Andrew H. Beck

Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival

Automated quantification of thousands of morphologic features in microscopic images of breast cancer allows the construction of a robust prognostic model. An Automated Pathologist Reads Cancer Biopsies How is a camera different from the human eye? Only the eye’s images undergo extensive secondary processing as they are interpreted by the human brain. But what if we could program a computer to do the secondary processing? A pathologist reading a cancer biopsy slide matches his or her brain’s memory of certain cancer-related features (tubules, atypical nuclei, and mitosis) against the tissue. This decades-old scoring system is still standard in most places for prognosis and treatment of cancer, despite its variability and often unreliability. Now, Beck et al. have created an automated pathologist by replacing the human brain with sophisticated image processing software and instructing it to find quantitative aspects of breast cancer tissue that predict prognosis. The software located a set of features that strongly predicted breast cancer outcome in both training and validation samples. With an image analysis protocol they termed C-Path, the authors set their program loose on a set of samples from patients in the Netherlands. From more than 6000 features, the software found a set that were associated with samples from patients who had died sooner. The key aspect of this analysis was that these features were not predefined by a pathologist as being relevant to cancer; instead, the software itself found the cancer-related features among the very large set of measurements of the image. Classifying the tissue as epithelial or stromal, an important part of cancer diagnosis, took a bit of extra work: The authors needed to provide the software with some hand-marked samples so it could learn the difference. The C-Path score yielded information above and beyond that from many other measures of cancer severity including pathology grade, estrogen receptor status, tumor size, and lymph node status. In another, completely independent group of women from Vancouver, the C-Path score was also associated with overall survival. An unexpected finding was that the features that were the best predictors of patient survival were not from the cancer itself but were from the adjacent stromal tissue. Women with worse outcomes tended to have thin cords of epithelial cells infiltrating the stroma, which resulted in high-risk stromal matrix variability scores. These patients also tended to have more inflammatory cells in the stroma (picked up as dark areas by the software). Replacing the human brain with an unbiased image processing system can extract more information from microcopy images and discover new biological aspects of cancer tissue. The morphological interpretation of histologic sections forms the basis of diagnosis and prognostication for cancer. In the diagnosis of carcinomas, pathologists perform a semiquantitative analysis of a small set of morphological features to determine the cancer’s histologic grade. Physicians use histologic grade to inform their assessment of a carcinoma’s aggressiveness and a patient’s prognosis. Nevertheless, the determination of grade in breast cancer examines only a small set of morphological features of breast cancer epithelial cells, which has been largely unchanged since the 1920s. A comprehensive analysis of automatically quantitated morphological features could identify characteristics of prognostic relevance and provide an accurate and reproducible means for assessing prognosis from microscopic image data. We developed the C-Path (Computational Pathologist) system to measure a rich quantitative feature set from the breast cancer epithelium and stroma (6642 features), including both standard morphometric descriptors of image objects and higher-level contextual, relational, and global image features. These measurements were used to construct a prognostic model. We applied the C-Path system to microscopic images from two independent cohorts of breast cancer patients [from the Netherlands Cancer Institute (NKI) cohort, n = 248, and the Vancouver General Hospital (VGH) cohort, n = 328]. The prognostic model score generated by our system was strongly associated with overall survival in both the NKI and the VGH cohorts (both log-rank P ≤ 0.001). This association was independent of clinical, pathological, and molecular factors. Three stromal features were significantly associated with survival, and this association was stronger than the association of survival with epithelial characteristics in the model. These findings implicate stromal morphologic structure as a previously unrecognized prognostic determinant for breast cancer.

Inconsistency in large pharmacogenomic studies

Two large-scale pharmacogenomic studies were published recently in this journal. Genomic data are well correlated between studies; however, the measured drug response data are highly discordant. Although the source of inconsistencies remains uncertain, it has potential implications for using these outcome measures to assess gene–drug associations or select potential anticancer drugs on the basis of their reported results.

A Diverse Array of Cancer-Associated MTOR Mutations Are Hyperactivating and Can Predict Rapamycin Sensitivity

Genes encoding components of the PI3K-AKT-mTOR signaling axis are frequently mutated in cancer, but few mutations have been characterized in MTOR, the gene encoding the mTOR kinase. Using publicly available tumor genome sequencing data, we generated a comprehensive catalog of mTOR pathway mutations in cancer, identifying 33 MTOR mutations that confer pathway hyperactivation. The mutations cluster in six distinct regions in the C-terminal half of mTOR and occur in multiple cancer types, with one cluster particularly prominent in kidney cancer. The activating mutations do not affect mTOR complex assembly, but a subset reduces binding to the mTOR inhibitor DEPTOR. mTOR complex 1 (mTORC1) signaling in cells expressing various activating mutations remains sensitive to pharmacologic mTOR inhibition, but is partially resistant to nutrient deprivation. Finally, cancer cell lines with hyperactivating MTOR mutations display heightened sensitivity to rapamycin both in culture and in vivo xenografts, suggesting that such mutations confer mTOR pathway dependency.

