Heterogeneity In Treatment Response Research Articles

Heterogeneous Treatment Effects after Inspiratory Muscle Training during Recovery from Postacute COVID-19 Syndrome.

The objective of this study is to investigate whether heterogeneous treatment effects occur for changes in inspiratory muscle strength, perceived dyspnea, and health-related quality of life after 8 wk of unsupervised home-based inspiratory muscle training (IMT) in adults with postacute coronavirus disease 2019 (COVID-19) syndrome. In total, 147 adults with self-reported prior COVID-19 either completed an 8-wk home-based IMT intervention ( n = 111, 92 females, 48 ± 11 yr, 9.3 ± 3.6 months postacute COVID-19 infection) or acted as "usual care" wait list controls ( n = 36, 34 females, 49 ± 12 yr, 9.4 ± 3.2 months postacute COVID-19 infection). Applying a Bayesian framework, we found clear evidence of heterogeneity of treatment response for inspiratory muscle strength: the estimated difference between standard deviations (SD) of the IMT and control groups was 22.8 cm H 2 O (75% credible interval (CrI), 4.7-37.7) for changes in maximal inspiratory pressure (MIP) and 86.8 pressure time units (75% CrI, 55.7-116.7) for sustained MIP (SMIP). Conversely, there were minimal differences in the SD between the IMT and the control group for changes in perceived dyspnea and health-related quality of life, providing no evidence of heterogeneous treatment effects. Higher cumulative power during the IMT intervention was related to changes in MIP ( β = 10.9 cm H 2 O (95% CrI, 5.3-16.8) per 1 SD) and SMIP ( β = 63.7 (32.2-95.3) pressure time units per 1 SD), clearly indicating an IMT dose response for changes in inspiratory muscle strength. Older age (>50 yr), a longer time postacute COVID-19 (>3 months), and greater severity of dyspnea at baseline were also associated with smaller improvements in inspiratory muscle strength. Heterogeneous individual responses occurred after an 8-wk home-based IMT program in people with postacute COVID-19 syndrome. Consistent with standard exercise theory, larger improvements in inspiratory muscle strength are strongly related to a greater cumulative dose of IMT.

Machine learning-based causal models for predicting the response of individual patients to dexamethasone treatment as prophylactic antiemetic

Risk-based strategies are widely used for decision making in the prophylaxis of postoperative nausea and vomiting (PONV), a major complication of general anesthesia. However, whether risk is associated with individual treatment effect remains uncertain. Here, we used machine learning-based algorithms for estimating the conditional average treatment effect (CATE) (double machine learning [DML], doubly robust [DR] learner, forest DML, and generalized random forest) to predict the treatment response heterogeneity of dexamethasone, the first choice for prophylactic antiemetics. Electronic health record data of 2026 adult patients who underwent general anesthesia from January to June 2020 were analyzed. The results indicated that only a small subset of patients respond to dexamethasone treatment, and many patients may be non-responders. Estimated CATE did not correlate with predicted risk, suggesting that risk may not be associated with individual treatment responses. The current study suggests that predicting treatment responders by CATE models may be more appropriate for clinical decision making than conventional risk-based strategy.

Ketamine Treatment for Alcohol Use Disorder: A Systematic Review.

