Genomic analysis of castration sensitive and resistant prostate cancer patients by multiple-gene targeted sequencing

e17525 Background: Previous studies showed the significant diversity of the genomic landscapes between castration sensitive prostate cancer (CSPC) and castration resistant prostate cancer (CRPC). AR, TP53/RB1, and BRCA2/ATM have been reported to contribute to the development of CRPC. In addition, CDK12 has recently reported to lead to the worse response to AR-targeted therapies. We are aimed to clarify the landscape of somatic gene alterations in CSPC and CRPC by multiple-gene targeted sequencing and discover the differential genes between the two treatment stages in order to guide clinical treatment. Methods: We recruited 413 prostate cancer patients including 230 CSPC and 183 CRPC. Cell-free DNA was extracted from plasma samples. Targeted sequencing of 50 genes was performed, which involved in DNA damage repair (DDR) pathway, AR pathway, TP53/RB1, etc. Chi square test or Fisher test were used to analyze the differences between two cohorts. Results: A total of 33.04% (76/230) CSPC patients carried somatic gene alterations, including 16.96% (39/230) in DDR pathway, 19.57% (45/230) in AR pathway and 7.39% (17/230) in TP53/RB1. The most frequent altered gene in CSPC was FOXA1 (9.13%,21/230), followed by NCOR2 (6.52%,15/230) and TP53 (5.65%,13/230). For CRPC patients, 66.67% (122/183) carried somatic gene alterations, including 44.81% (82/230) in DDR pathway, 49.73% (91/230) in AR pathway and 25.68% (47/183) in TP53/RB1. The most frequent mutated gene in CRPC was AR (33.33%,61/183), followed by FOXA1 (23.50%,43/183) and CDK12 (17.49%,32/183). Mutation frequencies of certain genes in CRPC patients were significantly higher than CSPC patients, including CDK12 (p < 0.001), BRCA2 (p = 0.006), ATM (p = 0.003), ATR (p = 0.007), AR (p < 0.001), FOXA1 (p = 0.001), SPOP (p = 0.002), ZBTB16 (p = 0.02), TP53 (p = 0.001), RB1 (p = 0.04) and PTEN (p = 0.001). Conclusions: It was the first study which explored the genomic alterations in Chinese CSPC and CRPC patients by liquid biopsy. These findings confirmed the genomic diversity between CSPC patients and CRPC patients, which could guide the individualized and precise management of prostate cancer in general practice. Furthermore, our findings indicated that somatic alterations in AR, CDK12, BRCA2, ATM and TP53/RB1 might contribute to the development of CRPC.

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A machine learning approach to optimizing cell-free DNA sequencing panels: with an application to prostate cancer

10.1101/2020.04.30.069658 ◽

2020 ◽

Author(s):

Clinton L. Cario ◽

Emmalyn Chen ◽

Lancelote Leong ◽

Nima C. Emami ◽

Karen Lopez ◽

...

Keyword(s):

Prostate Cancer ◽

Machine Learning ◽

Cancer Patients ◽

In Silico ◽

Single Point ◽

Targeted Sequencing ◽

Whole Genome Sequence ◽

Test Set ◽

Hybrid Capture ◽

Localized Disease

AbstractBackgroundCell-free DNA’s (cfDNA) use as a biomarker in cancer is challenging due to genetic heterogeneity of malignancies and rarity of tumor-derived molecules. Here we describe and demonstrate a novel machine-learning guided panel design strategy for improving the detection of tumor variants in cfDNA. Using this approach, we first generated a model to classify and score candidate variants for inclusion on a prostate cancer targeted sequencing panel. We then used this panel to screen tumor variants from prostate cancer patients with localized disease in both in silico and hybrid capture settings.MethodsWhole Genome Sequence (WGS) data from 550 prostate tumors was analyzed to build a targeted sequencing panel of single point and small (<200bp) indel mutations, which was subsequently screened in silico against prostate tumor sequences from 5 patients to assess performance against commonly used alternative panel designs. The panel’s ability to detect tumor-derived cfDNA variants was then assessed using prospectively collected cfDNA and tumor foci from a test set 18 prostate cancer patients with localized disease undergoing radical proctectomy.ResultsThe panel generated from this approach identified as top candidates mutations in known driver genes (e.g. HRAS) and prostate cancer related transcription factor binding sites (e.g. MYC, AR). It outperformed two commonly used designs in detecting somatic mutations found in the cfDNA of 5 prostate cancer patients when analyzed in an in silico setting. Additionally, hybrid capture and 2,500X sequencing of cfDNA molecules using the panel resulted in detection of tumor variants in all 18 patients of a test set, where 15 of the 18 patients had detected variants found in multiple foci.ConclusionMachine learning-prioritized targeted sequencing panels may prove useful for broad and sensitive variant detection in the cfDNA of heterogeneous diseases. This strategy has implications for disease detection and monitoring when applied to the cfDNA isolated from prostate cancer patients.

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