Microenvironmental niche divergence shapes BRCA1-dysregulated ovarian cancer morphological plasticity

Quantifying and interpreting biologically meaningful spatial signatures within tumor microenvironments

The tumor microenvironment (TME) plays a crucial role in orchestrating tumor cell behavior and cancer progression. Recent advances in spatial profiling technologies have uncovered novel spatial signatures, including univariate distribution patterns, bivariate spatial relationships, and higher-order structures. These signatures have the potential to revolutionize tumor mechanism and treatment. In this review, we summarize the current state of spatial signature research, highlighting computational methods to uncover spatially relevant biological significance. We discuss the impact of these advances on fundamental cancer biology and translational research, address current challenges and future research directions.

Pathologist-level diagnosis of ulcerative colitis inflammatory activity level using an automated histological grading method

BACKGROUND AND AIMS

Inflammatory bowel disease (IBD) is a global disease that is evolving with increasing incidence. However, there are few works on computationally assisted diagnosis of IBD based on pathological images. Therefore, based on the UK and Chinese IBD diagnostic guidelines, our study established an artificial intelligence-assisted diagnostic system for histologic grading of inflammatory activity in ulcerative colitis (UC).

METHODS

We proposed an efficient deep-learning (DL) method for grading inflammatory activity in whole-slide images (WSIs) of UC pathology. Our model was constructed using 603 UC WSIs from Nanjing Drum Tower Hospital for model train set and internal test set. We collected 212 UC WSIs from Zhujiang Hospital as an external test set. Initially, the pre-trained ResNet50 model on the ImageNet dataset was employed to extract image patch features from UC patients. Subsequently, a multi-instance learning (MIL) approach with embedded self-attention was utilized to aggregate tissue image patch features, representing the entire WSI. Finally, the model was trained based on the aggregated features and WSI annotations provided by senior gastrointestinal pathologists to predict the level of inflammatory activity in UC WSIs.

RESULTS

In the task of distinguishing the presence or absence of inflammatory activity, the Area Under Curve (AUC) value in the internal test set is 0.863 (95% confidence interval [CI] 0.829, 0.898), with a sensitivity of 0.913 (95% [CI] 0.866, 0.961), and specificity of 0.816 (95% [CI] 0.771, 0.861). The AUC in the external test set is 0.947 (95% confidence interval [CI] 0.939, 0.955), with a sensitivity of 0.889 (905% [CI] 0.837, 0.940), and specificity of 0.858 (95% [CI] 0.777, 0.939). For distinguishing different levels of inflammatory activity in UC, the average Macro-AUC in the internal test set and the external test set are 0.827 (95% [CI] 0.803, 0.850) and 0.908 (95% [CI] 0.882, 0.935). the average Micro-AUC in the internal test set and the external test set are 0.816 (95% [CI] 0.792, 0.840) and 0.898 (95% [CI] 0.869, 0.926).

CONCLUSIONS

Comparative analysis with diagnoses made by pathologists at different expertise levels revealed that the algorithm reached a proficiency comparable to the pathologist with 5 years of experience. Furthermore, our algorithm performed superior to other MIL algorithms.

Role of artificial intelligence in digital pathology for gynecological cancers

The diagnosis of cancer is typically based on histopathological sections or biopsies on glass slides. Artificial intelligence (AI) approaches have greatly enhanced our ability to extract quantitative information from digital histopathology images as a rapid growth in oncology data. Gynecological cancers are major diseases affecting women's health worldwide. They are characterized by high mortality and poor prognosis, underscoring the critical importance of early detection, treatment, and identification of prognostic factors. This review highlights the various clinical applications of AI in gynecological cancers using digitized histopathology slides. Particularly, deep learning models have shown promise in accurately diagnosing, classifying histopathological subtypes, and predicting treatment response and prognosis. Furthermore, the integration with transcriptomics, proteomics, and other multi-omics techniques can provide valuable insights into the molecular features of diseases. Despite the considerable potential of AI, substantial challenges remain. Further improvements in data acquisition and model optimization are required, and the exploration of broader clinical applications, such as the biomarker discovery, need to be explored.

The unveiled mosaic of intra-tumor heterogeneity in ovarian cancer through spatial transcriptomic technologies: A systematic review

Epithelial ovarian cancer is a significant global health issue among women. Diagnosis and treatment pose challenges due to difficulties in predicting patient responses to therapy, primarily stemming from gaps in understanding tumor chemoresistance mechanisms. Recent advancements in transcriptomic technologies like single-cell RNA sequencing and spatial transcriptomics have greatly improved our understanding of ovarian cancer intratumor heterogeneity and tumor microenvironment composition. Spatial transcriptomics, in particular, comprises a plethora of technologies that enable the detection of hundreds of transcriptomes and their spatial distribution within a histological section, facilitating the study of cell types, states, and interactions within the tumor and its microenvironment. Studies investigating the spatial distribution of gene expression in ovarian cancer masses have identified specific features that impact prognosis and therapy outcomes. Emerging evidence suggests that specific spatial patterns of tumor cells and their immune and non-immune microenvironment significantly influence therapy response, as well as the behavior and progression of primary tumors and metastatic sites. The importance of spatially contextualizing ovarian cancer transcriptomes is underscored by these findings, which will advance our understanding and therapeutic approaches for this complex disease.

