Response Rate as a Regulatory End Point in Single-Arm Studies of Advanced Solid Tumors | Targeted and Immune Cancer Therapy | JAMA Oncology | JAMA Network
[Skip to Navigation]
Access to paid content on this site is currently suspended due to excessive activity being detected from your IP address Please contact the publisher to request reinstatement.
Kwak  EL, Bang  Y-J, Camidge  DR,  et al.  Anaplastic lymphoma kinase inhibition in non-small-cell lung cancer.  N Engl J Med. 2010;363(18):1693-1703.PubMedGoogle ScholarCrossref
Flaherty  KT, Puzanov  I, Kim  KB,  et al.  Inhibition of mutated, activated BRAF in metastatic melanoma.  N Engl J Med. 2010;363(9):809-819.PubMedGoogle ScholarCrossref
Byrd  JC, O’Brien  S, James  DF.  Ibrutinib in relapsed chronic lymphocytic leukemia.  N Engl J Med. 2013;369(13):1278-1279.PubMedGoogle Scholar
Sherman  RE, Li  J, Shapley  S, Robb  M, Woodcock  J.  Expediting drug development—the FDA’s new “breakthrough therapy” designation.  N Engl J Med. 2013;369(20):1877-1880.PubMedGoogle ScholarCrossref
Hay  M, Thomas  DW, Craighead  JL, Economides  C, Rosenthal  J.  Clinical development success rates for investigational drugs.  Nat Biotechnol. 2014;32(1):40-51.PubMedGoogle ScholarCrossref
Lipson  D, Capelletti  M, Yelensky  R,  et al.  Identification of new ALK and RET gene fusions from colorectal and lung cancer biopsies.  Nat Med. 2012;18(3):382-384.PubMedGoogle ScholarCrossref
Beadling  C, Jacobson-Dunlop  E, Hodi  FS,  et al.  KIT gene mutations and copy number in melanoma subtypes.  Clin Cancer Res. 2008;14(21):6821-6828.PubMedGoogle ScholarCrossref
Kwok  M, Foster  T, Steinberg  M.  Expedited programs for serious conditions: an update on breakthrough therapy designation.  Clin Ther. 2015;37(9):2104-2120.PubMedGoogle ScholarCrossref
Cavallo  J. A look ahead: how the FDA is adapting in the era of precision medicine. ASCO Post. 2013;4(17).,-2013/a-look-ahead-how-the-fda-is-adapting-in-the-era-of-precision-medicine.aspx. Accessed February 3, 2016.
Kesselheim  AS, Wang  B, Franklin  JM, Darrow  JJ.  Trends in utilization of FDA expedited drug development and approval programs, 1987-2014: cohort study.  BMJ. 2015;351:h4633.PubMedGoogle ScholarCrossref
Lamont  EB, Hayreh  D, Pickett  KE,  et al.  Is patient travel distance associated with survival on phase II clinical trials in oncology?  J Natl Cancer Inst. 2003;95(18):1370-1375.PubMedGoogle ScholarCrossref
Oxnard  GR, Morris  MJ, Hodi  FS,  et al.  When progressive disease does not mean treatment failure: reconsidering the criteria for progression.  J Natl Cancer Inst. 2012;104(20):1534-1541.PubMedGoogle ScholarCrossref
Dahlberg  SE, Shapiro  GI, Clark  JW, Johnson  BE.  Evaluation of statistical designs in phase I expansion cohorts: the Dana-Farber/Harvard Cancer Center experience.  J Natl Cancer Inst. 2014;106(7):dju163.PubMedGoogle ScholarCrossref
Blumenthal  GM, Karuri  SW, Zhang  H,  et al.  Overall response rate, progression-free survival, and overall survival with targeted and standard therapies in advanced non-small-cell lung cancer: US Food and Drug Administration trial-level and patient-level analyses.  J Clin Oncol. 2015;33(9):1008-1014.PubMedGoogle ScholarCrossref
Hirsch  BR, Califf  RM, Cheng  SK,  et al.  Characteristics of oncology clinical trials: insights from a systematic analysis of  JAMA Intern Med. 2013;173(11):972-979.PubMedGoogle ScholarCrossref
Therasse  P, Arbuck  SG, Eisenhauer  EA,  et al.  New guidelines to evaluate the response to treatment in solid tumors.  J Natl Cancer Inst. 2000;92(3):205-216.PubMedGoogle ScholarCrossref
Eisenhauer  EA, Therasse  P, Bogaerts  J,  et al.  New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1).  Eur J Cancer. 2009;45(2):228-247.PubMedGoogle ScholarCrossref
Bowman  AW, Azzalini  A.  Computational aspects of nonparametric smoothing with illustrations from the sm library.  Comput Stat Data Anal. 2003;42(4):545-560.Google ScholarCrossref
Kruskal  WH.  Ordinal measures of association.  J A Stat Assoc. 1958;53(284):814-861.Google ScholarCrossref
DeLong  ER, DeLong  DM, Clarke-Pearson  DL.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.  Biometrics. 1988;44(3):837-845.PubMedGoogle ScholarCrossref
Oxnard  GR.  Strategies for overcoming acquired resistance to epidermal growth factor receptor: targeted therapies in lung cancer.  Arch Pathol Lab Med. 2012;136(10):1205-1209.PubMedGoogle ScholarCrossref
Jänne  PA, Yang  JC-H, Kim  D-W,  et al.  AZD9291 in EGFR inhibitor-resistant non-small-cell lung cancer.  N Engl J Med. 2015;372(18):1689-1699.PubMedGoogle ScholarCrossref
Cross  DAE, Ashton  SE, Ghiorghiu  S,  et al.  AZD9291, an irreversible EGFR TKI, overcomes T790M-mediated resistance to EGFR inhibitors in lung cancer.  Cancer Discov. 2014;4(9):1046-1061.PubMedGoogle ScholarCrossref
Jänne  PA, Ou  S-HI, Kim  D-W,  et al.  Dacomitinib as first-line treatment in patients with clinically or molecularly selected advanced non-small-cell lung cancer: a multicentre, open-label, phase 2 trial.  Lancet Oncol. 2014;15(13):1433-1441.PubMedGoogle ScholarCrossref
Rajakulendran  T, Adam  DN.  Spotlight on pembrolizumab in the treatment of advanced melanoma.  Drug Des Devel Ther. 2015;9:2883-2886.PubMedGoogle Scholar
Simon  R.  Optimal two-stage designs for phase II clinical trials.  Control Clin Trials. 1989;10(1):1-10.PubMedGoogle ScholarCrossref
Molecular Analysis for Therapy Choice (NCI MATCH). 2015. Accessed November 10, 2015.
Shaw  AT, Ou  S-HI, Bang  Y-J,  et al.  Crizotinib in ROS1-rearranged non-small-cell lung cancer.  N Engl J Med. 2014;371(21):1963-1971.PubMedGoogle ScholarCrossref
Original Investigation
June 2016

