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The figures depicts the optimization of PFO-D4 cholesterol probe

Supplemental Table 1. Adjusted p-Values of SH-4-54 and 27HC (10 uM) single and combination treatment by 2Way ANOVA with Tukey's Multiple Comparisons Posthoc Statistical Analysis Supplemental Table 2. Adjusted p-Values of C188-9 and 27HC (10 uM) single and combination treatment by 2Way ANOVA with Tukey's Multiple Comparisons Posthoc Statistical Analysis

Background Two prostate cancer (PC) classification methods based on transcriptome profiles, a de novo method referred to as the “Prostate Cancer Classification System” (PCS) and a variation of the established PAM50 breast cancer algorithm, were recently proposed. Both studies concluded that most human PC can be assigned to one of three tumor subtypes, two categorized as luminal and one as basal, suggesting the two methods reflect consistency in underlying biology. Despite the similarity, differences and commonalities between the two classification methods have not yet been reported. Methods Here, we describe a comparison of the PCS and PAM50 classification systems. PCS and PAM50 signatures consisting of 37 (PCS37) and 50 genes, respectively, were used to categorize 9,947 PC patients into PCS and PAM50 classes. Enrichment of hallmark gene sets and luminal and basal marker gene expression were assessed in the same datasets. Finally, survival analysis was performed to compare PCS and PAM50 subtypes in terms of clinical outcomes. Results PCS and PAM50 subtypes show clear differential expression of PCS37 and PAM50 genes. While only three genes are shared in common between the two systems, there is some consensus between three subtype pairs (PCS1 versus Luminal B, PCS2 versus Luminal A, and PCS3 versus Basal) with respect to gene expression, cellular processes, and clinical outcomes. PCS categories displayed better separation of cellular processes and luminal and basal marker gene expression compared to PAM50. Although both PCS1 and Luminal B tumors exhibited the worst clinical outcomes, outcomes between aggressive and less aggressive subtypes were better defined in the PCS system, based on larger hazard ratios observed. Conclusion The PCS and PAM50 classification systems are similar in terms of molecular profiles and clinical outcomes. However, the PCS system exhibits greater separation in multiple clinical outcomes and provides better separation of prostate luminal and basal characteristics.

We recently reported that restoring the CYP27A1–27hydroxycholesterol axis had antitumor properties. Thus, we sought to determine the mechanism by which 27HC exerts its anti–prostate cancer effects. As cholesterol is a major component of membrane microdomains known as lipid rafts, which localize receptors and facilitate cellular signaling, we hypothesized 27HC would impair lipid rafts, using the IL6–JAK–STAT3 axis as a model given its prominent role in prostate cancer. As revealed by single molecule imaging of DU145 prostate cancer cells, 27HC treatment significantly reduced detected cholesterol density on the plasma membranes. Further, 27HC treatment of constitutively active STAT3 DU145 prostate cancer cells reduced STAT3 activation and slowed tumor growth in vitro and in vivo. 27HC also blocked IL6-mediated STAT3 phosphorylation in nonconstitutively active STAT3 cells. Mechanistically, 27HC reduced STAT3 homodimerization, nuclear translocation, and decreased STAT3 DNA occupancy at target gene promoters. Combined treatment with 27HC and STAT3 targeting molecules had additive and synergistic effects on proliferation and migration, respectively. Hallmark IL6–JAK–STAT gene signatures positively correlated with CYP27A1 gene expression in a large set of human metastatic castrate-resistant prostate cancers and in an aggressive prostate cancer subtype. This suggests STAT3 activation may be a resistance mechanism for aggressive prostate cancers that retain CYP27A1 expression. In summary, our study establishes a key mechanism by which 27HC inhibits prostate cancer by disrupting lipid rafts and blocking STAT3 activation. Implications: Collectively, these data show that modulation of intracellular cholesterol by 27HC can inhibit IL6–JAK–STAT signaling and may synergize with STAT3-targeted compounds.

PURPOSE Multiparametric magnetic resonance imaging (mpMRI) is used widely for prostate cancer (PCa) evaluation. Approximately 35% of aggressive tumors, however, are not visible on mpMRI. We sought to identify the molecular alterations associated with mpMRI-invisible tumors and determine whether mpMRI visibility is associated with PCa prognosis. METHODS Discovery and validation cohorts included patients who underwent mpMRI before radical prostatectomy and were found to harbor both mpMRI-visible (Prostate Imaging and Reporting Data System 3 to 5) and -invisible (Prostate Imaging and Reporting Data System 1 or 2) foci on surgical pathology. Next-generation sequencing was performed to determine differential gene expression between mpMRI-visible and -invisible foci. A genetic signature for tumor mpMRI visibility was derived in the discovery cohort and assessed in an independent validation cohort. Its association with long-term oncologic outcomes was evaluated in a separate testing cohort. RESULTS The discovery cohort included 10 patients with 26 distinct PCa foci on surgical pathology, of which 12 (46%) were visible and 14 (54%) were invisible on preoperative mpMRI. Next-generation sequencing detected prioritized genetic mutations in 14 (54%) tumor foci (n = 8 mpMRI visible, n = 6 mpMRI invisible). A nine-gene signature (composed largely of cell organization/structure genes) associated with mpMRI visibility was derived (area under the curve = 0.89), and the signature predicted MRI visibility with 75% sensitivity and 100% specificity (area under the curve = 0.88) in the validation cohort. In the testing cohort (n = 375, median follow-up 8 years) there was no significant difference in biochemical recurrence, distant metastasis, or cancer-specific mortality in patients with predicted mpMRI-visible versus -invisible tumors (all P > .05). CONCLUSION Compared with mpMRI-invisible disease, mpMRI-visible tumors are associated with underexpression of cellular organization genes. mpMRI visibility does not seem to be predictive of long-term cancer outcomes, highlighting the need for biopsy strategies that detect mpMRI-invisible tumors.

