The multimodality cell segmentation challenge: toward universal solutions.

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multimodality cell segmentation benchmark, comprising more than 1,500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.

A bibliometric analysis of the gender gap in the authorship of leading medical journals.

BACKGROUND: Monitoring gender representation is critical to achieve diversity and equity in academia. One way to evaluate gender representation in academia is to examine the authorship of research publications. This study sought to determine the gender of first and senior authors of articles in leading medical journals and assess trends in the gender gap over time. METHODS: We gather bibliometric data on original research articles (n = 10,558) published in 2010-2019 in five leading medical journals to audit publication and citation frequency by gender. We explored their association with scientific fields, geographical regions, journals, and collaboration scope. RESULTS: We show that there are fewer women as senior (24.8%) than leading authors (34.5%, p < 0.001). The proportion of women varied by country with 9.1% last authors from Austria, 0.9% from Japan, and 0.0% from South Korea. The gender gap decreased longitudinally and faster for the last (-24.0 articles/year, p < 0.001) than first authors (-14.5 articles/year, p = 0.024) with pronounced country-specific variability. We also demonstrate that usage of research keywords varied by gender, partly accounting for the difference in citation counts. CONCLUSIONS: In summary, gender representation has increased, although with country-specific variability. The study frame can be easily applied to any journal and time period to monitor changes in gender representation in science.

The publishing of medical research papers has traditionally been dominated by men. To better understand whether gender diversity in the authorship of research papers has changed recently, we analyzed over 10,000 articles published between 2010 and 2019 in five top medical journals. Usually, the first author is recognized as the leading contributor, whereas the last author supervises the study. We found that there were fewer women in senior positions compared to first author positions. The percentage of women as authors varied across countries. Over time, the gender gap decreased, but at different rates depending on the author's position and country. Keywords selected by researchers to describe their work varied between genders. Our findings show progress in gender representation, but with country-specific differences. This study can be used as a model to track gender representation in other journals and time periods.

A bibliometric analysis of geographic disparities in the authorship of leading medical journals.

BACKGROUND: It has previously been reported that authors from developing countries are underrepresented in medical journals. Here, we aimed to build a comprehensive landscape of the geographical representation in medical research publications. METHODS: We collected bibliometric data of original research articles (n = 10,558) published between 2010 and 2019 in five leading medical journals and geolocated these by the institute of the corresponding authors. We introduced two simple metrics, the International Research Impact and the Domestic Self-Citation Index, to assess publishing and citing patterns by cities and countries. RESULTS: We show that only 32 countries published more than 10 publications in 10 years equaling 98.9% of all publications. English-speaking countries USA (48.2%), UK (15.9%), Canada (5.3%), and Australia (3.2%) are most represented, but with a declining trend in recent years. When normalized to citation count, 9/32 countries published ≥ 10% more than expected. In total, 85.7% of the publication excess originate from the USA and UK. We demonstrate similar geographical bias at the municipal level. Finally, we discover that journals more commonly publish studies from the country in which the journal is based and authors are more likely to cite work from their own country. CONCLUSIONS: The study reveals Anglocentric dominance, domestic preference, but increased geographical representation in recent years in medical publishing. Similar audits could mitigate possible national and regional disparities in any academic field.

Geographical representation in authorships of research articles is insufficiently understood. We analyzed data from over 10,000 research articles published between 2010­2019 in top medical journals. Anglocentric countries (USA, UK, Canada, and Australia) accounted for most publications, but their proportion has recently declined. When considering citations, i.e. formal references connecting new findings to observations from previously published articles, 1/3 of the studied countries published ≥10% more articles than expected. When publishing and citing articles, journals and researchers tended to favor publications from their own countries. While some improvement in geographical representation has occurred, our findings expose an Anglocentric bias and national preference, which might bias medical publishing. The approach used in this study may be used in future efforts to monitor geographical representation in publication authorships.

Real-world experience of novel multiple myeloma treatments in a large, single-center cohort in Finland.

