Elai Davicioni

The long noncoding RNA SChLAP1 promotes aggressive prostate cancer and antagonizes the SWI/SNF complex

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.

Translational Activation of Snail1 and Other Developmentally Regulated Transcription Factors by YB-1 Promotes an Epithelial-Mesenchymal Transition

Increased expression of the transcription/translation regulatory protein Y-box binding protein-1 (YB-1) is associated with cancer aggressiveness, particularly in breast carcinoma. Here we establish that YB-1 levels are elevated in invasive breast cancer cells and correlate with reduced expression of E-cadherin and poor patient survival. Enforced expression of YB-1 in noninvasive breast epithelial cells induced an epithelial-mesenchymal transition (EMT) accompanied by enhanced metastatic potential and reduced proliferation rates. YB-1 directly activates cap-independent translation of messenger RNAs encoding Snail1 and other transcription factors implicated in downregulation of epithelial and growth-related genes and activation of mesenchymal genes. Hence, translational regulation by YB-1 is a restriction point enabling coordinated expression of a network of EMT-inducing transcription factors, likely acting together to promote metastatic spread.

Discovery and Validation of a Prostate Cancer Genomic Classifier that Predicts Early Metastasis Following Radical Prostatectomy

Purpose 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. Methods 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. Results 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. Conclusion 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.

Identification of a PAX-FKHR Gene Expression Signature that Defines Molecular Classes and Determines the Prognosis of Alveolar Rhabdomyosarcomas

Alveolar rhabdomyosarcomas (ARMS) are aggressive soft-tissue sarcomas affecting children and young adults. Most ARMS tumors express the PAX3-FKHR or PAX7-FKHR (PAX-FKHR) fusion genes resulting from the t(2;13) or t(1;13) chromosomal translocations, respectively. However, up to 25% of ARMS tumors are fusion negative, making it unclear whether ARMS represent a single disease or multiple clinical and biological entities with a common phenotype. To test to what extent PAX-FKHR determine class and behavior of ARMS, we used oligonucleotide microarray expression profiling on 139 primary rhabdomyosarcoma tumors and an in vitro model. We found that ARMS tumors expressing either PAX-FKHR gene share a common expression profile distinct from fusion-negative ARMS and from the other rhabdomyosarcoma variants. We also observed that PAX-FKHR expression above a minimum level is necessary for the detection of this expression profile. Using an ectopic PAX3-FKHR and PAX7-FKHR expression model, we identified an expression signature regulated by PAX-FKHR that is specific to PAX-FKHR-positive ARMS tumors. Data mining for functional annotations of signature genes suggested a role for PAX-FKHR in regulating ARMS proliferation and differentiation. Cox regression modeling identified a subset of genes within the PAX-FKHR expression signature that segregated ARMS patients into three risk groups with 5-year overall survival estimates of 7%, 48%, and 93%. These prognostic classes were independent of conventional clinical risk factors. Our results show that PAX-FKHR dictate a specific expression signature that helps define the molecular phenotype of PAX-FKHR-positive ARMS tumors and, because it is linked with disease outcome in ARMS patients, determine tumor behavior.

The oestrogen receptor alpha-regulated lncRNA NEAT1 is a critical modulator of prostate cancer

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.

Molecular Classification of Rhabdomyosarcoma—Genotypic and Phenotypic Determinants of Diagnosis: A Report from the Children's Oncology Group

