Abstract Background Cell-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. Methods Whole 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. Results The 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. Conclusion Machine 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.
Abstract Purpose Prostate cancer is the most commonly diagnosed neoplasm in American men. Although existing biomarkers may detect localized prostate cancer, additional strategies are necessary for improving detection and identifying aggressive disease that may require further intervention. One promising, minimally invasive biomarker is cell-free DNA (cfDNA), which consist of short DNA fragments released into circulation by dying or lysed cells that may reflect underlying cancer. Here we investigated whether differences in cfDNA concentration and cfDNA fragment size could improve the sensitivity for detecting more advanced and aggressive prostate cancer. Materials and Methods This study included 268 individuals: 34 healthy controls, 112 men with localized prostate cancer who underwent radical prostatectomy (RP), and 122 men with metastatic castration-resistant prostate cancer (mCRPC). Plasma cfDNA concentration and fragment size were quantified with the Qubit 3.0 and the 2100 Bioanalyzer. The potential relationship between cfDNA concentration or fragment size and localized or mCRPC prostate cancer was evaluated with descriptive statistics, logistic regression, and area under the curve analysis with cross-validation. Results Plasma cfDNA concentrations were elevated in mCRPC patients in comparison to localized disease (OR 5 ng/mL = 1.34, P = 0.027) or to being a control (OR 5 ng/mL = 1.69, P = 0.034). Decreased average fragment size was associated with an increased risk of localized disease compared to controls (OR 5bp = 0.77, P = 0.0008). Conclusion This study suggests that cfDNA concentration and average cfDNA fragment size may provide a quick, cost-effective approach to help determine which patients will benefit most from further screening and/or disease monitoring to help improve prostate cancer outcomes.
<p><strong>Introduction</strong> Cow’s milk is a dietary staple for children in North America. Though clinical guidelines suggest children transition from whole (3.25% fat) milk to reduced (1% or 2%) fat milk at age 2 years, recent epidemiological evidence supports a link between whole milk consumption and lower adiposity in children. The purpose of this trial is to determine which milk fat recommendation minimises excess adiposity and optimises child nutrition and growth.</p> <p><strong>Methods and analysis</strong> Cow’s Milk Fat Obesity pRevention Trial will be a pragmatic, superiority, parallel group randomised controlled trial involving children receiving routine healthcare aged 2 to 4–5 years who are participating in the TARGet Kids! practice-based research network in Toronto, Canada. Children (n=534) will be randomised to receive one of two interventions: (1) a recommendation to consume whole milk or (2) a recommendation to consume reduced (1%) fat milk. The primary outcome is adiposity measured by body mass index z-score and waist circumference z-score; secondary outcomes will be cognitive development (using the Ages and Stages Questionnaire), vitamin D stores, cardiometabolic health (glucose, high-sensitivity C-reactive protein, non-high density lipoprotein (non-HDL), low density lipoprotein (LDL), triglyceride, HDL and total cholesterol, insulin and diastolic and systolic blood pressure), sugary beverage and total energy intake (measured by 24 hours dietary recall) and cost effectiveness. Outcomes will be measured 24 months postrandomisation and compared using analysis of covariance (ANCOVA), adjusting for baseline measures.</p> <p><strong>Ethics and dissemination</strong> Ethics approval has been obtained from Unity Health Toronto and The Hospital for Sick Children. Results will be presented locally, nationally and internationally and published in a peer-reviewed journal. The findings may be helpful to nutrition guidelines for children in effort to reduce childhood obesity using a simple, inexpensive and scalable cow’s milk fat intervention.</p>
