The characteristics of tumor-infiltrating lymphocytes (TIL) are essential in cancer prognosticati... more The characteristics of tumor-infiltrating lymphocytes (TIL) are essential in cancer prognostication and treatment through the ability to indicate the tumor’s capacity to evade the immune system (e.g., as evidenced by nodal involvement). Machine learning technologies have demonstrated remarkable success for localizing TILs, though these methods require extensive curation of manual annotations or restaining procedures that can degrade tissue quality, resulting in imprecise annotation. In this study, we co-registered tissue slides stained for both hematoxylin and eosin (H&E) and immunofluorescence (IF) as means to rapidly perform large-scale annotation of nuclei. We integrated the following approaches to improve the prediction of TILs: 1) minimized tissue degradation on same-section tissue restaining, 2) developed a scoring algorithm to improve the selection of patches for machine learning modeling and 3) utilized a graph neural network deep learning approach to identify relevant conte...
Aim: Tandem bisulfite (BS) and oxidative bisulfite (oxBS) conversion on DNA followed by hybridiza... more Aim: Tandem bisulfite (BS) and oxidative bisulfite (oxBS) conversion on DNA followed by hybridization to Infinium HumanMethylation BeadChips allows nucleotide resolution of 5-hydroxymethylcytosine genome-wide. Here, the authors compared data quality acquired from BS-treated and oxBS-treated samples. Materials & methods: Raw BeadArray data from 417 pairs of samples across 12 independent datasets were included in the study. Probe call rates were compared between paired BS and oxBS treatments controlling for technical variables. Results: oxBS-treated samples had a significantly lower call rate. Among technical variables, DNA-specific extraction kits performed better with higher call rates after oxBS conversion. Conclusion: The authors emphasize the importance of quality control during oxBS conversion to minimize information loss and recommend using a DNA-specific extraction kit for DNA extraction and an oxBSQC package for data preprocessing.
DNA methylation-based copy number variation (CNV) calling software offers the advantages of provi... more DNA methylation-based copy number variation (CNV) calling software offers the advantages of providing both genetic (copy-number) and epigenetic (methylation) state information from a single genomic library. This method is advantageous when looking at large-scale chromosomal rearrangements such as the loss of the short arm of chromosome 3 (3p) in renal cell carcinoma and the codeletion of the short arm of chromosome 1 and the long arm of chromosome 19 (1p/19q) commonly seen in histologically defined oligodendrogliomas. Herein, we present MethylMasteR: a software framework that facilitates the standardization and customization of methylation-based CNV calling algorithms in a single R package deployed using the Docker software framework. This framework allows for the easy comparison of the performance and the large-scale CNV event identification capability of four common methylation-based CNV callers. Additionally, we incorporated our custom routine, which was among the best performing...
Figure S1. Details of SVM BRCA1-like classifier. (A) Overview of copy number mapping algorithm fo... more Figure S1. Details of SVM BRCA1-like classifier. (A) Overview of copy number mapping algorithm for generating the input for training the SVM BRCA1-like classifier. (B) Receiver-operation characteristic curves (ROC) of the classifier applied to training and test set (AUC = 1.00 and 0.75, respectively). (C-D) Correlation of SVM BRCA1-like probability scores with published HR-deficiency metrics (HRD-LOH and LST scores). ***P T) deamination events in TCGA hormone-receptor-positive breast tumors. A P value indicates statistical significance from linear model adjusting for age, tumor stage, and ER, PR, and HER2 positivity. **P
Table S1. SVM BRCA1-like status and BRCA1ness-MLPA profiles in 10 breast cancer cell lines. Table... more Table S1. SVM BRCA1-like status and BRCA1ness-MLPA profiles in 10 breast cancer cell lines. Table S2A. TCGA breast tumors with SVM BRCA1-like status. Table S2B. METABRIC breast tumors with SVM BRCA1-like status. Table S3. Complete subject characteristics of TCGA and METABRIC breast tumors with SVM BRCA1-like status. Table S4. Differentially methylated CpGs in BRCA1-like tumors identified by DMRcate. Table S5A. Hypermethylated DMRs (n = 108) from DMRcate analysis. Table 5SB. Hypomethylated DMRs (n = 94) from DMRcate analysis. Table S6A. GOBP terms associated with 158 unique genes from 108 hypermethylated DMRs. Table S6B. GOBP terms associated with 131 unique genes from 94 hypermethylated DMRs. (XLSX 923 kb)
Figure S1. Forest plot for the top 10 Bonferroni-significant CpGs from the meta-analysis on the a... more Figure S1. Forest plot for the top 10 Bonferroni-significant CpGs from the meta-analysis on the association between continuous GA and offspring DNA methylation at birth adjusted for estimated cell proportions. Figure S2. Sensitivity analysis: Correlation of the point estimates for the no complications model main association of DNA methylation with gestational age (y-axis representing 3648 participants from 17 cohorts) with point estimates for a meta-analysis after excluding three cohorts (MoBa1, MoBa2 and ALSPAC) that were included in a previous publication1,2 (x-axis representing 2190 participants from 14 cohorts). Figure S3. Correlations between methylation and gene expression levels for selected four pairs. First, we created residuals for mRNA expression and residuals for DNA methylation and used linear regression models to evaluate correlations between expression residuals and methylation residuals. These residual models were adjusted for covariates, estimated white blood cell p...
