Immunogenomic Analyses of Advanced Serous Ovarian Cancer Reveal Immune Score is a Strong Prognostic Factor and an Indicator of Chemosensitivity

Immunomic longitudinal profiling of the NeoPembrOv trial identifies drivers of immunoresistance in high-grade ovarian carcinoma

PD-1/PD-L1 blockade has so far shown limited survival benefit for high-grade ovarian carcinomas. By using paired samples from the NeoPembrOv randomized phase II trial (NCT03275506), for which primary outcomes are published, and by combining RNA-seq and multiplexed immunofluorescence staining, we explore the impact of NeoAdjuvant ChemoTherapy (NACT) ± Pembrolizumab (P) on the tumor environment, and identify parameters that correlated with response to immunotherapy as a pre-planned exploratory analysis. Indeed, i) combination therapy results in a significant increase in intraepithelial CD8+PD-1+ T cells, ii) combining endothelial and monocyte gene signatures with the CD8B/FOXP3 expression ratio is predictive of response to NACT + P with an area under the curve of 0.93 (95% CI 0.85-1.00) and iii) high CD8B/FOXP3 and high CD8B/ENTPD1 ratios are significantly associated with positive response to NACT + P, while KDR and VEGFR2 expression are associated with resistance. These results indicate that targeting regulatory T cells and endothelial cells, especially VEGFR2+ endothelial cells, could overcome immune resistance of ovarian cancers.

A prediction nomogram for perineural invasion in colorectal cancer patients: a retrospective study

BACKGROUND

Perineural invasion (PNI), as the fifth recognized pathway for the spread and metastasis of colorectal cancer (CRC), has increasingly garnered widespread attention. The preoperative identification of whether colorectal cancer (CRC) patients exhibit PNI can assist clinical practitioners in enhancing preoperative decision-making, including determining the necessity of neoadjuvant therapy and the appropriateness of surgical resection. The primary objective of this study is to construct and validate a preoperative predictive model for assessing the risk of perineural invasion (PNI) in patients diagnosed with colorectal cancer (CRC).

MATERIALS AND METHODS

A total of 335 patients diagnosed with colorectal cancer (CRC) at a single medical center were subject to random allocation, with 221 individuals assigned to a training dataset and 114 to a validation dataset, maintaining a ratio of 2:1. Comprehensive preoperative clinical and pathological data were meticulously gathered for analysis. Initial exploration involved conducting univariate logistic regression analysis, with subsequent inclusion of variables demonstrating a significance level of p < 0.05 into the multivariate logistic regression analysis, aiming to ascertain independent predictive factors, all while maintaining a p-value threshold of less than 0.05. From the culmination of these factors, a nomogram was meticulously devised. Rigorous evaluation of this nomogram's precision and reliability encompassed Receiver Operating Characteristic (ROC) curve analysis, calibration curve assessment, and Decision Curve Analysis (DCA). The robustness and accuracy were further fortified through application of the bootstrap method, which entailed 1000 independent dataset samplings to perform discrimination and calibration procedures.

RESULTS

The results of multivariate logistic regression analysis unveiled independent risk factors for perineural invasion (PNI) in patients diagnosed with colorectal cancer (CRC). These factors included tumor histological differentiation (grade) (OR = 0.15, 95% CI = 0.03-0.74, p = 0.02), primary tumor location (OR = 2.49, 95% CI = 1.21-5.12, p = 0.013), gross tumor type (OR = 0.42, 95% CI = 0.22-0.81, p = 0.01), N staging in CT (OR = 3.44, 95% CI = 1.74-6.80, p < 0.001), carcinoembryonic antigen (CEA) level (OR = 3.13, 95% CI = 1.60-6.13, p = 0.001), and platelet-to-lymphocyte ratio (PLR) (OR = 2.07, 95% CI = 1.08-3.96, p = 0.028).These findings formed the basis for constructing a predictive nomogram, which exhibited an impressive area under the receiver operating characteristic (ROC) curve (AUC) of 0.772 (95% CI, 0.712-0.833). The Hosmer-Lemeshow test confirmed the model's excellent fit (p = 0.47), and the calibration curve demonstrated consistent performance. Furthermore, decision curve analysis (DCA) underscored a substantial net benefit across the risk range of 13% to 85%, reaffirming the nomogram's reliability through rigorous internal validation.

CONCLUSION

We have formulated a highly reliable nomogram that provides valuable assistance to clinical practitioners in preoperatively assessing the likelihood of perineural invasion (PNI) among colorectal cancer (CRC) patients. This tool holds significant potential in offering guidance for treatment strategy formulation.

Prediction of immunocyte infiltration and prognosis in postoperative hepatitis B virus-related hepatocellular carcinoma patients using magnetic resonance imaging

Background

The immune microenvironment (IME) is closely associated with prognosis and therapeutic response of hepatitis B virus-related hepatocellular carcinoma (HBV-HCC). Multi-parametric magnetic resonance imaging (MRI) enables non-invasive assessment of IME and predicts prognosis in HBV-HCC. We aimed to construct an MRI prediction model of the immunocyte-infiltration subtypes and explore its prognostic significance.

Methods

HBV-HCC patients at the First Affiliated Hospital of Sun Yat-sen University (Guangzhou, China) with radical surgery (between 1 October and 30 December 2021) were prospectively enrolled. Patients with pathologically proven HCC (between 1 December 2013 and 30 October 2019) were retrospectively enrolled. Pearson correlation analysis was used to examine the relationship between the immunocyte-infiltration counts and MRI parameters. An MRI prediction model of immunocyte-infiltration subtypes was constructed in prospective cohort. Kaplan-Meier survival analysis was used to analyse its prognostic significance in the retrospective cohort.

Results

Twenty-four patients were prospectively enrolled to construct the MRI prediction model. Eighty-nine patients were retrospectively enrolled to determine its prognostic significance. MRI parameters (relative enhancement, ratio of the apparent diffusion coefficient value of tumoral region to peritumoral region [rADC], T1 value) correlated significantly with the immunocyte-infiltration counts (leukocytes, T help cells, PD1+Tc cells, B lymphocytes). rADC differed significantly between high and low immunocyte-infiltration groups (1.47 ± 0.36 vs 1.09 ± 0.25, P = 0.009). The area under the curve of the MRI model was 0.787 (95% confidence interval 0.587-0.987). Based on the MRI model, the recurrence-free time was longer in the high immunocyte-infiltration group than in the low immunocyte-infiltration group (P = 0.026).

Conclusions

MRI is a non-invasive method for assessing the IME and immunocyte-infiltration subtypes, and predicting prognosis in post-operative HBV-HCC patients.

First-Line Chemoimmunotherapy versus Sequential Platinum-Based Chemotherapy Followed by Immunotherapy in Patients with Non-Small Cell Lung Cancer with ≤49% Programmed Death-Ligand 1 Expression: A Real-World Multicenter Retrospective Study

BACKGROUND

The long overall survival (OS) observed among patients with non-small cell lung cancer (NSCLC) with high programmed death-ligand 1 (PD-L1) expression in chemoimmunotherapy (CIT) groups in previous phase III trials suggests the limited efficacy of CIT among the subgroup with ≤49% PD-L1 expression on tumor cells. Hence, sequential treatment with first-line platinum-based chemotherapy followed by second-line immune checkpoint inhibitor treatment (SEQ) is an option. This study examined whether first-line CIT would provide better outcomes than SEQ in patients with advanced NSCLC with ≤49% PD-L1 expression.

METHODS

This retrospective study evaluated patients with untreated NSCLC who received first-line CIT or SEQ at nine hospitals in Japan. OS, progression-free survival (PFS), PFS-2 (the time from first-line treatment to progression to second-line treatment or death), and other related outcomes were evaluated between the CIT and SEQ groups.

RESULTS

Among the 305 enrolled patients, 234 eligible patients were analyzed: 165 in the CIT group and 69 in the SEQ group. The COX proportional hazards model suggested a significant interaction between PD-L1 expression and OS (p = 0.006). OS in the CIT group was significantly longer than that in the SEQ group in the 1-49% PD-L1 expression subgroup but not in the <1% PD-L1 expression subgroup. Among the subgroup with 1-49% PD-L1 expression, the CIT group exhibited longer median PFS than the SEQ group (CIT: 9.3 months (95% CI: 6.7-14.8) vs. SEQ:5.5 months (95% CI: 4.5-6.1); p < 0.001), while the median PFS in the CIT group was not statistically longer than the median PFS-2 in the SEQ group (p = 0.586). There was no significant difference between the median PFS in the CIT and SEQ groups among the <1% PD-L1 expression subgroup (p = 0.883); the median PFS-2 in the SEQ group was significantly longer than the median PFS in the CIT group (10.5 months (95% CI: 5.9-15.3) vs. 6.4 months (95% CI: 4.9-7.5); p = 0.024).

CONCLUSIONS

CIT is recommended for patients with NSCLC with 1-49% PD-L1 expression because it significantly improved OS and PFS compared to SEQ. CIT had limited benefits in patients with <1% PD-L1 expression, and the median PFS-2 in the SEQ group was significantly longer than the median PFS in the CIT group. These findings will help physicians select the most suitable treatment option for patients with NSCLC, considering PD-L1 expressions.

