Tag Archives: personalised medicine


Caveats: I have not taken notes in every talk of every session, a lack of notes for a particular speaker does not constitute disinterest on my part, I simply took notes for the talks that were directly related to my current work. If I have misquoted, misrepresented or misunderstood anything, and you are the speaker concerned, or a member of the team involved in the work, please leave a comment on the post, and I will rectify the situation accordingly.

5.1    Mark Lawler, QUB, Belfast: “Personalised Cancer Medicine; Are we there yet?”

Another talk from Mark who was an excellent chair for some conference sessions as well. One of the biggest problems with personalized medicine is that some data is already silo’d, or at very best fragmented.

In the UK getting science into clinical practice within the NHS is really predicated on the evidence that it reduces costs, is transformational in terms of treatment and adds value to the current system. So the bar is set quite high.

This was contrasted with the INCa Tumour Molecular Profiling Programme which is running in France with colorectal and lung cancers. This is drawing on 28 labs around Europe. INCa appears to be run under the auspices of the Institut National du Cancer.

Critical resource: http://www.e-cancer.fr/en

Mark felt that empowering patient advocacy was going to be an important drive in NHS uptake of new technologies and tests. But equally important was increasing personalized medicine literacy amongst GPs, policymakers and the insurance industry.

5.2    Nazneen Rahman, ICR, London “Implementing large-scale, high-throughput cancer predisposition genomic testing in the clinic”

Nazneen is obviously interested in testing germline mutations unlike much of the rest of the cancer programme which was focused on somatic mutation detection. Consequently working with blood draws and not biopsy material.

There are >100 predisposition genes implicated in 40+ cancers and there is variable contribution depending on the mutation and the cancer type. 15% of ovarian cancers result from germline variants, and this falls to 2-3% of all cancers. For this kind of screening a negative result is just as important as a positive one.

On the NHS testing for about half these predisposition genes is already available but even basic BRAF testing is not rolled out completely so tests have ‘restricted access’.

What is really needed is more samples. Increased sample throughput drives ‘mainstreaming of cancer genetics’. And three phases need to be tested – data generation, data analysis and data interpretation.

Critical resource: http://mcgprogramme.com/

They are using a targeted panel (CAPPA – which I believe is a TruSight Cancer Panel) where every base must be covered to at least 50x, which means mean target coverage of samples approaches 1000x even for germline detection. There’s a requirement for a <8week TAT and positive and negative calls must be made. It was acknowledged that there will be a switch to WEX/WES ‘in time’ when it is cheap.

The lab runs rapid runs on a HiSeq 2500 at a density of 48 samples per run. This gives a capacity of 500+ samples per week (so I assume there’s more than one 2500 available!). 50ng of starting DNA is required and there is a very low failure rate. 2.5k samples have been run to date. 384 of these were for BRCA1/2. 3 samples have failed and 15 required ‘Sanger filling’.

In terms of analysis Stampy is used for the aligner and Platypus for variant calling due to its superior handling of indels. A modified version of ExomeDepth is used for CNV calling and internal development produced coverage evaluation and HGVS parsers. All pathogenic mutations are still validated with Sanger or another validation method.

Data interpretation is the bottleneck now, its intensive work for pathogenic variants, and VOUS are an issue – they cannot be analysed in a context independent fashion and are ‘guilty until proven innnocent’ in the clinicians mind.

They have also performed exome sequencing of 1k samples, and observed an average of 117 variants per individual of clinical significance to cancer and 16% of the population has a rare BRCA variant.

Nazneen prefers to assume that VOUS are not implicated in advance, we should stick to reporting what is known, until such time a previous VOUS is declared to be pathogenic in some form. But we should be able to autoclassify 95% of the obvious variants, reducing some of the interpretation burden. Any interpretation pipeline needs to be dynamic and iteratively improved with decision trees built into the software. As such control variant data is important, ethnic variation is a common trigger for VOUS, where the variant is not in the reference sequence, but is a population level variant for an ethnic group.

