Distinct transcriptome architectures underlying lupus establishment and exacerbation.

Systemic lupus erythematosus (SLE) is a complex autoimmune disease involving multiple immune cells. To elucidate SLE pathogenesis, it is essential to understand the dysregulated gene expression pattern linked to various clinical statuses with a high cellular resolution. Here, we conducted a large-scale transcriptome study with 6,386 RNA sequencing data covering 27 immune cell types from 136 SLE and 89 healthy donors. We profiled two distinct cell-type-specific transcriptomic signatures: disease-state and disease-activity signatures, reflecting disease establishment and exacerbation, respectively. We then identified candidate biological processes unique to each signature. This study suggested the clinical value of disease-activity signatures, which were associated with organ involvement and therapeutic responses. However, disease-activity signatures were less enriched around SLE risk variants than disease-state signatures, suggesting that current genetic studies may not well capture clinically vital biology. Together, we identified comprehensive gene signatures of SLE, which will provide essential foundations for future genomic and genetic studies.

Single-cell dissection of tumor microenvironmental response and resistance to cancer therapy.

Cancer treatment strategies have evolved significantly over the years, with chemotherapy, targeted therapy, and immunotherapy as major pillars. Each modality leads to unique treatment outcomes by interacting with the tumor microenvironment (TME), which imposes a fundamental selective pressure on cancer progression. The advent of single-cell profiling technologies has revolutionized our understanding of the intricate and heterogeneous nature of the TME at an unprecedented resolution. This review delves into the commonalities and differential manifestations of how cancer therapies reshape the microenvironment in diverse cancer types. We highlight how groundbreaking immune checkpoint blockade (ICB) strategies alone or in combination with tumor-targeting treatments are endowed with comprehensive mechanistic insights when decoded at the single-cell level, aiming to drive forward future research directions on personalized treatments.

Deregulated transcription factors in the emerging cancer hallmarks.

Cancer progression is a multifaceted process that entails several stages and demands the persistent expression or activation of transcription factors (TFs) to facilitate growth and survival. TFs are a cluster of proteins with DNA-binding domains that attach to promoter or enhancer DNA strands to start the transcription of genes by collaborating with RNA polymerase and other supporting proteins. They are generally acknowledged as the major regulatory molecules that coordinate biological homeostasis and the appropriate functioning of cellular components, subsequently contributing to human physiology. TFs proteins are crucial for controlling transcription during the embryonic stage and development, and the stability of different cell types depends on how they function in different cell types. The development and progression of cancer cells and tumors might be triggered by any anomaly in transcription factor function. It has long been acknowledged that cancer development is accompanied by the dysregulated activity of TF alterations which might result in faulty gene expression. Recent studies have suggested that dysregulated transcription factors play a major role in developing various human malignancies by altering and rewiring metabolic processes, modifying the immune response, and triggering oncogenic signaling cascades. This review emphasizes the interplay between TFs involved in metabolic and epigenetic reprogramming, evading immune attacks, cellular senescence, and the maintenance of cancer stemness in cancerous cells. The insights presented herein will facilitate the development of innovative therapeutic modalities to tackle the dysregulated transcription factors underlying cancer.

iGenSig-Rx: an integral genomic signature based white-box tool for modeling cancer therapeutic responses using multi-omics data.

Multi-omics sequencing is poised to revolutionize clinical care in the coming decade. However, there is a lack of effective and interpretable genome-wide modeling methods for the rational selection of patients for personalized interventions. To address this, we present iGenSig-Rx, an integral genomic signature-based approach, as a transparent tool for modeling therapeutic response using clinical trial datasets. This method adeptly addresses challenges related to cross-dataset modeling by capitalizing on high-dimensional redundant genomic features, analogous to reinforcing building pillars with redundant steel rods. Moreover, it integrates adaptive penalization of feature redundancy on a per-sample basis to prevent score flattening and mitigate overfitting. We then developed a purpose-built R package to implement this method for modeling clinical trial datasets. When applied to genomic datasets for HER2 targeted therapies, iGenSig-Rx model demonstrates consistent and reliable predictive power across four independent clinical trials. More importantly, the iGenSig-Rx model offers the level of transparency much needed for clinical application, allowing for clear explanations as to how the predictions are produced, how the features contribute to the prediction, and what are the key underlying pathways. We anticipate that iGenSig-Rx, as an interpretable class of multi-omics modeling methods, will find broad applications in big-data based precision oncology. The R package is available: https://github.com/wangxlab/iGenSig-Rx .

