A regulatory network comprising let-7 miRNA and SMUG1 is associated with good prognosis in ER+ breast tumours.

Single-strand selective uracil-DNA glycosylase 1 (SMUG1) initiates base excision repair (BER) of uracil and oxidized pyrimidines. SMUG1 status has been associated with cancer risk and therapeutic response in breast carcinomas and other cancer types. However, SMUG1 is a multifunctional protein involved, not only, in BER but also in RNA quality control, and its function in cancer cells is unclear. Here we identify several novel SMUG1 interaction partners that functions in many biological processes relevant for cancer development and treatment response. Based on this, we hypothesized that the dominating function of SMUG1 in cancer might be ascribed to functions other than BER. We define a bad prognosis signature for SMUG1 by mapping out the SMUG1 interaction network and found that high expression of genes in the bad prognosis network correlated with lower survival probability in ER+ breast cancer. Interestingly, we identified hsa-let-7b-5p microRNA as an upstream regulator of the SMUG1 interactome. Expression of SMUG1 and hsa-let-7b-5p were negatively correlated in breast cancer and we found an inhibitory auto-regulatory loop between SMUG1 and hsa-let-7b-5p in the MCF7 breast cancer cells. We conclude that SMUG1 functions in a gene regulatory network that influence the survival and treatment response in several cancers.

Assessing heterogeneity in spatial data using the HTA index with applications to spatial transcriptomics and imaging.

MOTIVATION: Tumour heterogeneity is being increasingly recognized as an important characteristic of cancer and as a determinant of prognosis and treatment outcome. Emerging spatial transcriptomics data hold the potential to further our understanding of tumour heterogeneity and its implications. However, existing statistical tools are not sufficiently powerful to capture heterogeneity in the complex setting of spatial molecular biology. RESULTS: We provide a statistical solution, the HeTerogeneity Average index (HTA), specifically designed to handle the multivariate nature of spatial transcriptomics. We prove that HTA has an approximately normal distribution, therefore lending itself to efficient statistical assessment and inference. We first demonstrate that HTA accurately reflects the level of heterogeneity in simulated data. We then use HTA to analyze heterogeneity in two cancer spatial transcriptomics datasets: spatial RNA sequencing by 10x Genomics and spatial transcriptomics inferred from H&E. Finally, we demonstrate that HTA also applies to 3D spatial data using brain MRI. In spatial RNA sequencing, we use a known combination of molecular traits to assert that HTA aligns with the expected outcome for this combination. We also show that HTA captures immune-cell infiltration at multiple resolutions. In digital pathology, we show how HTA can be used in survival analysis and demonstrate that high levels of heterogeneity may be linked to poor survival. In brain MRI, we show that HTA differentiates between normal ageing, Alzheimer's disease and two tumours. HTA also extends beyond molecular biology and medical imaging, and can be applied to many domains, including GIS. AVAILABILITY AND IMPLEMENTATION: Python package and source code are available at: https://github.com/alonalj/hta. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Subtype-specific transcriptional regulators in breast tumors subjected to genetic and epigenetic alterations.

MOTIVATION: Breast cancer consists of multiple distinct tumor subtypes, and results from epigenetic and genetic aberrations that give rise to distinct transcriptional profiles. Despite previous efforts to understand transcriptional deregulation through transcription factor networks, the transcriptional mechanisms leading to subtypes of the disease remain poorly understood. RESULTS: We used a sophisticated computational search of thousands of expression datasets to define extended signatures of distinct breast cancer subtypes. Using ENCODE ChIP-seq data of surrogate cell lines and motif analysis we observed that these subtypes are determined by a distinct repertoire of lineage-specific transcription factors. Furthermore, specific pattern and abundance of copy number and DNA methylation changes at these TFs and targets, compared to other genes and to normal cells were observed. Overall, distinct transcriptional profiles are linked to genetic and epigenetic alterations at lineage-specific transcriptional regulators in breast cancer subtypes. AVAILABILITY AND IMPLEMENTATION: The analysis code and data are deposited at https://bitbucket.org/qzhu/breast.cancer.tf/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Immune phenotype of tumor microenvironment predicts response to bevacizumab in neoadjuvant treatment of ER-positive breast cancer.