Study on correlation between CXCL13 and prognosis and immune characteristics of ovarian cancer

Ovarian cancer (OC) has a limited immunotherapeutic response; hence, this study aimed to investigate the relationship between CXC-chemokine ligand 13 (CXCL13) expression and overall survival (OS) rate, key immune pathways, degree of immune cell infiltration, and progressive disease (PD)-1 checkpoint blockade. A total of 703 differentially expressed genes were obtained from "The Cancer Genome Atlas" (TCGA) database based on the immune and stromal scores of 379 OC patients for getting the targeted gene CXCL13. The association between CXCL13 and OS in OC patients, biological function annotation of CXCL13, and its correlation with immune components were assessed. The results indicated that upregulated CXCL13 expression was positively correlated with better OC patient prognosis. CXCL13 expression was associated with 6 immune-related pathways, 10 immune cells, and PD-1 expression of OC micro-environment. Moreover, high expression of CXCL13 was related to a better tumor response and more extended tumor-stable stage after PD-1 blocking therapy in IMvigor210. The study concluded that CXCL13 could be a prognostic marker and a potential immunotherapy target for OC patients, especially PD-1 checkpoint blockade.

Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology

Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology.

Deciphering the tumor immune microenvironment from a multidimensional omics perspective: insight into next-generation CAR-T cell immunotherapy and beyond

Tumor immune microenvironment (TIME) consists of intra-tumor immunological components and plays a significant role in tumor initiation, progression, metastasis, and response to therapy. Chimeric antigen receptor (CAR)-T cell immunotherapy has revolutionized the cancer treatment paradigm. Although CAR-T cell immunotherapy has emerged as a successful treatment for hematologic malignancies, it remains a conundrum for solid tumors. The heterogeneity of TIME is responsible for poor outcomes in CAR-T cell immunotherapy against solid tumors. The advancement of highly sophisticated technology enhances our exploration in TIME from a multi-omics perspective. In the era of machine learning, multi-omics studies could reveal the characteristics of TIME and its immune resistance mechanism. Therefore, the clinical efficacy of CAR-T cell immunotherapy in solid tumors could be further improved with strategies that target unfavorable conditions in TIME. Herein, this review seeks to investigate the factors influencing TIME formation and propose strategies for improving the effectiveness of CAR-T cell immunotherapy through a multi-omics perspective, with the ultimate goal of developing personalized therapeutic approaches.

Proteomic characterization of epithelial ovarian cancer delineates molecular signatures and therapeutic targets in distinct histological subtypes

Clear cell carcinoma (CCC), endometrioid carcinoma (EC), and serous carcinoma (SC) are the major histological subtypes of epithelial ovarian cancer (EOC), whose differences in carcinogenesis are still unclear. Here, we undertake comprehensive proteomic profiling of 80 CCC, 79 EC, 80 SC, and 30 control samples. Our analysis reveals the prognostic or diagnostic value of dysregulated proteins and phosphorylation sites in important pathways. Moreover, protein co-expression network not only provides comprehensive view of biological features of each histological subtype, but also indicates potential prognostic biomarkers and progression landmarks. Notably, EOC have strong inter-tumor heterogeneity, with significantly different clinical characteristics, proteomic patterns and signaling pathway disorders in CCC, EC, and SC. Finally, we infer MPP7 protein as potential therapeutic target for SC, whose biological functions are confirmed in SC cells. Our proteomic cohort provides valuable resources for understanding molecular mechanisms and developing treatment strategies of distinct histological subtypes.

KRT19 is a Promising Prognostic Biomarker and Associates with Immune Infiltrates in Serous Ovarian Cystadenocarcinoma

Background

Ovarian cancer (OV) is the highest prevalent gynecologic tumor with complicated pathogenesis; high-grade serous ovarian cystadenocarcinoma (HGSOC) is the most epidemiological and malignant subtype of OV. Keratin type I cytoskeleton 19 (KRT19) is an intermediate filament protein which plays essential roles in the maintenance of epithelial cells. However, its role in OV remains largely unknown.

Methods

Bioinformatic analysis with various databases was conducted in this study. In details, KRT19 expression was assessed using databases including The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Gene Expression Omnibus (GEO) and Human Protein Atlas (HPA). GO-KEGG and GSEA analysis were performed by R packages. The biological function of KRT19 was analyzed based on the single-cell sequencing information from CancerSEA database. The association of KRT19 expression with immunomodulator and chemokine was predicted via the TISIDB database.

Results

The expression of KRT19 was significantly upregulated in ovarian samples compared with normal controls. KRT19 expression was negatively associated with prognosis in OV, and further analysis revealed that KRT19 had promising diagnostic significance in distinguishing OV cancer from normal samples. GO-KEGG and GSEA analysis indicated that KRT19 was associated with multiple biological functions and pathways including epidermis development, apical junction, inflammatory response, and epithelial mesenchymal transition. By using different GEO series, we found that KRT19 was differentially expressed in OV-associated tissues. Furthermore, the increased KRT19 expression was positively correlated with the immune infiltration levels of the most immune cells in OV.

Conclusion

This study demonstrated that KRT19 is a promising prognosis and diagnosis biomarker that determines cancer progression and is correlated with tumor immune cells infiltration in OV, suggesting being a molecular target for immunotherapies.

Overview of Tumor Heterogeneity in High-Grade Serous Ovarian Cancers

Ovarian cancers encompass a group of neoplasms originating from germinal tissues and exhibiting distinct clinical, pathological, and molecular features. Among these, epithelial ovarian cancers (EOCs) are the most prevalent, comprising five distinct tumor histotypes. Notably, high-grade serous ovarian cancers (HGSOCs) represent the majority, accounting for over 70% of EOC cases. Due to their silent and asymptomatic behavior, HGSOCs are generally diagnosed in advanced stages with an evolved and complex genomic state, characterized by high intratumor heterogeneity (ITH) due to chromosomal instability that distinguishes HGSOCs. Histologically, these cancers exhibit significant morphological diversity both within and between tumors. The histologic patterns associated with solid, endometrioid, and transitional (SET) and classic subtypes of HGSOCs offer prognostic insights and may indicate specific molecular profiles. The evolution of HGSOC from primary to metastasis is typically characterized by clonal ITH, involving shared or divergent mutations in neoplastic sub-clones within primary and metastatic sites. Disease progression and therapy resistance are also influenced by non-clonal ITH, related to interactions with the tumor microenvironment and further genomic changes. Notably, significant alterations occur in nonmalignant cells, including cancer-associated fibroblast and immune cells, during tumor progression. This review provides an overview of the complex nature of HGSOC, encompassing its various aspects of intratumor heterogeneity, histological patterns, and its dynamic evolution during progression and therapy resistance.