Alcohol use disorder (AUD) is a chronic, recurrent condition that demonstrates significant heterogeneity in treatment response to first-line agents. Ketamine may have a therapeutic role in substance use disorders; however, research on this topic is limited. The objective of this systematic review is to qualitatively synthesize the current evidence of ketamine treatment for alcohol use disorder and evaluate its efficacy. A systematic review of Medline, PsycINFO, CINAHL, the Cochrane Library, and Google Scholar was performed to identify completed human studies in English or Spanish (from inception to July 2022) that assess the effectiveness of ketamine therapy for alcohol use disorder. This review was registered on the Open Science Framework. Data were descriptively summarized and presented in tables and tested via narrative synthesis methodology. The risk of bias was measured with Cochrane Collaboration tools and a case series quality assessment tool. A total of 11 studies with 854 adult patients in three different countries (the USA, the UK, and Russia) were analyzed. Sample sizes ranged from 5 to 211 people. Seven studies included patients with alcohol use disorder, one study focused on heavy drinkers, and three studies elaborated extensively on alcohol withdrawal. The overall proportion of patients achieving abstinence and reduced consumption was most favorable in people receiving combination ketamine and psychotherapy treatment. The results were mixed with respect to relapse, craving, and withdrawal. Ketamine may be an effective therapeutic modality for people with alcohol use disorders who fail to respond to FDA-approved first-line agents. More robust clinical trials are necessary to provide a more accurate assessment of efficacy, safety profile, and dosing strategies for ketamine utilization in alcohol use disorder.

Clinical Characteristics and Survival Analysis of Patients with Chronic Myelomonocytic Leukemia

To investigate the clinical characteristics, prognostic factors and efficacy of hypomethylating agent (HMA) in patients with chronic myelomonocytic leukemia (CMML). The clinical data of 37 newly diagnosed patients with CMML was analyzed retrospectively, and their clinical characteristics and the efficacy of HMA were summarized. Kaplan-Meier and Log-rank test were used for univariate survival analysis, and Cox proportional hazards regression model was used for multivariate analysis. The median age at diagnosis was 67 years old. Their common manifestations included fatigue, bleeding, abnormal blood routine and fever. Most patients had splenomegaly. According to FAB classification, there were 6 cases of myelodysplastic CMML and 31 cases of myeloproliferative CMML, while according to WHO classification, 8 patients belonged to CMML-0, 9 patients to CMML-1 and 20 patients to CMML-2. At the time of diagnosis, the median white blood cell count was 32.84×109/L, median hemoglobin (Hb) was 101 g/L, median platelet count was 65×109/L, median absolute monocyte count was 9.53×109//L, median absolute neutrophil count (ANC) was 11.29×109//L and median lactate dehydrogenase (LDH) was 374 U/L. Cytogenetic abnormalities were found in 4 cases among the 31 patients who underwent karyotype analysis or fluorescence in situ hybridization detection. There were 12 patients who had analyzable results and gene mutations were identified in 11 cases, including ASXL1, NRAS, TET2, SRSF2 and RUNX1. Among the 6 patients who were treated with HMA and could be evaluated for efficacy, 2 patients achieved complete remission, 1 patient achieved partial remission and 2 patients achieved clinical benefit. Compared with the non-HMA treatment group, overall survival (OS) time was not significantly prolonged in the HMA treatment group. Univariate analysis showed that Hb<100 g/L, ANC≥12×109/L, LDH≥250 U/L and peripheral blood (PB) blasts ≥5% were significantly associated with poor OS, while WHO classification CMML-2, Hb<100 g/L, ANC≥12×109/L, LDH≥250 U/L and PB blasts≥5% were significantly associated with poor leukemia-free survival (LFS) (P<0.05). Multivariate analysis showed that ANC≥12×109/L and PB blasts≥5% were significantly associated with poor OS and LFS (P<0.05). CMML has high heterogeneity in clinical characteristics, genetic changes, prognosis and treatment response. HMA can not significantly improve the survival of CMML patients. ANC≥12×109/L and PB blasts≥5% are independent prognostic factors of OS and LFS in patients with CMML.