Screening and Identification of a Prognostic Model of Ovarian Cancer by Combination of Transcriptomic and Proteomic Data

The integration of transcriptome and proteome analysis can lead to the discovery of a myriad of biological insights into ovarian cancer. Proteome, clinical, and transcriptome data about ovarian cancer were downloaded from TCGA's database. A LASSO-Cox regression was used to uncover prognostic-related proteins and develop a new protein prognostic signature for patients with ovarian cancer to predict their prognosis. Patients were brought together in subgroups using a consensus clustering analysis of prognostic-related proteins. To further investigate the role of proteins and protein-coding genes in ovarian cancer, additional analyses were performed using multiple online databases (HPA, Sangerbox, TIMER, cBioPortal, TISCH, and CancerSEA). The final resulting prognosis factors consisted of seven protective factors (P38MAPK, RAB11, FOXO3A, AR, BETACATENIN, Sox2, and IGFRb) and two risk factors (AKT_pS473 and ERCC5), which can be used to construct a prognosis-related protein model. A significant difference in overall survival (OS), disease-free interval (DFI), disease-specific survival (DSS), and progression-free interval (PFI) curves were found in the training, testing, and whole sets when analyzing the protein-based risk score (p < 0.05). We also illustrated a wide range of functions, immune checkpoints, and tumor-infiltrating immune cells in prognosis-related protein signatures. Additionally, the protein-coding genes were significantly correlated with each other. EMTAB8107 and GSE154600 single-cell data revealed that the genes were highly expressed. Furthermore, the genes were related to tumor functional states (angiogenesis, invasion, and quiescence). We reported and validated a survivability prediction model for ovarian cancer based on prognostic-related protein signatures. A strong correlation was found between the signatures, tumor-infiltrating immune cells, and immune checkpoints. The protein-coding genes were highly expressed in single-cell RNA and bulk RNA sequencing, correlating with both each other and tumor functional states.

signifinder enables the identification of tumor cell states and cancer expression signatures in bulk, single-cell and spatial transcriptomic data

Over the last decade, many studies and some clinical trials have proposed gene expression signatures as a valuable tool for understanding cancer mechanisms, defining subtypes, monitoring patient prognosis, and therapy efficacy. However, technical and biological concerns about reproducibility have been raised. Technical reproducibility is a major concern: we currently lack a computational implementation of the proposed signatures, which would provide detailed signature definition and assure reproducibility, dissemination, and usability of the classifier. Another concern regards intratumor heterogeneity, which has never been addressed when studying these types of biomarkers using bulk transcriptomics. With the aim of providing a tool able to improve the reproducibility and usability of gene expression signatures, we propose signifinder, an R package that provides the infrastructure to collect, implement, and compare expression-based signatures from cancer literature. The included signatures cover a wide range of biological processes from metabolism and programmed cell death, to morphological changes, such as quantification of epithelial or mesenchymal-like status. Collected signatures can score tumor cell characteristics, such as the predicted response to therapy or the survival association, and can quantify microenvironmental information, including hypoxia and immune response activity. signifinder has been used to characterize tumor samples and to investigate intra-tumor heterogeneity, extending its application to single-cell and spatial transcriptomic data. Through these higher-resolution technologies, it has become increasingly apparent that the single-sample score assessment obtained by transcriptional signatures is conditioned by the phenotypic and genetic intratumor heterogeneity of tumor masses. Since the characteristics of the most abundant cell type or clone might not necessarily predict the properties of mixed populations, signature prediction efficacy is lowered, thus impeding effective clinical diagnostics. Through signifinder, we offer general principles for interpreting and comparing transcriptional signatures, as well as suggestions for additional signatures that would allow for more complete and robust data inferences. We consider signifinder a useful tool to pave the way for reproducibility and comparison of transcriptional signatures in oncology.

A random forest-based metabolic risk model to assess the prognosis and metabolism-related drug targets in ovarian cancer

As one of the most common gynecologic malignant tumors, ovarian cancer is usually diagnosed at an advanced and incurable stage because of its early asymptomatic onset. Increasing research into tumor biology has demonstrated that abnormal cellular metabolism precedes tumorigenesis, therefore it has become an area of active research in academia. Cellular metabolism is of great significance in cancer diagnostic and prognostic studies. In this study, we integrated The Cancer Genome Atlas dataset with multiple Gene Expression Omnibus ovarian cancer datasets, identified 17 metabolic pathways with prognostic values using the random forest algorithm, constructed a metabolic risk scoring model based on metabolic pathway enrichment scores, and classified patients with ovarian cancer into two subtypes. Then, we systematically investigated the differences between different subtypes in terms of prognosis, differential gene expression, immune signature enrichment, Hallmark signature enrichment, and somatic mutations. As well, we successfully predicted differences in sensitivity to immunotherapy and chemotherapy drugs in patients with different metabolic risk subtypes. Moreover, we identified 5 drug targets associated with high metabolic risk and low metabolic risk ovarian cancer phenotypes through the weighted correlation network analysis and investigated their roles in the genesis of ovarian cancer. Finally, we developed an XGBoost classifier for predicting metabolic risk types in patients with ovarian cancer, producing a good predictive effect. In light of the above study, the research findings will provide valuable information for prognostic prediction and personalized medical treatment of patients with ovarian cancer.

Targeted therapy and immunotherapy: Diamonds in the rough in the treatment of epithelial ovarian cancer

Currently, for ovarian cancer, which has the highest mortality rate among all gynecological cancers, the standard treatment protocol is initial tumor cytoreductive surgery followed by platinum-based combination chemotherapy. Although the survival rate after standard treatment has improved, the therapeutic effect of traditional chemotherapy is very limited due to problems such as resistance to platinum-based drugs and recurrence. With the advent of the precision medicine era, molecular targeted therapy has gradually entered clinicians' view, and individualized precision therapy has been realized, surpassing the limitations of traditional therapy. The detection of genetic mutations affecting treatment, especially breast cancer susceptibility gene (BRCA) mutations and mutations of other homologous recombination repair defect (HRD) genes, can guide the targeted drug treatment of patients, effectively improve the treatment effect and achieve a better patient prognosis. This article reviews different sites and pathways of targeted therapy, including angiogenesis, cell cycle and DNA repair, and immune and metabolic pathways, and the latest research progress from preclinical and clinical trials related to ovarian cancer therapy.

The Senescence-Related Signature Predicts Prognosis and Characterization of Tumor Microenvironment Infiltration in Pancreatic Cancer

Background. Senescence is thought to be an imperative effect on the development of cancer. However, few studies pay an attention to the senescence-associated genes in pancreatic cancer (PC). The prognostic value of senescence-related genes (SRGs) and their involvement in tumor microenvironment (TME) in the PC remain obscure. The aim of this research was to investigate the prognostic role of senescence-associated genes and their affection in TME in PC. Methods. The transcriptome and clinical information of PC patients were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Two SRG-mediated molecular clusters were comprehensively identified. In total, data from the 285 PC patients were randomly used to develop a senescence-associated gene signature in the training set and verified in the validation set. Immune microenvironment analysis pertained to senescence-related genes was performed. Results. A SRG_score including five senescence-associated genes was established to separate PC patients into two risk groups. High-risk patients had worse overall survival than low-risk patients. The result of the multivariate Cox regression analysis identified the risk score and stage as independent prognostic factors for PC patients. Receiver operating characteristic curve (ROC) analysis confirmed the credible predictive ability of the nomogram. The area under time-dependent ROC curve (AUC) reached 0.746 at 1 year, 0.781 at 3 years, and 0.868 at 5 years in the training set and 0.653 at 1 year, 0.755 at 3 years, and 0.785 at 5 years in the validation set. Moreover, the SRG_score was associated with TME, tumor mutation burden (TMB), and chemotherapeutic drug sensitivity. Conclusions. This study found that the novel SRG_score could be an independent prognostic target for PC patients. Senescence-associated genes had a vital impact on the immune microenvironment and the treatment of PC patients.

Identification of Novel Hypoxia Subtypes for Prognosis Based on Machine Learning Algorithms

Objective

A reduced level or tension or the deprivation of oxygen is termed hypoxia. It is common for tumours to outgrow their natural source of nutrients, which causes hypoxia in some tumour regions. Hypoxia affects ovarian cancer (OC) in several ways.

Methods

In this study, the expression patterns of prognostic hypoxia-related genes were curated, and consensus clustering analyses were performed to determine hypoxia subtypes in OC included in The Cancer Genome Atlas cohort. Two hypoxia-related subtypes were observed and considered for further investigation. The analyses of differentially expressed genes (DEGs), gene ontology, mutation, and immune cell infraction were performed to explore the underlying molecular mechanisms.

Results

In total, 377 patients with OC were classified into two subgroups based on the subtype of hypoxia. The clinical outcome was considerably poor for patients with hypoxia subtype 2. DEG and protein-protein interaction analyses revealed that the expression levels of CLIP2 and SH3PXD2A were low in OC tissues. Immune cell infarction analysis revealed that the subtypes were associated with the tumour microenvironment (TME).

Conclusion

Our findings established the existence of two distinctive, complex, and varied hypoxia subtypes in OC. Findings from the quantitative analysis of hypoxia subtypes in patients improved our understanding of the characteristics of the TME and may facilitate the development of more efficient treatment regimens.

Adaptive transcriptomic and immune infiltrate responses in the tumor immune microenvironment following neoadjuvant chemotherapy in high grade serous ovarian cancer reveal novel prognostic associations and activation of pro-tumorigenic pathways

The high rate of ovarian cancer recurrence and chemoresistance necessitates further research into how chemotherapy affects the tumor immune microenvironment (TIME). While studies have shown that immune infiltrate increases following neoadjuvant (NACT) chemotherapy, there lacks a comprehensive understanding of chemotherapy-induced effects on immunotranscriptomics and cancer-related pathways and their relationship with immune infiltrate and patient responses. In this study, we performed NanoString nCounter^® PanCancer IO360 analysis of 31 high grade serous ovarian cancer (HGSOC) patients with matched pre-treatment biopsy and post-NACT tumor. We observed increases in pro-tumorigenic and immunoregulatory pathways and immune infiltrate following NACT, with striking increases in a cohort of genes centered on the transcription factors ATF3 and EGR1. Using quantitative PCR, we analyzed several of the top upregulated genes in HGSOC cell lines, noting that two of them, ATF3 and AREG, were consistently upregulated with chemotherapy exposure and significantly increased in platinum resistant cells compared to their sensitive counterparts. Furthermore, we observed that pre-NACT immune infiltrate and pathway scores were not strikingly related to platinum free interval (PFI), but post-NACT immune infiltrate, pathway scores, and gene expression were. Finally, we found that higher levels of a cohort of proliferative and DNA damage-related genes was related to shorter PFI. This study underscores the complex alterations in the ovarian TIME following chemotherapy exposure and begins to untangle how immunologic factors are involved in mediating chemotherapy response, which will allow for the future development of novel immunologic therapies to combat chemoresistance.

Current Advances in PD-1/PD-L1 Blockade in Recurrent Epithelial Ovarian Cancer

Immunotherapies have revolutionized the treatment of a variety of cancers. Epithelial ovarian cancer is the most lethal gynecologic malignancy, and the rate of advanced tumor progression or recurrence is as high as 80%. Current salvage strategies for patients with recurrent ovarian cancer are rarely curative. Recurrent ovarian cancer is a “cold tumor”, predominantly due to a lack of tumor antigens and an immunosuppressive tumor microenvironment. In trials testing programmed death-1 (PD-1)/programmed death ligand 1 (PD-L1) blockade as a monotherapy, the response rate was only 8.0-22.2%. In this review, we illustrate the status of cold tumors in ovarian cancer and summarize the existing clinical trials investigating PD-1/PD-L1 blockade in recurrent ovarian cancer. Increasing numbers of immunotherapy combination trials have been set up to improve the response rate of EOC. The current preclinical and clinical development of immunotherapy combination therapy to convert an immune cold tumor into a hot tumor and their underlying mechanisms are also reviewed. The combination of anti-PD-1/PD-L1 with other immunomodulatory drugs or therapies, such as chemotherapy, antiangiogenic therapies, poly (ADP-ribose) polymerase inhibitors, adoptive cell therapy, and oncolytic therapy, could be beneficial. Further efforts are merited to transfer these results to a broader clinical application.