Antiangiogenic drugs are potentially a useful supplement to neoadjuvant chemotherapy for a subgroup of patients with human epidermal growth factor receptor 2 (HER2) negative breast cancer, but reliable biomarkers for improved response are lacking. Here, we report on a randomized phase II clinical trial to study the added effect of bevacizumab in neoadjuvant chemotherapy with FEC100 (5-fluorouracil, epirubicin and cyclophosphamide) and taxanes (n = 132 patients). Gene expression from the tumors was obtained before neoadjuvant treatment, and treatment response was evaluated by residual cancer burden (RCB) at time of surgery. Bevacizumab increased the proportion of complete responders (RCB class 0) from 5% to 20% among patients with estrogen receptor (ER) positive tumors (P = .02). Treatment with bevacizumab was associated with improved 8-year disease-free survival (P = .03) among the good responders (RCB class 0 or I). Patients treated with paclitaxel (n = 45) responded better than those treated with docetaxel (n = 21; P = .03). Improved treatment response was associated with higher proliferation rate and an immune phenotype characterized by high presence of classically activated M1 macrophages, activated NK cells and memory activated CD4 T cells. Treatment with bevacizumab increased the number of adverse events, including hemorrhage, hypertension, infection and febrile neutropenia, but despite this, the ECOG status was not affected.

Serum levels of inflammation-related markers and metabolites predict response to neoadjuvant chemotherapy with and without bevacizumab in breast cancers.

Angiogenesis is necessary for tumor growth and has been targeted in breast cancer; however, it is unclear which patients will respond and benefit from antiangiogenic therapy. We report noninvasive monitoring of patient response to neoadjuvant chemotherapy given alone or in combination with anti-vascular endothelial growth factor (bevacizumab) in a randomized clinical trial. At four time points during neoadjuvant chemotherapy ± bevacizumab of receptor tyrosine-protein kinase erbB-2-negative breast cancers, we measured metabolites and inflammation-related markers in patient's serum. We report significant changes in the levels of several molecules induced by bevacizumab, the most prominent being an increase in pentraxin 3 (PTX3) and von Willebrand factor (VWF). Serum levels of AXL, VWF and pulmonary and activation-regulated cytokine (PARC/CCL18) reflected response to chemotherapy alone or in combination with bevacizumab. We further analyzed serum cytokines in relation to tumor characteristics such as gene expression, tumor metabolites and tumor infiltrating leukocytes. We found that VWF and growth-differentiation factor 15 tumor mRNA levels correlated with their respective serum protein levels suggesting that these cytokines may be produced by tumors and outflow to the bloodstream while influencing the tumor microenvironment locally. Finally, we used binomial logistic regression which allowed to predict patient's response using only 10 noninvasive biomarkers. Our study highlights the potential of monitoring circulating levels of cytokines and metabolites during breast cancer therapy.

Loss of progesterone receptor is associated with distinct tyrosine kinase profiles in breast cancer.

PURPOSE: The aim of this study was to assess protein tyrosine kinase profiles in primary breast cancer samples in correlation with the distinct hormone and growth receptor profiles ER, PR, and HER2. EXPERIMENTAL DESIGN: Pamchip® microarrays were used to measure the phosphorylation of 144 tyrosine kinase substrates in 29 ER+ breast cancer samples and cell lines MCF7, BT474 and ZR75-1. mRNA expression data from the METABRIC cohort and publicly available PR chip-sequencing data were used for validation purposes, together with RT-PCR. RESULTS: In ER+ breast tumors and cell lines, we observed that the loss of PR expression correlated to higher kinase activity in samples and cell lines that were HER2-. A number of kinases, representing mostly proteins within the PI3K/AKT pathway, were identified as responsible for the differential phosphorylation between PR- and PR+ in ER+/HER2- tumors. We used the METABRIC cohort to analyze mRNA expression from 977 ER+/HER2- breast cancers. Twenty four kinase-encoding genes were identified as differentially expressed between PR+ and PR-, dividing ER+/HER2- samples in two distinct clusters with significant differences in survival (p < 0.05). Four kinase genes, LCK, FRK, FGFR4, and MST1R, were identified as potential direct targets of PR. CONCLUSIONS: Our results suggest that the PR status has a profound effect on tyrosine kinases, especially for FGFR4 and LCK genes, in ER+/HER2- breast cancer patients. The influence of these genes on the PI3K/AKT signaling pathway may potentially lead to novel drug targets for ER+/PR- breast cancer patients.

Coagulation factor V in breast cancer: a p53-regulated tumor suppressor and predictive marker for treatment response to chemotherapy.

