Subtype Classification Research Articles

Neurological phenotypes and treatment outcomes in Eagle syndrome: systematic review and meta-analysis.

Eagle syndrome is caused by an elongated styloid process affecting carotid arteries and cranial nerves. Pain, dysphagia, tinnitus, paresthesia (classic subtype), and neurovascular events (vascular subtype) may be triggered by head movements or arise spontaneously. However, Eagle syndrome remains underappreciated in the neurological community. We aimed to determine the most common neurological and non-neurological clinical presentations in patients with Eagle syndrome and to assess the clinical outcome post-surgical resection in comparison to non-surgical therapies. We conducted a systematic review of patient-level data on adults with Eagle syndrome, following PRISMA guidelines. We extracted data on demographics, presenting symptoms, neurological deficits, radiological findings, and treatments, including outcomes and complications, from studies in multiple indexing databases published between 2000 and 2023. The study protocol is registered with PROSPERO. In total, 285 studies met inclusion criteria, including 497 patients with Eagle syndrome (mean age 47.3 years; 49.8% female). Classical Eagle (370 patients, 74.5%) was more frequent than vascular Eagle syndrome (117 patients, 23.5%, p < 0.0001). Six patients (1.2%) presented with both variants and the subvariant for four patients (0.8%) was unknown. There was a male preponderance (70.1% male) in the vascular subtype. A history of tonsillectomy was more frequent in classic (48/153 cases) than in vascular (2/33 cases) Eagle syndrome (Odds Ratio 5.2, 95% CI [1.2-22.4]; p = 0.028). By contrast, cervical movements as trigger factors were more prevalent in vascular (12/33 cases) than in classic (7/153 cases) Eagle syndrome (Odds Ratio 7.95, 95% CI [2.9-21.7]; p = 0.0001). Headache and Horner syndrome were more frequent in vascular Eagle syndrome and dysphagia and neck pain more prominent in classic Eagle syndrome (all p < 0.01). Surgically treated patients achieved overall better outcomes than medically treated ones: Eighty-one (65.9%) of 123 medically treated patients experienced improvement or complete resolution, while the same applied to 313 (97.8%) of 320 surgical patients (Odds Ratio 1.49, 95% CI [1.1-2.0]; p = 0.016). Eagle syndrome is underdiagnosed with potentially serious neurovascular complications, including ischemic stroke. Surgical treatment achieves better outcomes than conservative management. Although traditionally the domain of otorhinolaryngologist, neurologist should include this syndrome in differential diagnostic considerations because of the varied neurological presentations that are amenable to effective treatment.

Enhancer demethylation-regulated gene score identified molecular subtypes, inspiring immunotherapy or CDK4/6 inhibitor therapy in oesophageal squamous cell carcinoma

The 5-year survival rate of oesophageal squamous cell carcinoma (ESCC) is approximately 20%. The prognosis and drug response exhibit substantial heterogeneity in ESCC, impeding progress in survival outcomes. Our goal is to identify a signature for tumour subtype classification, enabling precise clinical treatments. Utilising pre-treatment multi-omics data from an ESCC dataset (n=310), an enhancer methylation-eRNA-target gene regulation network was constructed and validated by invitro experiments. Four machine learning methods collectively identified core target genes, establishing an Enhancer Demethylation-Regulated Gene Score (EDRGS) model for classification. The molecular function of EDRGS subtyping was explored in scRNA-seq (n=60) and bulk-seq (n=310), and the EDRGS's potential to predict treatment response was assessed in datasets of various cancer types. EDRGS stratified ESCCs into EDRGS-high/low subtypes, with EDRGS-high signifying a less favourable prognosis in ESCC and nine additional cancer types. EDRGS-high exhibited an immune-hot but immune-suppressive phenotype with elevated immune checkpoint expression, increased T cell infiltration, and IFNγ signalling in ESCC, suggesting a better response to immunotherapy. Notably, EDRGS outperformed PD-L1 in predicting anti-PD-1/L1 therapy effectiveness in ESCC (n=42), kidney renal clear cell carcinoma (KIRC, n=181), and bladder urothelial carcinoma (BLCA, n=348) cohorts. EDRGS-low showed a cell cycle-activated phenotype with higher CDK4 and/or CDK6 expression, demonstrating a superior response to the CDK4/6 inhibitor palbociclib, validated in ESCC (n=26), melanoma (n=18), prostate cancer (n=15) cells, and PDX models derived from patients with pancreatic cancer (n=30). Identification of EDRGS subtypes enlightens ESCC categorisation, offering clinical insights for patient management in immunotherapy (anti-PD-1/L1) and CDK4/6 inhibitor therapy across cancer types. This study was supported by funding from the National Key R&D Program of China (2021YFC2501000, 2020YFA0803300), the National Natural Science Foundation of China (82030089, 82188102), the CAMS Innovation Fund for Medical Sciences (2021-I2M-1-018, 2022-I2M-2-001, 2021-I2M-1-067), the Fundamental Research Funds for the Central Universities (3332021091).