Incorporating gene level information is desirable but rarely used. For instance information about how variable a gene is would be useful in assessing whether something was likely to be pathogenic – against a background which may be highly changeable vs. one that changes little.

Although variants are generally stratified into 5 levels of significance they really need to be collapsed down into a binary state of ‘do something’ or ‘do nothing’. A number of programs help in the classification including SIFT, PolyPhen, MAPP, AlignGVD, NN-Splice, MutationTaster. The report also has Google Scholar link outs (considered to be easier to query sanely than PubMed).

To speed analysis all the tools are used to precompute scores for every base substitution possible in the panel design.

5.3    Timothy Caulfield, University of Alberta, Canada: “Marketing the Myth of Personalised Prevention in the Age of Genomics”

No notes, here but an honorable mention for Tim who gave what was easily the most entertaining talk of the conference focusing on the misappropriation of genomics health by the snake oil industries of genomic matched dating, genomic influenced exercise regimes and variant led diets.  He also asked the dangerous question that if you 1) eat healthily 2) don’t smoke 3) drink in moderation 4) exercise is there really any value in personalized medicine except for a few edge cases? Health advice hasn’t changed much in decades. And people still live unhealthily. You won’t change this by offering them a genetic test and asking them to modify their behavior. If you ever have a chance to see Tim speak, it’s worth attending. He asked for a show of hands who had done 23andMe. Quite shocking for a genetics conference 3 people had their hand in the air. Myself, Tim and one of the other speakers.

HGV2014 Meeting Report, Session 2 “THE TRACTABLE CANCER GENOME”

Caveats: I have not taken notes in every talk of every session, a lack of notes for a particular speaker does not constitute disinterest on my part, I simply took notes for the talks that were directly related to my current work. If I have misquoted, misrepresented or misunderstood anything, and you are the speaker concerned, or a member of the team involved in the work, please leave a comment on the post, and I will rectify the situation accordingly.

2.1    Lillian Su, University of Toronto: “Prioritising Therapeutic Targets in the Context of Intratumour Heterogeneity”

The central question is how we can move towards molecular profiling of a patient. Heterogeneity of cancers includes not just inter-patient difference but also intra-patient differences, either within a tumour itself, or a primary tumour and its secondary metastases.

Lillian was reporting the on the IMPACT study, which has no fresh biopsy material available so works exclusively from FFPE samples Their initial work has been using a 40 gene TruSeq Custom Amplicon hotspot panel, but they are in the process of developing their own ‘550 gene’ panel which will have the report integrated with the EHR system.

The 550 gene panel has 52 hereditary hotspots, 51 full length genes and the rest presumably hotspot location. There’s also 45 SNP’s for QA/sample tracking.

Lillian went on to outline the difference between trial types and the effects of inter-individual differences. Patients can be stratified into ‘umbrella’ trials – which are histology let, or ‘basket’ trials which are led by genetic mutations (as well as N-of-1 studies where you have unmatched comparisons of drugs).

But none of this addresses the intra-patient heterogeneity, it’s not really considered in clinical trial design. Not all genes have good concordance in terms of the mutation spectra between primary and metastatic stakes (PIK3CA was given as an example). What is really required is a knowledge base of tumour heterogeneity before a truly effective trial design can be constructed. And how do you link alterations to clinical actions?

Critical paper: http://www.nature.com/nm/journal/v20/n6/abs/nm.3559.html “Whole-exome sequencing and clinical interpretation of formalin-fixed, paraffin-embedded tumor samples to guide precision cancer medicine”

Lillian outlined the filtering strategy for variants from FFPE and matched bloods. This was a MAF of <1% in 1kG data, a VAF of >5% and a DP>500x for the tumour, and DP>50x in matched bloods. Data was cross-referenced with COSMIC, TCGA, LDSBs and existing clinical trials, and missense mutations characterized with Polyphen, SIGT, LRT (likelihood ratio test) and MutationTaster.

They are able to pick out events like KRAS G12 mutations that are enriched on treatment, and this is a driver mutation, so the treatment enriches the driver over time.