Immune-and Metabolism-Associated Molecular Classification of Ovarian Cancer

Ovarian cancer (OV) is a complex gynecological disease, and its molecular characteristics are not clear. In this study, the molecular characteristics of OV subtypes based on metabolic genes were explored through the comprehensive analysis of genomic data. A set of transcriptome data of 2752 known metabolic genes was used as a seed for performing non negative matrix factorization (NMF) clustering. Three subtypes of OV (C1, C2 and C3) were found in analysis. The proportion of various immune cells in C1 was higher than that in C2 and C3 subtypes. The expression level of immune checkpoint genes TNFRSF9 in C1 was higher than that of other subtypes. The activation scores of cell cycle, RTK-RAS, Wnt and angiogenesis pathway and ESTIMATE immune scores in C1 group were higher than those in C2 and C3 groups. In the validation set, grade was significantly correlated with OV subtype C1. Functional analysis showed that the extracellular matrix related items in C1 subtype were significantly different from other subtypes. Drug sensitivity analysis showed that C2 subtype was more sensitive to immunotherapy. Survival analysis of differential genes showed that the expression of PXDN and CXCL11 was significantly correlated with survival. The results of tissue microarray immunohistochemistry showed that the expression of PXDN was significantly correlated with tumor size and pathological grade. Based on the genomics of metabolic genes, a new OV typing method was developed, which improved our understanding of the molecular characteristics of human OV.

Novel MicroRNA-Regulated Transcript Networks Are Associated with Chemotherapy Response in Ovarian Cancer

High-grade serous ovarian cancer (HGSOC) is a highly lethal gynecologic cancer, in part due to resistance to platinum-based chemotherapy reported among 20% of patients. This study aims to generate novel hypotheses of the biological mechanisms underlying chemotherapy resistance, which remain poorly understood. Differential expression analyses of mRNA- and microRNA-sequencing data from HGSOC patients of The Cancer Genome Atlas identified 21 microRNAs associated with angiogenesis and 196 mRNAs enriched for adaptive immunity and translation. Coexpression network analysis identified three microRNA networks associated with chemotherapy response enriched for lipoprotein transport and oncogenic pathways, as well as two mRNA networks enriched for ubiquitination and lipid metabolism. These network modules were replicated in two independent ovarian cancer cohorts. Moreover, integrative analyses of the mRNA/microRNA sequencing and single-nucleotide polymorphisms (SNPs) revealed potential regulation of significant mRNA transcripts by microRNAs and SNPs (expression quantitative trait loci). Thus, we report novel transcriptional networks and biological pathways associated with resistance to platinum-based chemotherapy in HGSOC patients. These results expand our understanding of the effector networks and regulators of chemotherapy response, which will help to improve the management of ovarian cancer.

The Combined Signatures of Hypoxia and Cellular Landscape Provides a Prognostic and Therapeutic Biomarker in HBV-Related Hepatocellular Carcinoma

Prognosis and treatment options of hepatitis B virus‐related hepatocellular carcinoma (HBV‐HCC) are generally based on tumor burden and liver function. Yet, tumor growth and therapeutic resistance of HBV‐HCC are strongly influenced by intratumoral hypoxia and cells infiltrating the tumor microenvironment (TME). We, therefore, studied whether linking parameters associated with hypoxia and TME cells could have a better prediction of prognosis and therapeutic responses. Quantification of 109 hypoxia‐related genes and 64 TME cells was performed in 452 HBV‐HCC tumors. Prognostic hypoxia and TME cells signatures were determined based on Cox regression and meta‐analysis for generating the Hypoxia‐TME classifier. Thereafter, the prognosis, tumor, and immune characteristics as well as the benefit of therapies in Hypoxia‐TME defined subgroups were analyzed. Patients in the Hypoxia^low/TME^high subgroup showed a better prognosis and therapeutic responses than any other subgroups, which can be well elucidated based on the differences in terms of immune‐related molecules, tumor somatic mutations, and cancer cellular signaling pathways. Notably, our analysis furthermore demonstrated the synergistic influence of hypoxia and TME on tumor metabolism and proliferation. Besides, the classifier allowed a further subdivision of patients with early‐ and late‐HCC stages. In addition, the Hypoxia‐TME classifier was validated in another independent HBV‐HCC cohort (n = 144) and several pan‐cancer cohorts. Overall, the Hypoxia‐TME classifier showed a pretreatment predictive value for prognosis and therapeutic responses, which might provide new directions for strategizing patients with optimal therapies.

Immune Subtypes Characterization Identifies Clinical Prognosis, Tumor Microenvironment Infiltration, and Immune Response in Ovarian Cancer

Objective: Because of the modest immunotherapeutic response among ovarian carcinoma (OC) patients, it is significant to evaluate antitumor immune response and develop more effective precision immunotherapeutic regimens. Here, this study aimed to determine diverse immune subtypes of OC.

Methods: This study curated the expression profiles of prognostic immunologically relevant genes and conducted consensus clustering analyses for determining immune subtypes among OC patients in TCGA cohort. With Boruta algorithm, characteristic genes were screened for conducting an immune scoring system through principal component analysis algorithm. The single-sample gene set enrichment analysis and ESTIAMTE methods were adopted for quantifying the immune infiltrates and responses to chemotherapeutic agents were estimated with pRRophetic algorithm. Two immunotherapeutic cohorts were used for investigating the efficacy of immune score in predicting therapeutic benefits.

Results: Two immune subtypes were conducted among 377 OC patients. Immune subtype 2 was characterized by worse clinical prognosis, more frequent genetic variations and mutations, enhanced immune infiltrates, and increased expression of MHC molecules and programmed cell death protein 1/programmed death ligand 1 (PD-1/PD-L1). In total, 30 prognosis-relevant characteristic immune subtype–derived genes were identified for constructing the immune score of OC patients. High immune score was linked with more dismal prognosis, decreased immune infiltrations, and expression of MHC molecules. High immune score presented favorable sensitivity to doxorubicin and vinorelbine and reduced sensitivity to cisplatin. In addition, immune score possessed the potential in predicting benefits from anti–PD-1/PD-L1 therapy.

Conclusion: Collectively, our findings propose two complex and diverse immune subtypes of OC. Quantitative assessment of immune subtypes in individual patients strengthens the understanding of tumor microenvironment features and promotes more effective immunotherapeutic regimens.

A CD8+ T Cell-Related Genes Expression Signature Predicts Prognosis and the Efficacy of Immunotherapy in Breast Cancer

Immunotherapy has been applied to patients with breast cancer. However, only part of patients benefits from the current immunotherapy. Accurate prediction of individual response to immunotherapy can be beneficial for breast cancer management. CD8+ T cells are the main force of anti-tumor immunity. This study aimed to establish a CD8+ T cell-related gene expression signature for prediction of breast cancer prognostic and immunotherapy efficacy. RNA-seq transcriptomic data was the basics of this research. Weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis established the prognostic signature. We identified 290 CD8+ T cell-related genes in the training set and established a risk-score model based on 8-genes panel (SOCS1, IL10, CAMK4, CXCL13, KIR2DS4, TESPA1, CD70 and ICAM4). Subsequently, univariate Cox regression analysis suggested that high risk-score was a risk factor for breast cancer (HR = 3.1, 95%CI 2.0-4.8, P < 0.001). In tumor microenvironment, high-risk tumors present decreased tumor infiltrating CD8+ T cells and increased M2 macrophages. The low-risk patients may benefit more from immune checkpoint blockade immunotherapy than the high-risk patients. Moreover, breast tumors which sensitive to immune checkpoint inhibitor (ICI) showed higher IL10 expression.

Single-Cell Transcriptome Reveals the Metabolic and Clinical Features of a Highly Malignant Cell Subpopulation in Pancreatic Ductal Adenocarcinoma

Background: Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with a high mortality rate. PDAC exhibits significant heterogeneity as well as alterations in metabolic pathways that are associated with its malignant progression. In this study, we explored the metabolic and clinical features of a highly malignant subgroup of PDAC based on single-cell transcriptome technology.

Methods: A highly malignant cell subpopulation was identified at single-cell resolution based on the expression of malignant genes. The metabolic landscape of different cell types was analyzed based on metabolic pathway gene sets. In vitro experiments to verify the biological functions of the marker genes were performed. PDAC patient subgroups with highly malignant cell subpopulations were distinguished according to five glycolytic marker genes. Five glycolytic highly malignant-related gene signatures were used to construct the glycolytic highly malignant-related genes signature (GHS) scores.

Results: This study identified a highly malignant tumor cell subpopulation from the single-cell RNA sequencing (scRNA-seq) data. The analysis of the metabolic pathway revealed that highly malignant cells had an abnormally active metabolism, and enhanced glycolysis was a major metabolic feature. Five glycolytic marker genes that accounted for the highly malignant cell subpopulations were identified, namely, EN O 1, LDHA, PKM, PGK1, and PGM1. An in vitro cell experiment showed that proliferation rates of PANC-1 and CFPAC-1 cell lines decreased after knockdown of these five genes. Patients with metabolic profiles of highly malignant cell subpopulations exhibit clinical features of higher mortality, higher mutational burden, and immune deserts. The GHS score evaluated using the five marker genes was an independent prognostic factor for patients with PDAC.

Conclusion: We revealed a subpopulation of highly malignant cells in PDAC with enhanced glycolysis as the main metabolic feature. We obtained five glycolytic marker gene signatures, which could be used to identify PDAC patient subgroups with highly malignant cell subpopulations, and proposed a GHS prognostic score.

Development and Validation of a Tumor Mutation Burden-Related Immune Prognostic Signature for Ovarian Cancers

Ovarian cancer (OC), one of the most common malignancies of the female reproductive system, is characterized by high incidence and poor prognosis. Tumor mutation burden (TMB), as an important biomarker that can represent the degree of tumor mutation, is emerging as a key indicator for predicting the efficacy of tumor immunotherapy. In our study, the gene expression profiles of OC were downloaded from TCGA and GEO databases. Subsequently, we analyzed the prognostic value of TMB in OC and found that a higher TMB score was significantly associated with a better prognosis (p = 0.004). According to the median score of TMB, 9 key TMB related immune prognostic genes were selected by LASSO regression for constructing a TMB associated immune risk score (TMB-IRS) signature, which can effectively predict the prognosis of OC patients (HR = 2.32, 95% CI = 1.68–3.32; AUC = 0.754). Interestingly, TMB-IRS is also closely related to the level of immune cell infiltration and immune checkpoint molecules (PD1, PD-L1, CTLA4, PD-L2) in OC. Furthermore, the nomogram combined with TMB-IRS and a variety of clinicopathological features can more comprehensively evaluate the prognosis of patients. In conclusion, we explored the relationship between TMB and prognosis and validated the TMB-IRS signature based on TMB score in an independent database (HR = 1.60, 95% CI = 1.13–2.27; AUC = 0.639), which may serve as a novel biomarker for predicting OC prognosis as well as possible therapeutic targets.

