Context matters-consensus molecular subtypes of colorectal cancer as biomarkers for clinical trials

The Colorectal Cancer Subtyping Consortium identified four gene expression consensus molecular subtypes, CMS1 (immune), CMS2 (canonical), CMS3 (metabolic), and CMS4 (mesenchymal), using multiple microarray or RNA-sequencing datasets of primary tumor samples mainly from early stage colon cancer patients. Consequently, rectal tumors and stage IV tumors (possibly reflective of more aggressive disease) were underrepresented, and no chemo-and/or radiotherapy pretreated samples or metastatic lesions were included. In view of their possible effect on gene expression and consequently subtype classification, sample source and treatments received by the patients before collection must be carefully considered when applying the classifier to new datasets. Recently, several correlative analyses of clinical trials demonstrated the applicability of this classification to the metastatic setting, confirmed the prognostic value of CMS subtypes after relapse and hinted at differential sensitivity to treatments. Here, we discuss why contexts and equivocal factors need to be taken into account when analyzing clinical trial data, including potential selection biases, type of platform, and type of algorithm used for subtype prediction. This perspective article facilitates both our clinical and research understanding of the application of this classifier to expedite subtype-based clinical trials

MCAM: A Database to Accelerate the Identification of Functional Cell Adhesion Molecules

In the post-genomic era, computational identification of cell adhesion molecules (CAMs) becomes important in defining new targets for diagnosis and treatment of various diseases including cancer. Lack of a comprehensive CAM-specific database restricts our ability to identify and characterize novel CAMs. Therefore, we developed a comprehensive mammalian cell adhesion molecule (MCAM) database. The current version is an interactive Web-based database, which provides the resources needed to search mouse, human and rat-specific CAMs and their sequence information and characteristics such as gene functions and virtual gene expression patterns in normal and tumor tissues as well as cell lines. Moreover, the MCAM database can be used for various bioinformatics and biological analyses including identifying CAMs involved in cell-cell interactions and homing of lymphocytes, hematopoietic stem cells and malignant cells to specific organs using data from high-throughput experiments. Furthermore, the database can also be used for training and testing existing transmembrane (TM) topology prediction methods specifically for CAM sequences. The database is freely available online at http://app1.unmc.edu/mcam

Genomic aberrations in normal tissue adjacent to HER2-amplified breast cancers: field cancerization or contaminating tumor cells?

Field cancerization effects as well as isolated tumor cell foci extending well beyond the invasive tumor margin have been described previously to account for local recurrence rates following breast conserving surgery despite adequate surgical margins and breast radiotherapy. To look for evidence of possible tumor cell contamination or field cancerization by genetic effects, a pilot study (Study 1: 12 sample pairs) followed by a verification study (Study 2: 20 sample pairs) were performed on DNA extracted from HER2-positive breast tumors and matching normal adjacent mammary tissue samples excised 1-3 cm beyond the invasive tumor margin. High-resolution molecular inversion probe (MIP) arrays were used to compare genomic copy number variations, including increased HER2 gene copies, between the paired samples; as well, a detailed histologic and immunohistochemical (IHC) re-evaluation of all Study 2 samples was performed blinded to the genomic results to characterize the adjacent normal tissue composition bracketing the DNA-extracted samples. Overall, 14/32 (44 %) sample pairs from both studies produced genome-wide evidence of genetic aberrations including HER2 copy number gains within the adjacent normal tissue samples. The observed single-parental origin of monoallelic HER2 amplicon haplotypes shared by informative tumor-normal pairs, as well as commonly gained loci elsewhere on 17q, suggested the presence of contaminating tumor cells in the genomically aberrant normal samples. Histologic and IHC analyses identified occult 25-200 μm tumor cell clusters overexpressing HER2 scattered in more than half, but not all, of the genomically aberrant normal samples re-evaluated, but in none of the genomically normal samples. These genomic and microscopic findings support the conclusion that tumor cell contamination rather than genetic field cancerization represents the likeliest cause of local clinical recurrence rates following breast conserving surgery, and mandate caution in assuming the genomic normalcy of histologically benign appearing peritumor breast tissue

