunohistochemistry to determine the presence of specific cells as a marker of the TLS. This has now been proven to be an underestimate of the true number of TLS. Thus, we propose a methodology for the automated identification and quantification of TLS, based on H&E slides. We subsequently determined the mathematical criteria defining a TLS. TLS regions were identified through a deep convolutional neural network and segmentation of lymphocytes was performed through an ellipsoidal model. This methodology had a 92.87% specificity at 95% sensitivity, 88.79% specificity at 98% sensitivity and 84.32% specificity at 99% sensitivity level based on 144 TLS annotated H&E slides implying that the automated approach was able to reproduce the histopathologists' assessment with great accuracy. We showed that the minimum number of lymphocytes within TLS is 45 and the minimum TLS area is 6,245µm2. Furthermore, we have shown that the density of the lymphocytes is more than 3 times those outside of the TLS. The mean density and standard deviation of lymphocytes within a TLS area are 0.0128/µm2 and 0.0026/µm2 respectively compared to 0.004/µm2 and 0.001/µm2 in non-TLS regions. The proposed methodology shows great potential for automated identification and quantification of the TLS density on digital H&E slides.

Interobserver variability in the assessment of stromal tumor-infiltrating lymphocytes (sTILs) in triple-negative invasive breast carcinoma influences the association with pathological complete response: the IVITA study.

High stromal tumor-infiltrating lymphocytes (sTILs) in triple-negative breast cancer (TNBC) are associated with pathological complete response (pCR) after neoadjuvant chemotherapy (NAC). Histopathological assessment of sTILs in TNBC biopsies is characterized by substantial interobserver variability, but it is unknown whether this affects its association with pCR. Here, we aimed to investigate the degree of interobserver variability in an international study, and its impact on the relationship between sTILs and pCR. Forty pathologists assessed sTILs as a percentage in digitalized biopsy slides, originating from 41 TNBC patients who were treated with NAC followed by surgery. Pathological response was quantified by the MD Anderson Residual Cancer Burden (RCB) score. Intraclass correlation coefficients (ICCs) were calculated per pathologist duo and Bland-Altman plots were constructed. The relation between sTILs and pCR or RCB class was investigated. The ICCs ranged from -0.376 to 0.947 (mean: 0.659), indicating substantial interobserver variability. Nevertheless, high sTILs scores were significantly associated with pCR for 36 participants (90%), and with RCB class for eight participants (20%). Post hoc sTILs cutoffs at 20% and 40% resulted in variable associations with pCR. The sTILs in TNBC with RCB-II and RCB-III were intermediate to those of RCB-0 and RCB-I, with lowest sTILs observed in RCB-I. However, the limited number of RCB-I cases precludes any definite conclusions due to lack of power, and this observation therefore requires further investigation. In conclusion, sTILs are a robust marker for pCR at the group level. However, if sTILs are to be used to guide the NAC scheme for individual patients, the observed interobserver variability might substantially affect the chance of obtaining a pCR. Future studies should determine the 'ideal' sTILs threshold, and attempt to fine-tune the patient selection for sTILs-based de-escalation of NAC regimens. At present, there is insufficient evidence for robust and reproducible sTILs-guided therapeutic decisions.

Associations between initiating antihypertensive regimens on stage I-III colorectal cancer outcomes: A Medicare SEER cohort analysis.

