A5089542879- Cancer Immunotherapy and Biomarkers
- Pancreatic and Hepatic Oncology Research
- Colorectal Cancer Treatments and Studies
- Epigenetics and DNA Methylation
- Cancer Genomics and Diagnostics
- Lung Cancer Treatments and Mutations
- RNA modifications and cancer
- Esophageal Cancer Research and Treatment
- Health Systems, Economic Evaluations, Quality of Life
- EEG and Brain-Computer Interfaces
- Lung Cancer Diagnosis and Treatment
- Prostate Cancer Treatment and Research
- Cancer-related Molecular Pathways
- Radiomics and Machine Learning in Medical Imaging
- Neutropenia and Cancer Infections
- Pharmaceutical Economics and Policy
- Medical Imaging Techniques and Applications
- Attention Deficit Hyperactivity Disorder
- Cancer survivorship and care
- Neonatal Respiratory Health Research
- Birth, Development, and Health
- ECG Monitoring and Analysis
- Cardiac Arrhythmias and Treatments
- Congenital heart defects research
- Computational Drug Discovery Methods
University of Toronto
2022-2024
Ontario Institute for Cancer Research
2021-2024
Shanghai Jiao Tong University
2019
Shanghai First People's Hospital
2019
Princess Margaret Cancer Centre
2016-2018
University Health Network
2016
Abstract Metastatic prostate cancer remains a major clinical challenge and metastatic lesions are highly heterogeneous difficult to biopsy. Liquid biopsy provides opportunities gain insights into the underlying biology. Here, using sensitive enrichment-based sequencing technology, we provide analysis of 60 175 plasma DNA methylomes from patients with localized cancer, respectively. We show that cell-free methylome can capture variations beyond tumor. A global hypermethylation in samples is...
Abstract Early kinetics of circulating tumor DNA (ctDNA) in plasma predict response to pembrolizumab but typically requires sequencing matched tissue or fixed gene panels. We analyzed genome-wide methylation and fragment-length profiles using cell-free methylated immunoprecipitation (cfMeDIP-seq) 204 samples from 87 patients before during treatment with a pan-cancer phase II investigator-initiated trial (INSPIRE). trained signature independent array data The Cancer Genome Atlas quantify...
Systematic documentation of chemotoxicities in outpatient clinics is challenging. Incorporating patient-reported outcome (PRO) measures clinical workflows can be an efficient strategy to strengthen the assessment symptomatic treatment toxicities oncology practice. We compared adequateness, feasibility, and acceptability toxicity using systematic, prospective, application PRO Common Toxicity Criteria for Adverse Events (PRO-CTCAE) tool. At a comprehensive cancer center, data abstraction...
Abstract Limited studies to date have investigated the detectability of cell-free DNA (cfDNA) markers in asymptomatic individuals prior a cancer diagnosis. Here, we performed cfDNA methylation profiling blood up seven years breast diagnosis addition matched cancer-free controls (n=150). We identified differentially methylated signatures that discriminated from pre-diagnosis cases over five and demonstrate these were reflective profiles tissue. report classification range detected at Stage I...
Abstract Cell-free DNA (cfDNA) epigenetic and fragmentomic profiling has emerged as prominent non-invasive approaches for early cancer detection subtyping. However, owing to difficulties in observing the development of human malignancies, most biomarker evolution studies date have primarily examined biologics following a diagnosis. Utilizing cfDNA screening tool disease requires assessment blood plasma samples collected from asymptomatic individuals prior diagnosis cancers. Here, we leverage...
<p>Overall survival (OS) and progression free (PFS) in included patients by cohort. (A) Kaplan-meier curves are shown indicating the OS PFS of five histology-specific cohorts. (B) Forest plot hazard ratios for each cohort a Cox proportional hazards model, with Cohort A as reference level.</p>
<p>Predicting survival outcomes using cancer-specific methylation (CSM) scores at baseline and cycle 3 of pembrolizumab. We computed CSM across the trial cohort. At both 3, patients were split into above- below-median groups. Survival are shown, with hazard ratios p-values adjusted for cohort a Cox model. PFS analysis excludes one patient who progressed before collection sample.</p>
<p>Association of tumor burden with cancer mutation concentration (CMC), as well cancer-specific methylation (CSM) and fragment length score (FLS). We computed CMC from personalized tumor-informed arrays. also CSM FLS fragmentomic analysis respectively data cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq) assays. Coefficients p-values were using Spearman correlations.</p>
<p>Examples of cancer-specific methylation score calculation. Cancer-specific scores were computed using the sum inferred absolute values for all reads overlapping an independently-trained signature. Here, we show examples illustrating how levels are from coverage depths in 300 bp bins while adjusting density CpGs.</p>
<p>CMC and CSM early kinetics discordant cases</p>
<p>Cell-free DNA from cancer patients demonstrates greater fragment length variability. (A) Genome wide lengths were computed and averaged within 5 megabase windows. Normalized short-to-long ratios calculated. Shaded confidence intervals represent 80% 95% of the data. (B) The variance mean across compared log fold change between normal variances are shown for each window. Windows with significantly different identified using non-parametric Ansari-Bradley tests. Neighbouring windows...
