Justin Burgener
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View article: Supplementary Figure 14 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Supplementary Figure 14 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
Cancer-specific methylation (CSM) and fragment length scores (FLS) are moderately correlated. CSM and FLS scores were computed for each sample. Correlation testing between FLS and log-adjusted CSM was performed using Spearman's method.
View article: Supplementary Figure 13 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Supplementary Figure 13 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
Increase in both ctDNA metrics identifies a subgroup with particularly poor outcome. Post-hoc analysis of ΔCSM and ΔCMC together demonstrates that a decrease in either metric was sufficient to result in significantly improved PFS and OS, w…
View article: Data from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Data from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
Early kinetics of circulating tumor DNA (ctDNA) in plasma predict response to pembrolizumab but typically requires sequencing of matched tumor tissue or fixed gene panels. We analyzed genome-wide methylation and fragment-length profiles us…
View article: Supplementary Figure 10 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Supplementary Figure 10 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
Predicting survival outcomes using cancer mutation concentration (CMC) at baseline and cycle 3 of pembrolizumab. CMC was determined using a tumor-informed bespoke approach across the trial cohort. At both baseline and cycle 3, patients wer…
View article: Supplementary Figure 11 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Supplementary Figure 11 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
Multivariable analysis of OS and PFS using cancer mutation concentration (CMC) at baseline and cycle 3 of pembrolizumab. CMC was determined using a bespoke targeted approach across the trial cohort. At both baseline and cycle 3, patients w…
View article: Data from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Data from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
Early kinetics of circulating tumor DNA (ctDNA) in plasma predict response to pembrolizumab but typically requires sequencing of matched tumor tissue or fixed gene panels. We analyzed genome-wide methylation and fragment-length profiles us…
View article: Supplementary Figure 9 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Supplementary Figure 9 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
Multivariable analysis of survival using cancer-specific methylation (CSM) scores at baseline and cycle 3 of pembrolizumab. We computed CSM scores across the trial cohort. At both baseline and cycle 3, patients were split into above- or be…
View article: Supplementary Figure 3 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Supplementary Figure 3 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
Examples of cancer-specific methylation score calculation. Cancer-specific methylation scores were computed using the sum of inferred absolute methylation values for all reads overlapping an independently-trained cancer-specific signature.…
View article: Supplementary Figure 16 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Supplementary Figure 16 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
Multivariate Cox analysis of the change in fragment length score (FLS) from baseline to cycle 3 of pembrolizumab. Covariates in clude cohort, PD-L1 expression, and tumor mutation burden.
View article: Supplementary Figure 15 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Supplementary Figure 15 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
Predicting survival outcomes using fragment length score (FLS) at baseline and cycle 3 of pembrolizumab. FLS was determined as the mean of the log2 transformed cancer-to-normal ratio of the length of each fragment in a given cfMeDIP-seq sa…
View article: Supplementary Figure 2 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Supplementary Figure 2 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
Overall survival (OS) and progression free survival (PFS) in included patients by cohort. (A) Kaplan-meier curves are shown indicating the OS and PFS of patients in five histology-specific cohorts. (B) Forest plot of the hazard ratios for …
View article: Supplementary Figure 8 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Supplementary Figure 8 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
Predicting survival outcomes using cancer-specific methylation (CSM) scores at baseline and cycle 3 of pembrolizumab. We computed CSM scores across the trial cohort. At both baseline and cycle 3, patients were split into above- and below-m…
View article: Supplementary Figure 6 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Supplementary Figure 6 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
Non-negative matrix factorization identifies characteristic cancer-associated signatures of shorter fragment lengths and greater nucleosome core occupancy. (A) Genome-wide fragment lengths were used as features in a two-component non-negat…
View article: Supplementary Figure 12 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Supplementary Figure 12 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
Multivariate Cox analysis of change in cancer mutation concentration (CMC) from baseline to cycle 3 of pembrolizumab. Covariates include cohort, PD-L1 expression, and tumor mutation burden
View article: Supplementary Figure 10 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Supplementary Figure 10 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
