Novel tumor sampling strategies to enable microarray gene expression signatures in breast cancer: a study to determine feasibility and reproducibility in the context of clinical care

Tumor acquisition for biomarker research in lung cancer

The biopsy collection data from two lung cancer trials that required fresh tumor samples be obtained for microarray analysis were reviewed. In the trial for advanced disease, microarray data were obtained on 50 patient samples, giving an overall success rate of 60.2%. The majority of the specimens were obtained through CT-guided lung biopsies (N = 30). In the trial for early-stage patients, 28 tissue specimens were collected from excess tumor after surgical resection with a success rate of 85.7%. This tissue procurement program documents the feasibility in obtaining fresh tumor specimens prospectively that could be used for molecular testing.

Genomic approach towards personalized anticancer drug therapy

Stratification of patients for multidrug response is a promising strategy for cancer treatment. Genome-based prediction models have great potential for this purpose because the extent of drug sensitivity may be attributed to the heterogeneity of the underlying genetic characteristics of cancer. However, microarray data is difficult to analyze and is not reproducible. Several machine-learning algorithms have therefore been developed in a repeatable manner. Random forests algorithm, which uses an ensemble approach based on classification and regression trees, appears to be superior for predicting multidrug sensitivity. This is because ensemble methods are more effective when there are much more predictors than samples. Here, we review recent advances in the development of classification algorithms using microarray technology for prediction of anticancer sensitivity, discuss the availability of ensemble methods for prediction models, and present data regarding the identification of potential responders to FOLFOX therapy using random forests algorithm.

Microarray: an instrument for cancer surgeons of the future?

Microarray enables the study of thousands of genes simultaneously. While still in its infancy as a technique and with a number of barriers to be overcome, microarray is allowing scientists to thoroughly examine the molecular pathways of cancer pathogenesis. However, the adoption of microarray as a clinically applicable technique has been slow coming. Current literature suggests roles in the diagnosis of tumours of unknown origin, in the evaluation of prognostic markers, and in guiding treatment for recurrent and resistant malignancy. This review outlines the science of microarray and draws on clinical examples, including osteosarcoma, breast, prostate and pancreatic carcinomas, to highlight the potential of microarray as a technique of surgical importance.

The biology underlying molecular imaging in oncology: from genome to anatome and back again

Cancers are complex, evolving, multiscale ecosystems that are characterized by profound spatial and temporal heterogeneity. The interactions in cancer are non-linear in that small changes in one variable can have large changes on another. These multiple interacting phenotypes and spatial scales can best be understood with appropriate mathematical and computational models. Imaging is central to this investigation because it can non-destructively and longitudinally characterize spatial variations in the tumour phenotype and environment so that the system dynamics over time can be captured quantitatively.

Detection of treatment-induced changes in signaling pathways in gastrointestinal stromal tumors using transcriptomic data

Cell signaling plays a central role in the etiology of cancer. Numerous therapeutics in use or under development target signaling proteins; however, off-target effects often limit assignment of positive clinical response to the intended target. As direct measurements of signaling protein activity are not generally feasible during treatment, there is a need for more powerful methods to determine if therapeutics inhibit their targets and when off-target effects occur. We have used the Bayesian Decomposition algorithm and data on transcriptional regulation to create a novel methodology, Differential Expression for Signaling Determination (DESIDE), for inferring signaling activity from microarray measurements. We applied DESIDE to deduce signaling activity in gastrointestinal stromal tumor cell lines treated with the targeted therapeutic imatinib mesylate (Gleevec). We detected the expected reduced activity in the KIT pathway, as well as unexpected changes in the p53 pathway. Pursuing these findings, we have determined that imatinib-induced DNA damage is responsible for the increased activity of p53, identifying a novel off-target activity for this drug. We then used DESIDE on data from resected, post-imatinib treatment tumor samples and identified a pattern in these tumors similar to that at late time points in the cell lines, and this pattern correlated with initial clinical response. The pattern showed increased activity of ETS domain-containing protein Elk-1 and signal transducers and activators of transcription 3 transcription factors, which are associated with the growth of side population cells. DESIDE infers the global reprogramming of signaling networks during treatment, permitting treatment modification that leverages ongoing drug development efforts, which is crucial for personalized medicine.