THE VALUE OF FUTURE RESEARCH FOR P53 AS A PREDICTIVE BIOMARKER IN EARLY- STAGE SQUAMOUS CELL LUNG CANCER

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1 THE VALUE OF FUTURE RESEARCH FOR P53 AS A PREDICTIVE BIOMARKER IN EARLY- STAGE SQUAMOUS CELL LUNG CANCER Joshua A. Roth, PhD, MHA Hutchinson Institute for Cancer Outcomes Research Fred Hutchinson Cancer Research Center Seattle, WA SMDM European Meeting Antwerp, Belgium June 10, 2014 Disclosures: None

2 Background: Lung Cancer Epidemiology Lung cancer is the leading cause of cancer death worldwide Squamous cell carcinomas account for about 30% of all lung cancers 1 OCen slow growingà Detected at earlier stages Survival prognosis is much befer for pagents diagnosed at an early- stage (I/II), but poor vs. other cancers 1 Stage I/II 5- year survival=40% Stage III 5- year survival=26% Stage IV 5- year survival=4% For early- stage squamous cell carcinoma pagents, treatment typically begins with surgery to resect the tumor 2 Adjuvant chemotherapy to reduce recurrence risk? 1 SEER, Cancer Statistics Review, 2013; 2 NCCN Guidelines,

3 Background: Adjuvant Chemotherapy HR: 1.01 (0.78, 1.30) HR: 0.74 (0.60, 0.91) HR: 0.66 (0.53, 0.83) Douillard et al., Journal of Thoracic Oncology,

4 Background: P53 Expression TesGng TP53 is a tumor suppressor gene that encodes P53 Caretaker : Block cell cycle progression and/or induce apoptosis in response to cellular stresses such as DNA damage P53 protein expression levels may be able to idengfy pagent sub- groups that benefit from standard plagnum- based regimens 1,2 P53 posigve pagents may derive survival benefit 1,2 P53 negagve pagents may NOT derive survival benefit 1,2 1 Pierceall et al., Ann Onc, 2012; 2 Tsao et al., JCO,

5 Background: P53- Chemotherapy InteracGon No Chemotherapy Chemotherapy P53 PosiGve P53 NegaGve Tsao et al., JCO,

6 Study RaGonale P53 tesgng has potengal clinical uglity to guide adjuvant chemotherapy decisions, but there is substangal uncertainty in current evidence Small sample sizes Wide confidence intervals Should addigonal studies be conducted to reduce uncertainty? Value of InformaGon analysis can be used to systemagcally and quangtagvely inform this decision 6

7 Study ObjecGves 1.) Design a decision model to evaluate a P53 tesgng strategy vs. standard care in early- stage squamous cell lung cancer 2.) Use the model to idengfy high value parameters that can evaluated in a future trial 7

8 Methods: Model Structure Distant Recurrence P53 Expression Testing Strategy P53 Positive: Indicated For Adjuvant Chemotherapy Receive Adjuvant Chemotherapy Adverse Event No Distant Recurrence Patients with Completely Resected Stage I-II Squamous Cell Lung Cancer P53 Negative: Not Indicated For Adjuvant Chemotherapy No Adverse Event Distant Recurrence No Distant Recurrence Standard Care Strategy Not Indicated for Adjuvant Chemotherapy Do Not Receive Adjuvant Chemotherapy Distant Recurrence No Distant Recurrence Note: All patients are followed until death 8

9 Methods: Selected Model Parameters Point Estimate Low Value High Value Distribution Reference P53 Testing Strategy Inputs Proportion P53 Positive 50.0% 45.0% 55.0% Beta Pierceall, 2012 P53 Positive Patients Mean Overall Survival (Years), No Chemo N/A Pierceall, 2012 Overall Survival Hazard Ratio, Chemo vs. No Chemo Log-Normal Tsao, 2007 Time from Recurrence to Death (Years) Normal Pirker, 2009 Proportion Choosing to Receive Chemo 50.0% 5.0% 95.0% Beta Assumption P53 Negative Patients Mean Overall Survival (Years), No Chemo N/A Pierceall, 2012 Overall Survival Hazard Ratio, Chemo vs. No Chemo Log-Normal Tsao, 2007 Time from Recurrence to Death (Years) Normal Pirker, 2009 Proportion Choosing to Receive Chemo 10.0% 5.0% 15.0% Beta Assumption Standard Care Strategy Inputs* Mean Overall Survival (Years), No Chemo N/A Calculated Mean Overall Survival (Years), Chemo N/A Calculated Time from Recurrence to Death (Years) N/A Calculated Proportion Choosing to Receive Chemo 10.0% 5.0% 15.0% Beta Gray, 2010 Health State Utility Value Inputs No Evidence of Disease Beta Manser, 2006 On Chemotherapy Beta Manser, 2006 Chemotherapy Grade 3/4 Adverse Event Beta Nafees, 2008 Distant Recurrence Beta Nafees, 2008 Direct Medical Expenditure Inputs P53 Expression Testing $300 $210 $390 Normal CPT Cisplatin+Vinorelbine (Per Cycle) $960 $670 $1,250 Normal CMS, 2014 Cost After Distant Recurrence (Per Month) $8,650 $6,060 $11,250 Normal Mariotto, 2011 *Standard care survival outcomes were calculated using weighted averages from the P53 strategy sub-groups 9

