Corrigendum: Childhood cerebral visual impairment subtype classification based on an extensive versus a limited test battery.

[This corrects the article DOI: 10.3389/fnins.2023.1266201.].

Updates in Molecular Profiling of Pancreatic Ductal Adenocarcinoma.

Outcomes from pancreatic ductal adenocarcinoma (PDAC) remain poor and better methods of prognostication and therapeutic approaches are needed. Recent advances in cancer genomics have led to the development of molecular subtypes of PDAC associated with clinical outcomes. Current evidence also suggests that the subtypes have differential response to first-line chemotherapy regimens. PDAC is also characterized by different stroma and immune environments. Further work is needed to confirm the utility of these subtypes to predicting response to different systemic therapies.

Comprehensive Transcriptome Analysis Expands lncRNA Functional Profiles in Breast Cancer.

Breast cancer is a heterogeneous disease that arises as a multi-stage process involving multiple cell types. Patients diagnosed with the same clinical stage and pathological classification may have different prognoses and therapeutic responses due to alterations in molecular genetics. As an essential marker for the molecular subtyping of breast cancer, long non-coding RNAs (lncRNAs) play a crucial role in gene expression regulation, cell differentiation, and the maintenance of genomic stability. Here, we developed a modular framework for lncRNA identification and applied it to a breast cancer cohort to identify novel lncRNAs not previously annotated. To investigate the potential biological function, regulatory mechanisms, and clinical relevance of the novel lncRNAs, we elucidated the genomic and chromatin features of these lncRNAs, along with the associated protein-coding genes and putative enhancers involved in the breast cancer regulatory networks. Furthermore, we uncovered that the expression patterns of novel and annotated lncRNAs identified in breast cancer were related to the hormone response in the PAM50 subtyping criterion, as well as the immune response and progression states of breast cancer across different immune cells and immune checkpoint genes. Collectively, the comprehensive identification and functional analysis of lncRNAs revealed that these lncRNAs play an essential role in breast cancer by altering gene expression and participating in the regulatory networks, contributing to a better insight into breast cancer heterogeneity and potential avenues for therapeutic intervention.

Predicting invasive disease-free survival in ER-positive, HER2-negative early breast cancer using the PAM50 risk-of-recurrence score: a retrospective analysis using single-center long-term follow-up data of postmenopausal Japanese patients.

BACKGROUND: The prognostic value of the risk-of-recurrence (ROR) score calculated using PAM50 has been validated using clinical trials and patient cohorts. This study aimed to investigate the prognostic value of the PAM50 ROR score in Japanese patients with early breast cancer using long-term follow-up data. METHODS: We enrolled postmenopausal patients with ER-positive, HER2-negative, stage I-II breast cancer who had undergone surgery at the Kyoto University Hospital between 2008 and 2014. The intrinsic subtype and ROR score were calculated using PAM50. The primary endpoint was invasive disease-free survival (IDFS). RESULTS: We enrolled 146 patients, of whom 47 (32%) patients had node-positive disease, and 36 (25%) had received neoadjuvant or adjuvant chemotherapy. The proportions of intrinsic subtypes for luminal A, luminal B, HER2-enriched, and basal-like subtypes were 67%, 27%, 3%, and 2%, respectively. The median follow-up duration was 8.4 (range 6.3-10.0) years, and 21 IDFS events were observed. Based on the ROR score, 37%, 33%, and 30% of the patients were classified as low, intermediate, and high risks, respectively. Patients in the high-risk group had a significantly worse 8-year IDFS rate than those in the low-to-intermediate-risk groups (75.1% vs. 91.6%, p = 0.04). The same trend was observed in patients with and without neoadjuvant or adjuvant chemotherapy. CONCLUSIONS: Using long-term follow-up data, this study showed that the ROR score can predict the prognosis of ER-positive, HER2-negative early breast cancer in Japanese postmenopausal patients. Further investigations are required to confirm the prognostic value of the ROR score in Asian populations.

Next-generation lung cancer pathology: Development and validation of diagnostic and prognostic algorithms.