Table S1. Cohort-specific results from epigenome-wide association analyses of gestational age. Ta... more Table S1. Cohort-specific results from epigenome-wide association analyses of gestational age. Table S2. Normalization technique and phenotype definitions used by each cohort. Table S3. Bonferroni-significant CpGs from the meta-analysis on the association between continuous gestational age (no complications model) and offspring DNA methylation at birth adjusted for estimated cell counts. Table S4. Bonferroni-significant CpGs from the meta-analysis on the association between continuous gestational age (all births model) and offspring DNA methylation at birth adjusted for estimated cell counts. Table S5. Gene regions that had at least three consecutive Bonferroni significant CpG sites from the continuous gestational age analyses (no complications model). Table S6. DMRs (n = 2375) for gestational age in relation to newborn methylation (no complication model) identified by using both comb-p (P
Results using EPIC microarray to investigate maternal CRP in the pregnancy and delivery with cord... more Results using EPIC microarray to investigate maternal CRP in the pregnancy and delivery with cord blood DNA methylation. Results from model 1 adjusting for maternal age, smoking, income, pre-pregnancy BMI, and plate. Model 2 additionally adjusts for cell type distribution using the cord blood reference as published by Bakulski et al. 2016. Model 3 adjusts for model 1 covariates and cell type distribution using the cord blood reference published by Gervin, Salas et al. 2019. Results are from first trimester (week 8), second trimester (week 20), third trimester (week 36), cord blood, cumulative (AUC) or persistent (extreme tertiles) CRP.<br>Supports the publication:<br>Yeung, E.H., Guan, W., Zeng, X. et al. Cord blood DNA methylation reflects cord blood C-reactive protein levels but not maternal levels: a longitudinal study and meta-analysis. Clin Epigenet 12, 60 (2020). https://doi.org/10.1186/s13148-020-00852-2<br>
Figure S1. Estimated cell purity by flow cytometry per cell type. Figure S2. Heatmap based on a h... more Figure S1. Estimated cell purity by flow cytometry per cell type. Figure S2. Heatmap based on a hierarchical cluster of purified cell types and cell mixtures based on the array SNPs. Figure S3. Association between the top 20 principal components and potential confounders for DNA methylation. Figure S4. Iterative testing of different L-DMR library sizes using the IDOL optimization algorithm. Table S1. Cell composition percentages for the artificial reconstruction samples. Figure S5. Comparison of several probe selection methods and estimated cell proportions using constrained projection/quadratic programming (CP/QP) versus the reconstructed (true) DNA fraction in the artificial DNA mixtures. Figure S6. Bland-Altman plots comparing the mean differences between the estimated cell fraction using three deconvolution methods and the true fraction in the artificial mixture per cell type. Figure S7. Comparison of the estimated cell proportions using CP/QP using an IDOL-optimized library res...
