Aung Ko Win
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View article: Cancer risks for MSH6 pathogenic variant carriers
Cancer risks for MSH6 pathogenic variant carriers Open
These refined, age- and sex-specific risk estimates for MSH6 PV carriers inform tailored surveillance strategies, supporting delayed CRC screening and individualised counselling on risk-reducing surgery for women.
View article: A prediction model for metachronous colorectal cancer: development and validation
A prediction model for metachronous colorectal cancer: development and validation Open
Background Being able to estimate the risk of metachronous disease in a patient with colorectal cancer (CRC) could enable risk-appropriate surveillance. The aim of this study was to develop a risk-prediction model to estimate individual 10…
View article: Management of Riedel Thyroiditis With Completion Thyroidectomy to Relieve Persistent Compressive Symptoms
Management of Riedel Thyroiditis With Completion Thyroidectomy to Relieve Persistent Compressive Symptoms Open
Riedel thyroiditis is a rare IgG4-related condition characterized by chronic inflammation that results in partial or total fibrosis of the thyroid gland. Medical management aimed at reducing the autoimmune inflammatory response is generall…
View article: Intratumoural pks Escherichia coli is associated with risk of metachronous colorectal cancer and adenoma development in people with Lynch syndrome
Intratumoural pks Escherichia coli is associated with risk of metachronous colorectal cancer and adenoma development in people with Lynch syndrome Open
This work was funded by an NHMRC Investigator grant (GNT1194896) and a Cancer Australia/Cancer Council NSW co-funded grant (GNT2012914).
View article: Metachronous colorectal cancer risks after extended or segmental resection in <i>MLH1</i>, <i>MSH2</i>, and <i>MSH6</i> Lynch syndrome: multicentre study from the Prospective Lynch Syndrome Database
Metachronous colorectal cancer risks after extended or segmental resection in <i>MLH1</i>, <i>MSH2</i>, and <i>MSH6</i> Lynch syndrome: multicentre study from the Prospective Lynch Syndrome Database Open
This first prospective observational study evaluates the impact of extended versus segmental colorectal surgery on the risk of metachronous colorectal cancer (CRC) in patients with Lynch syndrome, analyzing data from the Prospective Lynch …
View article: Risks of colorectal and extracolonic cancers following colorectal cancer: a systematic review and meta-analysis
Risks of colorectal and extracolonic cancers following colorectal cancer: a systematic review and meta-analysis Open
Background Colorectal cancer survivors face increased risks of developing new primary cancers in colorectum and other anatomical sites. This systematic review aimed to estimate primary colorectal and extracolonic cancers risks following co…
View article: Adenomas from individuals with pathogenic biallelic variants in the MUTYH and NTHL1 genes demonstrate base excision repair tumour mutational signature profiles similar to colorectal cancers, expanding potential diagnostic and variant classification applications
Adenomas from individuals with pathogenic biallelic variants in the MUTYH and NTHL1 genes demonstrate base excision repair tumour mutational signature profiles similar to colorectal cancers, expanding potential diagnostic and variant classification applications Open
SBS18+SBS36 and SBS30 were enriched in adenomas at comparable proportions to those observed in CRCs from biallelic MUTYH and biallelic NTHL1 cases, respectively. Therefore, testing adenomas may improve the identification of biallelic cases…
View article: Table S2 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Table S2 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci. Open
Supplementary Table S2 shows the QC and harmonisation steps for both the GECCO and CCFR datasets
View article: Table S7 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Table S7 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci. Open
Supplementary Table S7 shows the number of risk regions that are common to both CCFR and GECCO datasets in stage 1 DEPTH analysis, as well as whether they are detected by logistic regression analysis or were in previous CRC GWAS studies.
View article: Table S11 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Table S11 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci. Open
Supplementary Table S11 shows the ranking of SNPs at the HLA locus.
View article: Figure S1 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Figure S1 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci. Open
Supplementary Figure S1 showing the flow chart of the analyses for identifying novel, unique, and common risk regions from DEPTH and logistic regression analyses.
View article: Table S5 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Table S5 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci. Open
Supplementary Table S5 shows the summary of known colorectal risk loci that are located in our CCFR and GECCO datasets.
View article: Data from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Data from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci. Open
Background: DEPendency of association on the number of Top Hits (DEPTH) is an approach to identify candidate susceptibility regions by considering the risk signals from overlapping groups of sequential variants across the genome. Methods: …
View article: Table S8 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Table S8 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci. Open
Supplementary Table S8 shows the number of risk regions that are common to both CCFR and GECCO in stage 2 DEPTH analysis, as well as whether they are detected by logistic regression analysis or were in previous CRC GWAS studies.
View article: Table S7 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Table S7 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci. Open
Supplementary Table S7 shows the number of risk regions that are common to both CCFR and GECCO datasets in stage 1 DEPTH analysis, as well as whether they are detected by logistic regression analysis or were in previous CRC GWAS studies.
View article: Figure S2 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Figure S2 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci. Open
Supplementary Figure S2 of a principle components analysis showing the CCFR and GECCO datasets cluster with European Caucasians.
View article: Table S8 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Table S8 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci. Open
Supplementary Table S8 shows the number of risk regions that are common to both CCFR and GECCO in stage 2 DEPTH analysis, as well as whether they are detected by logistic regression analysis or were in previous CRC GWAS studies.
View article: Table S4 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Table S4 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci. Open
Supplementary Table S4 shows the number of participants filtered out at each QC stage (according to Supplementary Table S2).
View article: Figure S5 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Figure S5 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci. Open
Supplementary Figure S5 showing the QQ and Manhattan plots of conventional GWAS on the GECCO and CCFR datasets
View article: Table S10 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Table S10 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci. Open
Supplementary Table S10 shows the logistic regression summary statistics for both the CCFR and GECCO datasets.
View article: Table S9 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Table S9 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci. Open
Supplementary Table S9 shows the number of risk regions that are common to both CCFR and GECCO in stage 3 DEPTH analysis, as well as whether they are detected by logistic regression analysis or were in previous CRC GWAS studies.
View article: Figure S1 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Figure S1 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci. Open
Supplementary Figure S1 showing the flow chart of the analyses for identifying novel, unique, and common risk regions from DEPTH and logistic regression analyses.
View article: Figure S3 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Figure S3 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci. Open
Supplementary Figure S3 shows further filtering of SNPs that had greater than 20% difference in odds ratio or P values when comparing odds ratios and P values before and after adjusting for the first four principal components.
View article: Figure S3 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Figure S3 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci. Open
Supplementary Figure S3 shows further filtering of SNPs that had greater than 20% difference in odds ratio or P values when comparing odds ratios and P values before and after adjusting for the first four principal components.
View article: Table S6 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Table S6 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci. Open
Supplementary Table S6 shows the number of risk regions identified by first stage DEPTH analysis.
View article: Table S9 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Table S9 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci. Open
Supplementary Table S9 shows the number of risk regions that are common to both CCFR and GECCO in stage 3 DEPTH analysis, as well as whether they are detected by logistic regression analysis or were in previous CRC GWAS studies.
View article: Figure S4 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Figure S4 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci. Open
Supplementary Figure S4 shows how DEPTH common risk regions are defined.
View article: Figure S5 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Figure S5 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci. Open
Supplementary Figure S5 showing the QQ and Manhattan plots of conventional GWAS on the GECCO and CCFR datasets