Multiclonal Invasion in Breast Tumors Identified by Topographic Single Cell Sequencing.

Ductal carcinoma in situ (DCIS) is an early-stage breast cancer that infrequently progresses to invasive ductal carcinoma (IDC). Genomic evolution has been difficult to delineate during invasion due to intratumor heterogeneity and the low number of tumor cells in the ducts. To overcome these challenges, we developed Topographic Single Cell Sequencing (TSCS) to measure genomic copy number profiles of single tumor cells while preserving their spatial context in tissue sections. We applied TSCS to 1,293 single cells from 10 synchronous patients with both DCIS and IDC regions in addition to exome sequencing. Our data reveal a direct genomic lineage between in situ and invasive tumor subpopulations and further show that most mutations and copy number aberrations evolved within the ducts prior to invasion. These results support a multiclonal invasion model, in which one or more clones escape the ducts and migrate into the adjacent tissues to establish the invasive carcinomas.

Analysis of laboratory data transmission between two healthcare institutions using a widely used point-to-point health information exchange platform: a case report.

Objective: The objective was to identify information loss that could affect clinical care in laboratory data transmission between 2 health care institutions via a Health Information Exchange platform. Materials and Methods: Data transmission results of 9 laboratory tests, including LOINC codes, were compared in the following: between sending and receiving electronic health record (EHR) systems, the individual Health Level Seven International (HL7) Version 2 messages across the instrument, laboratory information system, and sending EHR. Results: Loss of information for similar tests indicated the following potential patient safety issues: (1) consistently missing specimen source; (2) lack of reporting of analytical technique or instrument platform; (3) inconsistent units and reference ranges; (4) discordant LOINC code use; and (5) increased complexity with multiple HL7 versions. Discussion and Conclusions: Using an HIE with standard messaging, SHIELD (Systemic Harmonization and Interoperability Enhancement for Laboratory Data) recommendations, and enhanced EHR functionality to support necessary data elements would yield consistent test identification and result value transmission.

Utility of Whole Slide Imaging for Intraoperative Consultation: Experience of a Large Academic Center.

CONTEXT.­: In the United States, review of digital whole slide images (WSIs) using specific systems is approved for primary diagnosis but has not been implemented for intraoperative consultation. OBJECTIVE.­: To evaluate the safety of review of WSIs and compare the efficiency of review of WSIs and glass slides (GSs) for intraoperative consultation. DESIGN.­: Ninety-one cases previously submitted for frozen section evaluation were randomly selected from 8 different anatomic pathology subspecialties. GSs from these cases were scanned on a Leica Aperio AT2 scanner at ×20 magnification (0.25 µm/pixel). The slides were deidentified, and a short relevant clinical history was provided for each slide. Nine board-certified general pathologists who do not routinely establish primary diagnoses using WSIs reviewed the WSIs using Leica Aperio ImageScope viewing software. After a washout period of 2-3 weeks, the pathologists reviewed the corresponding GSs using a light microscope (Olympus BX43). The pathologists recorded the diagnosis and time to reach the diagnosis. Intraobserver concordance, time to diagnosis, and specificity and sensitivity compared to the original diagnosis were evaluated. RESULTS.­: The rate of intraobserver concordance between GS results and WSI results was 93.7%. Mean time to diagnosis was 1.25 minutes for GSs and 1.76 minutes for WSIs (P < .001). Specificity was 91% for GSs and 90% for WSIs; sensitivity was 92% for GSs and 92% for WSIs. CONCLUSIONS.­: Time to diagnosis was longer with WSIs than with GSs, and scanning GSs and uploading the data to whole slide imaging systems takes time. However, review of WSIs appears to be a safe alternative to review of GSs. Use of WSIs allows reporting from a remote site during a public health emergency such as the COVID-19 pandemic and facilitates subspecialty histopathology services.

Patient-calibrated agent-based modelling of ductal carcinoma in situ (DCIS): from microscopic measurements to macroscopic predictions of clinical progression.