Transcriptional profiling of long non-coding RNAs and novel transcribed regions across a diverse panel of archived human cancers

BackgroundMolecular characterization of tumors has been critical for identifying important genes in cancer biology and for improving tumor classification and diagnosis. Long non-coding RNAs, as a new, relatively unstudied class of transcripts, provide a rich opportunity to identify both functional drivers and cancer-type-specific biomarkers. However, despite the potential importance of long non-coding RNAs to the cancer field, no comprehensive survey of long non-coding RNA expression across various cancers has been reported.ResultsWe performed a sequencing-based transcriptional survey of both known long non-coding RNAs and novel intergenic transcripts across a panel of 64 archival tumor samples comprising 17 diagnostic subtypes of adenocarcinomas, squamous cell carcinomas and sarcomas. We identified hundreds of transcripts from among the known 1,065 long non-coding RNAs surveyed that showed variability in transcript levels between the tumor types and are therefore potential biomarker candidates. We discovered 1,071 novel intergenic transcribed regions and demonstrate that these show similar patterns of variability between tumor types. We found that many of these differentially expressed cancer transcripts are also expressed in normal tissues. One such novel transcript specifically expressed in breast tissue was further evaluated using RNA in situ hybridization on a panel of breast tumors. It was shown to correlate with low tumor grade and estrogen receptor expression, thereby representing a potentially important new breast cancer biomarker.ConclusionsThis study provides the first large survey of long non-coding RNA expression within a panel of solid cancers and also identifies a number of novel transcribed regions differentially expressed across distinct cancer types that represent candidate biomarkers for future research.

Oncogenic Role of Fusion-circRNAs Derived from Cancer-Associated Chromosomal Translocations

Chromosomal translocations encode oncogenic fusion proteins that have been proven to be causally involved in tumorigenesis. Our understanding of whether such genomic alterations also affect non-coding RNAs is limited, and their impact on circular RNAs (circRNAs) has not been explored. Here, we show that well-established cancer-associated chromosomal translocations give rise to fusion circRNAs (f-circRNA) that are produced from transcribed exons of distinct genes affected by the translocations. F-circRNAs contribute to cellular transformation, promote cell viability and resistance upon therapy, and have tumor-promoting properties in in vivo models. Our work expands the current knowledge regarding molecular mechanisms involved in cancer onset and progression, with potential diagnostic and therapeutic implications.

Antibody therapy targeting the CD47 protein is effective in a model of aggressive metastatic leiomyosarcoma

Antibodies against CD47, which block tumor cell CD47 interactions with macrophage signal regulatory protein-α, have been shown to decrease tumor size in hematological and epithelial tumor models by interfering with the protection from phagocytosis by macrophages that intact CD47 bestows upon tumor cells. Leiomyosarcoma (LMS) is a tumor of smooth muscle that can express varying levels of colony-stimulating factor-1 (CSF1), the expression of which correlates with the numbers of tumor-associated macrophages (TAMs) that are found in these tumors. We have previously shown that the presence of TAMs in LMS is associated with poor clinical outcome and the overall effect of TAMs in LMS therefore appears to be protumorigenic. However, the use of inhibitory antibodies against CD47 offers an opportunity to turn TAMs against LMS cells by allowing the phagocytic behavior of resident macrophages to predominate. Here we show that interference with CD47 increases phagocytosis of two human LMS cell lines, LMS04 and LMS05, in vitro. In addition, treatment of mice bearing subcutaneous LMS04 and LMS05 tumors with a novel, humanized anti-CD47 antibody resulted in significant reductions in tumor size. Mice bearing LMS04 tumors develop large numbers of lymph node and lung metastases. In a unique model for neoadjuvant treatment, mice were treated with anti-CD47 antibody starting 1 wk before resection of established primary tumors and subsequently showed a striking decrease in the size and number of metastases. These data suggest that treatment with anti-CD47 antibodies not only reduces primary tumor size but can also be used to inhibit the development of, or to eliminate, metastatic disease.

