Evaluation of a short RNA within Prostate Cancer Gene 3 in the predictive role for future cancer using non-malignant prostate biopsies.

BACKGROUND: Prostate Cancer 3 (PCA3) is a long non-coding RNA (ncRNA) upregulated in prostate cancer (PCa). We recently identified a short ncRNA expressed from intron 1 of PCA3. Here we test the ability of this ncRNA to predict the presence of cancer in men with a biopsy without PCa. METHODS: We selected men whose initial biopsy did not identify PCa and selected matched cohorts whose subsequent biopsies revealed PCa or benign tissue. We extracted RNA from the initial biopsy and measured PCA3-shRNA2, PCA3 and PSA (qRT-PCR). RESULTS: We identified 116 men with and 94 men without an eventual diagnosis of PCa in 2-5 biopsies (mean 26 months), collected from 2002-2008. The cohorts were similar for age, PSA and surveillance period. We detected PSA and PCA3-shRNA2 RNA in all samples, and PCA3 RNA in 90% of biopsies. The expression of PCA3 and PCA3-shRNA2 were correlated (Pearson's r = 0.37, p<0.01). There was upregulation of PCA3 (2.1-fold, t-test p = 0.02) and PCA3-shRNA2 (1.5-fold) in men with PCa on subsequent biopsy, although this was not significant for the latter RNA (p = 0.2). PCA3 was associated with the future detection of PCa (C-index 0.61, p = 0.01). This was not the case for PCA3-shRNA2 (C-index 0.55, p = 0.2). CONCLUSIONS: PCA3 and PCA3-shRNA2 expression are detectable in historic biopsies and their expression is correlated suggesting co-expression. PCA3 expression was upregulated in men with PCa diagnosed at a future date, the same did not hold for PCA3-shRNA2. Futures studies should explore expression in urine and look at a time course between biopsy and PCa detection

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Atividade biológica de extratos acetato de etila, etanólico e aquoso de timbó (Lonchocarpus floribundus) sobre carrapato bovino

Os extratos acetato de etila, etanólico e aquoso de raízes de Lonchocarpus floribundus foram utilizados, a fim de avaliar a atividade biológica sobre carrapato bovino. Carrapatos adultos foram coletados em bovinos infestados artificialmente, separados em grupos de dez indivíduos, pesados e imersos, separadamente, nos extratos de raízes de L. Floribundus, nas concentrações de 5, 25, 50, 75 e 100 mg mL-1. Para a avaliação em larvas, foram utilizados indivíduos de 14 a 21 dias, os quais foram imersos nos extratos nas concentrações de 1, 5, 10, 15 e 20 mg mL-1. Após o tratamento, cada grupo foi colocado em placa de Petri e incubado a 27 ± 1 ºC e umidade relativa de 80 ± 5%. Os extratos avaliados não foram eficazes para induzir, acima de 50%, a mortalidade de fêmeas ingurgitadas. Os extratos acetato de etila e etanólico induziram 100% de mortalidade de larvas. Entretanto, quanto aos valores de concentração letal mediana (CL50), o extrato etanólico (CL50 = 2,1 mg mL-1) foi mais tóxico que o extrato acetato de etila (CL50 = 4,1 mg mL-1). O extrato etanólico estimou concentração inibitória mediana (CI50) de 3,0 mg mL-1 e foi mais tóxico que os demais extratos quanto a este parâmetro de avaliação. Entre os três extratos avaliados, os extratos acetato de etila e etanólico apresentaram os melhores resultados quanto ao controle de reprodução de R. (B.) microplus, atingindo 100% na concentração de 5 mg mL-1. Os extratos de raízes de L. Floribundus apresentaram atividade biológica sobre carrapato bovino

Driver Fusions and Their Implications in the Development and Treatment of Human Cancers.

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics

The Cancer Genome Atlas (TCGA) has catalyzed systematic characterization of diverse genomic alterations underlying human cancers. At this historic junction marking the completion of genomic characterization of over 11,000 tumors from 33 cancer types, we present our current understanding of the molecular processes governing oncogenesis. We illustrate our insights into cancer through synthesis of the findings of the TCGA PanCancer Atlas project on three facets of oncogenesis: (1) somatic driver mutations, germline pathogenic variants, and their interactions in the tumor; (2) the influence of the tumor genome and epigenome on transcriptome and proteome; and (3) the relationship between tumor and the microenvironment, including implications for drugs targeting driver events and immunotherapies. These results will anchor future characterization of rare and common tumor types, primary and relapsed tumors, and cancers across ancestry groups and will guide the deployment of clinical genomic sequencing