Complicated Form of Medication Overuse Headache Is Severe Version of Chronic Migraine.

Background: MOH (medication overuse headache) is regarded as a complication of chronic migraines (CMs), with a general acknowledgment of reciprocal triggering between these two conditions. The present study aims to investigate the clinical parameters of relevance for the development of MOH among patients with CM, as well as for the subtype classification of MOHs. Method: We compared two groups of CM patients, with and without MOH, separated based on their demographic data and migraine characteristics. A subgroup of MOH accompanied by psychiatric co-morbidities (depression, anxiety, sleep disorder) was delineated, and the clinical features of relevance for the progression of MOH into the complicated state were evaluated. Results: The study revealed a higher prevalence of a family history of migraine in both the MOH and potentially complicated MOH subgroups (p < 0.001, p = 0.036), along with a higher prevalence of bilateral pain localization (p = 0.033, 0.021). Symptoms commonly associated with migraines, such as nausea, vomiting, photophobia, phonophobia, and osmophobia, were more common in both the MOH and potentially complicated MOH subgroups (p < 0.05). Furthermore, a positive correlation was found for the frequency (p < 0.001) and severity (p = 0.010) of migraine attacks and the duration of headaches (p = 0.007), atopy (p = 0.017), sleep disturbances (p = 0.011), and emotional stress (p = 0.022) in the MOH group. Conclusion: We found a positive correlation between the prevalence of MOH among patients with CM and a family history of migraines, higher frequency and intensity of headaches, bilateral manifestation, sleep disturbances, and emotional stress. Moreover, symptoms accompanying migraines were found to be more prevalent in individuals with MOH and potentially complicated MOH.

Multinomial Logistic Regression with Adaptive Regularization for Cancer Subtype Classification via Multi-omics Data

Background: Integrating multi-omics data for cancer classification brings complementary biological insights while also facing challenges such as data integration, gene grouping, and adaptive weight construction. Objective: This paper aims to address the challenges faced by the cancer subtype classification and gene screening based on multi-omics data. Methods: Multinomial logistic regression with adaptive regularization (MLRAR) was proposed by integrating DNA methylation, gene mutation, and RNA-seq information. A data preprocessing strategy that effectively utilizes multi-omics information was presented, and the local maximum quasiclique merging (lmQCM) algorithm was implemented to group genes. Biological pathway information was utilized to evaluate the significance of gene groups, while the significance of each gene within a group was evaluated by integrating mutation information, information theory, and methylation information. Results: Compared to MRlasso, MRGL, MSGL, MROGL, AMRSOGL, and AGLRMR, the proposed method yielded improvements in subtype classification accuracy of breast cancer by 2.6%, 2.9%, 3.5%, 2.3%, 2.0%, and 1.8%, respectively. In addition, MLRAR also achieved significant improvements in ovarian cancer by 8.2%, 5.0%, 6.8%, 5.2%, 12.7%, and 6.3%, respectively. Conclusion: The proposed method can effectively deal with data integration, gene grouping, and adaptive weight construction.

Machine Learning-Based Etiologic Subtyping of Ischemic Stroke Using Circulating Exosomal microRNAs.

