Clinicopathologic features and biochemical recurrence are sensitive, but not specific, predictors of metastatic disease and lethal prostate cancer. We hypothesize that a genomic expression signature ...detected in the primary tumor represents true biological potential of aggressive disease and provides improved prediction of early prostate cancer metastasis.
A nested case-control design was used to select 639 patients from the Mayo Clinic tumor registry who underwent radical prostatectomy between 1987 and 2001. A genomic classifier (GC) was developed by modeling differential RNA expression using 1.4 million feature high-density expression arrays of men enriched for rising PSA after prostatectomy, including 213 who experienced early clinical metastasis after biochemical recurrence. A training set was used to develop a random forest classifier of 22 markers to predict for cases--men with early clinical metastasis after rising PSA. Performance of GC was compared to prognostic factors such as Gleason score and previous gene expression signatures in a withheld validation set.
Expression profiles were generated from 545 unique patient samples, with median follow-up of 16.9 years. GC achieved an area under the receiver operating characteristic curve of 0.75 (0.67-0.83) in validation, outperforming clinical variables and gene signatures. GC was the only significant prognostic factor in multivariable analyses. Within Gleason score groups, cases with high GC scores experienced earlier death from prostate cancer and reduced overall survival. The markers in the classifier were found to be associated with a number of key biological processes in prostate cancer metastatic disease progression.
A genomic classifier was developed and validated in a large patient cohort enriched with prostate cancer metastasis patients and a rising PSA that went on to experience metastatic disease. This early metastasis prediction model based on genomic expression in the primary tumor may be useful for identification of aggressive prostate cancer.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
As in many different areas of science and technology, most important problems in bioinformatics rely on the proper development and assessment of binary classifiers. A generalized assessment of the ...performance of binary classifiers is typically carried out through the analysis of their receiver operating characteristic (ROC) curves. The area under the ROC curve (AUC) constitutes a popular indicator of the performance of a binary classifier. However, the assessment of the statistical significance of the difference between any two classifiers based on this measure is not a straightforward task, since not many freely available tools exist. Most existing software is either not free, difficult to use or not easy to automate when a comparative assessment of the performance of many binary classifiers is intended. This constitutes the typical scenario for the optimization of parameters when developing new classifiers and also for their performance validation through the comparison to previous art.
In this work we describe and release new software to assess the statistical significance of the observed difference between the AUCs of any two classifiers for a common task estimated from paired data or unpaired balanced data. The software is able to perform a pairwise comparison of many classifiers in a single run, without requiring any expert or advanced knowledge to use it. The software relies on a non-parametric test for the difference of the AUCs that accounts for the correlation of the ROC curves. The results are displayed graphically and can be easily customized by the user. A human-readable report is generated and the complete data resulting from the analysis are also available for download, which can be used for further analysis with other software. The software is released as a web server that can be used in any client platform and also as a standalone application for the Linux operating system.
A new software for the statistical comparison of ROC curves is released here as a web server and also as standalone software for the LINUX operating system.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Prostate cancers remain indolent in the majority of individuals but behave aggressively in a minority. The molecular basis for this clinical heterogeneity remains incompletely understood. Here we ...characterize a long noncoding RNA termed SChLAP1 (second chromosome locus associated with prostate-1; also called LINC00913) that is overexpressed in a subset of prostate cancers. SChLAP1 levels independently predict poor outcomes, including metastasis and prostate cancer-specific mortality. In vitro and in vivo gain-of-function and loss-of-function experiments indicate that SChLAP1 is critical for cancer cell invasiveness and metastasis. Mechanistically, SChLAP1 antagonizes the genome-wide localization and regulatory functions of the SWI/SNF chromatin-modifying complex. These results suggest that SChLAP1 contributes to the development of lethal cancer at least in part by antagonizing the tumor-suppressive functions of the SWI/SNF complex.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
The androgen receptor (AR) plays a central role in establishing an oncogenic cascade that drives prostate cancer progression. Some prostate cancers escape androgen dependence and are often associated ...with an aggressive phenotype. The oestrogen receptor alpha (ERα) is expressed in prostate cancers, independent of AR status. However, the role of ERα remains elusive. Using a combination of chromatin immunoprecipitation (ChIP) and RNA-sequencing data, we identified an ERα-specific non-coding transcriptome signature. Among putatively ERα-regulated intergenic long non-coding RNAs (lncRNAs), we identified nuclear enriched abundant transcript 1 (NEAT1) as the most significantly overexpressed lncRNA in prostate cancer. Analysis of two large clinical cohorts also revealed that NEAT1 expression is associated with prostate cancer progression. Prostate cancer cells expressing high levels of NEAT1 were recalcitrant to androgen or AR antagonists. Finally, we provide evidence that NEAT1 drives oncogenic growth by altering the epigenetic landscape of target gene promoters to favour transcription.
