Clonal heterogeneity in ER+ breast cancer reveals the proteasome and PKC as potential therapeutic targets.

Intratumoral heterogeneity impacts the success or failure of anti-cancer therapies. Here, we investigated the evolution and mechanistic heterogeneity in clonal populations of cell models for estrogen receptor positive breast cancer. To this end, we established barcoded models of luminal breast cancer and rendered them resistant to commonly applied first line endocrine therapies.

Genomic characteristics and clinical significance of CD56+ circulating tumor cells in small cell lung cancer.

Circulating tumor cells (CTC) have been studied in various solid tumors but clinical utility of CTC in small cell lung cancer (SCLC) remains unclear. The aim of the CTC-CPC study was to develop an EpCAM-independent CTC isolation method allowing isolation of a broader range of living CTC from SCLC and decipher their genomic and biological characteristics. CTC-CPC is a monocentric prospective non-interventional study including treatment-naïve newly diagnosed SCLC.

A Comprehensive Analysis of Cutaneous Melanoma Patients in Greece Based on Multi-Omic Data.

Cutaneous melanoma (CM) is the most aggressive type of skin cancer, and it is characterised by high mutational load and heterogeneity. In this study, we aimed to analyse the genomic and transcriptomic profile of primary melanomas from forty-six Formalin-Fixed, Paraffin-Embedded (FFPE) tissues from Greek patients. Molecular analysis for both germline and somatic variations was performed in genomic DNA from peripheral blood and melanoma samples, respectively, exploiting whole exome and targeted sequencing, and transcriptomic analysis.

A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research.

Recent advances in sequencing and biotechnological methodologies have led to the generation of large volumes of molecular data of different omics layers, such as genomics, transcriptomics, proteomics and metabolomics. Integration of these data with clinical information provides new opportunities to discover how perturbations in biological processes lead to disease. Using data-driven approaches for the integration and interpretation of multi-omics data could stably identify links between structural and functional information and propose causal molecular networks with potential impact on cancer pathophysiology.

Radiogenomic Analysis of F-18-Fluorodeoxyglucose Positron Emission Tomography and Gene Expression Data Elucidates the Epidemiological Complexity of Colorectal Cancer Landscape.

Purpose: Transcriptomic profiling has enabled the neater genomic characterization of several cancers, among them colorectal cancer (CRC), through the derivation of genes with enhanced causal role and informative gene sets. However, the identification of small-sized gene signatures, which can serve as potential biomarkers in CRC, remains challenging, mainly due to the great genetic heterogeneity of the disease.

Methods: We developed and exploited an analytical framework for the integrative analysis of CRC datasets, encompassing transcriptomic data and positron emission tomography (PET) measurements.

Integrative Bioinformatic Analysis of Transcriptomic Data Identifies Conserved Molecular Pathways Underlying Ionizing Radiation-Induced Bystander Effects (RIBE).

Ionizing radiation-induced bystander effects (RIBE) encompass a number of effects with potential for a plethora of damages in adjacent non-irradiated tissue. The cascade of molecular events is initiated in response to the exposure to ionizing radiation (IR), something that may occur during diagnostic or therapeutic medical applications. In order to better investigate these complex response mechanisms, we employed a unified framework integrating statistical microarray analysis, signal normalization, and translational bioinformatics functional analysis techniques.