Ischemic stroke is a major cause of mortality worldwide. Proper etiological subtyping of ischemic stroke is crucial for tailoring treatment strategies. This study explored the utility of circulating microRNAs encapsulated in extracellular vesicles (EV-miRNAs) to distinguish the following ischemic stroke subtypes: large artery atherosclerosis (LAA), cardioembolic stroke (CES), and small artery occlusion (SAO). Using next-generation sequencing (NGS) and machine-learning techniques, we identified differentially expressed miRNAs (DEMs) associated with each subtype. Through patient selection and diagnostic evaluation, a cohort of 70 patients with acute ischemic stroke was classified: 24 in the LAA group, 24 in the SAO group, and 22 in the CES group. Our findings revealed distinct EV-miRNA profiles among the groups, suggesting their potential as diagnostic markers. Machine-learning models, particularly logistic regression models, exhibited a high diagnostic accuracy of 92% for subtype discrimination. The collective influence of multiple miRNAs was more crucial than that of individual miRNAs. Additionally, bioinformatics analyses have elucidated the functional implications of DEMs in stroke pathophysiology, offering insights into the underlying mechanisms. Despite limitations like sample size constraints and retrospective design, our study underscores the promise of EV-miRNAs coupled with machine learning for ischemic stroke subtype classification. Further investigations are warranted to validate the clinical utility of the identified EV-miRNA biomarkers in stroke patients.

Integrated analysis of single-cell RNA-seq, bulk RNA-seq, Mendelian randomization, and eQTL reveals T cell-related nomogram model and subtype classification in rheumatoid arthritis.

Rheumatoid arthritis (RA) is a systemic disease that attacks the joints and causes a heavy economic burden on humans worldwide. T cells regulate RA progression and are considered crucial targets for therapy. Therefore, we aimed to integrate multiple datasets to explore the mechanisms of RA. Moreover, we established a T cell-related diagnostic model to provide a new method for RA immunotherapy. scRNA-seq and bulk-seq datasets for RA were obtained from the Gene Expression Omnibus (GEO) database. Various methods were used to analyze and characterize the T cell heterogeneity of RA. Using Mendelian randomization (MR) and expression quantitative trait loci (eQTL), we screened for potential pathogenic T cell marker genes in RA. Subsequently, we selected an optimal machine learning approach by comparing the nine types of machine learning in predicting RA to identify T cell-related diagnostic features to construct a nomogram model. Patients with RA were divided into different T cell-related clusters using the consensus clustering method. Finally, we performed immune cell infiltration and clinical correlation analyses of T cell-related diagnostic features. By analyzing the scRNA-seq dataset, we obtained 10,211 cells that were annotated into 7 different subtypes based on specific marker genes. By integrating the eQTL from blood and RA GWAS, combined with XGB machine learning, we identified a total of 8 T cell-related diagnostic features (MIER1, PPP1CB, ICOS, GADD45A, CD3D, SLFN5, PIP4K2A, and IL6ST). Consensus clustering analysis showed that RA could be classified into two different T-cell patterns (Cluster 1 and Cluster 2), with Cluster 2 having a higher T-cell score than Cluster 1. The two clusters involved different pathways and had different immune cell infiltration states. There was no difference in age or sex between the two different T cell patterns. In addition, ICOS and IL6ST were negatively correlated with age in RA patients. Our findings elucidate the heterogeneity of T cells in RA and the communication role of these cells in an RA immune microenvironment. The construction of T cell-related diagnostic models provides a resource for guiding RA immunotherapeutic strategies.

NNBGWO-BRCA marker: Neural Network and binary grey wolf optimization based Breast cancer biomarker discovery framework using multi-omics dataset

Background and ObjectiveBreast cancer is a multifaceted condition characterized by diverse features and a substantial mortality rate, underscoring the imperative for timely detection and intervention. The utilization of multi-omics data has gained significant traction in recent years to identify biomarkers and classify subtypes in breast cancer. This kind of research idea from part to whole will also be an inevitable trend in future life science research. Deep learning can integrate and analyze multi-omics data to predict cancer subtypes, which can further drive targeted therapies. However, there are few articles leveraging the nature of deep learning for feature selection. Therefore, this paper proposes a Neural Network and Binary grey Wolf Optimization based BReast CAncer bioMarker (NNBGWO-BRCAMarker) discovery framework using multi-omics data to obtain a series of biomarkers for precise classification of breast cancer subtypes. MethodsNNBGWO-BRCAMarker consists of two phases: in the first phase, relevant genes are selected using the weights obtained from a trained feedforward neural network; in the second phase, the binary grey wolf optimization algorithm is leveraged to further screen the selected genes, resulting in a set of potential breast cancer biomarkers. ResultsThe SVM classifier with RBF kernel achieved a classification accuracy of 0.9242 ± 0.03 when trained using the 80 biomarkers identified by NNBGWO-BRCAMarker, as evidenced by the experimental results. We conducted a comprehensive gene set analysis, prognostic analysis, and druggability analysis, unveiling 25 druggable genes, 16 enriched pathways strongly linked to specific subtypes of breast cancer, and 8 genes linked to prognostic outcomes. ConclusionsThe proposed framework successfully identified 80 biomarkers from the multi-omics data, enabling accurate classification of breast cancer subtypes. This discovery may offer novel insights for clinicians to pursue in further studies.

