7th November, Translational Science: how to move from biology to clinical applications

Transcription

1 7th November, 2014 Translational Science: how to move from biology to clinical applications 1

2 Translational science: How to move from biology to clinical applications Translational cancer genomics and proteomics Stefan Sleijfer Dept Medical Oncology, Erasmus MC Cancer Institute

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4 Rotterdam: New York Times: Nr. 10 on the list places to visit in 2014

5 Rotterdam Netherlands Brazil: 3-0 (World Cup 2014) 9 players from Feyenoord

6 Erasmus University Medical Center Largest hospital in the Netherlands University Medical center: 1. Patient care: Patient care Rotterdam city center (± 500,000 people) Referral center 2. Education (± 1500 MD students; 500 Health Policy and Management) 3. Research (best European institution for clinical research (Times Higher Education))

7 Department of Medical Oncology 2 sites: Center Location Daniel den Hoed (this year: 100 years old) In 2017: all activities on one location 65 clinical beds extensive outpatient clinic 3000 new patients/year

8 Focus of care and research: personalized cancer treatment Current treatment approach: still a lot to improve: All patients with a certain tumor type and stage equally treated Overlooks the great differences within tumor types and between individuals: i.e. 1st line treatment patients with metastatic breast cancer: 50-60%: benefits at the expense of side-effects 40-50%: no benefit at all, only side-effects

9 Personalized cancer medicine Giving: The right drug, to the right patient, at the right dose, from the right moment onwards, till the right moment Promises personalized treatment: Higher chance to benefit when treated with less toxicity Better cost-effectiveness To get there, more insight into: Cancer cell biology ( era of precision medicine : identifying tumor-drivers and drugs inhibiting these) Tumor environment Pharmacology Quality of life of patients and their relatives

10 And that is not easy Cancer cells: What are the drivers? For most tumors: no tumor-drivers known: Multiple rather than single factor determine outcome to systemic agents Tumor environment: What is the function of the cells surrounding tumors? How to overcome tumor cell induced-immune tolerance? Pharmacology: Which factors affect pharmacology and to what extent? Which physical and mental factors determine quality of life? And overall: all factors constantly change over time and under treatment pressure

11 Research vision The treatment of an individual with cancer is determined by specific characteristics of that individual patient, the cancer cells, and their environment, and needs to be constantly adjusted according to the changes observed in these characteristics. Or in other words: Playing chess with cancer : Determine what a patient wants, identify anti-tumor drugs based on characteristics tumor and its environment, treat at drug doses based on patient characteristics Monitor molecular evolution of tumor cells, immune system, drug levels, and quality of life during treatment Adjust treatment if necessary

12 Research organigram Five research lines Tumor type independent Close collaboration between basic scientists and clinicians! Department Chair Translational Cancer Genomics and Proteomics Translational Pharmacology Translational Onco-Immunology Clinical Trials Palliative and Supportive Care Breast cancer Genomics and Proteomics Personalized Medicine Tumor Immunology Early Clinical Trials Palliative and Supportive Care Integrated Genomics of Treatment Resistance Experimental Systemic Therapy of Urogenital Hereditary Cancer Rest

13 Examples of current large research programs Examples from research line Translational Genomics and Proteomics Research on tumor-cell related factors important for treatment decision making Vision: concept of playing chess with cancer : Start treatment based on metastatic tumor cell characteristics Monitor evolution of tumor cells during treatment At progression, adjust treatment based on emerging characteristics Many requirements: Better insight into mechanisms underlying drug sensitivity/resistance Techniques to assess and analyze tumor cell characteristics in detail Methods to obtain metastatic tumor cells repeatedly during treatment

14 Center for Personalized Cancer Treatment: We re taking it personally

15 One Week Three Weeks Three step approach Biopsy of metastasic lesion: Resembles other metastases better than primary tumor Characterization by DNA sequencing (and other techniques): Fast track : 49 genes Discovery track : 1,952 genes involved in cancer Center for Personalized Cancer Treatment Patient with Metastatic Disease 2-4 Biopsies Pathological Analysis DNA Isolation ng ng Patient Stratification Research IonTorrent PGM SOLiD 5500xl Treatment Actionable Mutations > genes + Biomarker Discovery Profiling Cancer Pathways and Processes Start Targeted Therapy Allocation Fase1 Clinical Trial Systems Biology Targeted Resequencing ±2000 genes Response monitoring Resistance / Recurrence / Progression Cure Databanking Bioinformatic analysis Mutations, INDELs, Copy Number Variations in vitro / in vivo Modeling of Hypotheses

16 CPCT clinical trials Treatments after taking biopsy: 1. Prospective trials on basis of DNA profile with experimental regimens i.e.: For mutated k-ras; MDM2 amplifications; wtp53, mutated BRAF 2. Identification of profiles associated with outcome in context standard treatments Drug Tamoxifen Letrozole/Anastrozole/Exemestane Everolimus +exemestane Cetuximab/Panitumumab Erlotinib/Gefitinib Vemurafenib Sunitinib Pazopanib Sorafenib Imatinib Everolimus Ipilimumab Abiraterone Disease Breast cancer Breast cancer Breast cancer Colorectal cancer NSCLC Melanoma RCC/pNET/GIST RCC/ sarcoma HCC/RCC GIST RCC/pNET Melanoma Prostate cancer

17 DNA yield (ng) Biopsies and DNA yield Safe procedure From almost 1000 patients biopsies In the vast majority sufficient material Extended + limited sequencing ng 20 Limited sequencing only 0 0 No sequencing Biopsy samples with tumor cellularity > 20%

