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Abstract Background Zilovertamab is a humanized monoclonal antibody targeting ROR1, an onco-embryonic antigen expressed by malignant cells of a variety of solid tumors, including breast cancer. A prior phase 1 study showed that zilovertamab was well tolerated and effective in inhibiting ROR1-signaling, which leads to activation of ERK1/2, NF-κB, and NRF2 target genes. This phase 1b study evaluated the safety and tolerability of zilovertamab with paclitaxel in patients with advanced breast cancer. Patients and methods Eligible patients had locally advanced, unresectable, or metastatic HER2− breast cancer with Eastern Cooperative Group performance status of 0–2 and without prior taxane therapy in the advanced setting. Study treatment included 600 mg of zilovertamab administered intravenously (IV) on Days 1 and 15 of Cycle 1 and then Day 1 of each 28-day cycle along with paclitaxel weekly at 80 mg/m2 IV. Results Study patients had received a median of 4 prior therapies (endocrine therapy + chemotherapy) for locally advanced, unresectable, or metastatic disease. No patient discontinued therapy due to toxicity ascribed to zilovertamab. Adverse events were consistent with the known safety profile of paclitaxel. Of 16 patients, 6 (38%) had a partial response, and 6/16 (38%) patients had stable disease as best tumor response. Conclusion The combination of zilovertamab and paclitaxel was safe and well tolerated in heavily pre-treated advanced breast cancer patients. Further evaluation of ROR1 targeting in breast cancer patients with zilovertamab is warranted. Trial Registration: NCT02776917. Registered on ClinicalTrials.gov on 05/17/2016.

Abstract Background The growing amount of high dimensional biomolecular data has spawned new statistical and computational models for risk prediction and disease classification. Yet, many of these methods do not yield biologically interpretable models, despite offering high classification accuracy. An exception, the top-scoring pair (TSP) algorithm derives parameter-free, biologically interpretable single pair decision rules that are accurate and robust in disease classification. However, standard TSP methods do not accommodate covariates that could heavily influence feature selection for the top-scoring pair. Herein, we propose a covariate-adjusted TSP method, which uses residuals from a regression of features on the covariates for identifying top scoring pairs. We conduct simulations and a data application to investigate our method, and compare it to existing classifiers, LASSO and random forests. Results Our simulations found that features that were highly correlated with clinical variables had high likelihood of being selected as top scoring pairs in the standard TSP setting. However, through residualization, our covariate-adjusted TSP was able to identify new top scoring pairs, that were largely uncorrelated with clinical variables. In the data application, using patients with diabetes (n = 977) selected for metabolomic profiling in the Chronic Renal Insufficiency Cohort (CRIC) study, the standard TSP algorithm identified (valine-betaine, dimethyl-arg) as the top-scoring metabolite pair for classifying diabetic kidney disease (DKD) severity, whereas the covariate-adjusted TSP method identified the pair (pipazethate, octaethylene glycol) as top-scoring. Valine-betaine and dimethyl-arg had, respectively, ≥ 0.4 absolute correlation with urine albumin and serum creatinine, known prognosticators of DKD. Thus without covariate-adjustment the top-scoring pair largely reflected known markers of disease severity, whereas covariate-adjusted TSP uncovered features liberated from confounding, and identified independent prognostic markers of DKD severity. Furthermore, TSP-based methods achieved competitive classification accuracy in DKD to LASSO and random forests, while providing more parsimonious models. Conclusions We extended TSP-based methods to account for covariates, via a simple, easy to implement residualizing process. Our covariate-adjusted TSP method identified metabolite features, uncorrelated from clinical covariates, that discriminate DKD severity stage based on the relative ordering between two features, and thus provide insights into future studies on the order reversals in early vs advanced disease states.

