Prediction Of Therapy Outcome Research Articles

Prostate cancer therapy outcome prediction: are miRNAs a suitable guide for therapeutic decisions?

Radical prostatectomy, radiotherapy, chemotherapy, and androgen-deprivation therapy are among the most common treatment options for different forms of prostate cancer (PCa). However, making therapeutic decisions is difficult due to the lack of reliable prediction markers indicating therapy outcomes in clinical practice. The involvement of miRNAs in all mechanisms of the PCa development and their easy detection characterize them as attractive PCa biomarkers. Although there are extensive data on the role of miRNAs in PCa therapy resistance and sensitivity development, the issues of whether they could be used as a guide for therapy choice and, if so, how we can progress toward this goal, remain unclear. Thus, generalizable reviews and studies which summarize, compare, and analyze data on miRNA involvement in responses to different types of PCa therapies are required. Data on the involvement of miRNAs in therapy responses, on the role of cross-miRNA expression in different therapies, and on miRNA targets were analyzed in order to determine the miRNA-related factors which can lend perspective to the future development of personalized predictors of PCa sensitivity/resistance to therapies. The data available on the miRNAs associated with different PCa therapies (resistance and sensitivity therapies) are summarized and analyzed in this study, including analyses using bioinformatics resources. Special attention was dedicated to the mechanisms of the development of therapy resistance. A comprehensive combined analysis of the current data revealed a panel of miRNAs that were shown to be most closely associated with the PCa therapy response and were found to regulate the genes involved in PCa development via cell proliferation regulation, epithelial-mesenchymal transition (EMT), apoptosis, cell-cycle progression, angiogenesis, metastasis and invasion regulation, androgen-independent development, and colony formation. The selected miRNA-based panel has the potential to be a guide for therapeutic decision making in the effective treatment of PCa.

Identification of a new anoikis-related gene signature for prognostic significance in head and neck squamous carcinomas.

Anoikis, a mode of programmed cell death, is essential for normal development and homeostasis in the organism and plays an important role in the onset and progression of cancers. The authors of this research sought to establish a gene signature associated with anoikis to predict therapy outcomes and patient prognosis for individuals with head and neck squamous cell carcinoma (HNSCC). Transcriptome data of anoikis-related genes (ARGs) in individuals with HNSCC were retrieved from public databases to aid in the formulation of the gene signature. A novel ARG signature was then created using a combination of the Least Absolute Shrinkage and Selection Operator regression and Cox regression analysis. The relationship between ARGs and tumor immune microenvironment in HNSCC was explored using single-cell analysis. HNSCC individuals were classified into high-risk and low-risk groups as per the median value of risk score. The study also investigated the variations in the infiltration status of immune cells, tumor microenvironment, sensitivity to immunotherapy and chemotherapeutics, as well as functional enrichment between the low-risk and high-risk categories. A total of 18 ARGs were incorporated in the formulation of the signature. Our signature's validity as a standalone predictive predictor was validated by multivariate Cox regression analysis and Kaplan-Meier survival analysis. Generally, the prognosis was worse for high-risk individuals. Subjects in the low-risk groups had a better prognosis and responded in a better way to combination immunotherapy, had higher immunological ratings and activity levels, and had more immune cell infiltration. In addition, gene set enrichment analysis findings showed that the low-risk subjects exhibited heightened activity in several immune-related pathways. However, the high-risk patients responded better to chemotherapy. The aim of this research was to develop a new ARG signature to predict the prognosis and sensitivity to immunotherapeutic and chemotherapeutic schemes for HNSCC patient. As a result, this could help spur the creation of new chemotherapeutics and immunotherapeutic approaches for patients with HNSCC.

Treatment Engagement as a Predictor of Therapy Outcome Following Cognitive Behaviour Therapy for Autistic Children.

