Computational Intelligence Methods for Bioinformatics and Biostatistics : 10th International Meeting, CIBB 2013, Nice, France, June 20-22, 2013, Revised Selected Papers

International audienc

A Network-based Approach to Breast Cancer Systems Medicine.

Breast cancer is the most commonly diagnosed cancer and the second leading cause of cancer death in women. Although recent improvements in the prevention, early detection, and treatment of breast cancer have led to a significant decrease in the mortality rate, the identification of an optimal therapeutic strategy for each patient remains a difficult task because of the heterogeneous nature of the disease. Clinical heterogeneity of breast cancer is in part explained by the vast genetic and molecular heterogeneity of this disease, which is now emerging from large-scale screening studies using \u201c-omics\u201d technologies (e.g. microarray gene expression profiling, next-generation sequencing). This genetic and molecular heterogeneity likely contributes significantly to therapy response and clinical outcome. The recent advances in our understanding of the molecular nature of breast cancer due, in particular, to the explosion of high-throughput technologies, is driving a shift away from the \u201cone-dose-fits-all\u201d paradigm in healthcare, to the new \u201cPersonalized Cancer Care\u201d paradigm. The aim of \u201cPersonalized Cancer Care\u201d is to select the optimal course of clinical intervention for individual patients, maximizing the likelihood of effective treatment and reducing the probability of adverse drug reactions, according to the molecular features of the patient. In light to this medical scenario, the aim of this project is to identify novel molecular mechanisms that are altered in breast cancer through the development of a computational pipeline, in order to propose putative biomarkers and druggable target genes for the personalized management of patients. Through the application of a Systems Biology approach to reverse engineer Gene Regulatory Networks (GRNs) from gene expression data, we built GRNs around \u201chub\u201d genes transcriptionally correlating with clinical-pathological features associated with breast tumor expression profiles. The relevance of the GRNs as putative cancer-related mechanisms was reinforced by the occurrence of mutational events related to breast cancer in the \u201chub\u201d genes, as well as in the neighbor genes. Moreover, for some networks, we observed mutually exclusive mutational patterns in the neighbors genes, thus supporting their predicted role as oncogenic mechanisms. Strikingly, a substantial fraction of GRNs were overexpressed in Triple Negative Breast Cancer patients who acquired resistance to therapy, suggesting the involvement of these networks in mechanisms of chemoresistance. In conclusion, our approach allowed us to identify cancer molecular mechanisms frequently altered in breast cancer and in chemorefractory tumors, which may suggest novel cancer biomarkers and potential drug targets for the development of more effective therapeutic strategies in metastatic breast cancer patients

Cutaneous Melanoma Classification: The Importance of High-Throughput Genomic Technologies

Cutaneous melanoma is an aggressive tumor responsible for 90% of mortality related to skin cancer. In the recent years, the discovery of driving mutations in melanoma has led to better treatment approaches. The last decade has seen a genomic revolution in the field of cancer. Such genomic revolution has led to the production of an unprecedented mole of data. High-throughput genomic technologies have facilitated the genomic, transcriptomic and epigenomic profiling of several cancers, including melanoma. Nevertheless, there are a number of newer genomic technologies that have not yet been employed in large studies. In this article we describe the current classification of cutaneous melanoma, we review the current knowledge of the main genetic alterations of cutaneous melanoma and their related impact on targeted therapies, and we describe the most recent highthroughput genomic technologies, highlighting their advantages and disadvantages. We hope that the current review will also help scientists to identify the most suitable technology to address melanoma-related relevant questions. The translation of this knowledge and all actual advancements into the clinical practice will be helpful in better defining the different molecular subsets of melanoma patients and provide new tools to address relevant questions on disease management. Genomic technologies might indeed allow to better predict the biological - and, subsequently, clinical - behavior for each subset of melanoma patients as well as to even identify all molecular changes in tumor cell populations during disease evolution toward a real achievement of a personalized medicine

Patterns of injury and violence in Yaoundé Cameroon: an analysis of hospital data.

