Clinical Academic Group
Personalized Oncological Surgery
Each year 5000 patients are diagnosed with colorectal cancer in Denmark. Current oncological and surgical treatment of patients with cancer is performed according to standardized pathways. Even with high adherence to these pathways and with state-of-the-art oncological, perioperative and surgical treatment, one out of four patients suffers from complications within 30 days after colorectal cancer surgery and one out of three patients will develop disease recurrence. The financial burden is substantial as major complications after colorectal cancer surgery increase costs up to three times. Only one out of three patients with complications return to work within three months compared with every second patient without. The proportion of patients who never return to work is also doubled after postoperative complications. Preventing recurrence after surgery will also result in a dramatic reduction in overall costs. There is evidence supporting that delay of surgery will not translate into reduced cancer related survival. In current treatment strategies, optimizing the patient according to the different phases of the treatment trajectory, from diagnosis to full recovery, is not optimal as too many patients either do not get the right surgical or oncological therapy or have complications without getting the benefit.
There is compelling evidence showing that a tailored approach, delivering the right treatment at the right time for the right patient will have dramatic effects on the prevention of complications and recurrence after surgery. A personalized surgical approach is thus needed in order to stratify patients according to phenotypes and genotypes which can be linked with improved treatment pathways. In order to succeed it is necessary to include basic scientists specialized in big data, experts in translational techniques in the laboratory and a multidisciplinary team with experts from each phase of the entire patient care pathway.
The CAG-POS (Clinical Academic Group – Personalized Oncological Surgery) vision is to implement a generic research approach by combining clinical and basic research through Personalized Medicine (PM) treatments to patients undergoing oncological surgery. CAG-POS data spectrum is spanning before, during and after surgery, for every crucial part of the oncological surgical patient pathway. The aim is to improve oncological outcomes after surgery powered by the standardization and complexity reduction when organizing big data, building accurate prediction models and using prediction models within a clinical environment.
The work of the CAG-POS is organized in seven Work Packages (WP).
The CAG-POS WP1 will build upon the preliminary results from the elite research consortium (Enhanced Perioperative Oncology Consortium) and will evolve the decision-support capabilities of the POS platform by: i) enriching the Big Data foundation to include a wider spectrum of health data, ii) building prediction models around specific clinical questions, iii) validating the prediction model efficiency within a clinical environment by running window trials and iv) building patients-like-me tools targeting an interface friendly to healthcare actors.
The WP1 development is based on robust and internationally validated opensource tools which allow cohort creation and analyses in real-time, giving both junior and senior researchers the possibility to explore scientific hypotheses much quicker, standardized and easier than ever before. This will stimulate the consolidation of the CAG-POS as it will help support and direct the hypotheses with great agility.
A preexisting promising strategy to detect circulating tumor cells (CTC) will be implemented and further developed with the possibility to perform single cell extraction with subsequent sequencing of both DNA for mutational analyses but also the RNA of the CTCs.
Ex vivo and in vitro based methodology will be used to assess the immune phenotype of the patient and also the dynamics between tumor cells and the immune system.
The current multidisciplinary team (MDT) meeting involving a surgeon and a specialist in oncology, pathology and radiology is a crucial, integrated part of the national cancer treatment packages and is implemented in every department treating patients with colorectal cancer. In CAG-POS the MDT concept will be expanded integrating the POS platform in the decision making progress where Big data will be included in order to make an individualized prediction of essential outcomes such as risk of surgical complication, risk of short term mortality and risk of recurrence.
Metagenome sequencing will create the basis of a library of bacterial species in the POS platform. Data will characterize the intratumoral microbiota and identify bacterial drivers. Optimized fluorescence in situ hybridization (FISH) will be used to visualize the spatial organization of biofilms and detection of known bacterial drivers.
The principle of window trials in oncological research is to test experimental drugs or other interventions directed toward the tumor before surgery and then examine the effect of this intervention on the patient immune system and tumor microenvironment. Patients with colorectal cancer will be included based on a deep phenotypic profile including detailed information regarding comorbidities as well as detailed information on immune and tumor profile created based on the POS platform.
Focus will be put on interventions directed towards improving host response to colorectal cancer in order to reduce the tumor burden before surgery and clear micrometastatic disease. The biomarkers from these trials will be analysed in WP2 and WP3 and data will be integrated prospectively in the PM research tool in WP1.
Patient Generated Health Data (PGHD) using an Internet of Things (IoT) approach will be collected to take advantage of high-quality measurements via low cost sensors. Monitoring will be performed at home, before and after surgery, to enable a more detailed description of each individual patient.
The project´s initial funding was 2.85 million DKK. Moreover, an additional 7.7 million DKK was received by the Agency for Digitalisation as part of the National Strategy for Artificial Intelligence.