Recent genome analysis of human prostate cancers demonstrated that both AR gene amplification and TP53 mutation are among the most frequently observed alterations in advanced prostate cancer. However, the biological role of these dual genetic alterations in prostate tumorigenesis is largely unknown. In addition, there are no biologically relevant models that can be used to assess the molecular mechanisms for these genetic abnormalities. Here, we report a novel mouse model, in which elevated transgenic AR expression and Trp53 deletion occur simultaneously in mouse prostatic epithelium to mimic human prostate cancer cells. These compound mice developed an earlier onset of high-grade prostatic intraepithelial neoplasia and accelerated prostate tumors in comparison with mice harboring only the AR transgene. Histological analysis showed prostatic sarcomatoid and basaloid carcinomas with massive squamous differentiation in the above compound mice. RNA-sequencing analyses identified a robust enrichment of the signature genes for human prostatic basal cell carcinomas in the above prostate tumors. Master regulator analysis revealed SOX2 as a transcriptional regulator in prostatic basal cell tumors. Elevated expression of SOX2 and its downstream target genes were detected in prostatic tumors of the compound mice. Chromatin immunoprecipitation analyses implicate a coregulatory role of AR and SOX2 in the expression of prostatic basal cell signature genes. Our data demonstrate a critical role of SOX2 in prostate tumorigenesis and provide mechanistic insight into prostate tumor aggressiveness and progression mediated by aberrant AR and p53 signaling pathways.

Rationale: Our objective was to develop a circulating tumor cell (CTC)-RNA assay for characterizing clinically relevant RNA signatures for the assessment of androgen receptor signaling inhibitor (ARSI) sensitivity in metastatic castration-resistant prostate cancer (mCRPC) patients. Methods: We developed the NanoVelcro CTC-RNA assay by combining the Thermoresponsive (TR)-NanoVelcro CTC purification system with the NanoString nCounter platform for cellular purification and RNA analysis. Based on the well-validated, tissue-based Prostate Cancer Classification System (PCS), we focus on the most aggressive and ARSI-resistant PCS subtype, i.e., PCS1, for CTC analysis. We applied a rigorous bioinformatic process to develop the CTC-PCS1 panel that consists of prostate cancer (PCa) CTC-specific RNA signature with minimal expression in background white blood cells (WBCs). We validated the NanoVelcro CTC-RNA assay and the CTC-PCS1 panel with well-characterized PCa cell lines to demonstrate the sensitivity and dynamic range of the assay, as well as the specificity of the PCS1 Z score (the likelihood estimate of the PCS1 subtype) for identifying PCS1 subtype and ARSI resistance. We then selected 31 blood samples from 23 PCa patients receiving ARSIs to test in our assay. The PCS1 Z scores of each sample were computed and compared with ARSI treatment sensitivity. Results: The validation studies using PCa cell line samples showed that the NanoVelcro CTC-RNA assay can detect the RNA transcripts in the CTC-PCS1 panel with high sensitivity and linearity in the dynamic range of 5-100 cells. We also showed that the genes in CTC-PCS1 panel are highly expressed in PCa cell lines and lowly expressed in background WBCs. Using the artificial CTC samples simulating the blood sample conditions, we further demonstrated that the CTC-PCS1 panel is highly specific in identifying PCS1-like samples, and the high PCS1 Z score is associated with ARSI resistance samples. In patient bloods, ARSI-resistant samples (ARSI-R, n=14) had significantly higher PCS1 Z scores as compared with ARSI-sensitive samples (ARSI-S, n=17) (Rank-sum test, P=0.003). In the analysis of 8 patients who were initially sensitive to ARSI (ARSI-S) and later developed resistance (ARSI-R), we found that the PCS1 Z score increased from the time of ARSI-S to the time of ARSI-R (Pairwise T-test, P=0.016). Conclusions: Using our new methodology, we developed a first-in-class CTC-RNA assay and demonstrated the feasibility of transforming clinically-relevant tissue-based RNA profiling such as PCS into CTC tests. This approach allows for detecting RNA expression relevant to clinical drug resistance in a non-invasive fashion, which can facilitate patient-specific treatment selection and early detection of drug resistance, a goal in precision oncology.