Ductal carcinoma in situ (DCIS)--a significant precursor to invasive breast cancer--is typically diagnosed as microcalcifications in mammograms. However, the effective use of mammograms and other patient data to plan treatment has been restricted by our limited understanding of DCIS growth and calcification. We develop a mechanistic, agent-based cell model and apply it to DCIS. Cell motion is determined by a balance of biomechanical forces. We use potential functions to model interactions with the basement membrane and amongst cells of unequal size and phenotype. Each cell's phenotype is determined by genomic/proteomic- and microenvironment-dependent stochastic processes. Detailed "sub-models" describe cell volume changes during proliferation and necrosis; we are the first to account for cell calcification. We introduce the first patient-specific calibration method to fully constrain the model based upon clinically-accessible histopathology data. After simulating 45 days of solid-type DCIS with comedonecrosis, the model predicts: necrotic cell lysis acts as a biomechanical stress relief and is responsible for the linear DCIS growth observed in mammography; the rate of DCIS advance varies with the duct radius; the tumour grows 7-10mm per year--consistent with mammographic data; and the mammographic and (post-operative) pathologic sizes are linearly correlated--in quantitative agreement with the clinical literature. Patient histopathology matches the predicted DCIS microstructure: an outer proliferative rim surrounds a stratified necrotic core with nuclear debris on its outer edge and calcification in the centre. This work illustrates that computational modelling can provide new insight on the biophysical underpinnings of cancer. It may 1-day be possible to augment a patient's mammography and other imaging with rigorously-calibrated models that help select optimal surgical margins based upon the patient's histopathologic data.

Metastatic neuroendocrine tumour in the breast: a potential mimic of in-situ and invasive mammary carcinoma.

AIMS: The aim of this study was to review the clinicopathological characteristics of neuroendocrine tumours (NETs) metastasizing to the breast, in order to identify features that could be useful in distinguishing these metastatic lesions from primary breast neoplasms. METHODS AND RESULTS: Eighteen metastatic NETs in the breast were identified from two large hospitals over a 15-year period. Eleven (62%) tumours originated in the gastrointestinal tract, 5 (28%) originated in the lung, and the other two were of indeterminate origin. Eight (44%) cases were initially misdiagnosed as primary mammary carcinomas. In retrospect, all metastatic tumours exhibited architectural and cytological features that would suggest neuroendocrine differentiation. Immunohistochemistry can further aid in the distinction between metastatic neuroendocrine and primary mammary carcinoma. All 11 tumours from the gastrointestinal tract expressed CDX-2, 3 (60%) of five tumours from the lung expressed thyroid transcription factor-1, and only 2 (11%) of 18 showed weak oestrogen receptor positivity. Additionally, unlike primary carcinomas, the majority (82%) of metastatic NETs were negative for cytokeratin 7, and all were negative for gross cystic disease fluid protein 15 and mammoglobin. CONCLUSIONS: There is a high propensity for metastatic NETs to mimic primary breast carcinomas. Careful attention to cytological and architectural features can help to identify cases that require further immunophenotypic workup with a panel of tissue-specific antibodies. However, clinical history is paramount for optimal diagnosis.

The deployment of a tissue request tracking system for the CHTN: a case study in managing change in informatics for biobanking operations.

BACKGROUND: Managing change has not only been recognized as an important topic in medical informatics, but it has become increasingly important in translational informatics. The move to share data, together with the increasing complexity and volume of the data, has precipitated a transition from locally stored worksheet and flat files to relational data bases with object oriented interfaces for data storage and retrieval. While the transition from simple to complex data structures, mirroring the transition from simple to complex experimental technologies, seems natural, the human factor often fails to be adequately addressed leading to failures in managing change. METHODS: We describe here a case study in change management applied to an application in translational informatics that touches upon changes in hardware, software, data models, procedures, and terminology standards. We use the classic paper by Riley and Lorenzi to dissect the problems that arose, the solutions that were implemented, and the lessons learned. RESULTS: The entire project from requirements gathering through completion of migration of the system took three years. Double data entry into the old and new systems persisted for six months. Contributing factors hindering progress and solutions to facilitate managing the change were identified in seven of the areas identified by Riley and Lorenzi: communications, cultural changes in work practice, scope creep, leadership and organizational issues, and training. CONCLUSIONS: Detailed documentation of the agreed upon requirements for the new system along with ongoing review of the sources of resistance to change as defined by Riley and Lorenzi were the most important steps taken that contributed to the success of the project. Cultural changes in tissue collection mandated by standards requirements introduced by the Cancer Bioinformatics Grid (CaBIG) and excessive reliance on the outgoing system during a lengthy period of dual data entry were the primary sources of resistance to change.

