Tumour microenvironment-based molecular profiling reveals ideal candidates for high-grade serous ovarian cancer immunotherapy

Single cell and bulk RNA sequencing identifies tumor microenvironment subtypes and chemoresistance-related IGF1+ cancer-associated fibroblast in gastric cancer

BACKGROUND

The tumor microenvironment (TME) significantly influences prognosis and drug resistance in various tumors, yet its heterogeneity and the mechanisms affecting therapeutic response remain unclear in gastric cancer (GC).

METHODS

The heterogenous TME were explored with single-cell RNA-sequencing (scRNA-seq) data of 50 primary GC samples. We then identified four GC TME subtypes with nonnegative matrix factorization (NMF) and constructed a pearson nearest-centroid classifier based on subtype-specific upregulated genes. Genomic features and clinical significance of four subtypes were comprehensively evaluated. We reclustered fibroblasts to identify cancer-associated fibroblast (CAF) subtype associated with poor clinical outcomes. RT-qPCR and double immunofluorescence staining were applied to validate the findings. Cellchat analysis elucidated potential molecular mechanisms of the CAF subtype in GC disease progression and chemotherapy resistance.

FINDINGS

The GC TME exhibited high heterogeneity, influencing chemo-sensitivity. Four TME-based subtypes predicting response to immunotherapy and chemotherapy were identified and validated in 1406 GC patients. Among which, ISG1 subtype displayed higher fibroblasts infiltration and heightened oncogenic pathways, and inferior response to chemotherapy with unfavorable prognosis. Microsatellite instability-high (MSI-H) GCs within four TME subtypes showed immunological heterogeneity. We then reported an IGF1-overexpressing CAF was associated with chemo-resistance and GC recurrence. Cell communication analysis revealed IGF1+ CAF may induce drug-resistant phenotypes in tumor cells through IGF1-α6β4 integrin ligand-receptor binding and activation of EMT biological process.

INTERPRETATION

We identified four TME-based subtypes with different clinical outcomes and IGF1+ CAFs contributing to poor clinical outcomes in GC, which might provide guidance for individualized treatment and facilitate the development of novel therapeutic targets.

Fatty acid metabolism-related lncRNA prognostic signature for serous ovarian carcinoma

Background: To explore the role of fatty acid metabolism (FAM)-related lncRNAs in the prognosis and antitumor immunity of serous ovarian cancer (SOC). Materials & methods: A SOC FAM-related lncRNA risk model was developed and evaluated by a series of analyses. Additional immune-related analyses were performed to further assess the associations between immune state, tumor microenvironment and the prognostic risk model. Results: Five lncRNAs associated with the FAM genes were found and used to create a predictive risk model. The patients with a low-risk profile exhibited favorable prognostic outcomes. Conclusion: The established prognostic risk model exhibits better predictive capabilities for the prognosis of patients with SOC and offers novel potential therapy targets for SOC.

Personalization of Therapy in High-Grade Serous Tubo-Ovarian Cancer-The Possibility or the Necessity?

High-grade serous tubo-ovarian cancer (HGSTOC) is the most lethal tumor of the female genital tract. The foregoing therapy consists of cytoreduction followed by standard platinum/taxane chemotherapy; alternatively, for primary unresectable tumors, neo-adjuvant platinum/taxane chemotherapy followed by delayed interval cytoreduction. In patients with suboptimal surgery or advanced disease, different forms of targeted therapy have been accepted or tested in clinical trials. Studies on HGSTOC discovered its genetic and proteomic heterogeneity, epigenetic regulation, and the role of the tumor microenvironment. These findings turned attention to the fact that there are several distinct primary tumor subtypes of HGSTOC and the unique biology of primary, metastatic, and recurrent tumors may result in a differential drug response. This results in both chemo-refractoriness of some primary tumors and, what is significantly more frequent and destructive, secondary chemo-resistance of metastatic and recurrent HGSTOC tumors. Treatment possibilities for platinum-resistant disease include several chemotherapeutics with moderate activity and different targeted drugs with difficult tolerable effects. Therefore, the question appears as to why different subtypes of ovarian cancer are predominantly treated based on the same therapeutic schemes and not in an individualized way, adjusted to the biology of a specific tumor subtype and temporal moment of the disease. The paper reviews the genomic, mutational, and epigenetic signatures of HGSTOC subtypes and the tumor microenvironment. The clinical trials on personalized therapy and the overall results of a new, comprehensive approach to personalized therapy for ovarian cancer have been presented and discussed.