Genetic variability can modulate individual drug responses. A significant portion of pharmacogenetic variants reside in the noncoding genome yet it is unclear if the noncoding variants directly influence protein function and expression or are present on a haplotype including a functionally relevant genetic variation (synthetic association). Gene-wise variant burden (GVB) is a gene-level measure of deleteriousness, reflecting the cumulative effects of deleterious coding variants, predicted in silico. To test potential associations between noncoding and coding pharmacogenetic variants, we computed a drug-level GVB for 5099 drugs from DrugBank for 2504 genomes of the 1000 Genomes Project and evaluated the correlation between the long-known noncoding variant-drug associations in PharmGKB, with functionally relevant rare and common coding variants aggregated into GVBs. We obtained the area under the receiver operating characteristics curve (AUC) by comparing the drug-level GVB ranks against the corresponding pharmacogenetic variants-drug associations in PharmGKB. We obtained high overall AUCs (0.710 &plusmn, 0.022&ndash, 0.734 &plusmn, 0.018) for six different methods (i.e., SIFT, MutationTaster, Polyphen-2 HVAR, Polyphen-2 HDIV, phyloP, and GERP++), and further improved the ethnicity-specific validations (0.759 &plusmn, 0.066&ndash, 0.791 &plusmn, 0.078). These results suggest that a significant portion of the long-known noncoding variant-drug associations can be explained as synthetic associations with rare and common coding variants burden of the corresponding pharmacogenes.

Background Metastatic prostate cancer (PC) is highly lethal. The ability to identify primary tumors capable of dissemination is an unmet need in the quest to understand lethal biology and improve patient outcomes. Previous studies have linked chromosomal instability (CIN), which generates aneuploidy following chromosomal missegregation during mitosis, to PC progression. Evidence of CIN includes broad copy number alterations (CNAs) spanning > 300 base pairs of DNA, which may also be measured via RNA expression signatures associated with CNA frequency. Signatures of CIN in metastatic PC, however, have not been interrogated or well defined. We examined a published 70-gene CIN signature (CIN70) in untreated and castration-resistant prostate cancer (CRPC) cohorts from The Cancer Genome Atlas (TCGA) and previously published reports. We also performed transcriptome and CNA analysis in a unique cohort of untreated primary tumors collected from diagnostic prostate needle biopsies (PNBX) of localized (M0) and metastatic (M1) cases to determine if CIN was linked to clinical stage and outcome. Methods PNBX were collected from 99 patients treated in the VA Greater Los Angeles (GLA-VA) Healthcare System between 2000 and 2016. Total RNA was extracted from high-grade cancer areas in PNBX cores, followed by RNA sequencing and/or copy number analysis using OncoScan. Multivariate logistic regression analyses permitted calculation of odds ratios for CIN status (high versus low) in an expanded GLA-VA PNBX cohort (n = 121). Results The CIN70 signature was significantly enriched in primary tumors and CRPC metastases from M1 PC cases. An intersection of gene signatures comprised of differentially expressed genes (DEGs) generated through comparison of M1 versus M0 PNBX and primary CRPC tumors versus metastases revealed a 157-gene “metastasis” signature that was further distilled to 7-genes (PC-CIN) regulating centrosomes, chromosomal segregation, and mitotic spindle assembly. High PC-CIN scores correlated with CRPC, PC-death and all-cause mortality in the expanded GLA-VA PNBX cohort. Interestingly, approximately 1/3 of M1 PNBX cases exhibited low CIN, illuminating differential pathways of lethal PC progression. Conclusions Measuring CIN in PNBX by transcriptome profiling is feasible, and the PC-CIN signature may identify patients with a high risk of lethal progression at the time of diagnosis.

Additional file 1.

Studies suggest that the efficacy of cancer chemotherapy and immunotherapy is influenced by intestinal bacteria. However, the influence of the microbiome on radiation therapy is not as well understood, and the microbiome comprises more than bacteria. Here, we find that intestinal fungi regulate antitumor immune responses following radiation in mouse models of breast cancer and melanoma and that fungi and bacteria have opposite influences on these responses. Antibiotic-mediated depletion or gnotobiotic exclusion of fungi enhances responsiveness to radiation, whereas antibiotic-mediated depletion of bacteria reduces responsiveness and is associated with overgrowth of commensal fungi. Further, elevated intratumoral expression of Dectin-1, a primary innate sensor of fungi, is negatively associated with survival in patients with breast cancer and is required for the effects of commensal fungi in mouse models of radiation therapy.

INTRODUCTION AND OBJECTIVES:Up to 20% of patients with negative multiparametric magnetic resonance imaging (MRI) harbor Gleason score ≥7 prostate cancer (PCa). In this study, we sought to elucidate...