Non-small cell lung cancer (NSCLC) is one of the most common malignant tumors. In this study, we develop a clinically useful computational pathology platform for NSCLC that can be a foundation for multiple downstream applications and provide immediate value for patient care optimization and individualization. We train the primary multi-class tissue segmentation algorithm on a substantial, high-quality, manually annotated dataset of whole-slide images with lung adenocarcinoma and squamous cell carcinomas. We investigate two downstream applications. NSCLC subtyping algorithm is trained and validated using a large, multi-institutional (n = 6), multi-scanner (n = 5), international cohort of NSCLC cases (slides/patients 4,097/1,527). Moreover, we develop four AI-derived, fully explainable, quantitative, prognostic parameters (based on tertiary lymphoid structure and necrosis assessment) and validate them for different clinical endpoints. The computational platform enables the high-precision, quantitative analysis of H&E-stained slides. The developed prognostic parameters facilitate robust and independent risk stratification of patients with NSCLC.

The transcriptional landscape and clinico-biological characterization of human endogenous retroviruses in esophageal squamous cell carcinoma.

Human endogenous retroviruses (HERVs) are emerging as critical elements in host genomic regulation. Aberrant HERV transcription has been implicated in developmental and tissue-specific aging and pathological processes. In this study, we presented a comprehensive locus-specific characterization of the HERV expression landscape in esophageal squamous cell carcinoma (ESCC). We demonstrated the transcriptional diversity among patients and identified 12 clinically relevant HERVs in the SCH cohort, which were experimentally validated by Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR) in the CAMS cohort. ESCC patients were stratified into three HERV-based subtypes (HERVhigh, HERVmedian and HERVlow) with distinct clinical and biological characteristics. The HERVhigh subtype was associated with worse survival, increased CD4+ T cells infiltration and decreased metabolic activity, whereas the HERVlow subtype was characterized by abundant CD8+ T cells, increased metabolic activity, and better survival. The HERV-based tumor subtyping was further robustly validated by RNA sequencing and RT-qPCR in two additional external cohorts. Our findings demonstrate the clinical significance of HERVs for tumor subtyping and prognosis, provide insights into the functional role of HERVs and a valuable resource for developing novel biomarkers and therapeutic targets in ESCC.

Patient subtyping analysis of baseline multi-omic data reveals distinct pre-immune states associated with antibody response to seasonal influenza vaccination.

Understanding the molecular mechanisms underpinning diverse vaccination responses is critical for developing efficient vaccines. Molecular subtyping can offer insights into heterogeneous nature of responses and aid in vaccine design. We analyzed multi-omic data from 62 haemagglutinin seasonal influenza vaccine recipients (2019-2020), including transcriptomics, proteomics, glycomics, and metabolomics data collected pre-vaccination. We performed a subtyping analysis on the integrated data revealing five subtypes with distinct molecular signatures. These subtypes differed in the expression of pre-existing adaptive or innate immunity signatures, which were linked to significant variation in baseline immunoglobulin A (IgA) and hemagglutination inhibition (HAI) titer levels. It is worth noting that these differences persisted through day 28 post-vaccination, indicating the effect of initial immune state on vaccination response. These findings highlight the significance of interpersonal variation in baseline immune status as a crucial factor in determining the effectiveness of seasonal vaccines. Ultimately, incorporating molecular profiling could enable personalized vaccine optimization.

Identification of PANoptosis-relevant subgroups and predicting signature to evaluate the prognosis and immune landscape of patients with biliary tract cancer.

BACKGROUND: This study conducted molecular subtyping of biliary tract cancer patients based on 19 PANoptosis-related gene signatures. METHODS: Through consensus clustering, patients were categorized into two subtypes, A and B. By integrating multi-omics data and clinical information from different cohorts, we elucidated the association between different subtypes of biliary tract cancer and patient prognosis, which correlated with the immune infiltration characteristics of patients. RESULTS: LASSO regression analysis was performed on the 19 gene signatures, and we constructed and validated a 9-gene risk score prognostic model that accurately predicts the overall survival rate of different biliary tract cancer patients. Additionally, we developed a predictive nomogram demonstrating the clinical utility and robustness of our model. Further analysis of the risk score-based immune landscape highlighted potential associations with immune cell infiltration, chemotherapy, and immune therapy response. CONCLUSION: Our study provides valuable insights into personalized treatment strategies for biliary tract cancer, which are crucial for improving patient prognosis and guiding treatment decisions in clinical practice.