Artificial intelligence in ovarian cancer histopathology: a systematic review

This study evaluates the quality of published research using artificial intelligence (AI) for ovarian cancer diagnosis or prognosis using histopathology data. A systematic search of PubMed, Scopus, Web of Science, Cochrane CENTRAL, and WHO-ICTRP was conducted up to May 19, 2023. Inclusion criteria required that AI was used for prognostic or diagnostic inferences in human ovarian cancer histopathology images. Risk of bias was assessed using PROBAST. Information about each model was tabulated and summary statistics were reported. The study was registered on PROSPERO (CRD42022334730) and PRISMA 2020 reporting guidelines were followed. Searches identified 1573 records, of which 45 were eligible for inclusion. These studies contained 80 models of interest, including 37 diagnostic models, 22 prognostic models, and 21 other diagnostically relevant models. Common tasks included treatment response prediction (11/80), malignancy status classification (10/80), stain quantification (9/80), and histological subtyping (7/80). Models were developed using 1-1375 histopathology slides from 1-776 ovarian cancer patients. A high or unclear risk of bias was found in all studies, most frequently due to limited analysis and incomplete reporting regarding participant recruitment. Limited research has been conducted on the application of AI to histopathology images for diagnostic or prognostic purposes in ovarian cancer, and none of the models have been demonstrated to be ready for real-world implementation. Key aspects to accelerate clinical translation include transparent and comprehensive reporting of data provenance and modelling approaches, and improved quantitative evaluation using cross-validation and external validations. This work was funded by the Engineering and Physical Sciences Research Council.

Targeting tumor-stroma communication by blocking endothelin-1 receptors sensitizes high-grade serous ovarian cancer to PARP inhibition

PARP inhibitors (PARPi) have changed the treatment paradigm of high-grade serous ovarian cancer (HG-SOC). However, the impact of this class of inhibitors in HG-SOC patients with a high rate of TP53 mutations is limited, highlighting the need to develop combinatorial therapeutic strategies to improve responses to PARPi. Here, we unveil how the endothelin-1/ET-1 receptor (ET-1/ET-1R) axis, which is overexpressed in human HG-SOC and associated with poor prognosis, instructs HG-SOC/tumor microenvironment (TME) communication via key pro-malignant factors and restricts the DNA damage response induced by the PARPi olaparib. Mechanistically, the ET-1 axis promotes the p53/YAP/hypoxia inducible factor-1α (HIF-1α) transcription hub connecting HG-SOC cells, endothelial cells and activated fibroblasts, hence fueling persistent DNA damage signal escape. The ET-1R antagonist macitentan, which dismantles the ET-1R-mediated p53/YAP/HIF-1α network, interferes with HG-SOC/stroma interactions that blunt PARPi efficacy. Pharmacological ET-1R inhibition by macitentan in orthotopic HG-SOC patient-derived xenografts synergizes with olaparib to suppress metastatic progression, enhancing PARPi survival benefit. These findings reveal ET-1R as a mechanistic determinant in the regulation of HG-SOC/TME crosstalk and DNA damage response, indicating the use of macitentan in combinatorial treatments with PARPi as a promising and emerging therapy.

Effects of the exercise-inducible myokine irisin on proliferation and malignant properties of ovarian cancer cells through the HIF-1 α signaling pathway

Exercise has been shown to be associated with reduced risk and improving outcomes of several types of cancers. Irisin -a novel exercise-related myokine- has been proposed to exert beneficial effects in metabolic disorders including cancer. No previous studies have investigated whether irisin may regulate malignant characteristics of ovarian cancer cell lines. In the present study, we aimed to explore the effect of irisin on viability and proliferation of ovarian cancer cells which was examined by MTT assay. Then, we evaluated the migratory and invasive abilities of the cells via transwell assays. Moreover, the percentage of apoptosis induction was determined by flow cytometry. Furthermore, the mRNA expression level of genes related to the aerobic respiration (HIF-1α, c-Myc, LDHA, PDK1 and VEGF) was detected by real-time PCR. Our data revealed that irisin treatment significantly attenuated the proliferation, migration and invasion of ovarian cancer cells. Additionally, irisin induced apoptosis in ovarian cancer cells. We also observed that irisin regulated the expression of genes involved in aerobic respiration of ovarian cancer cells. Our results indicated that irisin may play a crucial role in inhibition of cell growth and malignant characteristics of ovarian cancer. These findings may open up avenues for future studies to identify the further therapeutic use of irisin in ovarian cancer management.

