Individualized Treatment Optimization Research Articles

Feasibility and clinical implementation of MRI-guided surface brachytherapy.

Best practices for high-dose-rate surface applicator brachytherapy treatment (SABT) have long relied on computed tomography (CT)-based imaging to visualize diseased sites for treatment planning. Compared with magnetic resonance (MR)-based imaging, CT provides insufficient soft tissue contrast. This work described the feasibility of clinical implementation of MR-based imaging in SABT planning to provide individualized treatment optimization. A 3D-printed phantom was used to fit Freiberg flap-style (Elekta, The Netherlands) applicator. Images were taken using an optimized pointwise encoding time reduction with radial acquisition (PETRA) MR sequence for catheter visualization, and a helical CT scan to generate parallel treatment plans. This clinical study included three patients undergoing SABT for Dupuytren's contracture/palmar fascial fibromatosis imaged with the same modalities.SABT planning was performed in Oncentra Brachy (Elekta Brachytherapy, The Netherlands) treatment planning software. A geometric analysis was conducted by comparing CT-based digitization with MR-based digitization. CT and MR dwell positions underwent a rigid registration, and average Euclidean distances between dwell positions were calculated. A dosimetric comparison was performed, including point-based dose difference calculations and volumetric segmentations with Dice similarity coefficient (DSC) calculations. Euclidean distances between dwell positions from CT-based and MR-based plans were on average 0.68 ±0.05 mm and 1.35 ±0.17 mm for the phantom and patients, respectively. The point dose difference calculations were on average 0.92% for the phantom and 1.98% for the patients. The D95 and D90 DSC calculations were both 97.9% for the phantom, and on average 93.6% and 94.2%, respectively, for the patients. The sub-millimeter accuracy of dwell positions and high DSC's (> 0.95) of the phantom demonstrated that digitization was clinically acceptable, and accurate treatment plans were produced using MR-only imaging. This novel approach, MRI-guided SABT, will lead to individualized prescriptions for potentially improved patient outcomes.

Expression Profiles of HOXC6 Predict the Survival of Glioblastoma Patients and Correlate with Cell Cycle.

The goal of this study was to investigate the homeobox (HOX) gene expression status and its prognostic value in glioblastoma multiforme (GBM) and to uncover the biological processes related to its expression. The prognostic value of HOX genes in GBM was systematically investigated by a genome-wide analysis of HOX gene expression profiles in GBM patient samples in The Cancer Genome Atlas (TCGA) project (microarray dataset) and validation datasets. Using the differentially expressed gene (DEG) analysis and a Cox regression model, we discovered that the HOXC6 could stratify patients into significantly different survival (p = 0.0012, log-rank test) groups in the training cohort. TCGA RNA-seq and GSE16011 datasets were used for validation. Multivariate Cox and stratification analysis indicated that HOXC6 was an independent prognostic factor after adjusting for other clinical covariates. Bioinformatic analysis suggested that the HOXC6 might be involved in the cell cycle-related biological processes and pathways that are well established in the context of glioblastoma tumorigenesis. We further explored the bioinformatic implications by gene set enrichment analysis (GSEA). Tumor cell biology experiments verified the role of HOXC6 in proliferation and cell cycle progression. In conclusion, HOXC6 might be a candidate biomarker gene for individual treatment optimization of glioblastoma. HOXC6 expression has a significant prognostic value and is related to the cell cycle process in glioblastoma.

Development and Validation of a Prognostic Signature Associated With Tumor Microenvironment Based on Autophagy-Related lncRNA Analysis in Hepatocellular Carcinoma.

Objective: The present study aimed to establish a prognostic signature based on the autophagy-related long non-coding RNAs (lncRNAs) analysis in patients with hepatocellular carcinoma (HCC).Methods: Patients with HCC from The Cancer Genome Atlas (TCGA) were taken as the training cohort, and patients from the International Cancer Genome Consortium (ICGC) were treated as the validation cohort. Autophagy-related lncRNAs were obtained via a co-expression network analysis. According to univariate and multivariate analyses, a multigene prognostic signature was constructed in the training cohort. The predictive power of the signature was confirmed in both cohorts. The detailed functions were investigated using functional analysis. The single-sample gene set enrichment analysis (ssGSEA) score was used to evaluate the tumor microenvironment. The expression levels of immunotherapy and targeted therapy targets between the two risk groups were compared. Finally, a nomogram was constructed by integrating clinicopathological parameters with independently predictive value and the risk score.Results: Four autophagy-related lncRNAs were identified to establish a prognostic signature, which separated patients into high- and low-risk groups. Survival analysis showed that patients in the high-risk group had a shorter survival time in both cohorts. A time-independent receiver-operating characteristic (ROC) curve and principal component analysis (PCA) confirmed that the prognostic signature had a robust predictive power and reliability in both cohorts. Functional analysis indicated that the expressed genes in the high-risk group are mainly enriched in autophagy- and cancer-related pathways. ssGSEA revealed that the different risk groups were associated with the tumor microenvironment. Moreover, the different risk groups had positive correlations with the expressions of specific mutant genes. Multivariate analysis showed that the risk score also exhibited excellent predictive power irrespective of clinicopathological characteristics in both cohorts. A nomogram was established. The nomogram showed good discrimination, with Harrell's concordance index (C-index) of 0.739 and good calibration.Conclusion: The four autophagy-related lncRNAs could be used as biological biomarkers and therapeutic targets. The prognostic signature and nomogram might aid clinicians in individual treatment optimization and clinical decision-making for patients with HCC.