Identification of a DNA damage repair-related LncRNA signature for predicting the prognosis and immunotherapy response of hepatocellular carcinoma.

BACKGROUND: DNA damage repair (DDR) may affect tumorigenesis and therapeutic response in hepatocellular carcinoma (HCC). Long noncoding RNAs (LncRNAs) can regulate DDR and play a vital role in maintaining genomic stability in cancers. Here, we identified a DDR-related prognostic signature in HCC and explored its potential clinical value. METHODS: Data of HCC samples were obtained from the Cancer Genome Atlas (TCGA), and a list of DDR-related genes was extracted from the Molecular Signatures database (MSigDB). A DDR-related lncRNAs signature associated to overall survival (OS) was constructed using the least absolute shrinkage and selection operator-cox regression, and was further validated by the Kaplan-Meier curve and receiver operating characteristic curve. A nomogram integrating other clinical risk factors was established. Moreover, the relationships between the signature with somatic mutation, immune landscape and drug sensitivity were explored. RESULTS: The prognostic model of 5 DDR-related lncRNAs was constructed and classified patients into two risk groups at median cut-off. The low-risk group had a better OS, and the signature was an independent prognostic indicator in HCC. A nomogram of the signature combined with TNM stage was constructed. TP53 gene was more frequently mutated in the high-risk group. Marked differences in immune cells were observed, such as CD4 + T cells, NK cells and macrophages, between the two groups. Moreover, an increase in the expression of immune checkpoint molecules was found in the high-risk group. The low-risk group presented with a significantly higher response to sorafenib or cisplatin. Finally, potential value of this signature was validated in real-world HCC patients. CONCLUSION: Our findings provided a promising insight into DDR-related lncRNAs in HCC and a personalized prediction tool for prognosis and therapeutic response.

Clinical validation of circulating GDF15/MIC-1 as a marker of response to docetaxel and survival in men with metastatic castration-resistant prostate cancer.

BACKGROUND: Elevated circulating growth differentiation factor (GDF15/MIC-1), interleukin 4 (IL4), and IL6 levels were associated with resistance to docetaxel in an exploratory cohort of men with metastatic castration-resistant prostate cancer (mCRPC). This study aimed to establish level 2 evidence of cytokine biomarker utility in mCRPC. METHODS: IntVal: Plasma samples at baseline (BL) and Day 21 docetaxel (n = 120). ExtVal: Serum samples at BL and Day 42 of docetaxel (n = 430). IL4, IL6, and GDF15 levels were measured by ELISA. Monocytes and dendritic cells were treated with 10% plasma from men with high or low GDF15 or recombinant GDF15. RESULTS: IntVal: Higher GDF15 levels at BL and Day 21 were associated with shorter overall survival (OS) (BL; p = 0.03 and Day 21; p = 0.004). IL4 and IL6 were not associated with outcomes. ExtVal: Higher GDF15 levels at BL and Day 42 predicted shorter OS (BL; p < 0.0001 and Day 42; p < 0.0001). Plasma from men with high GDF15 caused an increase in CD86 expression on monocytes (p = 0.03), but was not replicated by recombinant GDF15. CONCLUSIONS: Elevated circulating GDF15 is associated with poor prognosis in men with mCRPC receiving docetaxel and may be a marker of changes in the innate immune system in response to docetaxel resistance. These findings provide a strong rationale to consider GDF15 as a biomarker to guide a therapeutic trial of drugs targeting the innate immune system in combination with docetaxel in mCRPC.