Active engagement in one's therapy is a key contributor to successful outcomes. Research on child engagement in cognitive behaviour therapy (CBT) has largely focused on youth without autism. This longitudinal study examined multiple indicators of child engagement in relation to outcomes for autistic children who took part in CBT for emotion regulation. Data were collected from 60 autistic children who were between 8 and 13 years of age (86.7% male; Mage = 9.58 years, SD = 1.44 years; 75% White). Indicators of child engagement included independent observer ratings of in-session involvement, as measured by the Child Involvement Rating Scale, and therapist ratings of the therapeutic relationship and homework completion using single-item measures. Indicators of engagement were measured at early (i.e., first third), middle (i.e., mid third), and late (i.e., final third) stages of treatment. Parent-reported emotion regulation was the primary treatment outcome, as measured by the Emotion Regulation Checklist. After controlling for pre-treatment scores, in-session involvement significantly predicted some aspects of post-treatment emotion regulation, whereas therapeutic relationship and homework completion did not. Child in-session involvement throughout therapy may be particularly relevant for treatment change. Addressing issues related to in-session involvement early in treatment may help to promote therapeutic success for autistic children.

Objective wearable measures correlate with self-reported chronic pain levels in people with spinal cord stimulation systems

Spinal Cord Stimulation (SCS) is a well-established therapy for treating chronic pain. However, perceived treatment response to SCS therapy may vary among people with chronic pain due to diverse needs and backgrounds. Patient Reported Outcomes (PROs) from standard survey questions do not provide the full picture of what has happened to a patient since their last visit, and digital PROs require patients to visit an app or otherwise regularly engage with software. This study aims to assess the feasibility of using digital biomarkers collected from wearables during SCS treatment to predict pain and PRO outcomes. Twenty participants with chronic pain were recruited and implanted with SCS. During the six months of the study, activity and physiological metrics were collected and data from 15 participants was used to develop a machine learning pipeline to objectively predict pain levels and categories of PRO measures. The model reached an accuracy of 0.768 ± 0.012 in predicting the pain intensity of mild, moderate, and severe. Feature importance analysis showed that digital biomarkers from the smartwatch such as heart rate, heart rate variability, step count, and stand time can contribute to modeling different aspects of pain. The results of the study suggest that wearable biomarkers can be used to predict therapy outcomes in people with chronic pain, enabling continuous, real-time monitoring of patients during the use of implanted therapies.

Objective wearable measures and subjective questionnaires for predicting response to neurostimulation in people with chronic pain

BackgroundNeurostimulation is an effective therapy for treating and management of refractory chronic pain. However, the complex nature of pain and infrequent in-clinic visits, determining subject’s long-term response to the therapy remains difficult. Frequent measurement of pain in this population can help with early diagnosis, disease progression monitoring, and evaluating long-term therapeutic efficacy. This paper compares the utilization of the common subjective patient-reported outcomes with objective measures captured through a wearable device for predicting the response to neurostimulation therapy.MethodData is from the ongoing international prospective post-market REALITY clinical study, which collects long-term patient-reported outcomes from 557 subjects implanted by Spinal Cord Stimulator (SCS) or Dorsal Root Ganglia (DRG) neurostimulators. The REALITY sub-study was designed for collecting additional wearables data on a subset of 20 participants implanted with SCS devices for up to six months post implantation. We first implemented a combination of dimensionality reduction algorithms and correlation analyses to explore the mathematical relationships between objective wearable data and subjective patient-reported outcomes. We then developed machine learning models to predict therapy outcome based on the subject’s response to the numerical rating scale (NRS) or patient global impression of change (PGIC).ResultsPrincipal component analysis showed that psychological aspects of pain were associated with heart rate variability, while movement-related measures were strongly associated with patient-reported outcomes related to physical function and social role participation. Our machine learning models using objective wearable data predicted PGIC and NRS outcomes with high accuracy without subjective data. The prediction accuracy was higher for PGIC compared with the NRS using subjective-only measures primarily driven by the patient satisfaction feature. Similarly, the PGIC questions reflect an overall change since the study onset and could be a better predictor of long-term neurostimulation therapy outcome.ConclusionsThe significance of this study is to introduce a novel use of wearable data collected from a subset of patients to capture multi-dimensional aspects of pain and compare the prediction power with the subjective data from a larger data set. The discovery of pain digital biomarkers could result in a better understanding of the patient’s response to therapy and their general well-being.

