The Ability of Serum miRNAs to Act as Biomarkers for the Oral Squamous Cell

Carcinoma.

Christopher DickmanCathie Garnis

Experimental Medicine Research Day

Background

• Oral Squamous Cell Carcinoma (OSCC) is the most common form of head and neck cancer

• Poor survival rate due to rapid progression and frequent recurrence

• Understanding molecular alterations in OSCC may lead to drug targets and biomarkers

miRNAs

• Short 22 base pair molecules of RNA• Responsible for post-transcriptional genetic

regulation• Known to play a role in many cancer processes

Aims• We aim to find miRNAs that are significantly

different between the serum of individuals with oral cancer or carcinoma in situ (CIS) and healthy individuals that can be used as a biomarker for high risk lesions

Methods• Serum from 51 healthy individuals and 48

individuals with oral cancer or CIS were profiled for 742 miRNAs using RT-PCR

• 47 additional control samples and 32 additional cancer/CIS samples were included in a validation set

Haemolysis

• Analysis of serum miRNAs is made more difficult by the varying contribution of blood cell RNA to the serum

• 2 Options– Exclude samples with haemolysis– Exclude miRNAs affected by haemolysis

Data Analysis

• miRNAs normalized to the expression of miR-23b

• Creation of a subset using general discriminant analysis

• Subset analysis was performed using a generalized linear model (logistic)

• Best subsets of 4,3 and 2 miRNAs were discovered

Results

Validation

• Samples were run 8 per plate as opposed to one sample per two plates at ~1/8th the price

• Samples are run in triplicate– Variation among replicate increases in miRNAs

with low expression– There is difficulty in analyzing the data where all

replicates have a different CT value

Validation Results

Sources of Down-Regulated miRNAs

• Many of theses miRNAs are down regulated in cancer patients’ serum (miR-23a, 342-3p, 33a) what is their source?

• Often it’s assumed cancer related miRNAs are released from the tumor

• Immune system is known to be a large contributor to miRNA excretion

Conclusions

• We have identified candidates for a validation test of serum miRNA biomarkers for Oral Cancer

• Ongoing work must be preformed to determine what role overtraining plays in data analysis

Model Training AUC Validation AUC

23a, 205, 33a 342-3p .91 .77

23a, 205, 33a .90 .62

23a, 205 .83 .66

23a, 346 .85 NA

AcknowledgementsSupervisorDr. Cathie Garnis

Committee MembersDr. Calum MacaulayDr. Catherine Poh

Garnis Lab MembersSara MacLellanRebecca TowleDanielle TruongJames Lawson