Identification and Characterization of Poorly Differentiated Invasive Carcinomas in a Mouse Model of Pancreatic Neuroendocrine Tumorigenesis

Pancreatic neuroendocrine tumors (PanNETs) are a relatively rare but clinically challenging tumor type. In particular, high grade, poorly-differentiated PanNETs have the worst patient prognosis, and the underlying mechanisms of disease are poorly understood. In this study we have identified and characterized a previously undescribed class of poorly differentiated PanNETs in the RIP1-Tag2 mouse model. We found that while the majority of tumors in the RIP1-Tag2 model are well-differentiated insulinomas, a subset of tumors had lost multiple markers of beta-cell differentiation and were highly invasive, leading us to term them poorly differentiated invasive carcinomas (PDICs). In addition, we found that these tumors exhibited a high mitotic index, resembling poorly differentiated (PD)-PanNETs in human patients. Interestingly, we identified expression of Id1, an inhibitor of DNA binding gene, and a regulator of differentiation, specifically in PDIC tumor cells by histological analysis. The identification of PDICs in this mouse model provides a unique opportunity to study the pathology and molecular characteristics of PD-PanNETs

The expression level of HJURP has an independent prognostic impact and predicts the sensitivity to radiotherapy in breast cancer

INTRODUCTION. HJURP (Holliday Junction Recognition Protein) is a newly discovered gene reported to function at centromeres and to interact with CENPA. However its role in tumor development remains largely unknown. The goal of this study was to investigate the clinical significance of HJURP in breast cancer and its correlation with radiotherapeutic outcome. METHODS. We measured HJURP expression level in human breast cancer cell lines and primary breast cancers by Western blot and/or by Affymetrix Microarray; and determined its associations with clinical variables using standard statistical methods. Validation was performed with the use of published microarray data. We assessed cell growth and apoptosis of breast cancer cells after radiation using high-content image analysis. RESULTS. HJURP was expressed at higher level in breast cancer than in normal breast tissue. HJURP mRNA levels were significantly associated with estrogen receptor (ER), progesterone receptor (PR), Scarff-Bloom-Richardson (SBR) grade, age and Ki67 proliferation indices, but not with pathologic stage, ERBB2, tumor size, or lymph node status. Higher HJURP mRNA levels significantly decreased disease-free and overall survival. HJURP mRNA levels predicted the prognosis better than Ki67 proliferation indices. In a multivariate Cox proportional-hazard regression, including clinical variables as covariates, HJURP mRNA levels remained an independent prognostic factor for disease-free and overall survival. In addition HJURP mRNA levels were an independent prognostic factor over molecular subtypes (normal like, luminal, Erbb2 and basal). Poor clinical outcomes among patients with high HJURP expression were validated in five additional breast cancer cohorts. Furthermore, the patients with high HJURP levels were much more sensitive to radiotherapy. In vitro studies in breast cancer cell lines showed that cells with high HJURP levels were more sensitive to radiation treatment and had a higher rate of apoptosis than those with low levels. Knock down of HJURP in human breast cancer cells using shRNA reduced the sensitivity to radiation treatment. HJURP mRNA levels were significantly correlated with CENPA mRNA levels. CONCLUSIONS. HJURP mRNA level is a prognostic factor for disease-free and overall survival in patients with breast cancer and is a predictive biomarker for sensitivity to radiotherapy.National Institutes of Health, National Cancer Institute (R01 CA116481, P50 CA 5820, P30 CA 82103, U54 CA 112970); Office of Science; U.S. Department of Energy Office of Science, Office of Biological & Environmental Research (DE-AC02-05CH11231

T-cell receptor determinants of response to chemoradiation in locally-advanced HPV16-driven malignancies