BACKGROUND: Patients with cancer are at an increased risk of developing coagulation complications, and chemotherapy treatment increases the risk. Tumor progression is closely linked to the hemostatic system. Breast cancer tumors express coagulation factor V (FV), an essential factor in blood coagulation. The functional role of FV during treatment with chemotherapy is poorly understood and was explored in this study. OBJECTIVES: We aimed to investigate the role of FV in breast cancer progression by exploring associations with treatment response, gene regulation, and the functional effects of FV. METHODS: The receiver operating characteristic plotter was used to explore the predictive value of FV mRNA (F5) expression for treatment with FEC (5-fluorouracil, anthracycline, and cyclophosphamide). Breast cancer cohorts were analyzed to study treatment response to FEC. The effect of chemotherapy on F5 expression, the regulation of F5, and the functional effects of FV dependent and independent of chemotherapy were studied in breast cancer cell lines. RESULTS: F5 tumor expression was significantly higher in responders to FEC than in nonresponders. In vitro experiments revealed that anthracycline treatment increased the expression of F5. Inhibition and knockdown of p53 reduced the anthracycline-induced F5 expression. Mutation of a p53 half-site (c.158+1541/158+1564) in a luciferase plasmid reduced luciferase activity, suggesting that p53 plays a role in regulating F5. FV overexpression increased apoptosis and reduced proliferation slightly during anthracycline treatment. CONCLUSION: Our study identified F5 as a p53-regulated tumor suppressor candidate and a promising marker for response to chemotherapy. FV may have functional effects that are therapeutically relevant in breast cancer.

The breast cancer coagulome in the tumor microenvironment and its role in prognosis and treatment response to chemotherapy.

BACKGROUND: The procoagulant phenotype in cancer is linked to thrombosis, cancer progression, and immune response. A novel treatment that reduces the risk of both thrombosis and cancer progression without excess bleeding risk remains to be identified. OBJECTIVES: Here, we aimed to broadly investigate the breast tumor coagulome and its relation to prognosis, treatment response to chemotherapy, and the tumor microenvironment. METHODS: Key coagulation-related genes (n = 35) were studied in a Norwegian cohort with tumor (n = 134) and normal (n = 189) tissue and in the Cancer Genome Atlas (n = 1052) data set. We performed gene set variation analysis in the Norwegian cohort, and in the Cancer Genome Atlas cohort, associations with the tumor microenvironment and prognosis were evaluated. Analyses were performed with cBioPortal, Estimation of Stromal and Immune cells in Malignant Tumors Using Expression Data, Tumor Immune Estimation Resource, the integrated repository portal for tumor-immune system interactions, Tumor Immune Single-cell Hub 2, and the receiver operating characteristic plotter. Six independent breast cancer cohorts were used to study the tumor coagulome and treatment response to chemotherapy. RESULTS: Twenty-two differentially expressed coagulation-related genes were identified in breast tumors. Several coagulome factors were correlated with tumor microenvironment characteristics and were expressed by nonmalignant cells in the tumor microenvironment. PLAT and F8 were independent predictors of better overall survival and progression-free survival, respectively. F12 and PLAU were predictors of worse progression-free survival. The PROCR-THBD-PLAT signature showed a promising predictive value (area under the curve, 0.75; 95% CI, 0.69-0.81; P = 3.6 × 10-17) for combination chemotherapy with fluorouracil, epirubicin, and cyclophosphamide. CONCLUSION: The breast tumor coagulome showed potential in prediction of prognosis and chemotherapy response. Cells within the tumor microenvironment are sources of coagulome factors and may serve as therapeutic targets of coagulation factors.

Benign breast tumors may arise on different immunological backgrounds.

Benign breast tumors are a nonthreatening condition defined as abnormal cell growth within the breast without the ability to invade nearby tissue. However, benign lesions hold valuable biological information that can lead us toward better understanding of tumor biology. In this study, we have used two pathway analysis algorithms, Pathifier and gene set variation analysis (GSVA), to identify biological differences between normal breast tissue, benign tumors and malignant tumors in our clinical dataset. Our results revealed that one-third of all pathways that were significantly different between benign and malignant tumors were immune-related pathways, and 227 of them were validated by both methods and in the METABRIC dataset. Furthermore, five of these pathways (all including genes involved in cytokine and interferon signaling) were related to overall survival in cancer patients in both datasets. The cellular moieties that contribute to immune differences in malignant and benign tumors were analyzed using the deconvolution tool, CIBERSORT. The results showed that levels of some immune cells were specifically higher in benign than in malignant tumors, and this was especially the case for resting dendritic cells and follicular T-helper cells. Understanding the distinct immune profiles of benign and malignant breast tumors may aid in developing noninvasive diagnostic methods to differentiate between them in the future.

