Pan-urologic cancer genomic subtypes that transcend tissue of origin

AbstractUrologic cancers include cancers of the bladder, kidney, prostate, and testes, with common molecular features spanning different types. Here, we show that 1954 urologic cancers can be classified into nine major genomic subtypes, on the basis of multidimensional and comprehensive molecular characterization (including DNA methylation and copy number, and RNA and protein expression). Tissue dominant effects are first removed computationally in order to define these subtypes, which reveal common processes—reflecting in part tumor microenvironmental influences—driving cellular behavior across tumor lineages. Six of the subtypes feature a mixture of represented cancer types as defined by tissue or cell of origin. Differences in patient survival and in the manifestation of specific pathways—including hypoxia, metabolism, NRF2-ARE, Hippo, and immune checkpoint—can further distinguish the subtypes. Immune checkpoint markers and molecular signatures of macrophages and T cell infiltrates are relatively high within distinct subsets of each cancer type studied. The pan-urologic cancer genomic subtypes would facilitate information sharing involving therapeutic implications between tissue-oriented domains.</jats:p

Investigating prostate cancer tumour-stroma interactions - clinical and biological insights from an evolutionary game

BACKGROUND: Tumours are made up of a mixed population of different types of cells that include normal structures as well as ones associated with the malignancy, and there are multiple interactions between the malignant cells and the local microenvironment. These intercellular interactions, modulated by the microenvironment, effect tumour progression and represent a largely under appreciated therapeutic target. We use observations of primary tumor biology from prostate cancer to extrapolate a mathematical model: specifically; it has been observed that in prostate cancer three disparate cellular outcomes predominate: (i) the tumour remains well differentiated and clinically indolent - in this case the local stromal cells may act to restrain the growth of the cancer; (ii) early in its genesis the tumour acquires a highly malignant phenotype, growing rapidly and displacing the original stromal population (often referred to as small cell prostate cancer) - these less common aggressive tumours are relatively independent of the local microenvironment; and, (iii) the tumour co-opts the local stroma - taking on a classic stromagenic phenotype where interactions with the local microenvironment are critical to the cancer growth. METHODS: We present an evolutionary game theoretical construct that models the influence of tumour-stroma interactions in driving these outcomes. We consider three characteristic and distinct cellular populations: stromal cells, tumour cells that are self-reliant in terms of microenvironmental factors and tumour cells that depend on the environment for resources but can also co-opt stroma. &#xd;&#xa;RESULTS: Using evolutionary game theory we explore a number of different scenarios that elucidate the impact of tumour-stromal interactions on the dynamics of prostate cancer growth and progression and how different treatments in the metastatic setting can affect different types of tumors.&#xd;&#xa;CONCLUSIONS: The tumour microenvironment plays a crucial role selecting the traits of the tumour cells that will determine prostate cancer progression. Equally important, treatments like hormone therapy affect the selection of these cancer phenotypes making it very important to understand how they impact prostate cancer&#x2019;s somatic evolution

Genomic investigation of etiologic heterogeneity: methodologic challenges

Background: The etiologic heterogeneity of cancer has traditionally been investigated by comparing risk factor frequencies within candidate sub-types, defined for example by histology or by distinct tumor markers of interest. Increasingly tumors are being profiled for molecular features much more extensively. This greatly expands the opportunities for defining distinct sub-types. In this article we describe an exploratory analysis of the etiologic heterogeneity of clear cell kidney cancer. Data are available on the primary known risk factors for kidney cancer, while the tumors are characterized on a genome-wide basis using expression, methylation, copy number and mutational profiles. Methods: We use a novel clustering strategy to identify sub-types. This is accomplished independently for the expression, methylation and copy number profiles. The goals are to identify tumor sub-types that are etiologically distinct, to identify the risk factors that define specific sub-types, and to endeavor to characterize the key genes that appear to represent the principal features of the distinct sub-types. Results: The analysis reveals strong evidence that gender represents an important factor that distinguishes disease sub-types. The sub-types defined using expression data and methylation data demonstrate considerable congruence and are also clearly correlated with mutations in important cancer genes. These sub-types are also strongly correlated with survival. The complexity of the data presents many analytical challenges including, prominently, the risk of false discovery. Conclusions: Genomic profiling of tumors offers the opportunity to identify etiologically distinct sub-types, paving the way for a more refined understanding of cancer etiology. Electronic supplementary material The online version of this article (doi:10.1186/1471-2288-14-138) contains supplementary material, which is available to authorized users

