Large-scale Analysis Research Articles

Design Principles of DNA-Barcodes for Nanopore-FET Readout, Based on Molecular Dynamics and TCAD Simulations.

Nanopore field-effect transistor (NP-FET) devices hold great promise as sensitive single-molecule sensors, which provide CMOS-based on-chip readout and are also highly amenable to parallelization. A plethora of applications will therefore benefit from NP-FET technology, such as large-scale molecular analysis (e.g., proteomics). Due to its potential for parallelization, the NP-FET looks particularly well-suited for the high-throughput readout of DNA-based barcodes. However, to date, no study exists that unravels the bit-rate capabilities of NP-FET devices. In this paper, we design DNA-based barcodes by labeling a piece of double-stranded DNA with dumbbell-like DNA structures. We explore the impact of both the size of the dumbbells and their spacing on achievable bit-rates. The conformational fluctuations of this DNA-origami, as observed by molecular dynamics (MD) simulation, are accounted for when selecting label sizes. An experimentally informed 3D continuum nanofluidic-nanoelectronic device model subsequently predicts both the ionic current and FET signals. We present a barcode design for a conceptually generic NP-FET, with a 14 nm diameter pore, operating in conditions corresponding to experiments. By adjusting the spacing between the labels to half the length of the pore, we show that a bit-rate of 78 kbit·s-1 is achievable. This lies well beyond the state-of-the-art of ≈40 kbit·s-1, with significant headroom for further optimizations. We also highlight the advantages of NP-FET readout based on the larger signal size and sinusoidal signal shape.

Treatment patterns and clinical outcomes in patients with metastatic triple-negative breast cancer: a large-scale data analysis using the Japanese claims database.

This study evaluated treatment patterns and clinical outcomes among patients with metastatic triple-negative breast cancer (mTNBC) in real-world clinical settings in Japan. The treatment patterns, time to next treatment or death (TTNTD), time to treatment discontinuation, adverse events of interest, and medical costs of treating patients with mTNBC in first-, second-, and third-line settings were investigated using data of patients meeting the inclusion criteria between January 2017 and March 2022 in a Japanese medical claims database. The treatment regimens for mTNBC were defined according to the Japanese Breast Cancer Society Clinical Practice Guidelines. In this study, 2236 patients with mTNBC (median age 66.0years; 99.8% female) were included in the first-line cohort. Of these, 46.6% and 20.8% were included in the second- and third-line cohorts, respectively. The two most frequently used treatments were capecitabine (19.1%) and S-1 (tegafur-gimeracil-oteracil) (14.5%) in the first-line cohort, eribulin (18.3%) and bevacizumab/paclitaxel (14.4%) in the second-line cohort, and eribulin (19.4%) and bevacizumab/paclitaxel (17.5%) in the third-line cohort. The TTNTD shortened as the line of therapy progressed (median 8.0, 6.5, and 5.2months for the first-, second-, and third-line treatments, respectively). Nausea/vomiting and neutropenia/leukopenia occurred in 62.8% and 18.3% of all patients, respectively. The medical total costs per day were 6.7, 10.2, and 12.9 thousand yen during the first-/second-/third-line treatments, respectively. This study provides insight into current treatment patterns for mTNBC in Japan. The cost-benefit balance worsens with later-line treatment and a high unmet need for mTNBC drug treatment remains.

Comprehensive clinical and genetic analyses of circulating bile acids and their associations with diabetes and its indices.