Clinical, histopathologic, and immunohistochemical features of microglandular adenosis and transition into in situ and invasive carcinoma.

Microglandular adenosis (MGA) of the breast is widely known as a benign lesion that can mimic invasive carcinoma. In situ and invasive carcinomas have been described as arising in MGA, but which cases of MGA will progress to carcinoma is unclear. Criteria for distinguishing uncomplicated MGA, MGA with atypia (AMGA), and carcinoma arising in MGA (MGACA) are not standardized. The primary objective of this study was to illustrate the clinical, histopathologic, and immunophenotypical characteristics of MGA, AMGA, and MGACA in an effort to provide criteria for distinguishing the 3 types. We retrospectively identified 108 cases seen at M.D. Anderson Cancer Center between 1983 and 2007 that had a diagnosis of MGA. Of the 108 cases, 65 cases had available material for review. Inclusion criteria were glands of MGA expressing S-100 protein and lacking myoepithelial layer (smooth muscle actin negative). Eleven out of 65 cases qualified to have an MGA component; myoepithelial layer was detected in the remaining 54 cases and were classified as adenosis. Out of the 11 MGA patients, there were 3 patients with uncomplicated MGA, 2 had AMGA, and 6 had MGACA. Staining indices for the cell cycle markers p53 and Ki-67 were used to compare the 3 tumor categories. Additional staining for other tumor markers [estrogen and progesterone receptors, HER2, epidermal growth factor receptor (EGFR), c-kit, CK5/6, and CK18] were performed. Patient demographics, tumor radiologic features, and clinical follow-up data were collected for all cases. Multiple invasive histologic components were identified in each of the MGACA cases. All invasive MGACAs had a duct-forming component. In addition, basal-like component was present in 2 cases, aciniclike in 2, matrix producing in 4, sarcomatoid in 1, and adenoid cystic in 1. All tumors had strong and diffuse CK8/18 and EGFR expression but no estrogen receptor, progesterone receptor, HER2 (ie, triple negative), or CK5/6 expression. C-kit was focally expressed in 2 of the MGACAs. Ki-67 and p53 labeling indices was < 3% in all MGAs, 5% to 10% in the AMGAs, and > 30% in MGACAs. In a follow-up ranging from 14 days to 8 years, none of the MGA cases recurred. One of the AMGA cases recurred as invasive carcinoma in a background of AMGA after 8 years following incomplete excision of the lesion. Three out of 6 MGACA cases (50%) required multiple consecutive resections ending up with mastectomy due to involved margins by invasive or in situ carcinoma. Two out of 6 MGACA cases (34%) developed metastasis and died of disease. Our data showed that Ki-67 and p53 expression, in conjunction with the morphologic features, could be a reliable marker to distinguish MGA from AMGA and MGACA. Although 11 tumors were only included in our study, 64% of the tumors were carcinomas arising in MGA. This high incidence of MGACA may not represent the actual frequency of MGAs progressing into carcinoma and is likely due to referral bias in our institution. Nonetheless, the high association of carcinoma with MGA necessitates complete excision of MGA to rule out invasion. Although all the MGACA cases were triple negative and express EGFR (basal-like features), all the cases in our study showed a luminal type of differentiation by CK8/18 expression, indicating that MGACA may not fit well into the current proposed molecular classification of breast cancer.

DCIS Margins and Breast Conservation: MD Anderson Cancer Center Multidisciplinary Practice Guidelines and Outcomes.

