Prediction Of Therapy Outcome Research Articles

Genetic variations of adenosine kinase as predictable biomarkers of efficacy of vagus nerve stimulation in patients with pharmacoresistant epilepsy.

Vagus nerve stimulation (VNS) is an alternative treatment option for individuals with refractory epilepsy, with nearly 40% of patients showing no benefit after VNS and only 6%-8% achieving seizure freedom. It is presently unclear why some patients respond to treatment and others do not. Therefore, identification of biomarkers to predict efficacy of VNS is of utmost importance. The objective of this study was to explore whether genetic variations in genes involved in adenosine kinase (ADK), ecto-5'-nucleotidase (NT5E), and adenosine A1 receptor (A1R) are linked to outcome of VNS in patients with refractory epilepsy. Thirty single-nucleotide polymorphisms (SNPs), including 9 in genes encoding ADK, 3 in genes encoding NT5E, and 18 in genes encoding A1R, were genotyped in 194 refractory epilepsy patients who underwent VNS. The chi-square test and binary logistic regression were used to determine associations between genetic differences and VNS efficacy. A significant association between ADK SNPs rs11001109, rs7899674, and rs946185 and seizure reduction with VNS was found. Regardless of sex, age, seizure frequency and type, antiseizure drug use, etiology, and prior surgical history, all patients (10/10 patients [100%]) with minor allele homozygosity at rs11001109 (genotype AA) or rs946185 (AA) achieved > 50% seizure reduction and 4 patients (4/10 [40%]) achieved seizure freedom. VNS therapy demonstrated higher efficacy among carriers of minor allele rs7899674 (CG + GG) (68.3% vs 48.8% for patients with major allele homozygosity). Homozygous ADK SNPs rs11001109 (AA) and rs946185 (AA), as well as minor allele rs7899674 (CG + GG), may serve as useful biomarkers for prediction of VNS therapy outcome.

Systematic Evaluation of HLA-G 3'Untranslated Region Variants in Locally Advanced, Non-Metastatic Breast Cancer Patients: UTR-1, 2 or UTR-4 are Predictors for Therapy and Disease Outcome.

Despite major improvements in diagnostics and therapy in early as well as in locally advanced breast cancer (LABC), metastatic relapse occurs in about 20% of patients, often explained by early micro-metastatic spread into bone marrow by disseminated tumor cells (DTC). Although neoadjuvant chemotherapy (NACT) has been a successful tool to improve overall survival (OS), there is growing evidence that various environmental factors like the non-classical human leukocyte antigen-G (HLA-G) promotes cancer invasiveness and metastatic progression. HLA-G expression is associated with regulatory elements targeting certain single-nucleotide polymorphisms (SNP) in the HLA-G 3’ untranslated region (UTR), which arrange as haplotypes. Here, we systematically evaluated the impact of HLA-G 3’UTR polymorphisms on disease status, on the presence of DTC, on soluble HLA-G levels, and on therapy and disease outcome in non-metastatic LABC patients. Although haplotype frequencies were similar in patients (n = 142) and controls (n = 204), univariate analysis revealed that the UTR-7 haplotype was related to patients with low tumor burden, whereas UTR-4 was associated with tumor sizes >T1. Furthermore, UTR-4 was associated with the presence of DTC, but UTR-3 and UTR-7 were related to absence of DTC. Additionally, increased levels of soluble HLA-G molecules were found in patients carrying UTR-7. Regarding therapy and disease outcome, univariate and multivariate analysis highlighted UTR-1 or UTR-2 as a prognostic parameter indicative for a beneficial course of disease in terms of complete response towards NACT or progression-free survival (PFS). At variance, UTR-4 was an independent risk factor for a reduced OS besides already known parameters. Taken into account the most common HLA-G 3’UTR haplotypes (UTR-1–UTR-7, UTR-18), deduction of the UTR-1/2/4 haplotypes to specific SNPs revealed that the +3003C variant, unique for UTR-4, seemed to favor a detrimental disease outcome, while the +3187G and +3196G variants, unique for UTR-1 or UTR-2, were prognostic parameters for a beneficial course of disease. In conclusion, these data suggest that the HLA-G 3’UTR variants +3003C, +3187G, and +3196G are promising candidates for the prediction of therapy and disease outcome in LABC patients.

