Gel Stent Versus Trabeculectomy: The Randomized, Multicenter, Gold-Standard Pathway Study (GPS) of Effectiveness and Safety at 12 Months.

PURPOSE: To compare effectiveness and safety of the gel stent to trabeculectomy in open-angle glaucoma (OAG). DESIGN: Prospective, randomized, multicenter, noninferiority study. METHODS: Patients with OAG and intraocular pressure (IOP) 15 to 44 mm Hg on topical IOP-lowering medication were randomized 2:1 to gel stent implantation or trabeculectomy. Primary end point (surgical success): percentage of patients at month 12 achieving ≥20% IOP reduction from baseline without medication increase, clinical hypotony, vision loss to counting fingers, or secondary surgical intervention (SSI) in a noninferiority test with 24% margins. Secondary end points (month 12) included mean IOP and medication count, postoperative intervention rate, visual recovery, and patient-reported outcomes (PROs). Safety end points included adverse events (AEs). RESULTS: At month 12, the gel stent was statistically noninferior to trabeculectomy (between-treatment difference [Δ], -6.1%; 95% CI, -22.9%, 10.8%); 62.1% and 68.2% achieved the primary end point, respectively (P=.487); mean IOP and medication count reductions from baseline were significant (P<.001); and the IOP change-related Δ (2.8 mm Hg) favored trabeculectomy (P=.024). The gel stent resulted in fewer eyes requiring in-office postoperative interventions (P=.024 after excluding laser suture lysis), faster visual recovery (P≤.048), and greater 6-month improvements in visual function problems (ie, PROs; P≤.022). The most common AEs were reduced visual acuity at any time (gel stent, 38.9%; trabeculectomy, 54.5%) and hypotony (IOP <6 mm Hg at any time) (gel stent, 23.2%; trabeculectomy, 50.0%). CONCLUSIONS: At month 12, the gel stent was statistically noninferior to trabeculectomy, per the percentage of patients achieving ≥20% IOP reduction from baseline without medication increase, clinical hypotony, vision loss to counting fingers, or SSI. Trabeculectomy achieved a statistically lower mean IOP, numerically lower failure rate, and numerically lower need for supplemental medications. The gel stent resulted in fewer postoperative interventions, better visual recovery, and fewer AEs.

A novel prognostic biomarker CD3G that correlates with the tumor microenvironment in cervical cancer.

Cervical cancer (CESC) is the fourth most common and death-causing gynecological cancer, mostly induced by infection of human papillomavirus (HPV). Multiple components of the tumor microenvironment (TME), such as tumor infiltrating immune cells, could be targets of immunotherapy for HPV-related CESC. However, little is known about the TME of CESC until now. Here, we aimed to uncover the pathogenesis as well as to identify novel biomarkers to predict prognosis and immunotherapy efficacy for CESC. Combining the transcriptomic data and clinical characteristics, we identified differentially expressed genes in CESC samples from TCGA database by comparing the two groups with different ImmuneScore and StromalScore. Next, we detected ten key genes based on the PPI network and survival analyses with the univariate Cox regression model. Thereafter, we focused on CD3G, the only gene exhibiting increased RNA and protein expression in tumors by multiple analyses. Higher CD3G expression was associated with better survival; and it was also significantly associated with immune-related pathways through GSEA analysis. Furthermore, we found that CD3G expression was correlated with 16 types of TICs. Single cell RNA-sequencing data of CD3G in lymphocytes subgroup indicated its possible role in HPV defense. Hence, CD3G might be a novel biomarker in prognosis and immunotherapy for CESC patients.

Engineering of Neutrophil Membrane Camouflaging Nanoparticles Realizes Targeted Drug Delivery for Amplified Antitumor Therapy.

