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. 2015 Jun 30;6(18):16411-21.
doi: 10.18632/oncotarget.3845.

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

Shu Xia  1   2 , Manish Kohli  3 , Meijun Du  2 , Rachel L Dittmar  2 , Adam Lee  4 , Debashis Nandy  3 , Tiezheng Yuan  2 , Yongchen Guo  2 , Yuan Wang  2 , Michael R Tschannen  5 , Elizabeth Worthey  5 , Howard Jacob  5 , William See  6 , Deepak Kilari  6 , Xuexia Wang  7 , Raymond L Hovey  8 , Chiang-Ching Huang  7 , Liang Wang  2
Affiliations

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

Shu Xia et al. Oncotarget. .

Abstract

Liquid biopsies, examinations of tumor components in body fluids, have shown promise for predicting clinical outcomes. To evaluate tumor-associated genomic and genetic variations in plasma cell-free DNA (cfDNA) and their associations with treatment response and overall survival, we applied whole genome and targeted sequencing to examine the plasma cfDNAs derived from 20 patients with advanced prostate cancer. Sequencing-based genomic abnormality analysis revealed locus-specific gains or losses that were common in prostate cancer, such as 8q gains, AR amplifications, PTEN losses and TMPRSS2-ERG fusions. To estimate tumor burden in cfDNA, we developed a Plasma Genomic Abnormality (PGA) score by summing the most significant copy number variations. Cox regression analysis showed that PGA scores were significantly associated with overall survival (p < 0.04). After androgen deprivation therapy or chemotherapy, targeted sequencing showed significant mutational profile changes in genes involved in androgen biosynthesis, AR activation, DNA repair, and chemotherapy resistance. These changes may reflect the dynamic evolution of heterozygous tumor populations in response to these treatments. These results strongly support the feasibility of using non-invasive liquid biopsies as potential tools to study biological mechanisms underlying therapy-specific resistance and to predict disease progression in advanced prostate cancer.

Keywords: cell free DNA; liquid biopsy; next generation sequencing; plasma; prostate cancer.

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Conflict of interest statement

CONFLICTS OF INTEREST STATEMENT

The authors disclose no potential conflicts of interest.

Figures

Figure 1
Figure 1. cfDNA genomic abnormalities detected at specific chromosomal loci
PTEN loss at chromosome 10, TMPRSS2 at chromosome 21, and AR amplification at chromosome X are shown. Arrows indicate the locations of these chromosomal aberrations.
Figure 2
Figure 2. Plasma genomic abnormality (PGA) scores in 20 patients with advanced prostate cancer
Higher PGA scores indicated more tumor-associated somatic abnormalities in cfDNA and were associated with disease progression and overall survival.
Figure 3
Figure 3. PGA score differences between high and low volume prostate cancer patients (see main text for definition)
Average PGA score before treatment is significantly lower in low volume patients (n = 13) than in high volume patients (n = 7).
Figure 4
Figure 4. Comparison of PGA scores and TEff indexes in three representative CRPC patients
Chromosomes were shown on the x axis while GC-adjusted log2 ratios (black dots) in 1Mb windows were on the y axis. Red lines indicate the trend of copy number variations. Complete, partial and no responses to chemotherapy were displayed in A (patient 1060), B (patient 1005), and C (patient 1003), respectively. OS = overall survival.
Figure 5
Figure 5. Mutational profile changes between pre-ADT and post-ADT
Gene mutation pathway enrichment analysis was performed in the HSPC cohort receiving standard ADT. More gene mutations were observed in post- than in pre-treatment samples. Pathways involved in androgen biosynthesis, metabolism, and androgen receptor activation are among the most commonly mutated.
Figure 6
Figure 6. Mutational profile changes between pre- and post-chemotherapy
Many mutations detected after chemotherapy were not present in pre-treatment samples. The most common mutations in pre-treatment samples were in DNA repair-related hereditary breast cancer signaling pathways. The most common mutations in post-treatment samples occurred in the pathways related to AR regulation and resistance to chemotherapy including axonal guidance signaling, protein kinase A signaling, and renin-angiotensin signaling pathways.
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