Acquired resistance to cyclin-dependent kinases 4 and 6 (CDK4/6) inhibition in estrogen receptor-positive (ER+) breast cancer remains a significant clinical challenge. Efforts to uncover the mechanisms underlying resistance are needed to establish clinically actionable targets effective against resistant tumors. In this study, we sought to identify differentially expressed genes (DEGs) associated with acquired resistance to palbociclib in ER+ breast cancer. We performed next-generation transcriptomic RNA sequencing (RNA-seq) and pathway analysis in ER+ MCF7 palbociclib-sensitive (MCF7/pS) and MCF7 palbociclib-resistant (MCF7/pR) cells. We identified 2183 up-regulated and 1548 down-regulated transcripts in MCF7/pR compared to MCF7/pS cells. Functional analysis of the DEGs using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database identified several pathways associated with breast cancer, including ‘cell cycle’, ‘DNA replication’, ‘DNA repair’ and ‘autophagy’. Additionally, Ingenuity Pathway Analysis (IPA) revealed that resistance to palbociclib is closely associated with deregulation of several key canonical and metabolic pathways. Further studies are needed to determine the utility of these DEGs and pathways as therapeutics targets against ER+ palbociclib-resistant breast cancer.

Purpose: The management of triple-negative breast cancer (TNBC) remains a significant clinical challenge due to the lack of effective targeted therapies. Inhibitors of the cyclin-dependent kinases 4 and 6 (CDK4/6) are emerging as promising therapeutic agents against TNBC; however, cells can rapidly acquire resistance through multiple mechanisms that are yet to be identified. Therefore, determining the mechanisms underlying resistance to CDK4/6 inhibition is crucial to develop combination therapies that can extend the efficacy of the CDK4/6 inhibitors or delay resistance. This study aims to identify differentially expressed genes (DEG) associated with acquired resistance to palbociclib in ER− breast cancer cells. Methods: We performed next-generation transcriptomic sequencing (RNA-seq) and pathway analysis in ER− MDA-MB-231 palbociclib-sensitive (231/pS) and palbociclib-resistant (231/pR) cells. Results: We identified 2247 up-regulated and 1427 down-regulated transcripts in 231/pR compared to 231/pS cells. DEGs were subjected to functional analysis using Gene Ontology (GO) and the KEGG database which identified many transduction pathways associated with breast cancer, including the PI3K/AKT, PTEN and mTOR pathways. Additionally, Ingenuity Pathway Analysis (IPA) revealed that resistance to palbociclib is closely associated with altered cholesterol and fatty acid biosynthesis suggesting that resistance to palbociclib may be dependent on lipid metabolic reprograming. Conclusion: This study provides evidence that lipid metabolism is altered in TNBC with acquired resistance to palbociclib. Further studies are needed to determine if the observed lipid metabolic rewiring can be exploited to overcome therapy resistance in TNBC.

Background: Intestine epithelial hypoxia-inducible factor-1α (HIF-1α) plays a critical role in maintaining gut barrier function. The aim of this study was to determine whether pharmacological or genetic activation of intestinal HIF-1α ameliorates western diet–induced metabolic dysfunction–associated steatotic liver disease. Methods: Metabolic effects of pharmacological activation of HIF-1α by dimethyloxalylglycine were evaluated in HIF-α luciferase reporter (ODD-luc) mice. Male and/or female intestinal epithelial–specific Hif1α overexpression mice (Hif1α^{LSL/LSL;VilERcre}) and wild-type littermates (Hif1α^LSL/LSL) were fed with regular chow diet, high fructose (HFr) or high-fat (60% Kcal) high-fructose diet (HFHFr) for 8 weeks. Metabolic phenotypes were profiled. Results: Dimethyloxalylglycine treatment led to increased intestine HIF-α luciferase activity and decreased blood glucose levels in HFr diet–fed male ODD-luc mice. Male Hif1α^{LSL/LSL;VilERcre} mice exhibited markedly improved glucose tolerance compared to Hif1α^LSL/LSL mice in response to HFr diet. Eight weeks HFHFr feeding led to obesity in both Hif1α^{LSL/LSL;VilERcre} and Hif1α^LSL/LSL mice. However, male Hif1α^{LSL/LSL;VilERcre} mice exhibited markedly attenuated hepatic steatosis along with reduced liver size and liver weight compared to male Hif1α^LSL/LSL mice. Moreover, HFHFr-induced systemic inflammatory responses were mitigated in male Hif1α^{LSL/LSL;VilERcre} mice compared to male Hif1α^LSL/LSL mice, and those responses were not evident in female mice. Ileum RNA-seq analysis revealed that glycolysis/gluconeogenesis was up in male Hif1α^{LSL/LSL;VilERcre} mice, accompanied by increased epithelial cell proliferation. Moreover, an in vitro study showed that HIF stabilization enhances glycolysis in intestine organoids. Conclusions: Our data provide evidence that pharmacological or genetic activation of intestinal HIF-1α markedly ameliorates western diet–induced metabolic dysfunction–associated steatotic liver disease in a sex-dependent manner. The underlying mechanism is likely attributed to HIF-1α activation–induced upregulation of glycolysis, which, in turn, leads to enhanced epithelial cell proliferation and augmented gut barrier function.

Sample size calculation for adequate power analysis is critical in optimizing RNA-seq experimental design. However, the complexity increases for directly estimating sample size when taking into consideration confounding covariates. Although a number of approaches for sample size calculation have been proposed for RNA-seq data, most ignore any potential heterogeneity. In this study, we implemented a simulation-based and confounder-adjusted method to provide sample size recommendations for RNA-seq differential expression analysis. The data was generated using Monte Carlo simulation, given an underlined distribution of confounding covariates and parameters for a negative binomial distribution. The relationship between the sample size with the power and parameters, such as dispersion, fold change and mean read counts, can be visualized. We demonstrate that the adjusted sample size for a desired power and type one error rate of α is usually larger when taking confounding covariates into account. More importantly, our simulation study reveals that sample size may be underestimated by existing methods if a confounding covariate exists in RNA-seq data. Consequently, this underestimate could affect the detection power for the differential expression analysis. Therefore, we introduce confounding covariates for sample size estimation for heterogeneous RNA-seq data.