PURPOSE: Although the neutrophil membrane (NM)-based nanoparticulate delivery system has exhibited rapid advances in tumor targeting stemmed from the inherited instinct, the antitumor effect requires further improvement due to inefficient cellular internalization in the absence of specific interactions between NM-coated nanoparticles and tumor cells. METHODS: Herein, we fabricated drug-paclitaxel loaded NM camouflaging nanoparticles (TNM-PN) modified with tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), favorable for the cellular internalization. RESULTS: The results showed that TNM-PN exerted a significant cytotoxicity to tumor cells by TRAIL-mediated endocytosis and strong adhesion to inflamed endothelial cells in vitro. Due to TRAIL modification as well as the adhesive interactions between neutrophil and inflamed tumor vascular endothelial cells, tumors in TNM-PN group exhibited almost 2-fold higher fluorescence intensities than that of NM camouflaging nanoparticles and 3-fold higher than that of bare nanoparticles, respectively. Significant tumor inhibition and survival rates of mice were achieved in TNM-PN group as a consequence of prolonged blood circulations to 48 h and preferential tumor accumulations, which was ascribed to targeting adhesion originated from NM to immune evasion and subsequent excellent cellular internalization. CONCLUSION: The research unveiled a novel strategy of amplifying cellular internalization based on NM coating nanotechnology to boost antitumor efficacy.

Bayesian Two-stage Biomarker-based Adaptive Design for Targeted Therapy Development.

We propose a Bayesian two-stage biomarker-based adaptive randomization (AR) design for the development of targeted agents. The design has three main goals: (1) to test the treatment efficacy, (2) to identify prognostic and predictive markers for the targeted agents, and (3) to provide better treatment for patients enrolled in the trial. To treat patients better, both stages are guided by the Bayesian AR based on the individual patient's biomarker profiles. The AR in the first stage is based on a known marker. A Go/No-Go decision can be made in the first stage by testing the overall treatment effects. If a Go decision is made at the end of the first stage, a two-step Bayesian lasso strategy will be implemented to select additional prognostic or predictive biomarkers to refine the AR in the second stage. We use simulations to demonstrate the good operating characteristics of the design, including the control of per-comparison type I and type II errors, high probability in selecting important markers, and treating more patients with more effective treatments. Bayesian adaptive designs allow for continuous learning. The designs are particularly suitable for the development of multiple targeted agents in the quest of personalized medicine. By estimating treatment effects and identifying relevant biomarkers, the information acquired from the interim data can be used to guide the choice of treatment for each individual patient enrolled in the trial in real time to achieve a better outcome. The design is being implemented in the BATTLE-2 trial in lung cancer at the MD Anderson Cancer Center.

Bayesian two-step Lasso strategy for biomarker selection in personalized medicine development for time-to-event endpoints.

Clinical trial designs for targeted therapy development are progressing toward the goal of personalized medicine. Motivated by the need of ongoing efforts to develop targeted agents for lung cancer patients, we propose a Bayesian two-step Lasso procedure for biomarker selection under the proportional hazards model. We seek to identify the key markers that are either prognostic or predictive with respect to treatment from a large number of biomarkers. In the first step of our two-step strategy, we use the Bayesian group Lasso to identify the important marker groups, wherein each group contains the main effect of a single marker and its interactions with treatments. Applying a loose selection criterion in the first step, the goal of first step is to screen out unimportant biomarkers. In the second step, we zoom in to select the individual markers and interactions between markers and treatments in order to identify prognostic or predictive markers using the Bayesian adaptive Lasso. Our strategy takes a full Bayesian approach and is built upon rapid advancement of Lasso methodologies with variable selection. The proposed method is generally applicable to the development of targeted therapies in clinical trials. Our simulation study demonstrates the good performance of the two-step Lasso: Important biomarkers can typically be selected with high probabilities, and unimportant markers can be effectively eliminated from the model.

A five-gene and corresponding protein signature for stage-I lung adenocarcinoma prognosis.

