Background: Eukaryotic translation initiation factor 5A (eIF5A) regulates pancreatic cancer pathogenesis. Results: eIF5A was shown to control the expression of a set of key signaling molecules including RhoA and ROCK2, and to promote the invasive potential of pancreatic cancer cells. Conclusion: Hypusine/eIF5A/RhoA/ROCK cascade promotes pancreatic cancer cell metastasis. Significance: eIF5A may be a novel druggable target to treat metastatic pancreatic cancer.

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 co-immunoprecipitated 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 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.

Tumor cells rely upon membrane pliancy to escape primary lesions and invade secondary metastatic sites. This process relies upon localized assembly and disassembly cycles of F-actin that support and underlie the plasma membrane. Dynamic actin generates both spear-like and bleb structures respectively characterizing mesenchymal and amoeboid motility programs utilized by metastatic cells in three-dimensional matrices. The molecular mechanism and physiological trigger(s) driving membrane plasticity are poorly understood. mDia formins are F-actin assembly factors directing membrane pliancy in motile cells. mDia2 is functionally coupled with its binding partner DIP, regulating cortical actin and inducing membrane blebbing in amoeboid cells. Here we show that mDia2 and DIP co-tether to nascent blebs and this linkage is required for bleb formation. DIP controls mesenchymal/amoeboid cell interconvertability, while CXCL12 induces assembly of mDia2:DIP complexes to bleb cortices in 3D matrices. These results demonstrate how DIP-directed mDia2-dependent F-actin dynamics regulate morphological plasticity in motile cancer cells.

Nucleotide sequence differences on the whole-genome scale have been computed for 1,092 people from 14 populations publicly available by the 1000 Genomes Project. Total number of differences in genetic variants between 96,464 human pairs has been calculated. The distributions of these differences for individuals within European, Asian, or African origin were characterized by narrow unimodal peaks with mean values of 3.8, 3.5, and 5.1 million, respectively, and standard deviations of 0.1–0.03 million. The total numbers of genomic differences between pairs of all known relatives were found to be significantly lower than their respective population means and in reverse proportion to the distance of their consanguinity. By counting the total number of genomic differences it is possible to infer familial relations for people that share down to 6% of common loci identical-by-descent. Detection of familial relations can be radically improved when only very rare genetic variants are taken into account. Counting of total number of shared very rare single nucleotide polymorphisms (SNPs) from whole-genome sequences allows establishing distant familial relations for persons with eighth and ninth degrees of relationship. Using this analysis we predicted 271 distant familial pairwise relations among 1,092 individuals that have not been declared by 1000 Genomes Project. Particularly, among 89 British and 97 Chinese individuals we found three British–Chinese pairs with distant genetic relationships. Individuals from these pairs share identical-by-descent DNA fragments that represent 0.001%, 0.004%, and 0.01% of their genomes. With affordable whole-genome sequencing techniques, very rare SNPs should become important genetic markers for familial relationships and population stratification.

Morphological plasticity in response to environmental cues in migrating cancer cells requires F-actin cytoskeletal rearrangements. Conserved formin family proteins play critical roles in cell shape, tumor cell motility, invasion and metastasis, in part, through assembly of non-branched actin filaments. Diaphanous-related formin-2 (mDia2/Diaph3/Drf3/Dia) regulates mesenchymal-to-amoeboid morphological conversions and non-apoptotic blebbing in tumor cells by interacting with its inhibitor diaphanous-interacting protein (DIP), and disrupting cortical F-actin assembly and bundling. F-actin disruption is initiated by a CXCL12-dependent mechanism. Downstream CXCL12 signaling partners inducing mDia2-dependent amoeboid conversions remain enigmatic. We found in MDA-MB-231 tumor cells CXCL12 induces DIP and mDia2 interaction in blebs, and engages its receptor CXCR4 to induce RhoA-dependent blebbing. mDia2 and CXCR4 associate in blebs upon CXCL12 stimulation. Both CXCR4 and RhoA are required for CXCL12-induced blebbing. Neither CXCR7 nor other Rho GTPases that activate mDia2 are required for CXCL12-induced blebbing. The Rho Guanine Nucleotide Exchange Factor (GEF) Net1 is required for CXCL12-driven RhoA activation and subsequent blebbing. These results reveal CXCL12 signaling, through CXCR4, directs a Net1/RhoA/mDia-dependent signaling hub to drive cytoskeleton rearrangements to regulate morphological plasticity in tumor cells. These signaling hubs may be conserved during normal and cancer cells responding to chemotactic cues.

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: Metastatic 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) reflect unique genomic features relative to late relapse (lrTNBC; >2 years). Patients and Methods:We 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. Results:Patients with rrTNBC were higher stage at diagnosis (Chi-square p<0.0001) while lrTNBC were more likely to be non-basal PAM50 subtype (Chi-square p=0.03). Among 125 expression signatures, five immune signatures were significantly higher in nrTNBCs while lrTNBC were enriched for eight estrogen/luminal signatures (all FDR p<0.05). There was no significant difference in tumor mutation burden or percent genome altered across the groups. Among mutations, only TP53 mutations 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. Conclusions:We 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.

<p>Informatics analyses of translation proteins altered in PEAK1-depleted PDAC cells</p>