Digital spatial profiling of the microenvironment of muscle invasive bladder cancer

Muscle invasive bladder cancer (MIBC) is a molecularly diverse disease with varied clinical outcomes. Molecular studies typically employ bulk sequencing analysis, giving a transcriptomic snapshot of a section of the tumour. However, tumour tissues are not homogeneous, but are composed of distinct compartments such as the tumour and stroma. To investigate the molecular profiles of bladder cancer, whilst also maintaining the spatial complexity of the tumours, we employed whole transcriptome Digital Spatial Profiling (DSP). With this method we generated a dataset of transcriptomic profiles of tumour epithelium, stroma, and immune infiltrate. With these data we investigate the spatial relationship of molecular subtype signatures and ligand signalling events. We find that Basal/Squamous and Classical subtypes are mostly restricted to tumour regions, while the stroma-rich subtype signatures are abundant within the stroma itself. Additionally, we identify ligand signalling events occurring between tumour, stroma, and immune infiltrate regions, such as immune infiltrate derived GPNMB, which was highly correlated with VEGFA expression within the tumour. These findings give us new insights into the diversity of MIBC at a molecular level and provide a dataset with detailed spatial information that was not available before in bladder cancer research.

Tumor-intrinsic and Cancer-associated Fibroblast Subtypes Independently Predict Outcomes in Pancreatic Cancer.

To assess the utility of tumor-intrinsic and cancer-associated fibroblast (CAF) subtypes of pancreatic ductal adenocarcinoma (PDAC) in predicting response to neoadjuvant therapy (NAT) and overall survival (OS). PDAC remains a deadly disease with limited treatment options, and both the tumor as well as the microenvironment play an important role in pathogenesis. Gene expression-based tumor-intrinsic subtypes (classical and basal-like) have been shown to predict outcomes, but tumor microenvironment subtypes are still evolving. RNA-sequencing was performed on 114 deidentified resected PDAC tumors. Clinical data were collected by retrospective chart review. Single sample classifiers were used to determine classical and basal-like subtypes as well as tumor-permissive permCAF and tumor-restraining restCAF subtypes. Survival was analyzed using the log-rank test. Patients who received NAT had an increase in OS, with a median survival of 27.9 months compared with 20.1 months for those who did not receive NAT, but the difference did not reach statistical significance (hazard ratio: 0.64, P =0.076). Either tumor-intrinsic or CAF subtypes alone were associated with OS regardless of NAT or no NAT, and patients with classical or restCAF subtypes had the best outcomes. When evaluated together, patients with the classical-restCAF subtype had the best OS and basal-permCAF the worst OS ( P <0.0001). Patients undergoing NAT with the classical-restCAF subtype demonstrated the longest OS compared with the other groups ( P =0.00041). CAF subtypes have an additive effect over tumor-intrinsic subtypes in predicting survival with or without neoadjuvant FOLFIRINOX in PDAC. Molecular subtyping of both tumor and CAF compartments of PDAC may be important steps in selecting first-line systemic therapy.

Cutaneous lupus subtypes

A broad spectrum of dermatologic manifestations, known as cutaneous lupus erythematosus (CLE), may or may not be linked to the development of systemic disease. There are numerous subtypes of cutaneous lupus, such as acute cutaneous lupus (ACLE), subacute cutaneous lupus (SCLE), and chronic cutaneous lupus (CCLE). CCLE encompasses lupus tumidus, chilblain cutaneous lupus, LE profundus (LEP), and discoid lupus erythematosus (DLE). In order to diagnose these diseases, it is necessary to accurately classify the subtype. This is achieved through a combination of physical examination, laboratory studies, histology, antibody serology, and occasionally direct immunofluorescence, while also excluding systemic disease. The treatment of cutaneous lupus involves the provision of appropriate topical and systemic agents, as well as patient education regarding solar protection. In cases where the disease is pervasive, scarring, or treatment-refractory, systemic agents are recommended. In this chapter, we address the classification and diagnosis of the diverse subtypes of CLE and offer a comprehensive update on therapeutic management.

