Response, PFS or OS – what is the best endpoint in advanced colorectal cancer? Marc Buyse IDDI, Louvain-la-Neuve & Hasselt University

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Presentation transcript:

Response, PFS or OS – what is the best endpoint in advanced colorectal cancer? Marc Buyse IDDI, Louvain-la-Neuve & Hasselt University

Overall survival (OS) Progression-free (PFS) Tumor response (ORR) Biomarkers (including tumor measurements) POSSIBLE ENDPOINTS

REQUIREMENTS FOR IDEAL ENDPOINT IN TRIALS Ideal endpoint in clinical trialsshould capture all clinically relevant events be easy to measure have little opportunity for ascertainment bias be observed as early as possible be observed in as many patients as possible be statistically sensitive to real treatment benefits

CONSIDERATIONS FOR ENDPOINTS Ease of measurement Potential for bias Statistical sensitivity Clinical relevance OS  PFS  ORR  Tumor measurements / biomarkers  

Larger number of events at same follow-up time PFS less affected by competing risks (especially in elderly populations) PFS unaffected by effective rescue therapies and successive treatment lines Attenuation of treatment effect on OS vs. PFS REASONS FOR BETTER SENSITIVITY OF PFS AS COMPARED WITH OS

PFS Assumptions: Median PFS = 12 months in control group Median PFS = 16 months in experimental group HR =.75 (25% risk reduction) Median gain 4 months

OS Assumptions: Median OS = 24 months in control group Median OS = 28 months in experimental group HR =.86 (14% risk reduction) Median gain 4 months

SAMPLE SIZES To have 80% power of detecting HR =.75, 380 events are required

To have 80% power of detecting HR =.75, 380 events are required To have 80% power of detecting HR =.86, 1,380 deaths are required SAMPLE SIZES

OS is confounded by treatments received on progression –Reintroduction of same treatment (e.g. oxaliplatin) –Cross-overs in randomized trials –Other approved second-line treatments –Experimental agents Paradoxically, the better a new treatment, the less likely an OS benefit SECOND-LINE TREATMENTS Ref: de Gramont et al, JCO 2007; 25: 3224.

OXALIPLATIN REINTRODUCTION Ref: de Gramont et al, JCO 2007; 25: 3224.

POST-PROGRESSION SURVIVAL (PPS) Ref: Broglio and Berry, JNCI 2009;101:1642.

POWER FOR OS AS A FUNCTION OF SPP Ref: Broglio and Berry, JNCI 2009;101:1642.

POWER FOR OS AS A FUNCTION OF SPP Ref: Broglio and Berry, JNCI 2009;101:1642.

POWER FOR OS AS A FUNCTION OF SPP Ref: Broglio and Berry, JNCI 2009;101:1642.

21 patients with elevated PSA after prostatectomy and histological documentation of MUC1 antigen expression Weekly schedule Phase II trial of Interleukin-2 + a viral suspension of a recombinant vaccinia vector containing the sequence coding for the human MUC1 antigen Three-weekly schedule THE EXQUISITE SENSITIVITY OF BIOMARKERS

Protocol-defined “clinical” outcomes PSA response rate* Duration of PSA response Time to PSA progression Biomarker PSA measurements over time * PSA decreased to < 4 ng/ml or to < 50% of baseline level for at least 4 weeks « CLINICAL » OUTCOMES VS. BIOMARKER

PSA MEASUREMENTS OVER TIME

Model contains the following terms: Randomized treatment (Weekly or Three-weekly) Time Period (pre- vs. post-treatment) Interactions MODELLING OF PSA MEASUREMENTS

Treatment had an overall effect MODELLING OF PSA MEASUREMENTS

Weekly schedule had a more pronounced effect on PSA levels MODELLING OF PSA MEASUREMENTS

There were no pre-treatment differences in PSA levels between the two schedules (as expected) MODELLING OF PSA MEASUREMENTS

The weekly schedule had a significantly larger effect on PSA levels as compared with the three-weekly schedule MODELLING OF PSA MEASUREMENTS

Phase II trials should be randomized and use biomarkers rather than ORR, PFS or OS Interim analyses of phase III trials could use biomarkers But: Validation trials are required to show that biomarkers are predictive of clinical efficacy MODELLING OF PSA MEASUREMENTS

Differences in OS unlikely with active further Rx lines PFS arguably neither meaningful nor reliable Therefore: Search for biomarkers (including tumor measurements) Perform quantitative analyses of statistical surrogacy Revisit assumption of proportional hazards Why use a single endpoint ?! CONCLUSION : OS IS NO LONGER A USEFUL OR APPROPRIATE PRIMARY ENDPOINT FOR TRIALS