13 research outputs found
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Characterizing the Utility of Cell-free DNA in Prostate Cancer
Motivation: Prostate cancer remains the most commonly diagnosed neoplasm in American men, with existing biomarkers (i.e. PSA, nomograms, MRI) having varying levels of sensitivity and specificity in identifying more advanced and potentially aggressive disease. Tumor tissue biopsies remain the gold standard for confirming the presence of prostate cancer, as well as evaluating the genomic heterogeneity and clonal architecture that may be predictive of poor outcomes (i.e. recurrence and metastasis). However, tissue biopsies are limited in their ability to comprehensively assess tumors, and may lead to underestimation of disease grade and stage. These hurdles may be overcome with cell-free DNA (cfDNA), which allows for minimally invasive, repeated sampling through blood draws. This is particularly important when tumor tissue is unavailable during active surveillance or disease monitoring for the detection of residual disease or progression. Additionally, genomic interrogation via cfDNA sequencing typically requires prior knowledge of existing mutations from a patient’s tumor. The work presented here leverages a number of methods to ensure broad, yet sensitive detection of cfDNA variants for patients with localized prostate cancer, including sequencing with a machine-learning guided 2.5Mb targeted panel. In this dissertation, I investigate the use of cfDNA concentration, fragment size, and sequencing to identify advanced prostate cancer, as well as detect somatic mutations present in patient-matched tumors.Methods: The patient cohort included in these studies are composed of 268 individuals: 34 healthy individuals, 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 a Qubit fluorometer or Bioanalyzer utilizing a chip-based capillary electrophoresis method for nucleic acid analysis. Low-pass whole-genome and targeted sequencing were used to identify single nucleotide variants (SNVs), small insertions and deletions (indels), and copy number alterations (CNAs) for a subset of patients. Plasma cfDNA was barcoded with duplex Unique Molecular Identifiers (UMIs) to construct consensus reads and improve variant detection by leveraging duplicate reads and sequence complementarity of the two DNA strands. Extensive tissue sampling was used to capture tumor heterogeneity and provide a patient-specific gold standard for comparison of matched cfDNA.Results and Conclusions: Patients with advanced mCRPC had higher cfDNA concentration than men with localized disease or healthy controls, and those with localized disease had shorter average fragment sizes than controls. Importantly, cfDNA concentration and fragment size remained independent predictors after adjusting for age and PSA. We found that targeted sequencing of cfDNA—without a priori patient-specific tumor mutation information—identified somatic alterations found in matched tumor tissue from multiple regions, potentially allowing for dynamic monitoring of emerging resistant subclones throughout the course of disease. Detection of these concordant variants was associated with seminal vesicle invasion and the number of somatic variants found in the tumor tissue samples, predicating its use for patients with poor prognostic factors in a localized setting. Similar to cfDNA concentration, plasma cfDNA mutational burden was also found to increase with disease severity. The results from our studies demonstrate the ability of cfDNA to identify somatic variants in patients with heterogeneous, localized prostate cancer
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Characterizing the Utility of Cell-free DNA in Prostate Cancer
Motivation: Prostate cancer remains the most commonly diagnosed neoplasm in American men, with existing biomarkers (i.e. PSA, nomograms, MRI) having varying levels of sensitivity and specificity in identifying more advanced and potentially aggressive disease. Tumor tissue biopsies remain the gold standard for confirming the presence of prostate cancer, as well as evaluating the genomic heterogeneity and clonal architecture that may be predictive of poor outcomes (i.e. recurrence and metastasis). However, tissue biopsies are limited in their ability to comprehensively assess tumors, and may lead to underestimation of disease grade and stage. These hurdles may be overcome with cell-free DNA (cfDNA), which allows for minimally invasive, repeated sampling through blood draws. This is particularly important when tumor tissue is unavailable during active surveillance or disease monitoring for the detection of residual disease or progression. Additionally, genomic interrogation via cfDNA sequencing typically requires prior knowledge of existing mutations from a patient’s tumor. The work presented here leverages a number of methods to ensure broad, yet sensitive detection of cfDNA variants for patients with localized prostate cancer, including sequencing with a machine-learning guided 2.5Mb targeted panel. In this dissertation, I investigate the use of cfDNA concentration, fragment size, and sequencing to identify advanced prostate cancer, as well as detect somatic mutations present in patient-matched tumors.Methods: The patient cohort included in these studies are composed of 268 individuals: 34 healthy individuals, 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 a Qubit fluorometer or Bioanalyzer utilizing a chip-based capillary electrophoresis method for nucleic acid analysis. Low-pass whole-genome and targeted sequencing were used to identify single nucleotide variants (SNVs), small insertions and deletions (indels), and copy number alterations (CNAs) for a subset of patients. Plasma cfDNA was barcoded with duplex Unique Molecular Identifiers (UMIs) to construct consensus reads and improve variant detection by leveraging duplicate reads and sequence complementarity of the two DNA strands. Extensive tissue sampling was used to capture tumor heterogeneity and provide a patient-specific gold standard for comparison of matched cfDNA.Results and Conclusions: Patients with advanced mCRPC had higher cfDNA concentration than men with localized disease or healthy controls, and those with localized disease had shorter average fragment sizes than controls. Importantly, cfDNA concentration and fragment size remained independent predictors after adjusting for age and PSA. We found that targeted sequencing of cfDNA—without a priori patient-specific tumor mutation information—identified somatic alterations found in matched tumor tissue from multiple regions, potentially allowing for dynamic monitoring of emerging resistant subclones throughout the course of disease. Detection of these concordant variants was associated with seminal vesicle invasion and the number of somatic variants found in the tumor tissue samples, predicating its use for patients with poor prognostic factors in a localized setting. Similar to cfDNA concentration, plasma cfDNA mutational burden was also found to increase with disease severity. The results from our studies demonstrate the ability of cfDNA to identify somatic variants in patients with heterogeneous, localized prostate cancer
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A machine learning approach to optimizing cell-free DNA sequencing panels: with an application to prostate cancer.