Classification of the Stages of Nonalcoholic Steatohepatitis via Federated General Visual Representation Learning

ABSTRACTThe excess buildup of fat within a patient's liver, even in the absence of any previous alcohol consumption, defines nonalcoholic fatty liver disease (NAFLD). Accurate classification of NAFLD subtypes, especially nonalcoholic steatohepatitis (NASH), through histology biopsies is vital for risk stratification and patient management, including liver transplantation decisions. Therapeutic options targeting liver molecular pathways are being explored, as approved therapies for NASH/NAFLD are currently lacking. Deep learning‐driven pathological diagnosis holds promise for precise assessments, and the application of the federated learning version of the Big Transfer (BiT) model in this study represents a significant advancement in NAFLD/NASH classification and staging using histopathological imaging data. The AUC performance results of federated learning settings of “mainclass” and “multiclass” scenarios are obtained respectively as 0.9299 and 0.9582. The AUC results of each “subclass” are achieved as 0.9376, 0.9435, 0.9867, and 0.9618 respectively, for ballooning, inflammation, steatosis, and fibrosis. The effectiveness of the federated learning configurations in the scenarios categorized as “main‐class” and “multi‐class” is reflected in their respective accuracy (ACC) performance outcomes, which are recorded as 0.7650 and 0.6653. Furthermore, the ACC achievements for each specific “sub‐class” namely, ballooning, inflammation, steatosis, and fibrosis are 0.9016, 0.8568, 0.9203, and 0.8815, respectively.

Lesion-aware cross-phase attention network for renal tumor subtype classification on multi-phase CT scans

Multi-phase computed tomography (CT) has been widely used for the preoperative diagnosis of kidney cancer due to its non-invasive nature and ability to characterize renal lesions. However, since enhancement patterns of renal lesions across CT phases are different even for the same lesion type, the visual assessment by radiologists suffers from inter-observer variability in clinical practice. Although deep learning-based approaches have been recently explored for differential diagnosis of kidney cancer, they do not explicitly model the relationships between CT phases in the network design, limiting the diagnostic performance. In this paper, we propose a novel lesion-aware cross-phase attention network (LACPANet) that can effectively capture temporal dependencies of renal lesions across CT phases to accurately classify the lesions into five major pathological subtypes from time-series multi-phase CT images. We introduce a 3D inter-phase lesion-aware attention mechanism to learn effective 3D lesion features that are used to estimate attention weights describing the inter-phase relations of the enhancement patterns. We also present a multi-scale attention scheme to capture and aggregate temporal patterns of lesion features at different spatial scales for further improvement. Extensive experiments on multi-phase CT scans of kidney cancer patients from the collected dataset demonstrate that our LACPANet outperforms state-of-the-art approaches in diagnostic accuracy.

NGS panel enhance precise diagnosis of myeloid neoplasms under WHO-HAEM5 and International Consensus Classification: An observational study.

This study aimed to assess hematological diseases next-generation sequencing (NGS) panel enhances the diagnosis and classification of myeloid neoplasms (MN) using the 5th edition of the WHO Classification of Hematolymphoid Tumors (WHO-HAEM5) and the International Consensus Classification (ICC) of Myeloid Tumors. A cohort of 112 patients diagnosed with MN according to the revised fourth edition of the WHO classification (WHO-HAEM4R) underwent testing with a 141-gene NGS panel for hematological diseases. Ancillary studies were also conducted, including bone marrow cytomorphology and routine cytogenetics. The cases were then reclassified according to WHO-HAEM5 and ICC to assess the practical impact of these 2 classifications. The mutation detection rates were 93% for acute myeloid leukemia (AML), 89% for myelodysplastic syndrome (MDS), 94% for myeloproliferative neoplasm (MPN), and 100% for myelodysplasia/myeloproliferative neoplasm (MDS/MPN) (WHO-HAEM4R). NGS provided subclassified information for 26 and 29 patients with WHO-HAEM5 and ICC, respectively. In MPN, NGS confirmed diagnoses in 16 cases by detecting JAK2, MPL, or CALR mutations, whereas 13 "triple-negative" MPN cases revealed at least 1 mutation. NGS panel testing for hematological diseases improves the diagnosis and classification of MN. When diagnosed with ICC, NGS produces more classification subtype information than WHO-HAEM5.

