Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEry.

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity in health and disease. However, the lack of physical relationships among dissociated cells has limited its applications. To address this issue, we present CeLEry (Cell Location recovEry), a supervised deep learning algorithm that leverages gene expression and spatial location relationships learned from spatial transcriptomics to recover the spatial origins of cells in scRNA-seq. CeLEry has an optional data augmentation procedure via a variational autoencoder, which improves the method's robustness and allows it to overcome noise in scRNA-seq data. We show that CeLEry can infer the spatial origins of cells in scRNA-seq at multiple levels, including 2D location and spatial domain of a cell, while also providing uncertainty estimates for the recovered locations. Our comprehensive benchmarking evaluations on multiple datasets generated from brain and cancer tissues using Visium, MERSCOPE, MERFISH, and Xenium demonstrate that CeLEry can reliably recover the spatial location information for cells using scRNA-seq data.

Zero-inflated Poisson models with measurement error in the response.

Zero-inflated count data arise frequently from genomics studies. Analysis of such data is often based on a mixture model which facilitates excess zeros in combination with a Poisson distribution, and various inference methods have been proposed under such a model. Those analysis procedures, however, are challenged by the presence of measurement error in count responses. In this article, we propose a new measurement error model to describe error-contaminated count data. We show that ignoring the measurement error effects in the analysis may generally lead to invalid inference results, and meanwhile, we identify situations where ignoring measurement error can still yield consistent estimators. Furthermore, we propose a Bayesian method to address the effects of measurement error under the zero-inflated Poisson model and discuss the identifiability issues. We develop a data-augmentation algorithm that is easy to implement. Simulation studies are conducted to evaluate the performance of the proposed method. We apply our method to analyze the data arising from a prostate adenocarcinoma genomic study.

Awareness of the Harms of Continued Smoking Among Cancer Survivors.

BACKGROUND: Smoking cessation is an integral part of cancer survivorship. To help improve survivorship education, clinicians need an understanding of patient awareness of the harms of continued smoking. METHODS: Cancer survivors from Princess Margaret Cancer Centre (Toronto, ON) were surveyed on their awareness of the harms of continued smoking on cancer-related outcomes. Multivariable logistic regression models assessed factors associated with awareness and whether awareness was associated with subsequent cessation among smokers at diagnosis. RESULTS: Among 1118 patients, 23% were current smokers pre-diagnosis and 54% subsequently quit; 25% had lung and 30% head and neck cancers. Many patients reported being unaware that continued smoking results in greater cancer surgical complications (53%), increased radiation side effects (62%), decreased quality of life during chemotherapy (51%), decreased chemotherapy or radiation efficacy (57%), increased risk of death (40%), and increased development of second primaries (38%). Being a current smoker was associated with greater lack of awareness of some of these smoking harms (aORs = 1.53-2.20, P < 0.001-0.02), as was exposure to any second-hand smoke (aORs = 1.45-1.53, P = 0.006-0.04) and being diagnosed with early stage cancer (aORs = 1.38-2.31, P < 0.001-0.06). Among current smokers, those with fewer pack-years, being treated for cure, or had a non-tobacco-related cancer were more likely unaware. Awareness that continued tobacco use worsen quality of life after chemotherapy was associated with subsequent cessation (aOR = 2.26, P = 0.006). CONCLUSIONS: Many cancer survivors are unaware that continued smoking can negatively impact cancer-related outcomes. The impact of educating patients about the potential harms of continued smoking when discussing treatment plans should be further evaluated.

Body mass index and prognosis in patients with head and neck cancer.

BACKGROUND: Body mass index (BMI) has been associated variably with head and neck cancer outcomes. We evaluated the association between BMI at either diagnosis or at early adulthood head and neck cancer outcomes. METHODS: Patients with invasive head and neck squamous cell cancer at Princess Margaret Cancer Centre in Toronto, Canada, were surveyed on tobacco and alcohol exposure, performance status, comorbidities, and BMI at diagnosis. A subset also had data collected for BMI at early adulthood. RESULTS: With a median follow-up of 2.5 years, in 1279 analyzed patients, being overweight (hazard ratio [HR], 0.55; 95% confidence interval [CI], 0.4-0.8; p = .001) at diagnosis was associated with improved survival when compared with individuals with normal weight. In contrast, underweight patients at diagnosis were associated with a worse outcome (HR, 1.89; 95% CI, 1.2-3.1; p < .01). CONCLUSION: Being underweight at diagnosis was an independent, adverse prognostic factor, whereas being overweight conferred better prognosis. BMI in early adulthood was not associated strongly with head and neck cancer outcomes. © 2017 Wiley Periodicals, Inc. Head Neck 39: 1226-1233, 2017.

Validation of microRNA pathway polymorphisms in esophageal adenocarcinoma survival.

Polymorphisms in miRNA and miRNA pathway genes have been previously associated with cancer risk and outcome, but have not been studied in esophageal adenocarcinoma outcomes. Here, we evaluate candidate miRNA pathway polymorphisms in esophageal adenocarcinoma prognosis and attempt to validate them in an independent cohort of esophageal adenocarcinoma patients. Among 231 esophageal adenocarcinoma patients of all stages/treatment plans, 38 candidate genetic polymorphisms (17 biogenesis, 9 miRNA targets, 5 pri-miRNA, 7 pre-miRNA) were genotyped and analyzed. Cox proportional hazard models adjusted for sociodemographic and clinicopathological covariates helped assess the association of genetic polymorphisms with overall survival (OS) and progression-free survival (PFS). Significantly associated polymorphisms were then evaluated in an independent cohort of 137 esophageal adenocarcinoma patients. Among the 231 discovery cohort patients, 86% were male, median diagnosis age was 64 years, 34% were metastatic at diagnosis, and median OS and PFS were 20 and 12 months, respectively. GEMIN3 rs197412 (aHR = 1.37, 95%CI: [1.04-1.80]; P = 0.02), hsa-mir-124-1 rs531564 (aHR = 0.60, 95% CI: [0.53-0.90]; P = 0.05), and KIAA0423 rs1053667 (aHR = 0.51, 95% CI: [0.28-0.96]; P = 0.04) were found associated with OS. Furthermore, GEMIN3 rs197412 (aHR = 1.33, 95% CI: [1.03-1.74]; P = 0.03) and KRT81 rs3660 (aHR = 1.29, 95% CI: [1.01-1.64]; P = 0.04) were found associated with PFS. Although none of these polymorphisms were significant in the second cohort, hsa-mir-124-1 rs531564 and KIAA0423 rs1053667 had trends in the same direction; when both cohorts were combined together, GEMIN3 rs197412, hsa-mir-124-1 rs531564, and KIAA0423 rs1053667 remained significantly associated with OS. We demonstrate the association of multiple miRNA pathway polymorphisms with esophageal adenocarcinoma prognosis in a discovery cohort of patients, which did not validate in a separate cohort but had consistent associations in the pooled cohort. Larger studies are required to confirm/validate the prognostic value of these polymorphisms in esophageal adenocarcinoma.