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Fra-1 is a key driver of colon cancer metastasis and a Fra-1 classifier predicts disease-free survival.

  • Sedef Iskit‎ et al.
  • Oncotarget‎
  • 2015‎

Fra-1 (Fos-related antigen-1) is a member of the AP-1 (activator protein-1) family of transcription factors. We previously showed that Fra-1 is necessary for breast cancer cells to metastasize in vivo, and that a classifier comprising genes that are expressed in a Fra-1-dependent fashion can predict breast cancer outcome. Here, we show that Fra-1 plays an important role also in colon cancer progression. Whereas Fra-1 depletion does not affect 2D proliferation of human colon cancer cells, it impairs growth in soft agar and in suspension. Consistently, subcutaneous tumors formed by Fra-1-depleted colon cancer cells are three times smaller than those produced by control cells. Most remarkably, when injected intravenously, Fra-1 depletion causes a 200-fold reduction in tumor burden. Moreover, a Fra-1 classifier generated by comparing RNA profiles of parental and Fra-1-depleted colon cancer cells can predict the prognosis of colon cancer patients. Functional pathway analysis revealed Wnt as one of the central pathways in the classifier, suggesting a possible mechanism of Fra-1 function in colon cancer metastasis. Our results demonstrate that Fra-1 is an important determinant of the metastatic potential of human colon cancer cells, and that the Fra-1 classifier can be used as a prognostic predictor in colon cancer patients.


Systematic functional perturbations uncover a prognostic genetic network driving human breast cancer.

  • Tristan Gallenne‎ et al.
  • Oncotarget‎
  • 2017‎

Prognostic classifiers conceivably comprise biomarker genes that functionally contribute to the oncogenic and metastatic properties of cancer, but this has not been investigated systematically. The transcription factor Fra-1 not only has an essential role in breast cancer, but also drives the expression of a highly prognostic gene set. Here, we systematically perturbed the function of 31 individual Fra-1-dependent poor-prognosis genes and examined their impact on breast cancer growth in vivo. We find that stable shRNA depletion of each of nine individual signature genes strongly inhibits breast cancer growth and aggressiveness. Several factors within this nine-gene set regulate each other's expression, suggesting that together they form a network. The nine-gene set is regulated by estrogen, ERBB2 and EGF signaling, all established breast cancer factors. We also uncover three transcription factors, MYC, E2F1 and TP53, which act alongside Fra-1 at the core of this network. ChIP-Seq analysis reveals that a substantial number of genes are bound, and regulated, by all four transcription factors. The nine-gene set retains significant prognostic power and includes several potential therapeutic targets, including the bifunctional enzyme PAICS, which catalyzes purine biosynthesis. Depletion of PAICS largely cancelled breast cancer expansion, exemplifying a prognostic gene with breast cancer activity. Our data uncover a core genetic and prognostic network driving human breast cancer. We propose that pharmacological inhibition of components within this network, such as PAICS, may be used in conjunction with the Fra-1 prognostic classifier towards personalized management of poor prognosis breast cancer.


Integrated in vivo genetic and pharmacologic screening identifies co-inhibition of EGRF and ROCK as a potential treatment regimen for triple-negative breast cancer.

  • Sedef Iskit‎ et al.
  • Oncotarget‎
  • 2016‎

Breast cancer is the second most common cause of cancer-related deaths worldwide among women. Despite several therapeutic options, 15% of breast cancer patients succumb to the disease owing to tumor relapse and acquired therapy resistance. Particularly in triple-negative breast cancer (TNBC), developing effective treatments remains challenging owing to the lack of a common vulnerability that can be exploited by targeted approaches. We have previously shown that tumor cells have different requirements for growth in vivo than in vitro. Therefore, to discover novel drug targets for TNBC, we performed parallel in vivo and in vitro genetic shRNA dropout screens. We identified several potential drug targets that were required for tumor growth in vivo to a greater extent than in vitro. By combining pharmacologic inhibitors acting on a subset of these candidates, we identified a synergistic interaction between EGFR and ROCK inhibitors. This combination effectively reduced TNBC cell growth by inducing cell cycle arrest. These results illustrate the power of in vivo genetic screens and warrant further validation of EGFR and ROCK as combined pharmacologic targets for breast cancer.


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