Illumina Scientific Summit "Cancer Genomics" breakout

This years Illumina Scientific Summit was a great meeting, lots of great talks and great people to talk to at dinner and the bar. I already posted about a collaboration Geoff Smith presented and this post is some rough notes on the "Cancer Genomics" breakout that took part today.

Jen Stone from Illumina introduced the discussion session and talked about the promise of cancer genomics; what does it really mean for patients and doctors? She talked about four areas of focus: germline risk; healthy screening; profiling, diagnosis and treatment; and response, recurrence and MRD. Jen reintroduced everyone to the different datasets for each of these areas: GWAS for germline risk; TCGA & ICGC for discovery around profiling, diagnosis and treatment; circulating tumour DNA and cells for response, recurrence and MRD; and a single publication for healthy screening Evaluation of DNA from the Papanicolaou test to detect ovarian and endometrial cancers.

Jen also talked about how Illumina hopes to realise the promise of cancer genomics by continuing to innovate in sample prep and sequencing. Partnering with groups on collaborative research. And developing an environment that is receptive to genomic medicine (Understand Your Genome).

She also presented some experimental challenges; should we use targeted or whole genome sequencing, if targeted then what (exome, amplicon, etc), should we analyse DNA, RNA or Methylome, CTCs or ctDNA etc, et, etc. Analysis methods need to detect not just SNPS but also InDels and everything else, one method almost certainly does not fit all! Other issues like LOH, Heterogeneity, low input, stromal contamination, limits of sensitivity all need to be addressed.

So what does the future of Cancer genomics look like: Illumina used a mobile app to collect votes from attendees in real time and used the answers as a starting point for discussions. The app worked really well. It is always a bit of a risk doing a live demo and I'm always nervous that it is going to be a trainwreck presentation; but this worked really well. Hopefully the results from the votes will also be available after the session for attendees to reflect on. If not I've picked some of my favourite questions and the answers from participants.

There was a discussion on whether the term "the $1000 genome" is a force for good or bad? Does it set an unrealistic price point, can the bioinformatics challenges be met so they remove a large part of today's costs of analysis and interpretation, is it mis-interpreted by the public and the media?

One attendee pointed to New York Times OpEd piece article comparing the $99 cost of a 23andMe test (possibly $1000) to the $4000 cost of BRCA1/2! 

The questions and answers:

Cancer sequencing will be the standard for all patients in the next: about 45% of participants said either 3-5 years or 5-10 years.

In order for sequencing to be part of the standard, what are they key drivers?  90% said all of the above (Price, clinical utility, data analysis, workflow) are required but when asked which is most important then 65% chose clinical utility.

All Tumor DNA Sequencing must be analysed in conjunction with a paired normal sample?  almost 50% said yes, 25% said maybe.

Large projects such as ICGC, TCGA and Standup2Cancer are all generating multiple datasets per sample (T/N, RNASeq, Meth450, etc.). 70% of respondents sad they thought that only the subset of variants from these huge discovery datasets will be tested clinically, when responding to the statement "I think these studies are setting the stage such that:".

What is an acceptable turn-around time for an optimal cancer dataset in the research space?  2 weeks to 1 month was the majority choice. This is a tough challenge for core facilities like mine which are often capacity constrained. We need to work closely with our users to explain the limitations and set realistic expectations.

What is an acceptable turn-around time for an optimal cancer dataset in the clinical space? 95% said less than 2 weeks.

How important are long reads for cancer applications? only 25% said long reads were important. This is a topic close to my heart and I strongly believe long reads will be transformative for RNA-seq as isoforms can be directly counted. For DNA we should get more robust structural variation analysis. And both will hopefully be completed with fewer reads.

How important is phasing for cancer applications?  Most participants said "not very important", there was no "killer app" reported for cancer genomics.

We ran out of time to finish the questions and answers. All in all it was a very good breakout session. I certainly learned a lot from the discussions.