19 Background: The conventional model for clinical trial (CT) recruitment relies on clinicians to identify potential CTs for patients. Internet technology can be leveraged as a decision tool to enhance the CT recruitment process. Methods: An internet-based, clinician-facing decision tool was developed in genitourinary medical oncology clinic at a Comprehensive Cancer Center (CCC). The tool provided access to a real-time, tailored list of treatment CTs actively recruiting patients with PCa at the CCC based on clinical characteristics inputted by user. The clinical data was summarized. All clinicians (n = 9) with access to the decision tool completed a survey to assess effectiveness and satisfaction. Results: During a 9-month pilot period, user engagement increased from a baseline of 36 to 136 cases per month, with a total of 644 cases overall. Among cases, 525 had metastatic disease, 436 of which were metastatic castration resistant PCa (mCRPC). Overall, 145 cases were classified as having oligo-metastatic ( < = 3) PCa, 93 of whom were also mCRPC. Prior treatments received included abiraterone in hormone-sensitive PCa (HSPC 19.3%, CRPC 48.7%); enzalutamide (HSPC 3.7%, CRPC 34.9%) apalutamide (HSPC 1.3%, CRPC 6.9%), taxane (HSPC 17.2%, CRPC 27.8%), radium-223 (6.1%), sipuleucel-T (18.3%), parp inhibitors (4%), or check-point inhibitors (6%). Clinician-inputted genomics of cases included CDK12 (20.9%), MSI-high disease (13.6%), BRCA1/2 (32.7%), ARID1a (7.3%), ATM (21.8%), FANCA (4.5%), or CHEK2 (6.4%) and HDAC2 (0.9%). Among survey respondents, use of tool in clinic was reported sometimes (22%), often/always (78%). Results of decision tool were reported to inform treatment sometimes (22%) or often/always (78%). Respondents confidence in often/always knowing all available CTs increased from a baseline of 0% to 89%, and 89% of users reported very/complete satisfaction with decision tool. Conclusions: An internet-based CT decision tool for provides detailed clinical characteristics of patients for whom CTs are being considered at a CCC. Clinicians using the decision tool report high levels of satisfaction. The tool was effective in increasing confidence in knowledge of current available CTs. Data gathered in the decision tool may inform future CT development. Future research with expanded use of decision tool among referring clinicians will assess its impact in promoting diversity among CT participants.
Prostate cancer is the most commonly diagnosed neoplasm in American men. Although existing biomarkers may detect localized prostate cancer, additional strategies are necessary for improving detection and identifying aggressive disease that may require further intervention. One promising, minimally invasive biomarker is cell-free DNA (cfDNA), which consist of short DNA fragments released into circulation by dying or lysed cells that may reflect underlying cancer. Here we investigated whether differences in cfDNA concentration and cfDNA fragment size could improve the sensitivity for detecting more advanced and aggressive prostate cancer. This study included 268 individuals: 34 healthy controls, 112 men with localized prostate cancer who underwent radical prostatectomy (RP), and 122 men with metastatic castration-resistant prostate cancer (mCRPC). Plasma cfDNA concentration and fragment size were quantified with the Qubit 3.0 and the 2100 Bioanalyzer. The potential relationship between cfDNA concentration or fragment size and localized or mCRPC prostate cancer was evaluated with descriptive statistics, logistic regression, and area under the curve analysis with cross-validation. Plasma cfDNA concentrations were elevated in mCRPC patients in comparison to localized disease (OR5ng/mL = 1.34, P = 0.027) or to being a control (OR5ng/mL = 1.69, P = 0.034). Decreased average fragment size was associated with an increased risk of localized disease compared to controls (OR5bp = 0.77, P = 0.0008). This study suggests that while cfDNA concentration can identify mCRPC patients, it is unable to distinguish between healthy individuals and patients with localized prostate cancer. In addition to PSA, average cfDNA fragment size may be an alternative that can differentiate between healthy individuals and those with localized disease, but the low sensitivity and specificity results in an imperfect diagnostic marker. While quantification of cfDNA may provide a quick, cost-effective approach to help guide treatment decisions in advanced disease, its use is limited in the setting of localized prostate cancer.
