>**BREAKING NEWS** Marco Marra, BC Cancer Agency – “Sequencing Cancer Genomes and Transcriptomes: From New Pathology to Cancer Treatment.”

>Why sequence Cancer-ome: Most important: treatment-response difference. To match treatments to patients. Going to focus on that last one.

Two Anecdotes: Neuroblastoma (Olena Morozova and David Kaplan), and Papillary adenocarcinoma (tongue), primary unkown. 70 year difference in age. They have nothing in common except for “can sequence analysis present new treatment options?”

Background on Neuroblastoma. Most common cancer in infants, but not very common. 75 cases per year in Canada. Patients often have relapse and remission cycles after chemotherapy. Little improvement until recently, when Kaplan was able to show abiltity to enrich for tumour initiating cells (TICs). This gave a great model for more work.

Decided to have a look at Kaplan’s cells, and did transcriptome libraries (RNA-Seq) using PET, and sequenced a flow cell worth: giving 5Gb of raw seq from one sample, 4 from the other. Align to reference genome using custom database. (Probably Ryan Morin’s?) Junctions, etc.

Variants found that are B-cell related. Olena found markers, worked out lineage, and showed it was closer to B-cell malignancy than brain cancer sample. These cells also show neuroblastomas, when reintroduced to mice. So, is neuroblastoma like B-cell in expression? Yes, they seem to have a lot of traits in common. It appears as though the neuroblastoma is expressing early markers.

Thus, if you can target B-Cell markers, you’d have a clue.

David Kaplan verified and made sure that this was not contamination (Several markers). Showed that yes, the neuroblastoma cells are expressing b-cell markers, and that these are not B-cells. Thus, it seems that a drug that targets B-Cell markers could be used. (Rituximab, and Milatuzamab) Thus, we now have an insight that we wouldn’t have have had before. (Very small sample, but lots of promise.)

Anectdote 2: 80 year old male with adenoma of the tongue. Salivary gland origin possibly? Has had surgery and radiation and a Cat scan revealed lung nodules (no local recurrance.) There is no known standard chemotherapy that exists… so several guesses were made, and an EGFR inhibitor was tried.. Nothing changed. Thus, BC Cancer was approached: what can genome studies do? Didn’t know, but willing to try. Genome from formalin fixed sample (which is normally not done), and handful of WTSS from Fine-needle aspirates. (nanograms, which required amplification). 134Gb of aligned sequence across all libraries – about 110Gb to genome. (22X genome, 4X transcriptome.)

Data analysis, compared across many other in-house tumours, and looked for evidence of mutation. CNV was done from Genome. Integration with drug bank, to then provide appropriate candidates for treatment.

Comment on CNV: histograms shown: Showed that as many bases are found in single allele as diploid and then again, just as many in triploid and then some places at 4 and 5s. Was selected pressure involved in picking some places for gain, whereas much of the genome was involved in loss?

Investigated a few interesting high CNV regions, one of which contains RET. Some amplifications are highly specific, containing only a single gene, whereas they are surrounded by loss of CNV regions.

Looking at Expression level, you see a few interesting things. There is a lack of absolute correlation between changes in CNV and the expression of the gene.

When looking for intersection, ended up with some interesting features:
30 amplified genes in cancer pathways (kegg)
76 deleted genes in cancer pathways
~400 upregulated, ~400 downregulated genes
303 candidate non-synonymous snps
233 candidate novel coding SNPs
… more.

Went back to drugbank.ca (Yvonne and Jianghong?) When you merge that with target genes, you can find drugs specific to those targets. One of the key items on the list was RET.

Back to patient, the patient was using EGFR targetting drug. Why weren’t they responsive? Turns out that p10 and RB1 are lost in this patient… (see literature.. didn’t catch paper).

Pathway diagram made by Yvonne Li. Shows where mutations occur in pathways, gains and losses of expression are shown as well. Notice Lots of expression from RET, and no expression from p10. p10 regulates (negative) the RET pathway. Also increases of Mek and Ras. Suggests that in this tumour, activation of RET could be driving things.

Thus, came up with a short list of drugs. Favorite was Sunitinib. It’s fairly non-specific, used for renal cell carcinoma. Currently in clinical trials, tested for other cancers. Implications that RET is involved in some of those diseases (MEN2a, MEN2B and thyroid cancers.) RET sequence in patient was not likely to be mutated in patient.

CAT scans: response to Sunitinib and Erlotinib. When on the EGFR targetting drug, nodule grew. On Sunitinib, the cancer retreated!

Lots of Unanswered questions: Is RET really driving this tumour? Is drug really acting on RET? Is PTEN/RB1 loss responsible for erlotinib resistance in this tumour?

We don’t think we know everything, but can we use genome analysis to suggest treatment: YES!

First question: how did this work with ethics boards? How did they let you pass that test? Answer: this is not a path to treatment, it is a path towards making suggestion. In some cancers there is something called Hair Analysis. It can be considered or ignored. Same thing here: we didn’t administer… we just proposed a treatment.

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