312 Relevance of Anti-PLA2R Levels in Therapy Decision and Prediction of Therapy Outcome Using Cyclophosphamide and Steroids Treatment in Patients With Membranous Nephropathy

312 Relevance of Anti-PLA2R Levels in Therapy Decision and Prediction of Therapy Outcome Using Cyclophosphamide and Steroids Treatment in Patients With Membranous Nephropathy

Melanoma molecular subtyping and scoring model construction based on ligand-receptor pairs.

Melanoma is a malignancy of melanocytes, responsible for a high percentage of skin cancer mortality. Ligand-Receptor pairs, a type of cellular communication, are essential for tumor genesis, growth, metastasis, and prognosis. Yet, the role of Ligand-Receptor pairs in melanoma has not been fully elucidated. Our research focused on the function of Ligand-Receptor pairs in melanoma prognosis. We screened 131 melanoma prognosis corresponded ligand-receptor pairs by analyzing the TCGA data of melanoma and the 2293 LR pairs retrieved from the connectomeDB2020 database. And further developed subtypes of melanoma according to the expression of these ligand-receptor pairs by Consensus Clustering. Then we using lasso cox regression and stepwise multivariate regression analysis established a ligand-receptor pairs-based scoring model for the evaluation of melanoma prognosis. Our study demonstrated that the ligand-receptor pairs are vital to the molecular heterogeneity of melanoma, and characterized three different melanoma ligand-receptor pairs subtypes. Among them, the C3 subtype showed a better prognosis, while the C1 subtype exhibited a low prognosis state. And our analysis then found out that this could be related to the differed activation and inhabitation of the cell cycle and immune-related pathways. Using lasso cox regression and stepwise multivariate regression analysis, we further identified 9 key ligand-receptor pairs and established a scoring model that effectively correlated with the prognosis, immune pathways, and therapy of melanoma, showing that the LR.score model was a trustworthy and independent biomarker for melanoma prognosis evaluation. In sum, we found that ligand-receptor pairs are significantly associated with the prognosis and therapy of melanoma. And our ligand-receptor-based scoring model showed potential for the evaluation of melanoma prognosis and immune therapy outcome prediction, which is crucial to the survival for the patients.

Prediction of Tinnitus Treatment Outcomes Based on EEG Sensors and TFI Score Using Deep Learning

Tinnitus is a hearing disorder that is characterized by the perception of sounds in the absence of an external source. Currently, there is no pharmaceutical cure for tinnitus, however, multiple therapies and interventions have been developed that improve or control associated distress and anxiety. We propose a new Artificial Intelligence (AI) algorithm as a digital prognostic health system that models electroencephalographic (EEG) data in order to predict patients' responses to tinnitus therapies. The EEG data was collected from patients prior to treatment and 3-months following a sound-based therapy. Feature selection techniques were utilised to identify predictive EEG variables with the best accuracy. The patients' EEG features from both the frequency and functional connectivity domains were entered as inputs that carry knowledge extracted from EEG into AI algorithms for training and predicting therapy outcomes. The AI models differentiated the patients' outcomes into either therapy responder or non-responder, as defined by their Tinnitus Functional Index (TFI) scores, with accuracies ranging from 98%-100%. Our findings demonstrate the potential use of AI, including deep learning, for predicting therapy outcomes in tinnitus. The research suggests an optimal configuration of the EEG sensors that are involved in measuring brain functional changes in response to tinnitus treatments. It identified which EEG electrodes are the most informative sensors and how the EEG frequency and functional connectivity can better classify patients into the responder and non-responder groups. This has potential for real-time monitoring of patient therapy outcomes at home.

