Generalized monotone incremental forward stagewise method for modeling count data: application predicting micronuclei frequency

EMT activates exocytotic Rabs to coordinate invasion and immunosuppression in lung cancer

Epithelial-to-mesenchymal transition (EMT) underlies immunosuppression, drug resistance, and metastasis in epithelial malignancies. However, the way in which EMT orchestrates disparate biological processes remains unclear. Here, we identify an EMT-activated vesicular trafficking network that coordinates promigratory focal adhesion dynamics with an immunosuppressive secretory program in lung adenocarcinoma (LUAD). The EMT-activating transcription factor ZEB1 drives exocytotic vesicular trafficking by relieving Rab6A, Rab8A, and guanine nucleotide exchange factors from miR-148a-dependent silencing, thereby facilitating MMP14-dependent focal adhesion turnover in LUAD cells and autotaxin-mediated CD8+ T cell exhaustion, indicating that cell-intrinsic and extrinsic processes are linked through a microRNA that coordinates vesicular trafficking networks. Blockade of ZEB1-dependent secretion reactivates antitumor immunity and negates resistance to PD-L1 immune checkpoint blockade, an important clinical problem in LUAD. Thus, EMT activates exocytotic Rabs to drive a secretory program that promotes invasion and immunosuppression in LUAD.

DNA Damage/Repair Management in Cancers

DNA damage is well recognized as a critical factor in cancer development and progression. DNA lesions create an abnormal nucleotide or nucleotide fragment, causing a break in one or both chains of the DNA strand. When DNA damage occurs, the possibility of generated mutations increases. Genomic instability is one of the most important factors that lead to cancer development. DNA repair pathways perform the essential role of correcting the DNA lesions that occur from DNA damaging agents or carcinogens, thus maintaining genomic stability. Inefficient DNA repair is a critical driving force behind cancer establishment, progression and evolution. A thorough understanding of DNA repair mechanisms in cancer will allow for better therapeutic intervention. In this review we will discuss the relationship between DNA damage/repair mechanisms and cancer, and how we can target these pathways.

Penalized negative binomial models for modeling an overdispersed count outcome with a high-dimensional predictor space: Application predicting micronuclei frequency

Chromosomal aberrations, such as micronuclei (MN), have served as biomarkers of genotoxic exposure and cancer risk. Guidelines for the process of scoring MN have been presented by the HUman MicroNucleus (HUMN) project. However, these guidelines were developed for assay performance but do not address how to statistically analyze the data generated by the assay. This has led to the application of various statistical methods that may render different interpretations and conclusions. By combining MN with data from other high-throughput genomic technologies such as gene expression microarray data, we may elucidate molecular features involved in micronucleation. Traditional methods that can model discrete (synonymously, count) data, such as MN frequency, require that the number of explanatory variables (P) is less than the number of samples (N). Due to this limitation, penalized models have been developed to enable model fitting for such over-parameterized datasets. Because penalized models in the discrete response setting are lacking, particularly when the count outcome is over-dispersed, herein we present our penalized negative binomial regression model that can be fit when P > N. Using simulation studies we demonstrate the performance of our method in comparison to commonly used penalized Poisson models when the outcome is over-dispersed and applied it to MN frequency and gene expression data collected as part of the Norwegian Mother and Child Cohort Study. Our countgmifs R package is available for download from the Comprehensive R Archive Network and can be applied to datasets having a discrete outcome that is either Poisson or negative binomial distributed and a high-dimensional covariate space.