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Widespread alternative exon usage in clinically distinct subtypes of Invasive Ductal Carcinoma. Pan-cancer analysis of homozygous camm4 in primary tumours Giulietya rare tumour suppressors. Bioinformatics approaches to profile the tumor microenvironment for immunotherapeutic discovery. Giulietfa results in a complex interplay of diverse cellular signaling systems, where the immune cell component is now established to influence cancer progression and therapeutic response. It is experimentally difficult and laborious to comprehensively and systematically profile these distinct cell types from heterogeneous tumor samples in order to capitalize on potential therapeutic and biomarker discoveries.

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Identifying immunologically active or silent tumors may be an important indication for administration of therapy, and detecting early infiltration patterns may uncover factors that contribute to early risk. Thus far, direct detailed studies of the cell composition of tumor infiltration have been limited; with some studies giving approximate quantifications using immunohistochemistry and other small studies obtaining detailed measurements by isolating cells from excised tumors and sorting them using flow cytometry. Herein we utilize a machine learning based approach to identify lymphocyte markers with which we can quantify the presence of B cells, cytotoxic T-lymphocytes, T-helper 1, and T-helper 2 cells in any gene expression data set and apply it to studies of breast tissue.

By leveraging over 2, samples from existing large scale studies, we are able to find an inherent cell heterogeneity in clinically characterized immune infiltrates, a strong link between estrogen receptor activity and infiltration in normal and tumor tissues, changes with genomic complexity, and identify characteristic differences in lymphocyte expression among molecular groupings. With our extendable methodology for capturing cell type specific signal we systematically studied immune infiltration in breast cancer, finding an inverse correlation between beneficial lymphocyte infiltration and estrogen receptor activity in normal breast tissue and reduced infiltration in estrogen receptor negative tumors with high genomic complexity.

DNA methylation at enhancers identifies distinct breast cancer lineages. Body mass index and breast cancer survival: International Journal of Epidemiology. However, the underlying reasons remain unclear. We conducted a Mendelian randomization analysis to investigate a possible causal role of BMI in survival from breast cancer. Association between the BMI genetic score and breast cancer survival was analysed by Cox regression for each study separately. Study-specific hazard ratios were pooled using fixed-effect meta-analysis.

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Androgen receptor as potential therapeutic target in metastatic endometrial cancer. Annexin-A2 as predictor biomarker of recurrent disease in endometrial cancer. International Journal of Cancer. Molecular profiling of endometrial carcinoma precursor, primary and metastatic lesions suggests different targets for treatment in obese compared to non-obese patients. Common genetic variation in cellular transport genes and epithelial ovarian cancer EOC risk. Loss of ASRGL1 expression is an independent biomarker for disease-specific survival in endometrioid endometrial carcinoma. Multimodal imaging of orthotopic mouse model of endometrial carcinoma.

Cancer Epidemiology, Biomarkers and Prevention. Genome-wide significant risk associations for mucinous ovarian carcinoma. Collee; Rookus, Matti A. Identification of six new susceptibility loci for invasive epithelial ovarian cancer. Cis-eQTL analysis and functional validation of candidate susceptibility genes for high-grade serous ovarian cancer.


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