Melanoma is a deadly form of skin cancer that accounts for a disproportionally large proportion of cancer-related deaths in younger people. Compared with most other skin cancers, a feature of ...melanoma is its high metastatic capacity, although the mechanisms that confer this are not well understood. The Hippo pathway is a key regulator of organ growth and cell fate that is deregulated in many cancers. To analyse the Hippo pathway in cutaneous melanoma, we generated a transcriptional signature of melanoma cells that overexpressed YAP, the key downstream Hippo pathway oncoprotein. YAP-mediated transcriptional activity varied in melanoma cell lines but did not cluster with known genetic drivers of melanomagenesis such as BRAF and NRAS mutations. Instead, it correlated strongly with published gene expression profiles linked to melanoma cell invasiveness and varied throughout the metastatic cascade in melanoma patient tumours. Consistent with this, YAP was both necessary and sufficient for melanoma cell invasion in vitro. In vivo, YAP promoted spontaneous melanoma metastasis, whilst the growth of YAP-expressing primary tumours was impeded. Finally, we identified the YAP target genes AXL, THBS1 and CYR61 as key mediators of YAP-induced melanoma cell invasion. These data suggest that YAP is a critical regulator of melanoma metastasis.
Cyclin-dependent kinase 4/6 (CDK4/6) inhibitors are an established treatment in estrogen receptor-positive breast cancer and are currently in clinical development in melanoma, a tumor that exhibits ...high rates of CDK4 activation. We analyzed melanoma cells with acquired resistance to the CDK4/6 inhibitor palbociclib and demonstrate that the activity of PRMT5, a protein arginine methyltransferase and indirect target of CDK4, is essential for CDK4/6 inhibitor sensitivity. By indirectly suppressing PRMT5 activity, palbociclib alters the pre-mRNA splicing of MDM4, a negative regulator of p53, leading to decreased MDM4 protein expression and subsequent p53 activation. In turn, p53 induces p21, leading to inhibition of CDK2, the main kinase substituting for CDK4/6 and a key driver of resistance to palbociclib. Loss of the ability of palbociclib to regulate the PRMT5–MDM4 axis leads to resistance. Importantly, combining palbociclib with the PRMT5 inhibitor GSK3326595 enhances the efficacy of palbociclib in treating naive and resistant models and also delays the emergence of resistance. Our studies have uncovered a mechanism of action of CDK4/6 inhibitors in regulating the MDM4 oncogene and the tumor suppressor, p53. Furthermore, we have established that palbociclib inhibition of the PRMT5–MDM4 axis is essential for robust melanoma cell sensitivity and provide preclinical evidence that coinhibition of CDK4/6 and PRMT5 is an effective and well-tolerated therapeutic strategy. Overall, our data provide a strong rationale for further investigation of novel combinations of CDK4/6 and PRMT5 inhibitors, not only in melanoma but other tumor types, including breast, pancreatic, and esophageal carcinoma.
