Accurate prediction of prostate cancer clinical courses remains elusive. In this study, we performed whole genome copy number analysis on leukocytes of 273 prostate cancer patients using Affymetrix SNP6.0 chip. Copy number variations (CNV) were found across all chromosomes of the human genome. An average of 152 CNV fragments per genome was identified in the leukocytes from prostate cancer patients. The size distributions of CNV in the genome of leukocytes were highly correlative with prostate cancer aggressiveness. A prostate cancer outcome prediction model was developed based on large size ratio of CNV from the leukocyte genomes. This prediction model generated an average prediction rate of 75.2%, with sensitivity of 77.3% and specificity of 69.0% for prostate cancer recurrence. When combined with Nomogram and the status of fusion transcripts, the average prediction rate was improved to 82.5% with sensitivity of 84.8% and specificity of 78.2%. In addition, the leukocyte prediction model was 62.6% accurate in predicting short prostate specific antigen doubling time. When combined with Gleason’s grade, Nomogram and the status of fusion transcripts, the prediction model generated a correct prediction rate of 77.5% with 73.7% sensitivity and 80.1% specificity. To our knowledge, this is the first study showing that CNVs in leukocyte genomes are predictive of clinical outcomes of a human malignancy.

/pdf/genomic-copy-number-variations-in-the-genomes-of-leukocytes-18okm06gz3.pdf

Genomic Copy Number Variations in the Genomes of Leukocytes Predict Prostate Cancer Clinical Outcomes.

Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA

Androgen deprivation therapy (ADT) for hormone-sensitive prostate cancer (HSPC) and chemotherapy for castration-resistant prostate cancer (CRPC) are considered standard of care. However, there are no clinical tests or factors that can reliably predict treatment responses for the advanced prostate cancer patients. Examination of tumor component in circulation, also known as liquid biopsy, has shown promise for predicting treatment outcomes and survival. To capture the tumor-derived genomic alterations and decrease the surrogacy of using PSA measurements as is currently performed, we propose a Plasma Genomic Abnormality (PGA) score and Treatment Efficacy (TEff) index based on copy number variations as estimated by cell free DNA (cfDNA) in plasma. We first performed whole genome and targeted sequencing (NimbleGen comprehensive cancer panel) to examine plasma cfDNA for tumor-associated genetic and genomic aberrations in 10 HSPC patients receiving ADT and 10 CRPC patients receiving chemotherapy. For each patient, we tested plasma from two time points: pre-treatment and 4 months post-treatment. We then calculated the PGA score by summing the most significant genomic abnormalities with higher PGA score indicating greater tumor DNA content in cfDNA. TEff index was derived from comparison of PGA score difference between treatments with higher TEff index reflecting a better treatment response. The sequencing-based copy number analysis revealed locus-specific gains or losses including those previously reported in prostate cancers, such as 8q gains, AR amplifications, PTEN losses and TMPRSS2-ERG fusions. We found that overall PGA score was significantly higher in CRPC patients than in HSPC patients (p<0.0005), possibly reflecting increased tumor burden during the disease progression. Patients with low TEff index often demonstrated poor response to treatments, and patients with high initial PGA score or low TEff index tended to have poor overall survival. As examples of the PGA-based TEff associations with outcome we presented three CRPC patients including two with neuro-endocrine origin (small cell carcinoma). Comparison of mutational profiles between pre- and post-treatments showed that treatments caused more diverse gene mutations, in particular, in the genes involving androgen biosynthesis, AR activation, DNA repair and chemotherapy resistance, suggesting subclonal evolution of heterogeneous tumor genomes in response to these treatments. Our results strongly support the feasibility of using non-invasive liquid biopsy as a potential powerful tool to study biological mechanisms underlying therapy-specific resistance and monitor disease progression in the advanced prostate cancer. Further studies are needed to develop and validate the genomics-based PGA score and TEff index for clinical applications.

Citation Format: Liang Wang, Shu Xia, Meijun Du, Rachel Ditmar, Tiezheng Yuan, Yongchen Guo, Yuan Wang, Adam Lee, Michael Tschannen, Elizabeth Worthey, Howard Jacobs, Chiang-Ching Huang, Manish Kohli. Plasma genetic and genomic abnormalities predict treatment response and clinical outcome in advanced prostate cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 5231. doi:10.1158/1538-7445.AM2015-5231

Abstract 5231: Plasma genetic and genomic abnormalities predict treatment response and clinical outcome in advanced prostate cancer

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Abstract 5231: Plasma genetic and genomic abnormalities predict treatment response and clinical outcome in advanced prostate cancer