13 May Circulating Tumor DNA Linked to Recurrence After Colon Cancer Surgery
Posted at 12:42h
in Author Interviews, Biomarkers, Cancer Research, Colon Cancer, JAMA, Karolinski Institute
MedicalResearch.com Interview with:
[caption id="attachment_49076" align="alignleft" width="150"]
Dr. Olsson[/caption]
Louise Olsson MD PhD
Senior researcher
Department of Molecular Medicine and Surgery
Colorectal Surgery
Karolinski Institute
Stockholm, Sweden
MedicalResearch.com: What is the background for this study? What are the main findings?
Response: I read a very interesting paper back in 2006 “Detection and quantification of mutation in the plasma of patients with colorectal cancer”. Only some 60 % of patients with early colorectal cancer were detectable in this way whereas patients with stage IV disease all had a high concentration of APC mutations in their plasma. So the prospects of using the method for example, screening of primary colorectal cancer seemed limited but I thought wow, this is the test to detect recurrences and generalized disease during follow-up after surgery for colorectal cancer. After some discussion we started to collect plasma samples from patients at the hospital where I worked and that´s how my research began.

Jasleen Grewal, BSc.
Genome Sciences Centre
British Columbia Cancer Research Centre
Vancouver, British Columbia, Canada
MedicalResearch.com: What is the background for this study?
Response: Cancer diagnosis requires manual analysis of tissue appearance, histology, and protein expression. However, there are certain types of cancers, known as cancers of unknown primary, that are difficult to diagnose based purely on their appearance and a small set of proteins. In our precision medicine oncogenomics program, we needed an accurate approach to confirm diagnosis of biopsied samples and determine candidate tumour types for where the primary site of the cancer was uncertain. We developed a machine learning approach, trained on the gene expression data of over 10,688 individual tumours and healthy tissues, that has been able to achieve this task with high accuracy.
Genome sequencing offers a high-resolution view of the biological landscape of cancers. RNA-Seq in particular quantifies how much each gene is expressed in a given sample. In this study, we used the entire transcriptome, spanning 17,688 genes in the human genome, to train a machine learning method for cancer diagnosis. The resultant method, SCOPE, takes in the entire transcriptome and outputs an interpretable confidence score from across a set of 40 different cancer types and 26 healthy tissues.