Obesity is a US and global epidemic that exacerbates various cancers including prostate cancer (PC). A recent meta-analysis of men with PC found a 20% increased risk of PC-specific mortality for each 5 kg/m2 increase in BMI. To identify ways to exploit this, we performed an in vivo screen to identify kinases that were causally involved in tumor/growth in obese mice. This resulted in the identification of the AMPK-related kinase SNARK a.k.a NUAK2, a YAP1 target gene and positive feedback regulator by inhibition LATS1 kinase. To identify obesity essential kinases LAPC-4 cells were inoculated with an shRNA library of ~5,000 lentivirus targeting 513 kinases. 5 × 106 cells were grafted to chronically obese mice. Tumors were established to ~200 mm3 and a portion collected for reference. Remaining mice were randomized to continue on ad lib WD or 25% CR diet. Genome-integrated shRNA inserts were amplified using nested barcoded primers and sequenced using Illumina Hi-Seq 2000 and quantified. NAUK2 shRNAs (3 of 10) were significantly depleted in LAPC4 xenograft tumors from mice on a Western diet (WD), but not tumors from mice on an isocaloric 25% calorie-restricted (CR) diet. Analysis of NUAK2 gene expression in human PCs shows NUAK2 expression is higher in PC tissue vs. normal benign. In addition, Cox regression analysis showed that NUAK2 is associated with a 3-fold increased chance of developing metastasis disease in high NUAK2 group. Proteomic data from metastatic PC rapid autopsy biopsies shows NUAK2 phospho-target LATS1 Ser464 is higher in metastatic PCs vs. benign localized prostatic tissues. Western blot analysis of PC cell lines shows NUAK2 is elevated in comparison to primary prostate epithelial cells. To test if NUAK2 is an actionable target in PC cells, we utilized two narrow-spectrum kinase inhibitors from the literature, WZ4003 and HTH-02-006. Pharmacologic inhibition of NUAK2 slows 2D PC cell growth and 3D PC spheroid growth of various PC cell lines with an IC50 range between 1-10 uM, with HTH-02-006 being the more potent. In addition, NUAK2 kinase inhibition with WZ4003 and HTH-02-006 significantly slowed migration and Matrigel invasion of PC3 and DU145 cells. To test if NUAK2 is associated with obesity-enhanced PC progression, we tested NUAK2 inhibition in combination with palmitic acid supplementation. Our preliminary studies show that pharmacologic inhibition of NUAK2 sensitized PC cells to fatty acid-induced cell death. In conclusion, our in vivo shRNA screen identified NUAK2 as an essential kinase for tumors grown in obese mice. NUAK2 expression correlates with worse outcomes in PC and NUAK2 kinase activity is elevated in metastatic vs. localized PCs. Our preliminary data show that pharmacologic inhibition of NUAK2 slows PC cell growth and migration/invasion. Lastly, our preliminary studies show that NUAK2 may lower the threshold for fatty acid-induced toxicity. Together, these preliminary data suggest NUAK2 is a relevant actionable target in PC and may synergize with obese host physiology to slow PC tumor growth. Citation Format: Weiwei Fu, Megan Zhao, Amelia Schirmer, Erick J. Maravilla, Junhee Yoon, Sungyong You, Stephen J. Freedland, Everardo Macias. NUAK2 inhibition for prostate cancer [abstract]. In: Proceedings of the AACR Special Conference on the Hippo Pathway: Signaling, Cancer, and Beyond; 2019 May 8-11; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2020;18(8_Suppl):Abstract nr B07.

Introduction and Objective: Cisplatin-based chemotherapy is currently part of the standard of care for bladder cancer (BC). Unfortunately, some patients respond poorly to chemotherapy and have acquired or developed resistance. The molecular mechanisms underlying this resistance remain unclear. Here, we introduce a multidimensional proteomic analysis of a cisplatin-resistant BC model that provides different levels of protein information, including that of the global proteome and phosphoproteome. Methods: To characterize the global proteome and phosphoproteome in cisplatin-resistant BC cells, liquid chromatography-mass spectrometry/mass spectrometry experiments combined with comprehensive bioinformatics analysis were performed. Perturbed expression and phosphorylation levels of key kinases associated with cisplatin resistance were further studied using various cell biology assays, including Western blot analysis. Results: Analyses of protein expression and phosphorylation identified significantly altered proteins, which were also EGF dependent and independent. This suggests that protein phosphorylation plays a significant role in cisplatin-resistant BC. Additional network analysis of significantly altered proteins revealed CDK2, CHEK1, and ERBB2 as central regulators mediating cisplatin resistance. In addition to this, we identified the CDK2 network, which consists of CDK2 and its five substrates, as being significantly associated with poor survival after cisplatin chemotherapy. Conclusions: Collectively, these findings potentially provide a novel way of classifying higher-risk patients and may guide future research in developing therapeutic targets. Citation Format: Jae Hun Jung, Sungyong You, Jae Won Oh, Junhee Yoon, Austin Yeon, Muhammad Shahid, Eunho Cho, Vikram Sairam, Taeeun Park, Kwang Pyo Kim, Jayoung Kim. Characterization of the global proteome and phosphoproteome in cisplatin-resistant bladder cancers revealed the CDK2 network as a potential therapeutic target [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2019 May 18-21; Denver, CO. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(15_Suppl):Abstract nr B13.

e17509 Background: Genomic profiling has strongly impacted the contemporary understanding of prostate cancer (PCa). Clinical trials are now testing the utility of genomic classifiers such as the PCS (You, Ca Res 2016) and PAM50 (Zhao, JAMA Onc 2017) systems to optimize therapy selection. As contemporary tissue is not always readily available, especially in metastatic, castration-resistant PCa (mCRPC), a blood-based test would be better suited for assessing patients and predicting treatment response. Methods: The CTC-RNA assay combines the Thermoresponsive (TR)-NanoVelcro system with the NanoString nCounter platform. This allows for CTC purification and RNA analysis. Using a novel bioinformatics approach that accounts for differences in background signals between tissue and blood, we reconstructed the PCS and PAM50 panels to recapitulate both classifiers in this blood-based assay. A weighted Z-score and nearest centroid classifier were used to calculate gene expression and to assign PCS and PAM50 subtypes. Performance of the revised signatures and CTC-RNA assay was benchmarked on simulated spiked-blood specimens. An initial clinical test was performed using clinically annotated, banked blood specimens within the Translational Oncology Program Blood and Biospecimen Bank. Results: CTC-RNA profiles of C4-2B AR signaling inhibitor (ARSI)-resistant sublines were compared to parental C4-2B. C4-2B ARSI-resistant cells had significantly higher PCS1 Z scores, PCS1 probability, and basal probability compared to the parental C4-2B cells. Blood samples from 34 mCRPC patients prior to initiation of therapy with ARSIs (abiraterone, enzalutamide, or apalutamide) were then analyzed. Samples were classified as PCS1 (n = 3), PCS2 (n = 20), and PCS3 (n = 11); luminal A (n = 12), luminal B (n = 11), and basal (n = 11). The biochemical progression-free survival (bPFS) on ARSI and overall survival (OS) for PCS1/Basal vs. other are shown in the table. Conclusions: The CTC-RNA assay is capable of generating luminal-basal classifications such as those in the PCS and PAM50 systems. Given early data of these classifiers and their potential to guide therapeutic decisions, this approach may be useful as an alternative to biopsy to facilitate such decisions. Larger prospective studies will be needed to confirm and validate its clinical utility. [Table: see text]