Computational model predicts patient outcomes in Luminal B breast cancer treated with endocrine therapy and CDK4/6 inhibition.

PURPOSE: Development of a computational biomarker to predict, prior to treatment, the response to CDK4/6 inhibition (CDK4/6i) in combination with endocrine therapy in patients with breast cancer. EXPERIMENTAL DESIGN: A mechanistic mathematical model that accounts for protein signaling and drug mechanisms of action was developed and trained on extensive, publicly available data from breast cancer cell lines. The model was built to provide a patient-specific response score based on the expression of six genes (CCND1, CCNE1, ESR1, RB1, MYC and CDKN1A). The model was validated in five independent cohorts of 148 patients in total with early-stage or advanced breast cancer treated with endocrine therapy and CDK4/6i. Response was measured either by evaluating Ki67 levels and PAM50 risk of relapse (ROR) after neoadjuvant treatment or by evaluating progression-free survival (PFS). RESULTS: The model showed significant association with patient´s outcomes in all five cohorts. The model predicted high Ki67 (area under the curve; AUC (95% confidence interval) of 0.80 (0.64 - 0.92), 0.81 (0.60 - 1.00) and 0.80 (0.65 - 0.93)) and high PAM50 ROR (AUC of 0.78 (0.64 - 0.89)). This observation was not obtained in patients treated with chemotherapy. In the other cohorts, patient stratification based on the model prediction was significantly associated with PFS (hazard ratio=2.92 (95% CI 1.08 - 7.86), p=0.034 and HR=2.16 (1.02 4.55), p=0.043). CONCLUSION: A mathematical modeling approach accurately predicts patient outcome following CDK4/6i plus endocrine therapy, which marks a step towards more personalized treatments in patients with Luminal B breast cancer.

Functional effect of polymorphisms in 15q25 locus on CHRNA5 mRNA, bulky DNA adducts and TP53 mutations.

Genome-wide association studies have demonstrated that genetic polymorphisms influence the risk of developing lung cancer. Nicotinic acetylcholine receptor alpha3, alpha5 and beta4 genes (CHRNA3, CHRNA5 and CHRNB4) cluster at the 15q25.1 lung cancer susceptibility locus. We genotyped 310 patients with non-small cell lung cancer and a control group of 348 cancer-free individuals for seven sequence variants located in CHRNA3 and CHRNA5 genes. Two of the polymorphisms (rs3829787 and rs3841324) statistically influenced the risk of developing lung cancer. We found that four of the variants (rs3829787, rs3841324, rs588765 and rs3743073) were associated with differential levels of genetic alterations measured as the levels of hydrophobic DNA adducts in the adjacent histologically normal tissue of the lung cancer patients and as TP53 mutations in their lung tumors. The seven sequence variants formed three haplotypes with a frequency above 5%. The two most frequent haplotypes were associated with the risk of developing lung cancer and with smoking-related DNA alterations. We also found an association between CHRNA5 mRNA levels and the sequence variants or haplotypes. In conclusion, our results showed that several of the polymorphisms and their haplotypes in CHRNA5/CHRNA3 genes may have functional effects on (i) CHRNA5 mRNA levels, (ii) polycyclic aromatic hydrocarbon-DNA adduct levels, (iii) TP53 mutations and (iv) susceptibility to lung cancer.

DNA methylation at promoter regions of interleukin 1B, interleukin 6, and interleukin 8 in non-small cell lung cancer.

Epidemiologic and experimental evidences support the concept that inflammation promotes the development and progression of cancers. Interleukins (ILs) regulate the expression of several molecules and signaling pathways involved in inflammation. High expression of some ILs in the tumor microenvironment has been associated with a more virulent tumor phenotype. To examine the role of IL-1ß, IL-6, and IL-8 in non-small cell lung cancer, we measured mRNA levels and promoter DNA methylation in a panel of cultured human lung cells (n = 23) and in matched pair lung tumor versus adjacent non-tumorous tissues (n = 24). We found that lung cancer cells or tissues had significantly different DNA methylation and mRNA levels than normal human bronchial epithelial cells or adjacent non-tumorous tissues, respectively. High DNA methylation of ILs promoters in lung cancer cells or tissues was associated with low mRNA levels. We found an inverse correlation between DNA methylation of IL1B, IL6, and IL8 gene promoters and their corresponding mRNA levels, such inverse correlation was more significant for IL1B (i.e., all cancer cell lines used in this study had a hypermethylated IL1B promoter which was associated with silencing of the gene). Our results underline for the first time the role of epigenetic modifications in the regulation of the expression of key cytokines involved in the inflammatory response during lung cancer development.

