Remodeling of the Tumor Extracellular Matrix Activates YAP in Fibroblasts to Produce Cancer Associated Fibroblasts

When cells undergo transformation and initiate the formation of a solid tumor mass, they cause profound changes on the phenotypes of the cells that surround them1. However, in addition to the changes in cellular phenotype, there is a change in the extracellular matrix that coincides with tumor formation1. It has been demonstrated that the majority of solid tumors have increased stiffness in their extracellular matrix (ECM), which may lead to increased activation of pro-tumor signaling pathways, such as Src, FAK, and RhoA2-4. Recently, it was discovered that increased matrix stiffness may also lead to increased activity of the oncogenic YAP/TAZ complex, which is connected to the Hippo signaling pathways, transcriptional regulators that increase cellular proliferation, decreased cellular contact inhibition, increased cancer stem cell phenotype, and increased metastasis5. However, in a fibroblastrecent edition of Nature Cell Biology Calvo et al. demonstrated that 6.  Not only do the authors demonstrate that YAP/TAZ is active in CAFs, but YAP/TAZ is necessary for CAF development6. They show that CAF activation leads to matrix remodeling towards increased stiffness, via myosin light chain 2 (MYL9/MLC) expression, establishing a feed-forward loop where the ECM plays a vital role6.

The authors first isolated fibroblasts in different stages towards becoming a CAF and saw that both mechanical-responsive signaling machinery (SMA, FN1, Paxillin, MYL9, MYH10, DIAH1 & F-actin) and mechanical tension were increased in populations containing CAFs. Moreover, tumor cell invasion, and angiogenesis of the tumor microenvironment (shown via endomucin and second-harmonic microscopy) were increased in samples that contained more tumor-associated-like fibroblasts (indicated by vimentin)6.

Because of the role of cell-cell and cell-ECM contact in the Hippo signaling pathway, the authors sought to understand whether this pathway is activated in CAFs. They found that YAP, and its co-factor, TAZ to be only upregulated and co-localized in the nucleus of transformed fibroblasts; the target of the activated YAP-TAZ complex6.  Furthermore, upon depletion of YAP, the ability for CAFs to cause matrix stiffness by contraction lessened as well as CAFs ability to form collagen networks and facilitate angiogenesis. Interestingly, when TAZ was inhibited, there was no change in functionality, which may lead to a TAZ-independent function for YAP.

Upon microarray analysis of CAFs treated with siRNA that targets YAP, Calvo et al. found that the expression of many of the genes involved in mechanosensing and motility to be diminished6. Furthermore, when these individual genes were silenced, there was an overall decrease in the amount of cellular invasion of tumors. Many of the YAP-mediated genes, such as ANLN and DIAPH3, were involved in matrix remodeling and cellular invasion. Interestingly, modification of only one protein overexpression resulted in high amounts of matrix-remodeling and invasion: myosin regulatory light polypeptide 9 (MYL9). While not transcriptionally controlled by the YAP/TAZ complex, the authors demonstrate that YAP/TAZ is able to control MYL9 by post-translational modifications, placing YAP as a critical factor in regulating matrix-remodeling and invasion through MYL96.

Calvo et al. next posited that YAP/TAZ activation may not be exclusive to CAFs, but may also occur in normal fibroblasts when placed in a cancerous environment6. They found that fibroblasts placed in culture with tumor conditioned media had higher nuclear translocation of YAP, and higher gel contraction (akin to matrix stiffening) comparable to known promoters of pro-contractile function: L-alpha-lysophosphatidic acid (LPA) and transforming growth factor-beta (TGFβ). However, actomyosin inhibition (by blebbistatin) could not be rescued with LPA and TGFβ. Therefore, while soluble factors may activate matrix contraction, a functional cytoskeleton is essential for matrix contraction. Because of the necessary role of the cytoskeleton, the authors tested whether inhibition of RhoA kinase (ROCK), a kinase involved in regulating translocation and structure of the cell by the cytoskeleton, would affect the nuclear localization of YAP6. Inhibition of ROCK decreased YAP nuclearization and decreased the matrix stiffness. Of note, like ROCK inhibition, inhibition of Src also affected the nuclear localization of YAP as well as complex formation with TEAD1 and TEAD4. However, Src modulation of YAP is downstream of cytoskeletal changes in tension since Src inhibition did not affect stress fibers6.