In this single-center study, we aimed to describe the characteristics, treatment patterns, and outcomes of patients with multiple myeloma (MM) following treatment with bortezomib, carfilzomib, daratumumab, ixazomib, lenalidomide or pomalidomide-based regimens. Data were collected retrospectively from a study cohort of patients receiving a MM treatment in the Hospital District of Helsinki and Uusimaa (HUS) in Finland between 2016-2020. In total, 472 patients were included in the study. Median age was 68.2 years and nearly 25% had a high cytogenetic risk according to the International Myeloma Working Group categorization. In 2018-2020, the spectrum of regimens used as third- or later-line therapy was notably broader than in 2016-2017. The overall response rates for patients who received the most novel regimens (available ≤ 5 years) in second or third line of therapy (n = 67/430) and fourth line or later (n = 78/151) were 53.3% and 25.0%, respectively. In this real-world MM patient cohort, the response rates for these novel agents were lower compared to those reported in clinical trials. Given the higher cytogenetic risk profile and more advanced disease stage at the time when treated with novel agents, patients could have benefited from effective novel therapies earlier in their treatment pathway. What is the NEW aspect of your work? (ONE sentence) This study characterized the treatment of Finnish multiple myeloma patients during the era of most novel therapies (after 2016) and also included information on the cytogenetic risk profile of this real-world population.What is the CENTRAL finding of your work? (ONE sentence) There are clear differences between real-world populations treated with most novel combinations and those of randomized controlled trials (RCTs), which is reflected by the poorer treatment outcomes in the real-world setting.What is (or could be) the SPECIFIC clinical relevance of your work? (ONE sentence) Given the high cytogenetic risk profile and advanced disease stage at the time when treated with novel agents, patients could have benefited from effective novel therapies earlier in their treatment pathway.

Real-world evidence of multiple myeloma treated from 2013 to 2019 in the Hospital District of Helsinki and Uusimaa, Finland.

Background: The rapid development of multiple myeloma (MM) management underscores the value of real-world data. In our study we examined 509 adult MM patients treated with immunochemotherapy (ICT) with/without stem cell transplantation (SCT) from 2013 to 2019 in the Hospital District of Helsinki and Uusimaa, Finland. Materials & methods: Our study was based on computational analyses of data integrated into the hospital data lake. Results: After 2017, treatment pattern diversity increased with improved access to novel treatments. 5-year survivals were 74.4% (95% CI: 65.5-84.5) in SCT-eligible and 44.0% (95% CI: 37.6-51.4) in non-SCT subgroups. In the SCT-eligible subgroup, high first-year hospitalization costs were followed by stable resource requirements. Conclusion: Hospital data lakes can be adapted to carry out complex analysis of large MM cohorts.

To better understand how multiple myeloma (a type of blood cancer) is clinically managed, we examined 509 adult patients using advanced computer analysis and data stored in the Hospital District of Helsinki and Uusimaa information system. Our study found that after 2017, there was more variety in treatments due to better access to new therapies. Compared with a nontransplant group (44.0%), patients eligible for stem cell transplantation had a better 5-year survival rate (74.4%) and used higher levels of healthcare resources. Our study highlights the potential of hospital data systems to study large groups of multiple myeloma patients and inform strategies to tackle the burden associated with the treatment costs of multiple myeloma.

Computational textural mapping harmonises sampling variation and reveals multidimensional histopathological fingerprints.

BACKGROUND: Technical factors can bias H&E digital slides potentially compromising computational histopathology studies. Here, we hypothesised that sample quality and sampling variation can introduce even greater and undocumented technical fallacy. METHODS: Using The Cancer Genome Atlas (TCGA) clear-cell renal cell carcinoma (ccRCC) as a model disease, we annotated ~78,000 image tiles and trained deep learning models to detect histological textures and lymphocyte infiltration at the tumour core and its surrounding margin and correlated these with clinical, immunological, genomic, and transcriptomic profiles. RESULTS: The models reached 95% validation accuracy for classifying textures and 95% for lymphocyte infiltration enabling reliable profiling of ccRCC samples. We validated the lymphocyte-per-texture distributions in the Helsinki dataset (n = 64). Texture analysis indicated constitutive sampling bias by TCGA clinical centres and technically suboptimal samples. We demonstrate how computational texture mapping (CTM) can abrogate these issues by normalising textural variance. CTM-harmonised histopathological architecture resonated with both expected associations and novel molecular fingerprints. For instance, tumour fibrosis associated with histological grade, epithelial-to-mesenchymal transition, low mutation burden and metastasis. CONCLUSIONS: This study highlights texture-based standardisation to resolve technical bias in computational histopathology and understand the molecular basis of tissue architecture. All code, data and models are released as a community resource.

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

BackgroundRelatlimab 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.MethodsWe 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+TCRαß-Seq) combined with other multiomics profiling.ResultsThe 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.ConclusionAnti-LAG-3+anti-PD-1 therapy has profound effects on NK cells and Tregs in addition to CD8+ T cells.Trial registrationClinicalTrials.gov (NCT01968109)FundingCancer 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.

Fibroblast subsets in non-small cell lung cancer: Associations with survival, mutations, and immune features.