Necrotizing apoptosis-related genes prognosis and treatment effect analysis of osteosarcoma in children.

BACKGROUND: Immune cell homeostasis plays a crucial role in cancer research and therapeutic response. While chemotherapy and immunotherapy hold promise in treating osteosarcoma (OS), identifying patients who are likely to respond would significantly improve clinical practices. Necroptosis, a fundamental mechanism mediating chemotherapy and immunotherapy efficacy, offers valuable insights. In this context, subtypes based on necroptosis-related genes have been established to predict the response of OS patients to immunotherapy and chemotherapy. METHODS: We conducted a high-throughput screening test to identify necroptosis-associated genes that regulate the development of osteosarcoma. Subsequently, the ConsensusClusterPlus package was employed to classify OS patients into subtypes, enabling comparisons of prognosis and clinical information between these subtypes. Patients from the TARGET-OS and GSE21257 datasets were stratified into high-risk and low-risk groups, and their prognoses were compared. Additionally, we assessed the accuracy of the Risk Scoring Model in predicting prognosis, identified independent prognostic factors and explored potential chemotherapeutic agents and immunotherapy drugs. RESULTS: Through the intersection of expression profiles from the TARGET-OS and GSE21257 datasets, we have identified a total of 92 genes associated with necroptosis. Based on differences in the expression of these genes, patients were divided into three subtypes, and we investigated the differences in tumor-infiltrating immune cells, immune-related pathways, and prognosis among these subtypes. Our nomogram effectively differentiated subtypes with distinct responses to chemotherapy and immunotherapy. The established signature demonstrated superior prediction ability compared with single clinical indicators. CONCLUSIONS: This pioneering study unveils the prognostic role of necroptosis-related genes in OS patients, providing a promising alternative for prognostic prediction in clinical disease management. Moreover, our findings highlight the significance of immune cell homeostasis in cancer research and therapeutic response, underscoring its relevance in advancing current treatment strategies.

Development of a novel prognostic signature based on single-cell combined bulk RNA analysis in breast cancer.

BACKGROUND: Breast cancer (BC), a malignant tumor, is a significant cause of death and disability among women globally. Recent research indicates that copy number variation plays a crucial role in tumor development. In this study, we employed the Single-Cell Variational Aneuploidy Analysis (SCEVAN) algorithm to differentiate between malignant and non-malignant cells, aiming to identify genetic signatures with prognostic relevance for predicting patient survival. METHODS: We analyzed gene expression profiles and associated clinical data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Using the SCEVAN algorithm, we distinguished malignant from non-malignant cells and investigated cellular interactions within the tumor microenvironment (TME). We categorized TCGA samples based on differentially expressed genes (DEGs) between these cell types. Subsequent Kyoto Encyclopedia of Genes and Genomes pathway analysis was conducted. Additionally, we developed polygenic models for the DEGs using least absolute shrinkage and selection operator-penalized Cox regression analysis. To assess the prognostic accuracy of these characteristics, we generated Kaplan-Meier and receiver operating characteristic curves from training and validation datasets. We also monitored the expression variations of prognostic genes across the pseudotime of malignant cells. Patients were divided into high-risk and low-risk groups based on median risk scores to compare their TME and identify potential therapeutic agents. Lastly, polymerase chain reaction was used to validate seven pivotal genes. RESULTS: The SCEVAN algorithm identified distinct malignant and non-malignant cells in GSE180286. Cellchat analysis revealed significantly increased cellular communication, particularly between fibroblasts, endothelial cells and malignant cells. The DEGs were predominantly involved in immune-related pathways. TCGA samples were classified into clusters A and B based on these genes. Cluster A, enriched in immune pathways, was associated with poorer prognosis, whereas cluster B, predominantly involved in circadian rhythm pathways, showed better outcomes. We constructed a 14-gene prognostic signature, validated in a 1:1 internal TCGA cohort and external GEO datasets (GSE42568 and GSE146558). Kaplan-Meier analysis confirmed the prognostic signature's accuracy (p < 0.001). Receiver operating characteristic curve analysis demonstrated the predictive reliability of these prognostic features. Single-cell pseudotime analysis with monocle2 highlighted the distinct expression trends of these genes in malignant cells, underscoring the intratumoral heterogeneity. Furthermore, we explored the differences in TME between high- and low-risk groups and identified 16 significantly correlated drugs. CONCLUSION: Our findings suggest that the 14-gene prognostic signature could serve as a novel biomarker for forecasting the prognosis of BC patients. Additionally, the immune cells and pathways in different risk groups indicate that immunotherapy may be a crucial component of treatment strategies for BC patients.