Classification of anatomic structures in head and neck by CT-based radiomics

Background and purpose: Radiomics features are used to identify disease types and predict therapy outcomes. However, how the radiomics features are different among different anatomical structures has never been investigated. Hence, we analyzed the radiomics features of 22 anatomical structures in the head and neck area in CT images. Furthermore, we studied whether CT radiomics can classify anatomical structures of the head and neck using unsupervised machine-learning techniques. Materials and methods: We obtained IMRT/VMAT treatment planning data from 36 patients treated for head and neck cancers in a single institution. There were 1357 contours of more than 22 anatomical structures drawn on planning CTs. We calculated 174 radiomics features using the SIBEX program. First, we tested whether the radiomics features of anatomical structures were unique enough to classify all contours into 22 groups. We then developed a two-stage clustering technique to classify 22 anatomic structures into sub-groups with similar physiological or biological characteristics. Results: The heatmap of 174 radiomics features of 22 anatomical structures showed a distinct difference among tumors and other healthy structures. Radiomics features have allowed us to identify the eyes, lens, submandibular, pituitary glands, and thyroids with over 90% accuracy. The two-stage clustering of 22 structures resulted in six subgroups, which shared common characteristics such as fatty and bony tissues. Conclusions: We have shown that anatomical structures in head and neck tumors have distinguishable radiomics features. We could observe similarities of features among subgroups of the structures. The results suggest that CT radiomics can help distinguish the biological characteristics of head and neck lesions.

Pro- and anti-inflammatory cytokines Interleukin-6 and Interleukin-10 predict therapy outcome of female patients with posttraumatic stress disorder

PTSD patients show alterations of the immune system, mainly a ‘low-grade inflammation’. Psychotherapeutic treatments are meant to reduce symptom burden of PTSD patients but 30–50% of PTSD patients do not benefit from psychotherapy. Therefore, in this study, the predictive effect of cytokine levels on therapy outcome are investigated. Pro- (IL-6) and anti-inflammatory (IL-10) cytokines in female PTSD patients (N = 17) were assessed under acute stress during a Trier social stress test (TSST) before therapeutic treatment. The predictive effects of IL-6 and IL-10 on therapy outcome (SCL_GSI, BDI) after an inpatient psychotherapeutic treatment at the University Medical Center Carl Gustav Carus, Technische Universität Dresden was investigated. Areas under the curve with respect to ground (AUCG) and increase (AUCI) for IL-6 and IL-10 levels during the TSST were calculated and used as predictors in regression analyses with pre-treatment scores. Models including all three predictors show good model fits (R2 = 0.255 to 0.744). Models including AUCG and AUCI scores show superior fits compared with models including pre-treatment scores alone (ΔR2 = 0.196 to 0.444). IL-6 AUCG and AUCI scores are significant predictors for post-treatment SCL-GSI and BDI (β = −0.554 to 0.853), whereas IL-10 AUCG significantly predicts SCL-GSI and BDI (β = −0.449 to −0.509). Therefore, pro- and anti-inflammatory IL-6 and IL-10 levels under acute stress before therapy predict therapy outcome of female PTSD patients regarding general symptom burden and depressive symptoms. Future studies should further address the link between inflammation and therapy outcome, especially underlying mechanisms and influencing factors.

Degree of stemness predicts micro-environmental response and clinical outcomes of diffuse large B-cell lymphoma and identifies a potential targeted therapy.

