Uta Flucke

Mesenchymal neoplasms driven by protein kinases represent an increasing group with a broad clinicopathologic and genetic spectrum. Recently, tumors harboring a GAB1::ABL1 fusion have been reported, resulting in constitutive activation of the non-receptor tyrosine kinase ABL1. We describe six cases (one previously reported) investigated by fusion gene analysis and DNA-methylation profiling. Tumors were from three males and three females aged 7–71 years and arose in the neck, axilla, lower back, thigh, and digits. Lesions were excised. Available follow-up was uneventful. Neoplasms were circumscribed but unencapsulated, with infiltration of the surrounding tissue, and characterized by haphazardly arranged monomorphous spindle cells. There was a fibromyxoid stroma and staghorn-like vessels were present. Immunohistochemically, expression of CD34 (3/6), S100 (1/6), EMA (2/6), and GLUT1 (3/3) was observed. All cases harbored a GAB1::ABL1 fusion. RNA expression profile (n = 2) showed high-confidence similarity to dermatofibrosarcoma protuberans (DFSP). DNA-methylation profiling demonstrated that all cases clustered together in close proximity to DFSP. Application of the latest Heidelberg methylation sarcoma classifier did not confidentially classify these neoplasms. However, four showed low-confidence matches to NTRK-rearranged spindle cell neoplasms. GAB1::ABL1 spindle cell neoplasms represent a distinct member of the family of kinase-driven mesenchymal tumors with an indolent clinical course, while the activating ABL1 fusion raises the possibility of targeted therapy in selected cases.

The high heterogeneity of pediatric cancers presents significant diagnostic challenges, underscoring the need for accurate classification. Although molecular profiling supports first-line diagnostics and guides treatment, it can delay final diagnosis. While Nanopore-based methylation analysis has enabled rapid CNS tumor diagnosis, its application to pediatric solid tumors and lymphomas has remained largely unexplored. We developed Tucan, a deep-learning classifier trained on 3,818 methylation array profiles representing 84 subtypes, designed to classify tumors from sparse Nanopore methylation data. In retrospective validation (n=514), Tucan generated confident predictions (CFT ≥ 0.7) within 30 minutes of sequencing in 385 cases, achieving 372 correct diagnoses (F1-score: 0.98). In prospective testing (n=74; 63 classifiable), 52 samples reached the confidence threshold with 96% accuracy, confirming the original diagnosis in 47 cases and correctly refining or revising it in three. Together, Tucan enables rapid, high-confidence molecular classification of pediatric solid tumors and lymphomas.

Advanced and widespread molecular techniques have deepened our understanding of mesenchymal lesions, revealing considerable overlap among morphologically defined entities now known to be related to protein kinases (PKs). This paradigm shift is important for understanding oncogenesis and also in terms of treatment options and prognosis. Therefore, it is preferable to stratify these tumors molecularly instead of morphologically, as the different categories have clinical implications. Molecular analyses are an essential and integrated part of the diagnostic workup of tissue specimens, especially those of young patients. Involved PKs range from receptor tyrosine kinases (neurotrophic tyrosine receptor kinase [NTRK]1, 2, 3; anaplastic lymphoma kinase [ALK]; proto-oncogene 1 [ROS1]; proto-oncogene [RET]; and proto-oncogene/hepatocyte growth factor receptor [MET]; etc.) to intracytoplasmic serine/threonine kinases (RAF proteins) activating the same pathways. Morphological patterns vary from infantile fibrosarcoma(-like) to lipofibromatosis(-like), dermatofibrosarcoma protuberans(-like), and malignant peripheral nerve sheath tumor-like. However, there is considerable overlap histopathologically and immunohistochemically. Most of the neoplasms are (myo)fibroblastic in type, consisting of monomorphic cells. A hemangiopericytoma-like vasculature can be a diagnostic clue. The immunophenotype is characterized by variable expression of smooth muscle actin (SMA)/desmin/CD34 or CD34/S100. This review provides updates to understand the currently known spectrum of PK-related lesions, with emphasis on those occurring more rarely, to aid proper diagnoses and treatment. The aim is to contribute to a better holistic classification.