Prognostic immunologic signatures in epithelial ovarian cancer

Epithelial Ovarian Cancer (EOC) is a deadly gynecologic malignancy in which patients frequently develop recurrent disease following initial platinum-taxane chemotherapy. Analogous to many other cancer subtypes, EOC clinical trials have centered upon immunotherapeutic approaches, most notably programmed cell death 1 (PD-1) inhibitors. While response rates to these immunotherapies in EOC patients have been low, evidence suggests that ovarian tumors are immunogenic and that immune-related genomic profiles can serve as prognostic markers. This review will discuss recent advances in the development of immune-based prognostic signatures in EOC that predict patient clinical outcomes, as well as emphasize specific research areas that need to be addressed to drive this field forward.

Hypoxic Characteristic Genes Predict Response to Immunotherapy for Urothelial Carcinoma

Objective: Resistance to immune checkpoint inhibitors (ICIs) has been a massive obstacle to ICI treatment in metastatic urothelial carcinoma (MUC). Recently, increasing evidence indicates the clinical importance of the association between hypoxia and immune status in tumor patients. Therefore, it is necessary to investigate the relationship between hypoxia and prognosis in metastatic urothelial carcinoma. Methods: Transcriptomic and clinical data from 348 MUC patients who underwent ICI treatment from a large phase 2 trial (IMvigor210) were investigated in this study. The cohort was randomly divided into two datasets, a training set (n = 213) and a testing set (n = 135). Data of hypoxia-related genes were downloaded from the molecular signatures database (MSigDB), and screened by univariate and multivariate Cox regression analysis to construct a prognosis-predictive model. The robustness of the model was evaluated in two melanoma cohorts. Furthermore, an external validation cohort, the bladder cancer cohort, from the Cancer Genome Atlas (TCGA) database, was t used to explore the mechanism of gene mutation, immune cell infiltration, signaling pathway enrichment, and drug sensitivity. Results: We categorized patients as the high- or low- risk group using a four-gene hypoxia risk model which we constructed. It was found that patients with high-risk scores had significantly worse overall survival (OS) compared with those with low-risk scores. The prognostic model covers 0.71 of the area under the ROC curve in the training set and 0.59 in the testing set, which is better than the survival prediction of MUC patients using the clinical characteristics. Mutation analysis results showed that deletion mutations in RB1, TP53, TSC1 and KDM6A were correlated with hypoxic status. Immune cell infiltration analysis illustrated that the infiltration T cells, B cells, Treg cells, and macrophages was correlated with hypoxia. Functional enrichment analysis revealed that a hypoxic microenvironment activated inflammatory pathways, glucose metabolism pathways, and immune-related pathways. Conclusion: In this investigation, a four-gene hypoxia risk model was developed to evaluate the degree of hypoxia and prognosis of ICI treatment, which showed a promising clinical prediction value in MUC. Furthermore, the hypoxia risk model revealed a close relationship between hypoxia and the tumor immune microenvironment.

Immunotherapy in Ovarian Cancer: Thinking Beyond PD-1/PD-L1

Ovarian cancer (OC) is the most lethal gynecologic malignancy, affecting approximately 1 in 70 women with only 45% surviving 5 years after diagnosis. This disease typically presents at an advanced stage, and optimal debulking with platinum-based chemotherapy remains the cornerstone of management. Although most ovarian cancer patients will respond effectively to current management, 70% of them will eventually develop recurrence and novel therapeutic strategies are needed. There is a rationale for immune-oncological treatments (IO) in the managements of patients with OC. Many OC tumors demonstrate tumor infiltrating lymphocytes (TILs) and the degree of TIL infiltration is strongly and reproducibly correlated with survival. Unfortunately, results to date have been disappointing in relapsed OC. Trials have reported very modest single activity with various antibodies targeting PD-1 or PD-L1 resulting in response rate ranging from 4% to 15%. This may be due to the highly immunosuppressive TME of the disease, a low tumor mutational burden and low PD-L1 expression. There is an urgent need to improve our understanding of the immune microenvironment in OC in order to develop effective therapies. This review will discuss immune subpopulations in OC microenvironment, current immunotherapy modalities targeting these immune subsets and data from clinical trials testing IO treatments in OC and its combination with other therapeutic agents.

Prognostic value of preoperative soluble interleukin 2 receptor α as a novel immune biomarker in epithelial ovarian cancer

PURPOSE

Epithelial ovarian cancer (EOC) is regarded as the deadliest gynecological cancer, and the demand for novel noninvasive prognostic biomarkers remains significant. This study aimed to investigate the prognostic value of preoperative blood biomarkers in EOC patients.

METHODS

In total, 73 patients who had undergone ovarian mass resection were enrolled. Serum concentration of biomarkers, including soluble interleukin 2 receptor α (sIL-2R), was measured 1-2 weeks before surgery. Independent prognostic factors for progression-free survival (PFS) were investigated with multivariate Cox regression analysis. A prognostic model was subsequently developed and evaluated by discrimination, calibration and clinical net benefit. Furthermore, transcriptome data of 376 EOC cases from The Cancer Genome Atlas (TCGA) were analyzed with ESTIMATE, CIBERSORT and Maftools algorithm to evaluate the correlation of IL2RA expression with tumor immune microenvironment and immunotherapeutic response.

RESULTS

High sIL-2R concentration was found to be the only significant prognostic blood biomarker for PFS by multivariate Cox regression analysis in our center. A prognostic nomogram was developed with satisfactory discrimination, calibration and clinical net benefit. In addition, higher IL2RA expression was significantly associated with higher immune scores, activated CD4⁺ T cells, M2 macrophages and resting dendritic cells in TCGA data. Furthermore, IL2RA expression was closely related to TMB scores.

CONCLUSIONS

sIL-2R is a potential prognostic immune biomarker for EOC patients, and a comprehensive prognostic model comprising sIL-2R with satisfactory discrimination and clinical appliance was developed. Therefore, we recommend routine sIL-2R testing in EOC patients.

Gene Expression Subtyping Reveals Immune alterations:TCGA Database for Prognosis in Ovarian Serous Cystadenocarcinoma

Serous ovarian cancer is the most common and primary death type in ovarian cancer. In recent studies, tumor microenvironment and tumor immune infiltration significantly affect the prognosis of ovarian cancer. This study analyzed the four gene expression types of ovarian cancer in TCGA database to extract differentially expressed genes and verify the prognostic significance. Meanwhile, functional enrichment and protein interaction network analysis exposed that these genes were related to immune response and immune infiltration. Subsequently, we proved these prognostic genes in an independent data set from the GEO database. Finally, multivariate cox regression analysis revealed the prognostic significance of TAP1 and CXCL13. The genetic alteration and interaction network of these two genes were shown. Then, we established a nomogram model related to the two genes and clinical risk factors. This model performed well in Calibration plot and Decision Curve Analysis. In conclusion, we have obtained a list of genes related to the immune microenvironment with a better prognosis for serous ovarian cancer, and based on this, we have tried to establish a clinical prognosis model.

Landscape of Immune Microenvironment in Epithelial Ovarian Cancer and Establishing Risk Model by Machine Learning

Background

Epithelial ovarian cancer (EOC) is an extremely lethal gynecological malignancy and has the potential to benefit from the immune checkpoint blockade (ICB) therapy, whose efficacy highly depends on the complex tumor microenvironment (TME).

Method and Result

We comprehensively analyze the landscape of TME and its prognostic value through immune infiltration analysis, somatic mutation analysis, and survival analysis. The results showed that high infiltration of immune cells predicts favorable clinical outcomes in EOC. Then, the detailed TME landscape of the EOC had been investigated through “xCell” algorithm, Gene set variation analysis (GSVA), cytokines expression analysis, and correlation analysis. It is observed that EOC patients with high infiltrating immune cells have an antitumor phenotype and are highly correlated with immune checkpoints. We further found that dendritic cells (DCs) may play a dominant role in promoting the infiltration of immune cells into TME and forming an antitumor immune phenotype. Finally, we conducted machine-learning Lasso regression, support vector machines (SVMs), and random forest, identifying six DC-related prognostic genes (CXCL9, VSIG4, ALOX5AP, TGFBI, UBD, and CXCL11). And DC-related risk stratify model had been well established and validated.

Conclusion

High infiltration of immune cells predicted a better outcome and an antitumor phenotype in EOC, and the DCs might play a dominant role in the initiation of antitumor immune cells. The well-established risk model can be used for prognostic prediction in EOC.

Application of Immunoprofiling Using Multiplexed Immunofluorescence Staining Identifies the Prognosis of Patients with High-Grade Serous Ovarian Cancer

Immunoprofiling has an established impact on the prognosis of several cancers; however, its role and definition in high-grade serous ovarian cancer (HGSOC) are mostly unknown. This study is to investigate immunoprofiling which could be a prognostic factor in HGSOC. We produced tumor microarrays of 187 patients diagnosed with HGSOC. We performed a multiplexed immunofluorescence staining using Opal Multiplex IHC kit and quantitative analysis with Vectra-Inform system. The expression intensities of programmed death-ligand 1 (PD-L1), CD4, CD8, CD20, FoxP3, and CK in whole tumor tissues were evaluated. The enrolled patients showed general characteristics, mostly FIGO stage III/IV and responsive to chemotherapy. Each immune marker showed diverse positive densities, and each tumor sample represented its immune characteristics as an inflamed tumor or noninflamed tumor. No marker was associated with survival as a single one. Interestingly, high ratios of CD8 to FoxP3 and CD8 to PD-L1 were related to the favorable overall survival (77 vs. 39 months, 84 vs. 47 months, respectively), and CD8 to PD-L1 ratio was also a significant prognostic factor (HR 0.621, 95% CI 0.420-0.917, p = 0.017) along with well-known clinical prognostic factors. Additionally, CD8 to PD-L1 ratio was found to be higher in the chemosensitive group (p = 0.034). In conclusion, the relative expression levels of CD8, FoxP3, and PD-L1 were significantly related to the clinical outcome of patients with HGSOC, which could be a kind of significant immunoprofiling of ovarian cancer patients to apply for treatment.

