Ha, Gavin

There are no biomarkers predictive of resistance to docetaxel or cabazitaxel validated for patients with metastatic castration-resistant prostate cancer (mCRPC). We assessed the association between ABCB1 amplification and primary resistance to docetaxel or cabazitaxel for patients with mCRPC, using circulating cell-free DNA (cfDNA). Patients with ≥1 plasma sample drawn within 12 months before starting docetaxel (cohort A) or cabazitaxel (cohort B) for mCRPC were identified from the Dana–Farber Cancer Institute IRB approved database. Sparse whole genome sequencing was performed on the selected cfDNA samples and tumor fractions were estimated using the computational tool ichorCNA. We evaluated the association between ABCB1 amplification or other copy number alterations and primary resistance to docetaxel or cabazitaxel. Of the selected 176 patients, 45 samples in cohort A and 21 samples in cohort B had sufficient tumor content. No significant association was found between ABCB1 amplification and primary resistance to docetaxel (p = 0.58; odds ratio (OR) = 1.49) or cabazitaxel (p = 0.97; OR = 1.06). No significant association was found between exploratory biomarkers and primary resistance to docetaxel or cabazitaxel. In this study, ABCB1 amplification did not predict primary resistance to docetaxel or cabazitaxel for mCRPC. Future studies including ABCB1 amplification in a suite of putative biomarkers and a larger cohort may aid in drawing definitive conclusions.

174 Background: Docetaxel (D) and cabazitaxel (C) are standard chemotherapies for mCRPC. A reliable biomarker predictive of resistance to D or C is yet to be identified. We aimed to assess the association between genetic amplification of the multidrug resistance transporter ABCB1 (ABCB1 amp) and primary resistance (RES) to D or C for mCRPC, using cfDNA. Methods: A cohort (A) of 136 patients (pts) with at least 1 plasma sample drawn and stored within 1 year prior to starting D for mCRPC (2002-2014) and a cohort (B) of 42 pts with at least 1 plasma sample from within 1 year prior to starting C for mCRPC (2010-2016) were identified from the Dana-Farber Cancer Institute IRB approved database. Whole genome sequencing (WGS) at 0.1x coverage, termed ultra-low pass WGS (ULP-WGS), was performed on cfDNA extracted from the selected samples (1000μL/subject) and sequencing data were analyzed using a tool called ichorCNA to identify cases with sufficient tumor DNA content (>7%) for accurate detection of copy number alterations (CNAs) including ABCB1 amp. Primary objective was the association between ABCB1 amp and RES to D or C. RES was defined as lack of response (no PSA50 decline or radiologic response per RECIST criteria 1.1, within 4 months from treatment start). Odds ratio (OR) was used to compare odds of RES to D or C for pts with ABCB1 amp and P-values were calculated by Fisher’s exact test. Results: Of the selected 178 pts, 66 had tumor fraction >7%: 45 pts in cohort A and 21 in cohort B. No significant association was noted between ABCB1 amp and RES to D (P=0.7123; OR=1.600) or C (P=1.000; OR=1.0606). RES was observed in 26 pts (57.8%) of cohort A and 18 (85.7%) of cohort B. ABCB1 amp was found in 9 pts (20%; 95% CI, 9.6-34.6) in group A and 6 of them (66%) had RES to D. ABCB1 amp rate among D-resistant men was 23.1% (95% CI, 9.0-43.7). In group B, 2 pts (9.5%; 95% CI, 1.2-30.4) had ABCB1 amp and both of them had RES to C. ABCB1 rate among C-resistant pts was 11.1% (95% CI, 1.4-34.7). Conclusions: In this study, ABCB1 amp using cfDNA did not show statistically significant correlation with RES to D or C for pts with mCRPC. Future studies including ABCB1 amp in a suite of putative biomarkers and larger sample size may aid drawing definitive conclusions.

