Estudio de mercado

During the past five years, NGS technology has enabled to analyze MPN patients in more detail than ever before. Still, one of the most intriguing questions in MPN was and still is how different phenotypes can arise from the same driver mutation. Based on our experience from NGS studies of MPN patients, we suggest, that the phenotype of MPN patients is an integration of various genetic aberrations (Figure 35).

At the first level of these genetic changes, driver mutations in JAK2, CALR or MPL define the subset of MPN phenotypes. Mutations in CALR and MPL specifically enhance signaling of the MPL receptor and result in either ET or PMF phenotype. As there are no PV patients with CALR or MPL mutation or ET/PMF patients with JAK2-exon12 mutation, the effect of these mutations is definitive and non-reversible. The JAK2-V617F mutation is found in all MPN phenotypes, which might be due to the universal role of JAK2 in the signal transduction of cytokines and their receptors.

The next level is represented by somatic mutations. The modulating effect of somatic mutations seem to be less pronounced as compared to driver mutations as distinct somatic mutations might enhance or neutralize each other’s effect. In this context, clonal architecture might play a role and needs to be analyzed in large scale. Different clonal branches within a patient might add separate individual effects modulating the MPN phenotype. Interestingly, a recent study shows that the order of acquisition of somatic mutations has impact on treatment response and clinical correlates(146).

In another layer of complexity, the type of mutation within one gene might have huge impact, as seen in IDH1 and IDH2, were only specific mutations enable the enzyme to generate a new metabolite(137,138). Another example is DNMT3A, as DNMT3A-R882 mutant protein has been shown to inhibit wild type protein and to associate with the PRC1 complex(144). Observed frame shift or stop mutations in the DNMT3A might not support the exactly same effect due to major changes in the amino acid sequence. The final layer in our current version of this model contains germline alterations. Specific variations in the population have been shown to correlate with elevated numbers of specific blood lineages(154). In MPN, SNPs might have an impact on the severity of the symptoms and thereby, potentially influence age at diagnosis. In patients with JAK2-V617F and no other somatic mutation, germline variants might represent the feather that breaks the balance towards a red cell or megakaryocytic phenotype.

The model doesn’t have to end at this layer. As there are approximately 60% of patients, who do not carry somatic mutations additional to the driver mutations, other factors are likely to be uncovered in future. Potential candidates are genetic alterations affecting regulatory elements or miRNAs.

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Figure 35 Multiple facets of genotypes generate the MPN phenotype in patients

Proposed multi-layered model of how different genetic alterations might contribute to the MPN phenotypes. The gene names are colored according to the phenotype, which they are suggested to promote in a mutated state. Red: PV, yellow: ET, brown: PMF.

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4.8 Future

MPN are representing a rare group of disorders. As research needs to focus on subgroups of patients, local cohorts seem to reach a limit of what conclusions can be made. Currently, research groups are combining their cohorts in order to reach significance in their observations. In future, not only cohorts should be merged, also it will be important to combine NGS results with data from other available knowledge bases. This will help to make better predictions or potentially give insights, which would have been missed when analyzing NGS data as individual, self-contained experiments.

The need of running innovative analysis and complex experiments has been recognized. In hospitals, medical doctors have started work together with biologists and computer scientists to make use of the newest insights and provide detailed diagnoses. The implementation of NGS technologies in the daily routine recently created the fancy terms of precision diagnostics and personalized medicine.

However, as fast as medicine is incorporating new technologies, new challenges arise. BigData, a term which initially was used by giant internet companies, now also finds its way to research and soon also to medicine. The more data is generated by technologies like NGS methods, the more of these analyses infringe the anonymity of the patients. Anonymity is important in research, as the declaration of Helsinki, which sets ethical principles for medical research involving human subjects, clearly states, that study participants must not be affected by disadvantages as a result of the study. Therefore, proper data handling and protection of the study participants’ rights will play an important role, when data is combined or shared worldwide. At the same time, the general population started to use gadgets and apps to track health and record achievements in sports. Even whole genome analyses are offered to private persons. Therefore, the accumulation of personal data might also change the way the public values this private information. These recordings might be of interest in future studies, as they might offer information from pre-malignant phases or allow automatic administration of treatment based on on-time measurement, for example heart rate assessed by fitness trackers. It will be interesting to follow which potential can be unleashed from these new developments.

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