PURPOSE: Identification of effective markers for outcome is expected to improve the clinical management of non-small cell lung cancer (NSCLC). Here, we assessed in NSCLC the prognostic efficacy of genes, which we had previously found to be differentially expressed in an in vitro model of human lung carcinogenesis. EXPERIMENTAL DESIGN: Prediction algorithms and risk-score models were applied to the expression of the genes in publicly available NSCLC expression data sets. The prognostic capacity of the immunohistochemical expression of proteins encoded by these genes was also tested using formalin-fixed paraffin-embedded (FFPE) tissue specimens from 156 lung adenocarcinomas and 79 squamous cell carcinomas (SCCs). RESULTS: The survival of all-stages (P < 0.001, HR = 2.0) or stage-I (P < 0.001, HR = 2.84) adenocarcinoma patients that expressed the five-gene in vitro lung carcinogenesis model (FILM) signature was significantly poorer than that of patients who did not. No survival differences were observed between SCCs predicted to express or lack FILM signature. Moreover, all stages (P < 0.001, HR = 1.95) or stage-I (P = 0.001, HR = 2.6) adenocarcinoma patients predicted to be at high risk by FILM transcript exhibited significantly worse survival than patients at low risk. Furthermore, the corresponding protein signature was associated with poor survival (all stages, P < 0.001, HR = 3.6; stage-I, P < 0.001, HR = 3.5; stage-IB, P < 0.001, HR = 4.6) and mortality risk (all stages, P = 0.001, HR = 4.0; stage-I, P = 0.01, HR = 3.4; stage-IB, P < 0.001, HR = 7.2) in lung adenocarcinoma patients. CONCLUSIONS: Our findings highlight a gene and corresponding protein signature with effective capacity for identification of stage-I lung adenocarcinoma patients with poor prognosis that are likely to benefit from adjuvant therapy.

Reconstructive strategies for partial sacrectomy defects based on surgical outcomes.

BACKGROUND: Partial sacrectomy creates heterogeneous defects amenable to a wide variety of reconstructive techniques. Important factors to guide the choice of reconstruction technique have not been elucidated. The purpose of this study was to determine what factors best guide selection of reconstructive techniques following partial sacrectomy to optimize outcomes. METHODS: The authors conducted a 15-year retrospective review of all consecutive partial sacrectomy reconstructions performed at The University of Texas M. D. Anderson Cancer Center. They analyzed the relationship of patient, tumor, and treatment factors, including defect volume, to flap choice and surgical outcome. Defect volume was categorized as small (<400 cm3), moderate (400 to 2000 cm3), or large (>2000 cm3). RESULTS: Fifty patients underwent partial sacrectomy reconstruction: 25 (50 percent) gluteus-based, 13 (26 percent) vertical rectus abdominis musculocutaneous, four (8 percent) gluteal thigh, four (8 percent) paraspinous, and four (8 percent) other. The distribution of small, medium, and large defect volumes was 15 (30 percent), 25 (50 percent), and 10 (20 percent), respectively. Resection volume as a continuous variable (p = 0.023) and as a categorical variable (p = 0.016) was significantly associated with the type of reconstruction used. The overall complication rate was high (44 percent), but no factors, including flap choice, were significantly associated with complications. Defect volume was significantly correlated with time to tumor recurrence (Cox regression). The rates of wound-healing complications, however, were similar irrespective of defect volume. CONCLUSIONS: Resection volume was the major factor determining flap selection. Despite the worsening functional morbidity and oncologic prognosis associated with increased resection volumes, wound-related complications were similar among defect volume groups.

A simulation study for comparing testing statistics in response-adaptive randomization.

