Prediction Of Therapy Outcome Research Articles

Bone marrow infiltration in diffuse large B-cell lymphoma: impact of 18FDG-PET/CT in detection and prediction of therapy outcome

Bone marrow infiltration in diffuse large B-cell lymphoma: impact of 18FDG-PET/CT in detection and prediction of therapy outcome

Determining differences between therapists using an extended version of the facilitative interpersonal skills performance test.

The therapist-facilitative interpersonal skills (FIS) has shown to predict therapy outcomes, demonstrating that high FIS therapists are more effective than low FIS therapists. There is a need for more insight into the variability in strengths and weaknesses in therapist skills. This study investigates whether a revised and extended FIS-scoring leads to more differentiation in measuring therapists'interpersonal skills. Furthermore, we explorative examine whether subgroups of therapists can be distinguished in terms of differences in their interpersonal responses. Using secondary data analysis, 93 therapists were exposed to seven FIS-clips. Responses of therapists using the original and the extended FIS scoring were rated. Three factors were found on the extended FIS scoring distinguishing supportive, expressive, and persuasive interpersonal responses of therapists. A latent profile analysis enlightened the presence of six subgroups of therapists. Using the revised and extended FIS-scoring contributes to our understanding of the role of interpersonal skills in the therapeutic setting by unraveling the question what works for whom.

Preliminary results of anti-inflammatory cytokine concentrations predicting therapy outcome in panic disorder

BackgroundPatients with panic disorder (PD) show alterations of the immune reactivity to acute stress, which could serve as a marker for effective treatment. Nevertheless, the effect of immune reactivity under acute stress before treatment on therapy outcome remains unclear. MethodsA total of N = 16 PD patients performed the Trier Social Test. Blood sample collection of anti-inflammatory cytokine IL-10 accompanied the TSST. The Mobility Inventory was handed out for the assessment of avoidance behavior before and after treatment. Area under the curve with respect to the ground (AUCG) and increase (AUCI) were calculated for assessed cytokine levels and were used as predictors for therapy outcome in regression analyses. ResultsAUCG significantly predicts avoidance behavior in company after treatment (β = −0.007, p = .033) but not avoidance behavior alone (β = −0.003, p = .264). AUCI does not significantly predict therapy outcome. ConclusionHigher concentrations of anti-inflammatory cytokine IL-10 under acute stress before treatment predicts less avoidance behavior in company after therapy. Immune markers seem to play a crucial role in the maintenance of mental disorders such as PD. Underlying mechanisms and IL-10 as a marker for individualized treatments should be investigated in future studies.

Editorial Comment: FDG PET/CT Has Impact in Chimeric Antigen Receptor T-Cell Therapy Response Assessment and Outcome Prediction.

Editorial Comment: FDG PET/CT Has Impact in Chimeric Antigen Receptor T-Cell Therapy Response Assessment and Outcome Prediction.

Select or adjust? How information from early treatment stages boosts the prediction of non-response in internet-based depression treatment

Abstract Background Internet-based interventions produce comparable effectiveness rates as face-to-face therapy in treating depression. Still, more than half of patients do not respond to treatment. Machine learning (ML) methods could help to overcome these low response rates by predicting therapy outcomes on an individual level and tailoring treatment accordingly. Few studies implemented ML algorithms in internet-based depression treatment using baseline self-report data, but differing results hinder inferences on clinical practicability. This work compares algorithms using features gathered at baseline or early in treatment in their capability to predict non-response to a 6-week online program targeting depression. Methods Our training and test sample encompassed 1270 and 318 individuals, respectively. We trained random forest algorithms on self-report and process features gathered at baseline and after 2 weeks of treatment. Non-responders were defined as participants not fulfilling the criteria for reliable and clinically significant change on PHQ-9 post-treatment. Our benchmark models were logistic regressions trained on baseline PHQ-9 sum or PHQ-9 early change, using 100 iterations of randomly sampled 80/20 train-test-splits. Results Best performances were reached by our models involving early treatment characteristics (recall: 0.75–0.76; AUC: 0.71–0.77). Therapeutic alliance and early symptom change constituted the most important predictors. Models trained on baseline data were not significantly better than our benchmark. Conclusions Fair accuracies were only attainable by involving information from early treatment stages. In-treatment adaptation, instead of a priori selection, might constitute a more feasible approach for improving response when relying on easily accessible self-report features. Implementation trials are needed to determine clinical usefulness.

Dysbiosis and Associated Stool Features Improve Prediction of Response to Biological Therapy in Inflammatory Bowel Disease