Evaluation of KIR gene expression in patients with recurrent implantation failure

Infertility affects one out of ten reproductive age couples globally, while in Romania one out of six couples is reported to seek infertility counsel. The success of reproduction is highly dependent on maternal-fetal immune interaction. The balance between all activator and inhibitor signals between uterine natural killer (uNK) cells and the trophoblast represents a key factor and may influence embryo implantation. Uterine natural killer cells express surface receptors (KIR) which, after recognition of class I HLA molecules expressed by the trophoblast, may stimulate or inhibit the ability of uNK cells to produce soluble factors and may present a low toxicity required to maintain the embryo and allogeneic fetus. We aimed to prove that the investigation of the genetic background of uNK receptors represented by KIR genotyping may be a useful tool in the assessment of recurrent implantation failure (RIF), may help predict therapy outcome, and clarify disease pathogenesis. This is a prospective study conducted on 96 RIF patients and 80 fertile controls. We aimed to investigate whether the presence or absence of each gene or KIR genotype is correlated to the implantation success or to first-trimester miscarriage. The study results point out that the AA inhibitor genotype is associated with weak results in terms of achieving pregnancy following in vitro fertilization (IVF), and the presence or absence of certain genes is correlated to the success of implantation and to first-trimester miscarriage.

An Introduction to Machine Learning Approaches for Biomedical Research.

Machine learning (ML) approaches are a collection of algorithms that attempt to extract patterns from data and to associate such patterns with discrete classes of samples in the data—e.g., given a series of features describing persons, a ML model predicts whether a person is diseased or healthy, or given features of animals, it predicts weather an animal is treated or control, or whether molecules have the potential to interact or not, etc. ML approaches can also find such patterns in an agnostic manner, i.e., without having information about the classes. Respectively, those methods are referred to as supervised and unsupervised ML. A third type of ML is reinforcement learning, which attempts to find a sequence of actions that contribute to achieving a specific goal. All of these methods are becoming increasingly popular in biomedical research in quite diverse areas including drug design, stratification of patients, medical images analysis, molecular interactions, prediction of therapy outcomes and many more. We describe several supervised and unsupervised ML techniques, and illustrate a series of prototypical examples using state-of-the-art computational approaches. Given the complexity of reinforcement learning, it is not discussed in detail here, instead, interested readers are referred to excellent reviews on that topic. We focus on concepts rather than procedures, as our goal is to attract the attention of researchers in biomedicine toward the plethora of powerful ML methods and their potential to leverage basic and applied research programs.

Bedside determination of microcirculatory oxygen delivery and uptake: a prospective observational clinical study for proof of principle

Assessment of microcirculatory functional capacity is considered to be of prime importance for therapy guidance and outcome prediction in critically ill intensive care patients. Here, we show determination of skin microcirculatory oxygen delivery and consumption rates to be a feasible approach at the patient’s bedside. Real time laser-doppler flowmetry (LDF) and white light spectrophotometry (WLS) were used for assessment of thenar skin microperfusion, regional Hb and postcapillary venous oxygen saturation before and after forearm ischemia. Adapted Fick’s principle equations allowed for calculation of microcirculatory oxygen delivery and uptake. Patient groups with expected different microcirculatory status were compared [control (n = 20), sepsis-1/2 definition criteria identified SIRS (n = 10) and septic shock patients (n = 20), and the latter group further classified according to sepsis-3 definition criteria in sepsis (n = 10) and septic shock (n = 10)], respectively. In otherwise healthy controls, microcirculatory oxygen delivery and uptake approximately doubled after ischemia with maximum values (mDO2max and mVO2max) significantly lower in SIRS or septic patient groups, respectively. Scatter plots of mVO2max and mDO2max values defined a region of unphysiological low values not observed in control but in critically ill patients with the percentage of dots within this region being highest in septic shock patients. LDF and WLS combined with vasoocclusive testing reveals significant differences in microcirculatory oxygen delivery and uptake capacity between control and critically ill patients. As a clinically feasible technique for bedside determination of microcirculatory oxygen delivery and uptake, LDF and WLS combined with vasoocclusive testing holds promise for monitoring of disease progression and/or guidance of therapy at the microcirculatory level to be tested in further clinical trials.ClinicalTrials.gov: NCT01530932.

