As you know from very many of the meetings that many of the large scale sequencing efforts have identified a lot of driver mutations and the frequent mutations and we also know from the cytogenetics about a lot of aberrations. But if you look at the heterogeneity of the treatment outcome and drug response there’s no connection so far made to link those genomic alterations to individual drug responses. So much of the effort has been driven in the precision medicine to try to understand phenotype from genotype; our approach is the opposite. So we take the phenotype, OK this patient is responding or not responding, then we get back to the molecular features and the genomics and transcriptomics and proteomics to understand why. We also try to exploit that information to treat our patients in the clinic. So that was the main goal of our whole studies.
What was your approach?
Our approach is to tailor individualised treatment strategies for the multiple myeloma patients. The way we do it, we screen each multiple myeloma sample that we derive from clinic with all the oncology compounds approved and investigational. With the same samples we do NGS, the exome sequencing, we do RNA sequencing, we do have clinical information, the karyotype information, and all the clinical parameters and outcome. We also do proteomics. What we try to do with all the information from different levels of the systems biology to integrate the information and try to understand the molecular biology of the disease. One important thing that we do follow up our patients over time so during the four years’ time we take multiple sampling so we do test the real drug sensory landscape each time the patient is getting new treatment. Also we do follow the progression of the disease during the whole time so it’s very dynamic.
What were your findings?
If I sum up with the major finding is that we do see… the genomic landscape, you know, is very heterogeneous so we do see heterogeneity in the drug response too. We saw that but broadly the patients could be categorised based on the drug response into four different subgroups. One subgroup was very interesting in terms of they are very sensitive to many of the signal transduction inhibitors. So we looked into the data on survival, they actually had a poor survival. We looked into the genomics and then we saw that they have the highest mutational load and they have exclusively altered DNA repair genes and also many signalling pathway genes. So they have activated many escape routes and these are the very progressive patients. These are the patients that have gone through already multiple lines of treatment and the doctor doesn’t have any treatment for the patients anymore. So what our approach is and what we have done for these patients is we took the drug response data and the genomics data and we saw that this group of drugs are very active in the patients. So we combined the signal transduction molecules, the approved one, for example rapalogs we have combined with existing proteasome inhibitors, and we do see a dramatic response in those patients. We do see some patients go partial response and some patients… We also do another thing that you know that a very limited amount of drug is approved for one disease and whatever the stage of the disease sometime, except for the karyotype, many of the patients get the same line of treatment based on the diagnostic phase or relapsed phase. But it is very important to understand that not all the patients would respond the same way to the same treatment and the timing is the most important and the sequence. What our stake is that we do capture the sensory landscape each time the patient relapses so we know exactly what the patient is responsive to the treatment at the very moment. We do modify the treatment strategies, fine tune the treatment strategies, based on the response. We have very good success in translating the information to the clinic and we are very hopeful about it.
What were the implications?
The main implication of our study is to do this phenotype [?? 5:09] and combine the molecular information to exactly diagnose the staging of the disease and the right treatment at the right time for each of the patients. Precise treatments and individualised treatments for each patient which will ultimately improve the treatment outcome in the myeloma patients.