Association Between Nociplastic Pain and Pain Severity and Impact in Women With Chronic Pelvic Pain

Exploring the relationship between nociplastic pain and the severity and impact of pelvic pain symptoms could lend insight into the heterogeneous symptom presentation and treatment response that complicates management of chronic pelvic pain. In this prospective cross-sectional study, we sought to evaluate relationships between degree of nociplastic pain, measured by the Fibromyalgia (FM) Survey Score, and multiple aspects of the chronic pelvic pain (CPP) experience, including severity, frequency, tenderness during pelvic myofascial exam, interference with daily life, and high-impact pain. The study included 303 women who presented to a tertiary referral clinic for chronic pelvic pain and endometriosis. Multiple measures of pelvic pain, including pain severity, frequency, interference, pelvic myofascial pain, and high-impact pain were examined in General Linear Models with FM Survey Score as the primary predictor of interest in models controlling for endometriosis, surgical history, use of opioids, body mass index, and patient age. Higher level of nociplastic pain was associated with greater pelvic pain severity, frequency, interference, and pelvic myofascial pain (all P < .05). For all models, degree of nociplastic pain was more strongly associated with pain outcomes than the presence of endometriosis, and use of opioids was the only stronger predictor of worse pain outcomes. The likelihood of high impact pain increased 7% for each additional point on the FM Survey Score. Degree of nociplastic pain was robustly associated with severity, frequency, and impact of pelvic pain, and was independent of the presence of endometriosis, history of surgical procedures for pelvic pain, age, and BMI.Trial registration: not applicable PerspectiveThis article evaluates the impact of nociplastic pain on symptoms and functional status in chronic pelvic pain. These findings raise the possibility that a simple screening tool for nociplastic pain might provide clinically actionable information without the need for deep neurobiological phenotyping and may inform development of personalized management strategies.

Development of prediction models for one-year brain tumour survival using machine learning: a comparison of accuracy and interpretability

Background and ObjectivePrediction of survival in patients diagnosed with a brain tumour is challenging because of heterogeneous tumour behaviours and treatment response. Advances in machine learning have led to the development of clinical prognostic models, but due to the lack of model interpretability, integration into clinical practice is almost non-existent. In this retrospective study, we compare five classification models with varying degrees of interpretability for the prediction of brain tumour survival greater than one year following diagnosis. Methods1028 patients aged ≥16 years with a brain tumour diagnosis between April 2012 and April 2020 were included in our study. Three intrinsically interpretable ‘glass box’ classifiers (Bayesian Rule Lists [BRL], Explainable Boosting Machine [EBM], and Logistic Regression [LR]), and two ‘black box’ classifiers (Random Forest [RF] and Support Vector Machine [SVM]) were trained on electronic patients records for the prediction of one-year survival. All models were evaluated using balanced accuracy (BAC), F1-score, sensitivity, specificity, and receiver operating characteristics. Black box model interpretability and misclassified predictions were quantified using SHapley Additive exPlanations (SHAP) values and model feature importance was evaluated by clinical experts. ResultsThe RF model achieved the highest BAC of 78.9%, closely followed by SVM (77.7%), LR (77.5%) and EBM (77.1%). Across all models, age, diagnosis (tumour type), functional features, and first treatment were top contributors to the prediction of one year survival. We used EBM and SHAP to explain model misclassifications and investigated the role of feature interactions in prognosis. ConclusionInterpretable models are a natural choice for the domain of predictive medicine. Intrinsically interpretable models, such as EBMs, may provide an advantage over traditional clinical assessment of brain tumour prognosis by weighting potential risk factors and their interactions that may be unknown to clinicians. An agreement between model predictions and clinical knowledge is essential for establishing trust in the models decision making process, as well as trust that the model will make accurate predictions when applied to new data.

Deep phenotyping towards precision psychiatry of first-episode depression — the Brain Drugs-Depression cohort