Multiplex Dual-Target Reverse Transcription PCR for Subtyping Avian Influenza A(H5) Virus.

An increased risk for human infection with avian influenza A(H5N1) viruses is of concern. We developed an internally controlled, dual-target reverse transcription PCR for influenza A(H5) subtyping. This test could be used to detect influenza A(H5) in clinical samples.

Molecular characterizations of Cryptosporidium spp. in brown rat (Rattus norvegicus) from an animal feedlot in Xinjiang, China.

Cryptosporidium infection is a common occurrence in rodents worldwide. In this study, 435 wild brown rats were captured from an animal feedlot in Xinjiang, China, with a fecal sample obtained directly from the rectal contents of each rat. The DNA extracted from these fecal samples was analyzed for Cryptosporidium spp. using PCR targeting the SSU rRNA gene. The prevalence of Cryptosporidium infection in brown rats was found to be 5.5% (24 out of 435). Interestingly, the infection rates varied among different animal enclosures, with rates of 0% in the chicken coop (0/51), cowshed (0/3), and varying rates in other areas including the sheepfold (6.1%, 6/98), the pigsty (7.6%, 10/132), the dovecote (7.0%, 5/71), and outdoor environments (3.8%, 3/80). The study identified three species and one genotype of Cryptosporidium, namely C. occultus (n = 10), C. parvum (n = 4), C. ditrichi (n = 1), and Cryptosporidium rat genotype IV (n = 9). Additionally, two of the C. parvum isolates were successfully subtyped as IIdA19G1 (n = 2) at the gp60 gene. These results offer valuable insights into the prevalence and genetic diversity of Cryptosporidium in brown rats within the region.

Similar genetic profile in early and late stage urothelial tract cancer.

INTRODUCTION: Urothelial tract cancer (UTC) ranks as the tenth most prevalent cancer and holds the seventh position in terms of mortality worldwide. Despite its prevalence and mortality ranking, there are still gaps in the knowledge of the mutational landscape in patients with advanced disease who have limited therapeutic options after multiple lines of prior treatment. This study compares the genomic and transcriptomic landscape, and targeted treatment options between metastatic UTC (mUTC) patients treated with multiple lines of therapy compared to newly diagnosed, untreated Muscle Invasive Bladder Cancer (MIBC). METHODS: We compared genomic and clinical data from two cohorts: mUTC patients who received multiple lines of therapy and were referred to the Copenhagen Prospective Personalized Oncology (CoPPO) project at Rigshospitalet, University of Copenhagen. Data for MIBC UTC patients were acquired from the Cancer Genome Atlas Bladder Cancer (TCGA BLCA) cohort. Biopsies in CoPPO were performed at the time of enrollment. 523 highly important cancer-related genes (TrueSight Oncology-500 targeted sequencing panel) were used from both cohorts for comparative analysis. Analyses included RNA count data to compare predicted molecular subtypes in each cohort separately. RESULTS: Patients from the CoPPO cohort had a lower median age at first-line treatment than the TCGA BLCA cohort, with no significant gender disparity. The predominant histology was urothelial cell carcinoma in both cohorts. Genomic analysis revealed no significant difference between the top mutated genes in the two cohorts, specifically looking into DNA damage repair genes. Molecular subtyping indicated a higher frequency of neuroendocrine differentiation in the CoPPO cohort. 13% of patients in the CoPPO cohort received targeted therapy based on genomic findings, and 16% received non-targeted treatment, totaling 29% receiving CoPPO treatment (9 patients). The remaining 71% received best supportive care. Kaplan-Meier analysis showed a non-significant survival benefit for the intervention group in the CoPPO cohort. CONCLUSION: When focusing on 523 highly relevant cancer genes, the mutational profile of mUTC patients who have undergone numerous treatment lines resembles that of newly diagnosed MIBC. These alterations can be targeted, indicating the potential advantage of early genomic testing for personalized treatment within clinical trials.