Analysis of Spatial Molecular Data.

Digital analysis of pathology whole-slide images has been recently gaining interest in the context of cancer diagnosis and treatment. In particular, deep learning methods have demonstrated significant potential in supporting pathology analysis, recently detecting molecular traits never before recognized in pathology H&E whole-slide images (WSIs). Alongside these advancements in the digital analysis of WSIs, it is becoming increasingly evident that both spatial and overall tumor heterogeneity may be significant determinants of cancer prognosis and treatment outcome. In this chapter, we describe methods that aim to support these two elements. We describe both an end-to-end deep learning pipeline for producing limited spatial transcriptomics from WSIs with associated bulk gene expression data, as well as an algorithm for quantifying spatial tumor heterogeneity based on the results of this pipeline.

Human thymic putative CD8αα precursors exhibit a biased TCR repertoire in single cell AIRR-seq.

Thymic T cell development comprises T cell receptor (TCR) recombination and assessment of TCR avidity towards self-peptide-MHC complexes presented by antigen-presenting cells. Self-reactivity may lead to negative selection, or to agonist selection and differentiation into unconventional lineages such as regulatory T cells and CD8[Formula: see text] T cells. To explore the effect of the adaptive immune receptor repertoire on thymocyte developmental decisions, we performed single cell adaptive immune receptor repertoire sequencing (scAIRR-seq) of thymocytes from human young paediatric thymi and blood. Thymic PDCD1+ cells, a putative CD8[Formula: see text] T cell precursor population, exhibited several TCR features previously associated with thymic and peripheral ZNF683+ CD8[Formula: see text] T cells, including enrichment of large and positively charged complementarity-determining region 3 (CDR3) amino acids. Thus, the TCR repertoire may partially explain the decision between conventional vs. agonist selected thymocyte differentiation, an aspect of importance for the development of therapies for patients with immune-mediated diseases.

DNA methylation of the CYP1A1 enhancer is associated with smoking-induced genetic alterations in human lung.

CYP1A1 (cytochrome P4501A1) catalyze the conversion of polycyclic aromatic hydrocarbons into reactive metabolites, which may induce DNA damage. We hypothesized that DNA methylation of the CYP1A1 enhancer could be involved in inter-individual differences in mRNA levels of CYP1A1 or affect the smoking-induced DNA damage in human lung. Using DNA bisulfite conversion and pyrosequencing, we show that DNA methylation of the CYP1A1 enhancer is affected by smoking. In adjacent histologically normal lung from lung cancer patients (n = 120), low levels of DNA methylation of the CYP1A1 enhancer were related to high levels of smoking-induced hydrophobic DNA adduct (p < 0.03), and to the presence of TP53 or K-ras mutations in the corresponding lung tumors (p < 0.03). We found an inverse correlation between DNA methylation of the CYP1A1 enhancer and mRNA levels in vivo (Spearman r = -0.54; p < 0.0001). Thus, in lung tumor tissues, the CYP1A1 enhancer hypermethylation was associated with lower mRNA levels compared to adjacent histologically normal tissue (p < 0.0001). In vitro, using a panel of cultured human lung cells, we found hypermethylation of the CYP1A1 enhancer in cancer cell lines and an inverse correlation between DNA methylation and mRNA levels (Spearman r = -0.53; p = 0.003). Altogether, our results indicated that low levels of DNA methylation of the CYP1A1 enhancer in histologically normal human lung were associated with high CYP1A1 mRNA levels and with smoking-induced genetic alterations; thus, it may play a role in the initiation of lung carcinogenesis.

NHE-1 relocation outside cholesterol-rich membrane microdomains is associated with its benzo[a]pyrene-related apoptotic function.

