Validation of Vancomycin Area under the Concentration—Time Curve Estimation by the Bayesian Approach Using One-Point Samples for Predicting Clinical Outcomes in Patients with Methicillin-Resistant Staphylococcus aureus Infections

Area under the concentration–time curve (AUC)-guided vancomycin treatment is associated with decreased nephrotoxicity. It is preferable to obtain two samples to estimate the AUC. This study examined the usefulness of AUC estimation via trough concentration (Cmin)-only sampling of 260 adults infected with methicillin-resistant Staphylococcus aureus (MRSA) who received vancomycin. The exact Cmin sampling time was used for Bayesian estimation. A significantly higher early treatment response was observed in patients with a day 2 AUC ≥ 400 µg·h/mL than those with <400 µg·h/mL, and a significantly higher early nephrotoxicity rate was observed in patients with a day 2 AUC ≥ 600 µg·h/mL than those with <600 µg·h/mL. These AUC cutoff values constituted independent factors for each outcome. In sub-analysis, the discrimination ability for early clinical outcomes using these AUC cutoffs was confirmed only in patients with q12 vancomycin administration. A significant difference in early treatment response using the 400 µg·h/mL cutoff was obtained only in patients with low-risk infections. The usefulness of the vancomycin AUC target to decrease nephrotoxicity while assuring clinical efficacy was even confirmed with a single Cmin measurement. However, assessment with two samples might be required in patients with q24 administration or high/moderate-risk MRSA infections.

Combining Intravoxel Incoherent Motion Diffusion Weighted Imaging and Texture Analysis for a Nomogram to Predict Early Treatment Response to Concurrent Chemoradiotherapy in Cervical Cancer Patients

This study aimed to predict early treatment response to concurrent chemoradiotherapy (CCRT) by combining intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) with texture analysis (TA) for cervical cancer patients and to develop a nomogram for estimating the risk of residual tumor. Ninty-three cervical cancer patients underwent conventional MRI and IVIM-DWI before CCRT. We conducted TA using T2WI. The patients were allocated to partial response (PR) and complete response (CR) groups on the basis of posttreatment MRI. Multivariate logistic regression analysis on IVIM-DWI parameters and texture features was employed to filter the independent predictors and construct the predictive nomogram. Its discrimination and calibration performances were estimated. Multivariate analysis on the IVIM-DWI parameters showed that D and f were independent predictors (OR = 4.029 and 0.889, resp.; p < 0.05 ). However, the multivariate analysis on the texture features indicated that GLCM-correlation, GLRLM-LRE, and GLSZM-ZE were independent predictors (OR = 43.789, 9.774, and 23.738, resp.; p < 0.05 ). The combination of IVIM-DWI parameters and texture features exhibited the highest predictive performance (AUC = 0.975). The nomogram to identify the patients with high-risk residual tumors exhibited an acceptable predictive performance and stability with a C-index of 0.953. Decision curve analysis demonstrated the clinical use of the nomogram. The results demonstrate that D, f, GLCM-correlation, GLRLM-LRE, and GLSZM-ZE were independent predictors for cervical cancer. The nomogram combining IVIM-DWI parameters and texture features makes it possible to identify cervical cancer patients at a high risk of residual tumor after CCRT.

Predictors of Ocrelizumab Effectiveness in Patients with Multiple Sclerosis

SummaryData regarding effectiveness and safety of ocrelizumab in the post-marking setting are lacking. The aim of our study was to provide effectiveness and safety data of ocrelizumab treatment in patients with relapsing–remitting (RR-) and progressive multiple sclerosis (PMS) and to evaluate clinical and immunological predictors of early treatment response. In this single-center prospective observational study, we investigated effectiveness outcomes (time-to-confirmed disability worsening, time-to-first relapse, time-to-first evidence of MRI activity and time-to-first evidence of disease activity), clinical and immunological predictors of early treatment response, and incidence of adverse events (AEs). One hundred and fifty-three subjects were included (93 RRMS; 84 females). Median follow-up was 1.9 (1.3–2.7). At 2-year follow-up (FU), disability worsening-free survival were 90.5%, 64.7%, and 68.8% for RRMS, primary-progressive MS (PPMS), and secondary-progressive MS (SPMS) patients, respectively. At 2-year FU, 67.1%, 72.7%, and 81.3% of patients with RRMS, PPMS, and SPMS were free of MRI activity, with NEDA-3 percentages of 62.1%, 54.6%, and 55.1%, respectively. Lower baseline EDSS was independently associated with a reduced risk of disability worsening (HR(95%CI) = 1.45(1.05–2.00), p = 0.024) and previous treatment exposure was independently associated with increased probability of radiological activity (HR = 2.53(1.05–6.10), p = 0.039). At 6-month FU, CD8 + cell decrease was less pronounced in patients with inflammatory activity (p = 0.022). Six patients (3.9%) discontinued ocrelizumab due to severe AEs. Our findings suggest that ocrelizumab is an effective treatment in real-world patients with RRMS and PMS, with a manageable safety profile. Better outcomes were observed in treatment-naïve patients and in patients with a low baseline disability level. Depletion of CD8 + cells could underlie early therapeutic effects of ocrelizumab.

