Klein, Eric A.

3049 Background: For multi-cancer detection using cfDNA, TOO determination is critical to enable safe and efficient diagnostic follow-up. Previous array-based studies captured < 2% of genomic CpGs. Here, we report genome-wide fragment-level methylation patterns across 811 cancer cell methylomes representing 21 tumor types (97% of SEER cancer incidence), and define effects of this methylation database on TOO prediction within a machine learning framework. Methods: Genomic DNA from 655 formalin-fixed paraffin-embedded (FFPE) tumor tissues and 156 isolated cells from tumors was subjected to a prototype 30x whole-genome bisulfite sequencing (WGBS) assay, as previously reported in the Circulating Cell-free Genome Atlas (CCGA) study (NCT02889978). Two independent TOO models, one with and one without the methylation database, were fitted on training samples; each was used to predict on the test set. A WGBS classifier was used to detect cancer at 98% specificity; reported TOO results reflect percent agreement between predicted and true TOO among those detected cancers (166 cases: 81 stage I-III, 69 stage IV, 16 non-informative). Results: Genome-wide methylation data generated from this database allowed fragment-level analysis and coverage of ~30 million CpGs across the genome (~60-fold greater than array-based approaches). Incorrect TOO assignments decreased by 35% (20% to 13%) after incorporating methylation database information into TOO classification. Improvement was observed across all cancer types and was consistent in early-stage cancers (stage I-III). Respective performances in breast cancer (n = 23) were 87% vs 96%; in lung cancer (n = 32) were 85% vs 88%; in hepatobiliary (n = 10) were 70% vs 90%; and in pancreatic cancer (n = 17) were 94% vs 100%. Results using an optimized approach informed by these results in a large cohort of CCGA participants will be reported. Conclusions: Incorporating data from a large methylation database improved TOO performance in multiple cancer types. This supports feasibility of this methylation-based approach as an early cancer detection test across cancer types. Clinical trial information: NCT02889978.

1545 Background: Screening tests for early cancer detection are often criticized due to risk of overdiagnosis—detection of good prognosis cancers which may not require immediate treatment. We recently reported development of cfDNA sequencing approaches for cancer detection; longitudinal follow-up (F/U) data were utilized here to evaluate prognostic significance of cancer detection using cfDNA. Methods: Plasma cfDNA samples were subjected to whole-genome bisulfite sequencing (WGBS, 30X) as part of a previously-reported Circulating Cell-free Genome Atlas (CCGA; NCT02889978) substudy. This exploratory analysis evaluated the overall survival (OS) of training and test set participants (pts) with cancer (20 cancer types, any stage I-IV). Combining train and test set pts, univariate and multivariate analyses (Cox proportional hazards) assessed OS association with WGBS result (cancer detected vs not detected, set at 98% specificity), clinical stage (IV vs I-III), diagnostic method (symptom- vs screen-detected), sex, age, and histologic grade. Results: Of 827 pts from the training set with F/U (median 12.2 mo), 334 (40.4%) had WGBS-detected cancer. Among 127 (15.4%) pts with cancer that died during F/U, cancer was detected in 104 (81.9%). Results were similar in the test set. In univariate analyses all variables were associated with prognosis, including WGBS result (HR 7.7 p<0.001). In multivariate analyses accounting for other covariates, the three variables that most significantly remained prognostic were WGBS (HR 3.0, p<0.001), clinical stage (HR 3.3, p<0.001), and diagnostic method (HR 3.0, p<0.001). Validation of these findings is ongoing in an independent cohort of ~5,000 cancer pts from CCGA using an optimized assay; updated performance results will be reported. Conclusions: Cancers detected using WGBS of cfDNA had a worse prognosis than cancers not detected. WGBS cancer detection carried comparable prognostic significance as clinical stage. By preferentially detecting higher risk cancers, cancer detection using plasma cfDNA may avoid some of the overdiagnosis that has been seen with some existing cancer screening methods. Clinical trial information: NCT02889978.