Multimodal subtypes identified in Alzheimer's Disease Neuroimaging Initiative participants by missing-data-enabled subtype and stage inference.

Alzheimer's disease is a highly heterogeneous disease in which different biomarkers are dynamic over different windows of the decades-long pathophysiological processes, and potentially have distinct involvement in different subgroups. Subtype and Stage Inference is an unsupervised learning algorithm that disentangles the phenotypic heterogeneity and temporal progression of disease biomarkers, providing disease insight and quantitative estimates of individual subtype and stage. However, a key limitation of Subtype and Stage Inference is that it requires a complete set of biomarkers for each subject, reducing the number of datapoints available for model fitting and limiting applications of Subtype and Stage Inference to modalities that are widely collected, e.g. volumetric biomarkers derived from structural MRI. In this study, we adapted the Subtype and Stage Inference algorithm to handle missing data, enabling the application of Subtype and Stage Inference to multimodal data (magnetic resonance imaging, positron emission tomography, cerebrospinal fluid and cognitive tests) from 789 participants in the Alzheimer's Disease Neuroimaging Initiative. Missing-data Subtype and Stage Inference identified five subtypes having distinct progression patterns, which we describe by the earliest unique abnormality as 'Typical AD with Early Tau', 'Typical AD with Late Tau', 'Cortical', 'Cognitive' and 'Subcortical'. These new multimodal subtypes were differentially associated with age, years of education, Apolipoprotein E (APOE4) status, white matter hyperintensity burden and the rate of conversion from mild cognitive impairment to Alzheimer's disease, with the 'Cognitive' subtype showing the fastest clinical progression, and the 'Subcortical' subtype the slowest. Overall, we demonstrate that missing-data Subtype and Stage Inference reveals a finer landscape of Alzheimer's disease subtypes, each of which are associated with different risk factors. Missing-data Subtype and Stage Inference has broad utility, enabling the prediction of progression in a much wider set of individuals, rather than being restricted to those with complete data.

Transcriptomic subtyping of gastrointestinal malignancies.

Gastrointestinal (GI) cancers are highly heterogeneous at multiple levels. Tumor heterogeneity can be captured by molecular profiling, such as genetic, epigenetic, proteomic, and transcriptomic classification. Transcriptomic subtyping has the advantage of combining genetic and epigenetic information, cancer cell-intrinsic properties, and the tumor microenvironment (TME). Unsupervised transcriptomic subtyping systems of different GI malignancies have gained interest because they reveal shared biological features across cancers and bear prognostic and predictive value. Importantly, transcriptomic subtypes accurately reflect complex phenotypic states varying not only per tumor region, but also throughout disease progression, with consequences for clinical management. Here, we discuss methodologies of transcriptomic subtyping, proposed taxonomies for GI malignancies, and the challenges posed to clinical implementation, highlighting opportunities for future transcriptomic profiling efforts to optimize clinical impact.

Optimising nucleic acid recovery from rapid antigen tests for whole genome sequencing of respiratory viruses.