Immune-associated molecular occurrence and prognosis predictor of hepatocellular carcinoma: an integrated analysis of GEO datasets

Hepatocellular carcinoma (HCC) is the fifth most common cancer and the second most common cause of cancer-related deaths worldwide. As immune response failure is the main factor in the occurrence and poor prognosis of HCC, our study aimed to develop an immune-associated molecular occurrence and prognosis predictor (IMOPP) of HCC. To that end, we discovered a 4-gene immune-associated gene signature: C-C motif chemokine ligand 14 (CCL14), kallikrein B1 (KLKB1), vasoactive intestinal peptide receptor 1 (VIPR1), and cluster of differentiation 4 (CD4). When tested on three cohorts as an immune-associated molecular occurrence predictor (IMOP), it had high sensitivity, specificity, and area under the receiver operating characteristics curve. When tested as an immune-associated molecular prognosis predictor (IMPP), it stratified the HCC prognosis for overall survival (Kaplan-Meier analysis, log rank P = 0.0016; Cox regression, HR = 1.832, 95% CI = 1.173-2.859, P = 0.008) and disease-free survival (Kaplan-Meier analysis, log rank P = 0.0227). IMPP also significantly correlated with the clinicopathological characteristics of HCC; integrating it with clinicopathological characteristics improved the accuracy of a nomogram for overall survival prediction (C-index: 0.7097 vs. 0.6631). In HCC tumor microenviroments, the proportion of CD8⁺ T cells significantly differed between IMOP-stratified groups. We conclude that IMOPP can potentially predict the occurrence of HCC in high-risk populations and improve prognostic accuracy by providing new biomarkers for risk stratification. In addition, we believe that the IMOP mechanism may be related to its effect on the proportion of CD8⁺ T cells in tumor-infiltrating lymphocytes.

Immune checkpoint inhibitors in ovarian cancer: where do we stand?

Numerous retrospective studies have demonstrated that the density of intra-tumoral immune cell infiltration is prognostic in epithelial ovarian cancer (OC). These observations together with reports of programmed death ligand-1 (PD-L1) expression in advanced OC provided the rationale for investigating the benefit of programmed death-1 (PD1) or PD-L1 inhibition in OC. Unfortunately clinical trials to date evaluating PD1/PD-L1 inhibition in patients with relapsed OC have been disappointing. In this review we will discuss early results from single agent PD1/PD-L1 inhibitors and the strategies to enhance benefit from immune-oncology agents in OC, including proposing anti-PD-L1 in combination with other agents (cytotoxics, anti-angiogenics, poly(ADP-ribose) polymerase. (PARP) inhibitors, targeted therapies or other immunotherapies), as well as evaluating these agents earlier in the disease course, or in biomarker selected patients.

Chemotherapy with or without avelumab followed by avelumab maintenance versus chemotherapy alone in patients with previously untreated epithelial ovarian cancer (JAVELIN Ovarian 100): an open-label, randomised, phase 3 trial

BACKGROUND

Although most patients with epithelial ovarian cancer respond to frontline platinum-based chemotherapy, around 70% will relapse within 3 years. The phase 3 JAVELIN Ovarian 100 trial compared avelumab (anti-PD-L1 monoclonal antibody) in combination with chemotherapy followed by avelumab maintenance, or chemotherapy followed by avelumab maintenance, versus chemotherapy alone in patients with treatment-naive epithelial ovarian cancer.

METHODS

JAVELIN Ovarian 100 was a global, open-label, three-arm, parallel, randomised, phase 3 trial run at 159 hospitals and cancer treatment centres in 25 countries. Eligible women were aged 18 years and older with stage III-IV epithelial ovarian, fallopian tube, or peritoneal cancer (following debulking surgery, or candidates for neoadjuvant chemotherapy), and had an Eastern Cooperative Oncology Group performance status of 0 or 1. Patients were randomly assigned (1:1:1) via interactive response technology to receive chemotherapy (six cycles; carboplatin dosed at an area under the serum-concentration-time curve of 5 or 6 intravenously every 3 weeks plus paclitaxel 175 mg/m² every 3 weeks or 80 mg/m² once a week [investigators' choice]) followed by avelumab maintenance (10 mg/kg intravenously every 2 weeks; avelumab maintenance group); chemotherapy plus avelumab (10 mg/kg intravenously every 3 weeks) followed by avelumab maintenance (avelumab combination group); or chemotherapy followed by observation (control group). Randomisation was in permuted blocks of size six and stratified by paclitaxel regimen and resection status. Patients and investigators were masked to assignment to the two chemotherapy groups without avelumab at the time of randomisation until completion of the chemotherapy phase. The primary endpoint was progression-free survival assessed by blinded independent central review in all randomly assigned patients (analysed by intention to treat). Safety was analysed in all patients who received at least one dose of study treatment. This trial is registered with ClinicalTrials.gov, NCT02718417. The trial was fully enrolled and terminated at interim analysis due to futility, and efficacy is no longer being assessed.

FINDINGS

Between May 19, 2016 and Jan 23, 2018, 998 patients were randomly assigned (avelumab maintenance n=332, avelumab combination n=331, and control n=335). At the planned interim analysis (data cutoff Sept 7, 2018), prespecified futility boundaries were crossed for the progression-free survival analysis, and the trial was stopped as recommended by the independent data monitoring committee and endorsed by the protocol steering committee. Median follow-up for progression-free survival for all patients was 10·8 months (IQR 7·1-14·9); 11·1 months (7·0-15·3) for the avelumab maintenance group, 11·0 months (7·4-14·5) for the avelumab combination group, and 10·2 months (6·7-14·0) for the control group. Median progression-free survival was 16·8 months (95% CI 13·5-not estimable [NE]) with avelumab maintenance, 18·1 months (14·8-NE) with avelumab combination treatment, and NE (18·2 months-NE) with control treatment. The stratified hazard ratio for progression-free survival was 1·43 (95% CI 1·05-1·95; one-sided p=0·99) with the avelumab maintenance regimen and 1·14 (0·83-1·56; one-sided p=0·79) with the avelumab combination regimen, versus control treatment. The most common grade 3-4 adverse events were anaemia (69 [21%] patients in the avelumab maintenance group, 63 [19%] in the avelumab combination group, and 53 [16%] in the control group), neutropenia (91 [28%], 99 [30%], and 88 [26%]), and neutrophil count decrease (49 [15%], 45 [14%], and 59 [18%]). Serious adverse events of any grade occurred in 92 (28%) patients in the avelumab maintenance group, 118 (36%) in the avelumab combination group, and 64 (19%) in the control group. Treatment-related deaths occurred in one (<1%) patient in the avelumab maintenance group (due to atrial fibrillation) and one (<1%) patient in the avelumab combination group (due to disease progression).

INTERPRETATION

Although no new safety signals were observed, results do not support the use of avelumab in the frontline treatment setting. Alternative treatment regimens are needed to improve outcomes in patients with advanced epithelial ovarian cancer.

FUNDING

Pfizer and Merck KGaA, Darmstadt, Germany.

Systemic inflammation markers and cancer incidence in the UK Biobank

Systemic inflammation markers have been linked to increased cancer risk and mortality in a number of studies. However, few studies have estimated pre-diagnostic associations of systemic inflammation markers and cancer risk. Such markers could serve as biomarkers of cancer risk and aid in earlier identification of the disease. This study estimated associations between pre-diagnostic systemic inflammation markers and cancer risk in the prospective UK Biobank cohort of approximately 440,000 participants recruited between 2006 and 2010. We assessed associations between four immune-related markers based on blood cell counts: systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and risk for 17 cancer sites by estimating hazard ratios (HR) using flexible parametric survival models. We observed positive associations with risk for seven out of 17 cancers with SII, NLR, PLR, and negative associations with LMR. The strongest associations were observed for SII for colorectal and lung cancer risk, with associations increasing in magnitude for cases diagnosed within one year of recruitment. For instance, the HR for colorectal cancer per standard deviation increment in SII was estimated at 1.09 (95% CI 1.02-1.16) in blood drawn five years prior to diagnosis and 1.50 (95% CI 1.24-1.80) in blood drawn one month prior to diagnosis. We observed associations between systemic inflammation markers and risk for several cancers. The increase in risk the last year prior to diagnosis may reflect a systemic immune response to an already present, yet clinically undetected cancer. Blood cell ratios could serve as biomarkers of cancer incidence risk with potential for early identification of disease in the last year prior to clinical diagnosis.

TP53 variant allele frequency correlates with the chemotherapy response score in ovarian/fallopian tube/peritoneal high-grade serous carcinoma

Molecular findings in ovarian, fallopian tube, and peritoneal high-grade serous carcinoma (HGSCa) are emerging as potential prognostic indicators. The chemotherapy response score (CRS) has been proposed as a histologic-based prognostic factor in patients with HGSCa treated with neoadjuvant chemotherapy (NACT). No study details the relationship between the mutational landscape of HGSCa and the CRS. This study addresses this issue using next-generation sequencing (NGS). We retrospectively identified 25 HGSCas treated with NACT and pathology material available to calculate the CRS. All cases had NGS on the primary debulking specimen post-NACT. The three-tier Böhm CRS was applied to the omentum or adnexa and calculated as a combined score. Tumor mutation burden (TMB) and TP53 variant allele frequency (VAF) were calculated and used in correlative analysis. All cases had at least one mutation, most commonly TP53 (25 cases, 100%). Other mutations were BRCA2 (one case, 4%), ARID1A (two cases, 8%), and 1 (4%) of each of the following: ERBB2, NTRK3, STK11, NTRK2, TSC1, PIK3CA, NF1, NOTCH3, CDK2, SMAD4, and PMS2. TMB ranged from 2.58 to 7.75 (median 3.84). There was no statistically significant relationship between the TMB and omental CRS, R-squared = 0.011 (P = 0.62); adnexal CRS, R-squared = 0.005 (P = 0.74); or with the combined CRS, R-squared = 0.009 (P = 0.65). Statistically significant correlation was found between the TP53 VAF and the omental CRS (R-squared = 0.28, P = 0.007), adnexal CRS (R-squared = 0.26, P = 0.01), and the combined CRS (R-squared = 0.33, P = 0.0026). The TP53 VAF was adjusted for percent of tumor present on the slide resulting in an average per cell TP53 mutational load, resulting in similar results with a statistically significant correlation between the average per cell TP53 mutational load and the omental CRS (R-squared = 0.27, P = 0.02), adnexal CRS (R-squared = 0.16, P = 0.05), and the combined CRS (R-squared = 0.23, P = 0.02). In summary, NGS confirmed TP53 mutations in all cases of HGSCa. TMB showed no correlation with the CRS. TP53 VAF and average per cell TP53 mutational load showed significant correlation with the CRS, whether graded on the adnexa or omentum or as a combined score, indicating concordance between molecular and histological findings following NACT.

Avelumab alone or in combination with chemotherapy versus chemotherapy alone in platinum-resistant or platinum-refractory ovarian cancer (JAVELIN Ovarian 200): an open-label, three-arm, randomised, phase 3 study

BACKGROUND

Most patients with ovarian cancer will relapse after receiving frontline platinum-based chemotherapy and eventually develop platinum-resistant or platinum-refractory disease. We report results of avelumab alone or avelumab plus pegylated liposomal doxorubicin (PLD) compared with PLD alone in patients with platinum-resistant or platinum-refractory ovarian cancer.