18 CPCT in action

19 Alternatives needed for biopsies from solid metastases: liquid biopsies Biopsy from solid metastases: Pro: Enough material for multiple analyses Interaction with micro-environment Contra: Sampling error Not always possible; inaccessibility of lesions Cumbersome procedure for patients Repetitive sampling even more cumbersome Circulating tumor cells (CTCs) alternative?: Already described in 1869 ( carcinocythemia ):

20 %Probability of Survival %Probability of Survival Last decade Introduction of numerous CTC detection assays Many data, in particular with the CellSearch technique: Association with outcome to systemic therapy in metastatic breast, colorectal, prostate cancer, NSCLC, and many others: CTC at base-line CTC changes during therapy 100% 100% 90% 80% 70% 60% >18 Months 90% 80% 70% 60% Logrank p < >18 Months 50% 40% 30% 20% 10% 0% Logrank < Cox Hazards Ratio = chi-square = (p-value < ) ~10.1 Months # of Patients (Median Survival Time) < 5 CTCs at Baseline 90 (> 80 Weeks) > 5 CTCs at Baseline 87 (43.3 Weeks) 50% 40% 30% 20% 10% 0% ~8.2 Months Cox Hazards Ratio = chi-square = (p-value < ) # of Patients (Median Survival Time) < 5 CTC at Baseline & at 1st Follow-Up 81 (>80 Weeks) Decrease in CTC to < 5 at 1st Follow-Up 33 (62.6 Weeks) > 5 CTC at 1st Follow-Up 49 (35.4 Weeks) ~14.6 Months Time from Baseline (Weeks) [~4.3 Weeks / Month] Time from Baseline (Weeks) [~4.3 Weeks / Month] Christofanilli,NEJM 2004

21 Not the numbers but the characteristics that count

22 CTC characterisation/ Liquid biopsy Currently available tools for CTC characterization: Immunolabeling (eg HER2, EGF-R, IGF-R possible) FISH (eg HER2, androgen receptor) PCR (epithelial specific mrna and mirnas) Very attractive to guide cancer treatment enabling: Patient-friendly collection of metastatic tumor cells Allows repetitive sampling Possibly: better than biopsy from 1 metastasis: CTCs originate from several lesions: composite picture of the most important clones? CTC characterization more challenging than characterization of biopsies from metastatic lesions Shaffer et al, CCR 2007

23 CTCs Potential clinical value of CTC characterization by PCR Discordant ER and HER2 expression primary tumor vs CTC in MBC Clinical studies ongoing to explore clinical relevance of this: Do pts with ER+ primary tumors but ER- CTC treated with hormonal treatment worse? ER Her-2 primary tumor primary tumor Sieuwerts et al, CCR 2011

24 Promise of CTCs in prostate cancer Castration-refractory prostate cancer (CRPC) failing to docetaxel, several options: Enzalutamide (direct AR-blocker) Abiraterone (inhibitor of androgen synthesis) Cabazitaxel High need for predictive markers to guide treatment decision making Antonarakis et al: NEJM: ARv7: active splice variant lacking androgenes/enza-binding domain Presence ARV7 in CTCs predicts lack of reponse to enza/abi: Antonarakis, NEJM 2014

25 ARV7 in CTCs from CRPC patients However: CTC assay used not FDA-approved and not widely available (Adna-test) Association ARV7 and outcome to cabazitaxel is not known Methods: Set up assay to determine AR wt and splice variants in samples enriched for CTCs by CellSearch CRPC patients failing to docetaxel and about to start cabazitaxel (with high baseline CTC number (10 or more)) CTC enumeration, isolation and determination of ARV7 expression at base-line Presence of ARV7 associated with outcome?: CTCs response (< 5 CTCs after two cycles) PSA-response at 12 weeks Overall Survival

26 ARV7 in CTCs from CRPC patients Spiking experiments with prostate cancer cell lines with known AR variants in blood of healthy donors: Variants can be detected after 1 tumor cells is spiked into 7.5 ml HDB after CTCenrichment Patient samples (41 patients): ARwt present in all patients ARV7 present: 16/41 pts: 8/41 pts pre-treated with abiraterone: 5/8 ARV7 positive

27 ARV7 in CTCs from CRPC patients No difference in outcome to cabazitaxel between pts with or without ARV7 expression in CTCs: CTC response: ARV7 absent: 23% response ARV7present: 21% response PSA response OS In conclusion: AR splice variants determination in CTCs enriched by Cellsearch is feasible More ARV7+ CTCs in pts heavily pretreated with AR-targeting drugs In contrast to enza/abi, no impact of ARV7 on outcome to cabazitaxel If confirmed, important tool for treatment decision making

28 CTC characteristics to guide cancer treatment CareMore project (EU-FP7-sponsored): Study of tras/docetaxel in MBC with HER2 negative primary tumors but HER2 positive CTCs Development of methods enabling to assess Impact of phospho-her2 and PIK3CA mutations in CTCs on outcome CTCs: Green: ER Red: pher2

29 In conclusion Many determinants of outcome in cancer patients, which constantly change thereby impacting treatment decision making, including molecular characteristics of tumor cells Need for tools to monitor changes in tumor cell characteristics: liquid biopsies Circulating tumor cells attractive to implement in treatment decision making: Requires well-designed studies

30 Most importantly to move from biology to clinical applications Close collaboration between basic scientist and clinicians, having: Real interest in each other activities Knowledge on what is possible

31 The Rotterdam Translational Cancer and Genomics group & acknowledgments Nick Beije Els Berns Joan Bolt John Foekens Jan Willem Gratama Jozien Helleman Jaco Kraan John Martens Bianca Mostert Esther Reijm Anieta Sieuwerts Stefan Sleijfer Anne van Galen Marcel Smid Mai Van Vanja de Weerd Michael den Bakker (PA) Cees Verhoef (Surgery) Dirk Grunhagen (Surgery)