BackgroundMajor Depressive Disorder (MDD) is a heterogenous brain disorder, with potentially multiple psychosocial and biological disease mechanisms. This is also a plausible explanation for why patients do not respond equally well to treatment with first- or second-line antidepressants, i.e., one-third to one-half of patients do not remit in response to first- or second-line treatment.To map MDD heterogeneity and markers of treatment response to enable a precision medicine approach, we will acquire several possible predictive markers across several domains, e.g., psychosocial, biochemical, and neuroimaging.MethodsAll patients are examined before receiving a standardised treatment package for adults aged 18–65 with first-episode depression in six public outpatient clinics in the Capital Region of Denmark. From this population, we will recruit a cohort of 800 patients for whom we will acquire clinical, cognitive, psychometric, and biological data. A subgroup (subcohort I, n = 600) will additionally provide neuroimaging data, i.e., Magnetic Resonance Imaging, and Electroencephalogram, and a subgroup of patients from subcohort I unmedicated at inclusion (subcohort II, n = 60) will also undergo a brain Positron Emission Tomography with the [11C]-UCB-J tracer binding to the presynaptic glycoprotein-SV2A. Subcohort allocation is based on eligibility and willingness to participate. The treatment package typically lasts six months.Depression severity is assessed with the Quick Inventory of Depressive Symptomatology (QIDS) at baseline, and 6, 12 and 18 months after treatment initiation.The primary outcome is remission (QIDS ≤ 5) and clinical improvement (≥ 50% reduction in QIDS) after 6 months. Secondary endpoints include remission at 12 and 18 months and %-change in QIDS, 10-item Symptom Checklist, 5-item WHO Well-Being Index, and modified Disability Scale from baseline through follow-up. We also assess psychotherapy and medication side-effects.We will use machine learning to determine a combination of characteristics that best predict treatment outcomes and statistical models to investigate the association between individual measures and clinical outcomes.We will assess associations between patient characteristics, treatment choices, and clinical outcomes using path analysis, enabling us to estimate the effect of treatment choices and timing on the clinical outcome.DiscussionThe BrainDrugs-Depression study is a real-world deep-phenotyping clinical cohort study of first-episode MDD patients.Trial RegistrationRegistered at clinicaltrials.gov November 15th, 2022 (NCT05616559).

Risk Stratification for Oropharyngeal Squamous Cell Carcinoma Using Texture Analysis on CT - A Step Beyond HPV Status.

Background and Purpose: Human papillomavirus-associated oropharyngeal squamous cell carcinoma (OPSCC) is increasingly prevalent. Despite the overall more favorable outcome, the observed heterogeneous treatment response within this patient group highlights the need for additional means to prognosticate and guide clinical decision-making. Promising prediction models using radiomics from primary OPSCC have been derived. However, no model/s using metastatic lymphadenopathy exist to allow prognostication in those instances when the primary tumor is not seen. The aim of our study was to evaluate whether radiomics using metastatic lymphadenopathy allows for the development of a useful risk assessment model comparable to the primary tumor and whether additional knowledge of the HPV status further improves its prognostic efficacy. Materials and Methods: 80 consecutive patients diagnosed with stage III-IV OPSCC between February 2009 and October 2015, known human papillomavirus status, and pre-treatment CT images were retrospectively identified. Manual segmentation of primary tumor and metastatic lymphadenopathy was performed and the extracted texture features were used to develop multivariate assessment models to prognosticate treatment response. Results: Texture analysis of either the primary or metastatic lymphadenopathy from pre-treatment enhanced CT images can be used to develop models for the stratification of treatment outcomes in OPSCC patients. AUCs range from .78 to .85 for the various OPSCC groups tested, indicating high predictive capability of the models. Conclusions: This preliminary study can form the basis multi-centre trial that may help optimize treatment and improve quality of life in patients with OPSCC in the era of personalized medicine.

Comprehensive profiling of EGFR mutation subtypes reveals genomic-clinical associations in non-small-cell lung cancer patients on first-generation EGFR inhibitors