A signature based on neutrophil extracellular trap-related genes for the assessment of prognosis, immunoinfiltration, mutation and therapeutic response in hepatocellular carcinoma.

BACKGROUND: Liver cancer is a highly lethal and aggressive form of cancer that poses a significant threat to patient survival. Within this category, liver hepatocellular carcinoma (LIHC) represents the most common subtype of liver cancer. Despite decades of research and treatment, the overall survival rate for LIHC has not significantly improved. Improved models are necessary to differentiate high-risk cases and predict possible treatment options for LIHC patients. Recent studies have identified a set of genes associated with neutrophil extracellular traps (NETs) that may contribute to tumor growth and metastasis; however, their prognostic value in LIHC has yet to be established. This study aims to construct a prognostic signature based on a set of NET-related genes (NRGs) for patients diagnosed with LIHC. METHODS: The transcriptomic data and clinical information concerning LIHC patients were procured from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium LIHC (ICLIHC) databases, respectively. To determine the NRG subtypes, the k-means algorithm was employed, along with consensus clustering. The aforementioned analysis aided the construction of a prognostic signature utilizing the last absolute shrinkage and selection operator Cox analysis. To validate the prognostic model, an external dataset, receiver operating characteristic curve, and principal component analysis were utilized. Moreover, the immune microenvironment and the proportion of immune cells between high- and low-risk cases were scrutinized by ESTIMATE and CIBERSORT algorithms. Finally, gene set enrichment analysis was executed to investigate the potential mechanism of NRGs in the pathogenesis and prognosis of LIHC. RESULTS: Two molecular subtypes of LIHC were identified based on the expression patterns of differentially expressed NRGs (DE-NRGs). The two subtypes demonstrated significant differences in survival rates and immune cell expression levels. The study results demonstrated the role of NRGs in antigen presentation, which led to the promotion of tumor immune escape. A risk model was developed and validated with strong overall survival prediction ability. The model, comprising 34 NRGs, showed a strong ability to predict prognosis. CONCLUSION: We built a dependable prognostic signature based on NRGs for LIHC. We identified that NRGs could have a significant interaction in LIHC's immune microenvironment and therapeutic response. This finding offers insight into the molecular mechanisms and targeted therapy for LIHC.

Wnt/ß-catenin signaling in the development and therapeutic resistance of non-small cell lung cancer.

Wnt/ß-catenin signaling is a critical pathway that influences development and therapeutic response of non-small cell lung cancer (NSCLC). In recent years, many Wnt regulators, including proteins, miRNAs, lncRNAs, and circRNAs, have been found to promote or inhibit signaling by acting on Wnt proteins, receptors, signal transducers and transcriptional effectors. The identification of these regulators and their underlying molecular mechanisms provides important implications for how to target this pathway therapeutically. In this review, we summarize recent studies of Wnt regulators in the development and therapeutic response of NSCLC.

Sex differences in osteoarthritis prevalence, pain perception, physical function and therapeutics.

