When I was three years old my grandma passed away after a long fight with cancer. I should disclaim quickly: it never affected me greatly, since I was too young to remember anything. I know, though, that that experience was a gruelling one for my mum, who cared for our grandma during the therapy – battle with cancer ain’t pretty in general, and it was even worse then.
It would of course be easy to blame the health system of the communist regime in which we lived at the time but the truth is that the treatment strategy was pretty much the same all over the place: surgery, radiotherapy, chemotherapy. It doesn’t work? Let’s try harsher treatment. And the understanding of the molecular basis of the disease was obviously limited: today it is easy to forget that it wasn’t until the 1970s that oncogenes and tumor suppressor genes were discovered; that BRCA mutations were only described in the mid-90s.
My grandma was one of the many people who did not stand a chance: genome sequencing was still decades away, nobody even dreamt of targeted therapy, nobody really knew what personalized medicine was (or would be). This changed with the turn of the century, and early 2000s brought us an amazing success story: the case of Dr Lukas Wartman from the Washington University of St Louis.
Third time’s the charm
Lukas Wartman, as the story goes, is a cancer researcher from St Louis, who in the early 2000s was diagnosed with a form of leukemia very dangerous in adults, ALL. After initial chemotherapeutic treatment he went into remission. But the disease did not give up, and a few years later Wartman relapsed. Another round of chemo and a stem cell transplant put him back in remission. In 2011 Wartman relapsed again.
His odds were very poor. The 5-year overall survival after first relapse is at 10% in adult ALL. For the second relapse there isn’t even any survival data. And another round of chemotherapy did not help this time. Wartman was at this stage approached by his fellow-researchers from Washington University. In 2011, Tim Ley asked him to join a study looking to sequence a cancer genome.
The team generated not only cancer genome assemblies for Wartman’s cancer – which showed that he was, in reality, carrying two different types of cancer cells – but they also collected transcriptomic and exome data. This was analyzed by another WUStL researcher, Malachi Griffith, who discovered an abnormal expression of the FLT3 gene and identified a drug (usually used in kidney cancer treatment), which could be repurposed for the therapy of Wartman.
As far as I know, at this time Lukas Wartman remains relapse-free, and his story is a hopeful, inspiring example of what can be achieved today in the cancer therapy when using cancer genomic approaches.
Less is more
When we first issued a call for paper for our special issue on the genomics of cancer progression and heterogeneity, I thought that the majority of submissions (and retrospectively I can admit it was a very naïve thought) would be large sequencing cohorts looking at differences between cancer subtypes, whether on genomic, transcriptomic, or epigenomic level. This seemed to be a logical expansion of the current state of the field.
But then I talked to community members, in their labs and at conferences. And one trend started emerging from these chats: that the field of cancer genomics is more and more leaning towards personalized medicine.
Because, despite the deluge of cancer genomic data generated by initiatives such as TCGA and ICGC, we still know very little about the rarer cancer types. Or the unusual subtypes. And by the very definition of a rare event, no cohort looking at these will be large. Moreover, due to inherent heterogeneity of many cancers, even within a supposedly coherent cohort of patients there can still be a substantial amount of heterogeneity. When you add to this cancer evolution processes taking place during cancer progression, and the not uncommon within patient heterogeneity, the situation gets even more complex.
Suddenly even the best designed study, the most homogenous cohort is only a gathering of vaguely similarly diagnosed individuals. The question is: is that a problem? The answer seems to be a resounding ‘no’, and the community clearly is getting more and more comfortable with the idea that in cancer research sometimes less is more.
And here we start…
This week Genome Biology publishes the first articles to appear in our special issue on the genomics of cancer progression and heterogeneity. Michael Berger and Anna Varghese in the first Opinion piece of the issue discuss the difficulties we face when trying to translate hard-core genomics into hard-core medicine, and what they say is that while challenges are aplenty, there is a light at the end of the tunnel.
We also publish several original research articles. First, Kelly Frazer and colleagues introduce a simple, yet effective, method for estimation of biases in the analyses of clonal evolution and cancer heterogeneity. Two more Method articles published today touch on a recurring theme of the issue: that is how to deal with the analysis of the data that is, in a sense, incomplete. Zemin Zhang et al. describe MBASED: a method for the detection of allele-specific expression in the samples for which haplotype phasing information is not available. And Shirley Liu and colleagues give us MethylPurify: a method for studying methylomes of single cancer samples. This algorithm infers tumor purity and uses the estimates to identify differentially methylated regions.
In the coming weeks we will publish a number of excellent research articles, and a rich selection of Review and Opinion pieces. We are very excited to be able to share these with you – like a little child who learned a new word and cannot wait to tell her or his parents about this, we too, having obviously read all these great articles already, are impatiently waiting to put them in your hands.