Emerging evidence suggests that clinical variables should not be the only consideration in prognostic and risk stratification scoring systems; genomically informed tools carry equal, if not refined, insights regarding patient outcomes.
Emerging evidence suggests that clinical variables should not be the only consideration in prognostic and risk stratification scoring systems, explained Raajit Rampal, MD, PhD, who added that genomically informed tools carry equal, if not refined, insights regarding patient outcomes.
In a virtual presentation during the 2020 SOHO Annual Meeting, Rampal, assistant member and clinical director of the Leukemia Service at Memorial Sloan Kettering Cancer Center, cited the International Prognostic Scoring System (IPSS) as one such tool that has been used to inform prognosis and survival.
“The IPSS takes into account a number of clinical variables, such as age, constitutional symptoms, hemoglobin, leukocytes, and blood blasts, in order to prognosticate the patient’s risk of progression,” said Rampal.
The Dynamic IPSS (DIPSS) and DIPSS-PLUS are additional tools that can be used to calculate patients’ risk of progression.
Despite the phenotypic and prognostic heterogeneity of patients with myeloproliferative neoplasms (MPNs), the hallmark of these diseases is activation of the JAK/STAT pathway, said Rampal. However, in myelofibrosis, approximately half of patients have co-mutations, the most common of which include TET2, ASXL1, and DNMT3A mutations.
In 2013, a paper published in Leukemia demonstrated the prognostic implications of genomic variants in myelofibrosis, wherein SRSF2 (P = .007), EZH2 (P = .03), ASXL1 (P <.0001), and IDH1/2 mutations (P <.0001) were associated with a higher risk of disease progression in leukemic transformation.
Since then, several other mutations have emerged, showing similar prognostic impact. According to research from Memorial Sloan Kettering Cancer Center and The University of Texas MD Anderson Cancer Center, investigators demonstrated that NRAS and KRAS mutations confer worse overall survival and a higher risk of leukemic transformation.
Similarly, poor outcomes are seen when favorable-risk mutations, such as CALR, co-occur with high-risk mutations, such as ASXL1, said Rampal. In a study published in Leukemia in 2014, patients with CALR and ASXL1 mutations were found to have a median survival of 7 years versus 9.6 years in patients with only a CALR mutation.
“How do we integrate this in a clinically meaningful and useful manner? A number of scoring systems have been developed over the past several years, including the MIPSS70, the MIPSS70-plus, and the MIPSS70-plus 2.0 risk scores,” said Rampal.
In addition to accounting for clinical characteristics, the MIPSS70-plus accounts for genetic features, such as the absence of a type 1 CALR mutation, the presence of high molecular risk mutations, and an unfavorable karyotype and, in doing so, clearly distinguishes low-risk patients from high- and very high-risk patients.
Moreover, the interface for MIPSS70 is user friendly, said Rampal, allowing providers to easily input this information while in the clinic.
In addition to informing risk, the type and number of mutations patients harbor predicts for time to treatment failure with ruxolitinib (Jakofi), which is the current standard of care in intermediate- and high-risk myelofibrosis.
“Important work from MD Anderson from a number of years ago showed that the presence of ASXL1, EZH2, and DNMT3A mutations are associated with a quicker time to treatment failure,” said Rampal. “Having 3 or more mutations is associated with a worse prognosis in terms of treatment failure to ruxolitinib.”
In such a patient, it would not be unreasonable to consider allogeneic stem cell transplant, for which there are now also scoring systems that can be used to determine patients’ outcomes.
During the presentation, Rampal called attention to the myelofibrosis transplant scoring system (MTSS), which uses low-, intermediate-, high-, and very high-risk scores as surrogates for 5-year overall survival and non-relapse mortality rates. MTSS accounts for leukocyte count, platelet count, performance status, the presence of a CALR, MPL, and ASXL1 mutation, age, and whether the patient had a human leukocyte antigen mismatched unrelated donor.
Despite the incorporation of CALR, MPL, and ASXL1 mutations into the MTSS and recognition that genomics does play a role in the field, the field has yet to come to a consensus regarding the genetic markers that influence transplant outcomes, said Rampal.
“It’s important to recognize that the genetics are not static. When we look at the genetic profile at baseline, it may not reflect what happens 2 to 3 years down the line in a patient’s disease course. Given the possibility of clonal evolution over time, recurrent genomic testing should strongly be considered,” concluded Rampal.
Rampal R. Genomics in myelofibrosis: practical guidelines for its use in clinical practice. Presented at: SOHO 2020 Annual Meeting; September 9-12, 2020; Virtual. bit.ly/3iXcoxT.