Has_circ_0008285/miR-211-5p/SIRT-1 Axis Suppress Ovarian Cancer Cells Progression

The significant functional role of circular RNAs (circRNAs) in the progression of malignant tumors, including ovarian cancer, has been shown in various studies. In this study, we aimed to investigate the abnormal expression of hsa_circ_0008285 and its role in ovarian cancer pathogenesis. Quantitative real time polymerase chain reaction (qRT-PCR) and Western blot methods were used to detect the expression of hsa_circ_0008285 and some target genes in ovarian cancer tissues and related cell lines. To determine the functional roles of hsa_circ_0008285 in ovarian cancer, cell proliferation, apoptosis, and cell invasion assays were performed. Bioinformatics (Target scan, circ intractome) and luciferase reporter analyses were used to predict target genes. Results: In the present study, we first found that hsa_circ_0008285 was up regulated in ovarian cancer tissues and related cell lines. Bioinformatics, experimental data, and luciferase reporter analysis data showed miR-211-5p is a direct target of hsa_circ_0008285, while SIRT-1 is a direct target of miR-211-5p. Overexpression of hsa_circ_0008285 in cancer cells increased the expression of SIRT-1 and progression of cancer cells. Based on these results, inhibition of hsa_circ_0008285 expression could cause upregulation of miR-211-5p and down regulation of SIRT-1 and inhibited the proliferation and invasion of ovarian cancer cells. Conclusion: The results of the present study revealed that hsa_circ_0008285 suppressed ovarian cancer progression by regulating miR-211-5p expression to inhibit SIRT-1 expression.

Tumor-Infiltrating Lymphocytes (TILs) in Epithelial Ovarian Cancer: Heterogeneity, Prognostic Impact, and Relationship with Immune Checkpoints

Epithelial ovarian cancers (EOC) are often diagnosed at an advanced stage with carcinomatosis and a poor prognosis. First-line treatment is based on a chemotherapy regimen combining a platinum-based drug and a taxane-based drug along with surgery. More than half of the patients will have concern about a recurrence. To improve the outcomes, new therapeutics are needed, and diverse strategies, such as immunotherapy, are currently being tested in EOC. To better understand the global immune contexture in EOC, several studies have been performed to decipher the landscape of tumor-infiltrating lymphocytes (TILs). CD8+ TILs are usually considered effective antitumor immune effectors that immune checkpoint inhibitors can potentially activate to reject tumor cells. To synthesize the knowledge of TILs in EOC, we conducted a review of studies published in MEDLINE or EMBASE in the last 10 years according to the PRISMA guidelines. The description and role of TILs in EOC prognosis are reviewed from the published data. The links between TILs, DNA repair deficiency, and ICs have been studied. Finally, this review describes the role of TILs in future immunotherapy for EOC.

Deep-Learning to Predict BRCA Mutation and Survival from Digital H&E Slides of Epithelial Ovarian Cancer

BRCA 1/2 genes mutation status can already determine the therapeutic algorithm of high grade serous ovarian cancer patients. Nevertheless, its assessment is not sufficient to identify all patients with genomic instability, since BRCA 1/2 mutations are only the most well-known mechanisms of homologous recombination deficiency (HR-d) pathway, and patients displaying HR-d behave similarly to BRCA mutated patients. HRd assessment can be challenging and is progressively overcoming BRCA testing not only for prognostic information but more importantly for drugs prescriptions. However, HR testing is not already integrated in clinical practice, it is quite expensive and it is not refundable in many countries. Selecting patients who are more likely to benefit from this assessment (BRCA 1/2 WT patients) at an early stage of the diagnostic process, would allow an optimization of genomic profiling resources. In this study, we sought to explore whether somatic BRCA1/2 genes status can be predicted using computational pathology from standard hematoxylin and eosin histology. In detail, we adopted a publicly available, deep-learning-based weakly supervised method that uses attention-based learning to automatically identify sub regions of high diagnostic value to accurately classify the whole slide (CLAM). The same model was also tested for progression free survival (PFS) prediction. The model was tested on a cohort of 664 (training set: n = 464, testing set: n = 132) ovarian cancer patients, of whom 233 (35.1%) had a somatic BRCA 1/2 mutation. An area under the curve of 0.7 and 0.55 was achieved in the training and testing set respectively. The model was then further refined by manually identifying areas of interest in half of the cases. 198 images were used for training (126/72) and 87 images for validation (55/32). The model reached a zero classification error on the training set, but the performance was 0.59 in terms of validation ROC AUC, with a 0.57 validation accuracy. Finally, when applied to predict PFS, the model achieved an AUC of 0.71, with a negative predictive value of 0.69, and a positive predictive value of 0.75. Based on these analyses, we have planned further steps of development such as proving a reference classification performance, exploring the hyperparameters space for training optimization, eventually tweaking the learning algorithms and the neural networks architecture for better suiting this specific task. These actions may allow the model to improve performances for all the considered outcomes.

When breaks get hot: inflammatory signaling in BRCA1/2-mutant cancers

Genomic instability and inflammation are intricately connected hallmark features of cancer. DNA repair defects due to BRCA1/2 mutation instigate immune signaling through the cGAS/STING pathway. The subsequent inflammatory signaling provides both tumor-suppressive as well as tumor-promoting traits. To prevent clearance by the immune system, genomically instable cancer cells need to adapt to escape immune surveillance. Currently, it is unclear how genomically unstable cancers, including BRCA1/2-mutant tumors, are rewired to escape immune clearance. Here, we summarize the mechanisms by which genomic instability triggers inflammatory signaling and describe adaptive mechanisms by which cancer cells can 'fly under the radar' of the immune system. Additionally, we discuss how therapeutic activation of the immune system may improve treatment of genomically instable cancers.