Comprehensive Proteogenomic Profiling Reveals the Molecular Characteristics of Colorectal Cancer at Distinct Stages of Progression.

Colorectal cancer is the second most common malignant tumor worldwide. Analysis of the changes that occur during colorectal cancer progression could provide insights into the molecular mechanisms driving colorectal cancer development and identify improved treatment strategies. In this study, we performed an integrated multiomic analysis of 435 trace tumor samples from 148 patients with colorectal cancer, covering nontumor, intraepithelial neoplasia (IEN), infiltration, and advanced stage colorectal cancer phases. Proteogenomic analyses demonstrated that KRAS and BRAF mutations were mutually exclusive and elevated oxidative phosphorylation in the IEN phase. Chr17q loss and chr20q gain were also mutually exclusive, which occurred predominantly in the IEN and infiltration phases, respectively, and impacted the cell cycle. Mutations in TP53 were frequent in the advanced stage colorectal cancer phase and associated with the tumor microenvironment, including increased extracellular matrix rigidity and stromal infiltration. Analysis of the profiles of colorectal cancer based on consensus molecular subtype and colorectal cancer intrinsic subtype classifications revealed the progression paths of each subtype and indicated that microsatellite instability was associated with specific subtype classifications. Additional comparison of molecular characteristics of colorectal cancer based on location showed that ANKRD22 amplification by chr10q23.31 gain enhanced glycolysis in the right-sided colorectal cancer. The AOM/DSS-induced colorectal cancer carcinogenesis mouse model indicated that DDX5 deletion due to chr17q loss promoted colorectal cancer development, consistent with the findings from the patient samples. Collectively, this study provides an informative resource for understanding the driving events of different stages of colorectal cancer and identifying the potential therapeutic targets. Significance: Characterization of the proteogenomic landscape of colorectal cancer during progression provides a multiomic map detailing the alterations in each stage of carcinogenesis and suggesting potential diagnostic and therapeutic approaches for patients.

Histological Subtype Classification of Non-Small Cell Lung Cancer with Radiomics and 3D Convolutional Neural Networks

Histological Subtype Classification of Non-Small Cell Lung Cancer with Radiomics and 3D Convolutional Neural Networks

Early results of the PASS-01 trial: Pancreatic adenocarcinoma signature stratification for treatment-01.

LBA4004 Background: Over 60% of patients with pancreatic ductal adenocarcinoma (PDAC) present with metastatic disease. Both modified FOLFIRINOX (mFFX) and gemcitabine/nab-paclitaxel (GnP) are first-line options in advanced PDAC, however have not been compared prospectively in North American patients. Moreover, biomarkers to guide selection are lacking. Basal-like and Classical subtypes are prognostic, but their predictive impact is unknown. Patient-derived organoids (PDOs) are now feasible to study for drug pharmacotyping. Expedient molecular profiling with additional PDO drug sensitivities could enable better precision choices in PDAC. Methods: PASS-01 is a multi-center randomized phase II trial evaluating the benefit of 1st line mFFX vs GnP in de novo metastatic PDAC patients with ECOG PS 0-1, (germline BRCA1/2, PALB2 excluded) who have baseline tumor biopsies (bx) for whole genome/transcriptional sequencing (WGTS) and PDO generation/pharmacotyping with standard and novel drugs. The primary endpoint is the PFS of mFFX vs GnP (received at least 1 dose of assigned chemo, per protocol (PP)), 136 patients needed to reach 80% power to detect a difference in median PFS of 7 vs 5 months between mFFX and GnP at significance level of 0.3 in a 2-sided test. Secondary endpoints include: ORR, SAEs, OS, impact of RNA signatures and GATA6 expression on outcomes. Each patient is discussed at a molecular tumor board immediately following their 1st 8-week CT with the goal of recommending precision 2nd-line treatment options on progression. Results: This trial accrued 160 pts between 09/20 and 01/24, 45% in Canada, 55% in US with 140 eligible for 1st line PFS, data lock Mar 1/24 (see table). Median PFS (PP) was 5.1 mo for GnP and 4.0 mo for mFFX (p=0.14). Best response PR/SD for GnP: 29/45% and 24/35% for mFFX. SAEs attributed to the study were 3% GnP, 13% mFFX and 0.7% bx. Median OS (ITT) was 9.7 mo with GnP and 8.4 mo with mFFX, p=0.04. Of 113 patients in the PP analysis with progression, 64 (57%) received 2nd-line treatment (GnP, n= 30, mFFX n=34) Of these, a correlate-guided approach was delivered in 32 (50%), including 21 (66%) receiving chemo and 11 (34%) receiving a targeted or immunotherapy regimen. Correlative studies are underway. Preliminary analysis shows &gt;80% successful whole genomes and &gt;72% RNA signatures. PP patients include 9 % KRAS wild-type and 21% Basal-like PDAC. PDO-drug models have been established in 50%. Conclusions: Upfront multi-omic profiling of PDAC can be successfully incorporated into a multicenter randomized trial. While we have observed PP improved PFS and ITT longer OS favouring GnP in this cohort without gBRCA 1/2 or PALB2m, the benefit of chemo for advanced PDAC patients remains poor, with 43% unable to receive 2nd line, arguing strongly for the development of 1st-line biomarker selected strategies. Clinical trial information: NCT04469556 . [Table: see text]