Dysbiosis of the gut microbiota is considered a key contributor to inflammatory bowel disease (IBD) etiology. Here, we investigated potential associations between microbiota composition and the outcomes to biological therapies. The study prospectively recruited 296 patients with active IBD (203 with Crohn's disease, 93 with ulcerative colitis) initiating biological therapy. Quantitative microbiome profiles of pretreatment and posttreatment fecal samples were obtained combining flow cytometry with 16S amplicon sequencing. Therapeutic response was assessed by endoscopy, patient-reported outcomes, and changes in fecal calprotectin. The effect of therapy on microbiome variation was evaluated using constrained ordination methods. Prediction of therapy outcome was performed using logistic regression with 5-fold cross-validation. At baseline, 65.9% of patients carried the dysbiotic Bacteroides2 (Bact2) enterotype, with a significantly higher prevalence among patients with ileal involvement (76.8%). Microbiome variation was associated with the choice of biological therapy rather than with therapeutic outcome. Only anti-tumor necrosis factor-α treatment resulted in a microbiome shift away from Bact2, concomitant with an increase in microbial load and butyrogen abundances and a decrease in potentially opportunistic Veillonella. Remission rates for patients hosting Bact2 at baseline were significantly higher with anti-tumor necrosis factor-α than with vedolizumab (65.1% vs 35.2%). A prediction model, based on anthropometrics and clinical data, stool features (microbial load, moisture, and calprotectin), and Bact2 detection predicted treatment outcome with 73.9% accuracy for specific biological therapies. Fecal characterization based on microbial load, moisture content, calprotectin concentration, and enterotyping may aid in the therapeutic choice of biological therapy in IBD.

Dynamic and static predictive modelling of psychotherapy outcome-Comparison of two statistical approaches.

Improving prediction abilities in the therapy process can increase therapeutic success for a variety of reasons, such as more personalised treatment or resource optimisation. The increasingly applied methods of dynamic prediction seem to be very promising for this purpose. Prediction models are usually based on static approaches of frequentist statistics. However, the application of this statistical approach has been widely criticised in this research area. Bayesian statistics has been proposed in the literature as an alternative, especially for the task of dynamic modelling. In this study, we compare the performance of predicting therapy outcome over the course of therapy between both statistical approaches. Based on a sample of 341 patients, a logistic regression analysis was performed using both statistical approaches. Therapy success was conceptualised as reliable pre-post improvement in brief symptom inventory (BSI) scores. As predictors, we used the subscales of the Outcome Questionnaire (OQ-30) and the Helping Alliance Questionnaire (HAQ) measured every fifth session, as well as baseline BSI scores. The influence of the predictors during therapy differs between the frequentist and the Bayesian approach. In contrast, predictive validity is comparable with a mean area under the curve (AUC) of 0.76 in both model types. Bayesian statistic provides an innovative and useful alternative to the frequentist approach in predicting therapy outcome. The theoretical foundation is particularly well suited for dynamic prediction. Nevertheless, no differences in predictive validity were found in this study. More complex methodology as well as further research seems necessary to exploit the potential of Bayesian statistics in this area.

Hemodynamic forces from 4D flow magnetic resonance imaging for optimised prediction of cardiac resynchronisation therapy outcome

Abstract Background Patients with heart failure and left bundle branch block (LBBB) may receive cardiac resynchronisation therapy (CRT), but many patients have limited treatment response (1). The use of imaging to optimise patient selection has been repeatedly attempted with some success (2), and scar detected on late gadolinium enhancement (LGE) imaging has been associated with reduced response rates (3). Intraventricular hemodynamic force (HDF) analysis has been suggested as a novel marker for assessment of patients eligible for CRT (4). The aim of this study was therefore to investigate if septal scar from LGE or left ventricular HDF computed from cardiac magnetic resonance (CMR) imaging can improve prediction of CRT outcome. Methods Patients with heart failure, EF<35% and LBBB (n=22) underwent CMR with 4D flow at 1.5T or 3T (Achieva, Philips Healthcare) prior to CRT (Figure 1). Scar was assessed by LGE. Left ventricular HDF were computed based on the Navier-Stokes equations, using a validated module in the software Segment v3.3 R10057 (Medviso, Lund, Sweden) (5). Forces (N) were analysed in three orthogonal directions as root mean square (RMS) and peak values, reported in median [interquartile range]. Transversal/longitudinal HDF ratio was calculated for systole and diastole. Echocardiography was performed before and six months after CRT, and patients with end-systolic volume reduction ≥15% at follow-up were defined as responders. The Mann-Whitney U test was used for group comparisons, Fisher’s exact test for binary categorical data, Wilcoxon test for paired comparisons, and receiver operating characteristic analysis for prediction of CRT response. Results There was no difference between responders and non-responders in LGE (Table 1). Non-responders had smaller HDF than responders in the inferior-anterior direction in systole (RMS: 0.055 [0.027] vs 0.070 [0.032], p=0.04; peak: 0.11 [0.069] vs 0.15 [0.083], p=0.03), and in the apex-base direction in diastole (RMS: 0.087 [0.018] vs 0.11 [0.046], p=0.047). Non-responders had larger diastolic HDF ratio than responders (RMS: 0.89 [0.45] vs 0.67 [0.20], p=0.004; peak: 1.1 [0.51] vs 0.57 [0.40], p=0.01) (Figure 1). Receiver operating characteristic analysis of diastolic HDF ratio found an area under the curve of 0.88 (p=0.005). Identification of non-responders using diastolic HDF ratio with a specificity of 100%, resulted in a ratio of >0.87 for non-responders, with a sensitivity of 57%. Intragroup comparison found higher HDF ratio in systole than diastole for responders but not for non-responders (Figure 1). Conclusions Prediction of left ventricular remodelling following CRT may be optimized by hemodynamic force analysis from 4D flow CMR, while septal scar from LGE was not found indicative of CRT outcome in this pilot study. Larger-scale studies are required before clinical implementation of HDF analysis.Table 1Figure 1

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.