Prognostic significance of preoperative systemic inflammatory biomarkers in patients with hepatocellular carcinoma after microwave ablation and establishment of a nomogram

The study aimed to evaluate the prognostic significance of preoperative systemic inflammatory biomarkers including albumin to globulin ratio (AGR), neutrophil to lymphocyte ratio (NLR), lymphocyte to monocyte ratio (LMR), and platelet to lymphocyte ratio (PLR) and establish a nomogram in hepatocellular carcinoma (HCC) patients after microwave ablation (MWA). 192 HCC patients receiving MWA as initial therapy from the first ward of hepatobiliary surgery were classified as training cohort. Whereas, 84 patients from the second of hepatobiliary surgery were classified as validation cohort. Kaplan–Meier (KM) method and univariate analyses showed that AGR, NLR, LMR, and PLR were significantly associated with OS in the training cohort. Multivariate analysis including clinicopathologic features screened out independent predictors including ascites, tumor size, cancer embolus, AGR, and PLR. Based on those variables, a nomogram for predicting OS was established. The C-index was 0.794 in the training cohort and 0.772 in the validation cohort. Calibration plots identified the nomogram performed well with an ideal model. Compared with Barcelona Clinic Liver Cancer (BCLC) staging system and simple tumor size, the nomogram showed better predictive ability. Besides, the nomogram discovered the highest diagnostic accuracy in predicting postoperative clinical outcome than the combination of the present models with tumor size. In conclusion, the constructed nomogram could accurately predict individualized survival probability and might support clinician in individual treatment optimization and clinical decision-making.

Development of Personalized Nutrition: Applications in Lactose Intolerance Diagnosis and Management.

Recent discoveries in the “omics” field and the growing focus on preventive health have opened new avenues for personalized nutrition (PN), which is becoming an important theme in the strategic plans of organizations that are active in healthcare, food, and nutrition research. PN holds great potential for individual health optimization, disease management, public health interventions, and product innovation. However, there are still multiple challenges to overcome before PN can be truly embraced by the public and healthcare stakeholders. The diagnosis and management of lactose intolerance (LI), a common condition with a strong inter-individual component, is explored as an interesting example for the potential role of these technologies and the challenges of PN. From the development of genetic and metabolomic LI diagnostic tests that can be carried out in the home, to advances in the understanding of LI pathology and individualized treatment optimization, PN in LI care has shown substantial progress. However, there are still many research gaps to address, including the understanding of epigenetic regulation of lactase expression and how lactose is metabolized by the gut microbiota, in order to achieve better LI detection and effective therapeutic interventions to reverse the potential health consequences of LI.

Integration of mathematical model predictions into routine workflows to support clinical decision making in haematology

BackgroundIndividualization and patient-specific optimization of treatment is a major goal of modern health care. One way to achieve this goal is the application of high-resolution diagnostics together with the application of targeted therapies. However, the rising number of different treatment modalities also induces new challenges: Whereas randomized clinical trials focus on proving average treatment effects in specific groups of patients, direct conclusions at the individual patient level are problematic. Thus, the identification of the best patient-specific treatment options remains an open question. Systems medicine, specifically mechanistic mathematical models, can substantially support individual treatment optimization. In addition to providing a better general understanding of disease mechanisms and treatment effects, these models allow for an identification of patient-specific parameterizations and, therefore, provide individualized predictions for the effect of different treatment modalities.ResultsIn the following we describe a software framework that facilitates the integration of mathematical models and computer simulations into routine clinical processes to support decision-making. This is achieved by combining standard data management and data exploration tools, with the generation and visualization of mathematical model predictions for treatment options at an individual patient level.ConclusionsBy integrating model results in an audit trail compatible manner into established clinical workflows, our framework has the potential to foster the use of systems-medical approaches in clinical practice. We illustrate the framework application by two use cases from the field of haematological oncology.

An epidemiological study on the course of disease and therapeutic considerations in relapsing-remitting multiple sclerosis patients receiving injectable first-line disease-modifying therapies in Germany (EPIDEM).

