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Tsai, Harrison

https://orcid.org/0000-0003-1134-9331

Mayo Clinic

Johns Hopkins University

Citations 6

Deciphering the genomic fingerprint of small cell prostate cancer with potential clinical utility in radical prostatectomy tissues.
Alshalalfa, Mohammed , Haddad, Zaid , Ross, Ashley E. , Karnes, Jeffrey , Tsai, Harrison , Davicioni, Elai , Schaeffer, Edward M. , Lotan, Tamara Levin

Journal of Clinical Oncology | September 6, 2018 | Volume: 34 | Issue: 15_suppl | Pages: 5055-5055

10.1200/jco.2016.34.15_suppl.5055 Get It

Development and validation of genomic signature to predict ADT treatment failure.
Karnes, Jeffrey , Ashab, Hussam Al-Deen, Ross, Ashley E. , Tosoian, Jeffrey J. , Alshalalfa, Mohammed , Choeurng, Voleak, Yousefi, Kasra, Haddad, Zaid , Feng, Felix Y. , Dicker, Adam P. , Dicker, Adam P , Trock, Bruce J , Tsai, Harrison , Abdollah, Firas , Klein, Eric A. , Nguyen, Paul L. , Den, Robert B. , Davicioni, Elai , Lotan, Tamara Levin , Schaeffer, Edward M.

Journal of Clinical Oncology | September 6, 2018 | Volume: 34 | Issue: 15_suppl | Pages: 5018-5018

10.1200/jco.2016.34.15_suppl.5018 Get It

Development and Validation of a Prostate Cancer Genomic Signature that Predicts Early ADT Treatment Response Following Radical Prostatectomy
Karnes, R. Jeffrey , Sharma, Vidit , Choeurng, Voleak, Ashab, Hussam Al-Deen, Erho, Nicholas , Alshalalfa, Mohammed , Ross, Ashley E. , Yousefi, Kasra, Tsai, Harrison , Zhao, Shuang G. , Tosoian, Jeffrey J. , Haddad, Zaid , Takhar, Mandeep, Chang, S. Laura , Spratt, Daniel E. , Jenkins, Robert B. , Klein, Eric A. , Nguyen, Paul L. , Dicker, Adam P. , Feng, Felix Y. , Schaeffer, Edward M. , Spratt, Daniel E , Klein, Eric A , Trock, Bruce J , Zhao, Shuang G , Abdollah, Firas , Den, Robert B. , Davicioni, Elai , Lotan, Tamara Levin , Dicker, Adam P

Clinical Cancer Research | May 14, 2018 | Volume: 24 | Issue: 16 | Pages: 3908-3916

10.1158/1078-0432.ccr-17-2745 Get It

Abstract Purpose: Currently, no genomic signature exists to distinguish men most likely to progress on adjuvant androgen deprivation therapy (ADT) after radical prostatectomy for high-risk prostate cancer. Here we develop and validate a gene expression signature to predict response to postoperative ADT. Experimental Design: A training set consisting of 284 radical prostatectomy patients was established after 1:1 propensity score matching metastasis between adjuvant-ADT (a-ADT)-treated and no ADT–treated groups. An ADT Response Signature (ADT-RS) was identified from neuroendocrine and AR signaling–related genes. Two independent cohorts were used to form three separate data sets for validation (set I, n = 232; set II, n = 435; set III, n = 612). The primary endpoint of the analysis was postoperative metastasis. Results: Increases in ADT-RS score were associated with a reduction in risk of metastasis only in a-ADT patients. On multivariable analysis, ADT-RS by ADT treatment interaction term remained associated with metastasis in both validation sets (set I: HR = 0.18, Pinteraction = 0.009; set II: HR = 0.25, Pinteraction = 0.019). In a matched validation set III, patients with Low ADT-RS scores had similar 10-year metastasis rates in the a-ADT and no-ADT groups (30.1% vs. 31.0%, P = 0.989). Among High ADT-RS patients, 10-year metastasis rates were significantly lower for a-ADT versus no-ADT patients (9.4% vs. 29.2%, P = 0.021). The marginal ADT-RS by ADT interaction remained significant in the matched dataset (Pinteraction = 0.035). Conclusions: Patients with High ADT-RS benefited from a-ADT. In combination with prognostic risk factors, use of ADT-RS may thus allow for identification of ADT-responsive tumors that may benefit most from early androgen blockade after radical prostatectomy. We discovered a gene signature that when present in primary prostate tumors may be useful to predict patients who may respond to early ADT after surgery. Clin Cancer Res; 24(16); 3908–16. ©2018 AACR.
The long noncoding RNA landscape of neuroendocrine prostate cancer and its clinical implications
Ramnarine, Varune Rohan , Alshalalfa, Mohammed , Mo, Fan , Nabavi, Noushin , Erho, Nicholas , Takhar, Mandeep, Shukin, Robert, Brahmbhatt, Sonal, Gawronski, Alexander , Kobelev, Maxim , Nouri, Mannan , Lin, Dong , Tsai, Harrison , Karnes, R., Rubin, Mark A. , Zoubeidi, Amina , Gleave, Martin , Sahinalp, Cenk, Wyatt, Alexander W. , Beltran, Himisha , Wang, Yuzhuo , Collins, Colin C. , Karnes, R Jefferey, Lotan, Tamara Levin , Karnes, R. Jeffrey, Şahinalp, S. Cenk , Volik, Stanislav V. , Davicioni, Elai , Rubin, Mark A