BackgroundThe effect of chemoradiation on the anti-cancer immune response is being increasingly acknowledged; however, its clinical implications in treatment responses are yet to be fully understood. Human papillomavirus (HPV)-driven malignancies express viral oncogenic proteins which may serve as tumor-specific antigens and represent ideal candidates for monitoring the peripheral T-cell receptor (TCR) changes secondary to chemoradiotherapy (CRT).MethodsWe performed intra-tumoral and pre- and post-treatment peripheral TCR sequencing in a cohort of patients with locally-advanced HPV16-positive cancers treated with CRT. An in silico computational pipeline was used to cluster TCR repertoire based on epitope-specificity and to predict affinity between these clusters and HPV16-derived epitopes.ResultsIntra-tumoral repertoire diversity, intra-tumoral and post-treatment peripheral CDR3β similarity clustering were predictive of response. In responders, CRT triggered an increase peripheral TCR clonality and clonal relatedness. Post-treatment expansion of baseline peripheral dominant TCRs was associated with response. Responders showed more baseline clustered structures of TCRs maintained post-treatment and displayed significantly more maintained clustered structures. When applying clustering by TCR-specificity methods, responders displayed a higher proportion of intra-tumoral TCRs predicted to recognise HPV16 peptides.ConclusionsBaseline TCR characteristics and changes in the peripheral T-cell clones triggered by CRT are associated with treatment outcome. Maintenance and boosting of pre-existing clonotypes are key elements of an effective anti-cancer immune response driven by CRT, supporting a paradigm in which the immune system plays a central role in the success of CRT in current standard-of-care protocols

Prediction of epigenetically regulated genes in breast cancer cell lines

Methylation of CpG islands within the DNA promoter regions is one mechanism that leads to aberrant gene expression in cancer. In particular, the abnormal methylation of CpG islands may silence associated genes. Therefore, using high-throughput microarrays to measure CpG island methylation will lead to better understanding of tumor pathobiology and progression, while revealing potentially new biomarkers. We have examined a recently developed high-throughput technology for measuring genome-wide methylation patterns called mTACL. Here, we propose a computational pipeline for integrating gene expression and CpG island methylation profles to identify epigenetically regulated genes for a panel of 45 breast cancer cell lines, which is widely used in the Integrative Cancer Biology Program (ICBP). The pipeline (i) reduces the dimensionality of the methylation data, (ii) associates the reduced methylation data with gene expression data, and (iii) ranks methylation-expression associations according to their epigenetic regulation. Dimensionality reduction is performed in two steps: (i) methylation sites are grouped across the genome to identify regions of interest, and (ii) methylation profles are clustered within each region. Associations between the clustered methylation and the gene expression data sets generate candidate matches within a fxed neighborhood around each gene. Finally, the methylation-expression associations are ranked through a logistic regression, and their significance is quantified through permutation analysis. Our two-step dimensionality reduction compressed 90% of the original data, reducing 137,688 methylation sites to 14,505 clusters. Methylation-expression associations produced 18,312 correspondences, which were used to further analyze epigenetic regulation. Logistic regression was used to identify 58 genes from these correspondences that showed a statistically signifcant negative correlation between methylation profles and gene expression in the panel of breast cancer cell lines. Subnetwork enrichment of these genes has identifed 35 common regulators with 6 or more predicted markers. In addition to identifying epigenetically regulated genes, we show evidence of differentially expressed methylation patterns between the basal and luminal subtypes. Our results indicate that the proposed computational protocol is a viable platform for identifying epigenetically regulated genes. Our protocol has generated a list of predictors including COL1A2, TOP2A, TFF1, and VAV3, genes whose key roles in epigenetic regulation is documented in the literature. Subnetwork enrichment of these predicted markers further suggests that epigenetic regulation of individual genes occurs in a coordinated fashion and through common regulators

