Constrained Boundary Monitoring for Group Sequential Clinical Trials

Group sequential stopping rules are often used during the conduct of clinical trials in order to attain more ethical treatment of patients and to better address efficiency concerns. Because the use of such stopping rules materially affects the frequentist operating characteristics of the hypothesis test, it is necessary to choose an appropriate stopping rule during the planning of the study. It is often the case, however, that the number and timing of interim analyses are not precisely known at the time of trial design, and thus the implementation of a particular stopping rule must allow for flexible determination of the schedule of interim analyses. In this paper we consider the use of constrained stopping boundaries in the implementation of stopping rules. We compare this approach when used on various scales for the test statistic. When implemented on the scale of boundary crossing probabilities, this approach is identical to the error spending function approach of Lan & DeMets (1983)

Statistical techniques to construct assays for identifying likely responders to a treatment under evaluation from cell line genomic data

<p>Abstract</p> <p>Background</p> <p>Developing the right drugs for the right patients has become a mantra of drug development. In practice, it is very difficult to identify subsets of patients who will respond to a drug under evaluation. Most of the time, no single diagnostic will be available, and more complex decision rules will be required to define a sensitive population, using, for instance, mRNA expression, protein expression or DNA copy number. Moreover, diagnostic development will often begin with in-vitro cell-line data and a high-dimensional exploratory platform, only later to be transferred to a diagnostic assay for use with patient samples. In this manuscript, we present a novel approach to developing robust genomic predictors that are not only capable of generalizing from in-vitro to patient, but are also amenable to clinically validated assays such as qRT-PCR.</p> <p>Methods</p> <p>Using our approach, we constructed a predictor of sensitivity to dacetuzumab, an investigational drug for CD40-expressing malignancies such as lymphoma using genomic measurements of cell lines treated with dacetuzumab. Additionally, we evaluated several state-of-the-art prediction methods by independently pairing the feature selection and classification components of the predictor. In this way, we constructed several predictors that we validated on an independent DLBCL patient dataset. Similar analyses were performed on genomic measurements of breast cancer cell lines and patients to construct a predictor of estrogen receptor (ER) status.</p> <p>Results</p> <p>The best dacetuzumab sensitivity predictors involved ten or fewer genes and accurately classified lymphoma patients by their survival and known prognostic subtypes. The best ER status classifiers involved one or two genes and led to accurate ER status predictions more than 85% of the time. The novel method we proposed performed as well or better than other methods evaluated.</p> <p>Conclusions</p> <p>We demonstrated the feasibility of combining feature selection techniques with classification methods to develop assays using cell line genomic measurements that performed well in patient data. In both case studies, we constructed parsimonious models that generalized well from cell lines to patients.</p

