Melloni Giorgio E M, Guida Alessandro, Curigliano Giuseppe, Botteri Edoardo, Esposito Angela, Kamal Maude, Le Tourneau Christoph, Riva Laura, Magi Alberto, de Maria Ruggero, Pelicci Piergiuseppe, Mazzarella Luca
Giorgio E.M. Melloni, Harvard Medical School, Boston, MA; Giorgio E.M. Melloni and Laura Riva, Italian Institute of Technology; Alessandro Guida, Giuseppe Curigliano, Angela Esposito, Piergiuseppe Pelicci, and Luca Mazzarella, European Institute of Oncology; Giuseppe Curigliano and Piergiuseppe Pelicci, University of Milan, Milan; Alberto Magi, University of Florence, Florence; Ruggero de Maria, Catholic University, Rome, Italy; Edoardo Botteri, Norwegian Tumor Registry, Oslo, Norway; and Maude Kamal and Christoph Le Tourneau, Institut Curie, Paris, France.
JCO Precis Oncol. 2018 Nov;2:1-16. doi: 10.1200/PO.18.00015.
Trials that accrue participants on the basis of genetic biomarkers are a powerful means of testing targeted drugs, but they are often complicated by the rarity of the biomarker-positive population. Umbrella trials circumvent this by testing multiple hypotheses to maximize accrual. However, bigger trials have higher chances of conflicting treatment allocations because of the coexistence of multiple actionable alterations; allocation strategies greatly affect the efficiency of enrollment and should be carefully planned on the basis of relative mutation frequencies, leveraging information from large sequencing projects.
We developed software named Precision Trial Drawer (PTD) to estimate parameters that are useful for designing precision trials, most importantly, the number of patients needed to molecularly screen (NNMS) and the allocation rule that maximizes patient accrual on the basis of mutation frequency, systematically assigning patients with conflicting allocations to the drug associated with the rarer mutation. We used data from The Cancer Genome Atlas to show their potential in a 10-arm imaginary trial of multiple cancers on the basis of genetic alterations suggested by the past Molecular Analysis for Personalised Therapy (MAP) conference. We validated PTD predictions versus real data from the SHIVA (A Randomized Phase II Trial Comparing Therapy Based on Tumor Molecular Profiling Versus Conventional Therapy in Patients With Refractory Cancer) trial.
In the MAP imaginary trial, PTD-optimized allocation reduces number of patients needed to molecularly screen by up to 71.8% (3.5 times) compared with nonoptimal trial designs. In the SHIVA trial, PTD correctly predicted the fraction of patients with actionable alterations (33.51% [95% CI, 29.4% to 37.6%] in imaginary 32.92% [95% CI, 28.2% to 37.6%] expected) and allocation to specific treatment groups (RAS/MEK, PI3K/mTOR, or both).
PTD correctly predicts crucial parameters for the design of multiarm genetic biomarker-driven trials. PTD is available as a package in the R programming language and as an open-access Web-based app. It represents a useful resource for the community of precision oncology trialists. The Web-based app is available at https://gmelloni.github.io/ptd/shinyapp.html.
基于基因生物标志物招募参与者的试验是测试靶向药物的有力手段，但生物标志物阳性人群的罕见性常常使这类试验变得复杂。伞式试验通过测试多个假设来增加招募人数，从而规避这一问题。然而，由于多种可操作改变同时存在，规模更大的试验出现治疗分配冲突的可能性更高；分配策略对入组效率有很大影响，应根据相对突变频率，利用大型测序项目的信息进行精心规划。
我们开发了名为精准试验设计器（PTD）的软件，用于估计对设计精准试验有用的参数，最重要的是分子筛查所需患者数量（NNMS）以及根据突变频率最大化患者招募的分配规则，将分配冲突的患者系统地分配到与罕见突变相关的药物组。我们使用来自癌症基因组图谱的数据，基于过去个性化治疗分子分析（MAP）会议提出的基因改变，在一项针对多种癌症的10臂虚拟试验中展示了它们的潜力。我们将PTD的预测结果与SHIVA（一项比较基于肿瘤分子谱分析的治疗与难治性癌症患者传统治疗的随机II期试验）试验的真实数据进行了验证。
在MAP虚拟试验中，与非优化试验设计相比，PTD优化的分配方案可将分子筛查所需患者数量减少多达71.8%（减少3.5倍）。在SHIVA试验中，PTD正确预测了有可操作改变的患者比例（虚拟试验中为33.51%[95%CI，29.4%至37.6%]，预期为32.92%[95%CI，28.2%至37.6%]）以及分配到特定治疗组（RAS/MEK、PI3K/mTOR或两者）的情况。
PTD正确预测了多臂基因生物标志物驱动试验设计的关键参数。PTD以R编程语言包的形式提供，也有基于网络的开放访问应用程序。它为精准肿瘤学试验人员群体提供了一个有用的资源。基于网络的应用程序可在https://gmelloni.github.io/ptd/shinyapp.html获取。
