A Personalized Dose-Finding Algorithm Based on Adaptive Gaussian Process Regression.

Dose-finding studies play a crucial role in drug development by identifying the optimal dose(s) for later studies while considering tolerability. This not only saves time and effort in proceeding with Phase III trials but also improves efficacy. In an era of precision medicine, it is not ideal to assume patient homogeneity in dose-finding studies as patients may respond differently to the drug. To address this, we propose a personalized dose-finding algorithm that assigns patients to individualized optimal biological doses. Our design follows a two-stage approach. Initially, patients are enrolled under broad eligibility criteria. Based on the Stage 1 data, we fit a regression model of toxicity and efficacy outcomes on dose and biomarkers to characterize treatment-sensitive patients. In the second stage, we restrict the trial population to sensitive patients, apply a personalized dose allocation algorithm, and choose the recommended dose at the end of the trial. Simulation study shows that the proposed design reliably enriches the trial population, minimizes the number of failures, and yields superior operating characteristics compared to several existing dose-finding designs in terms of both the percentage of correct selection and the number of patients treated at target dose(s).

Bayesian inference for multivariate probit model with latent envelope.

The response envelope model proposed by Cook et al. (2010) is an efficient method to estimate the regression coefficient under the context of the multivariate linear regression model. It improves estimation efficiency by identifying material and immaterial parts of responses and removing the immaterial variation. The response envelope model has been investigated only for continuous response variables. In this paper, we propose the multivariate probit model with latent envelope, in short, the probit envelope model, as a response envelope model for multivariate binary response variables. The probit envelope model takes into account relations between Gaussian latent variables of the multivariate probit model by using the idea of the response envelope model. We address the identifiability of the probit envelope model by employing the essential identifiability concept and suggest a Bayesian method for the parameter estimation. We illustrate the probit envelope model via simulation studies and real-data analysis. The simulation studies show that the probit envelope model has the potential to gain efficiency in estimation compared to the multivariate probit model. The real data analysis shows that the probit envelope model is useful for multi-label classification.

Extracellular Microenvironment Alterations in Ductal Carcinoma In Situ and Invasive Breast Cancer Pathologies by Multiplexed Spatial Proteomics.

Ductal carcinoma in situ (DCIS) is a heterogeneous breast disease that remains challenging to treat due to its unpredictable progression to invasive breast cancer (IBC). Contemporary literature has become increasingly focused on extracellular matrix (ECM) alterations with breast cancer progression. However, the spatial regulation of the ECM proteome in DCIS has yet to be investigated in relation to IBC. We hypothesized that DCIS and IBC present distinct ECM proteomes that could discriminate between these pathologies. Tissue sections of pure DCIS, mixed DCIS-IBC, or pure IBC (n = 22) with detailed pathological annotations were investigated by multiplexed spatial proteomics. Across tissues, 1,005 ECM peptides were detected in pathologically annotated regions and their surrounding extracellular microenvironments. A comparison of DCIS to IBC pathologies demonstrated 43 significantly altered ECM peptides. Notably, eight fibrillar collagen peptides could distinguish with high specificity and sensitivity between DCIS and IBC. Lesion-targeted proteomic imaging revealed heterogeneity of the ECM proteome surrounding individual DCIS lesions. Multiplexed spatial proteomics reported an invasive cancer field effect, in which DCIS lesions in closer proximity to IBC shared a more similar ECM profile to IBC than distal counterparts. Defining the ECM proteomic microenvironment provides novel molecular insights relating to DCIS and IBC.

Spatial N-glycomics of the normal breast microenvironment reveals fucosylated and high-mannose N-glycan signatures related to BI-RADS density and ancestry.

Higher breast cancer mortality rates continue to disproportionally affect black women (BW) compared to white women (WW). This disparity is largely due to differences in tumor aggressiveness that can be related to distinct ancestry-associated breast tumor microenvironments (TMEs). Yet, characterization of the normal microenvironment (NME) in breast tissue and how they associate with breast cancer risk factors remains unknown. N-glycans, a glucose metabolism-linked post-translational modification, has not been characterized in normal breast tissue. We hypothesized that normal female breast tissue with distinct Breast Imaging and Reporting Data Systems (BI-RADS) categories have unique microenvironments based on N-glycan signatures that varies with genetic ancestries. Profiles of N-glycans were characterized in normal breast tissue from BW (n = 20) and WW (n = 20) at risk for breast cancer using matrix assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI). A total of 176 N-glycans (32 core-fucosylated and 144 noncore-fucosylated) were identified in the NME. We found that certain core-fucosylated, outer-arm fucosylated and high-mannose N-glycan structures had specific intensity patterns and histological distributions in the breast NME dependent on BI-RADS densities and ancestry. Normal breast tissue from BW, and not WW, with heterogeneously dense breast densities followed high-mannose patterns as seen in invasive ductal and lobular carcinomas. Lastly, lifestyles factors (e.g. age, menopausal status, Gail score, BMI, BI-RADS) differentially associated with fucosylated and high-mannose N-glycans based on ancestry. This study aims to decipher the molecular signatures in the breast NME from distinct ancestries towards improving the overall disparities in breast cancer burden.