Rhabdomyosarcoma (RMS) in children occurs as two major histological subtypes, embryonal (ERMS) and alveolar (ARMS). ERMS is associated with an 11p15.5 loss of heterozygosity (LOH) and may be confused with nonmyogenic, non-RMS soft tissue sarcomas. ARMS expresses the product of a genomic translocation that fuses FOXO1 (FKHR) with either PAX3 or PAX7 (P-F); however, at least 25% of cases lack these translocations. Here, we describe a genomic-based classification scheme that is derived from the combined gene expression profiling and LOH analysis of 160 cases of RMS and non-RMS soft tissue sarcomas that is at variance with conventional histopathological schemes. We found that gene expression profiles and patterns of LOH of ARMS cases lacking P-F translocations are indistinguishable from conventional ERMS cases. A subset of tumors that has been histologically classified as RMS lack myogenic gene expression. However, classification based on gene expression is possible using as few as five genes with an estimated error rate of less than 5%. Using immunohistochemistry, we characterized two markers, HMGA2 and TFAP2ss, which facilitate the differential diagnoses of ERMS and P-F RMS, respectively, using clinical material. These objectively derived molecular classes are based solely on genomic analysis at the time of diagnosis and are highly reproducible. Adoption of these molecular criteria may offer a more clinically relevant diagnostic scheme, thus potentially improving patient management and therapeutic RMS outcomes.

Validation of a Genomic Classifier that Predicts Metastasis Following Radical Prostatectomy in an At Risk Patient Population

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.

Tumour genomic and microenvironmental heterogeneity for integrated prediction of 5-year biochemical recurrence of prostate cancer: a retrospective cohort study

BACKGROUND Clinical prognostic groupings for localised prostate cancers are imprecise, with 30-50% of patients recurring after image-guided radiotherapy or radical prostatectomy. We aimed to test combined genomic and microenvironmental indices in prostate cancer to improve risk stratification and complement clinical prognostic factors. METHODS We used DNA-based indices alone or in combination with intra-prostatic hypoxia measurements to develop four prognostic indices in 126 low-risk to intermediate-risk patients (Toronto cohort) who will receive image-guided radiotherapy. We validated these indices in two independent cohorts of 154 (Memorial Sloan Kettering Cancer Center cohort [MSKCC] cohort) and 117 (Cambridge cohort) radical prostatectomy specimens from low-risk to high-risk patients. We applied unsupervised and supervised machine learning techniques to the copy-number profiles of 126 pre-image-guided radiotherapy diagnostic biopsies to develop prognostic signatures. Our primary endpoint was the development of a set of prognostic measures capable of stratifying patients for risk of biochemical relapse 5 years after primary treatment. FINDINGS Biochemical relapse was associated with indices of tumour hypoxia, genomic instability, and genomic subtypes based on multivariate analyses. We identified four genomic subtypes for prostate cancer, which had different 5-year biochemical relapse-free survival. Genomic instability is prognostic for relapse in both image-guided radiotherapy (multivariate analysis hazard ratio [HR] 4·5 [95% CI 2·1-9·8]; p=0·00013; area under the receiver operator curve [AUC] 0·70 [95% CI 0·65-0·76]) and radical prostatectomy (4·0 [1·6-9·7]; p=0·0024; AUC 0·57 [0·52-0·61]) patients with prostate cancer, and its effect is magnified by intratumoral hypoxia (3·8 [1·2-12]; p=0·019; AUC 0·67 [0·61-0·73]). A novel 100-loci DNA signature accurately classified treatment outcome in the MSKCC low-risk to intermediate-risk cohort (multivariate analysis HR 6·1 [95% CI 2·0-19]; p=0·0015; AUC 0·74 [95% CI 0·65-0·83]). In the independent MSKCC and Cambridge cohorts, this signature identified low-risk to high-risk patients who were most likely to fail treatment within 18 months (combined cohorts multivariate analysis HR 2·9 [95% CI 1·4-6·0]; p=0·0039; AUC 0·68 [95% CI 0·63-0·73]), and was better at predicting biochemical relapse than 23 previously published RNA signatures. INTERPRETATION This is the first study of cancer outcome to integrate DNA-based and microenvironment-based failure indices to predict patient outcome. Patients exhibiting these aggressive features after biopsy should be entered into treatment intensification trials. FUNDING Movember Foundation, Prostate Cancer Canada, Ontario Institute for Cancer Research, Canadian Institute for Health Research, NIHR Cambridge Biomedical Research Centre, The University of Cambridge, Cancer Research UK, Cambridge Cancer Charity, Prostate Cancer UK, Hutchison Whampoa Limited, Terry Fox Research Institute, Princess Margaret Cancer Centre Foundation, PMH-Radiation Medicine Program Academic Enrichment Fund, Motorcycle Ride for Dad (Durham), Canadian Cancer Society.

