2013

New concepts are emerging in the use of cancer vaccines and other forms of active immunotherapy such as checkpoint inhibitors both as monotherapy and in combination therapies. These include: (a) When used as monotherapy, certain cancer vaccines and checkpoint inhibitors have been shown to extend survival without an improvement in time to progression. (b) In contrast to the use of conventional therapeutic agents, cancer vaccines can alter the growth rate of tumors, leading to extended survival. (c) Debulking of tumor with surgery, chemotherapeutic agents, radiation, and/or hormones, prior to or in combination with vaccines can lead to enhanced time to progression and potentially enhance survival. (d) Certain chemotherapeutic agents and small molecule targeted therapeutics can positively alter the effector T-cell to regulatory T-cell ratio thus providing a window of opportunity to concurrently employ cancer vaccines. (e) Certain conventional therapeutics can alter the phenotype of human tumors to render them more susceptible to T-cell–mediated lysis, thus enhancing the efficacy of active immunotherapy. (f) Cancer vaccines can be developed to target transcription factors, which drive the epithelial-to-mesenchymal transition (EMT) process, “stemness” and drug resistance. This session will describe these and other emerging concepts in cancer active immunotherapy and cancer vaccines.

Cancer treatments are increasingly being developed along with a companion diagnostic assay that is used to select patients for treatment. For these assays, a cutoff must be identified that defines the assay result as positive or negative, with this information used in patient selection. Even for simple dichotomous DNA mutation-based tests, cutoff selection is required during development of the assay. Additional complexities in cutoff selection are the often limited information available to select a cutoff before enrollment in a pivotal study begins, and in the case where patients who test negative are unlikely to benefit, how many such patients must be treated to determine the clinical predictive value of the assay with a given cutoff. This session will describe strategies to identify and develop cutoffs for companion diagnostic assays, focusing on statistical and regulatory perspectives.

There is increasing interest to include patient-reported outcomes (PROs) in drug development programs and labeling. There are two FDA guidance documents providing information about methods involved with developing, qualifying, implementing, and analyzing PRO data. However, a host of methodological and logistical challenges are involved with designing PRO endpoints. This session will review some of these challenges and solutions used in prior drug development programs. Recent oncology labels with PROs include Jakafi and Zytiga, and approaches used in these cases will be described. The relationship of PROs to other patient-centered outcomes, and to outcomes used in other contexts such as comparative effectiveness research and performance evaluation, will be described.

Jakafi (ruxolitinib), a small-molecule drug against myelofibrosis, was approved by the USFDA in November 2012. It is the first treatment approved against myelofibrosis and also the sponsor’s (Incyte) first drug to market. The case study is fascinating for a variety of reasons. The drug clinically validated a novel mechanism of action in cancer by blocking the Janus-kinases JAK1 and JAK2, and the sponsor used a rapid development pace until approval (six years after an activating mutation in JAK2 was first identified in this cancer); and, importantly the agent illustrates the use of patient-reported outcome tools as a key element in facilitating approval.

In the pivotal trials, the primary endpoint was a 35% or greater reduction in spleen volume at 24 weeks in Comfort 1, and at 48 weeks in Comfort 2. That was achieved in Comfort 1 by 42% of Jakafi-treated patients and in Comfort 2 by 29%. In addition, Comfort 1 also included a secondary endpoint based on a patient-reported outcome: the proportion of patients with a 50% or greater reduction in a score of a total of six symptoms from baseline to week 24. This was measured using a novel instrument, the modified myelofibrosis symptom assessment form (MFSAF) v2.0 diary, which Incyte developed in consultation with patients and FDA during early phase 1/2 testing. The work on the MFSAF enabled the company to develop a special assessment protocol with the FDA for the phase 3 trial and tracking by MFSAF of all the symptoms enabled “full approval.”

A small discussion group at the 2012 AAADV focused on the agent, but also the role of the FDA and disease-focused groups, such as academicians and advocates to create and perhaps catalog the actual “burden of disease” for the specific disease-affected patients. This discussion group, led by Elizabeth Thompson of Susan G. Komen for the Cure gathered enthusiasm to extend this work. It is hoped that this year’s small group can continue these efforts and develop tangible plans to define the “burden of disease” thus creating tools and opportunities for new agents and interventions to reduce this burden.

Advances in genomic medicine have opened the possibility of conducting smaller, more informative clinical trials in which the drug to be tested is selected based on knowledge of the process, pathway or target most likely to be driving the growth of patient’s cancer. An extreme example of this is the “N of 1” clinical trial. This session will focus on the panelists’ experiences and perspectives on how such trials can be designed, conducted, and analyzed.

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It takes up to $2 billion to bring a drug to market, with less than 5 percent of researched cancer compounds making it to the pharmacist’s shelf. About the same number of people are dying from cancer now as did 30-40 years ago and cancer still accounts for more deaths than heart disease, an estimated 7.6 million deaths per year.
Significant advances in “big data” management and analysis give the oncology community the capability to share cancer data on a large scale. Everyone benefits from data sharing including academics, researchers, industry, and patients. Data sharing may accelerate research by helping companies design more efficient clinical trials through: ready-made comparisons of different treatment regimens, instant pseudo-experimental control groups to study the impact of risk factors, data standards and an opportunity to use multiple instances of small sample studies to develop valid epidemiological population estimates.