GigaScience | May 10, 2018 | Volume: 7 | Issue: 6 | Pages: No page available

10.1093/gigascience/giy050 Get It

Abstract Background Treatment-induced neuroendocrine prostate cancer (tNEPC) is an aggressive variant of late-stage metastatic castrate-resistant prostate cancer that commonly arises through neuroendocrine transdifferentiation (NEtD). Treatment options are limited, ineffective, and, for most patients, result in death in less than a year. We previously developed a first-in-field patient-derived xenograft (PDX) model of NEtD. Longitudinal deep transcriptome profiling of this model enabled monitoring of dynamic transcriptional changes during NEtD and in the context of androgen deprivation. Long non-coding RNA (lncRNA) are implicated in cancer where they can control gene regulation. Until now, the expression of lncRNAs during NEtD and their clinical associations were unexplored. Results We implemented a next-generation sequence analysis pipeline that can detect transcripts at low expression levels and built a genome-wide catalogue (n = 37,749) of lncRNAs. We applied this pipeline to 927 clinical samples and our high-fidelity NEtD model LTL331 and identified 821 lncRNAs in NEPC. Among these are 122 lncRNAs that robustly distinguish NEPC from prostate adenocarcinoma (AD) patient tumours. The highest expressed lncRNAs within this signature are H19, LINC00617, and SSTR5-AS1. Another 742 are associated with the NEtD process and fall into four distinct patterns of expression (NEtD lncRNA Class I, II, III, and IV) in our PDX model and clinical samples. Each class has significant (z-scores >2) and unique enrichment for transcription factor binding site (TFBS) motifs in their sequences. Enriched TFBS include (1) TP53 and BRN1 in Class I, (2) ELF5, SPIC, and HOXD1 in Class II, (3) SPDEF in Class III, (4) HSF1 and FOXA1 in Class IV, and (5) TWIST1 when merging Class III with IV. Common TFBS in all NEtD lncRNA were also identified and include E2F, REST, PAX5, PAX9, and STAF. Interrogation of the top deregulated candidates (n = 100) in radical prostatectomy adenocarcinoma samples with long-term follow-up (median 18 years) revealed significant clinicopathological associations. Specifically, we identified 25 that are associated with rapid metastasis following androgen deprivation therapy (ADT). Two of these lncRNAs (SSTR5-AS1 and LINC00514) stratified patients undergoing ADT based on patient outcome. Discussion To date, a comprehensive characterization of the dynamic landscape of lncRNAs during the NEtD process has not been performed. A temporal analysis of the PDX-based NEtD model has for the first time provided this dynamic landscape. TFBS analysis identified NEPC-related TF motifs present within the NEtD lncRNA sequences, suggesting functional roles for these lncRNAs in NEPC pathogenesis. Furthermore, select NEtD lncRNAs appear to be associated with metastasis and patients receiving ADT. Treatment-related metastasis is a clinical consequence of NEPC tumours. Top candidate lncRNAs FENDRR, H19, LINC00514, LINC00617, and SSTR5-AS1 identified in this study are implicated in the development of NEPC. We present here for the first time a genome-wide catalogue of NEtD lncRNAs that characterize the transdifferentiation process and a robust NEPC lncRNA patient expression signature. To accomplish this, we carried out the largest integrative study that applied a PDX NEtD model to clinical samples. These NEtD and NEPC lncRNAs are strong candidates for clinical biomarkers and therapeutic targets and warrant further investigation.
Deciphering the genomic fingerprint of small cell prostate cancer with potential clinical utility.
Alshalalfa, Mohammed , Haddad, Zaid , Ross, Ashley E. , Karnes, R. Jeffrey , Tsai, Harrison , Davicioni, Elai , Schaeffer, Edward M. , Lotan, Tamara Levin

Journal of Clinical Oncology | February 24, 2017 | Volume: 34 | Issue: 2_suppl | Pages: 303-303

