Abstract
Background
Inflammatory bowel disease (IBD), specifically ulcerative colitis(UC) is a pre-disposing factor for colorectal cancer (CRC), with risk in creasing with disease duration. CRC patients previously diagnosed with UC have poorer prognosis compared to sporadic CRC.
Aim
Through the integration of UC, CRC and normal tissue transcriptional profiles, we aim to identify at-risk patients through characterising a novel UC/CRC molecular subgroup. By identifying underpinning multiple drivers of disease behaviour, we will evaluate preventative/intervention treatments for UC/CRC at-risk patients and determine if a prior history of UC indicates additional CRC treatments.
Method
Publically-available transcriptional data and a novel in silico technique, Gene Expression Compositional Assignment, were used to consider similarities between UC/UC-CRC patients with normal/CRC samples and corresponding consensus molecular subgroups(CMS) groups. The microenvironment cell population (MCP)counter was used to assess cell-type proportions.
Results
UC/CRC samples were significantly more similar to sporadic CRC than normal/quiescent UC samples (p = 0.0001), with the majority of these alignments to metastasized tumours (Fig 1). CMS3 andCMS2 alignments were over- and under-represented respectively. The majority of UC-CRC samples aligned to the CMS unclassified group; which had significantly different high stromal and immune contents by MCP.
Conclusions
Initial results show this approach has potential to identify UC patients at-risk of developing CRC. CMS unclassified is an intermediate between the poorest prognosis groups, CMS1 and CMS4, with high immune and stromal scores. This corresponds with the UC stromal phenotype being more aggressive holding potential to connecting UC with CRC progression.
Inflammatory bowel disease (IBD), specifically ulcerative colitis(UC) is a pre-disposing factor for colorectal cancer (CRC), with risk in creasing with disease duration. CRC patients previously diagnosed with UC have poorer prognosis compared to sporadic CRC.
Aim
Through the integration of UC, CRC and normal tissue transcriptional profiles, we aim to identify at-risk patients through characterising a novel UC/CRC molecular subgroup. By identifying underpinning multiple drivers of disease behaviour, we will evaluate preventative/intervention treatments for UC/CRC at-risk patients and determine if a prior history of UC indicates additional CRC treatments.
Method
Publically-available transcriptional data and a novel in silico technique, Gene Expression Compositional Assignment, were used to consider similarities between UC/UC-CRC patients with normal/CRC samples and corresponding consensus molecular subgroups(CMS) groups. The microenvironment cell population (MCP)counter was used to assess cell-type proportions.
Results
UC/CRC samples were significantly more similar to sporadic CRC than normal/quiescent UC samples (p = 0.0001), with the majority of these alignments to metastasized tumours (Fig 1). CMS3 andCMS2 alignments were over- and under-represented respectively. The majority of UC-CRC samples aligned to the CMS unclassified group; which had significantly different high stromal and immune contents by MCP.
Conclusions
Initial results show this approach has potential to identify UC patients at-risk of developing CRC. CMS unclassified is an intermediate between the poorest prognosis groups, CMS1 and CMS4, with high immune and stromal scores. This corresponds with the UC stromal phenotype being more aggressive holding potential to connecting UC with CRC progression.
Original language | English |
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Publication status | Published - 06 Oct 2017 |
Event | Ulster Society of Gastroenterology Autumn Meeting 2017 - Belfast, United Kingdom Duration: 06 Oct 2017 → 06 Oct 2017 |
Conference
Conference | Ulster Society of Gastroenterology Autumn Meeting 2017 |
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Country/Territory | United Kingdom |
City | Belfast |
Period | 06/10/2017 → 06/10/2017 |
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A bioinformatics approach to identifying ulcerative colitis patients at-risk of developing colorectal cancer
Scanlon, E. (Author), Kennedy, R. (Supervisor) & Blayney, J. (Supervisor), Dec 2023Student thesis: Doctoral Thesis › Doctor of Philosophy