BackgroundInjuries are quickly becoming a leading cause of death globally, disproportionately affecting sub-Saharan Africa, where reports on the epidemiology of injuries are extremely limited. Reports on the patterns and frequency of injuries are available from Cameroon are also scarce. This study explores the patterns of trauma seen at the emergency ward of the busiest trauma center in Cameroon's capital city.Materials and methodsAdministrative records from January 1, 2007, through December 31, 2007, were retrospectively reviewed; information on age, gender, mechanism of injury, and outcome was abstracted for all trauma patients presenting to the emergency ward. Univariate analysis was performed to assess patterns of injuries in terms of mechanism, date, age, and gender. Bivariate analysis was used to explore potential relationships between demographic variables and mechanism of injury.ResultsA total of 6,234 injured people were seen at the Central Hospital of Yaoundé's emergency ward during the year 2007. Males comprised 71% of those injured, and the mean age of injured patients was 29 years (SD = 14.9). Nearly 60% of the injuries were due to road traffic accidents, 46% of which involved a pedestrian. Intentional injuries were the second most common mechanism of injury (22.5%), 55% of which involved unarmed assault. Patients injured in falls were more likely to be admitted to the hospital (p < 0.001), whereas patients suffering intentional injuries and bites were less likely to be hospitalized (p < 0.001). Males were significantly more likely to be admitted than females (p < 0.001)DiscussionPatterns in terms of age, gender, and mechanism of injury are similar to reports from other countries from the same geographic region, but the magnitude of cases reported is high for a single institution in an African city the size of Yaoundé. As the burden of disease is predicted to increase dramatically in sub-Saharan Africa, immediate efforts in prevention and treatment in Cameroon are strongly warranted

Mutation profiling of colorectal cancer for KRAS, BRAF, NRAS and PIK3CA genes in Indian patient cohort

&nbsp;This study investigated the molecular mechanisms that could predict the efficacy of Epidermal Growth Factor Receptor (EGFR) targeted therapy in Indian colorectal cancer (CRC) patients. KRAS, BRAF, NRAS and PIK3CA mutations was studied in 203 Indian CRC patients and mutation analyses of the biomarkers were correlated with clinicopathological findings.<br /

Evaluation of PD-L1 expression in various formalin-fixed paraffin embedded tumour tissue samples using SP263, SP142 and QR1 antibody clones

Background & objectives: Cancer cells can avoid immune destruction through the inhibitory ligand PD-L1. PD-1 is a surface cell receptor, part of the immunoglobulin family. Its ligand PD-L1 is expressed by tumour cells and stromal tumour infltrating lymphocytes (TIL). Methods: Forty-four cancer cases were included in this study (24 triple-negative breast cancers (TNBC), 10 non-small cell lung cancer (NSCLC) and 10 malignant melanoma cases). Three clones of monoclonal primary antibodies were compared: QR1 (Quartett), SP 142 and SP263 (Ventana). For visualization, ultraView Universal DAB Detection Kit from Ventana was used on an automated platform for immunohistochemical staining Ventana BenchMark GX. Results: Comparing the sensitivity of two different clones on same tissue samples from TNBC, we found that the QR1 clone gave higher percentage of positive cells than clone SP142, but there was no statistically significant difference. Comparing the sensitivity of two different clones on same tissue samples from malignant melanoma, the SP263 clone gave higher percentage of positive cells than the QR1 clone, but again the difference was not statistically significant. Comparing the sensitivity of two different clones on same tissue samples from NSCLC, we found higher percentage of positive cells using the QR1 clone in comparison with the SP142 clone, but once again, the difference was not statistically significant. Conclusion: The three different antibody clones from two manufacturers Ventana and Quartett, gave comparable results with no statistically significant difference in staining intensity/ percentage of positive tumour and/or immune cells. Therefore, different PD-L1 clones from different manufacturers can potentially be used to evaluate the PD- L1 status in different tumour tissues. Due to the serious implications of the PD-L1 analysis in further treatment decisions for cancer patients, every antibody clone, staining protocol and evaluation process should be carefully and meticulously validated

Bioinformatics and Machine Learning for Cancer Biology

Cancer is a leading cause of death worldwide, claiming millions of lives each year. Cancer biology is an essential research field to understand how cancer develops, evolves, and responds to therapy. By taking advantage of a series of “omics” technologies (e.g., genomics, transcriptomics, and epigenomics), computational methods in bioinformatics and machine learning can help scientists and researchers to decipher the complexity of cancer heterogeneity, tumorigenesis, and anticancer drug discovery. Particularly, bioinformatics enables the systematic interrogation and analysis of cancer from various perspectives, including genetics, epigenetics, signaling networks, cellular behavior, clinical manifestation, and epidemiology. Moreover, thanks to the influx of next-generation sequencing (NGS) data in the postgenomic era and multiple landmark cancer-focused projects, such as The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), machine learning has a uniquely advantageous role in boosting data-driven cancer research and unraveling novel methods for the prognosis, prediction, and treatment of cancer