The stable traits of melanoma genetics: an alternate approach to target discovery

<p>Abstract</p> <p>Background</p> <p>The weight that gene copy number plays in transcription remains controversial; although in specific cases gene expression correlates with copy number, the relationship cannot be inferred at the global level. We hypothesized that genes steadily expressed by 15 melanoma cell lines (CMs) and their parental tissues (TMs) should be critical for oncogenesis and their expression most frequently influenced by their respective copy number.</p> <p>Results</p> <p>Functional interpretation of 3,030 transcripts concordantly expressed (Pearson's correlation coefficient p-value < 0.05) by CMs and TMs confirmed an enrichment of functions crucial to oncogenesis. Among them, 968 were expressed according to the transcriptional efficiency predicted by copy number analysis (Pearson's correlation coefficient p-value < 0.05). We named these genes, "genomic delegates" as they represent at the transcriptional level the genetic footprint of individual cancers. We then tested whether the genes could categorize 112 melanoma metastases. Two divergent phenotypes were observed: one with prevalent expression of cancer testis antigens, enhanced cyclin activity, WNT signaling, and a Th17 immune phenotype (Class A). This phenotype expressed, therefore, transcripts previously associated to more aggressive cancer. The second class (B) prevalently expressed genes associated with melanoma signaling including <it>MITF</it>, melanoma differentiation antigens, and displayed a Th1 immune phenotype associated with better prognosis and likelihood to respond to immunotherapy. An intermediate third class (C) was further identified. The three phenotypes were confirmed by unsupervised principal component analysis.</p> <p>Conclusions</p> <p>This study suggests that clinically relevant phenotypes of melanoma can be retraced to stable oncogenic properties of cancer cells linked to their genetic back bone, and offers a roadmap for uncovering novel targets for tailored anti-cancer therapy.</p

Recurrent patterns of DNA copy number alterations in tumors reflect metabolic selection pressures.

Copy number alteration (CNA) profiling of human tumors has revealed recurrent patterns of DNA amplifications and deletions across diverse cancer types. These patterns are suggestive of conserved selection pressures during tumor evolution but cannot be fully explained by known oncogenes and tumor suppressor genes. Using a pan-cancer analysis of CNA data from patient tumors and experimental systems, here we show that principal component analysis-defined CNA signatures are predictive of glycolytic phenotypes, including 18F-fluorodeoxy-glucose (FDG) avidity of patient tumors, and increased proliferation. The primary CNA signature is enriched for p53 mutations and is associated with glycolysis through coordinate amplification of glycolytic genes and other cancer-linked metabolic enzymes. A pan-cancer and cross-species comparison of CNAs highlighted 26 consistently altered DNA regions, containing 11 enzymes in the glycolysis pathway in addition to known cancer-driving genes. Furthermore, exogenous expression of hexokinase and enolase enzymes in an experimental immortalization system altered the subsequent copy number status of the corresponding endogenous loci, supporting the hypothesis that these metabolic genes act as drivers within the conserved CNA amplification regions. Taken together, these results demonstrate that metabolic stress acts as a selective pressure underlying the recurrent CNAs observed in human tumors, and further cast genomic instability as an enabling event in tumorigenesis and metabolic evolution

Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes

Gene expression signatures of toxicity and clinical response benefit both safety assessment and clinical practice; however, difficulties in connecting signature genes with the predicted end points have limited their application. The Microarray Quality Control Consortium II (MAQCII) project generated 262 signatures for ten clinical and three toxicological end points from six gene expression data sets, an unprecedented collection of diverse signatures that has permitted a wide-ranging analysis on the nature of such predictive models. A comprehensive analysis of the genes of these signatures and their nonredundant unions using ontology enrichment, biological network building and interactome connectivity analyses demonstrated the link between gene signatures and the biological basis of their predictive power. Different signatures for a given end point were more similar at the level of biological properties and transcriptional control than at the gene level. Signatures tended to be enriched in function and pathway in an end point and model-specific manner, and showed a topological bias for incoming interactions. Importantly, the level of biological similarity between different signatures for a given end point correlated positively with the accuracy of the signature predictions. These findings will aid the understanding, and application of predictive genomic signatures, and support their broader application in predictive medicine

NELFE-Dependent MYC Signature Identifies a Unique Cancer Subtype in Hepatocellular Carcinoma.

