Machine Learning of Bone Marrow Histopathology Identifies Genetic and Clinical Determinants in Patients with MDS

Publisher Copyright: ©2021 American Association for Cancer Research.In myelodysplastic syndrome (MDS) and myeloproliferative neoplasm (MPN), bone marrow (BM) histopathology is assessed to identify dysplastic cellular morphology, cellularity, and blast excess. Yet, other morphologic findings may elude the human eye. We used convolutional neural networks to extract morphologic features from 236 MDS, 87 MDS/MPN, and 11 control BM biopsies. These features predicted genetic and cytogenetic aberrations, prognosis, age, and gender in multivariate regression models. Highest prediction accuracy was found for TET2 [area under the receiver operating curve (AUROC) = 0.94] and spliceosome mutations (0.89) and chromosome 7 monosomy (0.89). Mutation prediction probability correlated with variant allele frequency and number of affected genes per pathway, demonstrating the algorithms' ability to identify relevant morphologic patterns. By converting regression models to texture and cellular composition, we reproduced the classical del(5q) MDS morphology consisting of hypolobulated megakaryocytes. In summary, this study highlights the potential of linking deep BM histopathology with genetics and clinical variables. SIGNIFICANCE: Histopathology is elementary in the diagnostics of patients with MDS, but its high-dimensional data are underused. By elucidating the association of morphologic features with clinical variables and molecular genetics, this study highlights the vast potential of convolutional neural networks in understanding MDS pathology and how genetics is reflected in BM morphology.See related commentary by Elemento, p. 195.Peer reviewe

STAT3 Mutation Is Associated with STAT3 Activation in CD30+ ALK− ALCL

Peripheral T-cell lymphomas (PTCL) are a heterogeneous, and often aggressive group of non-Hodgkin lymphomas. Recent advances in the molecular and genetic characterization of PTCLs have helped to delineate differences and similarities between the various subtypes, and the JAK/STAT pathway has been found to play an important oncogenic role. Here, we aimed to characterize the JAK/STAT pathway in PTCL subtypes and investigate whether the activation of the pathway correlates with the frequency of STAT gene mutations. Patient samples from AITL (n = 30), ALCL (n = 21) and PTCL-NOS (n = 12) cases were sequenced for STAT3, STAT5B, JAK1, JAK3, and RHOA mutations using amplicon sequencing and stained immunohistochemically for pSTAT3, pMAPK, and pAKT. We discovered STAT3 mutations in 13% of AITL, 13% of ALK+ ALCL, 38% of ALK− ALCL and 17% of PTCL-NOS cases. However, no STAT5B mutations were found and JAK mutations were only present in ALK- ALCL (15%). Concurrent mutations were found in all subgroups except ALK+ ALCL where STAT3 mutations were always seen alone. High pY-STAT3 expression was observed especially in AITL and ALCL samples. When studying JAK-STAT pathway mutations, pY-STAT3 expression was highest in PTCLs harboring either JAK1 or STAT3 mutations and CD30+ phenotype representing primarily ALK− ALCLs. Further investigation is needed to elucidate the molecular mechanisms of JAK-STAT pathway activation in PTCL

STAT3 Mutation Is Associated with STAT3 Activation in CD30+ ALK− ALCL

Peripheral T-cell lymphomas (PTCL) are a heterogeneous, and often aggressive group of non-Hodgkin lymphomas. Recent advances in the molecular and genetic characterization of PTCLs have helped to delineate differences and similarities between the various subtypes, and the JAK/STAT pathway has been found to play an important oncogenic role. Here, we aimed to characterize the JAK/STAT pathway in PTCL subtypes and investigate whether the activation of the pathway correlates with the frequency of STAT gene mutations. Patient samples from AITL (n = 30), ALCL (n = 21) and PTCL-NOS (n = 12) cases were sequenced for STAT3, STAT5B, JAK1, JAK3, and RHOA mutations using amplicon sequencing and stained immunohistochemically for pSTAT3, pMAPK, and pAKT. We discovered STAT3 mutations in 13% of AITL, 13% of ALK+ ALCL, 38% of ALK− ALCL and 17% of PTCL-NOS cases. However, no STAT5B mutations were found and JAK mutations were only present in ALK- ALCL (15%). Concurrent mutations were found in all subgroups except ALK+ ALCL where STAT3 mutations were always seen alone. High pY-STAT3 expression was observed especially in AITL and ALCL samples. When studying JAK-STAT pathway mutations, pY-STAT3 expression was highest in PTCLs harboring either JAK1 or STAT3 mutations and CD30+ phenotype representing primarily ALK− ALCLs. Further investigation is needed to elucidate the molecular mechanisms of JAK-STAT pathway activation in PTCL