PURPOSE: Colorectal cancer (CRC) diagnosis is associated with high mortality in the United States and thus warrants the study of novel treatment approaches. Vascular changes are well observed in cancers and evidence indicates that antihypertensive (AH) medications may interfere with both tumor vasculature and in recruiting immune cells to the tumor microenvironment based on preclinical models. Extant literature also shows that AH medications are correlated with improved survival in some forms of cancer. Thus, this study sought to explore the impact of AH therapies on CRC outcomes. PATIENTS AND METHODS: This study was a non-interventional, retrospective analysis of patients aged 65 years and older with CRC diagnosed from January 1, 2007 to December 31st, 2012 in the Surveillance, Epidemiology, and End-Results (SEER)-Medicare database. The association between AH drug utilization on AJCC stage I-III CRC mortality rates in patients who underwent treatment for cancer was examined using Cox proportional hazards models. RESULTS: The study cohort consisted of 13,982 patients diagnosed with CRC. Adjusted Cox proportional hazards regression showed that among these patients, the use of AH drug was associated with decreased cancer-specific mortality (HR: 0.79, 95% CI: 0.75-0.83). Specifically, ACE inhibitors (hazard ratio [HR]: 0.84, 95% CI: 0.80-0.87), beta-blockers (HR: 0.87, 95% CI: 0.84-0.91), and thiazide diuretics (HR: 0.83, 95% CI: 0.80-0.87) were found to be associated with decreased mortality. An association was also found between adherence to AH therapy and decreased cancer-specific mortality (HR: 0.94, 95% CI: 0.90-0.98). CONCLUSION: Further research needs to be performed, but AH medications may present a promising, low-cost pathway to supporting CRC treatment for stage I-III cancers.

Modeling human tumor-immune environments in vivo for the preclinical assessment of immunotherapies.

Despite the significant contributions of immunocompetent mouse models to the development and assessment of cancer immunotherapies, they inadequately represent the genetic and biological complexity of corresponding human cancers. Immunocompromised mice reconstituted with a human immune system (HIS) and engrafted with patient-derived tumor xenografts are a promising novel preclinical model for the study of human tumor-immune interactions. Whilst overcoming limitations of immunocompetent models, HIS-tumor models often rely on reconstitution with allogeneic immune cells, making it difficult to distinguish between anti-tumor and alloantigen responses. Models that comprise of autologous human tumor and human immune cells provide a platform that is more representative of the patient immune-tumor interaction. However, limited access to autologous tissues, short experimental windows, and poor retention of tumor microenvironment and tumor infiltrating lymphocyte components are major challenges affecting the establishment and application of autologous models. This review outlines existing preclinical murine models for the study of immuno-oncology, and highlights innovations that can be applied to improve the feasibility and efficacy of autologous models.

Radiomics Assessment of the Tumor Immune Microenvironment to Predict Outcomes in Breast Cancer.

Background: The immune microenvironment of tumors provides information on prognosis and prediction. A prior validation of the immunoscore for breast cancer (ISBC) was made on the basis of a systematic assessment of immune landscapes extrapolated from a large number of neoplastic transcripts. Our goal was to develop a non-invasive radiomics-based ISBC predictive factor. Methods: Immunocell fractions of 22 different categories were evaluated using CIBERSORT on the basis of a large, open breast cancer cohort derived from comprehensive information on gene expression. The ISBC was constructed using the LASSO Cox regression model derived from the Immunocell type scores, with 479 quantified features in the intratumoral and peritumoral regions as observed from DCE-MRI. A radiomics signature [radiomics ImmunoScore (RIS)] was developed for the prediction of ISBC using a random forest machine-learning algorithm, and we further evaluated its relationship with prognosis. Results: An ISBC consisting of seven different immune cells was established through the use of a LASSO model. Multivariate analyses showed that the ISBC was an independent risk factor in prognosis (HR=2.42, with a 95% CI of 1.49-3.93; P<0.01). A radiomic signature of 21 features of the ISBC was then exploited and validated (the areas under the curve [AUC] were 0.899 and 0.815). We uncovered statistical associations between the RIS signature with recurrence-free and overall survival rates (both P<0.05). Conclusions: The RIS is a valuable instrument with which to assess the immunoscore, and offers important implications for the prognosis of breast cancer.

Comparative Genomics Reveals Distinct Immune-oncologic Pathways in African American Men with Prostate Cancer.