Since activation of YAP in CAFs  is connected to actomycin-mediated matrix stiffness, and this activation of YAP expresses MYL9, and expression of MYL9 results in matrix-remodeling towards stiffness, the authors posit that this pathway forms a feed-forward loop6. This loop could lead to constitutive activation of YAP pathway in CAFs, causing a robust response and stabilizing the CAF phenotype. However, it is not known what other mechanisms, as well as regulatory mechanisms of YAP, are involved in this process as well as whether the YAP-ECM tension pathway may play a regulatory role in normal fibroblasts.


1. Boudreau, A., van’t Veer, L. J. & Bissell, M. J. An “elite hacker”: breast tumors exploit the normal microenvironment program to instruct their progression and biological diversity. Cell adhesion & migration 6, 236-248, doi:10.4161/cam.20880 (2012).

2. Levental, K. R. et al. Matrix crosslinking forces tumor progression by enhancing integrin signaling. Cell 139, 891-906, doi:10.1016/j.cell.2009.10.027 (2009).

3. Guilluy, C. et al. The Rho GEFs LARG and GEF-H1 regulate the mechanical response to force on integrins. Nature cell biology 13, 722-727, doi:10.1038/ncb2254 (2011).

4. Sawada, Y. et al. Force sensing by mechanical extension of the Src family kinase substrate p130Cas. Cell 127, 1015-1026, doi:10.1016/j.cell.2006.09.044 (2006).

5. Harvey, K. F., Zhang, X. & Thomas, D. M. The Hippo pathway and human cancer. Nature reviews. Cancer 13, 246-257, doi:10.1038/nrc3458 (2013).

6. Calvo, F. et al. Mechanotransduction and YAP-dependent matrix remodelling is required for the generation and maintenance of cancer-associated fibroblasts. Nature cell biology 15, 637-646, doi:10.1038/ncb2756 (2013).


Finding the Right Cancer Culprits Using Mutational Heterogeneity

Imagine this: you are a police officer on patrol and you receive a call that multiple 30-year-old Caucasian males were seen breaking and entering; stealing heirlooms from a nearby neighborhood. The suspects were last seen entering a convention center and, to your dismay, you arrive to find the entire convention center is an antique show containing several 30-year-old Caucasian males carrying heirlooms. What do you do to apprehend your perpetrators?  You could arrest everyone that fits the description and interrogate them. On the other hand, you could scan the crowd for clues that there is a group of people that do not belong, or also radio to the police station for more information to narrow down the crowd. Needless to say, without gaining more contextual information for prudent discernment of the situation, you may arrest the wrong men and let the criminals go free.genomes

This is where cancer genomics is today; the sophistication of sequencing techniques have allowed for datasets that can detect every genomic mutation within cancer cells. Unfortunately, mutation rates are not equal among all genes. While this may seem a non-issue, this could lead scientists to ascertain that a mutated gene is associated with cancer when, in fact, the gene that “matches the description” is more susceptible to mutation, but has no role in oncogenesis. This is exactly what occurred to researchers who found high mutation rates of olfactory genes within lung cancer1. Doubtful of the role of olfactory genes in lung tumorigenesis, these scientists ultimately concluded that the mutation of olfactory genes had no role in the transformation of the lung epithelial cells1.

In Nature, Lawrence et al. further explored this issue, showing that failure to correct for the variability of mutation rates across the genome could lead to false positives for cancer associated genes1. To illustrate the importance of incorporating heterogeneity into the methodologies of data analysis, the authors compared a datasets with similar mutation frequencies to datasets that had different average mutation frequencies and found, when failing to take into account variability of mutations, there was an increase false categorization of cancer associated genes. Furthermore, the authors demonstrate that an analysis of an increasing sample size, as seen in the “big data” datasets of  American Society of Clinical Oncology’s “CancerLinQ™”2 and the Cancer Genome Atlas3, without correcting mutation rates, may exacerbate  the amount of false positives for cancer associated genes by decreasing the threshold needed to reach statistical significance. Lawrence postulated that heterogeneity may affect the detection of appropriate cancers by failing to correct for three contextual events: heterogeneity in mutation rates amongst samples of the same cancer type (patient-specific context), heterogeneity in mutation rates based on nucleotides surrounding a sequence (sequence-specific context), and heterogeneity in mutation rates based on the time that the gene is replicated or transcribed (replication/transcription-specific context).  Using the mutated olfactory genes mentioned above, along 3083 tumor-control pairs spanning 27 different cancer types, the authors demonstrate the importance of these contextually-discerning mutation rates and construct an algorithm for further context-based analysis, called MutSigCV.