Response Rate as a Regulatory End Point in Single-Arm Studies of Advanced Solid Tumors

Author Affiliations
  • 1Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
  • 2Department of Biostatistics and Epidemiology, Memorial Sloan Kettering Cancer Center, New York
  • 3Department of Radiology, Columbia University College of Physicians and Surgeons and New York Presbyterian Hospital, New York
  • 4Duke Cancer Care Research Program, Duke Cancer Institute, Dallas, Texas
JAMA Oncol. 2016;2(6):772-779. doi:10.1001/jamaoncol.2015.6315

Importance  Objective response rate (ORR) is an increasingly important end point for accelerated development of highly active anticancer therapies, yet its relationship to regulatory approval is not well characterized.

Objective  To identify circumstances in which a high ORR is associated with regulatory approval, and therefore might be an appropriate end point for definitive single-arm studies of anticancer therapies.

Data Source  A database of all oncology clinical trials registered at between October 1, 2007, and September 30, 2010.

Study Selection  Trials of palliative systemic therapies for 4 measurable solid tumor types, limited to those with trial arms of at least 20 patients reporting ORR per Response Evaluation Criteria in Solid Tumors (RECIST).

Data Extraction and Synthesis  A systematic search was used to identify the reported ORR for each eligible treatment arm that had been presented publicly.

Main Outcomes and Measures  For each treatment regimen, defined as a single-agent or unique combination of agents for 1 cancer type, the mean ORR and the maximum ORR statistically exceeded were calculated, and their association with regulatory approval was studied. A regimen was considered approved for a specific cancer type if it had received regulatory approval in any country for treatment of advanced cancer of that type.

Results  From 1800 trials, 874 eligible trial arms in 578 eligible trials were identified; 542 arms had ORR data available for 294 regimens. Maximum ORR and mean ORR were significantly associated with regulatory approval (τ = 0.27, P < .001; τ = 0.12, P = .01); this relationship was stronger for single-agent therapies (τ = 0.49; τ = 0.41) than for combination regimens (τ = 0.28; τ = 0.17). Evaluation of ORR thresholds between 20% and 60% as potential trial end points demonstrated that ORR statistically exceeding 30% with a single agent had 98% specificity and 89% positive predictive value for identifying regimens achieving regulatory approval.

Conclusions and Relevance  For single-agent regimens, high ORR was associated with regulatory approval; this relationship was less strong for combination regimens. Our data suggest that high ORR (eg, statistically exceeding an ORR of 30%) is an appropriate end point for single-arm trials aiming to demonstrate breakthrough activity of a single-agent anticancer therapy.