About OCCAMS
About OCCAMS
OCCAMS was formed in 2008 in order to develop a well powered clinical and molecular dataset that would advance research and ultimately patient outcomes from this poor prognosis cancer type.
We have mainly focussed on oesophageal adenocarcinoma including tumours of the gastro-oesophageal junction. Since it was formed OCCAMS became the main vehicle to provide whole genome sequencing (WGS) for the International Cancer Genome Consortium and more recently ICGC ARGO.
We have a well developed infrastructure for collecting samples and patient data. We welcome collaborations from members of the consortium who contribute tissues as well as from external collaborators.
Our aims
The aims of the OCCAMS study are to:
Better characterise the clinico-demographic risk factors for this cancer
As stated in this review of oesophageal cancer (doi.org/10.1038/nrdp.2017.48) incidence levels of OAC are rising in the developing world. It is therefore of upmost importance to understand the factors that are causing this rise.
Characterise the molecular genetic landscape (DNA, RNA, epigenome) to inform mechanistic and translational studies
The bulk of this work involves high coverage DNA Whole Genome Sequencing. A high level of coverage allows researchers to reliably state whether or not identified mutations are real or false, by comparing the tumour genetic profile with that of the patients normal tissue.
Determine disease sub-types and develop new clinically relevant classification systems
Previous classification systems are based on the position of the tumour in the oesophagus called the Siewert-Stein Classification (https://academic.oup.com/bjs/article/85/11/1457/6269563), and stage of the disease. The consortiums work has already lead to the establishment of a revised OAC classification method (https://pubmed.ncbi.nlm.nih.gov/19526624/), but we seek to expand this by utilising biological markers. This includes the routine collection of ctDNA for patient monitoring.
Develop and validate improved clinical staging and prognostic algorithms
One of our aims is to utilise ever expanding computational power to develop informatics techniques to cover all areas of the patient pathway. In light of this, the Fitzgerald group at Cambridge university has a number of non-biological laboratory PhD students and Postdoctoral Researchers covering areas such as Artificial Intelligence (AI), Engineering, and Bioinformatics.
Ascertain new therapeutic targets for clinical trials
It is well established that cancer is a mutation rich disease, some of which are meaningful and some are not. Our goal is to ascertain which mutations correspond to driver events, ones that significally impact the growth, expansion, resistance or spread of the tumour. One recent OCCAMS research paper surveyed 551 OAC samples, discovering some novel therapeutic targets for further investigation. (https://www.nature.com/articles/s41588-018-0331-5#)