Bile acids (BAs) are cholesterol-derived compounds that regulate glucose, lipid, and energy metabolism. Despite their significance in glucose homeostasis, the association between specific BA molecular species and their synthetic pathways with diabetes mellitus (DM) is unclear. Here, we used a recently validated stable-isotope dilution highperformance liquid chromatography with tandem mass spectrometry (LC-MS/MS) method to quantify a panel of BAs in fasting plasma from subjects (n=2,145) and explored structural and genetic determinants of BAs linked to DM, insulin resistance and obesity. Multiple 12α-hydroxylated BAs were associated with DM [adjusted odds ratios (aORs):1.3-1.9 (all P<0.05)] and insulin resistance [aORs:1.3-2.2 (all P<0.05)]. Conversely, multiple 6a-hydroxylated BAs and isolithocholic acid (Iso-LCA) were inversely associated with DM and obesity [aORs:0.3-0.9 (all P<0.05)]. Genome-wide association studies (GWAS) revealed multiple genome-wide significant loci linked with nine of the 14 DM-associated BAs, including a locus for Iso-LCA (rs11866815). Mendelian randomization analyses showed genetically elevated DCA levels were causally associated with higher BMI, and Iso-LCA levels were causally associated with reduced BMI and DM risk. In conclusion, comprehensive large-scale quantitative mass spectrometry and genetics analyses show circulating levels of multiple structurally specific BAs, especially DCA and Iso-LCA, are clinically associated with and genetically linked to obesity and DM.

Enhancements in Immediate Speech Emotion Detection: Harnessing Prosodic and Spectral Characteristics

Speech is essential to human communication for expressing and understanding feelings. Emotional speech processing has challenges with expert data sampling, dataset organization, and computational complexity in large-scale analysis. This study aims to reduce data redundancy and high dimensionality by introducing a new speech emotion recognition system. The system employs Diffusion Map to reduce dimensionality and includes Decision Trees and K-Nearest Neighbors(KNN)ensemble classifiers. These strategies are suggested to increase voice emotion recognition accuracy. Speech emotion recognition is gaining popularity in affective computing for usage in medical, industry, and academics. This project aims to provide an efficient and robust real-time emotion identification framework. In order to identify emotions using supervised machine learning models, this work makes use of paralinguistic factors such as intensity, pitch, and MFCC. In order to classify data, experimental analysis integrates prosodic and spectral information utilizing methods like Random Forest, Multilayer Perceptron, SVM, KNN, and Gaussian Naïve Bayes. Fast training times make these machine learning models excellent for real-time applications. SVM and MLP have the highest accuracy at 70.86% and 79.52%, respectively. Comparisons to benchmarks show significant improvements over earlier models.

Abstract PO1-24-06: Prolactin Hormone-Induced Mammary Differentiation and Anti-tumorigenic Role in Breast Cancer

Abstract The premise of differentiation therapy in cancer is a strategy that aims at engaging-forward differentiation and cellular reprograming restricting the proliferative, tumor repopulation, stemness, EMT and metastatic capacities of tumor cells leading to the cessation of the aggressive tumor phenotype and offering the cancer patients improved survival for decades. Therefore, deciphering molecular mechanisms deriving differentiation in normal mammary and breast cancer cells may allow the discovery of innovative differentiation-based biomarkers and therapeutics in breast cancer. Lactation is an intricate process that results in the ability of the breast cells to produce a complex nutritious biological fluid to nurse the new-born. While the beneficial effects of breastfeeding to the infant is irrefutable, its effects on maternal health are less investigated especially in relation to breast cancer that still affects 1 in 8 women in high income countries and 1 in 20 globally. Importantly, epidemiological studies have linked breastfeeding to reduced risk of breast cancer by promoting terminal differentiation of the breast epithelial cells. While breastfeeding process is regulated by multiple hormones, growth factors, and transcriptional regulators, the lactation hormone prolactin (PRL) is known to be directly implicated in the hormonal control of breastfeeding. Extensive research including our own work has shown that PRL/PRL receptor (PRLR) pathway derives mammary gland development and importantly mammary epithelial cell terminal differentiation allowing successful lactation. In contrast the role of PRL in breast cancer is still to be fully defined. Importantly, we have accumulated compelling evidence using in-silico publicly available patient data sets and molecular data implicating this pathway as a clinically relevant pro-differentiation pathway driving differentiation and cellular reprograming suppressing stemness, EMT, metastasis and tumorigenesis in breast cancer. To better characterize PRL signaling mediating its pro-differentiation effects, we used large-scale unbiased proteomics analysis of PRLR/interacting proteins in breast cancer cells. Our analyses revealed several novel PRLR-interactors. We have further confirmed these interactions through co-immunoprecipitations/western blotting analyses in breast cancer cells representative the different breast cancer subtypes. We also examined their functional contributions to mammary differentiation and tumorigenesis using in vitro and in vivo assays. Moreover, we defined their potential clinical relevance using bioinformatics in silico databases of breast cancer. Together our study delineates novel PRL/PRLR-downstream signaling mediators important for mammary differentiation and tumorigenesis and may thus be useful as biomarkers and/or therapeutic targets in breast cancer. Citation Format: Dana Hamam, Suhad Ali. Prolactin Hormone-Induced Mammary Differentiation and Anti-tumorigenic Role in Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-24-06.