OBJECTIVE: Women have a higher prevalence of osteoarthritis (OA) and worse clinical courses than men. However, the underlying factors and therapeutic outcomes of these sex-specific differences are incompletely researched. This review examines the current state of knowledge regarding sex differences in OA prevalence, risk factors, pain severity, functional outcomes, and use and response to therapeutics. METHODS: PubMed database was used with the title keyword combinations "{gender OR sex} AND osteoarthritis" plus additional manual search of the included papers for pertinent references, yielding 212 references. Additional references were added and 343 were reviewed for appropriateness. RESULTS: Globally, women account for 60% of people with osteoarthritis, with a greater difference after age 40. The higher risk for women may be due to differences in joint anatomy, alignment, muscle strength, hormonal influences, obesity, and/or genetics. At the same radiographic severity, women have greater pain severity than men, which may be explained by biologically distinct pain pathways, differential activation of central pain pathways, differences in pain sensitivity, perception, reporting, and coping strategies. Women have greater limitations of physical function and performance than men independent of BMI, OA severity, injury history, and amount of weekly exercise. Women also have greater use of analgesic medications than men but less use of arthroplasty and poorer prognosis after surgical interventions. CONCLUSIONS: The recognition of sex differences in OA manifestations and management could guide tailoring of sex-specific treatment protocols, and analysis of sex as a biological variable in future research would enhance development of precision medicine.

Deep Learning Radiomics Nomogram Based on Enhanced CT to Predict the Response of Metastatic Lymph Nodes to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer.

BACKGROUND: We aimed to construct and validate a deep learning (DL) radiomics nomogram using baseline and restage enhanced computed tomography (CT) images and clinical characteristics to predict the response of metastatic lymph nodes to neoadjuvant chemotherapy (NACT) in locally advanced gastric cancer (LAGC). METHODS: We prospectively enrolled 112 patients with LAGC who received NACT from January 2021 to August 2022. After applying the inclusion and exclusion criteria, 98 patients were randomized 7:3 to the training cohort (n = 68) and validation cohort (n = 30). We established and compared three radiomics signatures based on three phases of CT images before and after NACT, namely radiomics-baseline, radiomics-delta, and radiomics-restage. Then, we developed a clinical model, DL model, and a nomogram to predict the response of LAGC after NACT. We evaluated the predictive accuracy and clinical validity of each model using the receiver operating characteristic curve and decision curve analysis, respectively. RESULTS: The radiomics-delta signature was the best predictor among the three radiomics signatures. So, we developed and validated a DL delta radiomics nomogram (DLDRN). In the validation cohort, the DLDRN produced an area under the receiver operating curve of 0.94 (95% confidence interval, 0.82-0.96) and demonstrated adequate differentiation of good response to NACT. Furthermore, the DLDRN significantly outperformed the clinical model and DL model (p < 0.001). The clinical utility of the DLDRN was confirmed through decision curve analysis. CONCLUSIONS: In patients with LAGC, the DLDRN effectively predicted a therapeutic response in metastatic lymph nodes, which could provide valuable information for individualized treatment.

A Research Protocol for a Phase II Single-Arm Clinical Trial Assessing the Feasibility and Efficacy of Neoadjuvant Anastrozole in Patients With Luminal Breast Cancer and Low Proliferative Index: The ANNE Trial.

INTRODUCTION: Neoadjuvant endocrine therapy (NET) is recommended for the treatment of invasive breast cancer (BC), particularly luminal subtypes, in locally advanced stages. Previous randomized studies have demonstrated the benefits of aromatase inhibitors in this context. However, NET is typically reserved for elderly or frail patients who may not tolerate neoadjuvant chemotherapy. Identifying non-responsive patients early and extending treatment for responsive ones would be ideal, yet optimal strategies are awaited. AIMS: This non-randomized phase 2 clinical trial aims to assess NET feasibility and efficacy in postmenopausal stage II and III luminal BC patients, identifying predictive therapeutic response biomarkers. Efficacy will be gauged by patients with Ki67 ≤ 10% after 4 weeks and Preoperative Endocrine Prognostic Index (PEPI) scores 0 post-surgery. Study feasibility will be determined by participation acceptance rate (recruitment rate ≥50%) and inclusion rate (>2 patients/month). METHODS: Postmenopausal women with luminal, HER2-tumors in stages II and III undergo neoadjuvant anastrozole treatment, evaluating continuing NET or receiving chemotherapy through early Ki67 analysis after 2 to 4 weeks. The study assesses NET extension for up to 10 months, using serial follow-ups with standardized breast ultrasound and clinical criteria-based NET suspension. Clinical and pathological responses will be measured overall and in the luminal tumor A subgroup. Toxicity, health-related quality of life, and circulating biomarkers predicting early NET response will also be evaluated.