METHODS

JAVELIN Ovarian 200 was an open-label, parallel-group, three-arm, randomised, phase 3 trial, done at 149 hospitals and cancer treatment centres in 24 countries. Eligible patients were aged 18 years or older with epithelial ovarian, fallopian tube, or peritoneal cancer (maximum of three previous lines for platinum-sensitive disease, none for platinum-resistant disease) and an Eastern Cooperative Oncology Group performance status of 0 or 1. Patients were randomly assigned (1:1:1) via interactive response technology to avelumab (10 mg/kg intravenously every 2 weeks), avelumab plus PLD (40 mg/m² intravenously every 4 weeks), or PLD and stratified by disease platinum status, number of previous anticancer regimens, and bulky disease. Primary endpoints were progression-free survival by blinded independent central review and overall survival in all randomly assigned patients, with the objective to show whether avelumab alone or avelumab plus PLD is superior to PLD. Safety was assessed in all patients who received at least one dose of study treatment. This trial is registered with ClinicalTrials.gov, NCT02580058. The trial is no longer enrolling patients and this is the final analysis of both primary endpoints.

FINDINGS

Between Jan 5, 2016, and May 16, 2017, 566 patients were enrolled and randomly assigned (combination n=188; PLD n=190, avelumab n=188). At data cutoff (Sept 19, 2018), median duration of follow-up for overall survival was 18·4 months (IQR 15·6-21·9) for the combination group, 17·4 months (15·2-21·3) for the PLD group, and 18·2 months (15·8-21·2) for the avelumab group. Median progression-free survival by blinded independent central review was 3·7 months (95% CI 3·3-5·1) in the combination group, 3·5 months (2·1-4·0) in the PLD group, and 1·9 months (1·8-1·9) in the avelumab group (combination vs PLD: stratified HR 0·78 [repeated 93·1% CI 0·59-1·24], one-sided p=0·030; avelumab vs PLD: 1·68 [1·32-2·60], one-sided p>0·99). Median overall survival was 15·7 months (95% CI 12·7-18·7) in the combination group, 13·1 months (11·8-15·5) in the PLD group, and 11·8 months (8·9-14·1) in the avelumab group (combination vs PLD: stratified HR 0·89 [repeated 88·85% CI 0·74-1·24], one-sided p=0·21; avelumab vs PLD: 1·14 [0·95-1·58], one-sided p=0·83]). The most common grade 3 or worse treatment-related adverse events were palmar-plantar erythrodysesthesia syndrome (18 [10%] in the combination group vs nine [5%] in the PLD group vs none in the avelumab group), rash (11 [6%] vs three [2%] vs none), fatigue (ten [5%] vs three [2%] vs none), stomatitis (ten [5%] vs five [3%] vs none), anaemia (six [3%] vs nine [5%] vs three [2%]), neutropenia (nine [5%] vs nine [5%] vs none), and neutrophil count decreased (eight [5%] vs seven [4%] vs none). Serious treatment-related adverse events occurred in 32 (18%) patients in the combination group, 19 (11%) in the PLD group, and 14 (7%) in the avelumab group. Treatment-related adverse events resulted in death in one patient each in the PLD group (sepsis) and avelumab group (intestinal obstruction).

INTERPRETATION

Neither avelumab plus PLD nor avelumab alone significantly improved progression-free survival or overall survival versus PLD. These results provide insights for patient selection in future studies of immune checkpoint inhibitors in platinum-resistant or platinum-refractory ovarian cancer.

FUNDING

Pfizer and Merck KGaA, Darmstadt, Germany.

Catenin Alpha-2 Mutation Changes the Immune Microenvironment in Lung Adenocarcinoma Patients Receiving Immune Checkpoint Inhibitors

Background: Lung cancer has always been the most prevalent cancer. Lung adenocarcinoma (LUAD) is the most common lung cancer subtype and has a high tumor mutation rate. In addition to KRAS, EGFR, ALK, HER2, ROS1, and BRAF, which are known to have high mutation rates, we discovered some new mutated genes, such as catenin alpha-2 (CTNNA2), in LUAD patients treated with immune checkpoint inhibitors (ICIs). These mutant genes are potential therapeutic targets for LUAD.

Methods: We analyzed a cohort of LUAD patients with somatic mutation and survival data in the Cancer Genome Atlas (TCGA) database and a cohort of LUAD patients receiving immune checkpoint inhibitors with clinical data and whole-exome sequencing (WES) mutation data to evaluate the role of CTNNA2 gene mutation in LUAD. In addition, CIBERSORT was used to analyze the immune characteristics of CTNNA2 wild-type patients and CTNNA2 mutant-type patients, and gene set enrichment analysis (GSEA) was employed for pathway enrichment analysis. The results were verified by downloading data regarding the drug sensitivity of LUAD cell lines from the Genomics of Drug Sensitivity in Cancer (GDSC) database.

Results: We found that CTNNA2 mutation was associated with longer overall survival (OS) in LUAD patients. Analysis of the cohort from the Cancer Genome Atlas showed that patients with CTNNA2 mutation had more tumor neoantigens and a greater tumor mutation burden (TMB). Through further analysis of the tumor immune microenvironment, we found that in LUAD patients with CTNNA2 mutations, the gene expression levels of chemokine C-X-C motif chemokine 9 (CXCL9) and granzyme B (GZMB) were elevated, and the gene expression level of inhibitory receptor killer cell immunoglobulin-like receptor 2DL1 (KIR2DL1) was significantly reduced. These alterations might affect gene expression in macrophages, NK cells, and mast cell markers. In addition, LUAD patients with CTNNA2 mutation had a significantly increased number of mutations in DNA damage response (DDR) genes. The drug susceptibility results and gene set enrichment analysis showed that after CTNNA2 mutation occurred, changes were found in the DNA damage response pathway, the phosphoinositide 3-kinase (PI3K) pathway and others, indicating that CTNNA2 mutation can regulate the activation of PI3K and DDR pathways.

Conclusion: Our findings provide novel insights into the underlying pathogenesis of LUAD. CTNNA2 mutation can change the immune microenvironment, thereby improving patient prognosis. The results also suggest that CTNNA2 may become a new biomarker and therapeutic target for LUAD in the future.

Development and validation of glycolysis-related prognostic score for prediction of prognosis and chemosensitivity of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with aggressive biological behaviour. Its rapid proliferation and tumour growth require reprogramming of glucose metabolism or the Warburg effect. However, the association between glycolysis-related genes with clinical features and prognosis of PDAC is still unknown. Here, we used the meta-analysis to correlate the hazard ratios (HR) of 106 glycolysis genes from MSigDB by the cox proportional hazards regression analysis in 6 clinical data sets of PDAC patients to form a training cohort, and a single group of PDAC patients from the TCGA, ICGC, Arrayexpress and GEO databases to form the validation cohort. Then, a glycolysis-related prognosis (GRP) score based on 29 glycolysis prognostic genes was established in 757 PDAC patients from the training composite cohort and validated in 267 ICGC-CA validation cohort (all P < .05). In addition, including PADC, the prognostic value was also confirmed in other 7 out of 30 pan-cancer cohorts. The GRP score was significantly related to specific metabolism pathways, immune genes and immune cells in the patients with PADC (all P < .05). Finally, by combining with immune cells, the GRP score also well-predicted the chemosensitivity of patients with PADC in the TCGA cohort (AUC = 0.709). In conclusion, this study developed a GRP score for patients with PDAC in predicting prognosis and chemosensitivity for PDAC.

Association Between FSIP2 Mutation and an Improved Efficacy of Immune Checkpoint Inhibitors in Patients With Skin Cutaneous Melanoma

Background

Immune checkpoint inhibitors (ICIs) have shown remarkable success in treating skin cutaneous melanoma (SKCM); however, the response to treatment varies greatly between patients. Considering that the efficacy of ICI treatment is influenced by many factors, we selected the Fibrosheath interacting protein 2 (FSIP2) gene and systematically analyzed its potential to predict the efficacy of ICI treatment.

Methods

Patient data were collected from an ICI treatment cohort (n = 120) and a The Cancer Genome Atlas (TCGA)-SKCM cohort (n = 467). The data were divided into an FSIP2-mutant (MT) group and FSIP2-wild-type (WT) group according to FSIP2 mutation status. In this study, we analyzed the patients' overall survival rate, tumor mutational burden (TMB), neoantigen load (NAL), copy number variation (CNV), cell infiltration data and immune-related genes. We used gene set enrichment analysis (GSEA) to delineate biological pathways and processes associated with the efficacy of immunotherapy.

Results

The efficacy of ICI treatment of SKCM patients with FSIP2 mutation was significantly better than that of patients without FSIP2 mutation. The patients in the FSIP2-MT group had higher tumor immunogenicity and lower regulatory T cell (Treg) infiltration. Results of GSEA showed that pathways related to tumor progression (MAPK and FGFR), immunomodulation, and IL-2 synthesis inhibition were significantly downregulated in the FSIP2-MT group.

Conclusion

Our research suggests that the FSIP2 gene has the potential to predict the efficacy of ICI treatment. The high tumor immunogenicity and low Treg levels observed may be closely related to the fact that patients with FSIP2-MT can benefit from ICI treatment.

Stromal Score-Based Gene Signature: A Prognostic Prediction Model for Colon Cancer

BACKGROUND

Growing evidence has revealed the crucial roles of stromal cells in the microenvironment of various malignant tumors. However, efficient prognostic signatures based on stromal characteristics in colon cancer have not been well-established yet. The present study aimed to construct a stromal score-based multigene prognostic prediction model for colon cancer.

METHODS

Stromal scores were calculated based on the expression profiles of a colon cancer cohort from TCGA database applying the ESTIMATE algorithm. Linear models were used to identify differentially expressed genes between low-score and high-score groups by limma R package. Univariate, LASSO, and multivariate Cox regression models were used successively to select the prognostic gene signature. Two independent datasets from GEO were used as external validation cohorts.

RESULTS

Low stromal score was demonstrated to be a favorable factor to the overall survival of colon cancer patients in TCGA cohort (p = 0.0046). Three hundred and seven stromal score-related differentially expressed genes were identified. Through univariate, LASSO, and multivariate Cox regression analyses, a gene signature consisting of LEP, NOG, and SYT3 was recognized to build a prognostic prediction model. Based on the predictive values estimated by the established integrated model, patients were divided into two groups with significantly different overall survival outcomes (p < 0.0001). Time-dependent Receiver operating characteristic curve analyses suggested the satisfactory predictive efficacy for the 5-year overall survival of the model (AUC value = 0.733). A nomogram with great predictive performance combining the multigene prediction model and clinicopathological factors was developed. The established model was validated in an external cohort (AUC value = 0.728). In another independent cohort, the model was verified to be of significant prognostic value for different subgroups, which was demonstrated to be especially accurate for young patients (AUC value = 0.763).