Identifying Explainable Machine Learning Models and a Novel SFRP2+ Fibroblast Signature as Predictors for Precision Medicine in Ovarian Cancer

Ovarian cancer (OC) is a type of malignant tumor with a consistently high mortality rate. The diagnosis of early-stage OC and identification of functional subsets in the tumor microenvironment are essential to the development of patient management strategies. However, the development of robust models remains unsatisfactory. We aimed to utilize artificial intelligence and single-cell analysis to address this issue. Two independent datasets were screened from the Gene Expression Omnibus (GEO) database and processed to obtain overlapping differentially expressed genes (DEGs) in stage II-IV vs. stage I diseases. Three explainable machine learning algorithms were integrated to construct models that could determine the tumor stage and extract important characteristic genes as diagnostic biomarkers. Correlations between cancer-associated fibroblast (CAF) infiltration and characteristic gene expression were analyzed using TIMER2.0 and their relationship with survival rates was comprehensively explored via the Kaplan-Meier plotter (KM-plotter) online database. The specific expression of characteristic genes in fibroblast subsets was investigated through single-cell analysis. A novel fibroblast subset signature was explored to predict immune checkpoint inhibitor (ICI) response and oncogene mutation through Tumor Immune Dysfunction and Exclusion (TIDE) and artificial neural network algorithms, respectively. We found that Support Vector Machine-Shapley Additive Explanations (SVM-SHAP), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) successfully diagnosed early-stage OC (stage I). The area under the receiver operating characteristic curves (AUCs) of these models exceeded 0.990. Their overlapping characteristic gene, secreted frizzled-related protein 2 (SFRP2), was a risk factor that affected the overall survival of OC patients with stage II-IV disease (log-rank test: p < 0.01) and was specifically expressed in a fibroblast subset. Finally, the SFRP2+ fibroblast signature served as a novel predictor in evaluating ICI response and exploring pan-cancer tumor protein P53 (TP53) mutation (AUC = 0.853, 95% confidence interval [CI]: 0.829-0.877). In conclusion, the models based on SVM-SHAP, XGBoost, and RF enabled the early detection of OC for clinical decision making, and SFRP2+ fibroblast signature used in diagnostic models can inform OC treatment selection and offer pan-cancer TP53 mutation detection.

New Achievements from Molecular Biology and Treatment Options for Refractory/Relapsed Ovarian Cancer-A Systematic Review

Ovarian cancer (OC) has a high rate of mortality and is the fifth most common cause of death in females all over the world. The etiology is still unclear. Numerous factors such as smoking, obesity, and unhealthy diet may affect the risk of OC. Having a family history of breast and OC is one of the main risks for developing OC. Mutations of BRCA1/2 are associated with OC risk as well. The histopathological classification of OC reveals the four most common types: serous, clear cell, endometrioid, and mucinous; these are epithelial OC types, and other types are rare. Furthermore, OC can be subdivided into types I and II. Type I tumors are most probably caused by atypical proliferative tumors. Type II tumors include high-grade carcinoma of the serous type, carcinosarcoma, and carcinoma, which are not differentiated and generally originate from tubal intraepithelial carcinoma of the serous type. Typically, type I tumors are present in early stages, usually with good prognosis. Type II tumors are classified as high-grade tumors and are most often diagnosed at advanced FIGO stages with poor prognosis. High-grade serous OC accounts for 90% of serous OC. Tumor heterogeneity aggravates OC treatment. The standard care for primary epithelial ovarian cancer (EOC) is cytoreductive surgery followed by platinum-based chemotherapy. Neoadjuvant chemotherapy can be used in certain cases followed by cytoreductive surgery. The main prognostic factor is complete tumor resection. However, about 70% of patients relapse. Resistance to chemotherapeutic agents remains a major challenge in EOC treatment, in which many different factors are involved. In recent years, the examination of molecular parameters and their prognostic impact has become increasingly relevant in EOC, and furthermore, the use of immunotherapy has expanded the therapeutic range. As the clinical need is greatest for relapsed patients, this systematic review will focus on recent advances in molecular biology with prognostic and predictive markers and treatment options for recurrent/refractory OC. Inclusion criteria for the review: potential prospective or predictive biomarkers in preclinical or clinical use in relapsed and refractory OC, prognostic impact, clinical and preclinical trials, and immunotherapy. Exclusion criteria for the review: primary OC, no full text or abstract available, not the topic mentioned above, and text not available in English. Risk of bias: the included studies were evaluated descriptively for the topics mentioned above, and data were not compared with each other. The objective is to highlight the molecular mechanisms of the most promising targeted agents under clinical investigation to demonstrate their potential relevance in recurrent/refractory OC.