Robust language-based mental health assessments in time and space through social media

In the most comprehensive population surveys, mental health is only broadly captured through questionnaires asking about “mentally unhealthy days” or feelings of “sadness.” Further, population mental health estimates are predominantly consolidated to yearly estimates at the state level, which is considerably coarser than the best estimates of physical health. Through the large-scale analysis of social media, robust estimation of population mental health is feasible at finer resolutions. In this study, we created a pipeline that used ~1 billion Tweets from 2 million geo-located users to estimate mental health levels and changes for depression and anxiety, the two leading mental health conditions. Language-based mental health assessments (LBMHAs) had substantially higher levels of reliability across space and time than available survey measures. This work presents reliable assessments of depression and anxiety down to the county-weeks level. Where surveys were available, we found moderate to strong associations between the LBMHAs and survey scores for multiple levels of granularity, from the national level down to weekly county measurements (fixed effects β = 0.34 to 1.82; p < 0.001). LBMHAs demonstrated temporal validity, showing clear absolute increases after a list of major societal events (+23% absolute change for depression assessments). LBMHAs showed improved external validity, evidenced by stronger correlations with measures of health and socioeconomic status than population surveys. This study shows that the careful aggregation of social media data yields spatiotemporal estimates of population mental health that exceed the granularity achievable by existing population surveys, and does so with generally greater reliability and validity.

Proteomic Characterization of Undifferentiated Small Round Cell Sarcomas with EWSR1- and CIC::DUX4-Translocations Reveals Diverging Tumor Biology and Distinct Diagnostic Markers.

Undifferentiated small round cell sarcomas of bone and soft tissue (USRS) are a group of tumors with heterogenic genomic alterations sharing similar morphology. In the present study, we performed a comparative large-scale proteomic analysis of USRS (n=42) with diverse genomic translocations including classic Ewing sarcomas with EWSR1::FLI1 fusions (n=24) or EWSR1::ERG - fusions (n=4), sarcomas with an EWSR1 - rearrangement (n=2), CIC::DUX4 fusion (n=8), as well as tumors classified as USRS with no genetic data available (n=4). Proteins extracted from formalin-fixed, paraffin-embedded (FFPE) pretherapeutic biopsies were analyzed qualitatively and quantitatively using shot gun mass spectrometry (MS). More than 8000 protein groups could be quantified using data-independent acquisition. Unsupervised hierarchical cluster analysis based on proteomic data allowed stratification of the 42 cases into distinct groups reflecting the different molecular genotypes. Protein signatures that significantly correlated with the respective genomic translocations were identified and used to generate a heatmap of all 42 sarcomas with assignment of cases with unknown molecular genetic data to either the EWSR1- or CIC-rearranged groups. MS-based prediction of sarcoma subtypes was molecularly confirmed in two cases where next-generation sequencing was technically feasible. MS also detected proteins routinely used in the immunohistochemical approach for the differential diagnosis of USRS. BCL11B highly expressed in Ewing sarcomas and Bach2 as well as ETS-1 highly expressed in CIC::DUX4-associated sarcomas, were among proteins identified by the present proteomic study and were chosen for immunohistochemical confirmation of MS data in our study cohort. Differential expression of these 3 markers in the two genetic groups were further validated in an independent cohort of n= 34 USRS. Finally, our proteomic results point towards diverging signaling pathways in the different USRS subgroups.