Some cells within a diffuse large B-cell lymphoma (DLBCL) have the genotype of a stem cell, the proportion of which is termed degree of stemness. We interrogated correlations between the degree of stemness with immune and stromal cell scores and clinical outcomes in persons with DLBCL. We evaluated gene expression data on 1,398 subjects from Gene Expression Omnibus to calculate the degree of stemness. Subjects were classified into low- and high-stemness cohorts based on restricted cubic spline plots. Weighted gene co-expression network analysis (WGCNA) was used to screen for stemness-related genes. Immune and stromal scores correlated with the degree of stemness (both P < 0.001). A high degree of stemness correlated with a shorter progression-free survival (PFS; Hazard Ratio [HR; 95% Confidence Interval [CI] =1.90 (1.37, 2.64; P < 0.001) and a shorter survival (HR = 2.29 (1.53, 3.44; P < 0.001). CDC7 expression correlated with the degree of stemness, and CDC7-inhibitors significantly increased apoptosis (P < 0.01), the proportion of cells in G1 phase (P < 0.01), and inhibited lymphoma growth in a mice xenograft model (P = 0.04). Our data indicate correlations between the degree of stemness, immune and stromal scores, PFS, and survival. These data will improve the prediction of therapy outcomes in DLBCL and suggest potential new therapies.

A living biobank of canine mammary tumor organoids as a comparative model for human breast cancer

Mammary tumors in dogs hold great potential as naturally occurring breast cancer models in translational oncology, as they share the same environmental risk factors, key histological features, hormone receptor expression patterns, prognostic factors, and genetic characteristics as their human counterparts. We aimed to develop in vitro tools that allow functional analysis of canine mammary tumors (CMT), as we have a poor understanding of the underlying biology that drives the growth of these heterogeneous tumors. We established the long-term culture of 24 organoid lines from 16 dogs, including organoids derived from normal mammary epithelium or benign lesions. CMT organoids recapitulated key morphological and immunohistological features of the primary tissue from which they were derived, including hormone receptor status. Furthermore, genetic characteristics (driver gene mutations, DNA copy number variations, and single-nucleotide variants) were conserved within tumor-organoid pairs. We show how CMT organoids are a suitable model for in vitro drug assays and can be used to investigate whether specific mutations predict therapy outcomes. Specifically, certain CMT subtypes, such as PIK3CA mutated, estrogen receptor-positive simple carcinomas, can be valuable in setting up a preclinical model highly relevant to human breast cancer research. In addition, we could genetically modify the CMT organoids and use them to perform pooled CRISPR/Cas9 screening, where library representation was accurately maintained. In summary, we present a robust 3D in vitro preclinical model that can be used in translational research, where organoids from normal, benign as well as malignant mammary tissues can be propagated from the same animal to study tumorigenesis.

Quantitative pretreatment EEG predicts efficacy of ACTH therapy in infantile epileptic spasms syndrome

ObjectiveThis study aimed to determine the correlation between outcomes following adrenocorticotrophic hormone (ACTH) therapy and measurements of relative power spectrum (rPS), weighted phase lag index (wPLI), and graph theoretical analysis on pretreatment electroencephalography (EEG) in infants with non-lesional infantile epileptic spasms syndrome (IESS). MethodsTwenty-eight patients with non-lesional IESS were enrolled. Outcomes were classified based on seizure recurrence following ACTH therapy: seizure-free (F, n = 21) and seizure-recurrence (R, n = 7) groups. The rPS, wPLI, clustering coefficient, and betweenness centrality were calculated on pretreatment EEG and were statistically analyzed to determine the correlation with outcomes following ACTH therapy. ResultsThe rPS value was significantly higher in the delta frequency band in group R than in group F (p < 0.001). The wPLI values were significantly higher in the delta, theta, and alpha frequency bands in group R than in group F (p = 0.007, <0.001, and <0.001, respectively). The clustering coefficient in the delta frequency band was significantly lower in group R than in group F (p < 0.001). ConclusionsOur findings demonstrate the significant differences in power and functional connectivity between outcome groups. SignificanceThis study may contribute to an early prediction of ACTH therapy outcomes and thus help in the development of appropriate treatment strategies.