Background:In relapsing–remitting multiple sclerosis (RRMS), ‘no evidence of disease activity’ (NEDA) is regarded as a key treatment goal. The increasing number of treatments allows for individualized treatment optimization in patients with suboptimal response to first-line disease-modifying therapies (DMTs). Therefore, monitoring of clinical and subclinical disease activity on DMTs has been recognized as an important component of long-term patient management.Methods:EPIDEM was a multicenter non-interventional retrospective study in a large cohort of RRMS patients receiving injectable DMTs for at least 2 years in outpatient centers throughout Germany. It documented measures and ratings of disease activity on DMTs to characterize the factors that made the treating neurologists consider to switch therapy towards potentially more effective or better-tolerated drugs.Results:The cohort included predominantly female patients with a mean age of 45 years and a mean disease duration of 9.6 years, who had been continuously treated with an injectable DMT for a median duration of 54 months. Overall, 34.0% of the patients had experienced ⩾1 relapse on any DMT in the previous 2 years; 21.0% exhibited magnetic resonance imaging (MRI) activity, and the Kurtzke Expanded Disability Status Scale (EDSS) score increased by at least 0.5 points in 20.1%. Overall, 50.3% of the patients with EDSS progression and 70.6% of the patients with relapses were assessed as clinically stable by the neurologists. A change of treatment was considered in a fraction of patients with disease activity: in 22.8% of those with relapse activity, in 37.8% of those with MRI activity and in 20.1% of those with EDSS progression.Conclusion:The results of EPIDEM underline the importance of standardized evaluation and documentation of ongoing disease activity and disability deterioration. Judged from the present data, the current paradigm of low tolerance for disease activity and recommendations for early treatment optimization have not been turned fully into action as yet. More widespread implementation of current guideline recommendations may allow patients to more benefit from the growing panel of effective treatment options.

Neuropsychiatric genomics in precision medicine: diagnostics, gene discovery, and translation

Only a few years after its development, next-generation sequencing is rapidly becoming an essential part of clinical care for patients with serious neurological conditions, especially in the diagnosis of early-onset and severe presentations. Beyond this diagnostic role, there has been an explosion in definitive gene discovery in a range of neuropsychiatric diseases. This is providing new pointers to underlying disease biology and is beginning to outline a new framework for genetic stratification of neuropsychiatric disease, with clear relevance to both individual treatment optimization and clinical trial design. Here, we outline these developments and chart the expected impact on the treatment of neurological, neurodevelopmental, and psychiatric disease.

Somatostatin receptor-targeted radionuclide therapy for progressive meningioma: benefit linked to 68Ga-DOTATATE/-TOC uptake.

The prognosis of patients with progressive meningioma after failure of surgery and radiotherapy is poor. We retrospectively evaluated the safety and efficacy of somatostatin-receptor (SSTR)-targeted radionuclide therapy (177Lu-DOTATATE [n = 16], 90Y-DOTATOC [n = 3], or both [n = 1]) in patients with progressive, treatment-refractory meningiomas (5 World Health Organization [WHO] grade I, 7 WHO grade II, 8 WHO grade III) and in part multifocal disease (17 of 20 patients). SSTR radionuclide treatment (median of 3 treatment cycles, median administered dose/cycle 7400 MBq) led to a disease stabilization in 10 of 20 patients for a median time of 17 months. Stratification according to WHO grade showed a median progression-free survival (PFS) of 32.2 months for grade I tumors, 7.2 for grade II, and 2.1 for grade III. PFS at 6 months was 100% for grade I, 57% for grade II, and 0% for grade III. Median overall survival was 17.2 months in WHO grade III patients and not reached for WHO I and II at a median follow-up of 20 months. In the analysis of single meningioma lesions, maximal and mean standardized uptake values in pretherapeutic 68Ga-DOTATOC/-TATE PET/CT were significantly higher in those lesions with radiographic stability after 6 months. In line with this, high expression of SSTR via immunohistochemistry was associated with PFS >6 months. SSTR-targeted radionuclide treatment has activity in a subset of patients with meningioma. Expression of SSTR via immunohistochemistry or radionuclide uptake might serve as a predictive biomarker for outcome to facilitate individualized treatment optimization in patients with uni- and multifocal meningiomas.

