Profiling of selected angiogenesis-related genes in serous ovarian cancer patients

Immune mediator expression signatures are associated with improved outcome in ovarian carcinoma

Immune and inflammatory cascades may play multiple roles in ovarian cancer. We aimed to identify relationships between expression of immune and inflammatory mediators and patient outcomes. We interrogated differential gene expression of 44 markers and marker combinations (n = 1,978) in 1,656 ovarian carcinoma patient tumors, alongside matched 5-year overall survival (OS) data in silico. Using machine learning methods, we investigated whether genomic expression of these 44 mediators can discriminate between malignant and non-malignant tissues in 839 ovarian cancer and 115 non-malignant ovary samples. We furthermore assessed inflammation markers in 289 ovarian cancer patients’ sera in the Swedish Apolipoprotein MOrtality-related RISk (AMORIS) cohort. Expression of the 44 mediators could discriminate between malignant and non-malignant tissues with at least 96% accuracy. Higher expression of classical Th1, Th2, Th17, anti-parasitic/infection and M1 macrophage mediator signatures were associated with better OS. Contrastingly, inflammatory and angiogenic mediators, CXCL-12, C-reactive protein (CRP) and platelet-derived growth factor subunit A (PDGFA) were negatively associated with OS. Of the serum inflammatory markers in the AMORIS cohort, women with ovarian cancer who had elevated levels of haptoglobin (≥1.4 g/L) had a higher risk of dying from ovarian cancer compared to those with haptoglobin levels <1.4 g/L (HR = 2.09, 95% CI:1.38–3.16). Our findings indicate that elevated “classical” immune mediators, associated with response to pathogen antigen challenge, may confer immunological advantage in ovarian cancer, while inflammatory markers appear to have negative prognostic value. These highlight associations between immune protection, inflammation and clinical outcomes, and offer opportunities for patient stratification based on secretome markers.

Driver pattern identification over the gene co-expression of drug response in ovarian cancer by integrating high throughput genomics data

Multiple types of high throughput genomics data create a potential opportunity to identify driver patterns in ovarian cancer, which will acquire some novel and clinical biomarkers for appropriate diagnosis and treatment to cancer patients. To identify candidate driver genes and the corresponding driving patterns for resistant and sensitive tumors from the heterogeneous data, we combined gene co-expression modules with mutation modulators and proposed the method to identify driver patterns. Firstly, co-expression network analysis is applied to explore gene modules for gene expression profiles through weighted correlation network analysis (WGCNA). Secondly, mutation matrix is generated by integrating the CNV data and somatic mutation data, and a mutation network is constructed from the mutation matrix. Thirdly, candidate modulators are selected from significant genes by clustering vertexs of the mutation network. Finally, a regression tree model is utilized for module network learning, in which the obtained gene modules and candidate modulators are trained for the driving pattern identification and modulators regulatory exploration. Many identified candidate modulators are known to be involved in biological meaningful processes associated with ovarian cancer, such as CCL11, CCL16, CCL18, CCL23, CCL8, CCL5, APOB, BRCA1, SLC18A1, FGF22, GADD45B, GNA15, GNA11, and so on.

Driver pattern identification over the gene co-expression of drug response in ovarian cancer by integrating high throughput genomics data

Multiple types of high throughput genomics data create a potential opportunity to identify driver patterns in ovarian cancer, which will acquire some novel and clinical biomarkers for appropriate diagnosis and treatment to cancer patients. To identify candidate driver genes and the corresponding driving patterns for resistant and sensitive tumors from the heterogeneous data, we combined gene co-expression modules with mutation modulators and proposed the method to identify driver patterns. Firstly, co-expression network analysis is applied to explore gene modules for gene expression profiles through weighted correlation network analysis (WGCNA). Secondly, mutation matrix is generated by integrating the CNV data and somatic mutation data, and a mutation network is constructed from the mutation matrix. Thirdly, candidate modulators are selected from significant genes by clustering vertexs of the mutation network. Finally, a regression tree model is utilized for module network learning, in which the obtained gene modules and candidate modulators are trained for the driving pattern identification and modulators regulatory exploration. Many identified candidate modulators are known to be involved in biological meaningful processes associated with ovarian cancer, such as CCL11, CCL16, CCL18, CCL23, CCL8, CCL5, APOB, BRCA1, SLC18A1, FGF22, GADD45B, GNA15, GNA11, and so on.

Driver pattern identification over the gene co-expression of drug response in ovarian cancer by integrating high throughput genomics data

Multiple types of high throughput genomics data create a potential opportunity to identify driver patterns in ovarian cancer, which will acquire some novel and clinical biomarkers for appropriate diagnosis and treatment to cancer patients. To identify candidate driver genes and the corresponding driving patterns for resistant and sensitive tumors from the heterogeneous data, we combined gene co-expression modules with mutation modulators and proposed the method to identify driver patterns. Firstly, co-expression network analysis is applied to explore gene modules for gene expression profiles through weighted correlation network analysis (WGCNA). Secondly, mutation matrix is generated by integrating the CNV data and somatic mutation data, and a mutation network is constructed from the mutation matrix. Thirdly, candidate modulators are selected from significant genes by clustering vertexs of the mutation network. Finally, a regression tree model is utilized for module network learning, in which the obtained gene modules and candidate modulators are trained for the driving pattern identification and modulators regulatory exploration. Many identified candidate modulators are known to be involved in biological meaningful processes associated with ovarian cancer, such as CCL11, CCL16, CCL18, CCL23, CCL8, CCL5, APOB, BRCA1, SLC18A1, FGF22, GADD45B, GNA15, GNA11, and so on.

Systematic biobanking, novel imaging techniques, and advanced molecular analysis for precise tumor diagnosis and therapy: The Polish MOBIT project

Personalized and precision medicine is gaining recognition due to the limitations by standard diagnosis and treatment; many areas of medicine, from cancer to psychiatry, are moving towards tailored and individualized treatment for patients based on their clinical characteristics and genetic signatures as well as novel imaging techniques. Advances in whole genome sequencing have led to identification of genes involved in a variety of diseases. Moreover, biomarkers indicating severity of disease or susceptibility to treatment are increasingly being characterized. The continued identification of new genes and biomarkers specific to disease subtypes and individual patients is essential and inevitable for translation into personalized medicine, in estimating both, disease risk and response to therapy. Taking into consideration the mostly unsolved necessity of tailored therapy in oncology the innovative project MOBIT (molecular biomarkers for individualized therapy) was designed. The aims of the project are: (i) establishing integrative management of precise tumor diagnosis and therapy including systematic biobanking, novel imaging techniques, and advanced molecular analysis by collecting comprehensive tumor tissues, liquid biopsies (whole blood, serum, plasma), and urine specimens (supernatant; sediment) as well as (ii) developing personalized lung cancer diagnostics based on tumor heterogeneity and integrated genomics, transcriptomics, metabolomics, and radiomics PET/MRI analysis. It will consist of 5 work packages. In this paper the rationale of the Polish MOBIT project as well as its design is presented. (iii) The project is to draw interest in and to invite national and international, private and public, preclinical and clinical initiatives to establish individualized and precise procedures for integrating novel targeted therapies and advanced imaging techniques.