Frequency and prognostic impact of blood-circulating tumor mast cells in mastocytosis

Predictors of Clonality and Underlying Mastocytosis in Mast Cell Activation Syndromes

PURPOSE OF REVIEW

Mast cell (MC) activation can present with a wide range of symptoms. The mechanisms that cause such activation are varied. One of them is the presence of clonal MCs which is defined, within other possible changes, by the presence of a somatic, activating mutation in the KIT gene. The clinical course and prognosis of patients with this underlying disease may be different from other causes of MC activation (MCA). For this reason, it is important to early diagnose, or at least suspect, which patients with MCA are due to clonal MCs.

RECENT FINDINGS

The diagnosis of clonality must be made in a comprehensive manner. However, this paper reviews chronologically each of the stages from the patient's first visit to the doctor's office which can be indicative of clonality: clinical presentation of MCA, physical examination, analytical determinations of tryptase, and/or KIT mutational analysis and bone involvement, among others. The different clonality predictive scores proposed are also reviewed and compared. Although the gold standard for the diagnosis of certainty of MC clonality is the performance of a bone marrow (BM) biopsy, there are clinical symptoms, signs, and biological parameters suggestive of clonality, as well as predictive scores, which can guide (or rule out) an early diagnosis and avoid unnecessary BM biopsies.

The role of flow cytometry in the classification of myeloid disorders

The World Health Organization classification (WHO-HAEM5) and the International Consensus Classification (ICC 2022) of myeloid neoplasms are based on the integration of clinical, morphologic, immunophenotypic, and genomic data. Flow cytometric immunophenotyping (FCIP) allows the identification, enumeration, and characterization of hematopoietic cells, and is therefore a powerful tool in the diagnosis, classification, and monitoring of hematological neoplasms. The vast majority of flow cytometry (FCM) studies in chronic myeloid neoplasms focus on its role in myelodysplastic neoplasms (MDS). FCM can also be helpful for the assessment of myeloproliferative neoplasms (MPN) and MDS/MPN, including the early detection of evolving myeloid or lymphoid blast crisis and the characterization of monocytic subsets. The classification of acute myeloid leukemia (AML) is primarily based on cytogenetic and molecular findings; however, FCIP is needed for subclassification of AML, not otherwise specified (NOS; ICC)/AML defined by differentiation (WHO-HAEM5). The main role of FCM in AML remains in making a rapid diagnosis and as a tool for measurable residual disease monitoring. Machine learning and artificial intelligence approaches can be used to analyze and classify FCM data. This article, based on an invited lecture at the 106th Annual Meeting of the German Society of Pathology in 2023, reviews the role of FCM in the classification of myeloid neoplasms, including recent publications on the application of artificial intelligence.

The international consensus classification of mastocytosis and related entities

Mastocytosis is a neoplasm characterized by a clonal proliferation of mast cells, which accumulate in one or multiple organs, associated with an extremely heterogeneous clinical presentation. The disease can be limited to the skin (cutaneous mastocytosis) that is mostly seen in childhood and usually behaves in a benign fashion. Adult patients most often present with systemic disease with or without skin lesions. This includes indolent forms such as indolent systemic mastocytosis and its subvariant bone marrow mastocytosis, and smoldering systemic mastocytosis as well as aggressive forms including aggressive systemic mastocytosis, systemic mastocytosis with an associated myeloid neoplasm (previously called systemic mastocytosis with an associated hematologic neoplasm), and mast cell leukemia. In addition, mast cell sarcoma is a rare aggressive form of mastocytosis that can present in the skin as well as at extracutaneous sites. This review article focuses on the updates in mastocytosis of the 2022 international consensus classification (ICC).

Tumor Microenvironment Characterization in Breast Cancer Identifies Prognostic Pathway Signatures

Breast cancer is one of the most common female malignancies worldwide. Due to its early metastases formation and a high degree of malignancy, the 10 year-survival rate of metastatic breast cancer does not exceed 30%. Thus, more precise biomarkers are urgently needed. In our study, we first estimated the tumor microenvironment (TME) infiltration using the xCell algorithm. Based on TME infiltration, the three main TME clusters were identified using consensus clustering. Our results showed that the three main TME clusters cause significant differences in survival rates and TME infiltration patterns (log-rank test, p = 0.006). Then, multiple machine learning algorithms were used to develop a nine-pathway-based TME-related risk model to predict the prognosis of breast cancer (BRCA) patients (the immune-related pathway-based risk score, defined as IPRS). Based on the IPRS, BRCA patients were divided into two subgroups, and patients in the IPRS-low group presented significantly better overall survival (OS) rates than the IPRS-high group (log-rank test, p < 0.0001). Correlation analysis revealed that the IPRS-low group was characterized by increases in immune-related scores (cytolytic activity (CYT), major histocompatibility complex (MHC), T cell-inflamed immune gene expression profile (GEP), ESTIMATE, immune, and stromal scores) while exhibiting decreases in tumor purity, suggesting IPRS-low patients may have a strong immune response. Additionally, the gene-set enrichment analysis (GSEA) result confirmed that the IPRS-low patients were significantly enriched in several immune-associated signaling pathways. Furthermore, multivariate Cox analysis revealed that the IPRS was an independent prognostic biomarker after adjustment by clinicopathologic characteristics. The prognostic value of the IPRS model was further validated in three external validation cohorts. Altogether, our findings demonstrated that the IPRS was a powerful predictor to screen out certain populations with better prognosis in breast cancer and may serve as a potential biomarker guiding clinical treatment decisions.

Proposed European Competence Network on Mastocytosis-American Initiative in Mast Cell Diseases (ECNM-AIM) Response Criteria in Advanced Systemic Mastocytosis

Advanced systemic mastocytosis (AdvSM) is characterized by the presence of KIT D816V and other somatic mutations (eg, in SRSF2, ASXL1, and RUNX1) in 95% and 60% to 70% of patients, respectively. The biological and clinical consequences of AdvSM include multilineage involvement (eg, associated hematologic neoplasm) in 60% to 80% of patients, variable infiltration and damage (C-findings) of predominantly bone marrow and visceral organs through affected mast cell (MC) and non-MC lineages, and elevated levels of serum tryptase. Recently, the treatment landscape has substantially changed with the introduction of the multikinase/KIT inhibitor midostaurin and the selective KIT D816V inhibitor avapritinib. In this review, we discuss the evolution of AdvSM response criteria that have been developed to better capture clinical benefit (eg, improved responses and progression-free and overall survival). We propose refined response criteria from European Competence Network on Mastocytosis and American Initiative in Mast Cell Diseases investigators that use a tiered approach to segregate the effects of histopathologic (eg, bone marrow MC burden, tryptase), molecular (eg, KIT D816V variant allele frequency), clinical (eg, C-findings), and symptom response on long-term outcomes. These response criteria require evaluation in future prospective clinical trials of selective KIT inhibitors and other novel agents.