BACKGROUND: Response-adaptive randomizations are able to assign more patients in a comparative clinical trial to the tentatively better treatment. However, due to the adaptation in patient allocation, the samples to be compared are no longer independent. At large sample sizes, many asymptotic properties of test statistics derived for independent sample comparison are still applicable in adaptive randomization provided that the patient allocation ratio converges to an appropriate target asymptotically. However, the small sample properties of commonly used test statistics in response-adaptive randomization are not fully studied. METHODS: Simulations are systematically conducted to characterize the statistical properties of eight test statistics in six response-adaptive randomization methods at six allocation targets with sample sizes ranging from 20 to 200. Since adaptive randomization is usually not recommended for sample size less than 30, the present paper focuses on the case with a sample of 30 to give general recommendations with regard to test statistics for contingency tables in response-adaptive randomization at small sample sizes. RESULTS: Among all asymptotic test statistics, the Cook's correction to chi-square test (TMC) is the best in attaining the nominal size of hypothesis test. The William's correction to log-likelihood ratio test (TML) gives slightly inflated type I error and higher power as compared with TMC, but it is more robust against the unbalance in patient allocation. TMC and TML are usually the two test statistics with the highest power in different simulation scenarios. When focusing on TMC and TML, the generalized drop-the-loser urn (GDL) and sequential estimation-adjusted urn (SEU) have the best ability to attain the correct size of hypothesis test respectively. Among all sequential methods that can target different allocation ratios, GDL has the lowest variation and the highest overall power at all allocation ratios. The performance of different adaptive randomization methods and test statistics also depends on allocation targets. At the limiting allocation ratio of drop-the-loser (DL) and randomized play-the-winner (RPW) urn, DL outperforms all other methods including GDL. When comparing the power of test statistics in the same randomization method but at different allocation targets, the powers of log-likelihood-ratio, log-relative-risk, log-odds-ratio, Wald-type Z, and chi-square test statistics are maximized at their corresponding optimal allocation ratios for power. Except for the optimal allocation target for log-relative-risk, the other four optimal targets could assign more patients to the worse arm in some simulation scenarios. Another optimal allocation target, RRSIHR, proposed by Rosenberger and Sriram (Journal of Statistical Planning and Inference, 1997) is aimed at minimizing the number of failures at fixed power using Wald-type Z test statistics. Among allocation ratios that always assign more patients to the better treatment, RRSIHR usually has less variation in patient allocation, and the values of variation are consistent across all simulation scenarios. Additionally, the patient allocation at RRSIHR is not too extreme. Therefore, RRSIHR provides a good balance between assigning more patients to the better treatment and maintaining the overall power. CONCLUSION: The Cook's correction to chi-square test and Williams' correction to log-likelihood-ratio test are generally recommended for hypothesis test in response-adaptive randomization, especially when sample sizes are small. The generalized drop-the-loser urn design is the recommended method for its good overall properties. Also recommended is the use of the RRSIHR allocation target.

Identification of gene signatures and molecular markers for human lung cancer prognosis using an in vitro lung carcinogenesis system.

Lung cancer continues to be a major deadly malignancy. The mortality of this disease could be reduced by improving the ability to predict cancer patients' survival. We hypothesized that genes differentially expressed among cells constituting an in vitro human lung carcinogenesis model consisting of normal, immortalized, transformed, and tumorigenic bronchial epithelial cells are relevant to the clinical outcome of non-small cell lung cancer (NSCLC). Multidimensional scaling, microarray, and functional pathways analyses of the transcriptomes of the above cells were done and combined with integrative genomics to incorporate the microarray data with published NSCLC data sets. Up-regulated (n = 301) and down-regulated genes (n = 358) displayed expression level variation across the in vitro model with progressive changes in cancer-related molecular functions. A subset of these genes (n = 584) separated lung adenocarcinoma clinical samples (n = 361) into two clusters with significant survival differences. Six genes, UBE2C, TPX2, MCM2, MCM6, FEN1, and SFN, selected by functional array analysis, were also effective in prognosis. The mRNA and protein levels of one these genes-UBE2C-were significantly up-regulated in NSCLC tissue relative to normal lung and increased progressively in lung lesions. Moreover, stage I NSCLC patients with positive UBE2C expression exhibited significantly poorer overall and progression-free survival than patients with negative expression. Our studies with this in vitro model have lead to the identification of a robust six-gene signature, which may be valuable for predicting the survival of lung adenocarcinoma patients. Moreover, one of those genes, UBE2C, seems to be a powerful biomarker for NSCLC survival prediction.