ADC Values for Detecting Bowel Inflammation and Biologic Therapy Response in Patients With Crohn Disease: A Post Hoc Prospective Trial Analysis.

BACKGROUND. New biologic agents for Crohn disease (CD) create a need for noninvasive disease markers. DWI may assess bowel inflammation without contrast agents. OBJECTIVE. The purpose of this study was to evaluate ADC values for identifying bowel inflammation and therapeutic response in patients with CD treated with biologic therapy. METHODS. This study entailed post hoc analysis of prospective trial data. Analysis included 89 patients (median age, 37 years; 49 women, 40 men) with CD treated by biologic therapy who underwent MR enterography (MRE) at baseline and 46 weeks after therapy, from March 2013 to April 2021; 43 patients underwent ileocolonoscopy at both time points. Analysis was conducted at the level of small-bowel and colorectal segments (586 segments analyzed). MR index of activity (MaRIA) score and presence of endoscopic ulcers were determined at both time points. One observer measured bowel wall ADC. Diagnostic performance was evaluated. Dichotomous ADC assessments used a threshold of 1301 × 10-6 mm2/s based on initial ROC analysis; dichotomous MaRIA score assessments used a threshold of 11 (moderate to severe inflammation). A second observer repeated ADC measurements in 15 patients. RESULTS. At baseline, ADC had AUC of 0.92, sensitivity of 78.6%, specificity of 91.4%, and accuracy of 88.2% for detecting segments with MaRIA score 11 or greater. At baseline, AUC for detecting endoscopic ulcers was 0.96 for MaRIA score versus 0.87 for ADC (p < .001); sensitivity, specificity, and accuracy were 70.8%, 90.2%, and 85.1% for ADC and 86.2%, 96.2%, and 93.6% for MaRIA score. At follow-up, ADC had AUC of 0.87, sensitivity of 75.4%, specificity of 83.6%, and accuracy of 80.0% for detecting improvement in MaRIA score to less than 11. At follow-up, AUC for detecting endoscopic ulcer healing was 0.94 for MaRIA score versus 0.84 for ADC (p = .01); sensitivity, specificity, and accuracy were 70.7%, 95.8%, and 84.4% for ADC and 90.2%, 100.0%, and 95.6% for MaRIA score. Interobserver agreement for ADC, based on intraclass correlation coefficient, was 0.70 at baseline and 0.65 at follow-up. CONCLUSION. The findings do not support use of ADC rather than MaRIA scores for detecting biologic therapy response. CLINICAL IMPACT. ADC may have an adjunct role in assessing bowel inflammation in CD, but showed limited performance for detecting biologic therapy response.

Predicting the therapeutic response to valproic acid in childhood absence epilepsy through electroencephalogram analysis using machine learning.