Background Cisplatin-based chemotherapy is currently part of the standard of care for bladder cancer (BC). Unfortunately, some patients respond poorly to chemotherapy and have acquired or developed resistance. The molecular mechanisms underlying this resistance remain unclear. Here, we introduce a multidimensional proteomic analysis of a cisplatin-resistant BC model that provides different levels of protein information, including that of the global proteome and phosphoproteome. Methods To characterize the global proteome and phosphoproteome in cisplatin-resistant BC cells, liquid chromatography-mass spectrometry/mass spectrometry experiments combined with comprehensive bioinformatics analysis were performed. Perturbed expression and phosphorylation levels of key kinases associated with cisplatin resistance were further studied using various cell biology assays, including western blot analysis. Results Analyses of protein expression and phosphorylation identified significantly altered proteins, which were also EGF-dependent and independent. This suggests that protein phosphorylation plays a significant role in cisplatin-resistant BC. Additional network analysis of significantly altered proteins revealed CDK2, CHEK1, and ERBB2 as central regulators mediating cisplatin resistance. In addition to this, we identified the CDK2 network, which consists of CDK2 and its 5 substrates, as being significantly associated with poor survival after cisplatin chemotherapy. Conclusions Collectively, these findings potentially provide a novel way of classifying higher-risk patients and may guide future research in developing therapeutic targets.

The development of next-generation sequencing (NGS) technology allows to sequence whole exomes or genome. However, data analysis is still the biggest bottleneck for its wide implementation. Most laboratories still depend on manual procedures for data handling and analyses, which translates into a delay and decreased efficiency in the delivery of NGS results to doctors and patients. Thus, there is high demand for developing an automatic and an easy-to-use NGS data analyses system. We developed comprehensive, automatic genetic analyses controller named Mobile Genome Express (MGE) that works in smartphones or other mobile devices. MGE can handle all the steps for genetic analyses, such as: sample information submission, sequencing run quality check from the sequencer, secured data transfer and results review. We sequenced an Actrometrix control DNA containing multiple proven human mutations using a targeted sequencing panel, and the whole analysis was managed by MGE, and its data reviewing program called ELECTRO. All steps were processed automatically except for the final sequencing review procedure with ELECTRO to confirm mutations. The data analysis process was completed within several hours. We confirmed the mutations that we have identified were consistent with our previous results obtained by using multi-step, manual pipelines.

Background: Tissue-based gene signatures can predict clinical behavior in prostate cancer (PC). Our objective was to extend their application to circulating tumor cells (CTCs) and to show that changes in the signature were associated with changes in clinical behavior. Methods: Our approach combined the Thermoresponsive(TR)-NanoVelcro CTC purification system with the Nanostring nCounter system for cellular purification and transcriptomic analysis. The Prostate Cancer Classification System (PCS) panel was modified for use in CTCs. We selected 31 blood samples from 23 PC patients receiving androgen receptor signaling inhibitors (ARSI) and measured the PCS1 Z score (probability). These findings were compared with clinical outcome data (responsiveness/resistance). Results: A modified, 16-gene PCS1 signature was established and validated through a rigorous bioinformatics process. We performed analytical validation of our combined CTC-RNA system to ensure reproducibility and specificity. In patient bloods, ARSI-resistant samples (ARSI-R, n=14) had significantly higher PCS1 Z scores as compared with ARSI-sensitive samples (ARSI-S, n=17) (Rank-sum test, P=0.003). The analyzed bloods contained samples from 8 patients who developed resistance to an ARSI allowing for dynamic measurement of gene expression. Our analysis found that the PCS1 Z score increased at the time that ARSI-resistance emerged (Pairwise T-test, P=0.016). Conclusions:Using this new methodology, contemporary, clinically-relevant gene signatures such as PCS could be measured non-invasively in CTCs. These findings can be used to relate gene expression to clinical drug response. This approach also allowed for measurement of dynamic variations of gene expression in individual patients over time that correlated to ARSI sensitivity. Citation Format: Pai-Chi Teng, Yu Jen Jan, Junhee Yoon, Jie-Fu Chen, Pin-Jung Chen, Nu Yao, Shirley Cheng, Amber Lozano, Michael R. Freeman, Sungyong You, Hsian-Rong Tseng, Edwin M. Posadas. A circulating tumor cell assay for dynamic assessment of drug sensitivity in metastatic castration-resistant prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 453.

