No abstract

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.

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 to 10 mm 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 one 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.

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 exci...

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.