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PURPOSE This study investigated the effectiveness of algorithmic testing in hematopathology at the Brigham and Women's Hospital and Dana-Farber Cancer Institute (DFCI). The algorithm was predicated on test selection after an initial pathologic evaluation tomaximize cost-effective testing, especially for expensive molecular and cytogenetic assays. MATERIALS AND METHODS Standard ordering protocols (SOPs) for 17 disease categories were developed and encoded in a decision support application. Six months of retrospective data from application beta testing was obtained and compared with actual testing practices during that timeframe. In addition, 2 years of prospective data were also obtained from patients at one community satellite site. RESULTS A total of 460 retrospective cases (before introduction of algorithmic testing) and 109 prospective cases (following introduction)were analyzed. In the retrospective data, 61.7% of tests (509 of 825) were concordant with the SOPs while 38.3% (316 of 825) were overordered and 30.8%(227 of 736) of SOP-recommended tests were omitted. In the prospective data, 98.8% of testing was concordant (244 of 247 total tests) with only 1.2%overordered tests (3 of 247) and 7.6%omitted tests (20 of 264 SOP-recommended tests; overall P < .001). The cost of overordered tests before implementing SOP indicates a potential annualized saving of $1,347,520 in US dollars (USD) in overordered testing at Brigham and Women's Hospital/DFCI. Only two of 316 overordered tests (0.6%) returned any additional information, both for extremely rare clinical circumstances. CONCLUSION Implementation of SOPs dramatically improved test ordering practices, with a just right number of ancillary tests that minimizes cost and has no significant impact on acquiring key informative test results. [ABSTRACT FROM AUTHOR]

The development of targeted therapies for antiestrogen-resistant breast cancer requires a detailed understanding of its molecular characteristics. To further elucidate the molecular events underlying acquired resistance to the antiestrogens tamoxifen and fulvestrant, we established drug-resistant sublines from a single colony of hormone-dependent breast cancer MCF7 cells. These model systems allowed us to examine the cellular and molecular changes induced by antiestrogens in the context of a uniform clonal background. Global changes in both basal and estrogen-induced gene expression profiles were determined in hormone-sensitive and hormonal-resistant sublines using Affymetrix Human Genome U133 Plus 2.0 Arrays. Changes in DNA methylation were assessed by differential methylation hybridization, a high-throughput promoter CpG island microarray analysis. By comparative studies, we found distinct gene expression and promoter DNA methylation profiles associated with acquired resistance to fulvestrant versus tamoxifen. Fulvestrant resistance was characterized by pronounced up-regulation of multiple growth-stimulatory pathways, resulting in estrogen receptor alpha (ERalpha)-independent, autocrine-regulated proliferation. Conversely, acquired resistance to tamoxifen correlated with maintenance of the ERalpha-positive phenotype, although receptor-mediated gene regulation was altered. Activation of growth-promoting genes, due to promoter hypomethylation, was more frequently observed in antiestrogen-resistant cells compared with gene inactivation by promoter hypermethylation, revealing an unexpected insight into the molecular changes associated with endocrine resistance. In summary, this study provides an in-depth understanding of the molecular changes specific to acquired resistance to clinically important antiestrogens. Such knowledge of resistance-associated mechanisms could allow for identification of therapy targets and strategies for resensitization to these well-established antihormonal agents.

Motivation: To detect and select patterns of transcription factor binding sites (TFBSs) which distinguish genes directly regulated by estrogen receptor-alpha (ERalpha), we developed an innovative mixture model-based discriminate analysis for identifying ordered TFBS pairs., Results: Biologically, our proposed new algorithm clearly suggests that TFBSs are not randomly distributed within ERalpha target promoters (P-value < 0.001). The up-regulated targets significantly (P-value < 0.01) possess TFBS pairs, (DBP, MYC), (DBP, MYC/MAX heterodimer), (DBP, USF2) and (DBP, MYOGENIN); and down-regulated ERalpha target genes significantly (P-value < 0.01) possess TFBS pairs, such as (DBP, c-ETS1-68), (DBP, USF2) and (DBP, MYOGENIN). Statistically, our proposed mixture model-based discriminate analysis can simultaneously perform TFBS pattern recognition, TFBS pattern selection, and target class prediction; such integrative power cannot be achieved by current methods., Availability: The software is available on request from the authors., Contact: lali@iupui.edu, Supplementary Information: Supplementary data are available at Bioinformatics online.

Purpose: Aberrant DNA methylation, now recognized as a contributing factor to neoplasia, often shows definitive gene/sequence preferences unique to specific cancer types. Correspondingly, distinct combinations of methylated loci can function as biomarkers for numerous clinical correlates of ovarian and other cancers., Experimental Design: We used a microarray approach to identify methylated loci prognostic for reduced progression-free survival (PFS) in advanced ovarian cancer patients. Two data set classification algorithms, Significance Analysis of Microarray and Prediction Analysis of Microarray, successfully identified 220 candidate PFS-discriminatory methylated loci. Of those, 112 were found capable of predicting PFS with 95% accuracy, by Prediction Analysis of Microarray, using an independent set of 40 advanced ovarian tumors (from 20 short-PFS and 20 long-PFS patients, respectively). Additionally, we showed the use of these predictive loci using two bioinformatics machine-learning algorithms, Support Vector Machine and Multilayer Perceptron., Conclusion: In this report, we show that highly prognostic DNA methylation biomarkers can be successfully identified and characterized, using previously unused, rigorous classifying algorithms. Such ovarian cancer biomarkers represent a promising approach for the assessment and management of this devastating disease.