Non‐cutaneous melanomas most frequently involve the uveal tract and mucosal membranes, including the conjunctiva. In contrast to cutaneous melanoma, they often present at an advanced clinical stage, ...are associated with worse clinical outcomes and show poorer responses to immunotherapy. The mutational load within most non‐cutaneous melanomas reflects their lower ultraviolet light (UV) exposure. The genetic drivers within non‐cutaneous melanomas are heterogeneous. Within ocular melanomas, posterior uveal tract melanomas typically harbour one of two distinct, sets of driver mutations and alterations of clinical and biological significance. In contrast to posterior uveal tract melanomas, anterior uveal tract melanomas of the iris and conjunctival melanomas frequently carry both a higher mutational burden and specific mutations linked with UV exposure. The genetic drivers in iris melanomas more closely resemble those of the posterior uveal tract, whereas conjunctival melanomas harbour similar genetic driver mutations to cutaneous melanomas. Mucosal melanomas occur in sun‐shielded sites including sinonasal and oral cavities, nasopharynx, oesophagus, genitalia, anus and rectum, and their mutational landscape is frequently associated with a dominant process of spontaneous deamination and infrequent presence of UV mutation signatures. Genetic drivers of mucosal melanomas are diverse and vary with anatomic location. Further understanding of the causes of already identified recurrent molecular events in non‐cutaneous melanomas, identification of additional drivers in specific subtypes, integrative multi‐omics analyses and analysis of the tumor immune microenvironment will expand knowledge in this field. Furthermore, such data will likely uncover new therapeutic strategies which will lead to improved clinical outcomes in non‐cutaneous melanoma patients.
Purpose Patients with locally advanced prostate cancer after radical prostatectomy are candidates for secondary therapy. However, this higher risk population is heterogeneous. Many cases do not ...metastasize even when conservatively managed. Given the limited specificity of pathological features to predict metastasis, newer risk prediction models are needed. We report a validation study of a genomic classifier that predicts metastasis after radical prostatectomy in a high risk population. Materials and Methods A case-cohort design was used to sample 1,010 patients after radical prostatectomy at high risk for recurrence who were treated from 2000 to 2006. Patients had preoperative prostate specific antigen greater than 20 ng/ml, Gleason 8 or greater, pT3b or a Mayo Clinic nomogram score of 10 or greater. Patients with metastasis at diagnosis or any prior treatment for prostate cancer were excluded from analysis. A 20% random sampling created a subcohort that included all patients with metastasis. We generated 22-marker genomic classifier scores for 219 patients with available genomic data. ROC and decision curves, competing risk and weighted regression models were used to assess genomic classifier performance. Results The genomic classifier AUC was 0.79 for predicting 5-year metastasis after radical prostatectomy. Decision curves showed that the genomic classifier net benefit exceeded that of clinical only models. The genomic classifier was the predominant predictor of metastasis on multivariable analysis. The cumulative incidence of metastasis 5 years after radical prostatectomy was 2.4%, 6.0% and 22.5% in patients with low (60%), intermediate (21%) and high (19%) genomic classifier scores, respectively (p <0.001). Conclusions Results indicate that genomic information from the primary tumor can identify patients with adverse pathological features who are most at risk for metastasis and potentially lethal prostate cancer.
The biological underpinnings of therapeutic resistance to immune checkpoint inhibitors (ICI) in adolescent and young adult (AYA) melanoma patients are incompletely understood. Here, we characterize ...the immunogenomic profile and spatial architecture of the tumor microenvironment (TME) in AYA (aged ≤ 30 years) and older adult (aged 31-84 years) patients with melanoma, to determine the AYA-specific features associated with ICI treatment outcomes. We identify two ICI-resistant spatiotypes in AYA patients with melanoma showing stroma-infiltrating lymphocytes (SILs) that are distinct from the adult TME. The SIL
subtype was enriched in regulatory T cells in the peritumoral space and showed upregulated expression of immune checkpoint molecules, while the SIL
subtype showed a lack of immune activation. We establish a young immunosuppressive melanoma score that can predict ICI responsiveness in AYA patients and propose personalized therapeutic strategies for the ICI-resistant subgroups. These findings highlight the distinct immunogenomic profile of AYA patients, and individualized TME features in ICI-resistant AYA melanoma that require patient-specific treatment strategies.
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