BACKGROUND: Polycyclic aromatic hydrocarbons (PAHs), such as benzo[a]pyrene (B[a]P), are ubiquitous toxic environmental pollutants capable of inducing cell death. Intracellular pH plays a key role in the regulation of cell survival and death. Our previous works have demonstrated that intracellular alkalinization mediated by Na(+)/H(+) exchanger 1 (NHE-1) is a critical event involved in B[a]P-induced apoptosis. The aim of this study was to further elucidate the mechanisms of NHE-1 activation upon B[a]P exposure. METHODS: We tested the effects of plasma membrane cholesterol enrichment or depletion on B[a]P-induced NHE-1 activation related to apoptosis. We isolated cholesterol-rich plasma membrane microdomains to assess NHE-1 submembrane location and immunoprecipitated NHE-1 from the different sub-membrane fractions obtained to examine NHE-1 protein interactions during B[a]P-induced apoptosis. RESULTS: We found that NHE-1 is preferentially located in cholesterol-rich microdomains and that B[a]P activates NHE-1 via its relocation and binding of calmodulin outside these specialized plasma membrane microstructures; these events are necessary for the execution of the apoptosis-related intracellular alkalinization. CONCLUSION: Plasma membrane location of NHE-1 affects its protein interactions and apoptotic function.

Epigenetic alterations at distal enhancers are linked to proliferation in human breast cancer.

Aberrant DNA methylation is an early event in breast carcinogenesis and plays a critical role in regulating gene expression. Here, we perform genome-wide expression-methylation Quantitative Trait Loci (emQTL) analysis through the integration of DNA methylation and gene expression to identify disease-driving pathways under epigenetic control. By grouping the emQTLs using biclustering we identify associations representing important biological processes associated with breast cancer pathogenesis including regulation of proliferation and tumor-infiltrating fibroblasts. We report genome-wide loss of enhancer methylation at binding sites of proliferation-driving transcription factors including CEBP-ß, FOSL1, and FOSL2 with concomitant high expression of proliferation-related genes in aggressive breast tumors as we confirm with scRNA-seq. The identified emQTL-CpGs and genes were found connected through chromatin loops, indicating that proliferation in breast tumors is under epigenetic regulation by DNA methylation. Interestingly, the associations between enhancer methylation and proliferation-related gene expression were also observed within known subtypes of breast cancer, suggesting a common role of epigenetic regulation of proliferation. Taken together, we show that proliferation in breast cancer is linked to loss of methylation at specific enhancers and transcription factor binding and gene activation through chromatin looping.

Subtype and cell type specific expression of lncRNAs provide insight into breast cancer.

Long non-coding RNAs (lncRNAs) are involved in breast cancer pathogenesis through chromatin remodeling, transcriptional and post-transcriptional gene regulation. We report robust associations between lncRNA expression and breast cancer clinicopathological features in two population-based cohorts: SCAN-B and TCGA. Using co-expression analysis of lncRNAs with protein coding genes, we discovered three distinct clusters of lncRNAs. In silico cell type deconvolution coupled with single-cell RNA-seq analyses revealed that these three clusters were driven by cell type specific expression of lncRNAs. In one cluster lncRNAs were expressed by cancer cells and were mostly associated with the estrogen signaling pathways. In the two other clusters, lncRNAs were expressed either by immune cells or fibroblasts of the tumor microenvironment. To further investigate the cis-regulatory regions driving lncRNA expression in breast cancer, we identified subtype-specific transcription factor (TF) occupancy at lncRNA promoters. We also integrated lncRNA expression with DNA methylation data to identify long-range regulatory regions for lncRNA which were validated using ChiA-Pet-Pol2 loops. lncRNAs play an important role in shaping the gene regulatory landscape in breast cancer. We provide a detailed subtype and cell type-specific expression of lncRNA, which improves the understanding of underlying transcriptional regulation in breast cancer.

Multimodal human thymic profiling reveals trajectories and cellular milieu for T agonist selection.

To prevent autoimmunity, thymocytes expressing self-reactive T cell receptors (TCRs) are negatively selected, however, divergence into tolerogenic, agonist selected lineages represent an alternative fate. As thymocyte development, selection, and lineage choices are dependent on spatial context and cell-to-cell interactions, we have performed Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) and spatial transcriptomics on paediatric human thymu​​s. Thymocytes expressing markers of strong TCR signalling diverged from the conventional developmental trajectory prior to CD4+ or CD8+ lineage commitment, while markers of different agonist selected T cell populations (CD8αα(I), CD8αα(II), T(agonist), Treg(diff), and Treg) exhibited variable timing of induction. Expression profiles of chemokines and co-stimulatory molecules, together with spatial localisation, supported that dendritic cells, B cells, and stromal cells contribute to agonist selection, with different subsets influencing thymocytes at specific developmental stages within distinct spatial niches. Understanding factors influencing agonist T cells is needed to benefit from their immunoregulatory effects in clinical use.