5059 Background: Our objective is to develop a circulating tumor cell (CTC)-RNA assay for characterizing clinically relevant RNA signatures for the assessment of androgen receptor signaling inhibitors (ARSIs) sensitivity in metastatic castration-resistant prostate cancer (mCRPC) patients. Methods: We developed NanoVelcro CTC-RNA Assay by combining Thermoresponsive(TR)-NanoVelcro CTC purification system with NanoString nCounter platform for CTC purification and RNA analysis. Based on the well-validated, tissue-based Prostate Cancer Classification System (PCS), we selected the most aggressive and ARSI-resistant subtype- the PCS1, for CTC analysis. We applied a rigorous bioinformatic process to develop a CTC-PCS1 panel that is specific to PC CTCs. We validated NanoVelcro CTC-RNA Assay and CTC-PCS1 panel with PC cell lines to demonstrate sensitivity and specificity of the PCS1 Z score (the likelihood estimate of the PCS1 subtype) for identifying PCS1 subtype and ARSI resistance. We then selected 31 blood samples from 23 PC patients receiving ARSIs to test in our assay. The PCS1 Z score of each sample was computed and compared with ARSI treatment sensitivity. Results: We established a 16-gene CTC-PCS1 panel that consists of CTC-specific RNA signatures. The validation studies using PC cell lines showed that the assay can detect the RNA transcripts with high sensitivity and scalability in the range of 1-100 cells. We also showed that the genes in CTC-PCS1 panel is highly expressed in PC cells. We further demonstrated that the CTC-PCS1 panel is highly specific in identifying PCS1-like samples, and the high PCS1 Z score is associated with ARSI resistance. In patient bloods, ARSI-resistant samples (ARSI-R, n=14) had significantly higher PCS1 Z scores as compared with ARSI-sensitive samples (ARSI-S, n=17) (Rank-sum test, P=0.003). In 8 patients who were initially sensitive to ARSI (ARSI-S) and later developed resistance (ARSI-R), we found that the PCS1 Z score increased from the time of ARSI-S to the time of ARSI-R (Pairwise T-test, P=0.016). Conclusions: Using our new methodology, we developed a first-in-class CTC-RNA assay and demonstrated the feasibility of transforming clinically-relevant tissue-based RNA profiling into CTC tests. This approach allows for detecting RNA expression relevant to clinical drug resistance in a non-invasive fashion, which can facilitate patient-specific treatment selection and early detection of drug resistance- a goal in precision oncology.

126 Background: Up to 20% of patients with negative multiparametric magnetic resonance imaging (MRI) harbor Gleason score ≥7 prostate cancer (PCa). We sought to elucidate the molecular basis of and determine the prognostic significance of PCa visibility on MRI. Methods: We identified a retrospective cohort of patients who underwent MRI prior to prostatectomy with both MRI visible (PIRADS 3 – 5) and invisible PCa. MRI for each patient was re-reviewed and co-registered with whole-mount histopathology. DNA and RNA were co-isolated from all tumor foci pre-identified on FFPE specimens. High depth, targeted DNA and RNA next generation sequencing was performed to characterize the molecular profile of each tumor focus using the Oncomine Comprehensive Panel (DNA) and a custom targeted RNAseq panel assessing PCa relevant alterations. A multigene RNAseq model was developed and validated in two independent cohorts to predict MRI visible PCa and to determine the prognostic significance of MRI visibility. Results: A total of 26 primary tumor foci from 10 patients were analyzed. Of the 14 (54%) invisible lesions on MRI, 5 (36%) were Gleason 3+4 = 7 and the remainder were Gleason 6. We detected high-confidence prioritized PCa relevant mutations in 54% (14/26) of tumor foci, 43% (6/14) of which were in MRI invisible lesions. Notable point mutations were in APC, AR, ARID1B, ATM, ATRX, BRCA2, FAT1, MAP3K1, NF1, SPEN, SPOP, and TP53. A 9-gene RNA signature, the majority of which were under-expressed cellular organization and structure genes, was developed to predict MRI visibility with an AUC of 0.89. Validation of this signature in an independent data set (n = 16) yielded an AUC of 0.88 with a specificity of 100% for predicting MRI visible tumors. Using this signature in a cohort of 375 patients with clinical follow up, we found that predicted MRI visibility status was not an independent predictor of biochemical recurrence, metastasis-free survival, or PCa specific mortality (all p > 0.05). Conclusions: We observed under-expression of cellular organization and structural genes in MRI visible tumors compared to MRI invisible cancer foci. Using our validated signature to predict MRI visibility status, we found that MRI visibility is not a significant predictor of oncological outcomes.