Molecular cluster mining of high-grade serous ovarian cancer via multi-omics data analysis aids precise medicine

PURPOSE

HGSOC is a kind of gynecological cancer with high mortality and strong heterogeneity. The study used multi-omics and multiple algorithms to identify novel molecular subtypes, which can help patients obtain more personalized treatments.

METHODS

Firstly, the consensus clustering result was obtained using a consensus ensemble of ten classical clustering algorithms, based on mRNA, lncRNA, DNA methylation, and mutation data. The difference in signaling pathways was evaluated using the single-sample gene set enrichment analysis (ssGSEA). Meanwhile, the relationship between genetic alteration, response to immunotherapy, drug sensitivity, prognosis, and subtypes was further analyzed. Finally, the reliability of the new subtype was verified in three external datasets.

RESULTS

Three molecular subtypes were identified. Immune desert subtype (CS1) had little enrichment in the immune microenvironment and metabolic pathways. Immune/non-stromal subtype (CS2) was enriched in the immune microenvironment and metabolism of polyamines. Immune/stromal subtype (CS3) not only enriched anti-tumor immune microenvironment characteristics but also enriched pro-tumor stroma characteristics, glycosaminoglycan metabolism, and sphingolipid metabolism. The CS2 had the best overall survival and the highest response rate to immunotherapy. The CS3 had the worst prognosis and the lowest response rate to immunotherapy but was more sensitive to PARP and VEGFR molecular-targeted therapy. The similar differences among three subtypes were successfully validated in three external cohorts.

CONCLUSION

We used ten clustering algorithms to comprehensively analyze four types of omics data, identified three biologically significant subtypes of HGSOC patients, and provided personalized treatment recommendations for each subtype. Our findings provided novel views into the HGSOC subtypes and could provide potential clinical treatment strategies.

Cancer-mesothelial and cancer-macrophage interactions in the ovarian cancer microenvironment

The metastatic ovarian cancer microenvironment is characterized by an intricate interaction network between cancer cells and host cells. This complex heterotypic cancer-host cell crosstalk results in an environment that promotes cancer cell metastasis and treatment resistance, leading to poor patient prognosis and survival. In this review, we focus on two host cell types found in the ovarian cancer microenvironment: mesothelial cells and tumor-associated macrophages. Mesothelial cells make up the protective lining of organs in the abdominal cavity. Cancer cells attach and invade through the mesothelial monolayer to form metastatic lesions. Crosstalk between mesothelial and cancer cells can contribute to metastatic progression and chemotherapy resistance. Tumor-associated macrophages are the most abundant immune cell type in the ovarian cancer microenvironment with heterogeneous subpopulations exhibiting protumor or antitumor functions. Macrophage reprogramming toward a protumor or antitumor state can be influenced by chemotherapy and communication with cancer cells, resulting in cancer cell invasion and treatment resistance. A better understanding of cancer-mesothelial and cancer-macrophage crosstalk will uncover biomarkers of metastatic progression and therapeutic targets to restore chemotherapy sensitivity.