Common sensitizing mutations in epidermal growth factor receptor (cEGFR), including exon 19 deletions (19-Del) and exon 21 L858R substitution, are associated with high sensitivity to EGFR-TKIs in NSCLC patients. The treatment for NSCLC patients with uncommon EGFR (uEGFR) mutations remains a subject of debate due to heterogeneity in treatment responses. In this manuscript, the targeted next-generation sequencing (NGS) data of a large cohort of EGFR-mutated NSCLC patients was assessed to elucidate genomic profiles of tumors carrying cEGFR or uEGFR mutations. The results showed that NSCLC patients with uEGFR mutations were more likely to harbor co-occurring genetic alterations in the Hippo pathway and a higher TMB compared with cEGFR-positive patients. Smoking-related mutations were found to significantly enriched in uEGFR-positive patients. Subgroup analyses were performed to identify potential prognostic biomarkers in patients harboring various EGFR subtype mutations. L858R-positive patients with co-existing ARID2 mutations had shorter progression-free survival (PFS) than those who were L858R- or 19-Del-positive but ARID2-negative (median: 2.3 vs. 12.0 vs. 8.0 months, P = 0.038). Furthermore, mutational profiles, such as top frequently mutated genes and mutational signatures of patients with various EGFR subtype mutations were significantly different. Our study analyzed the mutational landscape of NSCLC patients harboring cEGFR and uEGFR mutations, revealing specific genomic characteristics associated with uEGFR mutations that might explain the poor prognosis of first-generation EGFR-TKIs.

PRSet: Pathway-based polygenic risk score analyses and software.

Polygenic risk scores (PRSs) have been among the leading advances in biomedicine in recent years. As a proxy of genetic liability, PRSs are utilised across multiple fields and applications. While numerous statistical and machine learning methods have been developed to optimise their predictive accuracy, these typically distil genetic liability to a single number based on aggregation of an individual's genome-wide risk alleles. This results in a key loss of information about an individual's genetic profile, which could be critical given the functional sub-structure of the genome and the heterogeneity of complex disease. In this manuscript, we introduce a 'pathway polygenic' paradigm of disease risk, in which multiple genetic liabilities underlie complex diseases, rather than a single genome-wide liability. We describe a method and accompanying software, PRSet, for computing and analysing pathway-based PRSs, in which polygenic scores are calculated across genomic pathways for each individual. We evaluate the potential of pathway PRSs in two distinct ways, creating two major sections: (1) In the first section, we benchmark PRSet as a pathway enrichment tool, evaluating its capacity to capture GWAS signal in pathways. We find that for target sample sizes of >10,000 individuals, pathway PRSs have similar power for evaluating pathway enrichment as leading methods MAGMA and LD score regression, with the distinct advantage of providing individual-level estimates of genetic liability for each pathway -opening up a range of pathway-based PRS applications, (2) In the second section, we evaluate the performance of pathway PRSs for disease stratification. We show that using a supervised disease stratification approach, pathway PRSs (computed by PRSet) outperform two standard genome-wide PRSs (computed by C+T and lassosum) for classifying disease subtypes in 20 of 21 scenarios tested. As the definition and functional annotation of pathways becomes increasingly refined, we expect pathway PRSs to offer key insights into the heterogeneity of complex disease and treatment response, to generate biologically tractable therapeutic targets from polygenic signal, and, ultimately, to provide a powerful path to precision medicine.

Acoustofluidic assembly of primary tumor-derived organotypic cell clusters for rapid evaluation of cancer immunotherapy

Cancer immunotherapy shows promising potential for treating breast cancer. While patients may have heterogeneous treatment responses for adjuvant therapy, it is challenging to predict an individual patient’s response to cancer immunotherapy. Here, we report primary tumor-derived organotypic cell clusters (POCCs) for rapid and reliable evaluation of cancer immunotherapy. By using a label-free, contactless, and highly biocompatible acoustofluidic method, hundreds of cell clusters could be assembled from patient primary breast tumor dissociation within 2 min. Through the incorporation of time-lapse living cell imaging, the POCCs could faithfully recapitulate the cancer-immune interaction dynamics as well as their response to checkpoint inhibitors. Superior to current tumor organoids that usually take more than two weeks to develop, the POCCs can be established and used for evaluation of cancer immunotherapy within 12 h. The POCCs can preserve the cell components from the primary tumor due to the short culture time. Moreover, the POCCs can be assembled with uniform fabricate size and cell composition and served as an open platform for manipulating cell composition and ratio under controlled treatment conditions with a short turnaround time. Thus, we provide a new method to identify potentially immunogenic breast tumors and test immunotherapy, promoting personalized cancer therapy.

