Kelvin K. Tsai, M.D., Ph.D. Laboratory for Tumor Epigenetics and Stemness (TES Lab) NATIONAL INSTITUTE OF CANCER RESEARCH NATIONAL HEALTH RESEARCH INSTITUTES.

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Presentation transcript:

Kelvin K. Tsai, M.D., Ph.D. Laboratory for Tumor Epigenetics and Stemness (TES Lab) NATIONAL INSTITUTE OF CANCER RESEARCH NATIONAL HEALTH RESEARCH INSTITUTES (NHRI), TAIWAN

Oncotype DX  : Knowledge-based but biased toward preselected markers Oncotype DX  : Knowledge-based but biased toward preselected markers MammaPrint  or PAM50: Computation-derived; not directly linked to tumor biology or pathways (cancer stemness, differentiation, etc.) MammaPrint  or PAM50: Computation-derived; not directly linked to tumor biology or pathways (cancer stemness, differentiation, etc.) None of them can guide the use of targeted therapeutics. None of them can guide the use of targeted therapeutics.

Cell clustersAcini/ducts Normal Tumor spheroidsCell clusters Neoplastic Structure differentiation Cell-cell interaction Structure HPDE (pancreatic ductal) RWPE-1 (prostatic glands) S-1 (mammary glands) PANC-1 (pancreatic cancer) LNCaP (prostate cancer) MDA-MB-231 (breast cancer) The TES Lab, National Health Research Institutes

Gastroenterology 2013;145:1110

*panSC: pancreatic stem cells; panCSCs: pancreatic cancer stem cells *HPDE, human pancreatic ductal epithelial cells; *DEG, differentially expressed genes Gastroenterology 2013;145:1110

*PDAC, pancreatic ductal adenocarcinoma *RS, Risk Score for poor survival Gastroenterology 2013;145:1110

Differentiation ATP9A ACOX3 CDC45L SLC40A1 AGR2 Reference RPL13A GAPDH To be chosen by data set testing Cancer stemness ASPM Undisclosed stem cell marker USPTO No. 61/824,679; PCT/US2014/38504

Patient 1 Patient 2 Patient 3 Patient 4 Risk Score Expected survival (year) Expected survival (year) Observed survival (year) Likelihood of survival beyond 1 year 90.4%70.8%59.0%< 0.1% Survival beyond 1 year Yes No USPTO No. 61/824,679; PCT/US2014/38504 * Overall survival and one-year survival rate of selected patients in the UCSF cohort as predicted by the PanGUIDE.

Accuracy 95% CI P value University of California, San Francisco cohort Clinico-pathological criteria 80.2%72.0%-88.4% PanGUIDE 95.0%89.6%-100.0% gene PDAssigner 80.5%69.2%-91.9% gene metastasis signature 57.3%40.2%-74.4%0.993 Johns Hopkins Medical Institutions cohort Clinico-pathological criteria 57.4%49.1%-65.6% PanGUIDE 83.3%66.3%-100.0% gene PDAssigner 58.6%44.8%-72.4% gene metastasis signature 68.4%56.9%-79.8%0.084 Northwestern Memorial Hospital cohort Clinico-pathological criteria 67.2%57.4%-77.1% PanGUIDE 81.2%67.8%-94.6% gene PDAssigner 68.6%58.8%-78.4% gene metastasis signature 64.0%53.8%-74.3%0.678 Gastroenterology 2013; Nat Med 2011; PLoS Med 2010

Gastroenterology 2013;145:1110

A 7-gene prognostic signature the PanGUIDE in pancreatic cancer. A 7-gene prognostic signature the PanGUIDE in pancreatic cancer. Stemness- and differentiation-associated; highly accurate Stemness- and differentiation-associated; highly accurate Applicable to patients with localized or metastatic pancreatic cancer due to shared tumor biology. Applicable to patients with localized or metastatic pancreatic cancer due to shared tumor biology. Detected on fresh frozen or FFPE samples. Detected on fresh frozen or FFPE samples. Multiplex qPCR, RNA-seq or NanoString Multiplex qPCR, RNA-seq or NanoString Outputs: 1. Standardized Risk Score 2. Overall survival 3. Yearly survival rate Outputs: 1. Standardized Risk Score 2. Overall survival 3. Yearly survival rate

Aggregates Acini α 6-integrin GM130 Hoechst Am J Pathol 2013;182:363

RS: relapse score HR: hazard ratio for post-OP relapse BWH: Brigham and Woman’s Hospital SU: Stanford University KI: Karolinska Institute JHU: Johns Hopkins University Am J Pathol 2013;182:363

Accuracy95% CI P value for C-index P value vs. clinical The Brigham and Women’s Hospital cohort Clinico-pathologic criteria* 61.7% %0.113 ProsGUIDE 93.9% %< The Chimei Foundational Medical Center cohort Clinico-pathologic criteria* 69.5% % ProsGUIDE 95.1% %< *Includes age, stage, PSA, and Gleason score. Am J Pathol 2013;182:363

Patient 1 Patient 2 Patient 3 Patient 4 Recurrence score by ProsGUIDE Predicted recurrence-free survival (years) > 4.61 Observed recurrence-free survival (years) Predicted 3-year recurrence rate 96.6%80.2%6.8%3.3% Observed recurrence before 3 years Yes No Three-year recurrence rates and recurrence-free survival of selected patients in the Brigham and Women’s Hospital cohort as predicted by ProsGUIDE. US 13/853,548; PCT/US13/34411

A 3-gene prognostic signature for prostate cancer A 3-gene prognostic signature for prostate cancer Differentiation-specific; highly accurate Differentiation-specific; highly accurate Applicable to patients with localized or metastatic prostate cancer due to shared tumor biology. Applicable to patients with localized or metastatic prostate cancer due to shared tumor biology. Fresh frozen or FFPE samples Fresh frozen or FFPE samples Multiplex qPCR, RNA-seq, NanoString or IHC Multiplex qPCR, RNA-seq, NanoString or IHC Output: 1. Standardized Risk Score 2. Recurrence-free survival 3. Yearly recurrence rate Output: 1. Standardized Risk Score 2. Recurrence-free survival 3. Yearly recurrence rate

Provides an individualized and accurate risk assessments that supersede clinico-pathologic criteria. Provides an individualized and accurate risk assessments that supersede clinico-pathologic criteria. Selects patients with early disease relapse or mortality for more aggressive neoadjuvant or adjuvant therapy. Selects patients with early disease relapse or mortality for more aggressive neoadjuvant or adjuvant therapy. Guides clinical decision-making and patient-tailored treatment plans. Guides clinical decision-making and patient-tailored treatment plans. Potentially improves the treatment outcome and/or the successful rate of clinical trials. Potentially improves the treatment outcome and/or the successful rate of clinical trials.

The TES Lab, NHRI Prof. Valerie M. Weaver Center for bioengineering and tissue regeneration, UCSF (3D culture models) Dr. Yan-Shen Shan, NCKUH (pancreatic cancer specimen and clinical data), Prof. Chi-Rong Li, Chung Shan Medical U (bioinformatics, statistics) Acknowledgement Funding sources: National Health Research Institutes Department of Health, Taiwan Ministry of Science and Technology Contact: Dr. Kelvin K. Tsai