PURPOSE: The role of immune-oncologic mechanisms of racial disparities in prostate cancer remains understudied. Limited research exists to evaluate the molecular underpinnings of immune differences in African American men (AAM) and European American men (EAM) prostate tumor microenvironment (TME). EXPERIMENTAL DESIGN: A total of 1,173 radiation-naïve radical prostatectomy samples with whole transcriptome data from the Decipher GRID registry were used. Transcriptomic expressions of 1,260 immune-specific genes were selected to assess immune-oncologic differences between AAM and EAM prostate tumors. Race-specific differential expression of genes was assessed using a rank test, and intergene correlational matrix and gene set enrichment was used for pathway analysis. RESULTS: AAM prostate tumors have significant enrichment of major immune-oncologic pathways, including proinflammatory cytokines, IFNα, IFNγ, TNFα signaling, ILs, and epithelial-mesenchymal transition. AAM TME has higher total immune content score (ICSHIGH) compared with 0 (37.8% vs. 21.9%, P = 0.003). AAM tumors also have lower DNA damage repair and are genomically radiosensitive as compared with EAM. IFITM3 (IFN-inducible transmembrane protein 3) was one of the major proinflammatory genes overexpressed in AAM that predicted increased risk of biochemical recurrence selectively for AAM in both discovery [HRAAM = 2.30; 95% confidence interval (CI), 1.21-4.34; P = 0.01] and validation (HRAAM = 2.42; 95% CI, 1.52-3.86; P = 0.0001) but not in EAM. CONCLUSIONS: Prostate tumors of AAM manifest a unique immune repertoire and have significant enrichment of proinflammatory immune pathways that are associated with poorer outcomes. Observed immune-oncologic differences can aid in a genomically adaptive approach to treating prostate cancer in AAM.

Prognostic assessment of resected colorectal liver metastases integrating pathological features, RAS mutation and Immunoscore.

Surgical resection of colorectal liver metastases combined with systemic treatment aims to maximize patient survival. However, recurrence rates are very high postsurgery. In order to assess patient prognosis after metastasis resection, we evaluated the main patho-molecular and immune parameters of all surgical specimens. Two hundred twenty-one patients who underwent, after different preoperative treatment, curative resection of 582 metastases were analyzed. Clinicopathological parameters, RAS tumor mutation, and the consensus Immunoscore (I) were assessed for all patients. Overall survival (OS) and time to relapse (TTR) were estimated using the Kaplan-Meier method and compared by log-rank tests. Cox proportional hazard models were used for uni- and multivariate analysis. Immunoscore and clinicopathological parameters (number of metastases, surgical margin, histopathological growth pattern, and steatohepatitis) were associated with relapse in multivariate analysis. Overall, pathological score (PS) that combines relevant clinicopathological factors for relapse, and I, were prognostic for TTR (2-year TTR rate PS 0-1: 49.8.% (95% CI: 42.2-58.8) versus PS 2-4: 20.9% (95% CI: 13.4-32.8), hazard ratio (HR) = 2.54 (95% CI: 1.82-3.53), p < 0.0000; and 2-year TTR rate I 0: 25.7% (95% CI: 16.3-40.5) versus I 3-4: 60% (95% CI: 47.2-76.3), HR = 2.87 (95% CI: 1.73-4.75), p = 0.0000). Immunoscore was also prognostic for OS (HR [I 3-4 versus I 0] = 4.25, 95% CI: 1.95-9.23; p = 0.0001). Immunoscore (HR [I 3-4 versus I 0] = 0.27, 95% CI: 0.12-0.58; p = 0.0009) and RAS mutation (HR [mutated versus WT] = 1.66, 95% CI: 1.06-2.58; p = 0.0265) were significant for OS. In conclusion, PS including relevant clinicopathological parameters and Immunoscore permit stratification of stage IV colorectal cancer patient prognosis in terms of TTR and identify patients with higher risk of recurrence. Immunoscore remains the major prognostic factor for OS.

Multi-omics analysis of tumor mutational burden combined with prognostic assessment in epithelial ovarian cancer based on TCGA database.

