Video Q&A: molecular profiling of breast cancer.

In this video Q&A, we talk to Professor Carlos Caldas about the identification of breast cancer subtypes through molecular profiling, and the clinical implications for diagnosis and treatment.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

“Omic” landscapes of breast cancer - the end of the beginning

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.published versio

A sparse regulatory network of copy-number driven expression reveals putative breast cancer oncogenes

The influence of DNA cis-regulatory elements on a gene's expression has been intensively studied. However, little is known about expressions driven by trans-acting DNA hotspots. DNA hotspots harboring copy number aberrations are recognized to be important in cancer as they influence multiple genes on a global scale. The challenge in detecting trans-effects is mainly due to the computational difficulty in detecting weak and sparse trans-acting signals amidst co-occuring passenger events. We propose an integrative approach to learn a sparse interaction network of DNA copy-number regions with their downstream targets in a breast cancer dataset. Information from this network helps distinguish copy-number driven from copy-number independent expression changes on a global scale. Our result further delineates cis- and trans-effects in a breast cancer dataset, for which important oncogenes such as ESR1 and ERBB2 appear to be highly copy-number dependent. Further, our model is shown to be efficient and in terms of goodness of fit no worse than other state-of the art predictors and network reconstruction models using both simulated and real data.Comment: Accepted at IEEE International Conference on Bioinformatics & Biomedicine (BIBM 2010

Integrating data from 3D CAD and 3D cameras for Real-Time Modeling

In a reversal of historic trends, the capital facilities industry is expressing an increasing desire for automation of equipment and construction processes. Simultaneously, the industry has become conscious that higher levels of interoperability are a key towards higher productivity and safer projects. In complex, dynamic, and rapidly changing three-dimensional (3D) environments such as facilities sites, cutting-edge 3D sensing technologies and processing algorithms are one area of development that can dramatically impact those projects factors. New 3D technologies are now being developed, with among them 3D camera. The main focus here is an investigation of the feasibility of rapidly combining and comparing – integrating – 3D sensed data (from a 3D camera) and 3D CAD data. Such a capability could improve construction quality assessment, facility aging assessment, as well as rapid environment reconstruction and construction automation. Some preliminary results are presented here. They deal with the challenge of fusing sensed and CAD data that are completely different in nature

A robust classifier of high predictive value to identify good prognosis patients in ER-negative breast cancer.

INTRODUCTION: Patients with primary operable oestrogen receptor (ER) negative (-) breast cancer account for about 30% of all cases and generally have a worse prognosis than ER-positive (+) patients. Nevertheless, a significant proportion of ER- cases have favourable outcomes and could potentially benefit from a less aggressive course of therapy. However, identification of such patients with a good prognosis remains difficult and at present is only possible through examining histopathological factors. METHODS: Building on a previously identified seven-gene prognostic immune response module for ER- breast cancer, we developed a novel statistical tool based on Mixture Discriminant Analysis in order to build a classifier that could accurately identify ER- patients with a good prognosis. RESULTS: We report the construction of a seven-gene expression classifier that accurately predicts, across a training cohort of 183 ER- tumours and six independent test cohorts (a total of 469 ER- tumours), ER- patients of good prognosis (in test sets, average predictive value = 94% [range 85 to 100%], average hazard ratio = 0.15 [range 0.07 to 0.36] p < 0.000001) independently of lymph node status and treatment. CONCLUSIONS: This seven-gene classifier could be used in a polymerase chain reaction-based clinical assay to identify ER- patients with a good prognosis, who may therefore benefit from less aggressive treatment regimens.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Genetic heterogeneity in breast cancer: the road to personalized medicine?

This is the final published version. It was originally published here: http://www.biomedcentral.com/1741-7015/11/151. DOI: 10.1186/1741-7015-11-151.More women die from breast cancer across the world today than from any other type of malignancy. The clinical course of breast cancer varies tremendously between patients. While some of this variability is explained by traditional clinico-pathological factors (including patient age, tumor stage, histological grade and estrogen receptor status), molecular profiling studies have defined breast cancer subtypes with distinct clinical outcomes. This mini-review considers recent studies which have used genomics technologies in an attempt to identify new biomarkers of prognosis and treatment response. These studies highlight the genetic heterogeneity that exists within breast cancers in space and time

The breast cancer genome--a key for better oncology.

Molecular classification has added important knowledge to breast cancer biology, but has yet to be implemented as a clinical standard. Full sequencing of breast cancer genomes could potentially refine classification and give a more complete picture of the mutational profile of cancer and thus aid therapy decisions. Future treatment guidelines must be based on the knowledge derived from histopathological sub-classification of tumors, but with added information from genomic signatures when properly clinically validated. The objective of this article is to give some background on molecular classification, the potential of next generation sequencing, and to outline how this information could be implemented in the clinic.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Maintaining Tumor Heterogeneity in Patient-Derived Tumor Xenografts.

Preclinical models often fail to capture the diverse heterogeneity of human malignancies and as such lack clinical predictive power. Patient-derived tumor xenografts (PDX) have emerged as a powerful technology: capable of retaining the molecular heterogeneity of their originating sample. However, heterogeneity within a tumor is governed by both cell-autonomous (e.g., genetic and epigenetic heterogeneity) and non-cell-autonomous (e.g., stromal heterogeneity) drivers. Although PDXs can largely recapitulate the polygenomic architecture of human tumors, they do not fully account for heterogeneity in the tumor microenvironment. Hence, these models have substantial utility in basic and translational research in cancer biology; however, study of stromal or immune drivers of malignant progression may be limited. Similarly, PDX models offer the ability to conduct patient-specific in vivo and ex vivo drug screens, but stromal contributions to treatment responses may be under-represented. This review discusses the sources and consequences of intratumor heterogeneity and how these are recapitulated in the PDX model. Limitations of the current generation of PDXs are discussed and strategies to improve several aspects of the model with respect to preserving heterogeneity are proposed.We are grateful to Cancer Research UK for supporting all authors