Rhophilin rho GTPase binding protein 1-antisense RNA 1 (RHPN1-AS1) promotes ovarian carcinogenesis by sponging microRNA-485-5p and releasing DNA topoisomerase II alpha (TOP2A)

Ovarian cancer (OC) is the most common and lethal gynecological cancer worldwide. Long non-coding RNAs (lncRNAs) and sponging microRNAs (miRNAs) serve as key regulators in the biological processes of OC. We sought to evaluate the effect of the RHPN1-AS1-miR-485-5p-DNA topoisomerase II alpha (TOP2A) axis in regulating OC progression. RHPN1-AS1, miR-485-5p, and TOP2A levels in OC tissues and cells were determined by RT-qPCR. The interaction of RHPN1-AS1/miR-485-5p/TOP2A was assessed using luciferase, RNA immunoprecipitation, and RNA pull-down assays. RHPN1-AS1 silencing allowed us to explore its biological function by measuring cell viability, proliferation, migration, invasion, and apoptosis in OC cells. In vivo experiments were performed to verify the in vitro findings. We found that the RHPN1-AS1 and TOP2A levels were significantly enhanced, whereas the miR-485-5p levels were reduced in OC tissues and cells. RHPN1-AS1 silencing attenuated cell growth, facilitated apoptosis in OC cells, and inhibited tumor growth in vivo. Notably, RHPN1-AS1 negatively regulating miR-485-5p promoted the TOP2A expression in OC cells. In conclusion, RHPN1-AS1 sponging miR-485-5p accelerated the progression of OC by elevating TOP2A expression, which makes it a promising target for the treatment of OC patients.

Visual Analytics for Hypothesis-Driven Exploration in Computational Pathology

Recent advances in computational and algorithmic power are evolving the field of medical imaging rapidly. In cancer research, many new directions are sought to characterize patients with additional imaging features derived from radiology and pathology images. The emerging field of Computational Pathology targets the high-throughput extraction and analysis of the spatial distribution of cells from digital histopathology images. The associated morphological and architectural features allow researchers to quantify and characterize new imaging biomarkers for cancer diagnosis, prognosis, and treatment decisions. However, while the image feature space grows, exploration and analysis become more difficult and ineffective. There is a need for dedicated interfaces for interactive data manipulation and visual analysis of computational pathology and clinical data. For this purpose, we present IIComPath, a visual analytics approach that enables clinical researchers to formulate hypotheses and create computational pathology pipelines involving cohort construction, spatial analysis of image-derived features, and cohort analysis. We demonstrate our approach through use cases that investigate the prognostic value of current diagnostic features and new computational pathology biomarkers.

Upregulated Expression of CYBRD1 Predicts Poor Prognosis of Patients with Ovarian Cancer

Cytochrome b reductase 1 (CYBRD1) promotes the development of ovarian serous cystadenocarcinoma (OV). We assessed the function of CYBRD1 in OV underlying The Cancer Genome Atlas (TCGA) database. The correlation between clinicopathological characteristics and CYBRD1 expression was estimated. The Cox proportional hazards regression model and the Kaplan-Meier method were applied to identify clinical features related to overall survival and disease-specific survival. Gene set enrichment analysis (GSEA) was applied to identify the relationship between CYBRD1 expression and immune infiltration. CYBRD1 expression in OV was significantly associated with poor outcomes of primary therapy and FIGO stage. Patients with high levels of CYBRD1 expression were prone to the development of a poorly differentiated tumor and experience of an unfavorable outcome. CYBRD1 expression had significant association with shorter OS and acts as an independent predictor of poor outcome. Moreover, enhanced CYBRD1 expression was positively associated with Tem, NK cells, and mast cells but negatively associated with CD56 bright NK cells and Th2 cells. CYBRD1 expression may serve as a diagnostic and prognostic indicator of OV patients. The mechanisms of poor prognosis of CYBRD1-mediated OV may include increased iron uptake, regulation of immune microenvironment, ferroptosis related pathway, and ERK signaling pathway, among which ferroptosis and ERK signaling pathway may be important pathways of CYBRD1-mediated OV. Furthermore, we verified that CYBRD1 was upregulated in OV and significant correlated with lymph nodes metastasis, advanced stage, poor-differentiated tumor, and poor clinical prognosis in East Hospital cohort. The results of this study may provide guidance for the development of optimal treatment strategies for OV.

Machine Learning and Artificial Intelligence-driven Spatial Analysis of the Tumor Immune Microenvironment in Pathology Slides

A better understanding of the tumor immune microenvironment (TIME) could lead to accurate diagnosis, prognosis, and treatment stratification. Although molecular analyses at the tissue and/or single cell level could reveal the cellular status of the tumor microenvironment, these approaches lack information related to spatial-level cellular distribution, co-organization, and cell-cell interaction in the TIME. With the emergence of computational pathology coupled with machine learning (ML) and artificial intelligence (AI), ML- and AI-driven spatial TIME analyses of pathology images could revolutionize our understanding of the highly heterogeneous and complex molecular architecture of the TIME. In this review we highlight recent studies on spatial TIME analysis of pathology slides using state-of-the-art ML and AI algorithms. PATIENT SUMMARY: This mini-review reports recent advances in machine learning and artificial intelligence for spatial analysis of the tumor immune microenvironment in pathology slides. This information can help in understanding the spatial heterogeneity and organization of cells in patient tumors.

Systemic Analysis of the DNA Replication Regulator MCM Complex in Ovarian Cancer and Its Prognostic Value

Microliposome maintenance (MCM) 2, MCM3, MCM4, MCM5, MCM6, and MCM7 are DNA replication regulators and are involved in the progression of multiple cancer types, but their role in ovarian cancer is still unclear. The purpose of this study is to clarify the biological function and prognostic value of the MCM complex in ovarian cancer (OS) progression. We analyzed DNA alterations, mRNA and protein levels, protein structure, PPI network, functional enrichment, and prognostic value in OC based on the Oncomine, cBioPortal, TCGA, CPTAC, PDB, GeneMANIA, DAVID, KEGG, and GSCALite databases. The results indicated that the protein levels of these DNA replication regulators were increased significantly. Moreover, survival analysis showed a prognostic signature based on the MCM complex, which performed moderately well in terms of OS prognostic prediction. Additionally, protein structure, functional enrichment, and PPI network analyses indicated that the MCM complex synergistically promoted OC progression by accelerating DNA replication and the cell cycle. In conclusion, our study suggested that the MCM complex might be a potential target and prognostic marker for OC patients.