GSEA enrichment using the curated set 7 (immune profiles) of the probes contained in the L-DMR ID... more GSEA enrichment using the curated set 7 (immune profiles) of the probes contained in the L-DMR IDOL library. (CSV 13Â kb)
The characteristics of tumor-infiltrating lymphocytes (TIL) are essential in cancer prognosticati... more The characteristics of tumor-infiltrating lymphocytes (TIL) are essential in cancer prognostication and treatment through the ability to indicate the tumor’s capacity to evade the immune system (e.g., as evidenced by nodal involvement). Machine learning technologies have demonstrated remarkable success for localizing TILs, though these methods require extensive curation of manual annotations or restaining procedures that can degrade tissue quality, resulting in imprecise annotation. In this study, we co-registered tissue slides stained for both hematoxylin and eosin (H&E) and immunofluorescence (IF) as means to rapidly perform large-scale annotation of nuclei. We integrated the following approaches to improve the prediction of TILs: 1) minimized tissue degradation on same-section tissue restaining, 2) developed a scoring algorithm to improve the selection of patches for machine learning modeling and 3) utilized a graph neural network deep learning approach to identify relevant conte...
Aim: Tandem bisulfite (BS) and oxidative bisulfite (oxBS) conversion on DNA followed by hybridiza... more Aim: Tandem bisulfite (BS) and oxidative bisulfite (oxBS) conversion on DNA followed by hybridization to Infinium HumanMethylation BeadChips allows nucleotide resolution of 5-hydroxymethylcytosine genome-wide. Here, the authors compared data quality acquired from BS-treated and oxBS-treated samples. Materials & methods: Raw BeadArray data from 417 pairs of samples across 12 independent datasets were included in the study. Probe call rates were compared between paired BS and oxBS treatments controlling for technical variables. Results: oxBS-treated samples had a significantly lower call rate. Among technical variables, DNA-specific extraction kits performed better with higher call rates after oxBS conversion. Conclusion: The authors emphasize the importance of quality control during oxBS conversion to minimize information loss and recommend using a DNA-specific extraction kit for DNA extraction and an oxBSQC package for data preprocessing.
DNA methylation-based copy number variation (CNV) calling software offers the advantages of provi... more DNA methylation-based copy number variation (CNV) calling software offers the advantages of providing both genetic (copy-number) and epigenetic (methylation) state information from a single genomic library. This method is advantageous when looking at large-scale chromosomal rearrangements such as the loss of the short arm of chromosome 3 (3p) in renal cell carcinoma and the codeletion of the short arm of chromosome 1 and the long arm of chromosome 19 (1p/19q) commonly seen in histologically defined oligodendrogliomas. Herein, we present MethylMasteR: a software framework that facilitates the standardization and customization of methylation-based CNV calling algorithms in a single R package deployed using the Docker software framework. This framework allows for the easy comparison of the performance and the large-scale CNV event identification capability of four common methylation-based CNV callers. Additionally, we incorporated our custom routine, which was among the best performing...
Figure S1. Details of SVM BRCA1-like classifier. (A) Overview of copy number mapping algorithm fo... more Figure S1. Details of SVM BRCA1-like classifier. (A) Overview of copy number mapping algorithm for generating the input for training the SVM BRCA1-like classifier. (B) Receiver-operation characteristic curves (ROC) of the classifier applied to training and test set (AUC = 1.00 and 0.75, respectively). (C-D) Correlation of SVM BRCA1-like probability scores with published HR-deficiency metrics (HRD-LOH and LST scores). ***P T) deamination events in TCGA hormone-receptor-positive breast tumors. A P value indicates statistical significance from linear model adjusting for age, tumor stage, and ER, PR, and HER2 positivity. **P
Table S1. SVM BRCA1-like status and BRCA1ness-MLPA profiles in 10 breast cancer cell lines. Table... more Table S1. SVM BRCA1-like status and BRCA1ness-MLPA profiles in 10 breast cancer cell lines. Table S2A. TCGA breast tumors with SVM BRCA1-like status. Table S2B. METABRIC breast tumors with SVM BRCA1-like status. Table S3. Complete subject characteristics of TCGA and METABRIC breast tumors with SVM BRCA1-like status. Table S4. Differentially methylated CpGs in BRCA1-like tumors identified by DMRcate. Table S5A. Hypermethylated DMRs (n = 108) from DMRcate analysis. Table 5SB. Hypomethylated DMRs (n = 94) from DMRcate analysis. Table S6A. GOBP terms associated with 158 unique genes from 108 hypermethylated DMRs. Table S6B. GOBP terms associated with 131 unique genes from 94 hypermethylated DMRs. (XLSX 923 kb)
Figure S1. Forest plot for the top 10 Bonferroni-significant CpGs from the meta-analysis on the a... more Figure S1. Forest plot for the top 10 Bonferroni-significant CpGs from the meta-analysis on the association between continuous GA and offspring DNA methylation at birth adjusted for estimated cell proportions. Figure S2. Sensitivity analysis: Correlation of the point estimates for the no complications model main association of DNA methylation with gestational age (y-axis representing 3648 participants from 17 cohorts) with point estimates for a meta-analysis after excluding three cohorts (MoBa1, MoBa2 and ALSPAC) that were included in a previous publication1,2 (x-axis representing 2190 participants from 14 cohorts). Figure S3. Correlations between methylation and gene expression levels for selected four pairs. First, we created residuals for mRNA expression and residuals for DNA methylation and used linear regression models to evaluate correlations between expression residuals and methylation residuals. These residual models were adjusted for covariates, estimated white blood cell p...