Abstract Background: Inflammation is linked to prostate cancer progression and is mediated by NF-κB. Tristetraprolin is a key node of NF-κB activation and we investigated its biological and prognostic role in lethal prostate cancer. Methods: In vitro assays assessed the function of tristetraprolin and the association between low mRNA tristetraprolin levels and lethal prostate cancer (metastatic disease or death) was assessed across independent prostatectomy cohorts: (i) nested case-control studies from Health Professionals Follow-up Study and Physicians’ Health Study, and (ii) prostatectomy samples from Cleveland Clinic, Mayo Clinic, Johns Hopkins and Memorial Sloan Kettering Cancer Center. Tristetraprolin expression levels in prostatectomy samples from patients with localized disease and biopsies of metastatic castration–resistant prostate cancer (mCRPC) were assessed in a Cornell University cohort. Results: In vitro tristetraprolin expression was inversely associated with NF-κB–controlled genes, proliferation, and enzalutamide sensitivity. Men with localized prostate cancer and lower quartile of tumor tristetraprolin expression had a significant, nearly two-fold higher risk of lethal prostate cancer after adjusting for known clinical and histologic prognostic features (age, RP Gleason score, T-stage). Tristetraprolin expression was also significantly lower in mCRPC compared with localized prostate cancer. Conclusions: Lower levels of tristetraprolin in human prostate cancer prostatectomy tissue are associated with more aggressive prostate cancer and may serve as an actionable prognostic and predictive biomarker. Impact: There is a clear need for improved biomarkers to identify patients with localized prostate cancer in need of treatment intensification, such as adjuvant testosterone suppression, or treatment de-intensification, such as active surveillance. Tristetraprolin levels may serve as informative biomarkers in localized prostate cancer.

5062 Background: Inflammation has been linked to prostate cancer (PCa) progression which may be mediated by the transcription factor nuclear factor kappa B (NFκB). Using a bioinformatic screen focused on NFκB pathway activation in lethal PCa, we identified the tumor suppressor ZFP36 as a key node of the NFκB network. We also showed in vitro that ZFP36 expression was inversely associated with both NFκB-controlled gene levels and proliferation and sensitivity to enzalutamide. We examined the role of ZFP36 and PCa progression in patient cohorts. Methods: The association between low mRNA expression of ZFP36 (levels in the lower quartile) and lethal PCa (defined as metastatic disease or death) was assessed with logistic regression among men with localized PCa in two independent cohorts treated with radical prostatectomy (RP). The discovery cohort was RP samples from Health Professional Follow-up Study and Physicians Health Study and the validation cohort was RP samples from Cleveland Clinic, Mayo Clinic, Johns Hopkins and MSKCC. In a third cohort from Cornell University, ZFP36 expression levels were assessed in RP samples from patients with localized PCa and biopsies of metastatic castration resistant prostate cancer (mCRPC). Results: Table 1 shows men with localized PCa and lower quartile of tumor ZFP36 expression had a nearly 2-fold higher risk of lethal PCa after adjusting for known clinical and histological prognostic features (age, RP Gleason score, T-stage); an association confirmed in the validation cohort. ZFP36 gene expression was also significantly lower in mCRPC (n=53) compared with a localized RP cohort (n=68) (p-value <0.0001). Conclusions: In humans lower ZFP36 in RP specimens is associated with clinically significant risk of lethal PCa after treatment with curative intent and lower ZFP36levels are found in metastatic tissue. [Table: see text]

5000 Background: It is clinically challenging to integrate genomic classifier results that report a continuous numerical risk of recurrence into treatment decisions for prostate cancer (PCa). We aimed to develop a novel clinical-genomic risk system that can readily be incorporated into treatment guidelines for localized PCa. Methods: Four multi-center cohorts (n = 6928 men; 5937 prospective samples and 991 retrospective samples with long-term follow-up) were utilized to identify and validate our clinical-genomic risk system in radical prostatectomy (RP) samples and subsequently in pre-treatment biopsy samples. All patients’ FFPE tissue underwent microarray analysis, and the expression values for 22 prespecified biomarkers that constitute Decipher were extracted. Cumulative incidence curves were constructed to estimate metastasis risk. C-indices were calculated to compare NCCN and CAPRA score to our clinical-genomic system. Results: With a median follow-up of 8 years for men in our RP cohort, the 10-year distant metastasis rates for NCCN low, favorable-intermediate, unfavorable-intermediate, and high-risk were 7.8%, 9.4%, 40.1%, and 41.4%, respectively. Our 3-tier clinical-genomic risk groups had 10-year distant metastasis rates of 3.7%, 30.7%, and 57.7%, for low, intermediate, and high-risk, which were validated in our pre-treatment biopsy cohort with 10-year rate of distant metastasis of 0%, 30.3%, and 63.2%, respectively. C-indices for the clinical-genomic system (0.84, 95%CI 0.62-0.92) were significantly improved over NCCN (0.71, 95%CI 0.59-0.84) and CAPRA (0.71, 95%CI 0.60-0.81) score. A total of 33.4% of men would be reclassified by the clinical-genomic system, and specifically 17.1%, 41.3%, and 19.4% of men in NCCN low, intermediate and high risk groups would be reclassified by our new system. Conclusions: The use of a readily available genomic classifier in combination with clinicopathologic variables can generate a simple to use 3-tier clinical-genomic risk system that is highly prognostic for distant metastasis, is more accurate than clinical risk, and can be easily incorporated into NCCN guidelines to inform treatment decisions.