Major Depressive Disorder: Understanding the Significance of Residual Symptoms and Balancing Efficacy with Tolerability

IntroductionMajor depressive disorder is a complex and frequent psychiatric condition that poses significant challenges to both the patients who experience it and the physicians who treat them. The goal of therapy is for patients to achieve remission, which requires identifying and measuring symptoms at the outset and throughout treatment to document both response and resistance to treatment. A number of validated instruments are available both for diagnosis of and response to treatment. Many factors affect a patient's ability to achieve remission, but although many patients do achieve remission, a significant number continue to have residual symptoms that cause functional impairment. MethodsReview of the literature for treatment of major depression, including mechanisms of action, individualized treatment optimization, residual symptom reduction, and minimization of side effects. ResultsFor sustained remission, all symptoms must be treated until they are undetectable. Patients who do not achieve remission after adequate treatment trials should be evaluated for adherence to treatment, as well as comorbid psychiatric and medical disorders. In these cases, consideration should be given to changing therapy by switching, combining, or augmenting initial therapy, as well as referring some patients to a psychiatrist for treatment with specialized modalities. Linking symptoms with malfunctioning brain circuits and neurotransmitters provides a targeted approach for achieving sustained remission. Neurobiology also provides a rational basis for combination therapy in patients with treatment-resistant depression, because it can aid selection of different drugs with different mechanisms of action or of multifunctional/multimodal antidepressant drugs that target more than 1 molecular mechanism. DiscussionRecent advances and better understanding of neurobiology provide a rational basis for individualizing treatment of patients with major depression.

Dynamics of hepatitis B virus resistance substitutions correlates with virological response in lamivudine-refractory patients with entecavir rescue monotherapy

Entecavir (ETV) demonstrates potent antiviral effects against lamivudine (LMV)-resistant hepatitis B virus (HBV). This study was designed to investigate the impact of LMV resistance mutations on the outcome of ETV rescue therapy in LMV-refractory patients. Twenty-six chronic hepatitis B patients who received ETV monotherapy for LMV resistance were enrolled. Dynamics of HBV DNA levels were monitored before and during ETV rescue therapy. Mutations in the HBV reverse transcriptase were examined by sequencing. LMV-resistant mutations (rtL180M and/or rtM204VI) were detected in 9 patients before ETV treatment and not in another 5 patients before and after the treatment. ETV therapy resulted in a greater reduction in the HBV DNA load in the patients with out LMV-associated mutations before treatment than in those with. Six patients with 100% LMV-resistant HBV variants at week 12 posttreatment had significantly (P<0.01) greater HBV DNA levels at the end of follow-up than the other patients studied. A comparable outcome was achieved between the patients with or without emergence of LMV-resistant mutations during the ETV treatment. In conclusion, patients without 100% LMV-resistant HBV mutants at week 12 and those without LMV-resistant mutations before treatment show a better response to ETV rescue therapy than the corresponding others. Therefore, individual treatment optimization is of significance in improving the efficacy of antiviral therapy for patients with chronic HBV infection.

Fetal and Neonatal Alloimmune Thrombocytopenia

Fetal and neonatal alloimmune thrombocytopenia constitutes the most common cause of severe thrombocytopenia in fetuses and neonates and of intracranial hemorrhage among term newborns. The cornerstone of therapy involves the use of steroids and intravenous immunoglobulins. Despite the risk of potentially devastating consequences to the fetus, fetal blood sampling has typically been used to document response to therapy. We propose a therapeutic algorithm based on risk stratification with individualized treatment optimization without the use of fetal blood sampling.

An Extension of Apriori Algorithm to Discover Individualized Treatment Optimization of Breast Cancer

Normally there is the very huge dataset in the application of medicine and bioinformatics. Traditional association algorithm produces too many rules in this kind of application, which are difficult to be identified and compared. In this work we attempt to propose an extension of Apriori algorithm to explore individualized treatment optimization of breast cancer. As the result of our method, the comparative association rules are produced. Thus, association rules algorithm become more practical and useful, especially in the field of medicine and bioinformatics.

Dynamics of Hepatitis B Virus Resistance to Lamivudine

Lamivudine was the first approved inhibitor of hepatitis B virus (HBV) reverse transcriptase (RT). Lamivudine resistance develops in 53% to 76% of patients after 3 years of treatment. We extensively characterized the dynamics of HBV quasispecies variant populations in four HBV-infected patients who developed lamivudine resistance. Virological breakthrough was preceded by 2 to 4 months by the emergence of quasispecies variants bearing amino acid substitutions at RT position 204, i.e., within the YMDD catalytic motif (rtM204V/I). Three patients had a gradual switch from a YMDD variant population at baseline to a 100% lamivudine-resistant variant population, whereas the remaining patient had a fluctuating pattern of resistance variant dynamics. Careful analysis of amino acid substitutions located outside domain C of HBV RT, including those known to partially restore replication capacities in vitro, showed that the in vivo replication of HBV variants is driven by multiple forces, including intrinsic replicative advantages conferred by mutations accumulating outside domain C and the changing environment in which these variants replicate. Our findings also suggest that individual treatment optimization will require sensitive methods capable of detecting the emergence of viral resistance before the relevant variants acquire optimal replicative capacities.