Agile development methodology of matched synthetic control for Bayesian trials in the era of genomic medicine

随着可访问技术的快速发展，在临床试验中产生了丰富的生物学和临床数据;应用深度学习方法将数字病理学特征与组学数据相结合，使我们能够充分描绘这些肿瘤，并了解哪些分子特征可能有助于治疗反应。所产生的多组学数据的数量为试验方法学家提供了宝贵的资源，以提取生物信息和数据，为未来的试验设计提供信息。具有预定义的嵌入式相关科学、完整的生物标志物、标志物富集设计或标志物适应性或治疗适应性设计的试验将持续增加。随机对照组允许比较治疗组而无需考虑混杂因素，随机研究并不总是可行的（或理想的），特别是对于罕见癌症如肉瘤或乳腺癌中的罕见亚组（例如，在这种情况下，使用外部对照来补充单臂数据可能是一种有吸引力的方法，可以探索。我们正在开发一种基于人工智能的集成基因组学临床工具，称为COUNTERPOINT，它将肿瘤基因组学，微环境和临床病理数据结合起来，以预测乳腺癌的疾病结局。该系统可扩展以利用新的生物学发现，目标是加速发现具有临床实施潜力的分子特征。例如，药物反应/临床结果可用于通过与癌症生物学家（Dr Huang、Dr Sadanandam、Dr Perou（位于查佩尔山））合作合并来自共临床PDX或类器官模型的数据来推断临床表型关联。然后，生物连通性可以为创新的试验设计提供信息，例如，通过将治疗特异性生物途径影响评分/模式作为生物反应适应性设计中的终点，或根据FDA的真实世界证据计划指南，为贝叶斯试验创建匹配的合成对照（黄，琼斯，雅普，郑，MRC/NIHR罕见癌症研究平台）。该博士项目将专注于审查和应用敏捷软件开发方法，以创建匹配合成对照的路线图（基于生物学）设计更有效的贝叶斯试验，基于乳腺癌（常见）和肉瘤（罕见癌症）的样本。

With the rapid advanced development in technologies that are accessible, wealth of biological and clinical data is generated in clinical trials; applying deep learning methods to integrate digital pathology features with omics data allow us fully to delineate these tumours and understand which molecular features may have contributed to the treatment response. The amount of multi-omics data generated provide valuable resource for trial methodologists to draw biological information and data to inform future trial designs. There will be continuous increased popularity of trials with pre-defined embedded correlative sciences, integral biomarkers, marker-enrichment designs, or marker-adaptive or treatment-adaptive designs. Randomised control arms allow for comparison of treatment arms without concern on confounding factors, a randomised study is not always feasible (or ideal), especially for rare cancer like sarcoma, or rare subgroup within breast cancer (e.g., basal-like tumour within estrogen receptor positive breast cancer) In such case, use of external controls to supplement single-arm data may be an attractive approach that can be explored.We are developing an artificial intelligence-based integrated genomics clinical tool, called COUNTERPOINT, which weave tumour genomics, microenvironment and clinicopathological data, to predict disease outcomes in breast cancer. This system is expandable to draw upon new biological findings with the goal to accelerate discovery of molecular features with potential for clinical implementation. For example, drug response/clinical outcome could be used to infer clinico-phenotypic associations by incorporating data from co-clinical PDX or organoid models in collaboration with cancer biologists (Dr Huang, Dr Sadanandam, Dr Perou (UNC at Chapel Hill)). Biological connectivity could then inform innovative trial designs e.g. by incorporating therapeutic-specific biological pathway impact scores/patterns as endpoints in biological-response adaptive designs, or by creation of matched synthetic controls for Bayesian trials, in line with the FDA's Real-World Evidence Program guidelines (Huang, Jones, Yap, Cheang, MRC/NIHR Rare Cancer Research platform).This PhD project will focus on review and apply agile software development methodology to create the roadmap of the creation of matched synthetic controls (based on biology) to design more efficient Bayesian trials based on exemplars from breast cancer (common) and sarcoma (rare cancer).