Markia A. Smith
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View article: Supplementary Figure 9 from Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer
Supplementary Figure 9 from Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer Open
Supplemental Figure 9A demonstrates the overlap between "p53-associated" signature from Troester et al. vs. "p53-like" signature from Choi et al; Supplemental Figure 9B shows the "p53-like" signature from Choi et (MDA) by UROMOL Subclasses…
View article: Supplementary Figure 11 from Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer
Supplementary Figure 11 from Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer Open
Supplemental Figure 11 shows the correlation between P53 Score by immune score, FOXM1 expression, Proliferation marker expression.
View article: Data from Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer
Data from Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer Open
Purpose:Improved risk stratification and predictive biomarkers of treatment response are needed for non–muscle-invasive bladder cancer (NMIBC). Here we assessed the clinical utility of targeted RNA and DNA molecular profiling in NMIBC.Expe…
View article: Supplementary Figure 6 from Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer
Supplementary Figure 6 from Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer Open
Supplemental Figure 6 shows Recurrence Free Survival in the Northwestern HGT1 cohort by immune score stratified by (1) High, Medium, Low; (2) High/Medium vs. Low.
View article: Supplementary Figure 5 from Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer
Supplementary Figure 5 from Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer Open
Supplemental Figure 5 shows immune score signature using the publicly available UROMOL.
View article: Supplementary Figure 8 from Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer
Supplementary Figure 8 from Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer Open
Supplemental Figure 8 shows the relationship between the Fraction of Genome Altered by UROMOL subgroups within the MSK cohort.
View article: Supplementary Figure 3 from Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer
Supplementary Figure 3 from Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer Open
Supplemental Figure 3 shows the relationship for UROMOL Subclasses in MSK and UNC cohorts between expression of Proliferation markers and FOXM1.
View article: Supplementary Figure 7 from Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer
Supplementary Figure 7 from Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer Open
Supplemental Figure 7 shows PDL1 (CD274) Expression, PD1 (PDCD1) Expression, and CTLA4 Expression by UROMOL Subclasses in MSK and UNC cohorts and RFS (by high, medium, low tertiles) in MSK, UROMOL, & Northwestern Cohorts.
View article: Supplementary Figure 4 from Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer
Supplementary Figure 4 from Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer Open
Supplemental Figure 4 shows the relationship between Immune Signature score by Patient Cohort and UROMOL subtype.
View article: Supplementary Figure 1 from Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer
Supplementary Figure 1 from Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer Open
Supplemental Figure 1 shows the quality check failure rate by Tumor Cellularity and by Stage and Grade.
View article: Supplementary Figure 10 from Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer
Supplementary Figure 10 from Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer Open
Supplemental Figure 10 shows the comparison of DNA based alterations in TP53 and other cell cycle regulation genes compared to "p53-associated" signature from Troester et al. (TP53 score), as well as the association between RFS with TP53 M…
View article: Supplementary Table from Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer
Supplementary Table from Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer Open
Supplementary Table 1, 2, 3
View article: Supplementary Figure 2 from Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer
Supplementary Figure 2 from Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer Open
Supplemental Figure 2A shows FGFR3 Expression by UROMOL Subclasses in MSK and UNC cohorts and Supplemental Figure 2B demonstrates the Inverse Relationship between FGFR3 expression and mutation with Immune Signature Score.