Abstract Early cancer diagnosis, especially while the disease is localized and before symptoms appear, results in significantly higher survival rates compared to late-stage diagnosis. At the time of diagnosis, it is also common to find multiple foci within the prostate in men with localized disease. Cell-free DNA (cfDNA) are short DNA fragments released into circulation by dying cells, which may reflect underlying disease biology and simultaneously allow for the identification of genetically distinct tumor subclones. The objectives of this study are to determine if cfDNA concentration can be used to distinguish between healthy individuals from patients with localized or metastatic castration-resistant prostate cancer (mCRPC), and if somatic mutations identified in tumor tissue are detectable in cfDNA. This study included samples from 277 individuals: 41 healthy donors, 112 patients with localized prostate cancer who underwent radical prostatectomy (RP) at UCSF, and 124 mCRPC patients. Whole peripheral blood was collected in EDTA tubes or PAXgene Blood ccfDNA tubes. Blood and matched tissue from adjacent normal seminal vesicles and multiple tumor regions (1-9 samples per patient) were collected from patients undergoing RP. Extraction of cfDNA was performed on double spun plasma using the Qiagen QIAamp Circulating Nucleic Acid Kits. After extraction, the concentration and fragment length distribution were measured with a Bioanalyzer 2100 Instrument. Fifty-seven samples (multiple tumor tissue foci and matched blood) from nine patients with localized disease were subjected to whole exome sequencing, and 22 samples from five patients were subjected to whole genome sequencing. All samples from 14 patients underwent targeted sequencing with a 2.5Mb panel generated via machine learning on TCGA prostate cancer sequence data. Somatic variant calling was performed with Broad Institute's Terra platform (GATK4/MuTect2) for tumor tissue and with the Curio platform for cfDNA to build consensus sequences leveraging duplex unique molecular tags. To estimate tumor fraction in cfDNA, ichorCNA was used to profile low pass whole genome sequence data. Total cfDNA concentration was able to distinguish between healthy and metastatic (p = 0.001) participants, adjusted for age; and also between localized and metastatic groups (p < 0.001), adjusted for age and PSA. Among the patients with localized disease, cfDNA concentration was not associated with age, PSA, Gleason score, or Decipher Score (a 22 gene metastasis risk-predicting RNA gene expression signature), suggesting the potential for this marker to be independent of factors that are commonly used to assess disease burden and risk of progression. Targeted sequencing of prostate tumors resulted in an average of 16 mutations with a range of 1 to 128 mutations per tissue region. The cfDNA allele frequencies for mutations identified in tumor tissue ranged from 0.5% to 20%. Four patients with variant detection in cfDNA also experienced biochemical recurrence. While copy number analysis identified clonal and potentially subclonal alterations in WGS tumor tissue, no alterations were found in WGS cfDNA. Further analysis of potential factors influencing variant detection in cfDNA (adverse pathology, starting amount of DNA, coverage) will be performed. Citation Format: Emmalyn Chen, Clinton L. Cario, Lancelote Leong, Karen Lopez, César Márquez, Carissa Chu, Patricia S. Li, Erica Oropeza, Imelda Tenggara, Janet Cowan, Jeffry P. Simko, Daniel K. Wells, Robin Kageyama, June M. Chan, Terence Friedlander, Rahul Aggarwal, Felix Feng, Pamela L. Paris, Peter R. Carroll, John S. Witte. Assessing the utility of cell-free DNA in identifying prostate cancer and characterizing tumor heterogeneity via targeted, whole exome, and whole genome multi-region sequencing [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 719.
5051 Background: Metastatic disease burden out of proportion to low serum PSA level is frequently used as a clinical surrogate for the diagnosis of treatment-emergent small cell neuroendocrine prostate cancer (t-SCNC), although many t-SCNC patients (pts) have normal or elevated PSA levels. The clinical and genomic characteristics of mCRPC pts who are Low PSA Secretors have not been previously described. Methods: Eligible mCRPC patients (pts) underwent image-guided needle biopsy. Formalin-fixed paraffin embedded tissue was evaluated with targeted next-generation DNA sequencing. Fresh frozen tissue from the same metastatic tumor underwent RNA-seq. A validated AR transcriptional signature was applied. Low PSA Secretors were defined as pts with PSA < 5 ng/mL plus ≥ 6 metastases on conventional imaging at the time of tumor biopsy. Clinical and genomic characteristics were compared between low PSA Secretors and all other pts. Results: Of 89 evaluable pts, 9 (10%) were identified as Low PSA Secretors. There was no difference between Low PSA Secretors and all other pts in: serum PSA at diagnosis, frequency of Gleason ≥ 8 adenocarcinoma at diagnosis, and serum level of LDH, alkaline phosphatase, or hemoglobin at the time of biopsy. Lung and/or liver metastases were more common in low PSA secretors (67% vs. 33%, p = 0.04). There was no difference in serum level of LDH, alkaline phosphatase, or hemoglobin. Tumor biopsies from Low PSA Secretors were more likely to fall within a previously defined t-SCNC transcriptional cluster (80% vs. 6%, p < 0.001). RB1 loss or inactivating mutations appeared to be enriched in Low PSA Secretors (40% vs. 12%, p = 0.09); there was no difference in frequency of TP53 alterations between subgroups. AR transcriptional signature scores were lower in the Low PSA Secretor group (median score -3.63 vs. 0.66, p < 0.001). Conclusions: Low serum PSA levels in relation to metastatic tumor burden may be a reliable surrogate for the detection of mCRPC that harbors the transcriptional and genomic hallmarks of t-SCNC. Validation studies are warranted.