Critical paper: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3436069/ “The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers”

Lillian sees WES/WGS as important as a long term investment rather than panels as well as the use of RNA-Seq in investigating heterogeneity. Ideally you want a machine learning system overlaid over the NGS datasets. Deep sequencing of tumours early might give you some idea of whether the tumour heterogeneity is pre-existing, or is it a result of tumoural selection over time. It was acknowledged that this was hard to do for every patient but would answer more long standing questions about the existence of resistant subclones being present and stable at the start of tumourogenesis.

2.2    Charles Lee, JAX labs: “Mouse PDX and Avatars: The Jackson Laboratory Experience”

PDX stands for “Patient Derived Xenografts”. This was an amazing talk, and as such I have few notes. The basic premise here is to take a tumour from a patient and segment it and implant the segments into immunodefficient mice where the tumours can grow. There was a lot of detail on the mouse strains involved, but the applications for this seem to be huge. Tumours can be treated in situ with a number of compounds and this information used to stratify patient treatment. The material can be used for CNV work, grown up for biobanking, expression profiling etc.

Fitting in with the previous talk, this model can also be used for investigating tumour heterogeneity as you can transplant different sections of the same tumour and then follow e.g. size in response to drug dosage in a number of animals all harbouring parts of the same original tumour.

Importantly this is not just limited to solid tumour work as AML human cell lines can also be established in the mice in a matter of weeks.

2.3    Frederica Di Nicolantonio, University of Torino, Italy: “Druggable Kinases in colorectal cancer”

The quote that stayed with me from the beginning of the talk was “Precision cancer medicine stands on exceptions”. The success stories of genomic guided medicine in cancer such as EGFR and ALK mutations are actually present in very small subsets of tumours. The ALK mutation is important in NSCLC tumours, but this is only 4% of tumours and only 2% respond. Colorectal cancer (CRC) is characterized by EGFR mutations and disruption of the RAS/RAF pathway.

However the situation is that you can’t just use mutation data to predict the response to a chemotherapeutic agent. BRAF mutations give different responses to drugs in melanomas vs. CRC because the melanomas have no expression of EGFR, owing to the differences in their embryonic origin.

Consequently in cell-line studies the important question to ask is are the gene expression profiles of the cell line appropriate to the tumour? This may determine the response to treatment, which may or not be the same depending on how the cell line has developed during its time in culture. Are cell lines actually a good model at all?

Frederica made a point that RNA-Seq might not be the best for determining outlier gene expression and immunohistochemistry was their preferred route to determine whether the cell line and tumour were still in sync in terms of gene expression/drug response.

2.4    Nick Orr, Institute of Cancer Research, London “Large-scale fine-mapping and functional characterisation identifies novel bresat cancer susceptibility loci at 9q31.2”

Nick started off talking about the various classes of risk alleles that exist for breast cancer. At the top of the list there are the high penetrance risk alleles in BRCA1 and BRCA2. In the middle there are moderate risk alleles at relatively low frequency in ATM and PALB2. Then there is a whole suite of common variants that are low risk, but population wide (FGFR2 mutations cited as an example).

With breast cancer the family history is still the most important predictive factor, but even so 50% of clearly familial breast cancer cases are genetically unexplained.

He went on to talk about the COGS study which has a website at http://nature.com/icogs which involved a large GWAS study of 10k cases and 12k controls. This was then followed up in a replication study of 45k cases and 45k controls.

Nick has been involved in the fine mapping follow up of the COGS data, but one of the important data points was an 11q13 association with TERT and FGFR2.

Critical paper: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3483423/ “Breast cancer risk-associated SNPs modulate the affinity of chromatin for FOXA1 and alter gene expression”

Data was presented on the fine mapping work that shows associated SNPs mapping to DNAseI hypersensitivity sites in MCF7 (a metastatic breast cancer cell line) as well as to transcription binding factor sites. This work relied on information from RegulomeDB: http://regulomedb.org/.

One of the most impressive feats of this talk was Nick reeling off 7 digit rsID’s repeatedly during his slides without stumbling over the numbers.

Work has also been performed to generate eQTLS. The GWAS loci are largely cis acting regulators of transcription factors.