Comprehensive Behavioral Treatment of an Older Adult Man with Trichotillomania

The Comprehensive Behavioral (ComB) model of treatment for trichotillomania (TTM) and other body-focused repetitive behaviors offers a framework for individualized, flexible intervention based on functional analysis. This case report focuses on the treatment of a patient who enrolled in the first randomized clinical trial of ComB for TTM (Carlson et al., 2021) as well as a long-term follow-up of participants from that trial conducted during the COVID-19 pandemic (Flannery et al., in press). Walter (pseudonym) entered the treatment trial at 69, having had TTM since age 17 but not received treatment for it. Walter showed clinically significant improvement in treatment, ultimately abstaining from hair pulling for two years. A single case from a parallel-groups trial cannot support strong conclusions about why his results were favorable, but qualitative review of Walter’s experience in therapy suggested that allowing him a good deal of collaborative input on the specific methods of implementation of ComB principles was helpful. Along with the general literature on patient age as a predictor of therapy outcome, Walter’s case serves as a reminder that older adults, even those with highly chronic clinical conditions, can benefit greatly from psychotherapy.

Smart Sensing and AI for Physical Therapy in IoT Era

It is well known that medical spending increase with disability status. Per capita spending for people with five or more limitations in activities of daily living (ADLs) is nearly five times the amount incurred by those with limitations in only one instrumental activities of daily living (IADLs). Physical therapy is the way to improve the motor capabilities however it takes a lot of time, it requires physiotherapists services, is often painful and the outcome are evaluated in subjective way. New technologies including smart sensors were adopted in healthcare including wearable solutions for cardiac and respiratory activity monitoring and successfully are contributing to reduce the costs of services. In the case of motor activity and particularly in physical rehabilitation the developments are still reduced the physical therapy services are using as hardware mechanical equipment without sensing, embedded processing and internet connectivity that significatively reduce the possibility to measure and evaluate the physical training outcomes in objective way. In this paper the disruptive solutions for physical therapy are presented that are based on hot technologies such as smart sensors, IoT, virtual reality (VR), mixed reality (MR), and artificial intelligence (AI). Applied AI may conduct to develop models, classifiers (gait classification) and short term or medium term prediction of physical therapy outcomes. Highly motivation of the patients under physical rehabilitation can be increased promoting serious game characterized by VR and MR scenarios.

1216P Prognostic potential of liquid biopsy in advanced HER2 positive esophagogastric adenocarcinoma under HER2 blockade and immune checkpoint therapy

Immune therapy and immune checkpoint inhibition (ICI) have drastically improved survival for an increasing number of entities and thereby revolutionized modern oncology. However, even in combined multi-agent approaches, therapy resistance presents a major limitation and imposes the need for tight and sensitive monitoring of the disease status. Liquid biopsy holds the potential to track both the tumor and the patient’s immune response and therefore to monitor even hardly accessible solid tumors as well as to predict therapy outcomes. In the work presented here, we explored the prognostic value of liquid biopsy in patients with advanced HER2+ esophagogastric adenocarcinoma (EGA) receiving first-line treatment of trastuzumab (αHER2) and nivolumab (αPD-1) in combination with ipilimumab (αCTLA-4) or FOLFOX as translational part of the INTEGA trial (NCT03409848). From blood sampled at baseline, after the first treatment cycle (2-3 weeks) and at the end of treatment, we isolated cell-free DNA (cfDNA) to quantify tumor burden and to derive the mutational landscape of the tumor from the contained circulating tumor DNA. Furthermore, we analyzed genomic DNA of blood cells via next-generation sequencing for imprints of the T cell receptor repertoire. We identified a potential tumor driver mutation from cfDNA at baseline in 88 % of cases implying that liquid biopsy is suitable to track malignant lesions in the setting of advanced HER2+ EGA. In some progressive patients, we found truncating or epitope-loss HER2 resistance mutations acquired on therapy, representing a new tumor escape mechanism under ICI with HER2 blockade. Analysis of the T cell immune repertoire in blood showed a significant correlation between high T cell richness and longer survival already after 2-3 weeks, which might represent a read-out for restored T cell activity upon ICI. Moreover, the crude amount of cfDNA per ml blood plasma after 2-3 weeks significantly correlated with response to treatment. Thus, the quantification of cfDNA could offer a relevant prognostic parameter to be implemented into the clinical praxis without the need for time-consuming and cost-intensive analyses.