Abstract Background The COVID-19 pandemic disrupted Alzheimer disease randomized clinical trials (RCTs), forcing investigators to make changes in the conduct of such trials while endeavoring to maintain their validity. Changing ongoing RCTs carries risks for biases and threats to validity. To understand the impact of exigent modifications due to COVID-19, we examined several scenarios in symptomatic and disease modification trials that could be made. Methods We identified both symptomatic and disease modification Alzheimer disease RCTs as exemplars of those that would be affected by the pandemic and considered the types of changes that sponsors could make to each. We modeled three scenarios for each of the types of trials using existing datasets, adjusting enrollment, follow-ups, and dropouts to examine the potential effects COVID-19-related changes. Simulations were performed that accounted for completion and dropout patterns using linear mixed effects models, modeling time as continuous and categorical. The statistical power of the scenarios was determined. Results Truncating both symptomatic and disease modification trials led to underpowered trials. By contrast, adapting the trials by extending the treatment period, temporarily stopping treatment, delaying outcomes assessments, and performing remote assessment allowed for increased statistical power nearly to the level originally planned. Discussion These analyses support the idea that disrupted trials under common scenarios are better continued and extended even in the face of dropouts, treatment disruptions, missing outcomes, and other exigencies and that adaptations can be made that maintain the trials’ validity. We suggest some adaptive methods to do this noting that some changes become under-powered to detect the original effect sizes and expected outcomes. These analyses provide insight to better plan trials that are resilient to unexpected changes to the medical, social, and political milieu.

Abstract Background Immunotherapeutic regulation of the tumor microenvironment in prostate cancer patients is not understood. Most antibody immunotherapies have not succeeded in prostate cancer. We showed previously that high-risk PCa patients have a higher density of tumor infiltrating B-cells in prostatectomy specimens. In mouse models, anti-CD20 antibody ablation of B-cells delayed PCa regrowth post-treatment. We sought to determine whether neoadjuvant anti-CD20 immunotherapy with rituximab could reduce CD20+ B cell infiltration of prostate tumors in patients. Methods An open label, single arm clinical trial enrolled eight high-risk PCa patients to receive one cycle of neoadjuvant rituximab prior to prostatectomy. Eleven clinical specimens with similar characteristics were selected as controls. Treated and control samples were concurrently stained for CD20 and digitally scanned in a blinded fashion. A new method of digital image quantification of lymphocytes was applied to prostatectomy sections of treated and control cases. CD20 density was quantified by a deconvolution algorithm in pathologist-marked tumor and adjacent regions. Statistical significance was assessed by one sided Welch’s t-test, at 0.05 level using a gatekeeper strategy. Secondary outcomes included CD3+ T-cell and PD-L1 densities. Results Mean CD20 density in the tumor regions of the treated group was significantly lower than the control group (p = 0.02). Mean CD3 density in the tumors was significantly decreased in the treated group (p = 0.01). CD20, CD3 and PD-L1 staining primarily occurred in tertiary lymphoid structures (TLS). Neoadjuvant rituximab was well-tolerated and decreased B-cell and T-cell density within high-risk PCa tumors compared to controls. Conclusions This is the first study to treat patients prior to surgical prostate removal with an immunotherapy that targets B-cells. Rituximab treatment reduced tumor infiltrating B and T-cell density especially in TLSs, thus, demonstrating inter-dependence between B- and T-cells in prostate cancer and that Rituximab can modify the immune environment in prostate tumors. Future studies will determine who may benefit from using rituximab to improve their immune response against prostate cancer. Trial registration NCT01804712, March 5th, 2013 https://clinicaltrials.gov/ct2/show/NCT01804712?cond=NCT01804712&draw=2&rank=1

Abstract Background Electronic cigarettes (e-cigarettes) are heavily marketed and widely perceived as helpful for quitting or reducing smoking intensity. We test whether ever-use of e-cigarettes among early adopters was associated with: 1) increased cigarette smoking cessation; and 2) reduced cigarette consumption. Methods A representative cohort of U.S. smokers (N = 2454) from the 2010 Tobacco Use Supplement to the Current Population Survey (TUS-CPS) was re-interviewed 1 year later. Outcomes were smoking cessation for 30+ days and change in cigarette consumption at follow-up. E-cigarettes use was categorized as for cessation purposes or for another reason. Multivariate regression was used to adjust for demographics and baseline cigarette dependence level. Results In 2011, an estimated 12 % of adult U.S. smokers had ever used e-cigarettes, and 41 % of these reported use to help quit smoking. Smokers who had used e-cigarettes for cessation were less likely to be quit for 30+ days at follow-up, compared to never-users who tried to quit (11.1 % vs 21.6 %; ORadj = 0.44, 95 % CI = 0.2–0.8). Among heavier smokers at baseline (15+ cigarettes per day (CPD)), ever-use of e-cigarettes was not associated with change in smoking consumption. Lighter smokers (