Xi Chen successfully completed the SAS Macro Language 1: Essentials e-course on January 23, 2016. The certificate confirms that Xi Chen completed the SAS Macro Language 1 e-course and achieved essential skills in SAS macros.
I am the authorized Consultant for eInstruction By Turning Technologies in South Carolina
Bill McIntosh
Phone :843-442-8888
Email : WKMcIntosh@Comcast.net
The presentation is intended for Clinical Trial programmers or statisticians who are working on the oncology lymphoma clinical trial studies. There are three types of studies in oncology: Solid Tumor, Lymphoma and Leukemia. The lymphoma studies usually follow Cheson while solid tumor follow RECIST (Response Evaluation Criteria in Solid Tumor) and Leukemia studies follow IWCLL(Internal Working Group on Chronic Lymphocytic Leukemia). There are two version of Cheson – 1999 and 2007. The presentation will be based on Cheson 2007.
The presentation will provide the brief introduction of Cheson 2007 such as legions (enlarged lymph node, nodal masses and extra nodal masses) and their types (target, non target and new) . The lymphoma studies need to collect the measurements of lesions (the longest diameter, its greatest transverse diameter and the sum of diameters), PET scan on those lesions, Bone Marrow assessment, Spleen and Liver assessment. Cheson 2007 explains how each assessment is made to determine responses (Complete Response, Partial Response, Stable Disease and Progression Disease).
Then, the paper will show how tumor data are streamlined in CDISC – mainly in SDTM and ADaM. The paper will introduce the new oncology SDTM domains - TU (Tumor Identification), TR (Tumor Results) and RS (Response) and oncology ADaM dataset – Time to Event (--TTE). The paper will show how Cheson 2007 data points are collected in SDTM domain - tumor measurements in TR and TU, PET scan in TR and TU, Bone Marrow in LB and FA, Spleen and Liver assessments in PE and response in RS. The paper will also show how ADaM time to event datasets can be used for oncology analysis such as OR(Overall Survival) and PFS (Progression Free Survival).
I am the authorized Consultant for eInstruction By Turning Technologies in South Carolina
Bill McIntosh
Phone :843-442-8888
Email : WKMcIntosh@Comcast.net
The presentation is intended for Clinical Trial programmers or statisticians who are working on the oncology lymphoma clinical trial studies. There are three types of studies in oncology: Solid Tumor, Lymphoma and Leukemia. The lymphoma studies usually follow Cheson while solid tumor follow RECIST (Response Evaluation Criteria in Solid Tumor) and Leukemia studies follow IWCLL(Internal Working Group on Chronic Lymphocytic Leukemia). There are two version of Cheson – 1999 and 2007. The presentation will be based on Cheson 2007.
The presentation will provide the brief introduction of Cheson 2007 such as legions (enlarged lymph node, nodal masses and extra nodal masses) and their types (target, non target and new) . The lymphoma studies need to collect the measurements of lesions (the longest diameter, its greatest transverse diameter and the sum of diameters), PET scan on those lesions, Bone Marrow assessment, Spleen and Liver assessment. Cheson 2007 explains how each assessment is made to determine responses (Complete Response, Partial Response, Stable Disease and Progression Disease).
Then, the paper will show how tumor data are streamlined in CDISC – mainly in SDTM and ADaM. The paper will introduce the new oncology SDTM domains - TU (Tumor Identification), TR (Tumor Results) and RS (Response) and oncology ADaM dataset – Time to Event (--TTE). The paper will show how Cheson 2007 data points are collected in SDTM domain - tumor measurements in TR and TU, PET scan in TR and TU, Bone Marrow in LB and FA, Spleen and Liver assessments in PE and response in RS. The paper will also show how ADaM time to event datasets can be used for oncology analysis such as OR(Overall Survival) and PFS (Progression Free Survival).
Pan-Cancer Epigenetic Biomarker Selection from Blood Sample Using SAS®Xi Chen
A key focus in current cancer research is the discovery of cancer biomarkers that allow earlier detection with high accuracy and lower costs for both patients and hospitals. Blood samples have long been used as a health status indicator, but DNA methylation signatures in blood have not been fully appreciated in cancer research. Historically, analysis of cancer has been conducted directly with the patient’s tumor or related tissues. Such analyses allow physicians to diagnose a patient’s health and cancer status; however, physicians must observe certain symptoms that prompt them to use biopsies or imaging to verify the diagnosis. This is a post-hoc approach. Our study will focus on epigenetic information for cancer detection, specifically information about DNA methylation in human peripheral blood samples in cancer discordant monozygotic twin-pairs. This information might be able to help us detect cancer much earlier, before the first symptom appears. Several other types of epigenetic data can also be used, but here we demonstrate the potential of blood DNA methylation data as a biomarker for pan-cancer using SAS® 9.3 and SAS® EM. We report that 55 methylation CpG sites measurable in blood samples can be used as biomarkers for early cancer detection and classification.
Pan-Cancer Epigenetic Biomarker Selection from Blood Sample Using SAS®Xi Chen
A key focus in current cancer research is the discovery of cancer biomarkers that allow earlier detection with high accuracy and lower costs for both patients and hospitals. Blood samples have long been used as a health status indicator, but DNA methylation signatures in blood have not been fully appreciated in cancer research. Historically, analysis of cancer has been conducted directly with the patient’s tumor or related tissues. Such analyses allow physicians to diagnose a patient’s health and cancer status; however, physicians must observe certain symptoms that prompt them to use biopsies or imaging to verify the diagnosis. This is a post-hoc approach. Our study will focus on epigenetic information for cancer detection, specifically information about DNA methylation in human peripheral blood samples in cancer discordant monozygotic twin-pairs. This information might be able to help us detect cancer much earlier, before the first symptom appears. Several other types of epigenetic data can also be used, but here we demonstrate the potential of blood DNA methylation data as a biomarker for pan-cancer using SAS® 9.3 and SAS® EM. We report that 55 methylation CpG sites measurable in blood samples can be used as biomarkers for early cancer detection and classification.