Attention deficit hyperactivity disorder subtypes classification: a machine learning approach with phenotypic information and brain tissue volume

Attention deficit hyperactivity disorder subtypes classification: a machine learning approach with phenotypic information and brain tissue volume

Relationship of adaptive subtyping and tumour heterogeneity of treatment response to neoadjuvant therapy in hormone receptor–positive HER2-negative early breast cancer: PENELOPE-B.

566 Background: The concept of intrinsic subtyping has been an important step for understanding of breast cancer (BC) as a heterogenous disease. However, these subtypes are not adapted to therapy-induced molecular plasticity. We have evaluated a high-risk luminal BC clinical trial cohort to identify new additional adaptive BC subtypes based on molecular alterations induced during neoadjuvant chemotherapy (NACT). Methods: A total of 1250 ER+/HER2- BC patients (pts) with residual disease after NACT and increased risk (CPS-EG score of ≥3 or 2 with ypN+) were randomized into the PENELOPE-B (NCT01864746) trial to receive palbociclib or placebo. Biomarker analysis was performed in 1411 pre- or postNACT tumor samples including 540 paired samples using the HTG EdgeSeq Oncology Biomarker Panel targeting 2549 genes (HTG Molecular Diagnostics Inc.), with assessment of the absolute assignment of breast cancer intrinsic molecular subtype (AIMS). For a subcohort of 29 pts, we evaluated triplicate samples before and after NACT as well as at the time of metastatic disease. Results: In paired biopsies, a total of 335 genes were significantly different between the pre- and the postNACT cohort. With hierarchical unsupervised clustering of these 335 genes, we characterized five different tumor subtypes with highly significant iDFS survival differences (p&lt;0.0001, n=539). We identified two large groups of tumors with excellent prognosis (adaptive cluster AC1 and AC2, together n=284, 6 events), as well as two groups with a poor prognosis; AC-3 (n=163, 82 events) and AC-4 (n=78, 29 events). The worst prognosis was observed in a small group of tumors with a pre-NACT Basal/HER2E AIMS subtype (n=14, 11 events). AC1 and AC3 are enriched for tumors with an AIMS subtype change from LumB to LumA during NACT, with improved iDFS for AC1. AC2 and AC4 are enriched for tumors with a constant LumA AIMS subtype before and after NACT, with improved iDFS for AC2. The low-risk adaptive subtypes AC1 and AC2 cover a total of 52.7% of pts with a combined event rate of 2.1%. In contrast, the best pretherapeutic conventional AIMS-subtype, preNACT LumA, covers 51.7% of pts, but still has an event rate of 15.4%. This suggests that adaptive subtypes are superior to classical AIMS subtypes for prediction of survival after NACT in luminal BC. Conclusions: Classical approach of intrinsic subtyping relies on constant gene expression, limiting its scope. Adaptive subtyping can complement the classical approach, focusing on those genes that are changed during therapy. Comparison of paired samples before and after therapy is superior to classical subtyping of baseline samples. This approach identified large subgroups of pts with excellent prognosis after NACT despite clinical high risk. This can be an important step to focus on high-risk pts for new neoadjuvant and post-neoadjuvant therapy concepts.