Background: Tumor mutation burden (TMB) is considered as a novel biomarker of response to immunotherapy and correlated with survival outcomes in various malignancies. Here, TMB-related genes (TRGs) expression signatures were constructed to investigate the association between TMB and prognosis in epithelial ovarian cancer (EOC), and the potential mechanism in immunoregulation was also explored. Methods: Based on somatic mutation data of 436 EOC samples from The Cancer Genome Atlas database, we examined the relationship between TMB level and overall survival (OS), as well as disease-free survival (DFS). Next, the TRGs signatures were constructed and validated. Differential abundance of immune cell infiltration, expression levels of immunomodulators and functional enrichment in high- and low-risk groups were also analyzed. Results: Higher TMB level revealed better OS and DFS, and correlated with earlier clinical stages in EOCs (P = 2.796e-04). The OS-related prognostic model constructed based on seven TRGs (B3GALT1, LIN7B, ANGPT2, D2HGDH, TAF13, PFDN4 and DNAJC19) significantly stratified EOC patients into high- and low-risk groups (P < 0.001). The AUC values of the seven-gene prognostic signature at 1 year, 3 years, and 5 years were 0.703, 0.758 and 0.777. While the DFS-related prognostic model was constructed based on the 4 TRGs (LPIN3, PXYLP1, IGSF23 and B3GALT1), with AUCs of 0.617, 0.756, and 0.731, respectively. Functional analysis indicated that immune-related pathways were enriched in low-risk groups. When considering the infiltration patterns of immune cells, we found higher proportions of follicular helper T (Tfh) cell and M1 macrophage, while lower infiltration of M0 macrophage in low-risk groups (P < 0.05). Accordingly, TMB levels of low-risk patients were significantly higher both in OS and DFS model (P < 0.01). Conclusions: Our TRGs-based models are reliable predictive tools for OS and DFS. High TMB may confer with an immunogenic microenvironment and predict favorable outcomes in EOCs.

Assessment of long non-coding RNA expression reveals novel mediators of the lung tumour immune response.

The tumour immune microenvironment is a crucial mediator of lung tumourigenesis, and characterizing the immune landscape of patient tumours may guide immunotherapy treatment regimens and uncover novel intervention points. We sought to identify the landscape of tumour-infiltrating immune cells in the context of long non-coding RNA (lncRNAs), known regulators of gene expression. We examined the lncRNA profiles of lung adenocarcinoma (LUAD) tumours by interrogating RNA sequencing data from microdissected and non-microdissected samples (BCCRC and TCGA). Subsequently, analysis of single-cell RNA sequencing data from lung tumours and flow-sorted healthy peripheral blood mononuclear cells identified lncRNAs in immune cells, highlighting their biological and prognostic relevance. We discovered lncRNA expression patterns indicative of regulatory relationships with immune-related protein-coding genes, including the relationship between AC008750.1 and NKG7 in NK cells. Activation of NK cells in vitro was sufficient to induce AC008750.1 expression. Finally, siRNA-mediated knockdown of AC008750.1 significantly impaired both the expression of NKG7 and the anti-tumour capacity of NK cells. We present an atlas of cancer-cell extrinsic immune cell-expressed lncRNAs, in vitro evidence for a functional role of lncRNAs in anti-tumour immune activity, which upon further exploration may reveal novel clinical utility as markers of immune infiltration.

Transcriptional and immunohistological assessment of immune infiltration in pancreatic cancer.

Pancreatic adenocarcinoma is characterized by a complex tumor environment with a wide diversity of infiltrating stromal and immune cell types that impact the tumor response to conventional treatments. However, even in this poorly responsive tumor the extent of T cell infiltration as determined by quantitative immunohistology is a candidate prognostic factor for patient outcome. As such, even more comprehensive immunophenotyping of the tumor environment, such as immune cell type deconvolution via inference models based on gene expression profiling, holds significant promise. We hypothesized that RNA-Seq can provide a comprehensive alternative to quantitative immunohistology for immunophenotyping pancreatic cancer. We performed RNA-Seq on a prospective cohort of pancreatic tumor specimens and compared multiple approaches for gene expression-based immunophenotyping analysis compared to quantitative immunohistology. Our analyses demonstrated that while gene expression analyses provide additional information on the complexity of the tumor immune environment, they are limited in sensitivity by the low overall immune infiltrate in pancreatic cancer. As an alternative approach, we identified a set of genes that were enriched in highly T cell infiltrated pancreatic tumors, and demonstrate that these can identify patients with improved outcome in a reference population. These data demonstrate that the poor immune infiltrate in pancreatic cancer can present problems for analyses that use gene expression-based tools; however, there remains enormous potential in using these approaches to understand the relationships between diverse patterns of infiltrating cells and their impact on patient treatment outcomes.