[Show abstract][Hide abstract] ABSTRACT: Oncocytomas are predominantly benign neoplasms possessing pathogenic mitochondrial mutations and accumulation of respiration-defective mitochondria, characteristics of unknown significance. Using exome and transcriptome sequencing, we identified two main subtypes of renal oncocytoma. Type 1 is diploid with CCND1 rearrangements, whereas type 2 is aneuploid with recurrent loss of chromosome 1, X or Y, and/or 14 and 21, which may proceed to more aggressive eosinophilic chromophobe renal cell carcinoma (ChRCC). Oncocytomas activate 5′ adenosine monophosphate-activated protein kinase (AMPK) and Tp53 (p53) and display disruption of Golgi and autophagy/lysosome trafficking, events attributed to defective mitochondrial function. This suggests that the genetic defects in mitochondria activate a metabolic checkpoint, producing autophagy impairment and mitochondrial accumulation that limit tumor progression, revealing a novel tumor-suppressive mechanism for mitochondrial inhibition with metformin. Alleviation of this metabolic checkpoint in type 2 by p53 mutations may allow progression to eosinophilic ChRCC, indicating that they represent higher risk.
[Show abstract][Hide abstract] ABSTRACT: Alternate transcripts from a single gene locus greatly enhance the combinatorial flexibility of the human transcriptome. Different patterns of exon usage have been observed when comparing normal tissue to cancers, suggesting that variant transcripts may play a role in the tumor phenotype.
Ribonucleic acid-sequencing (RNA-seq) data from breast cancer samples was used to identify an intronic start variant transcript of Acyl-CoA oxidase 2, ACOX2 (ACOX2-i9). Difference in expression between Estrogen Receptor (ER) positive and ER negative patients was assessed by the Wilcoxon rank sum test, and the findings validated in The Cancer Genome Atlas (TCGA) breast cancer dataset (BRCA). ACOX2-i9 expression was also assessed in cell lines using both quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) and Western blot analysis. Knock down by short hairpin RNA (shRNA) and colony formation assays were used to determine whether ACOX2-i9 expression would influence cellular fitness. The effect of ACOX2-i9 expression on patient survival was assessed by the Kaplan-Meier survival function, and association to clinical parameters was analyzed using a Fisher exact test.
The expression and translation of ACOX2-i9 into a 25 kDa protein was demonstrated in HepG2 cells as well as in several breast cancer cell lines. shRNA knock down of the ACOX2-i9 variant resulted in decreased cell viability of T47D and MDA-MB 436 cells. Moreover, expression of ACOX2-i9 was shown to be estrogen regulated, being induced by propyl pyrazoletriol and inhibited by tamoxifen and fulvestrant in ER+ T47D and Mcf-7 cells, but not in the ER- MDA-MB 436 cell line. This variant transcript showed expression predominantly in ER-positive breast tumors as assessed in our initial set of 53 breast cancers and further validated in 87 tumor/normal pairs from the TCGA breast cancer dataset, and expression was associated with better outcome in ER positive patients.
ACOX2-i9 is specifically enriched in ER+ breast cancers where expression of the variant is associated with improved outcome. These data identify variant ACOX2 as a potential novel therapeutic biomarker in ER+ breast tumors.
[Show abstract][Hide abstract] ABSTRACT: Metabotropic glutamate receptor 1 (mGluR1/Grm1) is a member of the G-protein-coupled receptor superfamily, which was once thought to only participate in synaptic transmission and neuronal excitability, but has more recently been implicated in non-neuronal tissue functions. We previously described the oncogenic properties of Grm1 in cultured melanocytes in vitro and in spontaneous melanoma development with 100 % penetrance in vivo. Aberrant mGluR1 expression was detected in 60-80 % of human melanoma cell lines and biopsy samples. As most human cancers are of epithelial origin, we utilized immortalized mouse mammary epithelial cells (iMMECs) as a model system to study the transformative properties of Grm1. We introduced Grm1 into iMMECs and isolated several stable mGluR1-expressing clones. Phenotypic alterations in mammary acinar architecture were assessed using three-dimensional morphogenesis assays. We found that mGluR1-expressing iMMECs exhibited delayed lumen formation in association with decreased central acinar cell death, disrupted cell polarity, and a dramatic increase in the activation of the mitogen-activated protein kinase pathway. Orthotopic implantation of mGluR1-expressing iMMEC clones into mammary fat pads of immunodeficient nude mice resulted in mammary tumor formation in vivo. Persistent mGluR1 expression was required for the maintenance of the tumorigenic phenotypes in vitro and in vivo, as demonstrated by an inducible Grm1-silencing RNA system. Furthermore, mGluR1 was found be expressed in human breast cancer cell lines and breast tumor biopsies. Elevated levels of extracellular glutamate were observed in mGluR1-expressing breast cancer cell lines and concurrent treatment of MCF7 xenografts with glutamate release inhibitor, riluzole, and an AKT inhibitor led to suppression of tumor progression. Our results are likely relevant to human breast cancer, highlighting a putative role of mGluR1 in the pathophysiology of breast cancer and the potential of mGluR1 as a novel therapeutic target.