10.1200/jco.2016.34.2_suppl.303 Get It

303 Background: Small cell (SC) neuroendocrine carcinoma of the prostate has very poor prognosis and does not respond well to androgen receptor (AR)-targeted therapies. Neuroendocrine prostate carcinomas are increasingly recognized to show a spectrum of morphologic changes, and may not always be distinguishable from adenocarcinoma (adeno) based on morphology alone. Here we hypothesize that SC carcinoma harbor a unique gene expression signature that, when measured in primary prostatic adenocarcinoma, may help guide subsequent management. Methods: In total, 617 whole genome expression profiles were retrieved from the Decipher GRID providing gene expression data for 1.4 million markers. 17 morphologically-diagnosed SC carcinoma samples were compared to 32 Gleason 8-10 and 77 Gleason 6 RP adeno samples with no prior treatment and no evidence of metastasis, and this comparison was used to define SC Genomic Fingerprint (SCGFt) through adjusted median fold difference and Wilcoxon test. An additional 493 RP adeno from Mayo Clinic and John Hopkins Hospital were used to evaluate the utility of the SCGFt for predicting outcome. Results: Based on genomic prognostic tests calculated using the Decipher assay including Decipher, Penney and microarray-derived Cell Cycle Progression, SC carcinomas have very poor prognosis compared to high grade adeno. A total of 356 genes defined the SCGFt with 258 gene upregulated in SC and 98 down-regulated. PEG10, HELLS and RB1-loss program are the most overexpressed genes in SC and AR-related genes the most down-regulated. As expected, SC lacked ERG and alternative ETS expression and showed relatively low SPINK1 expression. A median summarization of the 356 genes in SCGFt was used to build a small cell genomic score (SCGS). Evaluating SCGS in RP adeno cohorts showed that patients with high SCGS are at higher risk of developing metastasis after RP in a natural history cohort or after adjuvant hormonal therapy based on Kaplan Meier analysis (both p< 0.001). When restricted to patients with high Decipher scores, SCGS provided independent prognostic information. Conclusions: SC carcinoma has a distinct genomic profile that may be useful for treatment management of patients with adenocarcinoma.
Development and validation of an ADT resistance signature to predict adjuvant hormone treatment failure.
Karnes, Jeffrey , Ashab, Hussam Al-Deen, Ross, Ashley E. , Tosoian, Jeffrey J. , Erho, Nicholas , Choeurng, Voleak, Yousefi, Kasra, Haddad, Zaid , Feng, Felix Y. , Dicker, Adam P. , Jenkins, Robert B. , Trock, Bruce J , Tsai, Harrison , Abdollah, Firas , Klein, Eric A. , Nguyen, Paul L. , Den, Robert B. , Davicioni, Elai , Lotan, Tamara Levin , Schaeffer, Edward M. , Dicker, Adam P

Journal of Clinical Oncology | February 24, 2017 | Volume: 34 | Issue: 2_suppl | Pages: 106-106

10.1200/jco.2016.34.2_suppl.106 Get It

106 Background: Androgen deprivation therapy (ADT) is one of the main treatment options for locally advanced and metastatic prostate cancer. Neuroendocrine prostate cancer (NEPC) is inherently less sensitive or even resistant to ADT. NEPC can be observed de novo (e.g., small cell prostate cancer) but more commonly arises after exposure to ADT. We hypothesized that a gene expression signature of NEPC when measured in primary tumor specimens (RP) of prostatic adenocarcinoma may be useful for predicting patients with innate resistance to ADT. Methods: Expression profiles of 1023 PCa patients treated with RP were obtained from the Decipher GRID database. These were split into training (n=529) and validation (n=494) sets and stratified by the receipt of adjuvant ADT (n=243) or no adjuvant ADT (n=780). A literature review of ADT resistance and neuroendocrine genes identified 1,557 genes as candidates. This set was further filtered, using logistic regression to select a 52-gene ADT resistance signature (ARS). ARS was trained using a generalized linear model with lasso regularization. Survival c-index and Kaplan Meier was used to compare survival differences between treated and untreated patients with high and low ARS scores (defined by median split). Results: In validation cohorts, the ARS was predictive of metastasis in cohorts receiving adjuvant ADT (10-year metastasis free survival c-index of 0.69 (95% CI 0.59-0.78) as compared to 0.45 (95% CI 0.29-0.61) in patients not treated with ADT). Similarly in a separate cohort of untreated patients that received no ADT until after metastatic onset, ARS was not prognostic (c-index 0.53). Among ADT treated patients, those with low ARS scores had a 10 year MFS of 87%, versus 70% in those with high ARS scores (p<0.001). In the subset of men who received ADT after metastatic onset and who developed castrate-resistant prostate cancer (CRPC, n = 41), median time to treatment failure was 1 year in patients with high ARS compared to 2 years for those with low ARS scores (p=0.07). Conclusions: A 52-gene ADT resistance signature was developed which showed significant differences in metastasis-free survival among adjuvant hormone treated but not untreated patients.