Data-driven analysis of chemicals, proteins and pathways associated with peanut allergy: from molecular networking to biological interpretation

Peanut allergy is majorly related to severe food induced allergic reactions. Several food including cow’s milk, hen’s eggs, soy, wheat, peanuts, tree nuts (walnuts, hazelnuts, almonds, cashews, pecans and pistachios), fish and shellfish are responsible for more than 90 % of food allergies. Here, we provide promising insights using a large-scale data-driven analysis, comparing the mechanistic feature and biological relevance of different ingredients presents in peanuts, tree nuts (walnuts, almonds, cashews, pecans and pistachios) and soybean. Additionally, we have analysed the chemical compositions of peanuts in different processed form raw, boiled and dry-roasted. Using the data-driven approach we are able to generate new hypotheses to explain why nuclear receptors like the peroxisome proliferator-activated receptors (PPARs) and its isoform and their interaction with dietary lipids may have significant effect on allergic response. The results obtained from this study will direct future experimental and clinical studies to understand the role of dietary lipids and PPAR-isoforms to exert pro-inflammatory or anti-inflammatory functions on cells of the innate immunity and influence antigen presentation to the cells of the adaptive immunity.

So much for plain language: An analysis of the accessibility of U.S. federal laws over time.

So much for plain language: An analysis of the accessibility of U.S. federal laws over time.

BABS in Action: Merging Reproducibility and Scalability for Large-Scale Neuroimaging Analysis With BIDS Apps

BABS in Action: Merging Reproducibility and Scalability for Large-Scale Neuroimaging Analysis With BIDS Apps

Large-scale analysis of small molecule-RNA interactions using multiplexed RNA structure libraries

The large-scale analysis of small-molecule binding to diverse RNA structures is key to understanding the required interaction properties and selectivity for developing RNA-binding molecules toward RNA-targeted therapies. Here, we report a new system for performing the large-scale analysis of small molecule–RNA interactions using a multiplexed pull-down assay with RNA structure libraries. The system profiled the RNA-binding landscapes of G-clamp and thiazole orange derivatives, which recognizes an unpaired guanine base and are good probes for fluorescent indicator displacement (FID) assays, respectively. We discuss the binding preferences of these molecules based on their large-scale affinity profiles. In addition, we selected combinations of fluorescent indicators and different ranks of RNA based on the information and screened for RNA-binding molecules using FID. RNAs with high- and intermediate-rank RNA provided reliable results. Our system provides fundamental information about small molecule–RNA interactions and facilitates the discovery of novel RNA-binding molecules.

Comparison and Agreement between Cardiovascular Computed Tomography-Derived Mid-Diastolic and End-Diastolic Ventricular Volume in Patients with Congenital Heart Disease.

Prospective electrocardiogram (ECG)-triggered cardiovascular computed tomography (CCT) is primarily utilized for anatomical information in congenital heart disease (CHD) and has not been utilized for calculation of the end-diastolic volume (EDV); however, the mid-diastolic volume (MDV) may be measured. The objective of this study was to evaluate the feasibility and agreement between ventricular EDV and MDV. 31 retrospectively ECG-gated CCT were analyzed for the study of the 450 consecutive CCT. CCT images were processed using syngo.via with automatic contouring followed by manual adjustment of the endocardial borders of the left ventricles (LV) and right ventricles (RV) at end-diastolic and mid-diastolic phase (measured at 70% of cardiac cycle). The correlation and agreements between EDV and MDV were demonstrated using Spearman rank coefficient and intraclass correlation coefficient (ICC), respectively. Mean age ± SD was 28.8 ± 12.5 years, 19 were male (61.3%) and tetralogy of Fallot (TOF) was the most common diagnosis (58.1%), 35% (11/31) patients with a pacemaker, ICD or other such contraindication for a CMRI, 23% (7/31) with claustrophobia, and 6.5% (2/31) with developmental delay with refusal for sedation did not have a previous CMRI. The mean ± SD indexed LV EDV and LV MDV were 91.1 ± 24.5 and 84.8 ± 22.3 ml/m2, respectively. The mean ± SD indexed RV EDV and RV MDV were 136.8 ± 41 and 130.2 ± 41.5 ml/m2, respectively. EDV and MDV had a strong positive correlation and good agreement (ICC 0.92 for LV and 0.95 for RV). This agreement was preserved in a subset of patients (21) with dilated RV (indexed RV EDV z-score > 2). Intra-observer reliability (0.97 and 0.98 for LV and RV MDV, respectively) and inter-observer reliability (0.96 and 0.90 for LV and RV MDV, respectively) were excellent. In a select group of patients with CHD, measuring MDV by CCT is feasible and these values have good agreements with EDV. This may be used to derive functional data from prospectively ECG-triggered CCT studies. Further large-scale analysis is needed to determine accuracy and clinical correlation.