Effect of complicated, untreated and uncontrolled diabetes and pre-diabetes on treatment outcome among patients with pulmonary tuberculosis.

BACKGROUND AND OBJECTIVE: Patients with tuberculosis and diabetes have a higher risk of unfavourable anti-tuberculosis treatment outcomes. In the present study, we aimed to evaluate the effects of various diabetes statuses on the outcomes of patients with pulmonary tuberculosis. METHODS: Among the patients with pulmonary tuberculosis enrolled in the Korea Tuberculosis Cohort (KTBC) registry and the multicentre prospective cohort study of pulmonary tuberculosis (COSMOTB), those with diabetes and complicated diabetes were identified. The primary and secondary outcomes were unfavourable outcomes and mortality, respectively. The effect of diabetes and complicated diabetes on the outcomes was assessed using multivariable logistic regression analysis. Using COSMOTB, subgroup analyses were performed to assess the association between various diabetes statuses and outcomes. RESULTS: In the KTBC, diabetes (adjusted odds ratio [aOR] = 1.93, 95% CI = 1.64-2.26) and complicated diabetes (aOR = 1.96, 95% CI = 1.67-2.30) were significantly associated with unfavourable outcomes, consistent with the COSMOTB data analysis. Based on subgroup analysis, untreated diabetes at baseline was an independent risk factor for unfavourable outcomes (aOR = 2.72, 95% CI = 1.26-5.61). Prediabetes and uncontrolled diabetes increased unfavourable outcomes and mortality without statistical significance. CONCLUSION: Untreated and complicated diabetes at the time of tuberculosis diagnosis increases the risk of unfavourable outcomes and mortality.

The Matrix Stiffness Coordinates the Cell Proliferation and PD-L1 Expression via YAP in Lung Adenocarcinoma.

The extracellular matrix (ECM) exerts physiological activity, facilitates cell-to-cell communication, promotes cell proliferation and metastasis, and provides mechanical support for tumor cells. The development of solid tumors is often associated with increased stiffness. A stiff ECM promotes mechanotransduction, and the predominant transcription factors implicated in this phenomenon are YAP/TAZ, ß-catenin, and NF-κB. In this study, we aimed to investigate whether YAP is a critical mediator linking matrix stiffness and PD-L1 in lung adenocarcinoma. We confirmed that YAP, PD-L1, and Ki-67, a marker of cell proliferation, increase as the matrix stiffness increases in vitro using the lung adenocarcinoma cell lines PC9 and HCC827 cells. The knockdown of YAP decreased the expression of PD-L1 and Ki-67, and conversely, the overexpression of YAP increased the expression of PD-L1 and K-67 in a stiff-matrix environment (20.0 kPa). Additionally, lung cancer cells were cultured in a 3D environment, which provides a more physiologically relevant setting, and compared to the results obtained from 2D culture. Similar to the findings in 2D culture, it was confirmed that YAP influenced the expression of PD-L1 and K-67 in the 3D culture experiment. Our results suggest that matrix stiffness controls PD-L1 expression via YAP activation, ultimately contributing to cell proliferation.

Data-driven monitoring for phase II clinical trial designs based on percentile event time test.

The goal of phase II clinical trials is to evaluate the therapeutic efficacy of a new drug. Some investigators want to use the time-to-event endpoint as the primary endpoint of the phase II study to see the improvement of the therapeutic efficacy of a new drug in median survival time. Recently, median event time test (METT) has been proposed to provide a simple and straightforward rule which compares the observed median survival time with the prespecified threshold. However, median survival time would not be observed during the trial if the drug performs well and indeed cures most patients or if the accrual rate is so fast. To address the issues in clinical practice, we first propose a percentile event time test (PETT), which generalizes METT to any percentile of the survival time, and develop data-driven monitoring for phase II clinical trial designs based on PETT. We evaluate the performance of the method through simulations and illustrate the proposed method with a trial example.