3′-End Sequencing for Expression Quantification (3SEQ) from Archival Tumor Samples

Gene expression microarrays are the most widely used technique for genome-wide expression profiling. However, microarrays do not perform well on formalin fixed paraffin embedded tissue (FFPET). Consequently, microarrays cannot be effectively utilized to perform gene expression profiling on the vast majority of archival tumor samples. To address this limitation of gene expression microarrays, we designed a novel procedure (3′-end sequencing for expression quantification (3SEQ)) for gene expression profiling from FFPET using next-generation sequencing. We performed gene expression profiling by 3SEQ and microarray on both frozen tissue and FFPET from two soft tissue tumors (desmoid type fibromatosis (DTF) and solitary fibrous tumor (SFT)) (total n = 23 samples, which were each profiled by at least one of the four platform-tissue preparation combinations). Analysis of 3SEQ data revealed many genes differentially expressed between the tumor types (FDR<0.01) on both the frozen tissue (∼9.6K genes) and FFPET (∼8.1K genes). Analysis of microarray data from frozen tissue revealed fewer differentially expressed genes (∼4.64K), and analysis of microarray data on FFPET revealed very few (69) differentially expressed genes. Functional gene set analysis of 3SEQ data from both frozen tissue and FFPET identified biological pathways known to be important in DTF and SFT pathogenesis and suggested several additional candidate oncogenic pathways in these tumors. These findings demonstrate that 3SEQ is an effective technique for gene expression profiling from archival tumor samples and may facilitate significant advances in translational cancer research.

Three differentiation states risk-stratify bladder cancer into distinct subtypes

Current clinical judgment in bladder cancer (BC) relies primarily on pathological stage and grade. We investigated whether a molecular classification of tumor cell differentiation, based on a developmental biology approach, can provide additional prognostic information. Exploiting large preexisting gene-expression databases, we developed a biologically supervised computational model to predict markers that correspond with BC differentiation. To provide mechanistic insight, we assessed relative tumorigenicity and differentiation potential via xenotransplantation. We then correlated the prognostic utility of the identified markers to outcomes within gene expression and formalin-fixed paraffin-embedded (FFPE) tissue datasets. Our data indicate that BC can be subclassified into three subtypes, on the basis of their differentiation states: basal, intermediate, and differentiated, where only the most primitive tumor cell subpopulation within each subtype is capable of generating xenograft tumors and recapitulating downstream populations. We found that keratin 14 (KRT14) marks the most primitive differentiation state that precedes KRT5 and KRT20 expression. Furthermore, KRT14 expression is consistently associated with worse prognosis in both univariate and multivariate analyses. We identify here three distinct BC subtypes on the basis of their differentiation states, each harboring a unique tumor-initiating population.

Cancer-Associated PTEN Mutants Act in a Dominant-Negative Manner to Suppress PTEN Protein Function

PTEN dysfunction plays a crucial role in the pathogenesis of hereditary and sporadic cancers. Here, we show that PTEN homodimerizes and, in this active conformation, exerts lipid phosphatase activity on PtdIns(3,4,5)P3. We demonstrate that catalytically inactive cancer-associated PTEN mutants heterodimerize with wild-type PTEN and constrain its phosphatase activity in a dominant-negative manner. To study the consequences of homo- and heterodimerization of wild-type and mutant PTEN in vivo, we generated Pten knockin mice harboring two cancer-associated PTEN mutations (PtenC124S and PtenG129E). Heterozygous Pten(C124S/+) and Pten(G129E/+) cells and tissues exhibit increased sensitivity to PI3-K/Akt activation compared to wild-type and Pten(+/-) counterparts, whereas this difference is no longer apparent between Pten(C124S/-) and Pten(-/-) cells. Notably, Pten KI mice are more tumor prone and display features reminiscent of complete Pten loss. Our findings reveal that PTEN loss and PTEN mutations are not synonymous and define a working model for the function and regulation of PTEN.

The fibromatosis signature defines a robust stromal response in breast carcinoma

Breast cancer is a heterogeneous disease, and the influence of stromal gene and protein expression patterns on the biological and clinical heterogeneity of the disease is poorly understood. We previously demonstrated that evaluation of the gene expression patterns of two soft-tissue tumors (desmoid-type fibromatosis (DTF) and solitary fibrous tumor) could be used to identify distinct stromal reaction patterns in breast carcinoma. In the current study, we examined four additional data sets obtained from four different institutions and containing gene expression data from a total of 561 breast cancer patients. We identified a core set of 66 DTF-associated genes that were consistently coordinately expressed in a subset of 25–35% of breast cancers. Breast carcinomas defined by high levels of coordinated expression of DTF core genes tend to be lower grade, express estrogen receptor, and show significantly longer survival across the four data sets. Using multiple tissue microarrays of archival breast cancer specimens obtained from a total of 745 patients, we demonstrated that a subset of breast cancers show coordinate expression of DTF core proteins by stromal cells in the tumor microenvironment. We evaluated the protein expression of a single DTF core protein (SPARC) on a tissue microarray with clinical outcome data and demonstrated that breast cancers with strong stromal protein expression of SPARC show a trend for increased survival. Our data demonstrate that the DTF core gene set is a robust descriptor of a distinct stromal response that is associated with improved clinical outcome in breast cancer patients.