How biomedical papers accumulated their clinical citations: a large-scale retrospective analysis based on PubMed

How biomedical papers accumulated their clinical citations: a large-scale retrospective analysis based on PubMed

Transcriptome-wide analysis uncovers regulatory elements of the antennal transcriptome repertoire of bumblebee at different life stages.

Bumblebees are crucial pollinators, providing essential ecosystem services and global food production. The success of pollination services relies on the interaction between sensory organs and the environment. The antenna functions as a versatile multi-sensory organ, pivotal in mediating chemosensory/olfactory information, and governs adaptive responses to environmental changes. Despite an increasing number of RNA-sequencing studies on insect antenna, comprehensive antennal transcriptome studies at the different life stages were not elucidated systematically. Here, we quantified the expression profile and dynamics of coding/microRNA genes of larval head and antennal tissues from early- and late-stage pupa to the adult of Bombus terrestris as suitable model organism among pollinators. We further performed Pearson correlation analyses on the gene expression profiles of the antennal transcriptome from larval head tissue to adult stages, exploring both positive and negative expression trends. The positively correlated coding genes were primarily enriched in sensory perception of chemical stimuli, ion transport, transmembrane transport processes and olfactory receptor activity. Negatively correlated genes were mainly enriched in organic substance biosynthesis and regulatory mechanisms underlying larval body patterning and the formation of juvenile antennal structures. As post-transcriptional regulators, miR-1000-5p, miR-13b-3p, miR-263-5p and miR-252-5p showed positive correlations, whereas miR-315-5p, miR-92b-3p, miR-137-3p, miR-11-3p and miR-10-3p exhibited negative correlations in antennal tissue. Notably, based on the inverse expression relationship, positively and negatively correlated microRNA (miRNA)-mRNA target pairs revealed that differentially expressed miRNAs predictively targeted genes involved in antennal development, shaping antennal structures and regulating antenna-specific functions. Our data serve as a foundation for understanding stage-specific antennal transcriptomes and large-scale comparative analysis of transcriptomes in different insects.

Molecular characterization of the complete genome of a novel ormycovirus infecting the ectomycorrhizal fungus Hortiboletus rubellus.

Advancements in high-throughput sequencing and the development of new bioinformatics tools for large-scale data analysis play a crucial role in uncovering virus diversity and enhancing our understanding of virus evolution. The discovery of the ormycovirus clades, a group of RNA viruses that are phylogenetically distinct from all known Riboviria members and are found in fungi, highlights the value of these tools for the discovery of novel viruses. The aim of this study was to examine viral populations in fungal hosts to gain insights into the diversity, evolution, and classification of these viruses. Here, we report the molecular characterization of a newly discovered ormycovirus, which we have named "Hortiboletus rubellus ormycovirus 1" (HrOMV1), that was found in the ectomycorrhizal fungus Hortiboletus rubellus. The bipartite genome of HrOMV1, whose nucleotide sequence was determined by HTS and RLM-RACE, consists of two RNA segments (RNA1 and RNA2) that exhibit similarity to those of previously studied ormycoviruses in their organization and the proteins they encode. The presence of upstream, in-frame AUG triplets in the 5' termini of both RNA segments suggests that HrOMV1, like certain other ormycoviruses, employs a non-canonical translation initiation strategy. Phylogenetic analysis showed that HrOMV1 is positioned within the gammaormycovirus clade. Its putative RNA-dependent RNA polymerase (RdRp) exhibits sequence similarity to those of other gammaormycovirus members, the most similarity to that of Termitomyces ormycovirus 1, with 33.05% sequence identity. This protein was found to contain conserved motifs that are crucial for RNA replication, including the distinctive GDQ catalytic triad observed in gammaormycovirus RdRps. The results of this study underscore the significance of investigating the ecological role of mycoviruses in mycorrhizal fungi. This is the first report of an ormycovirus infecting a member of the ectomycorrhizal genus Hortiboletus.