BACKGROUND: Whole genome sequencing (WGS) of respiratory viruses from rapid antigen tests (RAT-WGS) is a novel approach to expanding genomic surveillance of respiratory infections. To date however, there are limited data on the genomic stability of these viruses on RATs. In this study, we investigated the effect of storage conditions and nucleic acid preservatives on the ability to enhance stability and improve recovery of respiratory virus genomes from RATs. METHODS: A mixture of common respiratory viruses was used to inoculate RATs at different environmental temperatures (4°C, 20°C and 36°C), with two preservative reagents (RNALater and DNA/RNA shield) Nucleic acid was extracted from RATs at two different timepoints (72 h and seven days) and subject to real-time multiplex respiratory PCR to detect a range of respiratory viruses. WGS was performed using target-enrichment with the TWIST Comprehensive Viral Research Panel. Defined metrics from an automated in-house bioinformatic pipeline were used to assess and compare viral genome recovery under different conditions. RESULTS: Nucleic acid degradation (indicated by relative change in PCR cycle threshold and WGS-based metrics) was most notable at 20 °C and 36 °C. Storage in either RNALater or DNA / RNA shield improved genome recovery for respiratory viruses across all temperature conditions, although this was most pronounced for RNALater. Subtyping of Influenza viruses demonstrated the applicability of RAT-WGS in downstream genomic epidemiological surveillance. CONCLUSIONS: Under simulated conditions, RAT-WGS demonstrated that (i) viral genomes were generally stable at 4°C at 72 h and 1 week, (ii) RNALater has a more significant preservation of nucleic acids compared to DNA/RNA Shield and (iii) genome recovery can be achieved using a sequencing depth of 500,000 reads per sample in RNALater, across all respiratory viruses and conditions.

Understanding machine learning applications in dementia research and clinical practice: a review for biomedical scientists and clinicians.

Several (inter)national longitudinal dementia observational datasets encompassing demographic information, neuroimaging, biomarkers, neuropsychological evaluations, and muti-omics data, have ushered in a new era of potential for integrating machine learning (ML) into dementia research and clinical practice. ML, with its proficiency in handling multi-modal and high-dimensional data, has emerged as an innovative technique to facilitate early diagnosis, differential diagnosis, and to predict onset and progression of mild cognitive impairment and dementia. In this review, we evaluate current and potential applications of ML, including its history in dementia research, how it compares to traditional statistics, the types of datasets it uses and the general workflow. Moreover, we identify the technical barriers and challenges of ML implementations in clinical practice. Overall, this review provides a comprehensive understanding of ML with non-technical explanations for broader accessibility to biomedical scientists and clinicians.

Microstructural characterization of multiple sclerosis lesion phenotypes using multiparametric longitudinal analysis.

BACKGROUND AND OBJECTIVES: In multiple sclerosis (MS), slowly expanding lesions were shown to be associated with worse disability and prognosis. Their timely detection from cross-sectional data at early disease stages could be clinically relevant to inform treatment planning. Here, we propose to use multiparametric, quantitative MRI to allow a better cross-sectional characterization of lesions with different longitudinal phenotypes. METHODS: We analysed T1 and T2 relaxometry maps from a longitudinal cohort of MS patients. Lesions were classified as enlarging, shrinking, new or stable based on their longitudinal volumetric change using a newly developed automated technique. Voxelwise deviations were computed as z-scores by comparing individual patient data to T1, T2 and T2/T1 normative values from healthy subjects. We studied the distribution of microstructural properties inside lesions and within perilesional tissue. RESULTS AND CONCLUSIONS: Stable lesions exhibited the highest T1 and T2 z-scores in lesion tissue, while the lowest values were observed for new lesions. Shrinking lesions presented the highest T1 z-scores in the first perilesional ring while enlarging lesions showed the highest T2 z-scores in the same region. Finally, a classification model was trained to predict the longitudinal lesion type based on microstructural metrics and feature importance was assessed. Z-scores estimated in lesion and perilesional tissue from T1, T2 and T2/T1 quantitative maps carry discriminative and complementary information to classify longitudinal lesion phenotypes, hence suggesting that multiparametric MRI approaches are essential for a better understanding of the pathophysiological mechanisms underlying disease activity in MS lesions.

Evolving treatment landscape in thymic epithelial tumors: From mechanism to therapy.