The MYC oncogene is dysregulated in approximately 30% of liver cancer. In an effort to exploit MYC as a therapeutic target, including in hepatocellular carcinoma (HCC), strategies have been developed on the basis of MYC amplification or gene translocation. Due to the failure of these strategies to provide accurate diagnostics and prognostic value, we have developed a Negative Elongation Factor E (NELFE)-Dependent MYC Target (NDMT) gene signature. This signature, which consists of genes regulated by MYC and NELFE, an RNA binding protein that enhances MYC-induced hepatocarcinogenesis, is predictive of NELFE/MYC-driven tumors that would otherwise not be identified by gene amplification or translocation alone. We demonstrate the utility of the NDMT gene signature to predict a unique subtype of HCC, which is associated with a poor prognosis in three independent cohorts encompassing diverse etiologies, demographics, and viral status. The application of gene signatures, such as the NDMT signature, offers patients access to personalized risk assessments, which may be utilized to direct future care

'Omic approaches to preventing or managing metastatic breast cancer

Early detection of metastasis-prone breast cancers and characterization of residual metastatic cancers are important in efforts to improve management of breast cancer. Applications of genome-scale molecular analysis technologies are making these complementary approaches possible by revealing molecular features uniquely associated with metastatic disease. Assays that reveal these molecular features will facilitate development of anatomic, histological and blood-based strategies that may enable detection prior to metastatic spread. Knowledge of these features also will guide development of therapeutic strategies that can be applied when metastatic disease burden is low, thereby increasing the probability of a curative response

Novel drug candidates for the treatment of metastatic colorectal cancer through global inverse gene-expression profiling

Drug-induced gene-expression profiles that invert disease profiles have recently been illustrated to be a starting point for drug repositioning. In this study, we validate this approach and focus on prediction of novel drugs for colorectal cancer, for which there is a pressing need to find novel antimetastatic compounds. We computationally predicted three novel and still unknown compounds against colorectal cancer: citalopram (an antidepressant), troglitazone (an antidiabetic), and enilconazole (a fungicide). We verified the compounds by in vitro assays of clonogenic survival, proliferation, and migration and in a subcutaneous mouse model. We found evidence that the mode of action of these compounds may be through inhibition of TGF{beta} signaling. Furthermore, one compound, citalopram, reduced tumor size as well as the number of circulating tumor cells and metastases in an orthotopic mouse model of colorectal cancer. This study proposes citalopram as a potential therapeutic option for patients with colorectal cancer, illustrating the potential of systems pharmacology

Intra-tumour signalling entropy determines clinical outcome in breast and lung cancer.

The cancer stem cell hypothesis, that a small population of tumour cells are responsible for tumorigenesis and cancer progression, is becoming widely accepted and recent evidence has suggested a prognostic and predictive role for such cells. Intra-tumour heterogeneity, the diversity of the cancer cell population within the tumour of an individual patient, is related to cancer stem cells and is also considered a potential prognostic indicator in oncology. The measurement of cancer stem cell abundance and intra-tumour heterogeneity in a clinically relevant manner however, currently presents a challenge. Here we propose signalling entropy, a measure of signalling pathway promiscuity derived from a sample's genome-wide gene expression profile, as an estimate of the stemness of a tumour sample. By considering over 500 mixtures of diverse cellular expression profiles, we reveal that signalling entropy also associates with intra-tumour heterogeneity. By analysing 3668 breast cancer and 1692 lung adenocarcinoma samples, we further demonstrate that signalling entropy correlates negatively with survival, outperforming leading clinical gene expression based prognostic tools. Signalling entropy is found to be a general prognostic measure, valid in different breast cancer clinical subgroups, as well as within stage I lung adenocarcinoma. We find that its prognostic power is driven by genes involved in cancer stem cells and treatment resistance. In summary, by approximating both stemness and intra-tumour heterogeneity, signalling entropy provides a powerful prognostic measure across different epithelial cancers

ELF5 Drives Lung Metastasis in Luminal Breast Cancer through Recruitment of Gr1+ CD11b+ Myeloid-Derived Suppressor Cells.