A priori prediction of local failure in brain metastasis after hypo-fractionated stereotactic radiotherapy using quantitative MRI and machine learning

This study investigated the effectiveness of pre-treatment quantitative MRI and clinical features along with machine learning techniques to predict local failure in patients with brain metastasis treated with hypo-fractionated stereotactic radiation therapy (SRT). The predictive models were developed using the data from 100 patients (141 lesions) and evaluated on an independent test set with data from 20 patients (30 lesions). Quantitative MRI radiomic features were derived from the treatment-planning contrast-enhanced T1w and T2-FLAIR images. A multi-phase feature reduction and selection procedure was applied to construct an optimal quantitative MRI biomarker for predicting therapy outcome. The performance of standard clinical features in therapy outcome prediction was evaluated using a similar procedure. Survival analyses were conducted to compare the long-term outcome of the two patient cohorts (local control/failure) identified based on prediction at pre-treatment, and standard clinical criteria at last patient follow-up after SRT. The developed quantitative MRI biomarker consists of four features with two features quantifying heterogeneity in the edema region, one feature characterizing intra-tumour heterogeneity, and one feature describing tumour morphology. The predictive models with the radiomic and clinical feature sets yielded an AUC of 0.87 and 0.62, respectively on the independent test set. Incorporating radiomic features into the clinical predictive model improved the AUC of the model by up to 16%, relatively. A statistically significant difference was observed in survival of the two patient cohorts identified at pre-treatment using the radiomics-based predictive model, and at post-treatment using the the RANO-BM criteria. Results of this study revealed a good potential for quantitative MRI radiomic features at pre-treatment in predicting local failure in relatively large brain metastases undergoing SRT, and is a step forward towards a precision oncology paradigm for brain metastasis.

Ellipsoid Zone Integrity and Visual Acuity Changes during Diabetic Macular Edema Therapy: A Longitudinal Study.

Purpose Ellipsoid zone (EZ) integrity is identified as a potential biomarker for therapy surveillance and outcome prediction of visual acuity (VA). However, only a few studies report long-term results of over 1 year of clinical and anatomical changes in patients with diabetic macular edema (DME). This study is aimed at describing the long-term VA and anatomical outcomes in spectral domain optical coherence tomography (OCT) (relative ellipsoid zone reflectivity ratio, central macular thickness, and volume) in patients with DME treated with antivascular endothelial growth factor (anti-VEGF) therapy. Furthermore, we studied the correlation between EZ integrity and changes in visual acuity. Methods 71 eyes of 71 patients were included in this retrospective study. Clinical characteristics were reviewed yearly. OCT data were assessed at baseline and after 1, 3, and 5 years. EZ parameters were quantified automatically. OCT parameters and visual outcome were correlated and analyzed in multivariable regression models. Results EZ reflectivity ratio correlated with functional outcome in DME patients from baseline to fifth year at all time points (for all p < 0.05). EZ reflectivity improved the most in the first year of treatment (0.68 to 0.75; p < 0.05) and declined gradually until year 5 of therapy (0.71; compared to baseline p > 0.05). Similarly, best VA was achieved after 1 year (0.40 logarithm of the minimum angle of resolution (logMAR) to 0.28 logMAR; p < 0.001) and declined gradually until year 5. Final VA in year 5 was comparable to baseline (0.45 logMAR, compared to baseline p > 0.05). Together with baseline VA, baseline EZ parameters did predict VA outcome after 1 year (p < 0.05). Concordantly, VA and EZ parameters from year 1 were associated with VA outcome in year 2. Conclusion This study described the long-term course of EZ changes during anti-VEGF treatment in DME patients. In addition, our results underlined the potential of EZ parameters as novel OCT biomarkers for prediction of VA outcomes during therapy.