Acquisition and validation of four pain subtypes of colon adenocarcinoma and prognostic analysis

BackgroundColon adenocarcinoma (COAD) is the most common type of colorectal cancer. Pain is a multidimensional unpleasant experience involving various molecular and cellular pathways. Nevertheless, the exploration of pain-related genes related to colon adenocarcinoma remains unclear yet. MethodsIn this study, the pathways enriched with pain-related genes were analyzed using Metascape. Then, we identified pain subtypes and classical subtypes and explored the link between them. Next, marker genes for different pain subtypes were identified, and their enrichment pathways were explored. These marker genes were then used to validate the pain subtypes. Subsequently, we performed an investigation of survival differences between pain subtypes by selecting specific top pathways in each subtype, calculating top pathway scores and pathway differences using heatmap and Kruskal test. Finally, we predicted the response of different pain subtypes to immunotherapy. ResultsA total of 146 pain-related genes were included in this study and we finally identified 4 pain subtypes and 4 stable subtypes. The marker genes for subtypes were validated by The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, revealing a worse prognosis for CS1. The genes of CS1, CS2, CS3 and CS4 markers were primarily enriched in the pathways of Focal adhesion, Human T cell leukemia virus1 infection, Metabolic pathway, and Pertussis, respectively. CS1 and CS4 were found to be more immunogenic. Moreover, CS1 was more sensitive to treatment with CTLA4 inhibitors, while CS4 was sensitive to treatment with PD-1 inhibitors. ConclusionsOur study’s identification of four pain subtypes of COAD provides new insights for personalized therapy for patients with COAD.

Development and validation of mFISHseq: A diagnostic test using multiplexed RNA-FISH–guided laser capture microdissection RNA sequencing.

3069 Background: Breast cancer (BCa) is a heterogeneous disease requiring precise diagnostic tools to guide effective treatment strategies. Current diagnostic assays, including various multigene assays, often fail to adequately address the complex biology of BCa subtypes. To address these limitations and enhance the understanding of BCa biology, we developed and validated a novel diagnostic, prognostic, and predictive tool, called mFISHseq. Methods: Our approach, mFISHseq, integrates multiplexed fluorescent in situ hybridization (FISH) of the four main BCa biomarkers, estrogen ( ESR1)/progesterone ( PGR)/Her2 ( ERBB2) receptors and Ki67 ( MKI67), which are used to guide laser capture microdissection (LCM) of regions of interest followed by RNA-sequencing. This technique ensures tumor purity, facilitates interrogation of tumor heterogeneity, consequently permitting unbiased analysis of whole transcriptome profiling data and explicitly quantifying the variability between different tumor regions. We validated mFISHseq on a retrospective cohort study involving 1,082 FFPE breast tumors with detailed clinicopathological data, informed consent, and ethical committee approval. Results: mFISHseq demonstrated excellent analytical validity with a 93% accuracy rate compared to standard immunohistochemistry (IHC), while providing more quantitative biomarker expression. Prespecified threshold values for mFISHseq derived from a split 70:30 training/test set showed exceptional concordance with IHC as demonstrated by area under the receiver operating characteristic (ROC) curves for all markers (AUC: MKI67=0.98, ERBB2=0.95, ESR1=0.95, PGR=0.93). Both RNA-FISH and -SEQ showed moderate to very strong correlations (Spearman’s r; ERBB2=0.41, MKI67=0.61, PGR=0.66, ESR1=0.75), thus highlighting the potential to use both orthogonal methodologies to cross-validate results. To demonstrate clinical validity, we developed a 293-gene intrinsic subtype classifier, showing substantial agreement to established classifiers like PAM50 and AIMS (Cohen’s κ= 0.75 and 0.73, respectively) and superior prognostic performance. We also report that LCM is an essential component of the mFISHseq workflow, since samples that did not undergo LCM showed reduced biomarker expression, elevated non-tumor gene expression, and misclassification of samples into less aggressive molecular subtypes (e.g., normal-like) and prognostic risk groups (e.g., high to low). Conclusions: The mFISHseq method showed excellent concordance with IHC and the use of LCM provides tumor-enriched samples that are devoid of contamination from non-tumor elements, thus providing unbiased spatially resolved interrogation of tumor heterogeneity. Altogether, mFISHseq solves a long-standing challenge in the precise diagnosis and classification of breast cancer subtypes.