RNA biomarkers associated with metastatic progression in prostate cancer: a multi-institutional high-throughput analysis of SChLAP1.

BACKGROUND Improved clinical predictors for disease progression are needed for localised prostate cancer, since only a subset of patients develop recurrent or refractory disease after first-line treatment. Therefore, we undertook an unbiased analysis to identify RNA biomarkers associated with metastatic progression after prostatectomy. METHODS Prostate cancer samples from patients treated with radical prostatectomy at three academic institutions were analysed for gene expression by a high-density Affymetrix GeneChip platform, encompassing more than 1 million genomic loci. In a discovery cohort, all protein-coding genes and known long non-coding RNAs were ranked by fold change in expression between tumours that subsequently metastasised versus those that did not. The top ranked gene was then validated for its prognostic value for metastatic progression in three additional independent cohorts. 95% of the gene expression assays were done in a Clinical Laboratory Improvements Amendments certified laboratory facility. All genes were assessed for their ability to predict metastatic progression by receiver-operating-curve area-under-the-curve analyses. Multivariate analyses were done for the primary endpoint of metastatic progression, with variables including Gleason score, preoperative prostate-specific antigen concentration, seminal vesicle invasion, surgical margin status, extracapsular extension, lymph node invasion, and expression of the highest ranked gene. FINDINGS 1008 patients were included in the study: 545 in the discovery cohort and 463 in the validation cohorts. The long non-coding RNA SChLAP1 was identified as the highest-ranked overexpressed gene in cancers with metastatic progression. Validation in three independent cohorts confirmed the prognostic value of SChLAP1 for metastatic progression. On multivariate modelling, SChLAP1 expression (high vs low) independently predicted metastasis within 10 years (odds ratio [OR] 2·45, 95% CI 1·70-3·53; p<0·0001). The only other variable that independently predicted metastasis within 10 years was Gleason score (8-10 vs 5-7; OR 2·14, 95% CI 1·77-2·58; p<0·0001). INTERPRETATION We identified and validated high SChLAP1 expression as significantly prognostic for metastatic disease progression of prostate cancer. Our findings suggest that further development of SChLAP1 as a potential biomarker, for treatment intensification in aggressive prostate cancer, warrants future study. FUNDING Prostate Cancer Foundation, National Institutes of Health, Department of Defense, Early Detection Research Network, Doris Duke Charitable Foundation, and Howard Hughes Medical Institute.

The homeoprotein six1 transcriptionally activates multiple protumorigenic genes but requires ezrin to promote metastasis.

The vast majority of deaths associated with cancer are a consequence of a complex phenotypic behavior, metastasis, by which tumor cells spread from their primary site of origin to regional and distant sites. This process requires the tumor cell to make numerous adjustments, both subtle and dramatic, to successfully reach, survive, and flourish at favorable secondary sites. It has been suggested that molecular mechanisms accounting for metastatic behavior can recapitulate those employed during embryogenesis. We have shown that the homeodomain transcription factor Six1, known to be required for normal development of migratory myogenic progenitor cells, is sufficient to promote metastatic spread in a mouse model of the pediatric skeletal muscle cancer rhabdomyosarcoma. Here, we report that Six1 is able to activate the expression of a set of protumorigenic genes (encoding cyclin D1, c-Myc, and Ezrin) that can control cell proliferation, survival, and motility. Although the role of Ezrin in cytoskeletal organization and adhesion has been well studied, the means by which its expression is regulated are poorly understood. We now show that the gene encoding Ezrin is a direct transcriptional target of Six1. Moreover, Ezrin is indispensable for Six1-induced metastasis and highly expressed in a panel of representative pediatric cancers. Our data indicate that Ezrin represents a promising therapeutic target for patients with advanced-stage rhabdomyosarcoma and perhaps other malignancies.