Microarray technology and its effect on breast cancer (re)classification and prediction of outcome

SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Comprehensive transcriptomic analysis of cell lines as models of primary tumors across 22 tumor types.

Cancer cell lines are a cornerstone of cancer research but previous studies have shown that not all cell lines are equal in their ability to model primary tumors. Here we present a comprehensive pan-cancer analysis utilizing transcriptomic profiles from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia to evaluate cell lines as models of primary tumors across 22 tumor types. We perform correlation analysis and gene set enrichment analysis to understand the differences between cell lines and primary tumors. Additionally, we classify cell lines into tumor subtypes in 9 tumor types. We present our pancreatic cancer results as a case study and find that the commonly used cell line MIA PaCa-2 is transcriptionally unrepresentative of primary pancreatic adenocarcinomas. Lastly, we propose a new cell line panel, the TCGA-110-CL, for pan-cancer studies. This study provides a resource to help researchers select more representative cell line models

Mammographic screening detects low-risk tumor biology breast cancers.

Overdiagnosis of breast cancer, i.e. the detection of slow-growing tumors that would never have caused symptoms or death, became more prevalent with the implementation of population-based screening. Only rough estimates have been made of the proportion of patients that are overdiagnosed and identification of those patients is difficult. Therefore, the aim of this study is to evaluate whether tumor biology can help identify patients with screen-detected tumors at such a low risk of recurrence that they are likely to be overdiagnosed. Furthermore, we wish to evaluate the impact of the transition from film-screen mammography (FSM) to the more sensitive full-field digital mammography (FFDM) on the biology of the tumors detected by each screening-modality. All Dutch breast cancer patients enrolled in the MINDACT trial (EORTC-10041) accrued 2007-2011, who participated in the national screening program (biennial screening ages 50-75) were included (n = 1,165). We calculated the proportions of high-, low- and among those the ultralow-risk tumors according to the 70-gene signature for patients with screen-detected (n = 775) and interval (n = 390) cancers for FSM and FFDM. Screen-detected cancers had significantly more often a low-risk tumor biology (68 %) of which 54 % even an ultralow-risk compared to interval cancers (53 % low-, of which 45 % ultralow-risk (p = 0.001) with an OR of 2.33 (p < 0.0001; 95 % CI 1.73-3.15). FFDM detected significantly more high-risk tumors (35 %) compared to FSM (27 %) (p = 0.011). Aside from favorable clinico-pathological factors, screen-detected cancers were also more likely to have a biologically low-risk or even ultralow-risk tumor. Especially for patients with screen-detected cancers the use of tools, such as the 70-gene signature, to differentiate breast cancers by risk of recurrence may minimize overtreatment. The recent transition in screening-modalities led to an increase in the detection of biologically high-risk cancers using FFDM

Marker genes for circulating tumour cells predict survival in metastasized breast cancer patients

We investigated the prognostic significance of circulating breast cancer cells in peripheral blood detected by quantitative RT-PCR of marker genes in patients with advanced breast cancer. Blood samples from 94 breast cancer patients with metastatic disease (M1) were examined for circulating tumour cells by studying the mRNA expression of CK19, p1B, PS2 and EGP2 by real-time PCR. Using a score function, developed for predicting circulating tumour cells by quadratic discriminant analysis (QDA), the four expression levels were combined into a single discriminant value. Tumour cells were present in 24 out of 94 (31%) of the patients. In 77% (72 out of 94) of the patients distant metastatic disease was localised in the bone. In 36% (26 out of 72) of the patients with bone metastases at the time of blood sampling, a positive QDA for the four genes was found, in contrast to only 14% (three out of 22) without bone involvement. Overall survival rates by Kaplan-Meier revealed no prognostic effect for the presence of bone metastases (P=0.93). However, patients with a positive QDA value did have a progression-free survival at 1 year of 3% and overall survival at 2 years of 17%, against 22 and 36% for patients with a negative QDA value (P=0.015 and 0.0053, respectively). Breast cancer patients with metastatic disease have a significantly worse progression-free and overall survival when circulating tumour cells can be detected in their peripheral bloo