Single-cell ATAC and RNA sequencing reveal pre-existing and persistent cells associated with prostate cancer relapse

Prostate cancer is heterogeneous and patients would benefit from methods that stratify those who are likely to respond to systemic therapy. Here, we employ single-cell assays for transposase-accessible chromatin (ATAC) and RNA sequencing in models of early treatment response and resistance to enzalutamide. In doing so, we identify pre-existing and treatment-persistent cell subpopulations that possess regenerative potential when subjected to treatment. We find distinct chromatin landscapes associated with enzalutamide treatment and resistance that are linked to alternative transcriptional programs. Transcriptional profiles characteristic of persistent cells are able to stratify the treatment response of patients. Ultimately, we show that defining changes in chromatin and gene expression in single-cell populations from pre-clinical models can reveal as yet unrecognized molecular predictors of treatment response. This suggests that the application of single-cell methods with high analytical resolution in pre-clinical models may powerfully inform clinical decision-making.

Circulating microRNAs Signature for Predicting Response to GLP1-RA Therapy in Type 2 Diabetic Patients: A Pilot Study

Type 2 diabetes (T2D) represents one of the major health issues of this century. Despite the availability of an increasing number of anti-hyperglycemic drugs, a significant proportion of patients are inadequately controlled, thus highlighting the need for novel biomarkers to guide treatment selection. MicroRNAs (miRNAs) are small non-coding RNAs, proposed as useful diagnostic/prognostic markers. The aim of our study was to identify a miRNA signature occurring in responders to glucagon-like peptide 1 receptor agonists (GLP1-RA) therapy. We investigated the expression profile of eight T2D-associated circulating miRNAs in 26 prospectively evaluated diabetic patients in whom GLP1-RA was added to metformin. As expected, GLP1-RA treatment induced significant reductions of HbA1c and body weight, both after 6 and 12 months of therapy. Of note, baseline expression levels of the selected miRNAs revealed two distinct patient clusters: “high expressing” and “low expressing”. Interestingly, a significantly higher percentage of patients in the high expression group reached the glycemic target after 12 months of treatment. Our findings suggest that the evaluation of miRNA expression could be used to predict the likelihood of an early treatment response to GLP1-RA and to select patients in whom to start such treatment, paving the way to a personalized medicine approach.

Early Detection of Response to Radiotherapy Treatment with 11C-Acetate PET Imaging

11C-Acetate radiotracer with Positron Emission Tomography (PET) imaging is currently used in cardiovascular imaging for perfusion and oxygen consumption measurement. It is also used, among other diseases, for prostate cancer as this radiotracer does not accumulate in the bladder. The present study reports the assessment of the radiotherapy treatment by measuring the tumor perfusion and oxygenation before and at mid-treatment by imaging with dynamic 11C-Acetate in patients with head and neck cancer. A pre-treatment dynamic 11C-Acetate and a clinical static 18F-FDG PET were conducted before initiation of the treatment, and the second 11C-Acetate dynamic scan was performed after four weeks of radiotherapy (i.e., after a dose of 35 Gy for a total of 70 Gy). The two-tissue compartment model was applied to 11C-Acetate images to extract the perfusion and oxygen consumption. The results showed a reduction in tumor volume by more than 50% compared to the initial volume in patient-1. Besides, patient-2 has displayed a more reduced tumor volume after 4 weeks of treatment. The 11C-Acetate rate constant k2 representing oxygen consumption increased after radiotherapy dose in both patients. This increase of k2 could reflect the reoxygenation process inside the tumor, and it can reflect the early treatment response. In conclusion, 11C-Acetate could predict the early changes in the tumor perfusion and the oxidative metabolism to optimally adjust the treatment.

Muscle microRNAs in the cerebrospinal fluid predict clinical response to nusinersen therapy in type II and type III spinal muscular atrophy patients

Objective: The antisense oligonucleotide nusinersen (spinraza) regulates splicing of the survival motor neuron 2 (SMN2) messenger RNA to increase survival motor neuron (SMN) protein expression and has improved ventilator free survival and motor function outcomes in infantile onset forms of SMA, treated early in the course of the disease. However, the response in later onset forms of SMA is highly variable and dependent on symptom severity and disease duration at treatment initiation. Therefore, we aimed to identify novel noninvasive biomarkers that could predict the response to nusinersen in type II and III SMA patients. Methods: This is a multi-center longitudinal cohort study including a total of 34 type II or III SMA patients. We applied unbiased next-generation sequencing to identify cell-free microRNAs in the cerebrospinal fluid (CSF) as candidate biomarkers to predict response to nusinersen. Motor function, assessed by the Hammersmith Functional Motor Scale Expanded (HFMSE), was considered the primary clinical outcome. HFMSE was conducted at baseline and 6 months post initiation of nusinersen therapy. Patients with an improvement ≥ 3 points or no change or decline < 0 points in the HFMSE total score were considered to be responders or non-responders, respectively. Results: Lower baseline levels of two muscle microRNAs (miR-206 and miR-133), alone or in combination, were associated with the pre-determined clinical response to nusinersen after 6 months therapy. Moreover, miR-206 levels were inversely correlated with the HFMSE score. Conclusions: Lower miR-206 and miR-133 in the CSF predict more robust clinical response to nusinersen treatment in later onset SMA patients. These novel findings have high clinical relevance for identifying early treatment response to nusinsersen in later onset SMA patients and call to test the ability of miRNAs to predict more sustained long-term benefit.