Recent published guidelines suggest that adequate margins for DCIS should be ≥ 2 mm after breast conserving surgery followed by radiotherapy (RT). Many groups now use this guideline as an absolute indication for additional surgery. This article describes detailed multidisciplinary practices including extensive preoperative/intraoperative pathologic/histologic image-guided assessment of margins, offering some patients with small low/intermediate grade DCIS no RT, the use/magnitude of radiation boost tailoring to margin width, and endocrine therapy for ER-positive DCIS. Use of these protocols over the past 20-years has resulted in 10-year local recurrence rates below 5% for patients with negative margins < 2 mm who received RT. Patients with margins < 2 mm who do not receive RT experience significantly higher local failure rates. Thus, there is not an absolute need to achieve wider negative surgical margins when < 2 mm for patients treated with RT and this should be determined by the multidisciplinary team. Utilization of these multidisciplinary treatment protocols and techniques may not be exportable and extrapolated to all hospitals, breast programs and systems as they can be complex and resource intensive.

Significance of p63 amplification and overexpression in lung cancer development and prognosis.

The fight against lung cancer is greatly compromised by the lack of effective early detection strategies. Genomic abnormalities and specifically the amplification of chromosomal region 3q26-3qter in lung cancer represent a major signature of neoplastic transformation. Here, we address the significance of p53 homologue p63 mapping to 3q27 in lung tumorigenesis. We analyzed p63 gene copy number (CN) by fluorescence in situ hybridization and expression by immunohistochemistry in tissue microarrays of 217 non-small cell lung cancers (NSCLCs) and correlated them with survival. We additionally characterized our findings in a subset of 24 NSCLCs by reverse transcription-PCR and Western blotting. We analyzed p63 CN and protein expression in 41 preinvasive squamous lesions. The p63 genomic sequence was amplified in 88% of squamous carcinomas, in 42% of large cell carcinomas, and in 11% of adenocarcinomas of the lung. The predominant splice variant of p63 expressed was DeltaNp63alpha. Western analyses revealed DeltaNp63alpha expression in normal bronchus and squamous carcinomas but not in normal lung or in adenocarcinomas. Furthermore, p63genomic amplification and protein staining intensity associated with better survival. We found a significant increase in CN in preinvasive lesions graded severe dysplasia or higher. Our data demonstrate that there is early and frequent genomic amplification of p63 in the development of squamous carcinoma of the lung and that patients with NSCLC showing amplification and overexpression of p63 have prolonged survival. These observations suggest that p63 genomic amplification has an early role in lung tumorigenesis and deserves additional evaluation as a biomarker for lung cancer progression.

2013 HIPAA Changes Provide Opportunities and Challenges for Researchers: Perspectives from a Cancer Center.

In 2013, the U.S. Department of Health and Human Services modified the Health Insurance Portability and Accountability Act Privacy Rule to "strengthen privacy and security protections" while "improving workability and effectiveness to increase flexibility for and decrease burden on regulated entities." In this article, we attempt to translate these generalized goals into the real-world implications of these changes. Under the new rules, researchers can obtain participants' permission to use their protected health information for more research activities with a single, upfront authorization (thereby reducing paperwork for participants, researchers, and institutional review boards) while providing potential participants with more information upon which to base their decisions about participation. The combined authorizations can be used in clinical trials and their optional substudies and in stand-alone biospecimen-banking research that includes authorization to permit future research use. We also suggest best practices for taking advantage of the flexibility offered by the new rules while maintaining strong privacy protections for human subjects.

The tissue microarray data exchange specification: a community-based, open source tool for sharing tissue microarray data.