Context: Nodular fasciitis (NF), myositis ossificans/fibro‐osseous pseudotumor of the digits (MO/FP) and aneurysmal bone cyst (ABC) are grouped under the category of ‘ USP6 ‐associated neoplasms’ (UANs) due to their shared characteristic of an underlying USP6 rearrangement. Objective: Our aim in this study is to investigate the diversity of USP6 rearrangements in UANs. Design: We performed a clinicopathological and molecular investigation using targeted RNA sequencing on 124 cases of NF, 19 cases of MO/PF and 32 cases of ABC. Additionally, NF cases were scored for the presence of typical morphological features to assess their correlation with the detected USP6 fusion partner. Results: In 85.4% of NF cases (88/103), we identified a USP6 rearrangement, with 46.6% exhibiting the classic MYH9::USP6 fusion and 53.4% displaying non‐ MYH9::USP6 fusions. Notably, classic histological features of NF strongly correlated with the presence of the MYH9::USP6 fusion, while specific NF locations (e.g. intramuscular, intra‐articular and intradermal) were significantly associated with non‐ MYH9 fusions. This study identified 22 novel USP6 fusion partners, demonstrating the fusion diversity within UANs. Additionally, there was minimal overlap in USP6 fusion partners between different UAN types. Conclusions: A significant entity‐ and location‐specific USP6 fusion partner diversity was revealed among the UAN members. This supports the hypothesis that this diversity may be linked to the cell of origin. Additionally, molecular analysis can be valuable in diagnosing non‐classical NF.

Supplementary Figure 1. Tumor Mutational Burden.

Purpose:Angiosarcomas (AS) are rare vascular sarcomas. Secondary AS (sAS) arise from DNA-damaging factors such as radiotherapy and UV radiation (UV-AS) or due to chronic lymphedema. The prognosis for advanced AS is poor, with limited treatment options. Immune checkpoint inhibition is not approved for AS, but high intratumoral T-cell density and frequent mutations in sAS may support efficacy.Patients and Methods:This prospective, single-arm, multicenter phase II trial assessed the efficacy and safety of cemiplimab (350 mg, intravenously every 3 weeks) in patients with locally advanced or metastatic sAS using a Simon’s two-stage design. The primary outcome was the best overall response rate within 24 weeks of treatment. Secondary outcomes included time to response, duration of response, progression-free survival, overall survival, and predictive biomarkers for treatment response.Results:Eighteen patients (12 with AS from radiotherapy, 3 with UV-AS, and 3 with AS due to chronic lymphedema) were treated with cemiplimab. The best overall response rate was 27.8% (4 partial responses, 1 complete response), with a time to response of 2.6 months and a duration of response of 6.9 months. The median progression-free survival was 3.7 months, and the median overall survival was 13.1 months. Grade ≥3 immune-related adverse events occurred in 33.3% of patients. High tumor mutational burden was observed in three patients with UV-AS, two of whom showed a response. High intratumoral CD3+ (P = 0.019), CD4 (P = 0.046), CD8+ (P = 0.026), and FoxP3+ (P = 0.026) T-cell densities; low platelet-to-lymphocyte ratio (P = 0.026); and Colidextribacter abundance were associated with response.Conclusions:Cemiplimab shows promising effectivity in sAS and warrants further investigation. Promising predictive blood and tissue biomarkers were identified, indicating potential for improved patient selection.

Supplementary Figure 4. Change in gut microbiome during treatment with cemiplimab.

Supplementary Table 1. Representativeness of Study Participants.

Supplementary Table 5. Immune cell infiltrate density in tumor vs stroma.

Supplementary Table 2. Adverse Events.