Multiple Myeloma Relapse Is Associated with Increased NFκB Pathway Activity and Upregulation of the Pro-Survival BCL-2 Protein BFL-1

Simple SummaryMultiple myeloma (MM) is a blood cancer that is still incurable because patients become insensitive to available treatments. The cancerous cells in MM misuse a signaling route, called the NFκB pathway, to make MM more difficult to treat. In our study, we used a new method to measure NFκB pathway activity in cancer cells of MM patients at different stages of disease. We found that NFκB pathway activity remains unchanged during disease development, is independent of the life expectancy of MM patients, and does not predict how well the cancer cells will respond to treatment. However, the cancer cells that survive after treatment of MM patients do show higher NFκB pathway activity. In a subgroup of these patients, the cancer cells also showed higher BFL-1 gene expression. BFL-1 can enhance cancer cell survival after treatment and may therefore be a potential new candidate to target in these patients.Multiple myeloma (MM) is a hematological malignancy that is still considered incurable due to the development of therapy resistance and subsequent relapse of disease. MM plasma cells (PC) use NFκB signaling to stimulate cell growth and disease progression, and for protection against therapy-induced apoptosis. Amongst its diverse array of target genes, NFκB regulates the expression of pro-survival BCL-2 proteins BCL-XL, BFL-1, and BCL-2. A possible role for BFL-1 in MM is controversial, since BFL-1, encoded by BCL2A1, is downregulated when mature B cells differentiate into antibody-secreting PC. NFκB signaling can be activated by many factors in the bone marrow microenvironment and/or induced by genetic lesions in MM PC. We used the novel signal transduction pathway activity (STA) computational model to quantify the functional NFκB pathway output in primary MM PC from diverse patient subsets at multiple stages of disease. We found that NFκB pathway activity is not altered during disease development, is irrespective of patient prognosis, and does not predict therapy outcome. However, disease relapse after treatment resulted in increased NFκB pathway activity in surviving MM PC, which correlated with increased BCL2A1 expression in a subset of patients. This suggests that BFL-1 upregulation, in addition to BCL-XL and BCL-2, may render MM PC resistant to therapy-induced apoptosis, and that BFL-1 targeting could provide a new approach to reduce therapy resistance in a subset of relapsed/refractory MM patients.

The future of personalized medicine in psoriasis

AbstractPsoriasis is a complex, heterogeneous condition arising from a combination of genetic, environmental, and immunological factors. Although many treatments exist for psoriasis, the heterogenous nature of the patients' response to treatment makes it difficult to predict whether a treatment will be effective for every individual patient or aspect of psoriasis. Precision medicine, otherwise known as personalized medicine, is a novel approach to improve clinical outcome in psoriasis, curb the risk of developing adverse events, and predict which treatment options will be effective for each patient. Here, we discuss several aspects of precision medicine related to psoriasis, including application of ‐omics technologies and clinical parameters to identify disease biomarkers and predict therapy outcomes; identification of predictors of psoriatic arthritis and other comorbidities; implementation of a personalized care model using mobile apps and telemedicine; and the development of machine learning algorithms that provide automated recommendations for providers and patients. These topics form the basis of the future of personalized medicine in psoriatic disease.