BACKGROUND: Tissue Microarrays (TMAs) allow researchers to examine hundreds of small tissue samples on a single glass slide. The information held in a single TMA slide may easily involve Gigabytes of data. To benefit from TMA technology, the scientific community needs an open source TMA data exchange specification that will convey all of the data in a TMA experiment in a format that is understandable to both humans and computers. A data exchange specification for TMAs allows researchers to submit their data to journals and to public data repositories and to share or merge data from different laboratories. In May 2001, the Association of Pathology Informatics (API) hosted the first in a series of four workshops, co-sponsored by the National Cancer Institute, to develop an open, community-supported TMA data exchange specification. METHODS: A draft tissue microarray data exchange specification was developed through workshop meetings. The first workshop confirmed community support for the effort and urged the creation of an open XML-based specification. This was to evolve in steps with approval for each step coming from the stakeholders in the user community during open workshops. By the fourth workshop, held October, 2002, a set of Common Data Elements (CDEs) was established as well as a basic strategy for organizing TMA data in self-describing XML documents. RESULTS: The TMA data exchange specification is a well-formed XML document with four required sections: 1) Header, containing the specification Dublin Core identifiers, 2) Block, describing the paraffin-embedded array of tissues, 3)Slide, describing the glass slides produced from the Block, and 4) Core, containing all data related to the individual tissue samples contained in the array. Eighty CDEs, conforming to the ISO-11179 specification for data elements constitute XML tags used in the TMA data exchange specification. A set of six simple semantic rules describe the complete data exchange specification. Anyone using the data exchange specification can validate their TMA files using a software implementation written in Perl and distributed as a supplemental file with this publication. CONCLUSION: The TMA data exchange specification is now available in a draft form with community-approved Common Data Elements and a community-approved general file format and data structure. The specification can be freely used by the scientific community. Efforts sponsored by the Association for Pathology Informatics to refine the draft TMA data exchange specification are expected to continue for at least two more years. The interested public is invited to participate in these open efforts. Information on future workshops will be posted at http://www.pathologyinformatics.org (API we site).

The Pan-Cancer analysis of pseudogene expression reveals biologically and clinically relevant tumour subtypes.

Although individual pseudogenes have been implicated in tumour biology, the biomedical significance and clinical relevance of pseudogene expression have not been assessed in a systematic way. Here we generate pseudogene expression profiles in 2,808 patient samples of seven cancer types from The Cancer Genome Atlas RNA-seq data using a newly developed computational pipeline. Supervised analysis reveals a significant number of pseudogenes differentially expressed among established tumour subtypes and pseudogene expression alone can accurately classify the major histological subtypes of endometrial cancer. Across cancer types, the tumour subtypes revealed by pseudogene expression show extensive and strong concordance with the subtypes defined by other molecular data. Strikingly, in kidney cancer, the pseudogene expression subtypes not only significantly correlate with patient survival, but also help stratify patients in combination with clinical variables. Our study highlights the potential of pseudogene expression analysis as a new paradigm for investigating cancer mechanisms and discovering prognostic biomarkers.

Challenges of the information age: the impact of false discovery on pathway identification.

BACKGROUND: Pathways with members that have known relevance to a disease are used to support hypotheses generated from analyses of gene expression and proteomic studies. Using cancer as an example, the pitfalls of searching pathways databases as support for genes and proteins that could represent false discoveries are explored. FINDINGS: The frequency with which networks could be generated from 100 instances each of randomly selected five and ten genes sets as input to MetaCore, a commercial pathways database, was measured. A PubMed search enumerated cancer-related literature published for any gene in the networks. Using three, two, and one maximum intervening step between input genes to populate the network, networks were generated with frequencies of 97%, 77%, and 7% using ten gene sets and 73%, 27%, and 1% using five gene sets. PubMed reported an average of 4225 cancer-related articles per network gene. DISCUSSION: This can be attributed to the richly populated pathways databases and the interest in the molecular basis of cancer. As information sources become enriched, they are more likely to generate plausible mechanisms for false discoveries.

Multiplicity: an organizing principle for cancers and somatic mutations.