Abstract Background . Cell-free DNA (cfDNA) sequencing enables serial temporal sampling, which offers the possibility of following the dynamics of biomarkers and clonal evolution in Multiple Myeloma (MM) over time. The use of cfDNA in clinical practice as a molecular biomarker and for monitoring response/resistance is dependent on a comprehensive profile of matched cfDNA and tumor DNA (tDNA) samples. Here we performed Ultra-Low Pass Whole Genome Sequencing (ULP-WGS) followed by whole-exome sequencing (WES) and targeted deep sequencing of matched cfDNA/tDNA samples from MM patients. Methods. We performed next generation sequencing of matched cfDNA/tDNA samples for 63 patients with newly diagnosed or relapsed MM, SMM, or MGUS. Libraries were constructed using the Kappa Hyper kit and sequenced by ultra-low-pass whole-genome sequencing (ULP-WGS, 0.1x coverage) to quantify tumor fraction within cfDNA. WES was performed on 30 matched samples cfDNA/tDNA/germline DNA from 10 patients with more than 5% of tumor fraction. Libraries were hybridized to the Nextera Rapid Capture Exome kit (Illumina) and then sequenced on HiSeq 4000 (Illumina). Targeted deep sequencing was performed on 32 matched cfDNA/tDNA samples from 16 patients using the HaloPlex HS technology (Agilent), allowing for molecular barcoding. Libraries were constructed according to the manufacturer's instructions and sequenced on HiSeq 2500 (Illumina). Sequencing data were analyzed using the Firehose pipelines, including MuTect, ABSOLUTE, ReCapSeg, GISTIC and MutSig. Results. We first used a cost-effective approach to establish the tumor content of cfDNA in a large-scale manner by ULP-WGS. Among 63 tested samples (53 MM, 6 SMM and 4 MGUS patient samples), the tumor fraction within cfDNA ranged from 0 to 81% with a mean of 10%. About 43% of these samples had tumor fraction greater than 5% within cfDNA. To assess whether cfDNA can capture the genetic diversity of MM and inform clinical management, we performed WES of matched cfDNA/tDNA/germline DNA samples for 10 patients (mean target coverage 194x). Copy number alterations (CNAs) assessed by WES (ReCapSeg) were consistent between cfDNA and tumor DNA. Similarly, focal CNAs assessed by GISTIC were consistent between tDNA and cfDNA. We then examined the overlap of somatic single nucleotide variants (SSNVs) between WES of cfDNA and matched tDNA. We found, on average, 100% of the clonal and 96% of the subclonal (range 54-100%) SSNVs that were detected in the tumor were confirmed to be present in cfDNA. Similarly, for mutations detected in the cfDNA, we found, on average, 100% of the clonal and 99% of the subclonal (range 98-100%) SSNVs were confirmed in the tumor. To assess whether targeted deep sequencing of cfDNA could be a good proxy for tumor biopsy we used a targeted deep sequencing approach of known MM driver genes. Libraries were prepared using unique molecular barcodes to avoid duplication rates, for 32 matched cfDNA/tDNA samples from 16 patients with MM. The mean target coverage was 596x. We found similar frequencies of altered MM driver genes in both cfDNA and tDNA, including KRAS, NRAS, and TP53, indicating that cfDNA can be used for precision medicine. Conclusions. Our study demonstrates that both WES and targeted deep sequencing of cfDNA are consistently representative of tumor DNA alterations in terms of CNAs, focal CNAs and SSNVs. This approach could therefore be used to longitudinally follow clonal evolution across the course of the disease and precision medicine in patients with MM. Disclosures Palumbo: Takeda: Employment, Honoraria; Janssen Cilag: Honoraria. Kumar:Noxxon Pharma: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Millennium: Consultancy, Research Funding; Skyline: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Research Funding; Kesios: Consultancy; Glycomimetics: Consultancy; BMS: Consultancy; Array BioPharma: Consultancy, Research Funding; Sanofi: Consultancy, Research Funding; AbbVie: Research Funding; Onyx: Consultancy, Research Funding. Roccaro:Takeda Pharmaceutical Company Limited: Honoraria. Facon:Amgen: Consultancy, Speakers Bureau; Novartis: Consultancy; Janssen: Consultancy, Speakers Bureau; Bristol: Consultancy; Millenium/Takeda: Consultancy; Celgene: Consultancy, Speakers Bureau; Karyopharm: Consultancy. Ghobrial:Celgene: Honoraria, Research Funding; BMS: Honoraria, Research Funding; Noxxon: Honoraria; Novartis: Honoraria; Takeda: Honoraria; Amgen: Honoraria.