Mary E. Edgerton

Ductal Carcinoma in Situ: State of the Science and Roadmap to Advance the Field

PURPOSE Ductal carcinoma in situ (DCIS) is the fourth leading cancer for women in the United States. Understanding of the biology and clinical behavior of DCIS is imperfect. This article highlights the current knowledge base and the scientific roadmap needed to advance the field. METHODS This article is based on work done by and consultations obtained from leading experts in the field over a 6-month period that culminated in a full-day symposium designed to systematically review the most pertinent MEDLINE published reports and develop a roadmap to elucidate the molecular steps of carcinogenesis, reduce the extent or prevent the need for therapies, eliminate recurrences, and reduce morbidity. RESULTS Expression profiling of pure DCIS will help elucidate the molecular characteristics that distinguish high-risk lesions from clinically irrelevant lesions. The development of new methods of extracting RNA from processed tissues may provide opportunities for research. Mammography often underestimates the pathologic extent of DCIS; other imaging methods need to be investigated for detection and monitoring of disease stability or progression. Novel biologic agents are being delivered in neoadjuvant clinical trials, and alternative methods for breast irradiation are being studied. Future trials of treatment versus no treatment for biologically selected cases of DCIS should be developed. CONCLUSION There is a critical need for a concerted international effort among patients with DCIS, clinicians, and basic scientists to conduct the research necessary to improve fundamental understanding of the biology and clinical behavior of DCIS and prevent development of invasive breast cancer.

Prediction of drug response in breast cancer using integrative experimental/computational modeling

Nearly 30% of women with early-stage breast cancer develop recurrent disease attributed to resistance to systemic therapy. Prevailing models of chemotherapy failure describe three resistant phenotypes: cells with alterations in transmembrane drug transport, increased detoxification and repair pathways, and alterations leading to failure of apoptosis. Proliferative activity correlates with tumor sensitivity. Cell-cycle status, controlling proliferation, depends on local concentration of oxygen and nutrients. Although physiologic resistance due to diffusion gradients of these substances and drugs is a recognized phenomenon, it has been difficult to quantify its role with any accuracy that can be exploited clinically. We implement a mathematical model of tumor drug response that hypothesizes specific functional relationships linking tumor growth and regression to the underlying phenotype. The model incorporates the effects of local drug, oxygen, and nutrient concentrations within the three-dimensional tumor volume, and includes the experimentally observed resistant phenotypes of individual cells. We conclude that this integrative method, tightly coupling computational modeling with biological data, enhances the value of knowledge gained from current pharmacokinetic measurements, and, further, that such an approach could predict resistance based on specific tumor properties and thus improve treatment outcome.

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.

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

BackgroundTissue 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.MethodsA 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.ResultsThe 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.ConclusionThe 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 tumor 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 tumor subtypes; and pseudogene expression alone can accurately classify the major histological subtypes of endometrial cancer. Across cancer types, the tumor 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.

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.

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.

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

Perry K D, Reynolds C, Rosen D G, Edgerton M E, Albarracin C T, Gilcrease M Z, Sahin A A, Abraham S C & Wu Y
(2011) Histopathology59, 619–630

Metastatic neuroendocrine tumour in the breast

Perry K D, Reynolds C, Rosen D G, Edgerton M E, Albarracin C T, Gilcrease M Z, Sahin A A, Abraham S C & Wu Y
(2011) Histopathology59, 619–630

Copy Number Imbalances between Screen- and Symptom-Detected Breast Cancers and Impact on Disease-Free Survival

Screening mammography results in the increased detection of indolent tumors. We hypothesized that screen- and symptom-detected tumors would show genotypic differences as copy number imbalances (CNI) that, in part, explain differences in the clinical behavior between screen- and symptom-detected breast tumors. We evaluated 850 women aged 40 and above diagnosed with stage I and II breast cancer at the University of Texas MD Anderson Cancer Center between 1985 and 2000 with information available on method of tumor detection (screen vs. symptoms). CNIs in screen- and symptom-detected tumors were identified using high-density molecular inversion probe arrays. Cox proportional modeling was used to estimate the effect of method of tumor detection on disease-free survival after adjusting for age, stage, and the CNIs. The majority of tumors were symptom detected (n = 603) compared with screen detected (n = 247). Copy number gains in chromosomes 2p, 3q, 8q, 11p, and 20q were associated with method of breast cancer detection (P < 0.00001). We estimated that 32% and 63% of the survival advantage of screen detection was accounted for by age, stage, nuclear grade, and Ki67 in women aged 50 to 70 and aged 40 to 87, respectively. In each age category, an additional 20% of the survival advantage was accounted for by CNIs associated with method of detection. Specific CNIs differ between screen- and symptom-detected tumors and explain part of the survival advantage associated with screen-detected tumors. Measurement of tumor genotype has the potential to improve discrimination between indolent and aggressive screen-detected tumors and aids patient and physician decision making about use of surgical and adjuvant treatments. Cancer Prev Res; 4(10); 1609–16. ©2011 AACR.