BACKGROUND: With the advent of whole-genome analysis for profiling tumor tissue, a pressing need has emerged for principled methods of organizing the large amounts of resulting genomic information. We propose the concept of multiplicity measures on cancer and gene networks to organize the information in a clinically meaningful manner. Multiplicity applied in this context extends Fearon and Vogelstein's multi-hit genetic model of colorectal carcinoma across multiple cancers. METHODS: Using the Catalogue of Somatic Mutations in Cancer (COSMIC), we construct networks of interacting cancers and genes. Multiplicity is calculated by evaluating the number of cancers and genes linked by the measurement of a somatic mutation. The Kamada-Kawai algorithm is used to find a two-dimensional minimum energy solution with multiplicity as an input similarity measure. Cancers and genes are positioned in two dimensions according to this similarity. A third dimension is added to the network by assigning a maximal multiplicity to each cancer or gene. Hierarchical clustering within this three-dimensional network is used to identify similar clusters in somatic mutation patterns across cancer types. RESULTS: The clustering of genes in a three-dimensional network reveals a similarity in acquired mutations across different cancer types. Surprisingly, the clusters separate known causal mutations. The multiplicity clustering technique identifies a set of causal genes with an area under the ROC curve of 0.84 versus 0.57 when clustering on gene mutation rate alone. The cluster multiplicity value and number of causal genes are positively correlated via Spearman's Rank Order correlation (rs(8) = 0.894, Spearman's t = 17.48, p < 0.05). A clustering analysis of cancer types segregates different types of cancer. All blood tumors cluster together, and the cluster multiplicity values differ significantly (Kruskal-Wallis, H = 16.98, df = 2, p < 0.05). CONCLUSION: We demonstrate the principle of multiplicity for organizing somatic mutations and cancers in clinically relevant clusters. These clusters of cancers and mutations provide representations that identify segregations of cancer and genes driving cancer progression.

A novel, patient-specific mathematical pathology approach for assessment of surgical volume: application to ductal carcinoma in situ of the breast.

We introduce a novel "mathematical pathology" approach, founded on a biophysical model, to identify robust patient-specific predictors of tumor growth useful in clinical practice to improve the accuracy of diagnosis/prognosis and intervention. In accordance with biological observations, our model simulates the diffusion-limited in situ tumors with a relatively short phase of fast initial growth, followed by a prolonged slow-growth phase where tumor size is constrained primarily by the relative weight of cell mitosis and death. The former phase may only last for a few months, so that at the time of diagnosis, we may assume that most tumors will have entered the phase where their size is changing slowly. Based on this prediction, we hypothesize that the volume of breast with ducts affected by in situ tumors at the time of diagnosis will be closely approximated by a model-derived mathematical function based on the ratio of tumor cell proliferation-to-apoptosis indices and on the extent of diffusion of cell nutrients (diffusion penetration length), which can be measured from immunohistochemical and morphometric analysis of patient histopathology specimens without the need for multiple-time measurements. We tested this idea in a retrospective study of 17 patients by staining breast tumor specimens containing ductal carcinoma in situ for mitosis with Ki-67 and for apoptosis with cleaved caspase-3 and counting cells positive for each marker. We also determined diffusion penetration by measuring the thickness of viable rims of tumor cells within ducts. Using the ensuing ratios, we applied the model to determine a predicted surgical volume or tumor size. We then corroborated our hypothesis by comparing the predicted size of each tumor based on our model with the actual size of the pathological specimen after tumor excision (R2 = 0.74-0.88). In addition, for the 17 cases studied, both histological grade and mammography were not found to correlate with tumor size (R2 = 0.08-0.47). We conclude that our mathematical pathology approach yields a high degree of accuracy in predicting the size of tumors based on the mitotic/apoptotic index and on diffusion penetration. By obtaining these ratios at the time of initial biopsy, pathologists can employ our model to predict the size of the tumor and thereby inform surgeons how much tissue to remove (surgical volume). We discuss how results from the model have implications concerning the current debate on recommendations for screening mammography, while the model itself may contribute to better planning of breast conservation surgery.

Selective genomic copy number imbalances and probability of recurrence in early-stage breast cancer.