Somatic STAT3 mutations in Felty syndrome: an implication for a common pathogenesis with large granular lymphocyte leukemia

Felty syndrome is a rare disease defined by neutropenia, splenomegaly, and rheumatoid arthritis. Sometimes the differential diagnosis between Felty syndrome and large granular lymphocyte leukemia is problematic. Recently, somatic STAT3 and STAT5B mutations were discovered in 30-40% of patients with large granular lymphocyte leukemia. Herein, we aimed to study whether these mutations can also be detected in Felty syndrome, which would imply the existence of a common pathogenic mechanism between these two disease entities. We collected samples and clinical information from 14 Felty syndrome patients who were monitored at the rheumatology outpatient clinic for Felty syndrome. Somatic STAT3 mutations were discovered in 43% (6/14) of Felty syndrome patients with deep amplicon sequencing targeting all STAT3 exons. Mutations were located in the SH2 domain of STAT3, which is a known mutational hotspot. No STAT5B mutations were found. In blood smears, overrepresentation of large granular lymphocytes was observed, and in the majority of cases the CD8(+) T-cell receptor repertoire was skewed when analyzed by flow cytometry. In bone marrow biopsies, an increased amount of phospho-STAT3 positive cells was discovered. Plasma cytokine profiling showed that ten of the 92 assayed cytokines were elevated both in Felty syndrome and large granular lymphocyte leukemia, and three of these cytokines were also increased in patients with uncomplicated rheumatoid arthritis. In conclusion, somatic STAT3 mutations and STAT3 activation are as frequent in Felty syndrome as they are in large granular lymphocyte leukemia. Considering that the symptoms and treatment modalities are also similar, a unified reclassification of these two syndromes is warranted.Peer reviewe

Single-cell characterization of anti–LAG-3 and anti–PD-1 combination treatment in patients with melanoma

Abstract Background: Relatlimab plus nivolumab (anti–lymphocyte-activation gene 3 plus anti–programmed death 1 [anti–LAG-3+anti–PD-1]) has been approved by the FDA as a first-line therapy for stage III/IV melanoma, but its detailed effect on the immune system is unknown. Methods: We evaluated blood samples from 40 immunotherapy-naive or prior immunotherapy–refractory patients with metastatic melanoma treated with anti–LAG-3+anti–PD-1 in a phase I trial using single-cell RNA and T cell receptor sequencing (scRNA+TCRαβ-Seq) combined with other multiomics profiling. Results: The highest LAG3 expression was noted in NK cells, Tregs, and CD8⁺ T cells, and these cell populations underwent the most significant changes during the treatment. Adaptive NK cells were enriched in responders and underwent profound transcriptomic changes during the therapy, resulting in an active phenotype. LAG3⁺ Tregs expanded, but based on the transcriptome profile, became metabolically silent during the treatment. Last, higher baseline TCR clonality was observed in responding patients, and their expanding CD8⁺ T cell clones gained a more cytotoxic and NK-like phenotype. Conclusion: Anti–LAG-3+anti–PD-1 therapy has profound effects on NK cells and Tregs in addition to CD8⁺ T cells. Trial registration: ClinicalTrials.gov (NCT01968109) Funding : Cancer Foundation Finland, Sigrid Juselius Foundation, Signe and Ane Gyllenberg Foundation, Relander Foundation, State funding for university-level health research in Finland, a Helsinki Institute of Life Sciences Fellow grant, Academy of Finland (grant numbers 314442, 311081, 335432, and 335436), and an investigator-initiated research grant from BMS