During pregnancy, the ETS transcription factor ELF5 establishes the milk-secreting alveolar cell lineage by driving a cell fate decision of the mammary luminal progenitor cell. In breast cancer, ELF5 is a key transcriptional determinant of tumor subtype and has been implicated in the development of insensitivity to anti-estrogen therapy. In the mouse mammary tumor virus-Polyoma Middle T (MMTV-PyMT) model of luminal breast cancer, induction of ELF5 levels increased leukocyte infiltration, angiogenesis, and blood vessel permeability in primary tumors and greatly increased the size and number of lung metastasis. Myeloid-derived suppressor cells, a group of immature neutrophils recently identified as mediators of vasculogenesis and metastasis, were recruited to the tumor in response to ELF5. Depletion of these cells using specific Ly6G antibodies prevented ELF5 from driving vasculogenesis and metastasis. Expression signatures in luminal A breast cancers indicated that increased myeloid cell invasion and inflammation were correlated with ELF5 expression, and increased ELF5 immunohistochemical staining predicted much shorter metastasis-free and overall survival of luminal A patients, defining a group who experienced unexpectedly early disease progression. Thus, in the MMTV-PyMT mouse mammary model, increased ELF5 levels drive metastasis by co-opting the innate immune system. As ELF5 has been previously implicated in the development of antiestrogen resistance, this finding implicates ELF5 as a defining factor in the acquisition of the key aspects of the lethal phenotype in luminal A breast cancer

Comparison of the mutational profiles of neuroendocrine breast tumours, invasive ductal carcinomas and pancreatic neuroendocrine carcinomas

The pathophysiology and the optimal treatment of breast neuroendocrine tumours (NETs) are unknown. We compared the mutational profiles of breast NETs (n = 53) with those of 724 publicly available invasive ductal carcinoma (IDC) and 98 pancreatic NET (PNET) cases. The only significantly different pathogenetic or unknown variant rate between breast NETs and IDCs was detected in the TP53 (11.3% in breast NETs and 41% in IDCs, adjusted p value 0.027) and ADCK2 (9.4% in breast NETs vs. 0.28% in IDCs, adjusted p value 0.045) genes. Between breast NETs and PNETs, different pathogenetic or unknown variant frequencies were detected in 30 genes. For example, MEN1 was mutated in only 6% of breast NETs and 37% in PNETs (adjusted p value 0.00050), and GATA3 pathogenetic or unknown variants were only found in 17.0% of breast NETs and 0% in PNETs (adjusted p value 0.0010). The most commonly affected oncogenic pathways in the breast NET cases were PI3K/Akt/mTOR, NOTCH and RTK-RAS pathways. Breast NETs had typically clock-like mutational signatures and signatures associated with defective DNA mismatch repair in their mutational landscape. Our results suggest that the breast NET mutational profile more closely resembles that of IDCs than that of PNETs. These results also revealed several potentially druggable targets, such as MMRd, in breast NETs. In conclusion, breast NETs are indeed a separate breast cancer entity, but their optimal treatment remains to be elucidated.Peer reviewe

MicroRNA Expression Characterizes Oligometastasis(es)

Cancer staging and treatment presumes a division into localized or metastatic disease. We proposed an intermediate state defined by ≤ 5 cumulative metastasis(es), termed oligometastases. In contrast to widespread polymetastases, oligometastatic patients may benefit from metastasis-directed local treatments. However, many patients who initially present with oligometastases progress to polymetastases. Predictors of progression could improve patient selection for metastasis-directed therapy.Here, we identified patterns of microRNA expression of tumor samples from oligometastatic patients treated with high-dose radiotherapy.Patients who failed to develop polymetastases are characterized by unique prioritized features of a microRNA classifier that includes the microRNA-200 family. We created an oligometastatic-polymetastatic xenograft model in which the patient-derived microRNAs discriminated between the two metastatic outcomes. MicroRNA-200c enhancement in an oligometastatic cell line resulted in polymetastatic progression.These results demonstrate a biological basis for oligometastases and a potential for using microRNA expression to identify patients most likely to remain oligometastatic after metastasis-directed treatment