BACKGROUND: Cancer-associated fibroblasts (CAFs) are molecularly heterogeneous mesenchymal cells that interact with malignant cells and immune cells and confer anti- and protumorigenic functions. Prior in situ profiling studies of human CAFs have largely relied on scoring single markers, thus presenting a limited view of their molecular complexity. Our objective was to study the complex spatial tumor microenvironment of non-small cell lung cancer (NSCLC) with multiple CAF biomarkers, identify novel CAF subsets, and explore their associations with patient outcome. METHODS: Multiplex fluorescence immunohistochemistry was employed to spatially profile the CAF landscape in 2 population-based NSCLC cohorts (n = 636) using antibodies against 4 fibroblast markers: platelet-derived growth factor receptor-alpha (PDGFRA) and -beta (PDGFRB), fibroblast activation protein (FAP), and alpha-smooth muscle actin (αSMA). The CAF subsets were analyzed for their correlations with mutations, immune characteristics, and clinical variables as well as overall survival. RESULTS: Two CAF subsets, CAF7 (PDGFRA-/PDGFRB+/FAP+/αSMA+) and CAF13 (PDGFRA+/PDGFRB+/FAP-/αSMA+), showed statistically significant but opposite associations with tumor histology, driver mutations (tumor protein p53 [TP53] and epidermal growth factor receptor [EGFR]), immune features (programmed death-ligand 1 and CD163), and prognosis. In patients with early stage tumors (pathological tumor-node-metastasis IA-IB), CAF7 and CAF13 acted as independent prognostic factors. CONCLUSIONS: Multimarker-defined CAF subsets were identified through high-content spatial profiling. The robust associations of CAFs with driver mutations, immune features, and outcome suggest CAFs as essential factors in NSCLC progression and warrant further studies to explore their potential as biomarkers or therapeutic targets. This study also highlights multiplex fluorescence immunohistochemistry-based CAF profiling as a powerful tool for the discovery of clinically relevant CAF subsets.

An immunity and pyroptosis gene-pair signature predicts overall survival in acute myeloid leukemia.

Treatment responses of patients with acute myeloid leukemia (AML) are known to be heterogeneous, posing challenges for risk scoring and treatment stratification. In this retrospective multi-cohort study, we investigated whether combining pyroptosis- and immune-related genes improves prognostic classification of AML patients. Using a robust gene pairing approach, which effectively eliminates batch effects across heterogeneous patient cohorts and transcriptomic data, we developed an immunity and pyroptosis-related prognostic (IPRP) signature that consists of 15 genes. Using 5 AML cohorts (n = 1327 patients total), we demonstrate that the IPRP score leads to more consistent and accurate survival prediction performance, compared with 10 existing signatures, and that IPRP scoring is widely applicable to various patient cohorts, treatment procedures and transcriptomic technologies. Compared to current standards for AML patient stratification, such as age or ELN2017 risk classification, we demonstrate an added prognostic value of the IPRP risk score for providing improved prediction of AML patients. Our web-tool implementation of the IPRP score and a simple 4-factor nomogram enables practical and robust risk scoring for AML patients. Even though developed for AML patients, our pan-cancer analyses demonstrate a wider application of the IPRP signature for prognostic prediction and analysis of tumor-immune interplay also in multiple solid tumors.

T and NK cell abundance defines two distinct subgroups of renal cell carcinoma.

Renal cell carcinoma (RCC) is considered as an immunogenic cancer. Because not all patients respond to current immunotherapies, we aimed to investigate the immunological heterogeneity of RCC tumors. We analyzedthe immunophenotype of the circulating, tumor, and matching adjacent healthy kidney immune cells from 52 nephrectomy patients with multi-parameter flow cytometry. Additionally, we studied the transcriptomic and mutation profiles of 20 clear cell RCC (ccRCC) tumors with bulk RNA sequencing and a customized pan-cancer gene panel. The tumor samples clustered into two distinct subgroups defined by the abundance of intratumoral CD3+ T cells (CD3high, 25/52) and NK cells (NKhigh, 27/52). CD3high tumors had an overall higher frequency of tumor infiltrating lymphocytes and PD-1 expression on the CD8+ T cells compared to NKhigh tumors. The tumor infiltrating T and NK cells had significantly elevated expression levels of LAG-3, PD-1, and HLA-DR compared to the circulating immune cells. Transcriptomic analysis revealed increased immune signaling (IFN-γ, TNF-α via NF-κB, and T cell receptor signaling) and kidney metabolism pathways in the CD3high subgroup. Genomic analysis confirmed the typical ccRCC mutation profile including VHL, PBRM1, and SETD2 mutations, and revealed PBRM1 as a uniquely mutated gene in the CD3high subgroup. Approximately half of the RCC tumors have a high infiltration of NK cells associated with a lower number of tumor infiltrating lymphocytes, lower PD-1 expression, a distinct transcriptomic and mutation profile, providing insights to the immunological heterogeneity of RCC which may impact treatment responses to immunological therapies.