Childhood absence epilepsy (CAE) is a common type of idiopathic generalized epilepsy, manifesting as daily multiple absence seizures. Although seizures in most patients can be adequately controlled with first-line antiseizure medication (ASM), approximately 25 % of patients respond poorly to first-line ASM. In addition, an accurate method for predicting first-line medication responsiveness is lacking. We used the quantitative electroencephalogram (QEEG) features of patients with CAE along with machine learning to predict the therapeutic effects of valproic acid in this population. We enrolled 25 patients with CAE from multiple medical centers. Twelve patients who required additional medication for seizure control or who were shifted to another ASM and 13 patients who achieved seizure freedom with valproic acid within 6 months served as the nonresponder and responder groups. Using machine learning, we analyzed the interictal background EEG data without epileptiform discharge before ASM. The following features were analyzed: EEG frequency bands, Hjorth parameters, detrended fluctuation analysis, Higuchi fractal dimension, Lempel-Ziv complexity (LZC), Petrosian fractal dimension, and sample entropy (SE). We applied leave-one-out cross-validation with support vector machine, K-nearest neighbor (KNN), random forest, decision tree, Ada boost, and extreme gradient boosting, and we tested the performance of these models. The responders had significantly higher alpha band power and lower delta band power than the nonresponders. The Hjorth mobility, LZC, and SE values in the temporal, parietal, and occipital lobes were higher in the responders than in the nonresponders. Hjorth complexity was higher in the nonresponders than in the responders in almost all the brain regions, except for the leads FP1 and FP2. Using KNN classification with theta band power in the temporal lobe yielded optimal performance, with sensitivity of 92.31 %, specificity of 76.92 %, accuracy of 84.62 %, and area under the curve of 88.46 %.We used various EEG features along with machine learning to accurately predict whether patients with CAE would respond to valproic acid. Our method could provide valuable assistance for pediatric neurologists in selecting suitable ASM.

Subcellular expression of CD30 in cutaneous mastocytosis-An important factor for targeted treatment.

BACKGROUND: The subcellular distribution of CD30 on mast cells and the presence of eosinophils in cutaneous mastocytosis require further investigation, especially as the cell surface expression of CD30 is critical for the therapeutic response of systemic mastocytosis to brentuximab vedotin. OBJECTIVE: Investigation of 147 biopsy specimens from 143 patients with cutaneous mastocytosis for mast cell density and distribution, frequency of CD30 expression, CD30 staining patterns, and presence and distribution of eosinophils. Correlation with clinical patterns. METHODS: Retrospective multicenter immunohistochemical study of CD30 expression, eosinophils and basic clinical data in cutaneous mastocytosis. RESULTS: CD30 expression was found in all samples (cut-off: ≥1%), whereby the staining was predominantly cytoplasmic in 99% of the samples. Additional membrane staining was detected in 62% of the samples. Surface expression of CD30 was more common in biopsy specimens with a high mast cell burden and in biopsy specimens with a higher CD30 expression rate. Eosinophils were admixed in 58% of the samples. Females and older patients showed a trend of a lower mast cell burden. LIMITATIONS: Retrospective study on formalin-fixed and paraffin-embedded tissue without functional analysis. CONCLUSION: Most cases of cutaneous mastocytosis show cell surface expression of CD30 expression and is, therefore, in principle, accessible for therapy with antibodies against CD30, provided the overall situation of the patient warrants.

Interaction between baseline BMI and baseline disease severity predicts greater improvement in negative symptoms in first-episode schizophrenia.

Several studies have reported that baseline symptom severity in patients with schizophrenia (SCZ) is associated with the efficacy of antipsychotic medication. Overweight/obesity is common in SCZ and has also been reported to be correlated with therapeutic response to antipsychotics. This study aimed to evaluate whether baseline body mass index (BMI) and disease severity were associated with improvements in negative symptoms in patients with first-episode and medication-naïve (FEMN) SCZ. A total of 241 FEMN patients were recruited in this study and treated with oral risperidone over 3 months. Clinical symptoms were measured by the Positive and Negative Syndrome Scale (PANSS) and BMI was assessed at baseline and 3-month follow-up. We found that baseline BMI was correlated with the baseline severity of symptoms. Baseline BMI or baseline disease severity was associated with improvement in negative symptoms after 3 months of treatment. Linear regression analysis indicated that the interaction of BMI and disease severity at baseline was associated with improvement in negative symptoms in the early stage of SCZ after controlling for sex, age, and dose of risperidone. Our study suggests that the interaction of baseline BMI and disease severity may play a role in predicting negative symptom improvement after 3 months of risperidone treatment.