10 Methods: Value of InformaGon (VOI) Analysis VOI can be calculated using net monetary benefit results from probabilisgc sensigvity analysis Net Monetary Benefit = (WTP*QALYs)- Cost A measure of the value to society associated with reducing uncertainty and expected opportunity loss Expected Value of Perfect InformaGon (EVPI) Expected Value of ParGal Perfect InformaGon (EVPPI) EVPPI can be used idengfy the parameters with the greatest value for future research Claxton et al., Health Economics, 1996; Ades et al., MDM, 2004; Strong et al., MDM,

11 Results: Uncertainty in Net Monetary Benefit Favors Standard Care Favors P53 Testing 11

12 Results: Expected Value of Perfect InformaGon Expected Value of Perfect Information (EVPI) at $100,000 per QALY for U.S. Population Per-Patient Expected Value of Perfect Information % of Simulations With Non-Optimal Decision: 23% Average Consequence of Non-Optimal Decision: $9,886 Per Patient EVPI: $2,274 U.S. Annual Affected Population % Running Count Reference Incidence of Lung Cancer in the U.S.: 224,000 ACS, 2014 Non-Small Cell Lung Cancer: 80.0% 179,200 ACS, 2014 Squamous Cell Carcinoma: 30.0% 53,760 SEER, 2013 Early-Stage 60.0% 32,256 NLST, 2013 Expected Rate of Technology Diffusion Year 1 Cumulative Diffusion: 2.5% Year 5 Cumulative Diffusion: 54.0% Year 10 Cumulative Diffusion: 100.0% 10-Year Population Expected Value of Perfect Information Expected Value of Perfect Information Millions'($USD)' $60 $50 $40 $30 $20 $10 $0 Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year 9 Year 10 Useful Lifetime of P53 Expression Testing Population EVPI: $358,405,000 ACS=American Cancer Society, SEER=Surveillance Epidemiology and End Results, NLST=National Lung Screening Trial 12

13 Results: Expected Value of ParGal Perfect InformaGon Expected Value of Perfect Information Millions ($USD) $400 $350 $300 $250 $200 $150 $100 $50 $0 $358 EVPI (All Parameters) $242 P53 Positive Hazard Ratio $33 P53 Positive Long- Term Survival $19 P53 Positive Chemo Decisions $1 P53 Negative Hazard Ratio Note: Values above reflect gradual technology diffusion and 3% annual discount rate 13

14 AssumpGons & LimitaGons The affected populagon is highly uncertain: Stage shic related to CT lung cancer screening? There is lifle evidence about chemotherapy decisions in P53 strategies or overall The useful lifegme is not clear There may be feasibility issues that are not captured in the model and could prohibit future research 14

15 Conclusions Future studies evaluagng P53 tesgng in early- stage squamous cell lung cancer are expected to be of high value Moderate uncertainty about opgmal strategy Large opportunity loss when wrong strategy is chosen Moderate affected populagon Study designs evaluagng the short- term (5- year) predicgve uglity of P53 tesgng are expected to be of highest value Next steps: Calculate expected value of sample informagon to assess the comparagve value of alternagve trial designs Compare the value of informagon for P53 tesgng to that of alternagve biomarkers in early- stage lung cancer (e.g. ERCC1, BRCA1, MSH) 15

16 QuesGons & Discussion Josh Roth Hutchinson InsGtute for Cancer Outcomes Research Fred Hutchinson Cancer Research Center SeaFle, WA 16