6P Unveiling the impact of intra-tumor heterogeneity in treatment response to achieve personalized medicine for endometrial cancer patients

6P Unveiling the impact of intra-tumor heterogeneity in treatment response to achieve personalized medicine for endometrial cancer patients

Chinese Research Progress Of Blood Eosinophils As Biomarkers In Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is a common chronic airway inflammatory disease. With its heterogeneity in clinical manifestations, treatment responses and prognosis, biomarkers that can predict treatment benefits and risk stratification of deterioration are very important. In recent years, many studies have reported that eosinophils are highly involved in the development of airway inflammation in COPD. The baseline blood eosinophil count(EOS) is related to the therapeutic effect of inhaled corticosteroids(ICS) and the risk of acute exacerbation in patients. The level of EOS may be affected by many factors such as geography, race, etc. In domestic clinical work, the actual reference data of EOS may not be consistent with the recommended reference threshold value in GOLD. Since there is no uniform and accurate epidemiological data on EOS in COPD patients in China, this review focuses on the research progress and controversy in the areas of EOS and COPD airway inflammation, development as biomarkers, and the role of guiding the clinical application of ICS, to discuss the setting of the critical value of EOS of Chinese COPD patients.

The Efficacy of Using Patient-Derived Organoids to Predict Treatment Response in Colorectal Cancer.

Colorectal cancer is an important cause of morbidity and mortality worldwide. The current treatment landscape includes chemotherapy, targeted therapy, immunotherapy, radiotherapy, and surgery. A key challenge to improving patient outcomes is the significant inter-patient heterogeneity in treatment response. Tumour organoids derived from the patients' tumours via surgically resected or endoscopically biopsied tissue, have emerged as promising models for personalised medicine. This review synthesises the findings, to date, of studies which have explored the efficacy of ex vivo organoid sensitivity testing for predicting treatment response. Most studies have focused on predicting the response to standard-of-care radiotherapy and chemotherapy options. There is strong evidence to support organoid sensitivity testing of ionising radiation, 5-fluorouracil, and irinotecan, and to a lesser extent, oxaliplatin and TAS-102. Fewer studies have used organoids to identify patients who are likely to benefit from novel treatment options that otherwise remain in clinical trials. This review also summarises recent advancements in organoid culture to include non-epithelial components of the tumour microenvironment, to allow testing of immunotherapy and certain targeted therapy options. Overall, further prospective trials will support the implementation of organoid-based personalised medicine for colorectal cancer patients in the future.

Personalizing atomoxetine dosing in children with ADHD: what can we learn from current supporting evidence.

There is marked heterogeneity in treatment response of atomoxetine in patients with attention deficit/hyperactivity disorder (ADHD), especially for the pediatric population.This review aims to evaluate current evidence to characterize the dose-exposure relationship, establish clinically relevant metrics for systemic exposure to atomoxetine, define a therapeutic exposure range, and to provide a dose-adaptation strategy before implementing personalized dosing for atomoxetine in children with ADHD. A comprehensive search was performed across electronic databases (PubMed and Embase) covering the period of January 1, 1985 to July 10, 2022, to summarize recent advances in the pharmacokinetics, pharmacogenomics/pharmacogenetics (PGx), therapeutic drug monitoring (TDM), physiologically based pharmacokinetics (PBPK), and population pharmacokinetics (PPK) of atomoxetine in children with ADHD. Some factors affecting the pharmacokinetics of atomoxetine were summarized, including food, CYP2D6 and CYP2C19 phenotypes, and drug‒drug interactions (DDIs). The association between treatment response and genetic polymorphisms of genes encoding pharmacological targets, such as norepinephrine transporter (NET/SLC6A2) and dopamine β hydroxylase (DBH), was also discussed. Based on well-developed and validated assays for monitoring plasma concentrations of atomoxetine, the therapeutic reference range in pediatric patients with ADHD proposed by several studies was summarized. However, supporting evidence on the relationship between systemic atomoxetine exposure levels and clinical response was far from sufficient. Personalizing atomoxetine dosage may be even more complex than anticipated thus far, but elucidating the best way to tailor the non-stimulant to a patient's individual need will be achieved by combining two strategies: detailed research in linking the pharmacokinetics and pharmacodynamics in pediatric patients, and better understanding in nature and causes of ADHD, as well as environmental stressors.