The influence of therapy alliance and treatment motivation in patients with Cluster B personality disorders on incidents in forensic hospitals

Purpose Nonspecific factors such as therapy alliance and treatment motivation have been shown to be predictive of therapy outcome. However, research investigating these factors among patients with personality disorders, or studies in the context of mandated treatment showed mixed results. A new theory furthermore speculates there may be differences between early formed therapeutic alliance (trait-like) versus alliance formed on the longer term (state-like). This paper aims to investigate the effects of therapy alliance and treatment motivation in 103 Dutch male forensic psychiatric patients with Cluster B personality disorders. Design/methodology/approach The authors used incidents as a measure of treatment outcome. They studied the effect of nonspecific factors on incidents in two phases, namely, 0 – 18 months and 18 – 36 months, along with known predictors of incidents (age, Historical items of the HCR-20 and psychopathy) as covariates. Findings Regression models predicting incidents in the first 18 months of treatment were nonsignificant. Incidents in the second 18 months were significantly predicted by models including alliance and motivation measured at the start of treatment, but not measures at 18 months and covariates. Predictors, except for age, were all nonsignificant. Practical implications These findings lend tentative support for the trait-like vs state-like theory of change through nonspecific factors. However, it may also be that other factors are more important in predicting therapy outcome in forensic psychiatric patients with Cluster B personality disorders. Originality/value The current study represents the first effort to study the effects of non-specific factors on therapeutic discourse in hospitalized offenders with Cluster B personality disorders.

Evaluation of 68Ga-PSMA-11 PET-MRI in Patients with Advanced Prostate Cancer Receiving 177Lu-PSMA-617 Therapy: A Radiomics Analysis.

Simple SummaryPSMA Therapy has recently become an additional therapeutic option in advanced prostate cancer. In the present study, the predictive and prognostic value of radiomic features from pretherapeutic PSMA PET-MRI are analyzed. Twenty-one patients with advanced prostate cancer underwent PSMA-therapy, including pretherapeutic PSMA PET-MRI. Radiomic features from PET- and MRI-sequences were extracted and processed to select the features differentiating responders and non-responders. Out of ten independent radiomic features differentiating between these two groups, the feature interquartile range from the T2 weighted images revealed the highest accuracy. PSA response and higher T2 interquartile range values might have impact on survival. This proof-of-concept study applies radiomic analysis to pretherapeutic PSMA PET-MRI before PSMA therapy, providing new parameters with potential predictive and prognostic value.177Lutetium PSMA-617 (Lu-PSMA) therapy in patients with metastatic castration resistant prostate cancer (mCRPC) has gained visibility through the ongoing phase III trial. The data on prediction of therapy outcome and survival out of pretherapeutic imaging parameters is still sparse. In this study, the predictive and prognostic value of radiomic features from 68Ga-PSMA-11 PET-MRI are analyzed. In total, 21 patients with mCRPC underwent 68Ga-PSMA-11 PET-MRI before Lu-PSMA therapy. The PET-positive tumor volume was defined and transferred to whole-body T2-, T1- and contrast-enhanced T1-weighted MRI-sequences. The radiomic features from PET and MRI sequences were extracted by using a freely available software package. For selecting features that allow differentiation of biochemical response (PSA decrease > 50%), a stepwise dimension reduction was performed. Logistic regression models were fitted, and selected features were tested for their prognostic value (overall survival) in all patients. Eight patients achieved biochemical response after Lu-PSMA therapy. Ten independent radiomic features differentiated well between responders and non-responders. The logistic regression model, including the feature interquartile range from T2-weighted images, revealed the highest accuracy (AUC = 0.83) for the prediction of biochemical response after Lu-PSMA therapy. Within the final model, patients with a biochemical response (p = 0.003) and higher T2 interquartile range values in pre-therapeutic imaging (p = 0.038) survived significantly longer. This proof-of-concept study provides first evidence on a potential predictive and prognostic value of radiomic analysis of pretherapeutic 68Ga-PSMA-11 PET-MRI before Lu-PSMA therapy.