P-Glycoprotein Activity at Diagnosis Does Not Predict Therapy Outcome and Survival in Canine B-Cell Lymphoma.

Simple SummaryClinical experience in human and canine clinics shows that following initial response to treatment, drug-resistant cancer cells frequently evolve and eventually, most tumors become resistant to all available therapies. The most straightforward cause of therapy resistance is linked to cellular alterations that prevent drugs from acting on their target. Drug efflux mediated by the ABC transporter P-glycoprotein (P-gp) contributes to unfavorable treatment outcome in several human malignancies. Here, we characterize a large cohort of canine B-cell lymphoma patients followed for over 7 years. We show that the intrinsic P-gp activity of tumor cells characterized at the time of diagnosis is not predictive for therapy outcome. Our results highlight the complexity of clinical drug resistance mechanisms and suggests that the relevance of P-gp in acquired resistance should be further investigated by the continuous monitoring of tumor cells during treatment.Various mechanisms are known to be involved in the development of multidrug resistance during cancer treatment. P-glycoprotein (P-gp) decreases the intracellular concentrations of cytotoxic drugs by an energy-dependent efflux mechanism. The aim of this study was to investigate the predictive value of P-gp function based on the evaluation of P-gp activity in tumor cells obtained from canine B-cell lymphoma patients at diagnosis. P-gp function of 79 immunophenotyped canine lymphoma samples was determined by flow cytometry using the Calcein assay. Dogs were treated with either the CHOP or the L-CHOP protocol, a subset of relapsed patients received L-asparaginase and lomustine rescue treatments. Among the 79 dogs, the median overall survival time was 417 days, and the median relapse-free period was 301 days. 47 percent of the samples showed high P-gp activity, which was significantly higher in Stage IV cancer patients compared to Stage II + III and V. Whereas staging was associated with major differences in survival times, we found that the intrinsic P-gp activity of tumor cells measured at diagnosis is not predictive for therapy outcome. Further studies are needed to identify the intrinsic and acquired resistant mechanisms that shape therapy response and survival in B-cell canine lymphoma patients.

An interim oral appliance as a screening tool during drug-induced sleep endoscopy to predict treatment success with a mandibular advancement device for obstructive sleep apnea.

PurposePrevious studies have shown a wide range of efficacy (29 to 71%) of a mandibular advancement device (MAD) in the treatment of obstructive sleep apnea (OSA). Currently, the ability to preselect suitable patients for MAD therapy based on individual characteristics related to upper airway collapsibility is limited. We investigated if the use of non-custom interim MAD during drug-induced sleep endoscopy (DISE) could be a valuable screening tool to predict MAD treatment outcome.MethodsIn a single-center prospective study including a consecutive series of patients with OSA, we compared DISE outcomes with a MAD in situ with polysomnography results after 3 months of using the same MAD that was used during DISE.ResultsOf 41 patients who completed the study, the median apnea–hypopnea index (AHI) was 16.0 events/h [IQR 7.4–23.4]. Respiratory outcomes on polysomnography, including apnea index (AI), total AHI, AHI in supine position, and oxygen desaturation index, all significantly improved after 3 months of MAD treatment. With complete improvement of the upper airway obstruction with the MAD in situ during DISE in supine position, patients were 6.3 times more likely to be a responder to MAD treatment compared to patients with a persisting complete obstruction, although not statistically significant (OR 6.3; 95%CI 0.9–42.7; p = 0.060).ConclusionThe potential predictive value with regard to MAD therapy outcomes of the use of an interim MAD during DISE would be an important finding, since the prediction of MAD therapy outcome is of great clinical and scientific interest. A study with a larger cohort should be performed to further investigate our findings.

High Neutrophil-Lymphocyte Ratio and Low Lymphocyte-Monocyte Ratio Combination after Thrombolysis Is a Potential Predictor of Poor Functional Outcome of Acute Ischemic Stroke.