Treatable traits in interstitial lung diseases: a call to action

Despite growing interest in the management of interstitial lung diseases (ILDs) and many clinical trials, treatment options for ILDs remain scarce. This reality stems from variability in disease pathogenesis across ILD subtypes, unpredictable natural history, and heterogeneous treatment responses. 1 Kokosi MA Margaritopoulos GA Wells AU Personalised medicine in interstitial lung diseases: number 6 in the Series “personalised medicine in respiratory diseases” edited by Renaud Louis and Nicolas Roche. Eur Respir Rev. 2018; 27170117 Crossref PubMed Scopus (10) Google Scholar Some data suggest that diverse forms of ILD might share pathogenetic and biological pathways and could therefore be amenable to similar treatment strategies. 1 Kokosi MA Margaritopoulos GA Wells AU Personalised medicine in interstitial lung diseases: number 6 in the Series “personalised medicine in respiratory diseases” edited by Renaud Louis and Nicolas Roche. Eur Respir Rev. 2018; 27170117 Crossref PubMed Scopus (10) Google Scholar , 2 Hoffmann-Vold AM Weigt SS Saggar R et al. Endotype-phenotyping may predict a treatment response in progressive fibrosing interstitial lung disease. EBioMedicine. 2019; 50: 379-386 Summary Full Text Full Text PDF PubMed Scopus (29) Google Scholar This evidence points to the existence of treatable traits—specific disease characteristics that are clinically relevant and modifiable through pharmacological or non-pharmacological interventions—that cut across traditional diagnostic categories. 3 McDonald VM Fingleton J Agusti A et al. Treatable traits: a new paradigm for 21st century management of chronic airway diseases: Treatable Traits Down Under International Workshop report. Eur Respir J. 2019; 531802058 Crossref Scopus (110) Google Scholar

Ex Vivo Functional Assay for Evaluating Treatment Response in Tumor Tissue of Head and Neck Squamous Cell Carcinoma.

Head and neck squamous cell carcinoma (HNSCC) displays a large heterogeneity in treatment response, and consequently in patient prognosis. Despite extensive efforts, no clinically validated model is available to predict tumor response. Here we describe a functional test for predicting tumor response to radiation and chemotherapy on the level of the individual patient. Resection material of 17 primary HNSCC patients was cultured ex vivo, irradiated or cisplatin-treated, after which the effect on tumor cell vitality was analyzed several days after treatment. Ionizing radiation (IR) affected tumor cell growth and viability with a clear dose-response relationship, and marked heterogeneity between tumors was observed. After a single dose of 5Gy, proliferation in IR-sensitive tumors dropped below 30% of the untreated level, while IR-resistant tumors maintained at least 60% of proliferation. IR-sensitive tumors showed on average a twofold increase in apoptosis, as well as an increased number and size of DNA damage foci after treatment. No differences in the homologous recombination (HR) proficiency between IR-sensitive and -resistant tumors were detected. Cisplatin caused a decrease in proliferation, as well as induction of apoptosis, again with marked variation between the samples. Our functional ex vivo assay discriminated between IR-sensitive and IR-resistant HNSCC tumors, and may also be suitable for predicting response to cisplatin. Its predictive value is currently under investigation in a prospective clinical study.