A modified in vitro clot lysis assay predicts outcomes and safety in acute ischemic stroke patients undergoing intravenous thrombolysis

The outcome of intravenous thrombolysis using recombinant tissue plasminogen activator (rt-PA) is only favorable in ≈ 40% of acute ischemic stroke (AIS) patients. Moreover, in ≈ 6–8% of cases, intracerebral hemorrhage (ICH) develops. We tested whether a modification of clot lysis assay (CLA), might predict therapy outcomes and safety. In this prospective observational study, blood samples of 231 AIS patients, all receiving intravenous rt-PA, were taken before thrombolysis. Cell-free DNA (cfDNA), CLA and CLA supplemented with cfDNA and histones (mCLA) were determined from the blood samples. Stroke severity was determined by NIHSS on admission. ICH was classified according to ECASSII. Short- and long-term outcomes were defined at 7 and 90 days post-event according to ΔNIHSS and by the modified Rankin Scale, respectively. Stroke severity demonstrated a step-wise positive association with cfDNA levels, while a negative association was found with the time to reach 50% lysis (50%CLT) parameter of CLA and mCLA. ROC analysis showed improved diagnostic performance of the mCLA. Logistic regression analysis proved that 50%CLT is a predictor of short-term therapy failure, while the AUC parameter predicts ICH occurrence. A modified CLA, supplemented with cfDNA and histones, might be a promising tool to predict short-term AIS outcomes and post-lysis ICH.

A mass transfer model for computational prediction of proliferation and therapy outcome of non-Hodgkin lymphoma

Mathematical modeling constitutes an emerging area of oncological research aiming to predict spatial and temporal evolution of tumors, by describing many different phenomena, which occur at different scales. Among these, modeling at the macroscopic scale has an interesting potential of application, when applied in a framework where actual diagnostic imaging is used to identify the metabolic tumor volume undergoing proliferation.Diffuse large B-cell lymphoma (DLBCL) is a cancer of B cells, a type of lymphocyte that is responsible for producing antibodies. The most common form of Non-Hodgkin lymphoma among adults, DLBCL can arise in any part of the body and may play a very aggressive malignancy. This paper aims to enforce a mass transfer modeling approach in order to gain deeper insight into the dynamics of tumor growth at the tissue scale and to develop a predictive, quantitative method for each patient at hand. A cohort of 18 patients has been successfully enrolled to validate the model. Results confirm that tumor proliferation, at the macroscopic scale, scores many nonlinear features, and show that the proposed model could be used by oncologists as a decision support tool towards personalized treatment optimization of solid tumors.

Therapy response prediction in major depressive disorder: current and novel genomic markers influencing pharmacokinetics and pharmacodynamics.

Major depressive disorderis connected withhigh rates of functional disability and mortality. About athird of the patients are at risk of therapy failure. Several pharmacogenetic markersespecially located in CYP450 genes such as CYP2D6 or CYP2C19 are of relevance for therapy outcome prediction in major depressive disorder but a further optimization of predictive tools is warranted. The article summarizes the current knowledge on pharmacogenetic variants, therapy effects and side effects of important antidepressive therapeutics, and sheds light on new methodological approaches for therapy response estimation based on genetic markers with relevance for pharmacokinetics, pharmacodynamics and disease pathology identified in genome-wide association studyanalyses, highlighting polygenic risk score analysis as a tool for further optimization of individualized therapy outcome prediction.

Predicting language treatment response in bilingual aphasia using neural network-based patient models

Predicting language therapy outcomes in bilinguals with aphasia (BWA) remains challenging due to the multiple pre- and poststroke factors that determine the deficits and recovery of their two languages. Computational models that simulate language impairment and treatment outcomes in BWA can help predict therapy response and identify the optimal language for treatment. Here we used the BiLex computational model to simulate the behavioral profile of language deficits and treatment response of a retrospective sample of 13 Spanish-English BWA who received therapy in one of their languages. Specifically, we simulated their prestroke naming ability and poststroke naming impairment in each language, and their treatment response in the treated and the untreated language. BiLex predicted treatment effects accurately and robustly in the treated language and captured different degrees of cross-language generalization in the untreated language in BWA. Our cross-validation approach further demonstrated that BiLex generalizes to predict treatment response for patients whose data were not used in model training. These findings support the potential of BiLex to predict therapy outcomes for BWA and suggest that computational modeling may be helpful to guide individually tailored rehabilitation plans for this population.

