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Cancer Precision Medicine: A Primer Rebecca C. Arend, MD Division of Gyn Oncology OUTLINE • Background • Where we have been • Where we are • Where we are going • • • • Targeted Therapy in Ovarian Cancer How to Individualized Targeted Therapy Personalized Medicine Initiative at UAB The Future of “Personalized or Precision Medicine” • We have a long way to go Ovarian Cancer Statistics • 21,290 new cases, 14,180 deaths • State of Alabama: 350 cases, 260 deaths • 5th in cancer deaths among women • More deaths than any other cancer of the female reproductive system • ~75% diagnosed at late stage (stage III/IV) • Most treated with surgical cytoreduction and adjuvant platinum- taxane based chemotherapy • Most patients recur within 2 years and receive multiple rounds of chemotherapy Ovarian Cancer - Progress in Outcome 70 IP THERAPY 46 34 22 Survival (mo) 58 CISPLATIN AGGRESSIVE SURGERY, COMBINATION CHEMOTHERAPY PACLITAXEL 10 -2 1975 1980 1985 1990 YEAR 1995 PACLITAXEL CARBO 2000 2005 Modified from David Spriggs FDA approved drugs for Ovarian Cancer 1978 2016 1978 1990 1991 1992 1996 1999 2005 2006 2014 2014 2016 2016 Cisplatin Altretamine Carboplatin Paclitaxel Topotecan Liposomal Doxorubicin (Accelerated) Liposomal Doxorubicin (Full) Gemcitabine Bevacizumab (platinum resistant) Olaparib (BRCA mutation carriers) Bevacizumab (platinum sensitive) Recaparib (somatic+germline BRCA) New Chemotherapy Approaches • Intraperitoneal chemotherapy • Neo-adjuvant chemotherapy • Dose dense taxanes • Anti-angiogenic therapy • PARP inhibitors Anti-angiogenic therapy: Targeted Therapy VEGF Inhibition Biologically-Targeted Drugs (Ovarian Cancer) 45 FDA Approval: November 2014 Bevacizumab 40 PFS ≥ 6 mos (%) 35 30 Sorafenib 25 20 Dasatinib Imatinib 15 10 5 A6 Temsirolimus Gefitinib Mifepristone Enzastaurin Lapatinib Vorinostat 0 0 5 10 15 Response Rate (%) 20 25 Bevacizumab • FDA approved for use in combination with chemotherapy in the treatment of women with platinum-resistant, recurrent ovarian cancer • Studied as primary therapy and consolidation with paclitaxel and carboplatin showed modest improvement in PFS and OS • Not FDA approved PARP inhibitors: Individualized Targeted Therapy Poly ADP ribose polymerase inhibitor that blocks enzymes in repairing damaged DNA BRCA and PARP Olaparib approved for women with advanced ovarian cancer with defective germline BRCA genes Proportion Of Hereditary Ovarian Cancer Ovarian Cancer 15% Sporadic Hereditary Recaparib approved for women with advanced ovarian cancer with defective germline and/or somatic BRCA genes Specific Pathways How do we individualize treatment? Can we use Genetic Sequencing? Personalized Medicine Initiative Division of Gyn Oncology at UAB Introduction • Background: Molecular profiling can play an important role in making treatment decisions and will be a critical component in optimizing personalized medicine cancer care. Utilizing personalized medicine in high-volume clinical environments requires interdisciplinary expertise. The lack of an organized infrastructure in performing next generation sequencing (NGS), integrating these results into the electronic medical record (EMR), and guiding clinicians on how to interpret these tests for clinical decision making remains a barrier in the implementation of personalized medicine. • Objectives: 1. To build an infrastructure of molecular profiling through NGS in patients with recurrent ovarian cancer that could impact clinical care in a Personalized Medicine Initiative (PMI). 2. To analyze the results of NGS on tumor and plasma cell free DNA (cfDNA) in patients with recurrent ovarian cancer. 3. To generate evidence of the feasibility of providing genotype-guided therapy to patients with recurrent ovarian cancer. Methods – Under IRB approval, patients with recurrent ovarian cancer were consented from September 2015 to November 2016. – NGS was performed on archival tumor and on cfDNA at the time of enrollment. – Before March 2016, NGS was performed on tumor using a 65 gene panel from Genomic Pathology Service (GPS) at Washington University. – After March 2016, all NGS was performed using a 315 gene FoundationOne (FO) panel at Foundation Medicine. – NGS was performed on cfDNA utilizing a 50 gene panel performed at Circulogene Theranostics. – A Personalized Medicine Letter (PML) summarizing the results of the tumor NGS and recommendations