Structural brain plasticity and inflammation are independently related to changes in depressive symptoms six months after an index ECT course.

Electroconvulsive therapy (ECT) is effective for treatment-resistant depression and leads to short-term structural brain changes and decreases in the inflammatory response. However, little is known about how brain structure and inflammation relate to the heterogeneity of treatment response in the months following an index ECT course. A naturalistic six-month study following an index ECT course included 20 subjects with treatment-resistant depression. Upon conclusion of the index ECT course and again after six months, structural magnetic resonance imaging scans and peripheral inflammation measures [interleukin-6 (IL-6), IL-8, tumor necrosis factor (TNF-α), and C-reactive protein] were obtained. Voxel-based morphometry processed with the CAT-12 Toolbox was used to estimate changes in gray matter volume. Between the end of the index ECT course and the end of follow-up, we found four clusters of significant decreases in gray matter volume (p < 0.01, FWE) and no regions of increased volume. Decreased HAM-D scores were significantly related only to reduced IL-8 level. Decreased volume in one cluster, which included the right insula and Brodmann's Area 22, was related to increased HAM-D scores over six months. IL-8 levels did not mediate or moderate the relationship between volumetric change and depression. Six months after an index ECT course, multiple regions of decreased gray matter volume were observed in a naturalistic setting. The independent relations between brain volume and inflammation to depressive symptoms suggest novel explanations of the heterogeneity of longer-term ECT treatment response.

Changing criminal thinking: An examination of heterogeneity in treatment effects in a sample of justice-involved persons with dual diagnoses.

Recent studies have indicated variable reductions in criminal thinking for justice-involved persons with mental illness exposed to cognitive-behavioral treatments. To date, however, no studies have identified risk factors for limited response or modeled observed disparities in responsivity to interventions aimed at reducing criminal thinking. Using an archival data set of 162 probationers with a dual diagnosis who were exposed to changing lives and changing outcomes, a latent profile analysis modeled unobserved heterogeneity in treatment response per observed changes in criminal thinking. Most participants endorsed significant changes in self-reported reactive criminal thinking with minimal changes in proactive criminal thinking. Neither self-reported pretreatment severity of psychopathology nor self-reported compliance with psychotropic medication predicted response to treatment. Although diagnosis also did not predict responsiveness, more favorable views of treatment predicted greater decreases in criminal thinking. Moreover, those expressing fewer levels of criminal thinking after treatment were also found to express a decrease in attitudes supportive of violence. Limitations and treatment recommendations are discussed, including the need for correctional treatments to improve responsiveness to individual treatment needs. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Targeting Skeletal Muscle Dysfunction With L-Carnitine for the Treatment of Patients With Chronic Obstructive Pulmonary Disease.

Chronic obstructive pulmonary disease (COPD) is a major medical problem and the world's third leading cause of death. COPD is a chronic disease with heterogeneous clinical symptoms, disease progression, and treatment responses. Besides pulmonary symptomatology, the common systemic clinical manifestations are cachexia, muscle weakness, and widespread comorbidities such as cardiovascular diseases, diabetes, osteoporosis, and infections. The adverse effects of pharmaceutical therapies contribute to the difficulty of health risk assessment and management of COPD patients. This review shows how skeletal muscle dysfunction and metabolic abnormalities contribute significantly to COPD patients' symptoms, functional activities, quality of life, and overall disease outcomes. Based on the clinical evidence of L-carnitine and derivatives as metabolic and muscle bioenergetic enhancers, we propose broader research and implementation of this nutraceutical agent as an effective, inexpensive, and safe adjuvant therapeutic for the long-term management of COPD patients. Moreover, we believe the management of COPD as a chronic disease should be shifted from symptomatic reactive pharmaceutical intervention to more constructive and non-toxic approaches using a single or combination of natural and nutritional agents with potential muscle metabolic enhancing and immunomodulating activities to achieve a better overall outcome for the patients in terms of morbidity, mortality, and medical cost-reduction.