Project Duration: 2021-2025
MOHCCN Consortium: Marathon of Hope - Quebec
Investigators: Simon Turcotte, Gerry Batist, An Tang, Dominique Trudel
Partners: Centre de recherche du Centre hospitalier de l’Université de Montréal; Montreal General Jewish Hospital
Approximately 9,600 patients die of colorectal cancer (CRC) yearly in Canada and the liver is the first and most common site of metastatic progression. CRC patients with liver metastases are treated using a “one size fits all” paradigm, with first line chemotherapy before and after surgical resection of all metastases, when possible. After these treatments, 80% of patients develop a recurrence. In addition, liver surgery may not add survival benefits in the 20-30% of patients who recur within one year of surgery. In those who do not recur, it is unclear whether the two to three months of post-operative chemotherapy have added survival benefits. To personalize patient care in order to avoid potentially high risk and ineffective treatments, we need new biological markers (biomarkers) to more reliably predict and assess treatment response and patient prognosis.
To discover useful biomarkers, we aim to analyze the gene sequences of liver metastases in combination with clinical data, microscopic features of the metastases, circulating blood derivatives, medical imaging, and the gut flora (microbiome).
Since 2011, we have prospectively followed a cohort of now 850 CRC patients treated with perioperative chemotherapy and operated on for liver metastases. In a subgroup of 219 patients, we have established a large tissue array for high throughput protein and cell analysis and gained access to CT-scan and tumor pathology images to investigate features associated with patient outcomes. In the prospective observational sub-study “Early Detection of Treatment Failure in Metastatic Colorectal Cancer Patients” (eDetect-mCRC - NCT05068531), we are now collecting serial biospecimens (blood, tumor, stools) in 100 of these patients. With MOH support, we will generate genomic sequencing of liver metastases from patients in these cohorts. Using artificial intelligence analysis tools, we will combine the genomic data with the clinical, pathological, imaging and microbiome data to identify the best set of candidate biomarkers.
This project is likely to guide the design of future prospective studies where candidate biomarkers will be used to test personalized treatment strategies, such as to guide the choice of upfront chemotherapy based on the likelihood of response, opting for liver-directed treatment other than major liver surgery in patients at high risk of post-operative recurrence, eliminating post-operative preventive (adjuvant) chemotherapy in patients who responded well to upfront chemotherapy and liver surgery, and testing new post-operative preventive (adjuvant) treatments in patients at high risk of recurrence. Additionally, this rich set of genomically annotated liver metastases will inform the potential use of new targeted therapies to treat this common and lethal cancer.