Rapid, ultra low coverage copy number profiling of cell-free DNA as a precision oncology screening strategy.

Current cell-free DNA (cfDNA) next generation sequencing (NGS) precision oncology workflows are typically limited to targeted and/or disease-specific applications. In advanced cancer, disease burden and cfDNA tumor content are often elevated, yielding unique precision oncology opportunities. We sought to demonstrate the utility of a pan-cancer, rapid, inexpensive, whole genome NGS of cfDNA approach (PRINCe) as a precision oncology screening strategy via ultra-low coverage (~0.01x) tumor content determination through genome-wide copy number alteration (CNA) profiling. We applied PRINCe to a retrospective cohort of 124 cfDNA samples from 100 patients with advanced cancers, including 76 men with metastatic castration-resistant prostate cancer (mCRPC), enabling cfDNA tumor content approximation and actionable focal CNA detection, while facilitating concordance analyses between cfDNA and tissue-based NGS profiles and assessment of cfDNA alteration associations with mCRPC treatment outcomes. Therapeutically relevant focal CNAs were present in 42 (34%) cfDNA samples, including 36 of 93 (39%) mCRPC patient samples harboring AR amplification. PRINCe identified pre-treatment cfDNA CNA profiles facilitating disease monitoring. Combining PRINCe with routine targeted NGS of cfDNA enabled mutation and CNA assessment with coverages tuned to cfDNA tumor content. In mCRPC, genome-wide PRINCe cfDNA and matched tissue CNA profiles showed high concordance (median Pearson correlation = 0.87), and PRINCe detectable AR amplifications predicted reduced time on therapy, independent of therapy type (Kaplan-Meier log-rank test, chi-square = 24.9, p < 0.0001). Our screening approach enables robust, broadly applicable cfDNA-based precision oncology for patients with advanced cancer through scalable identification of therapeutically relevant CNAs and pre-/post-treatment genomic profiles, enabling cfDNA- or tissue-based precision oncology workflow optimization

CXCL12γ Promotes Development of Metastatic Castration Resistant Prostate Cancer by Induction of Cancer Stem Cell and Neuroendocrine Phenotypes

There is evidence that cancer stem-like cells (CSC) and neuroendocrine behavior play critical roles in the pathogenesis and clinical course of metastatic castration-resistant prostate cancer (m-CRPC). However, there is limited mechanistic understanding of how CSC and neuroendocrine phenotypes impact the development of m-CRPC. In this study, we explored the role of the intracellular chemokine CXCL12γ in CSC induction and neuroendocrine differentiation and its impact on m-CRPC. CXCL12γ expression was detected in small-cell carcinoma of metastatic tissues and circulating tumor cells from m-CRPC patients and in prostate cancer cells displaying an neuroendocrine phenotype. Mechanistic investigations demonstrated that overexpression of CXCL12γ induced CSC and neuroendocrine phenotypes in prostate cancer cells through CXCR4-mediated PKCα/NFκB signaling, which promoted prostate tumor outgrowth, metastasis, and chemoresistance in vivo Together, our results establish a significant function for CXCL12γ in m-CRPC development and suggest it as a candidate therapeutic target to control aggressive disease.Significance: Expression of CXCL12γ induces the expression of a cancer stem cell and neuroendocrine phenotypes, resulting in the development of aggressive m-CRPC. Cancer Res; 78(8); 2026-39. ©2018 AACR

HR-MAS NMR Tissue Metabolomic Signatures Cross-validated by Mass Spectrometry Distinguish Bladder Cancer from Benign Disease

Effective diagnosis and surveillance of bladder cancer (BCa) is currently challenged by detection methods that are of poor sensitivity, particularly for low-grade tumors, resulting in unnecessary invasive procedures and economic burden. We performed HR-MAS NMR-based global metabolomic profiling and applied unsupervised principal component analysis (PCA) and hierarchical clustering performed on NMR data set of bladder-derived tissues and identified metabolic signatures that differentiate BCa from benign disease. A partial least-squares discriminant analysis (PLS-DA) model (leave-one-out cross-validation) was used as a diagnostic model to distinguish benign and BCa tissues. Receiver operating characteristic curve generated either from PC1 loadings of PCA or from predicted Y-values resulted in an area under curve of 0.97. Relative quantification of more than 15 tissue metabolites derived from HR-MAS NMR showed significant differences (<i>P</i> < 0.001) between benign and BCa samples. Noticeably, striking metabolic signatures were observed even for early stage BCa tissues (Ta-T1), demonstrating the sensitivity in detecting BCa. With the goal of cross-validating metabolic signatures derived from HR-MAS NMR, we utilized the same tissue samples to analyze 8 metabolites through gas chromatography–mass spectrometry (GC–MS)-targeted analysis, which undoubtedly complements HR-MAS NMR-derived metabolomic information. Cross-validation through GC–MS clearly demonstrates the utility of a straightforward, nondestructive, and rapid HR-MAS NMR technique for clinical diagnosis of BCa with even greater sensitivity. In addition to its utility as a diagnostic tool, these studies will lead to a better understanding of aberrant metabolic pathways in cancer as well as the design and implementation of personalized cancer therapy through metabolic modulation