CONCLUSION

The well-established model based on stromal score-related gene signature might serve as a promising tool for the prognostic prediction of colon cancer.

A new thinking: extended application of genomic selection to screen multiomics data for development of novel hypoxia-immune biomarkers and target therapy of clear cell renal cell carcinoma

Increasing evidences show the clinical significance of the interaction between hypoxia and immune in clear cell renal cell carcinoma (ccRCC) microenvironment. However, reliable prognostic signatures based on a combination of hypoxia and immune have not been well established. Moreover, many studies have only used RNA-seq profiles to screen the prognosis feature of ccRCC. Presently, there is no comprehensive analysis of multiomics data to mine a better one. Thus, we try and get it. First, t-SNE and ssGSEA analysis were used to establish tumor subtypes related to hypoxia-immune, and we investigated the hypoxia-immune-related differences in three types of genetic or epigenetic characteristics (gene expression profiles, somatic mutation, and DNA methylation) by analyzing the multiomics data from The Cancer Genome Atlas (TCGA) portal. Additionally, a four-step strategy based on lasso regression and Cox regression was used to construct a satisfying prognostic model, with average 1-year, 3-year and 5-year areas under the curve (AUCs) equal to 0.806, 0.776 and 0.837. Comparing it with other nine known prognostic biomarkers and clinical prognostic scoring algorithms, the multiomics-based signature performs better. Then, we verified the gene expression differences in two external databases (ICGC and SYSU cohorts). Next, eight hub genes were singled out and seven hub genes were validated as prognostic genes in SYSU cohort. Furthermore, it was indicated high-risk patients have a better response for immunotherapy in immunophenoscore (IPS) analysis and TIDE algorithm. Meanwhile, estimated by GDSC and cMAP database, the high-risk patients showed sensitive responses to six chemotherapy drugs and six candidate small-molecule drugs. In summary, the signature can accurately predict the prognosis of ccRCC and may shed light on the development of novel hypoxia-immune biomarkers and target therapy of ccRCC.

From the Immune Profile to the Immunoscore: Signatures for Improving Postsurgical Prognostic Prediction of Pancreatic Neuroendocrine Tumors

Objective

Immune infiltration plays an important role in tumor development and progression and shows promising prognostic value in numerous tumors. In this study, we aimed to identify the role of immune infiltration in pancreatic neuroendocrine tumors (Pan-NETs) and to establish an Immunoscore system to improve the prediction of postsurgical recurrence-free survival.

Methods

To derive transcriptional signatures and deconvolute specific immune populations, two GEO datasets containing 158 Pan-NET patients were reanalyzed to summarize the immune infiltration landscape and identify immune-related signatures. Using real-time reverse transcription-polymerase chain reaction, immunofluorescence and immunochemistry methods, candidate signatures were further detected. The least absolute shrinkage and selection operator (LASSO) logistic regression model used statistically significant survival predicators in the training cohort (n=125) to build an Immunoscore system. The prognostic and predictive accuracy was validated in an external independent cohort of 77 patients.

Results

The immune infiltration profile in Pan-NETs showed significant heterogeneity, among which accumulated immune cells, T lymphocytes and macrophages were predominant. Fourteen statistically significant immune-related signatures were further identified in the screening cohort. The Immunoscore system for Pan-NETs (ISpnet) consisting of six immune features (CCL19, IL-16, CD163, IRF4, CD8_PT and CD8_IT) was constructed to classify patients as high and low risk in the training cohort (cutoff value = 2.14). Low-risk patients demonstrated longer 5-year recurrence-free survival (HR, 0.061; 95% CI, 0.026 to 0.14; p < 0.0001), with fewer recurrences and better prognoses. To predict the individual risk of recurrence, a nomogram incorporating both immune signatures and clinicopathological characteristics was developed.

Conclusion

Our model, ISpnet, captures immune feature-associated prognostic indicators in Pan-NETs and represents the first immune feature-based score for the postsurgical prognostic prediction. The nomogram based on the ISpnet and independent clinical risk factors might facilitate decision-making regarding early recurrence risk monitoring, identify high-risk patients in need of adjuvant therapy, and provide auxiliary guidance for patients with Pan-NETs that may benefit from immunotherapy in clinical trials.

Dysregulated Immunological Functionome and Dysfunctional Metabolic Pathway Recognized for the Pathogenesis of Borderline Ovarian Tumors by Integrative Polygenic Analytics

The pathogenesis and molecular mechanisms of ovarian low malignant potential (LMP) tumors or borderline ovarian tumors (BOTs) have not been fully elucidated to date. Surgery remains the cornerstone of treatment for this disease, and diagnosis is mainly made by histopathology to date. However, there is no integrated analysis investigating the tumorigenesis of BOTs with open experimental data. Therefore, we first utilized a functionome-based speculative model from the aggregated obtainable datasets to explore the expression profiling data among all BOTs and two major subtypes of BOTs, serous BOTs (SBOTs) and mucinous BOTs (MBOTs), by analyzing the functional regularity patterns and clustering the separate gene sets. We next prospected and assembled the association between these targeted biomolecular functions and their related genes. Our research found that BOTs can be accurately recognized by gene expression profiles by means of integrative polygenic analytics among all BOTs, SBOTs, and MBOTs; the results exhibited the top 41 common dysregulated biomolecular functions, which were sorted into four major categories: immune and inflammatory response-related functions, cell membrane- and transporter-related functions, cell cycle- and signaling-related functions, and cell metabolism-related functions, which were the key elements involved in its pathogenesis. In contrast to previous research, we identified 19 representative genes from the above classified categories (IL6, CCR2 for immune and inflammatory response-related functions; IFNG, ATP1B1, GAS6, and PSEN1 for cell membrane- and transporter-related functions; CTNNB1, GATA3, and IL1B for cell cycle- and signaling-related functions; and AKT1, SIRT1, IL4, PDGFB, MAPK3, SRC, TWIST1, TGFB1, ADIPOQ, and PPARGC1A for cell metabolism-related functions) that were relevant in the cause and development of BOTs. We also noticed that a dysfunctional pathway of galactose catabolism had taken place among all BOTs, SBOTs, and MBOTs from the analyzed gene set databases of canonical pathways. With the help of immunostaining, we verified significantly higher performance of interleukin 6 (IL6) and galactose-1-phosphate uridylyltransferase (GALT) among BOTs than the controls. In conclusion, a bioinformatic platform of gene-set integrative molecular functionomes and biophysiological pathways was constructed in this study to interpret the complicated pathogenic pathways of BOTs, and these important findings demonstrated the dysregulated immunological functionome and dysfunctional metabolic pathway as potential roles during the tumorigenesis of BOTs and may be helpful for the diagnosis and therapy of BOTs in the future.

Biological bases of cancer immunotherapy

Immunotherapy has changed the landscape of cancer treatment and has significantly improved the outcome of several cancer types including breast, lung, colorectal and prostate. Neoantigen recognition and immune checkpoint inhibitors are nowadays the milestones of different immunotherapeutic regimes; however, high cost, primary and acquired resistance and the high variability of responses make their extensive use difficult. The development of better predictive biomarkers that represent tumour diversity shows promise because there is a significant body of clinical data showing a spectrum of immunotherapeutic responses that might be related back to their specific characteristics. This article makes a conceptual and historical review to summarise the main advances in our understanding of the role of the immune system in cancer, while describing the methodological details that have been successfully implemented on cancer treatments and that may hold the key to improved therapeutic approaches.

A Novel Immunotype-based Risk Stratification Model Predicts Postoperative Prognosis and Adjuvant TACE Benefit in Chinese Patients with Hepatocellular Carcinoma

Background and Aims: The tumor microenvironment can be divided into inflamed, immune-excluded and immune-desert phenotypes according to CD8⁺ T cell categories with differential programmed cell death protein 1 (PD-L1) expression. The study aims to construct a novel immunotype-based risk stratification model to predict postsurgical survival and adjuvant trans-arterial chemoembolization (TACE) response in patients with hepatocellular carcinoma (HCC).

Methods: A total of 220 eligible HCC patients participated in this study. CD8⁺ T cell infiltration and PD-L1 expression mode were estimated by immunohistochemical staining. A risk stratification model was developed and virtualized by a nomogram that integrated these independent prognostic factors. The postoperative prognosis and adjuvant TACE benefits were evaluated with a novel immunotype-based risk stratification model.

Results: A total of 220 patients were finally identified. Immune-desert, immune-excluded, and inflamed immunotypes represented 45%, 24%, and 31% of HCC, respectively. Univariate and multivariate analyses identified immunotype and PD-L1 expression mode as independent prognostic factors for overall survival time (OS) and recurrence-free survival time (RFS). The nomogram was constructed by integrating immunotype, PD-L1 expression, Barcelona Clinic Liver Cancer (BCLC) stage and tumor grade. The C-index was 0.794 in the training cohort and 0.813 in the validation cohort. A risk stratification system was constructed based on the nomogram classifying HCC patients into 3 risk groups. The average OS times in the low-risk, intermediate-risk and high-risk groups in all cohorts were 77.1 months (95% CI 71.4-82.9), 53.7 months (95% CI 48.2-59.2), and 25.6 months (95% CI 21.4-29.7), respectively. Further analysis showed that OS was significantly improved by adjuvant TACE in the low- and intermediate-risk groups (P=0.041 and P=0.010, respectively) but not in the high-risk group (P=0.398).

Conclusion: A novel immunotype-based risk stratification model was built to predict postoperative prognosis and adjuvant TACE benefit in HCC patients. These tools can assist in building a more customized method of HCC treatment.

Analysis in epithelial ovarian cancer identifies KANSL1 as a biomarker and target gene for immune response and HDAC inhibition

OBJECTIVE

There is an immunoreactive subtype of ovarian cancer with a favorable prognosis, but the majority of ovarian cancers have limited immune reactivity. The reason for this is poorly understood. This study aimed to approach this question by identifying prognostically relevant genes whose prognostic mRNA expression levels correlated with a genomic event.

METHODS

Expression microarray and 5-year survival data on 170 ovarian tumors and aCGH data on 45 ovarian cancer cell lines were used to identify amplified/deleted genes associated with prognosis. Three immune-response genes were identified mapping to epigenetically modified chromosome 6p21.3. Genes were searched for roles in epigenetic modification, identifying KANSL1. Genome-wide association studies were searched to identify genetic variants in KANSL1 associated with altered immune profile. Sensitivity to HDAC inhibition in cell lines with KANSL1 amplification/rearrangement was studied.

