ABSTRACT
Throughout the life sciences, biological populations undergo multiple phases of growth, often referred to as biphasic growth for the commonly encountered situation involving two phases. Biphasic population growth occurs over a massive range of spatial and temporal scales, ranging from microscopic growth of tumours over several days, to decades-long regrowth of corals in coral reefs that can extend for hundreds of kilometres. Different mathematical models and statistical methods are used to diagnose, understand and predict biphasic growth. Common approaches can lead to inaccurate predictions of future growth that may result in inappropriate management and intervention strategies being implemented. Here, we develop a very general computationally efficient framework, based on profile likelihood analysis, for diagnosing, understanding and predicting biphasic population growth. The two key components of the framework are as follows: (i) an efficient method to form approximate confidence intervals for the change point of the growth dynamics and model parameters and (ii) parameter-wise profile predictions that systematically reveal the influence of individual model parameters on predictions. To illustrate our framework we explore real-world case studies across the life sciences.
Subject(s)
Population GrowthABSTRACT
Precision medicine approaches that inform clinical management of individuals with cancer are progressively advancing. Patient-derived explants (PDEs) provide a patient-proximal ex vivo platform that can be used to assess sensitivity to standard of care (SOC) therapies and novel agents. PDEs have several advantages as a patient-proximal model compared to current preclinical models, as they maintain the phenotype and microenvironment of the individual tumor. However, the longevity of PDEs is not compatible with the timeframe required to incorporate candidate therapeutic options identified by whole exome sequencing (WES) of the patient's tumor. This review investigates how PDE longevity varies across tumor streams and how this is influenced by tissue preparation. Improving longevity of PDEs will enable individualized therapeutics testing, and thus contribute to improving outcomes for people with cancer.
ABSTRACT
Current in vitro therapeutic testing platforms lack relevance to tumor pathophysiology, typically employing cancer cell lines established as two-dimensional (2D) cultures on tissue culture plastic. There is a critical need for more representative models of tumor complexity that can accurately predict therapeutic response and sensitivity. The development of three-dimensional (3D) ex vivo culture of patient-derived organoids (PDOs), derived from fresh tumor tissues, aims to address these shortcomings. Organoid cultures can be used as tumor surrogates in parallel to routine clinical management to inform therapeutic decisions by identifying potential effective interventions and indicating therapies that may be futile. Here, this procedure aims to describe strategies and a detailed step-by-step protocol to establish bladder cancer PDOs from fresh, viable clinical tissue. Our well-established, optimized protocols are practical to set up 3D cultures for experiments using limited and diverse starting material directly from patients or patient-derived xenograft (PDX) tumor material. This procedure can also be employed by most laboratories equipped with standard tissue culture equipment. The organoids generated using this protocol can be used as ex vivo surrogates to understand both the molecular mechanisms underpinning urological cancer pathology and to evaluate treatments to inform clinical management.