Real-time genomic surveillance for enhanced control of infectious diseases and antimicrobial resistance

This article advocates for mobilizing pathogen genomic surveillance to contain and mitigate health threats from infectious diseases and antimicrobial resistance (AMR), building upon successes achieved by large-scale genome sequencing analysis of SARS-CoV-2 variants in guiding COVID-19 monitoring and public health responses and adopting a One Health approach. Capabilities of laboratory-based surveillance and epidemic alert systems should be enhanced by fostering (i) universal access to real-time whole genome sequence (WGS) data of pathogens to inform clinical practice, infection control, public health policies, and vaccine and antimicrobial drug research and development; (ii) integration of diagnostic microbiology data, data from testing asymptomatic individuals, pathogen sequence data, clinical data, and epidemiological data into surveillance programs; (iii) stronger cross-sectorial collaborations between healthcare, public health, animal health, and environmental surveillance and research using One Health approaches, toward understanding the ecology and transmission pathways of pathogens and AMR across ecosystems; (iv) international collaboration and interconnection of surveillance networks, harmonization of laboratory methods, and standardization of surveillance methods for global reporting, including on pathogen genomic variant or strain nomenclature; (v) responsible data sharing between surveillance networks, databases, and platforms according to FAIR (findability, accessibility, interoperability, and reusability) principles; and (vi) research on genomic surveillance system implementation and its cost-effectiveness for different pathogens and AMR threats across different settings. Regional and global One Health policies and governance initiatives should foster the concerted development and efficient utilization of pathogen genomic surveillance to protect the health of humans, animals, and the environment.

JAK-STAT signaling maintains homeostasis in T cells and macrophages

Immune cells need to sustain a state of constant alertness over a lifetime. Yet, little is known about the regulatory processes that control the fluent and fragile balance that is called homeostasis. Here we demonstrate that JAK-STAT signaling, beyond its role in immune responses, is a major regulator of immune cell homeostasis. We investigated JAK-STAT-mediated transcription and chromatin accessibility across 12 mouse models, including knockouts of all STAT transcription factors and of the TYK2 kinase. Baseline JAK-STAT signaling was detected in CD8+ T cells and macrophages of unperturbed mice—but abrogated in the knockouts and in unstimulated immune cells deprived of their normal tissue context. We observed diverse gene-regulatory programs, including effects of STAT2 and IRF9 that were independent of STAT1. In summary, our large-scale dataset and integrative analysis of JAK-STAT mutant and wild-type mice uncovered a crucial role of JAK-STAT signaling in unstimulated immune cells, where it contributes to a poised epigenetic and transcriptional state and helps prepare these cells for rapid response to immune stimuli.

NIFK, a Potential Prognostic and Immunological Biomarker in Pan-- Cancer Analysis, Significantly Regulates Proliferation and Metastasis of Colorectal Cancer.