Predicting survival outcomes using cancer mutation concentration (CMC) at baseline and cycle 3 of pembrolizumab. CMC was determined using a tumor-informed bespoke approach across the trial cohort. At both baseline and cycle 3, patients wer…
View article: Supplementary Figure 7 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Supplementary Figure 7 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
Association of tumor burden with cancer mutation concentration (CMC), as well as cancer-specific methylation (CSM) and fragment length score (FLS). We computed CMC from personalized tumor-informed mutation arrays. We also computed CSM and …
View article: Table S1 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Table S1 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
CMC and CSM early kinetics discordant cases
View article: Table S1 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Table S1 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
CMC and CSM early kinetics discordant cases
View article: Supplementary Figure 11 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Supplementary Figure 11 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
Multivariable analysis of OS and PFS using cancer mutation concentration (CMC) at baseline and cycle 3 of pembrolizumab. CMC was determined using a bespoke targeted approach across the trial cohort. At both baseline and cycle 3, patients w…
View article: Supplementary Figure 3 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Supplementary Figure 3 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
Examples of cancer-specific methylation score calculation. Cancer-specific methylation scores were computed using the sum of inferred absolute methylation values for all reads overlapping an independently-trained cancer-specific signature.…
View article: Supplementary Figure 6 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Supplementary Figure 6 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
Non-negative matrix factorization identifies characteristic cancer-associated signatures of shorter fragment lengths and greater nucleosome core occupancy. (A) Genome-wide fragment lengths were used as features in a two-component non-negat…
View article: Supplementary Figure 12 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Supplementary Figure 12 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
Multivariate Cox analysis of change in cancer mutation concentration (CMC) from baseline to cycle 3 of pembrolizumab. Covariates include cohort, PD-L1 expression, and tumor mutation burden
View article: Supplementary Figure 1 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Supplementary Figure 1 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
Overview of the timepoints included in cfMeDIP-seq analysis for each patient. (A) Every sample included by patient and timepoint. (B) Numbers of samples analyzed per cohort and timepoint.
View article: Supplementary Figure 16 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Supplementary Figure 16 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
Multivariate Cox analysis of the change in fragment length score (FLS) from baseline to cycle 3 of pembrolizumab. Covariates in clude cohort, PD-L1 expression, and tumor mutation burden.
View article: Supplementary Figure 7 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Supplementary Figure 7 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
Association of tumor burden with cancer mutation concentration (CMC), as well as cancer-specific methylation (CSM) and fragment length score (FLS). We computed CMC from personalized tumor-informed mutation arrays. We also computed CSM and …
View article: Supplementary Figure 4 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Supplementary Figure 4 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
Validation of the 200 CpG signature using publicly available WGBS data. A 200 CpG signature was generated using 450K array data from TCGA PanCanAtlas. We validated this signature in publicly available data from WGBS of esophageal squamous …
View article: Supplementary Figure 4 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Supplementary Figure 4 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
Validation of the 200 CpG signature using publicly available WGBS data. A 200 CpG signature was generated using 450K array data from TCGA PanCanAtlas. We validated this signature in publicly available data from WGBS of esophageal squamous …
View article: Supplementary Figure 15 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Supplementary Figure 15 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
Predicting survival outcomes using fragment length score (FLS) at baseline and cycle 3 of pembrolizumab. FLS was determined as the mean of the log2 transformed cancer-to-normal ratio of the length of each fragment in a given cfMeDIP-seq sa…
View article: Supplementary Figure 9 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Supplementary Figure 9 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
Multivariable analysis of survival using cancer-specific methylation (CSM) scores at baseline and cycle 3 of pembrolizumab. We computed CSM scores across the trial cohort. At both baseline and cycle 3, patients were split into above- or be…
View article: Supplementary Figure 1 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors
Supplementary Figure 1 from Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors Open
Overview of the timepoints included in cfMeDIP-seq analysis for each patient. (A) Every sample included by patient and timepoint. (B) Numbers of samples analyzed per cohort and timepoint.