A simple method for assigning genomic grade to individual breast tumours

<p>Abstract</p> <p>Background</p> <p>The prognostic value of grading in breast cancer can be increased with microarray technology, but proposed strategies are disadvantaged by the use of specific training data or parallel microscopic grading. Here, we investigate the performance of a method that uses no information outside the breast profile of interest.</p> <p>Results</p> <p>In 251 profiled tumours we optimised a method that achieves grading by comparing rank means for genes predictive of high and low grade biology; a simpler method that allows for truly independent estimation of accuracy. Validation was carried out in 594 patients derived from several independent data sets. We found that accuracy was good: for low grade (G1) tumors 83- 94%, for high grade (G3) tumors 74- 100%. In keeping with aim of improved grading, two groups of intermediate grade (G2) cancers with significantly different outcome could be discriminated.</p> <p>Conclusion</p> <p>This validates the concept of microarray-based grading in breast cancer, and provides a more practical method to achieve it. A simple R script for grading is available in an additional file. Clinical implementation could achieve better estimation of recurrence risk for 40 to 50% of breast cancer patients.</p

A gene expression profile for detection of sufficient tumour cells in breast tumour tissue: microarray diagnosis eligibility

<p>Abstract</p> <p>Background</p> <p>Microarray diagnostics of tumour samples is based on measurement of prognostic and/or predictive gene expression profiles. Typically, diagnostic profiles have been developed using bulk tumour samples with a sufficient amount of tumour cells (usually >50%). Consequentially, a diagnostic results depends on the minimal percentage of tumour cells within a sample. Currently, tumour cell percentage is assessed by conventional histopathological review. However, even for experienced pathologists, such scoring remains subjective and time consuming and can lead to ambiguous results.</p> <p>Methods</p> <p>In this study we investigated whether we could use transcriptional activity of a specific set of genes instead of histopathological review to identify samples with sufficient tumour cell content. Genome-wide gene expression measurements were used to develop a transcriptional gene profile that could accurately assess a sample's tumour cell percentage.</p> <p>Results</p> <p>Supervised analysis across 165 breast tumour samples resulted in the identification of a set of 13 genes which expression correlated with presence of tumour cells. The developed gene profile showed a high performance (AUC 0.92) for identification of samples that are suitable for microarray diagnostics. Validation on 238 additional breast tumour samples indicated a robust performance for correct classification with an overall accuracy of 91 percent and a kappa score of 0.63 (95%CI 0.47–0.73).</p> <p>Conclusion</p> <p>The developed 13-gene profile provides an objective tool for assessment whether a breast cancer sample contains sufficient tumour cells for microarray diagnostics. It will improve the efficiency and throughput for diagnostic gene expression profiling as it no longer requires histopathological analysis for initial tumour percentage scoring. Such profile will also be very use useful for assessment of tumour cell percentage in biopsies where conventional histopathology is difficult, such as fine needle aspirates.</p

Nearest Template Prediction: A Single-Sample-Based Flexible Class Prediction with Confidence Assessment

Gene-expression signature-based disease classification and clinical outcome prediction has not been widely introduced in clinical medicine as initially expected, mainly due to the lack of extensive validation needed for its clinical deployment. Obstacles include variable measurement in microarray assay, inconsistent assay platform, analytical requirement for comparable pair of training and test datasets, etc. Furthermore, as medical device helping clinical decision making, the prediction needs to be made for each single patient with a measure of its reliability. To address these issues, there is a need for flexible prediction method less sensitive to difference in experimental and analytical conditions, applicable to each single patient, and providing measure of prediction confidence. The nearest template prediction (NTP) method provides a convenient way to make class prediction with assessment of prediction confidence computed in each single patient's gene-expression data using only a list of signature genes and a test dataset. We demonstrate that the method can be flexibly applied to cross-platform, cross-species, and multiclass predictions without any optimization of analysis parameters

The promise of microarrays in the management and treatment of breast cancer

Breast cancer is the most common malignancy afflicting women from Western cultures. Developments in breast cancer molecular and cellular biology research have brought us closer to understanding the genetic basis of this disease. Recent advances in microarray technology hold the promise of further increasing our understanding of the complexity and heterogeneity of this disease, and providing new avenues for the prognostication and prediction of breast cancer outcomes. These new technologies have some limitations and have yet to be incorporated into clinical use, for both the diagnosis and treatment of women with breast cancer. The most recent application of microarray genomic technologies to studying breast cancer is the focus of this review