A number of studies of copy number imbalances (CNIs) in breast tumors support associations between individual CNIs and patient outcomes. However, no pattern or signature of CNIs has emerged for clinical use. We determined copy number (CN) gains and losses using high-density molecular inversion probe (MIP) arrays for 971 stage I/II breast tumors and applied a boosting strategy to fit hazards models for CN and recurrence, treating chromosomal segments in a dose-specific fashion (-1 [loss], 0 [no change] and +1 [gain]). The concordance index (C-Index) was used to compare prognostic accuracy between a training (n = 728) and test (n = 243) set and across models. Twelve novel prognostic CNIs were identified: losses at 1p12, 12q13.13, 13q12.3, 22q11, and Xp21, and gains at 2p11.1, 3q13.12, 10p11.21, 10q23.1, 11p15, 14q13.2-q13.3, and 17q21.33. In addition, seven CNIs previously implicated as prognostic markers were selected: losses at 8p22 and 16p11.2 and gains at 10p13, 11q13.5, 12p13, 20q13, and Xq28. For all breast cancers combined, the final full model including 19 CNIs, clinical covariates, and tumor marker-approximated subtypes (estrogen receptor [ER], progesterone receptor, ERBB2 amplification, and Ki67) significantly outperformed a model containing only clinical covariates and tumor subtypes (C-Index(full model), train[test]  =  0.72[0.71] ± 0.02 vs. C-Index(clinical + subtype model), train[test]  =  0.62[0.62] ± 0.02; p<10(-6)). In addition, the full model containing 19 CNIs significantly improved prognostication separately for ER-, HER2+, luminal B, and triple negative tumors over clinical variables alone. In summary, we show that a set of 19 CNIs discriminates risk of recurrence among early-stage breast tumors, independent of ER status. Further, our data suggest the presence of specific CNIs that promote and, in some cases, limit tumor spread.

Bagged gene shaving for the robust clustering of high-throughput data.

The analysis of high-throughput data sets, such as microarray data, often requires that individual variables (genes, for example) be grouped into clusters of variables with highly correlated values across all samples. Gene shaving is an established method for generating such clusters, but is overly sensitive to the input data: changing just one sample can determine whether or not an entire cluster is found. This paper describes a clustering method based on the bootstrap aggregation of gene shaving clusters, which overcomes this and other problems, and applies the new method to a large gene expression microarray dataset from brain tumour samples.

Gene expression meta-analysis supports existence of molecular apocrine breast cancer with a role for androgen receptor and implies interactions with ErbB family.

BACKGROUND: Pathway discovery from gene expression data can provide important insight into the relationship between signaling networks and cancer biology. Oncogenic signaling pathways are commonly inferred by comparison with signatures derived from cell lines. We use the Molecular Apocrine subtype of breast cancer to demonstrate our ability to infer pathways directly from patients' gene expression data with pattern analysis algorithms. METHODS: We combine data from two studies that propose the existence of the Molecular Apocrine phenotype. We use quantile normalization and XPN to minimize institutional bias in the data. We use hierarchical clustering, principal components analysis, and comparison of gene signatures derived from Significance Analysis of Microarrays to establish the existence of the Molecular Apocrine subtype and the equivalence of its molecular phenotype across both institutions. Statistical significance was computed using the Fasano & Franceschini test for separation of principal components and the hypergeometric probability formula for significance of overlap in gene signatures. We perform pathway analysis using LeFEminer and Backward Chaining Rule Induction to identify a signaling network that differentiates the subset. We identify a larger cohort of samples in the public domain, and use Gene Shaving and Robust Bayesian Network Analysis to detect pathways that interact with the defining signal. RESULTS: We demonstrate that the two separately introduced ER- breast cancer subsets represent the same tumor type, called Molecular Apocrine breast cancer. LeFEminer and Backward Chaining Rule Induction support a role for AR signaling as a pathway that differentiates this subset from others. Gene Shaving and Robust Bayesian Network Analysis detect interactions between the AR pathway, EGFR trafficking signals, and ErbB2. CONCLUSION: We propose criteria for meta-analysis that are able to demonstrate statistical significance in establishing molecular equivalence of subsets across institutions. Data mining strategies used here provide an alternative method to comparison with cell lines for discovering seminal pathways and interactions between signaling networks. Analysis of Molecular Apocrine breast cancer implies that therapies targeting AR might be hampered if interactions with ErbB family members are not addressed.