View article: Diffsig: Associating Risk Factors with Mutational Signatures
Diffsig: Associating Risk Factors with Mutational Signatures Open
BACKGROUND: Somatic mutational signatures elucidate molecular vulnerabilities to therapy, and therefore detecting signatures and classifying tumors with respect to signatures has clinical value. However, identifying the etiology of the mut…
View article: Supplementary Figure 5 from Diffsig: Associating Risk Factors with Mutational Signatures
Supplementary Figure 5 from Diffsig: Associating Risk Factors with Mutational Signatures Open
This figure shows the simulation results with liver cancer signatures from COSMIC
View article: Supplementary Figure 6 from Diffsig: Associating Risk Factors with Mutational Signatures
Supplementary Figure 6 from Diffsig: Associating Risk Factors with Mutational Signatures Open
This figure shows the evaluation results of false positives with simulation data on Diffsig
View article: Supplementary Figure 4 from Diffsig: Associating Risk Factors with Mutational Signatures
Supplementary Figure 4 from Diffsig: Associating Risk Factors with Mutational Signatures Open
This figure shows the simulation results on the 80% credible interval coverages and absolute errors of the estimates per simulation
View article: Supplementary Figure 5 from Diffsig: Associating Risk Factors with Mutational Signatures
Supplementary Figure 5 from Diffsig: Associating Risk Factors with Mutational Signatures Open
This figure shows the simulation results with liver cancer signatures from COSMIC
View article: Supplementary Figure 4 from Diffsig: Associating Risk Factors with Mutational Signatures
Supplementary Figure 4 from Diffsig: Associating Risk Factors with Mutational Signatures Open
This figure shows the simulation results on the 80% credible interval coverages and absolute errors of the estimates per simulation
View article: Supplementary Figure 2 from Diffsig: Associating Risk Factors with Mutational Signatures
Supplementary Figure 2 from Diffsig: Associating Risk Factors with Mutational Signatures Open
This figure shows the detected HiLDA Signatures on TCGA Breast Cancer Data
View article: Supplementary Figure 3 from Diffsig: Associating Risk Factors with Mutational Signatures
Supplementary Figure 3 from Diffsig: Associating Risk Factors with Mutational Signatures Open
This figure shows the distribution of the number of mutations in simulation compared to real data from TCGA breast cancer data
View article: Supplementary Methods from Diffsig: Associating Risk Factors with Mutational Signatures
Supplementary Methods from Diffsig: Associating Risk Factors with Mutational Signatures Open
This document provides a more detailed explanation of the compositional nature of contributions and identifiability of associations and the priors for the hyperparameters
View article: Supplementary Figure 3 from Diffsig: Associating Risk Factors with Mutational Signatures
Supplementary Figure 3 from Diffsig: Associating Risk Factors with Mutational Signatures Open
This figure shows the distribution of the number of mutations in simulation compared to real data from TCGA breast cancer data
View article: Supplementary Figure 7 from Diffsig: Associating Risk Factors with Mutational Signatures
Supplementary Figure 7 from Diffsig: Associating Risk Factors with Mutational Signatures Open
This figure shows TCGA results with a different set of breast cancer signatures
View article: Supplementary Figure 2 from Diffsig: Associating Risk Factors with Mutational Signatures
Supplementary Figure 2 from Diffsig: Associating Risk Factors with Mutational Signatures Open
This figure shows the detected HiLDA Signatures on TCGA Breast Cancer Data
View article: Supplementary Data from Diffsig: Associating Risk Factors with Mutational Signatures
Supplementary Data from Diffsig: Associating Risk Factors with Mutational Signatures Open
This data includes the variables used in the analysis including HRD scores (HRD_Score) and breast cancer subtypes (Call)
View article: Supplementary Figure 7 from Diffsig: Associating Risk Factors with Mutational Signatures
Supplementary Figure 7 from Diffsig: Associating Risk Factors with Mutational Signatures Open
This figure shows TCGA results with a different set of breast cancer signatures
View article: Data from Diffsig: Associating Risk Factors with Mutational Signatures
Data from Diffsig: Associating Risk Factors with Mutational Signatures Open
Background:Somatic mutational signatures elucidate molecular vulnerabilities to therapy, and therefore detecting signatures and classifying tumors with respect to signatures has clinical value. However, identifying the etiology of the muta…
View article: Supplementary Methods from Diffsig: Associating Risk Factors with Mutational Signatures
Supplementary Methods from Diffsig: Associating Risk Factors with Mutational Signatures Open
This document provides a more detailed explanation of the compositional nature of contributions and identifiability of associations and the priors for the hyperparameters
View article: Supplementary Figure 6 from Diffsig: Associating Risk Factors with Mutational Signatures
Supplementary Figure 6 from Diffsig: Associating Risk Factors with Mutational Signatures Open
This figure shows the evaluation results of false positives with simulation data on Diffsig