Describing the Mutational Characteristics of Myxofibrosarcoma: An AACR Project GENIE Analysis

Myxofibrosarcomas (MFS) are highly infiltrative soft tissue sarcomas that most commonly occur in adults within the sixth to seventh decades. Diagnosis relies on histopathological analysis as no definitive molecular markers have been identified. This study seeks to describe the mutational landscape of MFS, characterize mutations unique to certain populations, and identify mutations that may be of particular utility in diagnosis and treatment.

Using the AACR Project GENIE database, we identified a cohort of 202 patients with MFS. Patients were stratified by sex, age, race, and ethnicity. Tumors were categorized as primary, metastatic, locally recurrent, or distant organ metastases. Somatic mutations and copy number alterations were identified. Data were analyzed using R and RStudio, with p<0.05 denoting statistical significance.

We are the first to link the following mutations to MFS: NOTCH3, ALOX12B, SDHA, ETV6, NCOA2 and SOS2. The most common somatic mutations included TP53 (27.98%), ATRX (14.68%), NF1 (9.17%), and RB1 (7.80%). Homozygous deletions were most frequent in TP53 (28.7%), CDKN2A (20.5%), CDKN2B (19.48%), and RB1 (15.38%), while amplifications were most frequent in NCOR1 (6.29%) and FLCN (5.13%). Several mutations frequently co-occurred, while NF1 and RB1 demonstrated total mutual exclusivity. NCOA2 mutations were exclusive to White patients and NKX2-1 to non-White patients. Mutations in MAP2K4 and ALOX12B were unique to males, while SDHA mutations were unique to females.

As we enter the era of precision medicine, classifying cancers by molecular markers will become increasingly valuable. Our investigation enriches the literature by identifying novel mutations and mutations exclusive to certain demographic groups. These findings support a shift beyond histology toward molecularly informed diagnostics and pathway-directed therapeutic hypotheses for MFS. Next steps should validate candidate markers in independent cohorts and link genomic profiles to clinicopathologic features, disease course, and treatment response to improve clinical translation. These observations will help shape diagnostics and targeted therapies against MFS.