Prognostic implications of tumor-infiltrating T cells in early-stage endometrial cancer.

Patients with endometrial cancer differ in terms of the extent of T-cell infiltration; however, the association between T-cell subpopulations and patient outcomes remains unexplored. We characterized 285 early-stage endometrial carcinoma samples for T-cell infiltrates in a tissue microarray format using multiplex fluorescent immunohistochemistry. The proportion of T cells and their subpopulations were associated with clinicopathological features and relapse-free survival outcomes. CD3+ CD4+ infiltrates were more abundant in the patients with higher grade or non-endometrioid histology. Cytotoxic T cells (CD25+, PD-1+, and PD-L1+) were strongly associated with longer relapse-free survival. Moreover, CD3+ PD-1+ stromal cells were independent of other immune T-cell populations and clinicopathological factors in predicting relapses. Patients with high stromal T-cell fraction of CD3+ PD-1+ cells were associated with a 5-year relapse-free survival rate of 93.7% compared to 79.0% in patients with low CD3+ PD-1+ fraction. Moreover, in patients classically linked to a favorable outcome (such as endometrioid subtype and low-grade tumors), the stromal CD3+ PD-1+ T-cell fraction remained prognostically significant. This study supports that T-cell infiltrates play a significant prognostic role in early-stage endometrial carcinoma. Specifically, CD3+ PD-1+ stromal cells emerge as a promising novel prognostic biomarker.

Implementing a Functional Precision Medicine Tumor Board for Acute Myeloid Leukemia.

We generated ex vivo drug-response and multiomics profiling data for a prospective series of 252 samples from 186 patients with acute myeloid leukemia (AML). A functional precision medicine tumor board (FPMTB) integrated clinical, molecular, and functional data for application in clinical treatment decisions. Actionable drugs were found for 97% of patients with AML, and the recommendations were clinically implemented in 37 relapsed or refractory patients. We report a 59% objective response rate for the individually tailored therapies, including 13 complete responses, as well as bridging five patients with AML to allogeneic hematopoietic stem cell transplantation. Data integration across all cases enabled the identification of drug response biomarkers, such as the association of IL15 overexpression with resistance to FLT3 inhibitors. Integration of molecular profiling and large-scale drug response data across many patients will enable continuous improvement of the FPMTB recommendations, providing a paradigm for individualized implementation of functional precision cancer medicine. SIGNIFICANCE: Oncogenomics data can guide clinical treatment decisions, but often such data are neither actionable nor predictive. Functional ex vivo drug testing contributes significant additional, clinically actionable therapeutic insights for individual patients with AML. Such data can be generated in four days, enabling rapid translation through FPMTB.See related commentary by Letai, p. 290.This article is highlighted in the In This Issue feature, p. 275.

E-cadherin is a robust prognostic biomarker in colorectal cancer and low expression is associated with sensitivity to inhibitors of topoisomerase, aurora, and HSP90 in preclinical models.

Cell-cell and cell-matrix adhesion proteins that have been implicated in colorectal epithelial integrity and epithelial-to-mesenchymal transition could be robust prognostic and potential predictive biomarkers for standard and novel therapies. We analyzed in situ protein expression of E-cadherin (ECAD), integrin ß4 (ITGB4), zonula occludens 1 (ZO-1), and cytokeratins in a single-hospital series of Norwegian patients with colorectal cancer (CRC) stages I-IV (n = 922) using multiplex fluorescence-based immunohistochemistry (mfIHC) on tissue microarrays. Pharmacoproteomic associations were explored in 35 CRC cell lines annotated with drug sensitivity data on > 400 approved and investigational drugs. ECAD, ITGB4, and ZO-1 were positively associated with survival, while cytokeratins were negatively associated with survival. Only ECAD showed independent prognostic value in multivariable Cox models. Clinical and molecular associations for ECAD were technically validated on a different mfIHC platform, and the prognostic value was validated in another Norwegian series (n = 798). In preclinical models, low and high ECAD expression differentially associated with sensitivity to topoisomerase, aurora, and HSP90 inhibitors, and EGFR inhibitors. E-cadherin protein expression is a robust prognostic biomarker with potential clinical utility in CRC.

Clinical Impact of Immune Cells and Their Spatial Interactions in Diffuse Large B-Cell Lymphoma Microenvironment.