157 Background: Tissue-based gene signatures can predict clinical behavior in prostate cancer (PC). Our objective was to extend their application to circulating tumor cells (CTCs) and to show that changes in the signature were associated with changes in clinical behavior. Methods: Our approach combined the Thermoresponsive(TR)-NanoVelcro CTC purification system with the Nanostring nCounter system for cellular purification and transcriptomic analysis. The Prostate Cancer Classification System (PCS) panel was modified for use in CTCs. We selected 31 blood samples from 23 PC patients receiving androgen receptor signaling inhibitors (ARSI) and measured the PCS1 Z score (probability). These findings were compared with clinical outcome data (responsiveness/resistance). Results: A modified, 16-gene PCS1 signature was established and validated through a rigorous bioinformatics process. We performed analytical validation of our combined CTC-RNA system to ensure reproducibility and specificity. In patient bloods, ARSI-resistant samples (ARSI-R, n = 14) had significantly higher PCS1 Z scores as compared with ARSI-sensitive samples (ARSI-S, n = 17) (Rank-sum test, P = 0.003). The analyzed bloods contained samples from 8 patients who developed resistance to an ARSI allowing for dynamic measurement of gene expression. Our analysis found that the PCS1 Z score increased at the time that ARSI-resistance emerged (Pairwise T-test, P = 0.016). Conclusions: Using this new methodology, contemporary, clinically-relevant gene signatures such as PCS could be measured non-invasively in CTCs. These findings can be used to relate gene expression to clinical drug response. This approach also allowed for measurement of dynamic variations of gene expression in individual patients over time that correlated to ARSI sensitivity.

Next-generation sequencing (NGS) is becoming a standard for genetic analyses of clinical samples. DNAs retrieved from formalin-fixed, paraffin-embedded (FFPE) tissue specimens are commonly degraded, and specimens such as core biopsies are sometimes too small to obtain enough DNA for NGS applications. Thus, it is important to measure both the DNA quantity and quality accurately from clinical samples. However, there is no standard method for DNA quantity and quality analyses for NGS library preparation. We tested four different methods (PicoGreen, Qubit® fluorometry, TaqMan and SYBR-Green-based qPCR assay) and compared each to RNase P TaqMan as a reference control. We found that SYBR-Green-based qPCR assay provides a consistent and accurate DNA quantification while keeping its cost relatively low and the throughput high. We designed a dual-probe SYBR-Green qPCR assay for DNA quantity and quality assessment for targeted NGS library preparation. This assay provides a Dscore (degradation score) of the interrogated DNA by analyzing two different sizes of amplicons. We show an example of a clinical sample with a very high Dscore (high degradation). With a regular DNA quantification, without considering the degradation status, no correct NGS libraries were obtained. However, after optimizing the library condition by considering its poor DNA quality, a reasonably good library and sequencing results were obtained. In summary, we developed and presented a new DNA quantity and quality analysis qPCR assay for the targeted NGS library preparation. This assay may be mostly efficient for the clinical samples with high degradation and poor DNA quality.

Next-generation sequencing (NGS) has led to breakthroughs for genetic and genomic analyses and personalized medicine approaches for many diseases. More and more clinical laboratories are using NGS as a genetic screening tool for providing mutation information that is used to select the best treatment regimens for cancer patients. However, several obstacles prevent the routine implementation of NGS technology into the clinical molecular diagnosis setting: the sophisticated sample preparation process, high cost, time-consuming data analyses, as well as the reproducibility and accuracy of interpretation. To systematically evaluate the performance and quality of targeted NGS cancer panel analyses in clinical laboratories, we performed three different tests: i) laboratory-to-laboratory accuracy test, ii) intra-laboratory precision validation, and iii) limit of detection test, using formalin-fixed, paraffin-embedded cancer tissue specimens, cell lines and mutation positive DNA. A laboratory-to-laboratory accuracy test performed using 51 samples showed 100% sensitivity and 99.97% specificity. For the intra-laboratory precision test, 100% reproducibility was observed. For the limit of detection test, KRAS mutations from samples diluted from 70 to 2% of mutant allele frequencies were detected correctly. We believe that the present study demonstrated the feasibility of clinical implementation of a targeted NGS cancer panel analysis for personalized medicine.

Objectives Major public high-throughput functional genomic data repositories, including the Gene Expression Omnibus (GEO) and ArrayExpress have rapidly expanded. As a result, a large number of diverse high-throughput functional genomic data retrieval systems have been developed. However, high-throughput functional genomic data retrieval remains challenging. Methods We developed Gene Expression data Explore (GEE), the first powerful, flexible web and mobile search application for searching whole-genome epigenetic data and microarray data in public databases, such as GEO and ArrayExpress. Results GEE provides an elaborate, convenient interface of query generation competences not available via various high-throughput functional genomic data retrieval systems, including GEO, ArrayExpress, and Atlas. In particular, GEE provides a suitable query generator using eVOC, the Experimental Factor Ontology (EFO), which is well represented with a variety of high-throughput functional genomic data experimental conditions. In addition, GEE provides an experimental design query constructor (EDQC), which provides elaborate retrieval filter conditions when the user designs real experiments. Conclusions The web version of GEE is available at http://www.snubi.org/software/gee, and its app version is available from the Apple App Store.

