Post by MPN-MATE Admin » Thu Nov 07, 2024 10:21 am
Morning everyone...
I recently received this new article that emanates out of a German Laboratory in Munich. It purports to have more accurately found a way to simplify the MPN Tree of Classifications of MPN illnesses. It uses computer modelling & machine learning to advance it theories. However, they claim to produce a circa 98% rate of accuracy. Hence, always well worth leaning into and trying to understand what they are endeavouring to say...
Their conclusions are of some interest to my own lay-ways of thinking... Especially the one that suggests JAK2 VAF diminish during Blast Phase (BP). Once again denoting why following ones Allele Frequencies (to some extent) might prove useful... (in my view...). 
However, please also accept my apologies in advance for not presenting the graphical imagery that accompanies this article, (I do have the PDF file for any who may wish a copy).
I had copied the entire article here below, however it contained to many characters for MPN Voice website to allow. Full article also POSTED on MPN-MATE.com
mpn-mate.com/forum/viewtopi...
I have also prefaced it by Posting. the News Article, which helps to simplify the jargon used etc.
Hope you like me might find some of this article well worth the reading...
Best wishes
Steve
Simple Decision Tree Could Help Stratify Patients With MPN
Özge Özkaya, MSc, PhD | November 5, 2024
Mutations in the SRSF2, TET2, and RUNX1 genes mark the transition to the blast phase.
Driver mutations may change with time and mutations driving myeloproliferative neoplasms (MPN) such as myelofibrosis (MF) that are present at diagnosis may disappear, found a new study published in the scientific journal Leukemia.
Based on this finding, the authors suggested that a broader genetic screening at diagnosis as well as at clinical progression should be performed.
For the study, a team of researchers led by Manja Meggendorfer, PhD, from MLL Munich Leukemia Laboratory in Munich, Germany, conducted a thorough genetic analysis and developed a model relying on 12 genetic markers to accurately stratify patients with chronic-phase MPN according to World Health Organization groups.
When they compared samples at chronic and blast phases, the researchers found that a third of patients lost their MPN driver-gene mutation. Mutations in splicing and chromatin-modifying genes were, however, stable.
They reported that mutations in the TET2, SRSF2, and RUNX1 genes mark the transition to the blast phase with mutations in the TP53 gene also prevalent during the transition.
This, they concluded, indicates a shared founder clone of chronic and blast phases with different driver mutations and therefore different progressing capacities.
This finding was further supported by the gain of typical de novo acute myeloid leukemia gene mutations, together with a gain of complex karyotypes and genetic mutations in the RAS pathway.
The team also showed that the model could be simplified into a decision tree that could routinely be used in the clinic.
“Our model, incorporating these markers, can determine patients’ risk of transformation,” they wrote. They explained that patients with essential thrombocytopenia, who are typically considered low-risk, may actually be genetically high-risk.
“Consequently, expanding genetic analysis beyond JAK2, CALR, and MPL at diagnosis is crucial for accurate [myeloproliferative] classification, early high-risk patient identification, and timely intervention,” they concluded.
ORIGINAL ARTICLE - (references below)
Characterization of myeloproliferative neoplasms based on genetics only and prognostication of transformation to blast phase
Wencke & Meggendorfer et al. 2024, Leukemia
Abstract
Myeloproliferative neoplasms (MPN) are a heterogeneous group of clonal disorders characterized by aberrant hematopoietic proliferation and an intrinsic risk of progression to blast phase. The WHO classification 2022 identifies chronic myeloid leukemia and the BCR::ABL1 negative MPNs polycythemia vera, primary myelofibrosis and essential thrombocythemia as individual entities. However, overlaps, borderline findings or transitions between MPN subtypes occur and incomplete clinical data often complicates diagnosis. By conducting a thorough genetic analysis, we’ve developed a model that relies on 12 genetic markers to accurately stratify MPN patients. The model can be simplified into a decision tree for routine use. Comparing samples at chronic and blast phase revealed, that one third of patients lost their MPN driver-gene mutation, while mutations in splicing and chromatin modifying genes were stable, indicating a shared founder clone of chronic and blast phase with different driver mutations and therefore different progressing capacities. This was further supported by gain of typical de novo AML gene mutations, accompanied by gain of complex karyotypes and RAS pathway gene mutations. Our data suggest to perform a broader genetic screening at diagnosis and also at clinical progression, as driver mutations may change and the MPN-driver mutations present at diagnosis may disappear.
(see original article for all graphical imagery)
Up to 20% of MPN cases progress to BP and multiple progression factors have been identified. As shown in other studies, TP53 mutations play a significant role in leukemic transformation [21, 31], and SRSF2 mutations are found in all entities as an adverse risk factor [20]. However, to the best of our knowledge, the simultaneous and comprehensive exome-wide assessment of SNVs, CNVs, and CN-LOH events before and after progression has not yet been performed in a cohort of substantial size.
We analyzed 19 paired MPN-CP/MPN-BP samples, and all but one were classified in chronic phase into the group with mutations in ASXL1, SF3B1, SRSF2, U2AF1, or TP53, representing high-risk patients. Thus, the samples did not show entity-specific but rather prognosis-specific assignment.
The clonal hierarchy of MPN is heterogeneous, as exemplified in this study by the fact that independent clones in addition to the MPN clone can also drive leukemic transformation [32]. This is in line with a recent study of clonal dynamics in MPN based on single-cell genotype data, that showed that the emergence of new dominating clones underlines progression to MPN-BP in most instances [33]. In our study, 138 PV/ET/PMF cases of cohort 1 showed additional mutations, whereof 86/138 (62%) presented with the MPN-driver mutation within the dominant clone. However, any correlation to the progressing clone was not observable in the MPN-CP/MPN-BP cohort, showing 8/19 (42%) cases with a dominant MPN-driver clone at MPN-CP.
In summary, our study identifies 12 genetic markers sufficient for classifying chronic phase MPN patients according to WHO groups. We found that mutations in SRSF2, TET2, and RUNX1 mark the transition to BP, with TP53 mutations also prevalent during this shift. Our model, incorporating these markers, can determine patients’ risk of transformation, highlighting that ET patients, typically considered low-risk, may actually be high-risk genetically. Consequently, expanding genetic analysis beyond JAK2, CALR, and MPL at diagnosis is crucial for accurate MPN classification, early high-risk patient identification, and timely intervention.
REFERENCES
Walter, W., Nadarajah, N., Hutter, S. et al. Characterization of myeloproliferative neoplasms based on genetics only and prognostication of transformation to blast phase. Leukemia (2024). doi.org/10.1038/s41375-024-...