Lawrence et al. studied cancer samples of the same cancer type (3,083 tumor-normal pairs across 27 tumor types) with variable average mutation rate. The authors found that, among all pairs and tumor types, there was a 1,000-fold variance in median frequency of mutations within the sample size. In these samples, the lowest variances were amongst hematological and pediatric cancers while the highest were among tumors induced by environmental factors, such as smoking and radiation. Given the importance of having accurate knowledge of the variability of rate of mutation, this underscores the importance in treating different cancer types, as well as patients with the same cancer, with a context-specific treatment protocol.

However, correcting for mutational frequencies attributed to tissue types, and mutations caused by known carcinogens and differences in cancer types, the authors still found that there was high mutational variability within certain samples of the same cancer type. Since mutation variance cannot be wholly accounted for by carcinogens, Lawrence et al. postulated that nucleotide makeup of the gene sequence may play a role in the mutation rate variability. The authors tested mutational heterogeneity in multiple tumors by assaying for 96 possible mutations (taking into account flanking bases) that were simplified into a radial chart for analysis1. Lawrence et al found that certain tumor types clustered into certain mutated sequences with the same flanking nucleotides (for instance lung cancers had a really high C to A mutations) was predominate, but still varied, within a certain cancer type.

While both variance in median mutation rates, and predominance of a specific sequence mutation, within specific cancer types was significant, the most important aspect in mutational heterogeneity seems to be in regional areas across a whole genome of cancer types, attributing to an excess of fivefold differences in median mutation rates1. Lawrence et al. credited this to two factors: the amount a gene is transcribed for the time the DNA section is replicated. The authors discovered that mutation rates are highest in genes with low rates of transcription and late DNA replication events. Comparing falsely-implicated olfactory receptor genes to known cancer associated genes, Lawrence et al. demonstrate different transcription rates and different replication times, with olfactory genes being expressed at cells with lower rates and later replication times. In contrast, cancer associated genes have higher transcription rates and earlier replication times.  In other words, while normal and cancer associated genes are both gaining mutations, the events that lead to these mutations are different. Thus, without parsing out mutational rates compared to replication and transcription, one may falsely assume that similar mutation levels must determine a cancer associated genes.

In the end, the authors surmised that “the rich variation in mutational spectrum across tumours underscores the problems with using an overly simplistic model of the average mutational process for a tumour type and failing to account for heterogeneity within a tumor type.” They state that their new analysis algorithm, MutSigCV, takes into account these context dependent nuances, allowing for cancer genomic analysis of mutations that eliminates these false positives. Using MutSigCV, Lawrence et al. was able to take a list of 450 suspected cancer associated genes in lung carcinoma and narrow the list to 11 suspected genes; genes shown to be linked to cancer1. This underscores the importance of context-specific analysis of big data in terms of cancer genomics. Without such a process, the use of whole genome sequencing for mutation rates for novel drug targets may be inadequate, sending many pharmaceutical and biotech companies toward therapeutic targets that, while look like the right suspect, are just an innocent bystanders that “fit the description”.



1          Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature, doi:10.1038/nature12213 (2013).

2          DeMartino, J. K. & Larsen, J. K. Data Needs in Oncology: “Making Sense of The Big Data Soup”. Journal of the National Comprehensive Cancer Network 11, S-1-S-12 (2013).

3          Network, C. G. A. R. Comprehensive genomic characterization of squamous cell lung cancers. Nature 489, 519-525, doi:10.1038/nature11404 (2012).