PURPOSE: Tumor-infiltrating immune cells have prognostic significance and are attractive therapeutic targets. Yet, the clinical significance of their spatial organization and phenotype in diffuse large B-cell lymphoma (DLBCL) is unclear. EXPERIMENTAL DESIGN: We characterized T cells, macrophages, and their spatial interactions by multiplex IHC (mIHC) in 178 patients with DLBCL and correlated the data with patient demographics and survival. We validated the findings on gene expression data from two external DLBCL cohorts comprising 633 patients. RESULTS: Macrophage and T-cell contents divided the samples into T cell-inflamed (60%) and noninflamed (40%) subgroups. The T cell-inflamed lymphoma microenvironment (LME) was also rich in other immune cells, defining immune hot phenotype, which did not as such correlate with outcome. However, when we divided the patients according to T-cell and macrophage contents, LME characterized by high T-cell/low macrophage content or a corresponding gene signature was associated with superior survival [5-year overall survival (OS): 92.3% vs. 74.4%, P = 0.036; 5-year progression-free survival (PFS): 92.6% vs. 69.8%, P = 0.012]. High proportion of PD-L1- and TIM3-expressing CD163- macrophages in the T cell-inflamed LME defined a group of patients with poor outcome [OS: HR = 3.22, 95% confidence interval (CI), 1.63-6.37, Padj = 0.011; PFS: HR = 2.76, 95% CI, 1.44-5.28, Padj = 0.016]. Furthermore, PD-L1 and PD-1 were enriched on macrophages interacting with T cells. CONCLUSIONS: Our data demonstrate that the interplay between macrophages and T cells in the DLBCL LME is immune checkpoint dependent and clinically meaningful.

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

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.

Spatial immunoprofiling of the intratumoral and peritumoral tissue of renal cell carcinoma patients.

While the abundance and phenotype of tumor-infiltrating lymphocytes are linked with clinical survival, their spatial coordination and its clinical significance remain unclear. Here, we investigated the immune profile of intratumoral and peritumoral tissue of clear cell renal cell carcinoma patients (n = 64). We trained a cell classifier to detect lymphocytes from hematoxylin and eosin stained tissue slides. Using unsupervised classification, patients were further classified into immune cold, hot and excluded topographies reflecting lymphocyte abundance and localization. The immune topography distribution was further validated with The Cancer Genome Atlas digital image dataset. We showed association between PBRM1 mutation and immune cold topography, STAG1 mutation and immune hot topography and BAP1 mutation and immune excluded topography. With quantitative multiplex immunohistochemistry we analyzed the expression of 23 lymphocyte markers in intratumoral and peritumoral tissue regions. To study spatial interactions, we developed an algorithm quantifying the proportion of adjacent immune cell pairs and their immunophenotypes. Immune excluded tumors were associated with superior overall survival (HR 0.19, p = 0.02) and less extensive metastasis. Intratumoral T cells were characterized with pronounced expression of immunological activation and exhaustion markers such as granzyme B, PD1, and LAG3. Immune cell interaction occurred most frequently in the intratumoral region and correlated with CD45RO expression. Moreover, high proportion of peritumoral CD45RO+ T cells predicted poor overall survival. In summary, intratumoral and peritumoral tissue regions represent distinct immunospatial profiles and are associated with clinicopathologic characteristics.

Somatic mutations in lymphocytes in patients with immune-mediated aplastic anemia.

The prevalence and functional impact of somatic mutations in nonleukemic T cells is not well characterized, although clonal T-cell expansions are common. In immune-mediated aplastic anemia (AA), cytotoxic T-cell expansions are shown to participate in disease pathogenesis. We investigated the mutation profiles of T cells in AA by a custom panel of 2533 genes. We sequenced CD4+ and CD8+ T cells of 24 AA patients and compared the results to 20 healthy controls and whole-exome sequencing of 37 patients with AA. Somatic variants were common both in patients and healthy controls but enriched to AA patients' CD8+ T cells, which accumulated most mutations on JAK-STAT and MAPK pathways. Mutation burden was associated with CD8+ T-cell clonality, assessed by T-cell receptor beta sequencing. To understand the effect of mutations, we performed single-cell sequencing of AA patients carrying STAT3 or other mutations in CD8+ T cells. STAT3 mutated clone was cytotoxic, clearly distinguishable from other CD8+ T cells, and attenuated by successful immunosuppressive treatment. Our results suggest that somatic mutations in T cells are common, associate with clonality, and can alter T-cell phenotype, warranting further investigation of their role in the pathogenesis of AA.