As a binding protein of Ki67, NIFK plays an important role in the mitosis of cells and is closely related to the progression of specific types of tumors. However, there is still a lack of systematic analysis of NIFK in pan-cancer and insufficient research to explore its role in human tumors. We systematically evaluated the pan-cancer expression and mutation of NIFK in human cancers using data from The Cancer Genome Atlas (TCGA) through large-scale bioinformatics analysis. In addition, we explored the pan-cancer immunological characteristics of NIFK, especially in colorectal adenocarcinoma (COAD). Furthermore, we used single-cell sequencing to analyze the expression of NIFK in different cells of COAD tissues and performed GO, KEGG, and gene set enrichment analysis of NIFK in COAD. Lastly, we evaluated the effects of NIFK knockdown on the colorectal cancer cell lines in in vitro experiment. We found that NIFK was overexpressed in almost all types of tumors and showed significant prognostic efficacy. Additionally, correlations between NIFK and specific immune features, such as immune cell infiltration, immune checkpoint genes, TMB, and MSI, suggest that NIFK may be used to guide immunotherapy. Subsequently, it was found that the expression of NIFK was significantly upregulated in tumor cells through single-cell sequencing analysis, and the NIFK gene was closely associated with tumor progression and immune therapy response. Finally, we further elucidated the role of NIFK in colorectal cancer and found that downregulation of NIFK expression could inhibit the proliferation, migration, and invasion ability of colorectal cancer cells. The results of this study demonstrated that NIFK, as a member of the pan-cancer genes, will serve as a biomarker and a potential therapeutic target for a range of cancer types, providing new insight into precision medicine.

Kataegis in clinical and molecular subgroups of primary breast cancer

Kataegis is a hypermutation phenomenon characterized by localized clusters of single base pair substitution (SBS) reported in multiple cancer types. Despite a high frequency in breast cancer, large-scale analyses of kataegis patterns and associations with clinicopathological and molecular variables in established breast cancer subgroups are lacking. Therefore, WGS profiled primary breast cancers (n = 791) with associated clinical and molecular data layers, like RNA-sequencing data, were analyzed for kataegis frequency, recurrence, and associations with genomic contexts and functional elements, transcriptional patterns, driver alterations, homologous recombination deficiency (HRD), and prognosis in tumor subgroups defined by ER, PR, and HER2/ERBB2 status. Kataegis frequency was highest in the HER2-positive(p) subgroups, including both ER-negative(n)/positive(p) tumors (ERnHER2p/ERpHER2p). In TNBC, kataegis was neither associated with PAM50 nor TNBC mRNA subtypes nor with distant relapse in chemotherapy-treated patients. In ERpHER2n tumors, kataegis was associated with aggressive characteristics, including PR-negativity, molecular Luminal B subtype, higher mutational burden, higher grade, and expression of proliferation-associated genes. Recurrent kataegis loci frequently targeted regions commonly amplified in ER-positive tumors, while few recurrent loci were observed in TNBC. SBSs in kataegis loci appeared enriched in regions of open chromatin. Kataegis status was not associated with HRD in any subgroup or with distinct transcriptional patterns in unsupervised or supervised analysis. In summary, kataegis is a common hypermutation phenomenon in established breast cancer subgroups, particularly in HER2p subgroups, coinciding with an aggressive tumor phenotype in ERpHER2n disease. In TNBC, the molecular implications and associations of kataegis are less clear, including its prognostic value.

TGFβ-Responsive Stromal Activation Occurs Early in Serrated Colorectal Carcinogenesis

Activated TGFβ signaling in the tumor microenvironment, which occurs independently of epithelial cancer cells, has emerged as a key driver of tumor progression in late-stage colorectal cancer (CRC). This study aimed to elucidate the contribution of TGFβ-activated stroma to serrated carcinogenesis, representing approximately 25% of CRCs and often characterized by oncogenic BRAF mutations. We used a transcriptional signature developed based on TGFβ-responsive, stroma-specific genes to infer TGFβ-dependent stromal activation and conducted in silico analyses in 3 single-cell RNA-seq datasets from a total of 39 CRC samples and 12 bulk transcriptomic datasets consisting of 2014 CRC and 416 precursor samples, of which 33 were serrated lesions. Single-cell analyses validated that the signature was expressed specifically by stromal cells, effectively excluding transcriptional signals derived from epithelial cells. We found that the signature was upregulated during malignant transformation and cancer progression, and it was particularly enriched in CRCs with mutant BRAF compared to wild-type counterparts. Furthermore, across four independent precursor datasets, serrated lesions exhibited significantly higher levels of TGFβ-responsive stromal activation compared to conventional adenomas. This large-scale analysis suggests that TGFβ-dependent stromal activation occurs early in serrated carcinogenesis. Our study provides novel insights into the molecular mechanisms underlying CRC development via the serrated pathway.