Chemoresistance is a major obstacle in triple negative breast cancer (TNBC), the most aggressive breast cancer subtype. Here we identify hypoxia-induced ECM re-modeler, lysyl oxidase (LOX) as a key inducer of chemoresistance by developing chemoresistant TNBC tumors in vivo and characterizing their transcriptomes by RNA-sequencing. Inhibiting LOX reduces collagen cross-linking and fibronectin assembly, increases drug penetration, and downregulates ITGA5/FN1 expression, resulting in inhibition of FAK/Src signaling, induction of apoptosis and re-sensitization to chemotherapy. Similarly, inhibiting FAK/Src results in chemosensitization. These effects are observed in 3D-cultured cell lines, tumor organoids, chemoresistant xenografts, syngeneic tumors and PDX models. Re-expressing the hypoxiarepressed miR-142-3p, which targets HIF1A, LOX and ITGA5, causes further suppression of the HIF-1α/LOX/ITGA5/FN1 axis. Notably, higher LOX, ITGA5, or FN1, or lower miR-142-3p levels are associated with shorter survival in chemotherapy-treated TNBC patients. These results provide strong pre-clinical rationale for developing and testing LOX inhibitors to overcome chemoresistance in TNBC patients.

No abstract

DNA methylation profiling has become an important aspect of biomedical molecular analysis. Polymerase chain reaction (PCR) amplification of bisulphite-treated DNA is a processing step that is common to many currently used methods of quantitative methylation analysis. Preferential amplification of unmethylated alleles—known as PCR-bias—may significantly affect the accuracy of quantification. To date, no universal experimental approach has been reported to overcome the problem. This study presents an effective method of correcting biased methylation data. The procedure includes a calibration performed in parallel to the analysis of the samples under investigation. DNA samples with defined degrees of methylation are analysed. The observed deviation of the experimental results from the expected values is used for calculating a regression curve. The equation of the best-fitting curve is then used for correction of the data obtained from the samples of interest. The process can be applied irrespective of the locus interrogated and the number of sites analysed, avoiding an optimization of the amplification conditions for each individual locus.