Linking Tissue Microarchitectures to Rationalized Molecular Diagnostics in Glandular Cancers
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1 Linking Tissue Microarchitectures to Rationalized Molecular Diagnostics in Glandular Cancers 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
2 Problems with current molecular diagnostics 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.) None of them can guide the use of targeted therapeutics.
3 Modeling stem cells differentiation into tissue microarchitectures Structure Cell clusters Acini/ducts Normal Structure differentiation Cell clusters Tumor spheroids Neoplastic Cell-cell interaction 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
4 Recapitulating tubular differentiation of pancreatic stem cells Gastroenterology 2013;145:1110
5 Molecular profiling of pancreatic tubular differentiation *pansc: pancreatic stem cells; pancscs: pancreatic cancer stem cells *HPDE, human pancreatic ductal epithelial cells; *DEG, differentially expressed genes Gastroenterology 2013;145:1110
6 A tubulogenesis-specific specific prognostic signature in pancreatic cancer *PDAC, pancreatic ductal adenocarcinoma *RS, Risk Score for poor survival
7 The PanGUIDE genes Cancer stemness ASPM Undisclosed stem cell marker Differentiation ATP9A ACOX3 CDC45L SLC40A1 AGR2 Reference RPL13A GAPDH To be chosen by data set testing USPTO No. 61/824,679; PCT/US2014/38504
8 Survival prediction by the PanGUIDE assay Patient 1 Patient 2 Patient 3 Patient 4 Risk Score 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 Yes Yes No * Overall survival and one-year survival rate of selected patients in the UCSF cohort as predicted by the PanGUIDE. USPTO No. 61/824,679; PCT/US2014/38504
9 Prognostic accuracy of 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% 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% 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% Gastroenterology 2013; Nat Med 2011; PLoS Med 2010
10 ASPM as a poor prognostic marker in PDAC Gastroenterology 2013;145:1110
11 ASPM bolsters Wnt activity by stabilizing the dishevelled proteins Gastroenterology 2013;145:1110
12 ASPM maintains pancreatic cancer stemness
13 ASPM contributes to pancreatic cancer aggressiveness Gastroenterology 2013;145:1110
14 Summary of the PanGUIDE assay A 7-gene prognostic signature the PanGUIDE in pancreatic cancer. Stemness- and differentiation-associated; associated; highly accurate Applicable to patients with localized or metastatic pancreatic cancer due to shared tumor biology. Detected on fresh frozen or FFPE samples. Multiplex qpcr, RNA-seq or NanoString Outputs: 1. Standardized Risk Score 2. Overall survival 3. Yearly survival rate
15 Aggregates Acini
16 Transcriptional alterations specific to prostate acinar differentiation Am J Pathol 2013;182:363
17 A tissue microarchitecture-specific prognostic signature of prostate cancer 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
18 PDCD4, KLF6 and ABCG1 as differentiationspecific prognostic markers in prostate cancer Am J Pathol 2013;182:363
19 ProsGUIDE: a 3-gene prognostic signature in prostate cancer Am J Pathol 2013;182:363
20 Prediction accuracy of the ProsGUIDE Accuracy 95% CI P value for C-index P value vs. clinical The Brigham and Women s Hospital cohort Clinico-pathologic criteria* 61.7% % 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
21 Survival prediction by the ProsGUIDE assay Recurrence score by ProsGUIDE Predicted recurrence-free survival (years) Observed recurrence-free survival (years) Predicted 3-year recurrence rate Observed recurrence before 3 years Patient 1 Patient 2 Patient 3 Patient > 4.61 > % 80.2% 6.8% 3.3% Yes Yes No 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
22 Summary of the ProsGUIDE assay A 3-gene prognostic signature for prostate cancer Differentiation-specific; highly accurate Applicable to patients with localized or metastatic prostate cancer due to shared tumor biology. Fresh frozen or FFPE samples Multiplex qpcr, RNA-seq, NanoString or IHC Output: 1. Standardized Risk Score 2. Recurrence-free survival 3. Yearly recurrence rate
23 Clinical utility of PanGUIDE and ProsGUIDE 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. Guides clinical decision-making and patient-tailored tailored treatment plans. Potentially improves the treatment outcome and/or the successful rate of clinical trials.
24 Acknowledgement 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) Funding sources: National Health Research Institutes Department of Health, Taiwan Ministry of Science and Technology Contact: Dr. Kelvin K. Tsai (tsaik@nhri.org.tw)
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