(1) Background: Ischemic stroke is one of the leading causes of death and disability. An inflammatory response is observed in multiple stages of cerebral ischemia, particularly in the acute phase. Recent publications revealed that the neutrophil–lymphocyte ratio (NLR) and lymphocyte–monocyte ratio (LMR) may be used to predict long-term prognosis in acute ischemic stroke (AIS) after thrombolysis. To test whether there is a relationship between the combination of these parameters and long-term prognosis, we analyzed the NLR–LMR combination in AIS patients treated with intravenous recombinant tissue plasminogen activator (rtPA); (2) Methods: The study included 285 adults with a diagnosis of AIS and rtPA treatment within a 4.5 h time window. Blood samples were obtained at admission and 24 h after thrombolysis to calculate pre- and post-thrombolysis NLR and LMR. Clinical data, including NIHSS was registered on admission and day 1. The long-term outcome was defined 90 days post-event by the modified Rankin Scale (mRS). Therapy-associated intracranial hemorrhage (ICH) was classified according to ECASS II. Receiver operating characteristic curve (ROC) analysis was performed to determine optimal cutoffs of NLR and LMR as predictors of therapy outcomes; (3) Results: Patients were stratified by cutoffs of 5.73 for NLR and 2.08 for LMR. The multivariate logistic regression model, including all possible confounders, displayed no significant association between NLR or LMR with 3-months functional prognosis. The combination of high NLR–low LMR vs. low NRL–high LMR as obtained 24 h after thrombolysis was found to be an independent predictor of poor 3-months functional outcome (mRS ≥ 2; OR 3.407, 95% CI 1.449 to 8.011, p = 0.005). The proportion of patients between low NLR–high LMR and high NLR–low LMR groups from admission to day 1 showed no significant change in the good outcome group. On the other hand, in the poor outcome group (mRS ≥ 2), low NLR–high LMR and high NLR–low LMR groups displayed a significant shift in patient proportions from 67% and 21% at admission (p = 0.001) to 36% and 49% at 24 h after thrombolysis (p < 0.001), respectively; (4) Conclusions: Our study demonstrated for the first time that a high NLR–low LMR combination as observed at 24 h after thrombolysis can serve as an independent predictor of 3-months poor outcome in AIS patients. This simple and readily available data may help clinicians to improve the prognostic estimation of patients and may provide guidance in selecting patients for personalized and intensified care post-thrombolysis.

A Signature Based on Costimulatory Molecules for the Assessment of Prognosis and Immune Characteristics in Patients With Stomach Adenocarcinoma.

Although costimulatory molecules have been shown to boost antitumor immune responses, their significance in stomach adenocarcinoma (STAD) remains unknown. The purpose of this study was to examine the gene expression patterns of costimulatory molecule genes in patients with STAD and develop a predictive signature to aid in therapy selection and outcome prediction. We used 60 costimulatory family genes from prior research to conduct the first complete costimulatory molecular analysis in patients with STAD. In the two study groups, consensus clustering analysis based on these 60 genes indicated unique distribution patterns and prognostic differences. Using the least absolute shrinkage and selection operator and Cox regression analysis, we identified nine costimulatory molecular gene pairs (CMGPs) with prognostic value. With these nine CMGPs, we were able to develop a costimulatory molecule-related prognostic signature that performed well in an external dataset. For the patients with STAD, the signature was proven to be a risk factor independent of the clinical characteristics, indicating that this signature may be employed in conjunction with clinical considerations. A further connection between the signature and immunotherapy response was discovered. The patients with high mutation rates, an abundance of infiltrating immune cells, and an immunosuppressive milieu were classified as high-risk patients. It is possible that these high-risk patients have a better prognosis for immunotherapy since they have higher cytolytic activity scores and immunophenoscores of CTLA4 and PD-L1/PD-L2 blockers. Therefore, our signature may help clinicians in assessing patient prognosis and developing treatment plans.