Clinical and molecular characterization of HER2 amplified-pancreatic cancer

&lt;p&gt;Background: Pancreatic cancer is one of the most lethal and molecularly diverse malignancies. Repurposing of therapeutics that target specific molecular mechanisms in different disease types offers potential for rapid improvements in outcome. Although HER2 amplification occurs in pancreatic cancer, it is inadequately characterized to exploit the potential of anti-HER2 therapies.&lt;/p&gt; &lt;p&gt;Methods: HER2 amplification was detected and further analyzed using multiple genomic sequencing approaches. Standardized reference laboratory assays defined HER2 amplification in a large cohort of patients (n = 469) with pancreatic ductal adenocarcinoma (PDAC).&lt;/p&gt; &lt;p&gt;Results: An amplified inversion event (1 MB) was identified at the HER2 locus in a patient with PDAC. Using standardized laboratory assays, we established diagnostic criteria for HER2 amplification in PDAC, and observed a prevalence of 2%. Clinically, HER2- amplified PDAC was characterized by a lack of liver metastases, and a preponderance of lung and brain metastases. Excluding breast and gastric cancer, the incidence of HER2-amplified cancers in the USA is &#62;22,000 per annum.&lt;/p&gt; &lt;p&gt;Conclusions: HER2 amplification occurs in 2% of PDAC, and has distinct features with implications for clinical practice. The molecular heterogeneity of PDAC implies that even an incidence of 2% represents an attractive target for anti-HER2 therapies, as options for PDAC are limited. Recruiting patients based on HER2 amplification, rather than organ of origin, could make trials of anti-HER2 therapies feasible in less common cancer types.&lt;/p&gt

MINTbase v2.0: a comprehensive database for tRNA-derived fragments that includes nuclear and mitochondrial fragments from all The Cancer Genome Atlas projects.

MINTbase is a repository that comprises nuclear and mitochondrial tRNA-derived fragments (\u27tRFs\u27) found in multiple human tissues. The original version of MINTbase comprised tRFs obtained from 768 transcriptomic datasets. We used our deterministic and exhaustive tRF mining pipeline to process all of The Cancer Genome Atlas datasets (TCGA). We identified 23 413 tRFs with abundance of ≥ 1.0 reads-per-million (RPM). To facilitate further studies of tRFs by the community, we just released version 2.0 of MINTbase that contains information about 26 531 distinct human tRFs from 11 719 human datasets as of October 2017. Key new elements include: the ability to filter tRFs on-the-fly by minimum abundance thresholding; the ability to filter tRFs by tissue keywords; easy access to information about a tRF\u27s maximum abundance and the datasets that contain it; the ability to generate relative abundance plots for tRFs across cancer types and convert them into embeddable figures; MODOMICS information about modifications of the parental tRNA, etc. Version 2.0 of MINTbase contains 15x more datasets and nearly 4x more distinct tRFs than the original version, yet continues to offer fast, interactive access to its contents. Version 2.0 is available freely at http://cm.jefferson.edu/MINTbase/

The role of epithelial-to-mesenchymal plasticity in ovarian cancer progression and therapy resistance

Ovarian cancer is the most lethal of all gynecologic malignancies and the eighth leading cause of cancer-related deaths among women worldwide. The main reasons for this poor prognosis are late diagnosis; when the disease is already in an advanced stage, and the frequent development of resistance to current chemotherapeutic regimens. Growing evidence demonstrates that apart from its role in ovarian cancer progression, epithelial-to-mesenchymal transition (EMT) can promote chemotherapy resistance. In this review, we will highlight the contribution of EMT to the distinct steps of ovarian cancer progression. In addition, we will review the different types of ovarian cancer resistance to therapy with particular attention to EMT-mediated mechanisms such as cell fate transitions, enhancement of cancer cell survival, and upregulation of genes related to drug resistance. Preclinical studies of anti-EMT therapies have yielded promising results. However, before anti-EMT therapies can be effectively implemented in clinical trials, more research is needed to elucidate the mechanisms leading to EMT-induced therapy resistance