RESULTS

Expression of 196 genes was statistically significantly associated with survival, and expression levels correlated with copy number variations for 82 of them. Among these, 3 immune-response genes (HCP5, PSMB8, PSMB9) clustered together at epigenetically modified chromosome 6p21.3 and their expression was inversely correlated to epigenetic modification gene KANSL1. KANSL1 is amplified/rearranged in ovarian cancer, associated with lymphocyte profile, a biomarker for response to HDAC inhibition, and may drive expression of immune-response genes.

CONCLUSION

This study identifies 82 genes with prognostic relevance and genomic alteration in ovarian cancer. Among these, immune-response genes have correlated expression which is associated with 5-year survival. KANSL1 may be a master gene altering immune-response gene expression at 6p21.3 and drive response to HDAC inhibitors. Future research should investigate KANSL1 and determine whether targeting it alters the immune profile of ovarian cancer and improves survival, HDAC inhibition, and/or immunotherapy response.

Correlation of tumor-infiltrating immune cells of melanoma with overall survival by immunogenomic analysis

AIMS

Different types of tumor-infiltrating immune cells not only augment but also dampen antitumor immunity in the microenvironment of melanoma. Therefore, it is critical to provide an overview of tumor-infiltrating immune cells in melanoma and explore a novel strategy for immunotherapies.

METHODS

We analyzed the immune states of different stages in melanoma patients by the immune, stromal, and estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) scores. Immune cell types were identified by the estimating relative subsets of RNA transcripts (CIBERSORTx) algorithm in 471 melanoma and 324 healthy tissues. Moreover, we performed a gene set variation analysis (GSVA) to determine the differentially regulated pathways in the tumor microenvironment.

RESULTS

In melanoma cohorts, we found that ESTIMATE and immune scores were involved in survival or tumor clinical stage. Among the 22 immune cells, CD8⁺ T cells, M2 macrophages, and regulatory T cells (Tregs) showed significant differences using the CIBERSORTx algorithm. Furthermore, GSVA identified the immune cell-related pathways; the primary immunodeficiency pathway, intestinal immune network for IgA, and TGF-β pathways were identified as participants of the crosstalk in CD8⁺ T cells, Tregs, and M2 macrophages in the melanoma microenvironment.

CONCLUSION

These results reveal the cellular and molecular characteristics of immune cells in melanoma, providing a method for selecting targets of immunotherapies and promoting the efficacy of therapies for the treatment of melanoma.

Potential predictive value of SCN4A mutation status for immune checkpoint inhibitors in melanoma

Melanoma refers to a pigmented nevus with malignant changes. The preferred treatment for primary melanoma is surgical excision and postoperative radiotherapy, but the prognosis is poor. Immune checkpoint inhibitors (ICIs) have been remarkably successful in different types of cancers, but not all cancer patients can benefit from it. Therefore, it is essential to find predictable biomarkers and improve the accuracy of treatment. In this study, we used survival analysis, gene panorama analysis, immune cell enrichment analysis, TMB analysis, and GSEA to demonstrate that SCN4A gene mutations may be used as one of the indicators to predict the prognosis of melanoma patients undergoing ICI treatment. The research further indicates that SCN4A gene mutations improve the prognosis of ICI treatment. It is hoped that the effect of SCN4A on immunogenicity and tumor immunity can be demonstrated to further suggest the effect of this gene on the efficacy of ICIs.

ATM Mutations Benefit Bladder Cancer Patients Treated With Immune Checkpoint Inhibitors by Acting on the Tumor Immune Microenvironment

Immune checkpoint inhibitors (ICIs) have shown promising results in bladder cancer (BC). However, only some patients respond to ICIs. DNA repair defects (DDR) play an important role in the therapeutic response of bladder cancer. Therefore, we aimed to elucidate the association between ICIs in bladder cancer and ataxia telangiectasia mutated (ATM), a core component of the DNA repair system. From a collected immunotherapy cohort (n = 210) and The Cancer Genome Atlas (TCGA)-Bladder cancer cohort, which were both retrieved from publicly available resources, we performed a series of analyses to evaluate the prognostic value and potential mechanism of ATM in bladder cancer immunotherapy. We found that ATM-mutant (ATM-MT) bladder cancer patients derived greater benefit from ICIs [overall survival (OS), hazard ratio (HR) = 0.28, [95% confidence interval (CI), 0.16 to 0.51], P = 0.007] and showed a higher mutation load (P < 0.05) and immunogenicity (P < 0.05) than ATM-wild-type (ATM-WT) patients. The immune inflammatory response to antigenic stimulation, the regulation of the IFN pathway and the macrophage activation pathway were significantly enriched in the ATM-MT group (NES > 1, P < 0.05), while insulin-like growth factor receptor signaling pathways and vasculogenesis were significantly downregulated (NES < −1, P < 0.05). ATM mutation significantly upregulated the number of DNA damage repair pathway gene mutations (P < 0.05). ATM mutations resulted in increased bladder cancer sensitivity to 29 drugs (P < 0.05), including cisplatin and BMS-536924, an IGF-1R inhibitor. Our results demonstrate the importance of ATM as a prognostic signature in bladder cancer and reveal that ATM may impact the effects of ICIs by acting on the tumor immune microenvironment.

Prognosis of Lung Adenocarcinoma Patients With NTRK3 Mutations to Immune Checkpoint Inhibitors

Background

Immune checkpoint inhibitors (ICIs) are an important treatment modality that must be considered for patients with lung adenocarcinoma (LUAD). However, ICIs are effective only in some of these patients. Therefore, identifying biomarkers that accurately predict the prognosis of patients with LUAD treated with ICIs can help maximize their therapeutic benefits. This study aimed to identify a new potential predictor to better select and optimally benefit LUAD patients.

Methods

We first collected and analyzed a discovery immunotherapy cohort comprising clinical and mutation data for LUAD patients. Then, we evaluated whether the specific mutated genes can act as predictive biomarkers in this discovery immunotherapy cohort and further validated the findings in The Cancer Genome Atlas (TCGA) project LUAD cohort. Gene set enrichment analysis (GSEA) was used to explore possible alterations in DNA damage response (DDR) pathways within the gene mutation. Moreover, we analyzed whole-exome sequencing (WES) and drug sensitivity response data for LUAD cell lines in the Genomics of Drug Sensitivity in Cancer (GDSC) database.

Results

Among the mutated genes screened from both the ICI treatment and TCGA-LUAD cohorts, NTRK3 mutation (mutant-type NTRK3, NTRK3-MT) was strongly associated with immunotherapy. First, significant differences in overall survival (OS) were observed between patients with NTRK3-MT and those with NTRK3-WT in the ICI treatment cohort but not in the non-ICI-treated TCGA-LUAD cohort. We then analyzed the association of NTRK3-MT with clinical characteristics and found the tumor mutation burden (TMB) to be significantly higher in both NTRK3-MT cohorts. However, significant differences in neoantigen levels and smoking history were found only for NTRK3-MT in the LUAD cohort from TCGA. Furthermore, some immune-related genes and immune cell-related genes were significantly upregulated in patients with NTRK3-MT compared to those with NTRK3-WT. In addition, NTRK3 mutation affected the deregulation of some signaling pathways and the DDR pathway.

Conclusions

Our findings suggest that NTRK3-MT can predict the prognosis of patients with LUAD treated by ICIs and that it may have clinical significance for immunotherapy.

Prognostic nomogram based on immune scores for laryngeal squamous cell cancer

PURPOSE

Immune scores have been used as a prognostic factor for various types of cancer. However, the association between immune scores and the prognosis of laryngeal squamous cell cancer (LSCC) has not yet been investigated. This study aimed to explore the prognostic significance of immune scores and construct a clinical nomogram to predict the survival of patients with LSCC.

METHODS

The clinicopathological characteristics and immune scores of 102 patients with LSCC were obtained from TCGA database and a nomogram was developed. C-index and calibration curves were applied to assess the performance of the model.

RESULTS

Patients with higher immune scores had significantly better overall survival (OS). The prognostic nomogram presented a good performance in survival prediction.

CONCLUSIONS

High immune scores are correlated with improved OS in patients with LSCC. In addition, the nomogram developed for this study may assist clinicians in the prognostic evaluation of patients with LSCC.

A Prognostic Nomogram Based on Immune Scores Predicts Postoperative Survival for Patients with Hepatocellular Carcinoma

Background

Increasing research attention has focused on tumor-infiltrating immune cells. However, the threshold of an immune score for use in predicting overall survival (OS) and disease-free survival (DFS) in hepatocellular carcinoma (HCC) is not defined. This study aims at exploring the association between immune scores with prognosis and building a clinical nomogram for predicting the survival of HCC patients. Material and Methods. A total of 299 patients were enrolled in this study. Their clinical pathological characteristics and immune scores downloaded from The Cancer Genome Atlas (TCGA) database were analyzed. Survival differences between different immune score subgroups were compared, and a final nomogram was built using the Cox proportional hazards regression model. The predictive performance of the nomogram was assessed using the concordance index (C-index) and a calibration plot.

Results

All the patients were divided into three subgroups based on immune scores. Patients with medium and high immune scores had significantly better OS (HR and 95% CI: 0.417 [0.186-0.937] and 0.299 [0.146-0.616]) and DFS (HR and 95% CI: 0.575 [0.329-1.004] and 0.451 [0.278-0.733], respectively, compared with those with low immune scores. The C indices for OS and DFS were 0.748 (95% CI, 0.687-0.809) and 0.675 (95% CI, 0.630-0.720), respectively. A calibration plot used to determine the probability of survival at 3 or 5 years (OS and DFS) showed a significant agreement between nomogram predictions and actual observations.

Conclusions

Medium and high immune scores are significantly associated with prolonged OS and DFS in HCC patients. Nomograms built in this study can help doctors and patients assess prognosis and guide treatment.

Breakthrough concepts in immune-oncology: Cancer vaccines at the bedside

Clinical approval of the immune checkpoint blockade (ICB) agents for multiple cancer types has reinvigorated the long-standing work on cancer vaccines. In the pre-ICB era, clinical efforts focused on the Ag, the adjuvants, the formulation, and the mode of delivery. These translational efforts on therapeutic vaccines range from cell-based (e.g., dendritic cells vaccine Sipuleucel-T) to DNA/RNA-based platforms with various formulations (liposome), vectors (Listeria monocytogenes), or modes of delivery (intratumoral, gene gun, etc.). Despite promising preclinical results, cancer vaccine trials without ICB have historically shown little clinical activity. With the anticipation and expansion of combinatorial immunotherapeutic trials with ICB, the cancer vaccine field has entered the personalized medicine arena with recent advances in immunogenic neoantigen-based vaccines. In this article, we review the literature to organize the different cancer vaccines in the clinical space, and we will discuss their advantages, limits, and recent progress to overcome their challenges. Furthermore, we will also discuss recent preclinical advances and clinical strategies to combine vaccines with checkpoint blockade to improve therapeutic outcome and present a translational perspective on future directions.