was placed in the patients’ EMR. – Treatment for patients enrolled in the PMI ovarian project was determined by the patients’ physician. – NGS results detailing genomic alterations were stored in the Ovarian Personalized Medicine Initiative (OVPMI) database. Methods Patient Demographics Genomic Alterations Found in Tumor DNA TP53 is the most common genomic alteration found in >60% of recurrent ovarian cancer patients; whereas the majority of altered genes are seen in <1%. Results compared to Public databases TP53 BRCA1/2 (somatic) KRAS TCGA (n=270, n=412) MYC Ross (n=48) PIK3CA Rodriguez-Rodriguez (n= 36) NF1 Arend (n = 109) CDKN2A 0 10 20 30 40 50 % of Pt. 60 70 80 90 100 Circulating DNA in the bloodstream Primary tumor bloodstream circulating normal DNA circulating tumor DNA Metastatic tumor Summary of results from each genetic test • A total of 116 patients were enrolled • 62 sent to GPS, 55 patients with results • 54 sent to FO • 6 plasma samples sent to Circulogene • 49 patients with NGS results from both cfDNA and tumor (13 by GPS; 36 by FO) 13/49 (26.5%) patients had a TP53 genomic alteration in both the tumor and cfDNA. Of the 13, none had the same variant in their TP53 mutation. Overall, 36/49 (73.5%) showed no concordant genomic alterations. Summary of genomic alteration based on histology CDKN2A NF1 PIK3CA Papillary Serous Endometrioid Mixed Clear Cell Mucinous Other Undifferntiated Unknown MYC KRAS BRCA1/2 TP53 0 10 20 30 40 50 Total # of Pt. 60 70 80 90 Summary of pts that received targeted therapy FDA-approved targeted therapy potential benefits in the FO cohort (n=54) Summary of BRCA1/2 genetic alterations Summary of Treatments • 6/56 (10.7%) have received targeted therapy. • As of November 21, 2016, 2 patients were on targeted therapy based on their NGS results. • 1 patient on Olaparib (parp inhibitor) based on a somatic BRCA mutation detected (no germline mutation). • 1 patient on Pazopanib (tyrosine kinase inhibitor) for a FGFR mutation. • Targeted therapy was started and discontinued on 4 patients. • 2 patients received Trametinib (MEK inhibitor) for a KRAS or BRAF mutation; discontinued due to rash. • 1 patient received Olaparib for a PTEN mutation; discontinued due to progression of disease. • 1 patient received Olaparib based on somatic BRCA mutation (no germline mutation); discontinued due to progression of disease. 56/109 (54.1%) patients had actionable mutations with potential clinical benefit from FDA-approved targeted therapy based on NGS results Costs Associated with Targeted Therapy • Hospital initiative covered the cost for GPS testing (62 patients). • 35 patients on Medicare and Medicaid had 100% of the cost covered for FO NGS. • 19 patients with private insurance plus FO financial assistance program had 100% of cost covered. • One patient was uninsured and 100% of cost covered by FO. • Circulogene covered the cost for NGS on cfDNA. • Costs for targeted therapy were covered by insurance or the supplier. Conclusions • Ovarian cancer has a diverse genetic landscape and molecular profiling via NGS offers the opportunity to identify genetic alterations that can be utilized to direct therapy. • 51.4% of patients with recurrent ovarian cancer had a mutation that could be targeted with a commercially available drug. • Our study highlights the infrastructure and feasibility of implementing NGS into a clinical workflow to expand the potential treatment choices available to patients. • We have been successful in providing patients with NGSdirected therapy. Limitations Collection of archival tumor DNA may not represent the current genomic mutations. Given the heterogeneous nature of ovarian tumors, one sample of tumor (current or archival) may not show all mutations present in the cancer cfDNA represents mainly the genome of dying tumor cells Tumor and cfDNA were collected at different time points in the course of a patient’s disease. NGS companies use different gene panels, DNA extraction methods, bioinformatics platforms, and variant callers. There can be diversity in clinical interpretations of actionable mutations. Not all patients who receive NGS-directed therapy will respond. Retrospective Study – Tumor Bank Informed Consent (N=37) Surgery canceled (N=1) Diagnostic Laparoscopy (N=36) Primary Debulking (N=3) cfDNA Collected at the time of recurrence (N=4) cfDNA and Tumor DNA (N=14) HGPS with Pre and Post Treatment Specimens Collected (N=20) Exclusions: Liver Cirrhosis (N=1), Not High Grade pap-serous histology (N=6), No interval debulking performed (N=5), Complete pathologic response at interval debulking (N=1) Quality RNA extracted for Nanostring (N=19) Study Design Pre and Post Comparison Overall Molecules Exp. Value NR4A3 17.864 NR4A1 16.294 FOS 4.993 OSM 4.327 KLF4 4.309 DUSP5 4.241 SFRP2 4.064 NFATC1 3.598 WNT16 3.583 RASGRF2 3.215 Molecules Exp. Value RAD51 -2.653 HIST1H3B -2.471 FANCA -2.395 HIST1H3G -2.391 CCNA2 -2.143 HELLS -2.137 CCNB1 -2.111 E2F1 -2.094 TTK -2.034 HIST1H3H -2.009 Hereditary Ovarian Cancer Signaling Hereditary Ovarian Cancer Signaling Plasma vs. Tumor Variant Overlap Pre Neoadjuvant DNA Analysis Post Neoadjuvant DNA Analysis Top Four Mutations Pre-NACT Post-NACT TP53 Tumor Only Tumor and Plasma N=0 N=10 N=4 N=0 N= 9 N=5 PIK3CA N=3 N=3 N=0 N=0 N=3 N=2 N=2 N=2 N=0 KDR N=0 N=2 N=4 KIT N=3 N=2 N=3 N=3 N=3 N=1 Plasma Only Frequency of alterations, overall concordance, and potential targeted therapy TP53 Freq in Tissue 100 Freq in Plasma 68 Overall Concordance 36 KIT 35 39 64 PTEN 7 18 75 PIK3CA CTNNB1 21 0 36 14 79 86 BRAF 0 11 89 EGFR APC ATM FGFR2 GNAQ 0 0 0 0 0 11 7 7 7 7 KDR 7 RET Gene FDA Approved Therapy Clinical Trials WEE1 inhibitors 89 93 93 93 93 NA Tyrosine kinase inhibitors-imatinib, nilotinib, sorafenib, dasatinib, sunitinib MTOR inhibitors- everolimus, temsirolimus; Parp inhibitorolaparib MTOR inhibitors- everolimus, temsirolimus MTOR inhibitors- everolimus, temsirolimus BRAF inhibitors-vemurafenib, dabrafenib; MEK inhibitorstametinib, cobimentinib lapatinib, erlotinib, gefitinib NA Parp inhibitor- olaparib Multikinase inhibitors -pazopanib, ponatinib MEK inhibitors-tametinib, cobimentinib 7 93 Cabozantinib, axitinib, sorafenib, regorafenib, sunitinib Anti angiogenics 0 7 93 Multikinase inhibitors – vandetanib, ponatinib, sunitinib RET inhibitors KRAS 7 0 93 MEK inhibitors – trametinib, cobimentinib MEK inhibitors ABL1 0 4 96 ABL inhibitor-imatinib, nilotinib, dasatinib, bosutinib, ponatinib Tyrosine kinase inhibitors ERBB2 0 4 96 ERBB4 FBXW7 GNAS IDH1 JAK2 MET 0 0 0 0 0 0 4 4 4 4 4 4 96 96 96 96 96 96 NRAS 0 4 96 PTPN11 0 4 SMAD4 0 SMARCB1 VHL Tyrosine kinase inhibitors MTOR/Parp inhibitors MTOR inhibitors WNT inhibitors MEK inhibitors EGFR inhibitors WNT inhibitors ATM inhibitors Multikinase inhibitors PKC inhibitors 96 lapatinib, afatnib, neratinib, dacomitinib, ado-trastuzumab, emtansine, pertuzumab Tyrosine kinase inhibitors – gefitinib, lapatinib MTOR inhibitors - everolimus , temsirolimus MEK inhibitors – trametinib, cobimentinib NA NA MET inhibitors- crizotinib, cabozantinib BRAF inhibitors-vemurafenib, dabrafenib; MEK inhibitorstametinib, cobimentinib NA PTPN11/SHP2 inhibitors 4 96 NA TGFβ inhibitors, anti angiogenics 0 4 96 NA EZH2 inhibitors 0 4 96 Tyrosine kinase inhibitors – sunitinib, sorafenib, MTOR inhibitors – everolimus, temsirolimus VEGF inhibitors HER2 inhibitors Pan-Her inhibitors MTOR inhibitors ERK inhibitors IDH1 inhibitors JAK2 inhibitors MET inhibitors MAP/ERK inhibitors Conclusions • Mutations are more frequently detected in the plasma compared to the tumor. • Tumor heterogeneity is better captured by circulating cfDNA than from a small biopsy of tumor. • More mutation changes are picked up in the plasma following therapy. • Caution must be taken in interpreting the results for clinical purposes because much is still unknown about the dynamic biology of circulating cfDNA. • Since cfDNA represents mainly the genome of dying tumor cells, it may not be appropriate to use these mutations to guide targeted therapy. • Further research is required to identify the utility of cfDNA as a dynamic “liquid” tumor biopsy tool Acknowledgments Arend Lab • Angelina Londoño, Ph.D. • Ashwini Katre, M.S. • Naveed Farrukh B.S. • Mary Kat Smith B.S. • Taylor Turner, M.D. • Haller Smith, M.D. • Allison Montgomery B.S. • Cindy Tawfik B.S. • Zach Dobbin, M.D. Don Buchsbaum, PhD Andres Forero, M.D. Ronald D. Alvarez, M.D. Eddy S. Yang, M.D. Shuko Harada, M.D. Charles A. Leath III, M.D. Warner K. Huh, M.D. Lyse Norian, PhD Circulogene • Chen Yeh, M.D. • Andrew Ford, Personalized Medicine Grant through UAB’s Personalized Medicine Institution, Circulogene Theranostics, UAB Cancer Center, T32 5T32CA183926-02 Research Training Program in Basic and Translational Oncology, ABOG Early Career Grant, Norma Livingston Foundation, and Patients that enrolled in our study.