Data-efficient and weakly supervised computational pathology on whole-slide images

Deep-learning methods for computational pathology require either manual annotation of gigapixel whole-slide images (WSIs) or large datasets of WSIs with slide-level labels and typically suffer from poor domain adaptation and interpretability. Here we report an interpretable weakly supervised deep-learning method for data-efficient WSI processing and learning that only requires slide-level labels. The method, which we named clustering-constrained-attention multiple-instance learning (CLAM), uses attention-based learning to identify subregions of high diagnostic value to accurately classify whole slides and instance-level clustering over the identified representative regions to constrain and refine the feature space. By applying CLAM to the subtyping of renal cell carcinoma and non-small-cell lung cancer as well as the detection of lymph node metastasis, we show that it can be used to localize well-known morphological features on WSIs without the need for spatial labels, that it overperforms standard weakly supervised classification algorithms and that it is adaptable to independent test cohorts, smartphone microscopy and varying tissue content.

Automated Co-in Situ Hybridization and Immunofluorescence Using Archival Tumor Tissue

In situ hybridization (ISH) and immunohistochemistry (IHC) are valuable tools for molecular pathology and cancer research. Recent advances in multiplex technology, assay automation, and digital image analysis have enabled the development of co-ISH IHC or immunofluorescence (IF) methods, which allow researchers to simultaneously view and quantify expression of mRNA and protein within the preserved tissue spatial context. These data are vital to the study of the control of gene expression in the complex tumor microenvironment.

LncRNA TTN-AS1 acts as sponge for miR-15b-5p to regulate FBXW7 expression in ovarian cancer

Emerging evidence showed that long noncoding RNA (lncRNA) plays crucial roles in regulating various cancer biological behaviors. Titin-antisense RNA1 (TTN-AS1) has been reported to have crucial roles in cancers but its role in ovarian cancer remains unknown. The levels of TTN-AS1, microNRA-15b-5p (miR-15b-5p), and F-box and WD repeat domain containing 7 (FBXW7) in ovarian cancer cells were measured by quantitative reverse-transcription PCR. Targets for TTN-AS1 and miR-15b-5p were predicted by bioinformatic tools, and validated by luciferase activity reporter assay. Cell proliferation, colony formation, and cell apoptosis were analyzed with cell counting kit-8 assay, colony formation assay, and flow cytometry. Correlation of TTN-AS1 and FBXW7 was analyzed at gene expression profiling interactive analysis. TTN-AS1 was found decreased expression in ovarian cancer tissues and cells. Dual-luciferase activity validated TTN-AS1 and FBXW7 shared binding site in miR-15b-5p. Functional assays showed TTN-AS1 overexpression inhibits ovarian cancer cell proliferation, colony formation but promotes apoptosis. Rescue experiments showed that knockdown of FBXW7 could partially counteracted the effects of TTN-AS1 overexpression on ovarian cancer cell behaviors. Our results indicated that the TTN-AS1/miR-15b-5p/FBXW7 axis identified in this work could help to identify treatment biomarkers for ovarian cancer.

Geospatial immune variability illuminates differential evolution of lung adenocarcinoma

Remarkable progress in molecular analyses has improved our understanding of the evolution of cancer cells toward immune escape1-5. However, the spatial configurations of immune and stromal cells, which may shed light on the evolution of immune escape across tumor geographical locations, remain unaddressed. We integrated multiregion exome and RNA-sequencing (RNA-seq) data with spatial histology mapped by deep learning in 100 patients with non-small cell lung cancer from the TRACERx cohort6. Cancer subclones derived from immune cold regions were more closely related in mutation space, diversifying more recently than subclones from immune hot regions. In TRACERx and in an independent multisample cohort of 970 patients with lung adenocarcinoma, tumors with more than one immune cold region had a higher risk of relapse, independently of tumor size, stage and number of samples per patient. In lung adenocarcinoma, but not lung squamous cell carcinoma, geometrical irregularity and complexity of the cancer-stromal cell interface significantly increased in tumor regions without disruption of antigen presentation. Decreased lymphocyte accumulation in adjacent stroma was observed in tumors with low clonal neoantigen burden. Collectively, immune geospatial variability elucidates tumor ecological constraints that may shape the emergence of immune-evading subclones and aggressive clinical phenotypes.

lncRNA RHPN1-AS1 Promotes Ovarian Cancer Growth and Invasiveness Through Inhibiting miR-1299

Background

Ovarian cancer (OC) is a big threat for public health. However, the molecular mechanism underlying OC development and progression remains unclear. Although the importance of lncRNA in cancer has been proven, how lncRNA is involved in OC is waiting for further investigation.

Materials and Methods

qRT-PCR was performed to test expression level. CCK8 and colony formation were conducted to analyze proliferation. Transwell was conducted to measure migration and invasion. Luciferase reporter assay and pulldown assay were utilized to validate RNA interaction.

Results

lncRNA RHPN1-AS1 was highly expressed in OC tissues. RHPN1-AS1 was positively correlated with OC progression and its high expression indicated a low survival rate. Moreover, knockdown of RHPN1-AS1 significantly inhibited the proliferation, migration and invasion of OC cells, and bioinformatics analysis identified that miR-1299 was sponged by RHPN1-AS1 in OC cells. Knockdown of RHPN1-AS1 markedly promoted miR-1299 expression. Of note, inhibition of miR-1299 reversed the roles of RHPN1-AS1 silencing on suppressing proliferation, migration and invasion.

Conclusion

Our study demonstrates that RHPN1-AS1 promotes OC progression via sponging miR-1299, suggesting RHPN1-AS1 may be a novel therapeutic target.