The consensus molecular subtypes of colorectal cancer

Colorectal cancer (CRC) is a frequently lethal disease with heterogeneous outcomes and drug responses. To resolve inconsistencies among the reported gene expression-based CRC classifications and facilitate clinical translation, we formed an international consortium dedicated to large-scale data sharing and analytics across expert groups. We show marked interconnectivity between six independent classification systems coalescing into four consensus molecular subtypes (CMSs) with distinguishing features: CMS1 (microsatellite instability immune, 14%), hypermutated, microsatellite unstable and strong immune activation; CMS2 (canonical, 37%), epithelial, marked WNT and MYC signaling activation; CMS3 (metabolic, 13%), epithelial and evident metabolic dysregulation; and CMS4 (mesenchymal, 23%), prominent transforming growth factor-beta activation, stromal invasion and angiogenesis. Samples with mixed features (13%) possibly represent a transition phenotype or intratumoral heterogeneity. We consider the CMS groups the most robust classification system currently available for CRC-with clear biological interpretability-and the basis for future clinical stratification and subtype-based targeted interventions

DNA methylation patterns identify subgroups of pancreatic neuroendocrine tumors with clinical association

Here we report the DNA methylation profile of 84 sporadic pancreatic neuroendocrine tumors (PanNETs) with associated clinical and genomic information. We identified three subgroups of PanNETs, termed T1, T2 and T3, with distinct patterns of methylation. The T1 subgroup was enriched for functional tumors and ATRX, DAXX and MEN1 wild-type genotypes. The T2 subgroup contained tumors with mutations in ATRX, DAXX and MEN1 and recurrent patterns of chromosomal losses in half of the genome with no association between regions with recurrent loss and methylation levels. T2 tumors were larger and had lower methylation in the MGMT gene body, which showed positive correlation with gene expression. The T3 subgroup harboured mutations in MEN1 with recurrent loss of chromosome 11, was enriched for grade G1 tumors and showed histological parameters associated with better prognosis. Our results suggest a role for methylation in both driving tumorigenesis and potentially stratifying prognosis in PanNETs

A Cross-Species Analysis of a Mouse Model of Breast Cancer-Specific Osteolysis and Human Bone Metastases Using Gene Expression Profiling

<p>Abstract</p> <p>Background</p> <p>Breast cancer is the second leading cause of cancer-related death in women in the United States. During the advanced stages of disease, many breast cancer patients suffer from bone metastasis. These metastases are predominantly osteolytic and develop when tumor cells interact with bone. <it>In vivo </it>models that mimic the breast cancer-specific osteolytic bone microenvironment are limited. Previously, we developed a mouse model of tumor-bone interaction in which three mouse breast cancer cell lines were implanted onto the calvaria. Analysis of tumors from this model revealed that they exhibited strong bone resorption, induction of osteoclasts and intracranial penetration at the tumor bone (TB)-interface.</p> <p>Methods</p> <p>In this study, we identified and used a TB microenvironment-specific gene expression signature from this model to extend our understanding of the metastatic bone microenvironment in human disease and to predict potential therapeutic targets.</p> <p>Results</p> <p>We identified a TB signature consisting of 934 genes that were commonly (among our 3 cell lines) and specifically (as compared to tumor-alone area within the bone microenvironment) up- and down-regulated >2-fold at the TB interface in our mouse osteolytic model. By comparing the TB signature with gene expression profiles from human breast metastases and an <it>in vitro </it>osteoclast model, we demonstrate that our model mimics both the human breast cancer bone microenvironment and osteoclastogenesis. Furthermore, we observed enrichment in various signaling pathways specific to the TB interface; that is, TGF-β and myeloid self-renewal pathways were activated and the Wnt pathway was inactivated. Lastly, we used the TB-signature to predict cyclopenthiazide as a potential inhibitor of the TB interface.</p> <p>Conclusion</p> <p>Our mouse breast cancer model morphologically and genetically resembles the osteoclastic bone microenvironment observed in human disease. Characterization of the gene expression signature specific to the TB interface in our model revealed signaling mechanisms operative in human breast cancer metastases and predicted a therapeutic inhibitor of cancer-mediated osteolysis.</p