"DEPHENCE" system-a novel regimen of therapy that is urgently needed in the high-grade serous ovarian cancer-a focus on anti-cancer stem cell and anti-tumor microenvironment targeted therapies

Ovarian cancer, especially high-grade serous type, is the most lethal gynecological malignancy. The lack of screening programs and the scarcity of symptomatology result in the late diagnosis in about 75% of affected women. Despite very demanding and aggressive surgical treatment, multiple-line chemotherapy regimens and both approved and clinically tested targeted therapies, the overall survival of patients is still unsatisfactory and disappointing. Research studies have recently brought some more understanding of the molecular diversity of the ovarian cancer, its unique intraperitoneal biology, the role of cancer stem cells, and the complexity of tumor microenvironment. There is a growing body of evidence that individualization of the treatment adjusted to the molecular and biochemical signature of the tumor as well as to the medical status of the patient should replace or supplement the foregoing therapy. In this review, we have proposed the principles of the novel regimen of the therapy that we called the "DEPHENCE" system, and we have extensively discussed the results of the studies focused on the ovarian cancer stem cells, other components of cancer metastatic niche, and, finally, clinical trials targeting these two environments. Through this, we have tried to present the evolving landscape of treatment options and put flesh on the experimental approach to attack the high-grade serous ovarian cancer multidirectionally, corresponding to the "DEPHENCE" system postulates.

Poor-prognosis molecular subtypes in adenocarcinomas of pancreato-biliary and gynecological origin: A systematic review

Pancreato-biliary and gynecological adenocarcinomas need better tools to predict clinical outcome. Potential prognostic mesenchymal(-like) transcriptome-based subtypes have been identified in these cancers. In this systematic review, we include studies into molecular subtyping and summarize biological and clinical features of the subtypes within and across sites of origin, searching for suggestions to improve classification and prognostication. PubMed and Embase were searched for original research articles describing potential mesenchymal(-like) mRNA-based subtypes in pancreato-biliary or gynecological adenocarcinomas. Studies limited to supervised clustering were excluded. Fourty-four studies, discussing cholangiocarcinomas, gallbladder, ampullary, pancreatic, ovarian, and endometrial adenocarcinomas were included. There was overlap in molecular and clinical features in mesenchymal(-like) subtypes across all adenocarcinomas. Approaches including microdissection were more likely to identify prognosis-associating subtypes. To conclude, molecular subtypes in pancreato-biliary and gynecological adenocarcinomas share biological and clinical characteristics. Furthermore, separation of stromal and epithelial signals should be applied in future studies into biliary and gynecological adenocarcinomas.

T-Cell Receptor Repertoire Sequencing and Its Applications: Focus on Infectious Diseases and Cancer

The immune system is a dynamic feature of each individual and a footprint of our unique internal and external exposures. Indeed, the type and level of exposure to physical and biological agents shape the development and behavior of this complex and diffuse system. Many pathological conditions depend on how our immune system responds or does not respond to a pathogen or a disease or on how the regulation of immunity is altered by the disease itself. T-cells are important players in adaptive immunity and, together with B-cells, define specificity and monitor the internal and external signals that our organism perceives through its specific receptors, TCRs and BCRs, respectively. Today, high-throughput sequencing (HTS) applied to the TCR repertoire has opened a window of opportunity to disclose T-cell repertoire development and behavior down to the clonal level. Although TCR repertoire sequencing is easily accessible today, it is important to deeply understand the available technologies for choosing the best fit for the specific experimental needs and questions. Here, we provide an updated overview of TCR repertoire sequencing strategies, providers and applications to infectious diseases and cancer to guide researchers’ choice through the multitude of available options. The possibility of extending the TCR repertoire to HLA characterization will be of pivotal importance in the near future to understand how specific HLA genes shape T-cell responses in different pathological contexts and will add a level of comprehension that was unthinkable just a few years ago.

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.