Background: Circulating tumor cells (CTCs) are blood borne tumor cells shed from all present disease sites, including primary and metastatic tumors. Researchers have been exploring the use of CTCs to identify important transcriptomic features such as androgen receptor splicing variants in prostate cancer (PCa). Newer transcriptomic profiles such as the prostate cancer classification system (PCS) have been developed that may be useful clinically. Over the past decade, we have been developing the NanoVelcro CTC purification system which includes 3 different stimuli-responsive strategies (temperature-dependent, glycan-stimulated, click chemistry-mediated) to capture and release viable CTCs with intact RNA. The purified CTCs can then be subjected to transcriptomic analysis such as quantitative PCR (qPCR), Droplet Digital PCR (ddPCR) and NanoString's nCounter system. In this study, we benchmarked the efficiency of these platforms for purification of CTCs from blood specimens and the feasibility for detection of PCa-related RNA signatures from purified CTCs. Methods: NanoVelcro CTC purification system is combined with qPCR and ddPCR platform to detect PCa-specific RNA targets such as full length AR (AR-FL), AR splicing variant 7 (AR-V7), KLK3 (prostate-specific antigen, PSA), FOLH1 (prostate-specific membrane antigen, PSMA), and long-noncoding RNA SChLAP1 (second chromosome locus associated with prostate-1) in purified CTCs from patients' blood samples. We also developed a modified PCS panel of 30 genes adapted to the nCounter platform and tested purified CTCs from PCa patient blood samples and performed differential expression analysis for PCa disease profiling. Results: PCa-related RNA signals were detected in 16/17 CTC(+) PCa patients, including 3 of 4 non-metastatic patients. We also tested the gene expressions of the modified PCS panel using 32 patient blood samples. Upon performing clustering of samples based on the RNA signatures, we found the differential expression of CTC-target genes grouped patients in a manner which strongly related to drug response and clinical status. Conclusions: With the evolution of our NanoVelcro CTC purification system, we have shown the capability of purifying CTCs with high efficiency, while retaining cell viability and molecular integrity, allowing for detection of PCa-specific RNA signatures from CTCs. Our non-invasive, blood-based assay will be useful for detecting and continuous monitoring molecular alterations related to disease evolvement, addressing an unmet need for prostate cancer clinical care. Citation Format: Yu Jen Jan, Jie-Fu Chen, Sungyong You, Junhee Yoon, Nu Yao, Shirley Cheng, Michael R. Freeman, Hsian-Rong Tseng, Edwin M. Posadas. NanoVelcro CTC Purification Systems for expressional analysis of circulating tumor cells from prostate cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1578.

Malignant pleural mesothelioma (MPM), a rare malignancy with a poor prognosis, is mainly caused by exposure to asbestos or other organic fibers, but the underlying genetic mechanism is not fully understood. Genetic alterations and causes for multiple primary cancer development including MPM are unknown. We used whole exome sequencing to identify somatic mutations in a patient with MPM and two additional primary cancers who had no evidence of venous, arterial, lymphovascular, or perineural invasion indicating dissemination of a primary lung cancer to the pleura. We found that the MPM had R282W, a key TP53 mutation, and genome-wide allelic loss or loss of heterozygosity, a distinct genomic alteration not previously described in MPM. We identified frequent inactivating SETDB1 mutations in this patient and in 68 additional MPM patients (mutation frequency: 10%, 7/69) by targeted deep sequencing. Our observations suggest the possibility of a new genetic mechanism in the development of either MPM or multiple primary cancers. The frequent SETDB1 inactivating mutations suggest there could be new diagnostic or therapeutic options for MPM.

The emergence of next-generation sequencing (NGS) has changed the paradigm for genetic and genomic studies in many medical and life science fields. Despite its growing popularity and importance in many life science applications, several factors such as complicated sample preparation, high cost, and time-consuming data analyses may prevent NGS applications from being more widely used in clinical and research settings. Therefore, it is crucial that the current methods are improved or newly developed to become faster, more robust, and accurate in NGS applications. Targeted sequencing, focusing on small but important gene sets or genetic regions, is a very powerful approach to screen disease-related key genes. Reductions in cost and experimental time as well as availability of targeted sequencing are fueling the use of NGS for many genetic applications. We have developed a new, simple, and robust sample preparation method called ‘NextDay Seq,’ enabling us to obtain targeted deep sequencing data within the next day of sample arrival. Researchers and clinicians can obtain sequencing data within 36 hours. Our method starts with a 15 minute DNA extraction kit followed by library preparation, sequencing, and then data analysis. The NextDay Seq library preparation method directly ligates adapters to targeted genes/amplicons. This new protocol does not require restriction enzyme digestion (i.e. Ion Torrent Ampliseq) or hybridization of the target region (i.e. Illumina TruSeq). It is faster and simpler than previously referenced library preparation kits. We have also developed a new data analysis tool called DanPA: a fast, accurate, and robust NGS data analysis tool. DanPA, yet developed mainly for targeted sequencing analysis, can also be used for whole exome or genome sequencing data analysis. DanPA detects any kind of reported mutations registered in the database such as Catalogue of Somatic Mutations in Cancer (COSMIC). We have so far analyzed 866 cancer FFPE tissue samples, 431 flash-frozen tissue samples, 18 cell lines samples, and 515 other biological or synthesized (i.e. plasmids) samples using these new inventions. Our system will help clinicians in better deciding which therapeutic options (personalized medicine) or biological applications are optimal to treat patients with specific mutations. Cancer patients should have exact mutation information in a timely manner in order to select treatment options. By combining our new FFPE DNA extraction method, NextDay Seq library preparation method, and DanPA data analysis software, we are able to provide key mutation data to patients, medical doctors, and researchers within 36 hours (next day). We believe this new innovative NGS system can change the paradigm for mutation screening in clinical and research applications. Citation Format: Pedro Mendez, Jun-Hee Yoon, Sharon Lee, James Kim, Jenny Dang, Thomas Kim, David Jablons, Il Jin Kim. A new innovative and robust targeted deep sequencing system: 36 hours turn round time from patient samples to the final mutation report. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3609.