Thymic epithelial tumors (TETs) are rare neoplasms of the anterior mediastinum that arise from thymic epithelial cells. Although surgery is the preferred treatment for resectable TETs, the options for unresectable or recurrent advanced-stage TETs are limited beyond platinum-based chemotherapy. The evolving landscape of TET treatments is marked by significant advancements in targeted therapies and immunotherapies, particularly with anti-angiogenic agents and immune checkpoint inhibitors (ICIs). While monotherapies demonstrated certain efficacy, the development of combination strategies is vital for improving patient outcomes. This review consolidates progress in anti-angiogenic therapies and ICIs, emphasizing the evolution of combination therapies of TETs. Furtherly, we particularly discuss new first-line strategies based on these advancements and emphasizes exploring novel treatments like antibody-drug conjugates, immunomodulatory drugs and cytokine-based agents for TETs. Mechanistically, the molecular features of TETs integrated with clinical diagnosis and targeted therapy, and immunophenotyping of TETs along with its impact on the efficacy and safety of immunotherapy are discussed. Thus, this review systemizes the development in the treatment landscape of TETs, integrating the corresponding molecular and immune mechanisms, aiming to provide new references for the treatment of TETs.

Comprehensive molecular epidemiology of Cryptosporidium species in Japan.

Cryptosporidium species, causing diarrheal illnesses in humans and animals worldwide, are under investigation for their molecular epidemiology in Japan. The study focuses on detecting Cryptosporidium species in humans, animals, water, and the environment, revealing three species in people: C. parvum, C. meleagridis, and C. hominis. Subtype IIa of the C. parvum gp60 gene is prevalent, indicating potential zoonotic transmission. Animal studies identified sixteen species, mainly cattle and pets, with C. parvum (subtype IIa) common in cattle and C. canis and C. felis prevalent in pets. Additionally, C. bovis and C. ryanae were found in cattle and sika deer. Knowledge gaps exist, particularly in water and environmental source typing, with limited research revealing five species and five genotypes, suggesting a significant role of water in transmission. Further research is needed to understand the molecular diversity and transmission dynamics across humans, animals, water, and the environment in Japan.

Enhancing RNA detection and breast cancer subtyping with a universal 3D-hybridization chain reaction system.

Breast cancer, a globally prevalent malignancy, is characterized by pronounced heterogeneity. Accurate subtyping requires the simultaneous detection of different biomarkers, which is crucial for personalized treatment strategies. However, existing methodologies are hindered by limited versatility and sensing performance. To overcome these hurdles, this study presents a universal 3D-Hybridization Chain Reaction (3D-HCR) system for RNA detection and subtype-specific diagnosis of breast cancer. The system integrated a universal trigger for HCR, thereby circumventing the need for complex sequence design and enabling the analysis of various RNA targets. Leveraging the spatial-confinement effect offered by DNA nanocarriers, this system exhibited superior amplification efficiency, achieving detection limits of 3.83 pM and 4.96 pM for PD-L1 mRNA and miR-21, respectively. Importantly, the system could differentiate between triple-negative breast cancer and estrogen receptor-positive breast cancer in both living cells and clinical tissues. These findings underscore the potential of the universal 3D-HCR system as a promising tool in clinical diagnostics. With its proven proficiency in breast cancer diagnostics and versatility in RNA analysis, this system holds the promise of broadening the horizons of precision medicine.

Themis: advancing precision oncology through comprehensive molecular subtyping and optimization.

Recent advances in tumor molecular subtyping have revolutionized precision oncology, offering novel avenues for patient-specific treatment strategies. However, a comprehensive and independent comparison of these subtyping methodologies remains unexplored. This study introduces 'Themis' (Tumor HEterogeneity analysis on Molecular subtypIng System), an evaluation platform that encapsulates a few representative tumor molecular subtyping methods, including Stemness, Anoikis, Metabolism, and pathway-based classifications, utilizing 38 test datasets curated from The Cancer Genome Atlas (TCGA) and significant studies. Our self-designed quantitative analysis uncovers the relative strengths, limitations, and applicability of each method in different clinical contexts. Crucially, Themis serves as a vital tool in identifying the most appropriate subtyping methods for specific clinical scenarios. It also guides fine-tuning existing subtyping methods to achieve more accurate phenotype-associated results. To demonstrate the practical utility, we apply Themis to a breast cancer dataset, showcasing its efficacy in selecting the most suitable subtyping methods for personalized medicine in various clinical scenarios. This study bridges a crucial gap in cancer research and lays a foundation for future advancements in individualized cancer therapy and patient management.