Advances in tissue-based imaging: impact on oncology research and clinical practice

INTRODUCTION

Tissue-based imaging has emerged as a critical tool in translational cancer research and is rapidly gaining traction within a clinical context. Significant progress has been made in the digital pathology arena, particularly in respect of brightfield and fluorescent imaging. Critically, the cellular context of molecular alterations occurring at DNA, RNA, or protein level within tumor tissue is now being more fully appreciated. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumor microenvironment, including the potential interplay between various cell types.

AREAS COVERED

This review summarizes the recent developments within the field of tissue-based imaging, centering on the application of these approaches in oncology research and clinical practice.

EXPERT OPINION

Significant advances have been made in digital pathology during the last 10 years. These include the use of quantitative image analysis algorithms, predictive artificial intelligence (AI) on large datasets of H&E images, and quantification of fluorescence multiplexed tissue imaging data. We believe that new methodologies that can integrate AI-derived histologic data with omic data, together with other forms of imaging data (such as radiologic image data), will enhance our ability to deliver better diagnostics and treatment decisions to the cancer patient.

Topological Tumor Graphs: A Graph-Based Spatial Model to Infer Stromal Recruitment for Immunosuppression in Melanoma Histology

Despite the advent of immunotherapy, metastatic melanoma represents an aggressive tumor type with a poor survival outcome. The successful application of immunotherapy requires in-depth understanding of the biological basis and immunosuppressive mechanisms within the tumor microenvironment. In this study, we conducted spatially explicit analyses of the stromal-immune interface across 400 melanoma hematoxylin and eosin (H&E) specimens from The Cancer Genome Atlas. A computational pathology pipeline (CRImage) was used to classify cells in the H&E specimen into stromal, immune, or cancer cells. The estimated proportions of these cell types were validated by independent measures of tumor purity, pathologists' estimate of lymphocyte density, imputed immune cell subtypes, and pathway analyses. Spatial interactions between these cell types were computed using a graph-based algorithm (topological tumor graphs, TTG). This approach identified two stromal features, namely stromal clustering and stromal barrier, which represented the melanoma stromal microenvironment. Tumors with increased stromal clustering and barrier were associated with reduced intratumoral lymphocyte distribution and poor overall survival independent of existing prognostic factors. To explore the genomic basis of these TTG-derived stromal phenotypes, we used a deep learning approach integrating genomic (copy number) and transcriptomic data, thereby inferring a compressed representation of copy number-driven alterations in gene expression. This integrative analysis revealed that tumors with high stromal clustering and barrier had reduced expression of pathways involved in naïve CD4 signaling, MAPK, and PI3K signaling. Taken together, our findings support the immunosuppressive role of stromal cells and T-cell exclusion within the vicinity of melanoma cells. SIGNIFICANCE: Computational histology-based stromal phenotypes within the tumor microenvironment are significantly associated with prognosis and immune exclusion in melanoma.

Up-regulation of circ_LARP4 suppresses cell proliferation and migration in ovarian cancer by regulating miR-513b-5p/LARP4 axis

Background

Ovarian cancer (OC) is a common fatal malignant tumor of female reproductive system worldwide. Growing studies have proofed that circular RNAs (circRNAs) engage in the regulation of various types of cancers. However, the underlying biological functions and effect mechanism of circular RNA_LARP4 (circ_LARP4) in OC have not been explored.

Methods

Quantitative real-time polymerase chain reaction (qRT-PCR) analysis was used to detect the expression of circ_LARP4 in OC cells. The function of circ_LARP4 was measured by cell counting kit-8 (CCK-8), colony formation assay and transwell assay. RNA immunoprecipitation (RIP) assay and luciferase reporter assays assessed the binding correlation between miR-513b-5p and circ_LARP4 (or LARP4).

Results

The expression of circ_LARP4 in OC cells was much lower than that in human normal ovarian epithelial cells. Overexpressing circ_LARP4 impaired cell proliferation, invasion and migration abilities. Circ_LARP4 worked as a competing endogenous RNA (ceRNA) to sponge miR-513b-5p. Furthermore, LARP4 was indirectly modulated by circ_LARP4 as the downstream target of miR-513b-5p, as well as the host gene of circ_LARP4.

Conclusion

Circ_LARP4 could hamper cell proliferation and migration by sponging miR-513b-5p to regulate the expression of LARP4. This research may provide some referential value to OC treatment.

Recent advances in tissue imaging for cancer research

Image analysis in clinical research has evolved at fast pace in the last decade. This review discusses basic concepts ranging from immunohistochemistry to advanced techniques such as multiplex imaging, digital pathology, flow cytometry and intravital microscopy. Tissue imaging ex vivo is still one of the gold-standards in the field due to feasibility. We describe here different protocols and applications of digital analysis providing basic and clinical researchers with an overview on how to analyse tissue images. In vivo imaging is not easily accessible to researchers; however, it provides invaluable dynamic information. Overall, we discuss a plethora of techniques that - when combined - constitute a powerful platform for basic and translational cancer research.

Sonodynamic Therapy Combined to 2-Deoxyglucose Potentiate Cell Metastasis Inhibition of Breast Cancer

Metastasis is a major dilemma of cancer therapy. It frequently occurs in breast cancer, which is the leading form of malignant tumor among females worldwide. Although there are therapies that provide a possible method for this challenge, such as chemotherapy, the tumoral metabolic pathway is unconventional and favors metastasis and proliferation. This magnifies the difficulty of treating breast cancer. In this study, we identified 2-deoxyglucose (2 DG) as an important glycolysis suppressor that can potentiate sonodynamic therapy (SDT) to inhibit migration and invasion. In addition, disruptions of the cell membrane microstructure were captured by a scanning electron microscope in cells treated with the co-therapy. Similarly, we detected blockages of the cell cycle process, using flow cytometry. Of note, we observed that hexokinase II (HK2), the rate-limiting enzyme of glycolysis, was notably uncoupled from the mitochondria in SDT + 2 DG co-therapy group. Furthermore, there was altered expression of HK2 and Glut1, which control glycolysis. Simultaneously, the in vivo results revealed that pulmonary metastasis was also seriously suppressed by SDT + 2 DG co-therapy. These results demonstrate this co-therapy is a promising strategy for breast cancer inhibition through metastasis and proliferation.