Multigene profiling of CTCs in mCRPC identifies a clinically relevant prognostic signature

The trend toward precision-based therapeutic approaches dictated by molecular alterations offers substantial promise for men with metastatic castration-resistant prostate cancer (mCRPC). However, current approaches for molecular characterization are primarily tissue based, necessitating serial biopsies to understand changes over time and are limited by the challenges inherent to extracting genomic material from predominantly bone metastases. Therefore, a circulating tumor cell (CTC)-based assay was developed to determine gene expression across a panel of clinically relevant and potentially actionable prostate cancer-related genes. CTCs were isolated from the whole blood of mCRPC patients (n = 41) and multiplex qPCR was performed to evaluate expression of prostate cancer-related target genes (n = 78). A large fraction of patients (27/41, 66%) had detectable CTCs. Increased androgen receptor (AR) expression (70% of samples) and evidence of Wnt signaling (67% of samples) were observed. The TMPRSS2:ERG fusion was expressed in 41% of samples, and the aggressive prostate cancer-associated long noncoding RNA SChLAP1 was upregulated in 70%. WNT5a [HR 3.62, 95% confidence interval (CI), 1.63-8.05, P = 0.002], AURKA (HR 5.56, 95% CI, 1.79-17.20, P = 0.003), and BMP7 (HR 3.86, 95% CI, 1.60-9.32, P = 0.003) were independently predictive of overall survival (FDR &lt; 10%) after adjusting for a panel of previously established prognostic variables in mCRPC (Halabi nomogram). A model including Halabi, WNT5a, and AURKA expression, termed the miCTC score, outperformed the Halabi nomogram alone (AUC = 0.89 vs. AUC = 0.70). Understanding the molecular landscape of CTCs has utility in predicting clinical outcomes in patients with aggressive prostate cancer and provides an additional tool in the arsenal of precision-based therapeutic approaches in oncology.Implications: Analysis of CTC gene expression reveals a clinically prognostic "liquid biopsy" signature in patients with metastatic castrate-resistance prostate cancer. Mol Cancer Res; 16(4); 643-54. ©2018 AACR

Rapid, ultra low coverage copy number profiling of cell-free DNA as a precision oncology screening strategy.

Current cell-free DNA (cfDNA) next generation sequencing (NGS) precision oncology workflows are typically limited to targeted and/or disease-specific applications. In advanced cancer, disease burden and cfDNA tumor content are often elevated, yielding unique precision oncology opportunities. We sought to demonstrate the utility of a pan-cancer, rapid, inexpensive, whole genome NGS of cfDNA approach (PRINCe) as a precision oncology screening strategy via ultra-low coverage (~0.01x) tumor content determination through genome-wide copy number alteration (CNA) profiling. We applied PRINCe to a retrospective cohort of 124 cfDNA samples from 100 patients with advanced cancers, including 76 men with metastatic castration-resistant prostate cancer (mCRPC), enabling cfDNA tumor content approximation and actionable focal CNA detection, while facilitating concordance analyses between cfDNA and tissue-based NGS profiles and assessment of cfDNA alteration associations with mCRPC treatment outcomes. Therapeutically relevant focal CNAs were present in 42 (34%) cfDNA samples, including 36 of 93 (39%) mCRPC patient samples harboring AR amplification. PRINCe identified pre-treatment cfDNA CNA profiles facilitating disease monitoring. Combining PRINCe with routine targeted NGS of cfDNA enabled mutation and CNA assessment with coverages tuned to cfDNA tumor content. In mCRPC, genome-wide PRINCe cfDNA and matched tissue CNA profiles showed high concordance (median Pearson correlation = 0.87), and PRINCe detectable AR amplifications predicted reduced time on therapy, independent of therapy type (Kaplan-Meier log-rank test, chi-square = 24.9, p &lt; 0.0001). Our screening approach enables robust, broadly applicable cfDNA-based precision oncology for patients with advanced cancer through scalable identification of therapeutically relevant CNAs and pre-/post-treatment genomic profiles, enabling cfDNA- or tissue-based precision oncology workflow optimization