Epithelial-mesenchymal transition (EMT) fosters cancer cell invasion and metastasis, the main cause of cancer-related mortality. Growing evidence that SNAIL and ZEB transcription factors, typically portrayed as master regulators of EMT, may be dispensable for this process, led us to re-investigate its mechanistic underpinnings. For this, we used an unbiased computational approach that integrated time-resolved analyses of chromatin structure and differential gene expression, to predict transcriptional regulators of TGFβ1-inducible EMT in the MCF10A mammary epithelial cell line model. Bioinformatic analyses indicated comparatively minor contributions of SNAIL proteins and ZEB1 to TGFβ1-induced EMT, whereas the AP-1 subunit JUNB was anticipated to have a much larger impact. CRISPR/Cas9-mediated loss-of-function studies confirmed that TGFβ1-induced EMT proceeded independently of SNAIL proteins and ZEB1. In contrast, JUNB was necessary and sufficient for EMT in MCF10A cells, but not in A549 lung cancer cells, indicating cell-type-specificity of JUNB EMT-regulatory capacity. Nonetheless, the JUNB-dependence of EMT-associated transcriptional reprogramming in MCF10A cells allowed to define a gene expression signature which was regulated by TGFβ1 in diverse cellular backgrounds, showed positively correlated expression with TGFβ signaling in multiple cancer transcriptomes, and was predictive of patient survival in several cancer types. Altogether, our findings provide novel mechanistic insights into the context-dependent control of TGFβ1-driven EMT and thereby may lead to improved diagnostic and therapeutic options.

Background: Given the poor prognosis of metastatic pancreatic adenocarcinoma (mPDAC), closer disease monitoring through liquid biopsy, most frequently based on serial measurements of cell-free mutated KRAS (KRASmut cfDNA), has become a highly active research focus, aiming to improve patients´ long-term outcome. However, most of the available data show only a limited predictive and prognostic value of single-parameter based methods. We hypothesized that a combined longitudinal analysis of KRASmut cfDNA and novel protein biomarkers could improve risk stratification and molecular monitoring of patients with mPDAC. Methods: We prospectively collected 160 plasma samples from 47 patients with mPDAC at our institution. Highly sensitive single-target ddPCR assays were employed to detect and quantify KRASmut cfDNA. Additionally, analysis of ten protein biomarkers was performed through Enzyme-linked Immunosorbent Assay (ELISA), and CA 19-9 dynamics were registered. Results: KRASmut cfDNA was detectable in 37/47 (78.7%) patients through course of study, and CA 19-9 levels were elevated in 40 out of 47 (85.1%) patients. KRASmut cfDNA increase at time of first follow-up could predict inferior PFS (Hazard ratio (HR) = 3.40, P = 0.0003) and OS (HR = 4.91, P < 0.0001). In contrast to CA 19-9 kinetics, not predictive of outcome, integrated analysis of KRASmut cfDNA combined with six evaluated circulating protein biomarkers, allowed basal risk stratification at time of first follow-up (HR = 10.2, P = 0.0014). Conclusion: A combined longitudinal analysis of KRASmut cfDNA with selected protein biomarkers offers significantly improved prognostic value for patients with mPDAC compared to single-parameter methods. This innovative approach is a step forward in the molecular monitoring of mPDAC and should be validated in further prospective studies.