BackgroundCell-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 (< 200 bp) 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 2500X 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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A machine learning approach to optimizing cell-free DNA sequencing panels: with an application to prostate cancer.
BackgroundCell-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 (< 200 bp) 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 2500X 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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Cell-free DNA concentration and fragment size as a biomarker for prostate cancer.
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
Recommended from our members
Cell-free DNA concentration and fragment size as a biomarker for prostate cancer.
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
Cell-Free DNA Detection of Tumor Mutations in Heterogeneous, Localized Prostate Cancer Via Targeted, Multiregion Sequencing.
Cell-free DNA (cfDNA) may allow for minimally invasive identification of biologically relevant genomic alterations and genetically distinct tumor subclones. Although existing biomarkers may detect localized prostate cancer, additional strategies interrogating genomic heterogeneity are necessary for identifying and monitoring aggressive disease. In this study, we aimed to evaluate whether circulating tumor DNA can detect genomic alterations present in multiple regions of localized prostate tumor tissue. Low-pass whole-genome and targeted sequencing with a machine-learning guided 2.5-Mb targeted panel were used to identify single nucleotide variants, small insertions and deletions (indels), and copy-number alterations in cfDNA. The majority of this study focuses on the subset of 21 patients with localized disease, although 45 total individuals were evaluated, including 15 healthy controls and nine men with metastatic castration-resistant prostate cancer. Plasma cfDNA was barcoded with duplex unique molecular identifiers. For localized cases, matched tumor tissue was collected from multiple regions (one to nine samples per patient) for comparison. Somatic tumor variants present in heterogeneous tumor foci from patients with localized disease were detected in cfDNA, and cfDNA mutational burden was found to track with disease severity. Somatic tissue alterations were identified in cfDNA, including nonsynonymous variants in FOXA1, PTEN, MED12, and ATM. Detection of these overlapping variants was associated with seminal vesicle invasion (P = .019) and with the number of variants initially found in the matched tumor tissue samples (P = .0005). Our findings demonstrate the potential of targeted cfDNA sequencing to detect somatic tissue alterations in heterogeneous, localized prostate cancer, especially in a setting where matched tumor tissue may be unavailable (ie, active surveillance or treatment monitoring)
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Artificial Intelligence Predictive Model for Hormone Therapy Use in Prostate Cancer.
BACKGROUND: Androgen deprivation therapy (ADT) with radiotherapy can benefit patients with localized prostate cancer. However, ADT can negatively impact quality of life, and there remain no validated predictive models to guide its use. METHODS: We used digital pathology images from pretreatment prostate tissue and clinical data from 5727 patients enrolled in five phase 3 randomized trials, in which treatment was radiotherapy with or without ADT, as our data source to develop and validate an artificial intelligence (AI)–derived predictive patient-specific model that would determine which patients would develop the primary end point of distant metastasis. The model used baseline data to provide a binary output that a given patient will likely benefit from ADT or not. After the model was locked, validation was performed using data from NRG Oncology/Radiation Therapy Oncology Group (RTOG) 9408 (n=1594), a trial that randomly assigned men to radiotherapy plus or minus 4 months of ADT. Fine–Gray regression and restricted mean survival times were used to assess the interaction between treatment and the predictive model and within predictive model–positive, i.e., benefited from ADT, and –negative subgroup treatment effects. RESULTS: Overall, in the NRG/RTOG 9408 validation cohort (14.9 years of median follow-up), ADT significantly improved time to distant metastasis. Of these enrolled patients, 543 (34%) were model positive, and ADT significantly reduced the risk of distant metastasis compared with radiotherapy alone. Of 1051 patients who were model negative, ADT did not provide benefit. CONCLUSIONS: Our AI-based predictive model was able to identify patients with a predominantly intermediate risk for prostate cancer likely to benefit from short-term ADT. (Supported by a grant [U10CA180822] from NRG Oncology Statistical and Data Management Center, a grant [UG1CA189867] from NCI Community Oncology Research Program, a grant [U10CA180868] from NRG Oncology Operations, and a grant [U24CA196067] from NRG Specimen Bank from the National Cancer Institute and by Artera, Inc. ClinicalTrials.gov numbers NCT00767286, NCT00002597, NCT00769548, NCT00005044, and NCT00033631.