Combining radiomic phenotypes of non-small cell lung cancer with liquid biopsy data may improve prediction of response to EGFR inhibitors

Among non-small cell lung cancer (NSCLC) patients with therapeutically targetable tumor mutations in epidermal growth factor receptor (EGFR), not all patients respond to targeted therapy. Combining circulating-tumor DNA (ctDNA), clinical variables, and radiomic phenotypes may improve prediction of EGFR-targeted therapy outcomes for NSCLC. This single-center retrospective study included 40 EGFR-mutant advanced NSCLC patients treated with EGFR-targeted therapy. ctDNA data included number of mutations and detection of EGFR T790M. Clinical data included age, smoking status, and ECOG performance status. Baseline chest CT scans were analyzed to extract 429 radiomic features from each primary tumor. Unsupervised hierarchical clustering was used to group tumors into phenotypes. Kaplan–Meier (K–M) curves and Cox proportional hazards regression were modeled for progression-free survival (PFS) and overall survival (OS). Likelihood ratio test (LRT) was used to compare fit between models. Among 40 patients (73% women, median age 62 years), consensus clustering identified two radiomic phenotypes. For PFS, the model combining radiomic phenotypes with ctDNA and clinical variables had c-statistic of 0.77 and a better fit (LRT p = 0.01) than the model with clinical and ctDNA variables alone with a c-statistic of 0.73. For OS, adding radiomic phenotypes resulted in c-statistic of 0.83 versus 0.80 when using clinical and ctDNA variables (LRT p = 0.08). Both models showed separation of K–M curves dichotomized by median prognostic score (p < 0.005). Combining radiomic phenotypes, ctDNA, and clinical variables may enhance precision oncology approaches to managing advanced non-small cell lung cancer with EGFR mutations.

Predicting optimal treatment outcomes using the Personalized Advantage Index for patients with persistent somatic symptoms

ABSTRACT Objective: Because individual patients with persistent somatic symptoms (PSS) respond differently to treatments, a better understanding of the factors that predict therapy outcomes are of high importance. Aggregating a wide selection of information into the treatment-decision process is a challenge for clinicians. Using the Personalized Advantage Index (PAI) this study aims to deal with this. Methods: Data from a multicentre RCT comparing CBT (N = 128) versus CBT enriched with emotion regulation training (ENCERT) (N = 126) for patients diagnosed with somatic symptom disorder were used to identify based on two machine learning approaches predictors of therapy outcomes. The identified predictors were used to calculate the PAI. Results: Five treatment unspecific predictors (pre-treatment somatic symptom severity, depression, symptom disability, health-related quality of life, age) and five treatment specific moderators (global functioning, early childhood traumatic events, gender, health anxiety, emotion regulation skills) were identified. Individuals assigned to their PAI-indicated optimal treatment had significantly lower somatic symptom severity at the end of therapy compared to those randomised to their non-optimal condition. Conclusion: Allowing patients to choose a personalised treatment seems to be meaningful. This could help to improve outcomes for PSS and reduce its high costs to the health care system.

Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysis

No tools are currently available to predict whether a patient suffering from major depressive disorder (MDD) will respond to a certain treatment. Machine learning analysis of magnetic resonance imaging (MRI) data has shown potential in predicting response for individual patients, which may enable personalized treatment decisions and increase treatment efficacy. Here, we evaluated the accuracy of MRI-guided response prediction in MDD. We conducted a systematic review and meta-analysis of all studies using MRI to predict single-subject response to antidepressant treatment in patients with MDD. Classification performance was calculated using a bivariate model and expressed as area under the curve, sensitivity, and specificity. In addition, we analyzed differences in classification performance between different interventions and MRI modalities. Meta-analysis of 22 samples including 957 patients showed an overall area under the bivariate summary receiver operating curve of 0.84 (95% CI 0.81–0.87), sensitivity of 77% (95% CI 71–82), and specificity of 79% (95% CI 73–84). Although classification performance was higher for electroconvulsive therapy outcome prediction (n = 285, 80% sensitivity, 83% specificity) than medication outcome prediction (n = 283, 75% sensitivity, 72% specificity), there was no significant difference in classification performance between treatments or MRI modalities. Prediction of treatment response using machine learning analysis of MRI data is promising but should not yet be implemented into clinical practice. Future studies with more generalizable samples and external validation are needed to establish the potential of MRI to realize individualized patient care in MDD.

Single-Domain Antibody Nuclear Imaging Allows Noninvasive Quantification of LAG-3 Expression by Tumor-Infiltrating Leukocytes and Predicts Response of Immune Checkpoint Blockade

Recent advances in the field of immune-oncology led to the discovery of next-generation immune checkpoints (ICPs). Lymphocyte activation gene-3 (LAG-3), being the most widely studied among them, is being explored as a target for the treatment of cancer patients. Several antagonistic anti-LAG-3 antibodies are being developed and are prime candidates for clinical application. Furthermore, validated therapies targeting cytotoxic T-lymphocyte–associated protein-4, programmed cell-death protein-1, or programmed cell-death ligand-1 showed that only subsets of patients respond. This finding highlights the need for better tools for patient selection and monitoring. The potential of molecular imaging to detect ICPs noninvasively in cancer is supported by several preclinical and clinical studies. Here, we report on a single-domain antibody to evaluate whole-body LAG-3 expression in various syngeneic mouse cancer models using nuclear imaging. <b>Methods:</b> SPECT/CT scans of tumor-bearing mice were performed 1 h after injection with radiolabeled single-domain antibody. Organs and tumors of mice were isolated and evaluated for the presence of the radiolabeled tracer and LAG-3–expressing immune cells using a γ-counter and flow cytometry respectively. PD-1/LAG-3–blocking antibodies were injected in MC38-bearing mice. <b>Results:</b> The radiolabeled single-domain antibody detected LAG-3 expression on tumor-infiltrating lymphocytes (TILs) as soon as 1 h after injection in MC38, MO4, and TC-1 cancer models. The single-domain antibody tracer visualized a compensatory upregulation of LAG-3 on TILs in MC38 tumors of mice treated with PD-1–blocking antibodies. When PD-1 blockade was combined with LAG-3 blockade, a synergistic effect on tumor growth delay was observed. <b>Conclusion:</b> These findings consolidate LAG-3 as a next-generation ICP and support the use of single-domain antibodies as tools to noninvasively monitor the dynamic evolution of LAG-3 expression by TILs, which could be exploited to predict therapy outcome.

Dialectical Behavior Therapy in the Treatment of Borderline Personality Disorder: Effectiveness of Therapy, Factors Predicting Therapy Outcomes and Drop-out

Borderline personality disorder (BPD) is a mental health problem characterized by rapid mood changes, impaired interpersonal relationships, anger, impulsivity, and self-harming behavior. Since BPD threatens the psychological and physical health as well as the functionality of the individual, its treatment becomes increasingly important. Although dialectical behavioral therapy (DBT) is one of the most effective methods for BPD, it is seen that the factors responsible for change in therapy are mostly not defined and drop-out is not adequately addressed in the outcome studies. In this article, it is aimed to examine the change and how the change occurs on the basis of DBT in BPD by investigating the factors that predict the therapy outcomes. For the purposes, firstly, DBT will be mentioned briefly; the effectiveness of DBT and the dropout rates will be reviewed. Then, the “change” in therapy will be discussed. Finally, the factors responsible for change will be summarized with research findings on that predicting therapy outcomes and drop-out. In the discussion section, along with the findings summarized, the “difficult to treat” bias towards BPD will be evaluated. Finally, suggestions for future studies will be presented.