Prediction of response to promising first-line chemotherapy in ovarian cancer patients with residual peritoneal tumors: practical biomarkers and robust multiplex models.

BACKGROUND: Platinum/taxane (TC) chemotherapy with debulking surgery stays the mainstay of the treatment in ovarian cancer patients with peritoneal metastasis, and recently its novel modality, intraperitoneal carboplatin with dose-dense paclitaxel (ddTCip), was shown to have greater therapeutic impact. Nevertheless, the response varies among patients and consequent recurrence, or relapse often occurs. Discovery of therapeutic response predictor to ddTCip and/or TC therapy is eagerly awaited to improve the treatment outcome. METHODS: Using datasets in 76 participants in our ddTCip study and published databases on patients received TC therapy, we first validated a total of 75 previously suggested markers, sought out more active biomarkers through the association analyses of genome-wide transcriptome and genotyping data with progression-free survival (PFS) and adverse events, and then developed multiplex statistical prediction models for PFS and toxicity by mainly using multiple regression analysis and the classification and regression tree (CART) algorithm. RESULTS: The association analyses revealed that SPINK1 could be a possible biomarker of ddTCip efficacy, while ABCB1 rs1045642 and ERCC1 rs11615 would be a predictor of hematologic toxicity and peripheral neuropathy, respectively. Multiple regression analyses and CART algorithm finally provided a potent efficacy prediction model using 5 gene expression data and robust multiplex toxicity prediction models-CART models using a total of 4 genotype combinations and multiple regression models using 15 polymorphisms on 12 genes. CONCLUSION: Biomarkers and multiplex models composed here could work well in the response prediction of ddTCip and/or TC therapy, which might contribute to realize optimal selection of the key therapy.

Letter to the Editor: "The relationship between imaging features, therapeutic response, and overall survival in pediatric diffuse intrinsic pontine glioma".

This letter to the editor discusses the findings of Yu et al. (2024), which highlight the prognostic significance of volumetric assessments over cross-product measurements in pediatric diffuse intrinsic pontine glioma (DIPG). The study's methodology enhances precision in monitoring therapeutic responses, offering insights into treatment adjustments based on detailed imaging features. Emphasizing the value of volumetric MRI, this letter suggests its potential to improve surgical planning and therapeutic strategies, thereby optimizing patient management. This approach could revolutionize treatment paradigms, emphasizing personalized care through advanced imaging techniques.

Characteristics in patients with very early therapeutic response of condoliase chemonucleolysis for lumbar disc herniation.

INTRODUCTION: Treatment of lumbar disc herniation (LDH) using condoliase chemonucleolysis (CC) requires more time than surgery to demonstrate therapeutic effects. This study aimed to identify patients who show significant improvement in leg pain very early after CC and to determine pretreatment factors that can predict a very early therapeutic response. METHODS: The study included 52 patients who underwent CC for treatment-resistant LDH. Scores for low back and leg pain measured by a numerical rating scale were assessed at four time points (1 day, 1 week, 1 month, and 3 months after CC). Patients who reported subjective pain relief the day after treatment and further exhibited an improved straight leg raising (SLR) angle compared to pretreatment were classified as "very early responders (VER)". RESULTS: Of the 52 patients, 39 (75%) were VER, and 13 (25%) were non-VER. The VER showed earlier improvement in leg pain. The VER had a significantly higher proportion of positive SLR test patients (p = 0.01) and a significantly smaller pretreatment SLR angle compared to the non-VER (VER vs. non-VER: 40.6 ± 19.0 vs. 63.1 ± 16.9, p < 0.001). There were no significant differences in the level, type, and size of LDH and the disc regression rate between the two groups. CONCLUSIONS: Patients with a smaller pretreatment SLR angle are more likely to experience very early or early symptomatic relief, with a significant and sustained reduction in leg pain up to 3 months after CC treatment.