Full-text · Article · Apr 2015 · Breast Cancer Research and Treatment
[Show abstract][Hide abstract] ABSTRACT: Clinical studies using prognostic and predictive signatures have shown that an immune signal emanating from whole tumors reflects the level of immune cell infiltration-a high immune signal linked to improved outcome. Factors regulating immune cell trafficking to the tumor, however, are not known. Previous work has shown that expression of interferon regulatory factor 5 (IRF5), a critical immune regulator, is lost in ~80% of invasive ductal carcinomas examined. We postulated that IRF5-positive and -negative breast tumors would differentially regulate immune cell trafficking to the tumor. Using a focused tumor inflammatory array, differences in cytokine and chemokine expression were examined between IRF5-positive and -negative MDA-MB-231 cells grown in three-dimensional culture. A number of cytokines/chemokines were found to be dysregulated between cultures. CXCL13 was identified as a direct target of IRF5 resulting in the enhanced recruitment of B and T cells to IRF5-positive tumor-conditioned media. The ability of IRF5 to regulate mediators of cell migration was confirmed by enzyme-linked immunosorbent assay, chromatin immunoprecipitation assay, small interfering RNA knockdown and immunofluorescence staining of human breast tumor tissues. Analysis of primary immune cell subsets revealed that IRF5 specifically recruits CXCR5(+) B and T cells to the tumor; CXCR5 is the receptor for CXCL13. Analysis of primary breast tumor tissues revealed a significant correlation between IRF5 and CXCL13 expression providing clinical relevance to the study. Together, these data support that IRF5 directly regulates a network of genes that shapes a tumor immune response and may, in combination with CXCL13, serve as a novel prognostic marker for antitumor immunity.Immunology and Cell Biology advance online publication, 23 December 2014; doi:10.1038/icb.2014.110.
No preview · Article · Dec 2014 · Immunology and Cell Biology
[Show abstract][Hide abstract] ABSTRACT: Classification of pediatric brain tumors with unusual histologic and clinical features may be a diagnostic challenge to the pathologist. We present a case of a 12-year-old girl with a primary intracranial tumor. The tumor classification was not certain initially, and the site of origin and clinical behavior were unusual. Genomic characterization of the tumor using a Clinical Laboratory Improvement Amendment (CLIA)-certified next-generation sequencing assay assisted in the diagnosis and translated into patient benefit, albeit transient. Our case argues that next generation sequencing may play a role in the pathological classification of pediatric brain cancers and guiding targeted therapy, supporting additional studies of genetically targeted therapeutics.
[Show abstract][Hide abstract] ABSTRACT: PALB2 links BRCA1 and BRCA2 in homologous recombinational repair of DNA double strand breaks (DSBs). Mono-allelic mutations
in PALB2 increase the risk of breast, pancreatic, and other cancers, and biallelic mutations cause Fanconi anemia (FA). Like Brca1 and Brca2, systemic knock-out of Palb2 in mice results in embryonic lethality. In this study, we generated a hypomorphic Palb2 allele expressing a mutant PALB2 protein unable to bind BRCA1. Consistent with an FA-like phenotype, cells from the mutant
mice showed hypersensitivity and chromosomal breakage when treated with mitomycin C, a DNA interstrand crosslinker. Moreover,
mutant males showed reduced fertility due to impaired meiosis and increased apoptosis in germ cells. Interestingly, mutant
meiocytes showed a significant defect in sex chromosome synapsis, which likely contributed to the germ cell loss and fertility
defect. Our results underscore the in vivo importance of the PALB2-BRCA1 complex formation in DSB repair and male meiosis.
[Show abstract][Hide abstract] ABSTRACT: This paper presents a deep learning approach for automatic detection and visual analysis of invasive ductal carcinoma (IDC) tissue regions in whole slide images (WSI) of breast cancer (BCa). Deep learning approaches are learn-from-data methods involving computational modeling of the learning process. This approach is similar to how human brain works using different interpretation levels or layers of most representative and useful features resulting into a hierarchical learned representation. These methods have been shown to outpace traditional approaches of most challenging problems in several areas such as speech recognition and object detection. Invasive breast cancer detection is a time consuming and challenging task primarily because it involves a pathologist scanning large swathes of benign regions to ultimately identify the areas of malignancy. Precise delineation of IDC in WSI is crucial to the subsequent estimation of grading tumor aggressiveness and predicting patient outcome. DL approaches are particularly adept at handling these types of problems, especially if a large number of samples are available for training, which would also ensure the generalizability of the learned features and classifier. The DL framework in this paper extends a number of convolutional neural networks (CNN) for visual semantic analysis of tumor regions for diagnosis support. The CNN is trained over a large amount of image patches (tissue regions) from WSI to learn a hierarchical part-based representation. The method was evaluated over a WSI dataset from 162 patients diagnosed with IDC. 113 slides were selected for training and 49 slides were held out for independent testing. Ground truth for quantitative evaluation was provided via expert delineation of the region of cancer by an expert pathologist on the digitized slides. The experimental evaluation was designed to measure classifier accuracy in detecting IDC tissue regions in WSI. Our method yielded the best quantitative results for automatic detection of IDC regions in WSI in terms of F-measure and balanced accuracy (71.80%, 84.23%), in comparison with an approach using handcrafted image features (color, texture and edges, nuclear textural and architecture), and a machine learning classifier for invasive tumor classification using a Random Forest. The best performing handcrafted features were fuzzy color histogram (67.53%, 78.74%) and RGB histogram (66.64%, 77.24%). Our results also suggest that at least some of the tissue classification mistakes (false positives and false negatives) were less due to any fundamental problems associated with the approach, than the inherent limitations in obtaining a very highly granular annotation of the diseased area of interest by an expert pathologist.
Full-text · Article · Feb 2014 · Proceedings of SPIE - The International Society for Optical Engineering