Applied models and molecular characteristics of small cell lung cancer.

Small cell lung cancer (SCLC) is a highly aggressive type of cancer frequently diagnosed with metastatic spread, rendering it surgically unresectable for the majority of patients. Although initial responses to platinum-based therapies are often observed, SCLC invariably relapses within months, frequently developing drug-resistance ultimately contributing to short overall survival rates. Recently, SCLC research aimed to elucidate the dynamic changes in the genetic and epigenetic landscape. These have revealed distinct subtypes of SCLC, each characterized by unique molecular signatures. The recent understanding of the molecular heterogeneity of SCLC has opened up potential avenues for precision medicine, enabling the development of targeted therapeutic strategies. In this review, we delve into the applied models and computational approaches that have been instrumental in the identification of promising drug candidates. We also explore the emerging molecular diagnostic tools that hold the potential to transform clinical practice and patient care.

Predicting drug response of small cell lung cancer cell lines based on enrichment analysis of complex gene signatures.

Advances in the field of genomics and transcriptomics have enabled researchers to identify gene signatures related to development and treatment of Small Cell Lung Cancer. In most cases, complex gene expression patterns are identified, comprising of genes with differential behavior. Most tools use single-genes as predictors of drug response, with only limited options for multi-gene use. Here we examine the potential of predicting drug response using these complex gene expression signatures by employing clustering and signal enrichment in Small Cell Lung Cancer. Our results demonstrate clustering genes from complex expression patterns helps identify differential activity of gene groups with alternate function which can then be used to predict drug response.

Genetic determinants of fungi-induced ROS production are associated with the risk of invasive pulmonary aspergillosis.

Reactive oxygen species (ROS) are an essential component of the host defense against fungal infections. However, little is known about how common genetic variation affects ROS-mediated antifungal host defense. In the present study, we investigated the genetic factors that regulate ROS production capacity in response to the two human fungal pathogens: Candida albicans and Aspergillus fumigatus. We investigated fungal-stimulated ROS production by immune cells isolated from a population-based cohort of approximately 200 healthy individuals (200FG cohort), and mapped ROS-quantitative trait loci (QTLs). We identified several genetic loci that regulate ROS levels (P < 9.99 × 10-6), with some of these loci being pathogen-specific, and others shared between the two fungi. These ROS-QTLs were investigated for their influence on the risk of invasive pulmonary aspergillosis (IPA) in a disease relevant context. We stratified hematopoietic stem-cell transplant (HSCT) recipients based on the donor's SNP genotype and tested their impact on the risk of IPA. We identified rs4685368 as a ROS-QTL locus that was significantly associated with an increased risk of IPA after controlling for patient age and sex, hematological malignancy, type of transplantation, conditioning regimen, acute graft-versus-host-disease grades III-IV, and antifungal prophylaxis. Collectively, this data provides evidence that common genetic variation can influence ROS production capacity, and, importantly, the risk of developing IPA among HSCT recipients. This evidence warrants further research for patient stratification based on the genetic profiling that would allow the identifications of patients at high-risk for an invasive fungal infection, and who would benefit the most from a preventive strategy.