EGF-SNX3-EGFR axis drives tumor progression and metastasis in triple-negative breast cancers

Epidermal growth factor receptor (EGFR) has critical roles in epithelial cell physiology. Over-expression and over-activation of EGFR have been implicated in diverse cancers, including triple-negative breast cancers (TNBCs), prompting anti-EGFR therapies. Therefore, developing potent therapies and addressing the inevitable drug resistance mechanisms necessitates deciphering of EGFR related networks. Here, we describe Sorting Nexin 3 (SNX3), a member of the recycling retromer complex, as a critical player in the epidermal growth factor (EGF) stimulated EGFR network in TNBCs. We show that SNX3 is an immediate and sustained target of EGF stimulation initially at the protein level and later at the transcriptional level, causing increased SNX3 abundance. Using a proximity labeling approach, we observed increased interaction of SNX3 and EGFR upon EGF stimulation. We also detected colocalization of SNX3 with early endosomes and endocytosed EGF. Moreover, we show that EGFR protein levels are sensitive to SNX3 loss. Transient RNAi models of SNX3 downregulation have a temporary reduction in EGFR levels. In contrast, long-term silencing forces cells to recover and overexpress EGFR mRNA and protein, resulting in increased proliferation, colony formation, migration, invasion in TNBC cells, and increased tumor growth and metastasis in syngeneic models. Consistent with these results, low SNX3 and high EGFR mRNA levels correlate with poor relapse-free survival in breast cancer patients. Overall, our results suggest that SNX3 is a critical player in the EGFR network in TNBCs with implications for other cancers dependent on EGFR activity.Chemical Immunolog

Alternative Polyadenylation Patterns for Novel Gene Discovery and Classification in Cancer

Certain aspects of diagnosis, prognosis, and treatment of cancer patients are still important challenges to be addressed. Therefore, we propose a pipeline to uncover patterns of alternative polyadenylation (APA), a hidden complexity in cancer transcriptomes, to further accelerate efforts to discover novel cancer genes and pathways. Here, we analyzed expression data for 1045 cancer patients and found a significant shift in usage of poly(A) signals in common tumor types (breast, colon, lung, prostate, gastric, and ovarian) compared to normal tissues. Using machine-learning techniques, we further defined specific subsets of APA events to efficiently classify cancer types. Furthermore, APA patterns were associated with altered protein levels in patients, revealed by antibody-based profiling data, suggesting functional significance. Overall, our study offers a computational approach for use of APA in novel gene discovery and classification in common tumor types, with important implications in basic research, biomarker discovery, and precision medicine approaches