Table S1. Cohort-specific results from epigenome-wide association analyses of gestational age. Ta... more Table S1. Cohort-specific results from epigenome-wide association analyses of gestational age. Table S2. Normalization technique and phenotype definitions used by each cohort. Table S3. Bonferroni-significant CpGs from the meta-analysis on the association between continuous gestational age (no complications model) and offspring DNA methylation at birth adjusted for estimated cell counts. Table S4. Bonferroni-significant CpGs from the meta-analysis on the association between continuous gestational age (all births model) and offspring DNA methylation at birth adjusted for estimated cell counts. Table S5. Gene regions that had at least three consecutive Bonferroni significant CpG sites from the continuous gestational age analyses (no complications model). Table S6. DMRs (n = 2375) for gestational age in relation to newborn methylation (no complication model) identified by using both comb-p (P
Results using EPIC microarray to investigate maternal CRP in the pregnancy and delivery with cord... more Results using EPIC microarray to investigate maternal CRP in the pregnancy and delivery with cord blood DNA methylation. Results from model 1 adjusting for maternal age, smoking, income, pre-pregnancy BMI, and plate. Model 2 additionally adjusts for cell type distribution using the cord blood reference as published by Bakulski et al. 2016. Model 3 adjusts for model 1 covariates and cell type distribution using the cord blood reference published by Gervin, Salas et al. 2019. Results are from first trimester (week 8), second trimester (week 20), third trimester (week 36), cord blood, cumulative (AUC) or persistent (extreme tertiles) CRP.<br>Supports the publication:<br>Yeung, E.H., Guan, W., Zeng, X. et al. Cord blood DNA methylation reflects cord blood C-reactive protein levels but not maternal levels: a longitudinal study and meta-analysis. Clin Epigenet 12, 60 (2020). https://doi.org/10.1186/s13148-020-00852-2<br>
Figure S1. Estimated cell purity by flow cytometry per cell type. Figure S2. Heatmap based on a h... more Figure S1. Estimated cell purity by flow cytometry per cell type. Figure S2. Heatmap based on a hierarchical cluster of purified cell types and cell mixtures based on the array SNPs. Figure S3. Association between the top 20 principal components and potential confounders for DNA methylation. Figure S4. Iterative testing of different L-DMR library sizes using the IDOL optimization algorithm. Table S1. Cell composition percentages for the artificial reconstruction samples. Figure S5. Comparison of several probe selection methods and estimated cell proportions using constrained projection/quadratic programming (CP/QP) versus the reconstructed (true) DNA fraction in the artificial DNA mixtures. Figure S6. Bland-Altman plots comparing the mean differences between the estimated cell fraction using three deconvolution methods and the true fraction in the artificial mixture per cell type. Figure S7. Comparison of the estimated cell proportions using CP/QP using an IDOL-optimized library res...
GSEA enrichment using the curated set 7 (immune profiles) of the probes contained in the L-DMR ID... more GSEA enrichment using the curated set 7 (immune profiles) of the probes contained in the L-DMR IDOL library. (CSV 13Â kb)
Uploads
Papers by Lucas Salas