Single-Cell TCR and Transcriptome Analysis: An Indispensable Tool for Studying T-Cell Biology and Cancer Immunotherapy

T cells have been known to be the driving force for immune response and cancer immunotherapy. Recent advances on single-cell sequencing techniques have empowered scientists to discover new biology at the single-cell level. Here, we review the single-cell techniques used for T-cell studies, including T-cell receptor (TCR) and transcriptome analysis. In addition, we summarize the approaches used for the identification of T-cell neoantigens, an important aspect for T-cell mediated cancer immunotherapy. More importantly, we discuss the applications of single-cell techniques for T-cell studies, including T-cell development and differentiation, as well as the role of T cells in autoimmunity, infectious disease and cancer immunotherapy. Taken together, this powerful tool not only can validate previous observation by conventional approaches, but also can pave the way for new discovery, such as previous unidentified T-cell subpopulations that potentially responsible for clinical outcomes in patients with autoimmunity or cancer.

Ferroptosis-Related Gene Signature Promotes Ovarian Cancer by Influencing Immune Infiltration and Invasion

Ovarian cancer is a kind of gynecological malignancy with high mortality. Ferroptosis is a new type of iron-dependent cell death characterized by the formation of lipid peroxides and excessive accumulation of reactive oxygen species. Studies have shown that ferroptosis modulates tumor genesis, progression, and invasion, including ovarian cancer. Based on the mRNA expression data from TCGA, we construct a scoring system using consensus clustering analysis, univariate Cox regression analysis, and least absolute selection operator. Then, we systematically evaluate the relationship between score and clinical characteristics of ovarian cancer. The result from the prediction of biofunction pathways shows that score serves as an independent prognostic marker for ovarian cancer and affects tumor progression by modulating tumor metastasis. Moreover, immunocytes such as activated CD4 T cell, activated CD8 T cell, regulatory T cells, macrophage, and stromal cells, including adipocytes, epithelial cells, and fibroblast infiltrate more in the tumor microenvironment in a high-score group, indicating ferroptosis can also affect tumor immune landscape. Critically, four potentially sensitive drugs, including staurosporine, epothilone B, DMOG, and HG6-64-1 based on the scores, are predicted, and DMOG is recognized as a novel targeted drug for ovarian cancer. In general, we construct the scoring system based on ferroptosis-related genes that can predict the prognosis of ovarian cancer patients and propose that ferroptosis may affect ovarian cancer progression by mediating tumor metastasis and immune landscape. Novel drugs to target ovarian cancer are also predicted.

Tumour microenvironment-based molecular profiling reveals ideal candidates for high-grade serous ovarian cancer immunotherapy

OBJECTIVE

Due to limited immunological profiles of high-grade serous ovarian cancer (HGSOC), we aimed to characterize its molecular features to determine whether a specific subset that can respond to immunotherapy exists.

MATERIALS AND METHODS

A training cohort of 418 HGSOC samples from TCGA was analysed by consensus non-negative matrix factorization. We correlated the expression patterns with the presence of immune cell infiltrates, immune regulatory molecules and other genomic or epigenetic features. Two independent cohorts containing 482 HGSOCs and in vitro experiments were used for validation.

RESULTS

We identified immune and non-immune groups where the former was enriched in signatures that reflect immune cells, infiltration and PD-1 signalling (all, P < 0.001), and presented with a lower chromosomal aberrations but increased neoantigens, tumour mutation burden, and microsatellite instability (all, P < 0.05); this group was further refined into two microenvironment-based subtypes characterized by either immunoactivation or carcinoma-associated fibroblasts (CAFs) and distinct prognosis. CAFs-immune subtype was enriched for factors that mediate immunosuppression and promote tumour progression, including highly expressed stromal signature, TGF-β signalling, epithelial-mesenchymal transition and tumour-associated M2-polarized macrophages (all, P < 0.001). Robustness of these immune-specific subtypes was verified in validation cohorts, and in vitro experiments indicated that activated-immune subtype may benefit from anti-PD1 antibody therapy (P < 0.05).

CONCLUSION

Our findings revealed two immune subtypes with different responses to immunotherapy and indicated that some HGSOCs may be susceptible to immunotherapies or combination therapies.