Dynamics of mutations in patients with ET treated with Imetelstat

Background: Imetelstat, a first in class specific telomerase inhibitor, induced hematologic responses in all patients (pts) with essential thrombocythemia (ET) in a recent phase-2 study, and molecular responses were seen within 1-12 months in the majority of pts carrying JAK2 V617F (JAK2m) and CALR mutations (CALRm) (Baerlocher et al., ASH 2014). It has been reported that the treatment response to imetelstat in pts with myelofibrosis (Tefferi et al., ASH 2014) was negatively influenced by mutations in ASXL1 and favorably impacted by mutations in SF3B1 and U2AF1. In pts with CALRm ET treated with Interferon-alpha (INFa) the response rate was lower if the patient carried more than one mutation in ASXL1, TET2, IDH2, CSF3R and SH2B3 (Kiladjian et al., ASH 2014). In addition, in JAK2m pts with polycythemia vera, TET2-mutated clones have been demonstrated to be resistant to IFNa therapy (Kiladjian et al., Leukemia 2010). Aims: Our aim was to assess the dynamics of additional mutations besides JAK2 V617F, CALR and MPL mutations in pts with ET treated with imetelstat, and to investigate their association with hematologic and molecular response. Methods: The study enrolled 18 pts with ET who had failed or were intolerant to ≥1 prior therapy, or refused standard therapy. Pts were treated with imetelstat 7.5 mg/kg or 9.4 mg/kg IV weekly until attainment of platelet count ~250-300x109/L followed by a maintenance phase with dosing titrated according to platelet count. DNA was extracted from granulocytes or leukocytes. Mutation analysis was performed by high-throughput sequencing of selectively amplified target sequences on a PGM Ion Torrent instrument covering the coding and adjacent intronic sequences of 15 genes known for mutations in MPN (ASXL1, CBL, DNMT3A, EZH2, IDH1, IDH2, JAK2, MPL, SF3B1, SRSF2, SOCS1, TET2, TP53, U2AF1 and ZRSR2). Samples were taken at baseline and up to 8 time points during treatment through cycle 26, with approximately 3 cycles between samples. Results: As a driver mutation, at baseline, 9 pts had JAK2V617F, 5 pts had CALR and 2 pts had MPL mutations (MPLm; one L and one K). Two pts were triple negative. A partial molecular response (according to Barosi et al., Blood 2009) was seen in 7/8 JAK2m pts and reductions in CALRm allele burden were between 15% and 55%. At baseline, 19 additional somatic mutations (11 missense, 4 frameshift, 3 nonsense, 1 splice site) were detected in 6/9 JAK2m and 2/5 CALRm pts, affecting the genes ASXL1 (n=3), DNMT3A (n=5), EZH2(n=1), SF3B1 (n=1), SOCS1 (n=2), TET2 (n=4) and TP53 (n=3). Two mutations in DNMT3A and ZRSR2 were detected in 1/2 MPLm pts and none were found in the triple negative pts. Of note, all but one mutated pts carried at least 2 mutations in addition to their driver mutation (up to 5 additional mutations). ASXL1 and SOCS1 mutations were only present in JAK2m pts, and these pts reached hematologic and partial molecular response. At time of best molecular response, a reduction of mutant allele burden corresponding to the reduction of the driver mutations was observed for mutations in ASXL1, EZH2, SOCS1 and some DNMT3A, TET2 and TP53 mutations, but not for SF3B1 and ZRSR2 mutations. Sequential analysis in a JAK2m patient with 4 additional mutations showed that all 4 mutated clones were sensitive to imetelstat treatment and followed the dynamics of the JAK2 mutation, and in a patient with 5 mutations in addition to the CALR mutation, 3/5 mutated clones were responsive. Three pts with a weaker molecular response had TP53 mutations which persisted over time, and 2 were accompanied by additional mutations. Conclusions: 9/18 (50%) pts in this study carried no additional mutations at baseline, and 50% carried 1-5 mutations in addition to the driver mutation, suggesting genetic instability in a subset of pts. Genetic instability might be enhanced in this pretreated patient cohort with a median time since diagnosis of 7.2 years (range 0.3-24.9). Clones with ASXL1 mutations, a known poor prognostic marker in MPN, appear to be sensitive to imetelstat treatment, and pts with 2-5 additional mutations had both hematologic and molecular responses. TP53 mutations were an exception, being associated with weaker molecular responses to imetelstat treatment. Additional analyses of associations between mutations, disease characteristics and response will be presented

Dynamics of mutations in patients with essential thrombocythemia treated with imetelstat.

In a phase-2 study, the telomerase inhibitor imetelstat induced rapid hematologic responses in all patients with essential thrombocythemia who were refractory or intolerant to prior therapies. Significant molecular responses were achieved within 3-6 months in 81% of patients with phenotypic driver mutations in JAK2, CALR and MPL. Here, we investigated the dynamics of additional somatic mutations in response to imetelstat. At study entry, 50% of patients carried 1-5 additional mutations in the genes ASXL1, CBL, DNMT3A, EZH2, IDH1, SF3B1, TET2, TP53 and U2AF1. Three patients with baseline mutations also had late-emerging mutations in TP53, IDH1 and TET2. Most clones with additional mutations were responsive to imetelstat and decreased with the driver mutation, including the poor prognostic ASXL1, EZH2 and U2AF1 mutations while SF3B1 and TP53 mutations were associated with poorer molecular response. Overall, phenotypic driver mutation response was significantly deeper in patients without additional mutations (P = 0.04) and correlated with longer duration of response. In conclusion, this detailed molecular analysis of highly pretreated and partly resistant patients with essential thrombocythemia reveals a high individual patient complexity. Moreover, imetelstat demonstrates potential to inhibit efficiently co-incident mutations occurring in neoplastic clones in patients with essential thrombocythemia. (ClinicalTrials.gov number, NCT01243073 N Engl J Med 2015; 373:920-928, DOI: 10.1056/NEJMoa1503479.)