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Western blots showing PEAk1 and YAP1 depletion in PDAC cells treated with two independent shRNAs

List of PEAK1 regulated proteins involved in cytoskeleton organization

In pancreatic ductal adenocarcinoma (PDAC), mutant KRAS stimulates the translation initiation factor eIF5A and upregulates the focal adhesion kinase PEAK1, which transmits integrin and growth factor signals mediated by the tumor microenvironment. Although eIF5A-PEAK1 signaling contributes to multiple aggressive cancer cell phenotypes, the downstream signaling processes that mediate these responses are uncharacterized. Through proteomics and informatic analyses of PEAK1-depleted PDAC cells, we defined protein translation, cytoskeleton organization, and cell-cycle regulatory pathways as major pathways controlled by PEAK1. Biochemical and functional studies revealed that the transcription factors YAP1 and TAZ are key targets of eIF5A-PEAK1 signaling. YAP1/TAZ coimmunoprecipitated with PEAK1. Interfering with eIF5A-PEAK1 signaling in PDAC cells inhibited YAP/TAZ protein expression, decreasing expression of stem cell–associated transcription factors (STF) including Oct4, Nanog, c-Myc, and TEAD, thereby decreasing three-dimensional (3D) tumor sphere growth. Conversely, amplified eIF5A-PEAK1 signaling increased YAP1/TAZ expression, increasing expression of STF and enhancing 3D tumor sphere growth. Informatic interrogation of mRNA sequence databases revealed upregulation of the eIF5A-PEAK1-YAP1-TEAD signaling module in PDAC patients. Taken together, our findings indicate that eIF5A-PEAK1-YAP signaling contributes to PDAC development by regulating an STF program associated with increased tumorigenicity. Cancer Res; 77(8); 1997–2007. ©2017 AACR.

Background Triple-negative breast cancer (TNBC) is a heterogeneous disease and we have previously shown that rapid relapse of TNBC is associated with distinct sociodemographic features. We hypothesized that rapid versus late relapse in TNBC is also defined by distinct clinical and genomic features of primary tumors. Methods Using three publicly-available datasets, we identified 453 patients diagnosed with primary TNBC with adequate follow-up to be characterized as ‘rapid relapse’ (rrTNBC; distant relapse or death ≤2 years of diagnosis), ‘late relapse’ (lrTNBC; > 2 years) or ‘no relapse’ (nrTNBC: > 5 years no relapse/death). We explored basic clinical and primary tumor multi-omic data, including whole transcriptome (n = 453), and whole genome copy number and mutation data for 171 cancer-related genes (n = 317). Association of rapid relapse with clinical and genomic features were assessed using Pearson chi-squared tests, t-tests, ANOVA, and Fisher exact tests. We evaluated logistic regression models of clinical features with subtype versus two models that integrated significant genomic features. Results Relative to nrTNBC, both rrTNBC and lrTNBC had significantly lower immune signatures and immune signatures were highly correlated to anti-tumor CD8 T-cell, M1 macrophage, and gamma-delta T-cell CIBERSORT inferred immune subsets. Intriguingly, lrTNBCs were enriched for luminal signatures. There was no difference in tumor mutation burden or percent genome altered across groups. Logistic regression mModels that incorporate genomic features significantly outperformed standard clinical/subtype models in training (n = 63 patients), testing (n = 63) and independent validation (n = 34) cohorts, although performance of all models were overall modest. Conclusions We identify clinical and genomic features associated with rapid relapse TNBC for further study of this aggressive TNBC subset.

Purpose: Cancers with short interval between diagnosis and metastasis are associated with aggressive clinical course. Specifically, metastatic relapse of triple-negative breast cancer (TNBC) within 2 years of initial primary diagnosis is associated with marked chemoresistance, rapid progression, and poor prognosis. We hypothesized that rapid relapse TNBCs (rrTNBC; distant metastatic relapse or death 2 years) or no relapse (nrTNBC; no distant relapse or death with at least 5 years follow-up). Patients and Methods: We identified 453 primary TNBCs from three publicly-available datasets and characterized each as rrTNBC, lrTNBC, or nrTNBC. We compiled primary tumor clinical and multi-omic data, including transcriptome (n=453), copy number alterations (CNAs; n=317), and mutations in 171 cancer-related genes (n=317), then calculated expression and immune signatures. Results: Patients with rrTNBC were higher stage at diagnosis (Chi-square p Citation Format: Yiqing Zhang, William Nock, Meghan Wyse, Zachary Weber, Elizabeth J Adams, Asad Sarah, Sinclair Stockard, David Tallman, Jasneet Singh, Junu Bae, Eric P Winer, Nancy U Lin, Yi-Zhou Jiang, Ding Ma, Peng Wang, Leming Shi, Wei Huang, Zhi-Ming Shao, Claire Verschraegen, Mathew Cherian, Maryam B Lustberg, Bhuvana Ramaswamy, Sagar Sardesai, Jeffrey VanDeusen, Nicole Williams, Wesolowski Robert, Daniel G Stover. Machine learning predicts rapid relapse in triple negative breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-05-02.

PurposeMetastatic relapse of triple-negative breast cancer (TNBC) within 2 years of diagnosis is associated with particularly aggressive disease and a distinct clinical course relative to TNBCs that relapse beyond 2 years. We hypothesized that rapid relapse TNBCs (rrTNBC; metastatic relapse or death 2 years).Patients and MethodsWe identified 453 primary TNBCs from three publicly-available datasets and characterized each as rrTNBc, lrTNBC, or ‘no relapse’ (nrTNBC: no relapse/death with at least 5 years follow-up). We compiled primary tumor clinical and multi-omic data, including transcriptome (n=453), copy number alterations (CNAs; n=317), and mutations in 171 cancer-related genes (n=317), then calculated published gene expression and immune signatures.ResultsPatients with rrTNBC were higher stage at diagnosis (Chi-square pTP53mutations were significantly more frequent in rrTNBC compared to lrTNBC (Fisher exact FDR p=0.009). To develop an optimal classifier, we used 77 significant clinical and ‘omic features to evaluate six modeling approaches encompassing simple, machine learning, and artificial neural network (ANN). Support vector machine outperformed other models with average receiver-operator characteristic area under curve >0.75.ConclusionsWe provide a new approach to define TNBCs based on timing of relapse. We identify distinct clinical and genomic features that can be incorporated into machine learning models to predict rapid relapse of TNBC.