Analysis of tumour ecological balance reveals resource-dependent adaptive strategies of ovarian cancer

BACKGROUND

Despite treatment advances, there remains a significant risk of recurrence in ovarian cancer, at which stage it is usually incurable. Consequently, there is a clear need for improved patient stratification. However, at present clinical prognosticators remain largely unchanged due to the lack of reproducible methods to identify high-risk patients.

METHODS

In high-grade serous ovarian cancer patients with advanced disease, we spatially define a tumour ecological balance of stromal resource and immune hazard using high-throughput image and spatial analysis of routine histology slides. On this basis an EcoScore is developed to classify tumours by a shift in this balance towards cancer-favouring or inhibiting conditions.

FINDINGS

The EcoScore provides prognostic value stronger than, and independent of, known risk factors. Crucially, the clinical relevance of mutational burden and genomic instability differ under different stromal resource conditions, suggesting that the selective advantage of these cancer hallmarks is dependent on the context of stromal spatial structure. Under a high resource condition defined by a high level of geographical intermixing of cancer and stromal cells, selection appears to be driven by point mutations; whereas, in low resource tumours featured with high hypoxia and low cancer-immune co-localization, selection is fuelled by aneuploidy.

INTERPRETATION

Our study offers empirical evidence that cancer fitness depends on tumour spatial constraints, and presents a biological basis for developing better assessments of tumour adaptive strategies in overcoming ecological constraints including immune surveillance and hypoxia.

Genomic and Transcriptomic Determinants of Therapy Resistance and Immune Landscape Evolution during Anti-EGFR Treatment in Colorectal Cancer

Despite biomarker stratification, the anti-EGFR antibody cetuximab is only effective against a subgroup of colorectal cancers (CRCs). This genomic and transcriptomic analysis of the cetuximab resistance landscape in 35 RAS wild-type CRCs identified associations of NF1 and non-canonical RAS/RAF aberrations with primary resistance and validated transcriptomic CRC subtypes as non-genetic predictors of benefit. Sixty-four percent of biopsies with acquired resistance harbored no genetic resistance drivers. Most of these had switched from a cetuximab-sensitive transcriptomic subtype at baseline to a fibroblast- and growth factor-rich subtype at progression. Fibroblast-supernatant conferred cetuximab resistance in vitro, confirming a major role for non-genetic resistance through stromal remodeling. Cetuximab treatment increased cytotoxic immune infiltrates and PD-L1 and LAG3 immune checkpoint expression, potentially providing opportunities to treat cetuximab-resistant CRCs with immunotherapy.

Long non-coding RNA FLJ33360 participates in ovarian cancer progression by sponging miR-30b-3p

Background

Long noncoding RNAs (lncRNAs) have been reported to play a key role in the development and progression of human malignancies. FLJ33360 is an lncRNA with unknown functions. This study was designed to determine the clinical significance and mechanism of FLJ33360 in ovarian cancer.

Materials and methods

The clinical significance of FLJ33360 in ovarian cancer was determined using the Gene Expression Profiling Interactive Analysis (GEPIA) database, Kaplan-Meier Plotter database, quantitative reverse transcription polymerase chain reaction (qRT-PCR) and statistical analysis. The regulatory relationships between FLJ33360 and miR-30b-3p were explored through bioinformatics, the Gene Expression Omnibus (GEO) database, the ArrayExpress database and meta-analysis. The possible pathways were predicted using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. In addition, the key target genes were identified using a protein-protein interaction (PPI) network, the Cancer Genome Atlas (TCGA) database, and correlation analysis.

Results

FLJ33360 expression was significantly downregulated in ovarian cancer tissue (P=0.0011) and was closely associated with International Federation of Gynecology and Obstetrics (FIGO) stage (P=0.027) and recurrence (P=0.002). FLJ33360 may have potential value in detecting ovarian cancer (area under the curve =0.793). Function analysis demonstrated that FLJ33360 can act as a molecular sponge of miR-30b-3p to regulate the expression of target genes that are mainly involved in positive regulation of smooth muscle cell migration, the unsaturated fatty acid metabolic process, and positive regulation of the epithelial to mesenchymal transition. Among these target genes, BCL2 is the hub gene.

Conclusion

FLJ33360 is a potential biomarker for early diagnosis and prognostic assessment in ovarian cancer and may regulate the expression of genes by sponging miR-30b-3p and thus participate in the development of ovarian cancer.

Measuring Clonal Evolution in Cancer with Genomics

Cancers originate from somatic cells in the human body that have accumulated genetic alterations. These mutations modify the phenotype of the cells, allowing them to escape the homeostatic regulation that maintains normal cell number. Viewed through the lens of evolutionary biology, the transformation of normal cells into malignant cells is evolution in action. Evolution continues throughout cancer growth, progression, treatment resistance, and disease relapse, driven by adaptation to changes in the cancer's environment, and intratumor heterogeneity is an inevitable consequence of this evolutionary process. Genomics provides a powerful means to characterize tumor evolution, enabling quantitative measurement of evolving clones across space and time. In this review, we discuss concepts and approaches to quantify and measure this evolutionary process in cancer using genomics.