<p>Predicting survival outcomes using fragment length score (FLS) at baseline and cycle 3 of pembrolizumab. FLS was determined as the mean log2 transformed cancer-to-normal ratio each in a given cfMeDIP-seq sample. At both 3, patients were split into above- or below-median groups. Survival are shown, with hazard ratios p-values adjusted for cohort Cox model. PFS analysis excludes one patient who progressed before collection sample.</p>
<p>Cancer-specific methylation (CSM) and fragment length scores (FLS) are moderately correlated. CSM FLS were computed for each sample. Correlation testing between log-adjusted was performed using Spearman's method.</p>
<p>Predicting survival outcomes using cancer mutation concentration (CMC) at baseline and cycle 3 of pembrolizumab. CMC was determined a tumor-informed bespoke approach across the trial cohort. At both 3, patients were split into above- or below-median groups. Survival are shown, with hazard ratios p-values adjusted for cohort Cox model. PFS analysis excludes one patient who progressed before collection sample.</p>
<p>Multivariate Cox analysis of the change in fragment length score (FLS) from baseline to cycle 3 pembrolizumab. Covariates clude cohort, PD-L1 expression, and tumor mutation burden.</p>
<p>Non-negative matrix factorization identifies characteristic cancer-associated signatures of shorter fragment lengths and greater nucleosome core occupancy. (A) Genome-wide were used as features in a two-component non-negative analysis. This revealed longer component. The weight the was elevated cell-free DNA cancer patients relative to normal controls. (B) distances ends centers also factorization. two components with different proportions intra-nucleosomal ends. signature more (C)...
<p>Multivariable analysis of OS and PFS using cancer mutation concentration (CMC) at baseline cycle 3 pembrolizumab. CMC was determined a bespoke targeted approach across the trial cohort. At both 3, patients were split into above- or below-median groups. Survival outcomes are shown in multivariable including cohort, PD-L1 expression, tumor burden (TMB).</p>
<div>Abstract<p>Early kinetics of circulating tumor DNA (ctDNA) in plasma predict response to pembrolizumab but typically requires sequencing matched tissue or fixed gene panels. We analyzed genome-wide methylation and fragment-length profiles using cell-free methylated immunoprecipitation (cfMeDIP-seq) 204 samples from 87 patients before during treatment with a pan-cancer phase II investigator-initiated trial (INSPIRE). trained signature independent array data The Cancer Genome...
<p>Increase in both ctDNA metrics identifies a subgroup with particularly poor outcome. Post-hoc analysis of ΔCSM and ΔCMC together demonstrates that decrease either metric was sufficient to result significantly improved PFS OS, whereas increase identified group outcomes. at cycle 3 excludes one patient who progressed before the collection sample.</p>
<p>Multivariable analysis of OS and PFS using cancer mutation concentration (CMC) at baseline cycle 3 pembrolizumab. CMC was determined a bespoke targeted approach across the trial cohort. At both 3, patients were split into above- or below-median groups. Survival outcomes are shown in multivariable including cohort, PD-L1 expression, tumor burden (TMB).</p>
<p>Multivariate Cox analysis of the change in fragment length score (FLS) from baseline to cycle 3 pembrolizumab. Covariates clude cohort, PD-L1 expression, and tumor mutation burden.</p>
<p>Non-negative matrix factorization identifies characteristic cancer-associated signatures of shorter fragment lengths and greater nucleosome core occupancy. (A) Genome-wide were used as features in a two-component non-negative analysis. This revealed longer component. The weight the was elevated cell-free DNA cancer patients relative to normal controls. (B) distances ends centers also factorization. two components with different proportions intra-nucleosomal ends. signature more (C)...
<p>Association of tumor burden with cancer mutation concentration (CMC), as well cancer-specific methylation (CSM) and fragment length score (FLS). We computed CMC from personalized tumor-informed arrays. also CSM FLS fragmentomic analysis respectively data cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq) assays. Coefficients p-values were using Spearman correlations.</p>
<p>Overall survival (OS) and progression free (PFS) in included patients by cohort. (A) Kaplan-meier curves are shown indicating the OS PFS of five histology-specific cohorts. (B) Forest plot hazard ratios for each cohort a Cox proportional hazards model, with Cohort A as reference level.</p>