About this trial
This phase II trial studies how well ponatinib hydrochloride works as second line therapy in treating patients with chronic myeloid leukemia in chronic phase that has not responded to initial treatment (first line) with imatinib mesylate, dasatinib, or nilotinib or cannot tolerate imatinib mesylate, dasatinib, or nilotinib. Ponatinib hydrochloride may stop or control the growth of cancer cells by blocking a protein needed for cell growth.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Diagnosis of Philadelphia chromosome (Ph)-positive (by cytogenetics or fluorescent in situ hybridization [FISH]) or breakpoint cluster region (BCR)-ABL-positive (by polymerase chain reaction [PCR]) CML in chronic phase.
Less than complete hematologic response (CHR) at or beyond 3 months
No partial cytogenetic response at or beyond 3 months
BCR-ABL1 ≥ 10% at or beyond 3 months
Disqualifiers
Prior therapy with other BCR-ABL-targeted TKIs except bosutinib, dasatinib or nilotinib. Participants with T315I mutations are eligible and will be analyzed separately.
Active NYHA cardiac class 3-4 heart disease
Any history of MI, unstable angina, cerebrovascular accident, or TIA
Any history of peripheral vascular infarction, including visceral infarction
Trial design
Single group
Treatments tested in this trial
Laboratory Biomarker Analysis
Other interventionBlood draws.
Ponatinib Hydrochloride
DrugStarting dose: 30 mg by mouth once a day.
Quality-of-Life Assessment
Other interventionSurveys completed.
Treatment groups
Trial outcomes
Primary outcomes
MCyR at 6 months (MCyR6)
The method of Kaplan and Meier will be used to estimate the unadjusted distribution of duration of MCyR. An appropriate time-to-event regression model will be fit to the event time data to assess the effects of patient covariates on the event time variable, with the particular model determined by preliminary goodness-of-fit analyses. The distribution of MCyR6 will be tabulated and effects of baseline patient covariates on this variable will be assessed by logistic regression.
Time-to-toxicity defined as any grade 3 or 4 drug-related adverse event that is not responsive to standard therapeutic management and requires permanent treatment discontinuation
Time-to-toxicity will be monitored using the Bayesian method of Thall, et al. The method of Kaplan and Meier will be used to estimate the unadjusted distribution of time to toxicity. An appropriate time-to-event regression model will be fit to the event time data to assess the effects of patient covariates on the event time variable, with the particular model determined by preliminary goodness-of-fit analyses.
Secondary outcomes
Duration of MCyR
The method of Kaplan and Meier will be used to estimate the unadjusted distribution of the duration of MCyR. An appropriate time-to-event regression model will be fit to the event time data to assess the effects of patient covariates on the event time variable, with the particular model determined by preliminary goodness-of-fit analyses.
Time to transformation to accelerated phase CML
The method of Kaplan and Meier will be used to estimate the unadjusted distribution of the time to transformation to accelerated phase CML. An appropriate time-to-event regression model will be fit to the event time data to assess the effects of patient covariates on the event time variable, with the particular model determined by preliminary goodness-of-fit analyses.
Time to transformation to blastic phase CML
The method of Kaplan and Meier will be used to estimate the unadjusted distribution of the time to transformation to blastic phase CML. An appropriate time-to-event regression model will be fit to the event time data to assess the effects of patient covariates on the event time variable, with the particular model determined by preliminary goodness-of-fit analyses.
MMR
An appropriate time-to-event regression model will be fit to the event time data to assess the effects of patient covariates on the event time variable, with the particular model determined by preliminary goodness-of-fit analyses. The distribution of MMR will be tabulated and effects of baseline patient covariates on this variable will be assessed by logistic regression.
Sponsors and contacts
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M.D. Anderson Cancer Center
Lead sponsor
National Cancer Institute (NCI)
Collaborator