greg – Sanguine Bio Researcher Blog

July 22, 2013

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 RhoA ^2-4. 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 recent 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.

References:

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).

July 18, 2013

iMATES: Immunotherapy’s Best Friend?

The ability to form an effective immunotherapy relies on our understanding of the nuances of the immune response. This lack of understanding may limit promising therapies from reaching the clinic. In classical immunological dogma, cytotoxic T lymphocytes (CTLs) are activated by antigen presenting cells, primed for targeting cells expressing a certain antigen in their Major Histocompatibility Complex I (MHC I)¹ , then expanded and sent to the targeted area. This methodology would allow for ex vivo expansion and activation of CTLs for therapeutic purposes. However, in the treatment paradigm of adoptive cytoxic T lymphocyte (CTL) therapy of virally-infected liver, immunomodulatory signals in the microenvironment of the infected organ cause CTLs to undergo exhaustion, inhibition, or apoptosis. Interestingly, collections of macrophages, known as granulomas, have been shown to form after mycobacterial infection and are thought to activate CTL function² . In a recent article in Nature Immunology, Huang et al. demonstrate that a unique subpopulation of inflammatory, monocytic myeloid cell clusters, named intra-hepatic myeloid-cell aggregates for T cell population expansion (or iMATEs), are able to expand CTLs at sites of infection through an antigen-independent mechanism.

The authors first studied the effects of inflammatory activation on CTLs using ex-vivo activated, adoptively-transferred CD90.1⁺CTLs in CD90.2⁺ mice; assaying for changes in CTL proliferation, increase in myeloid (CD11b⁺, Major Histocompatibility Class II-positive (MHCII⁺)) populations in mice treated with ligands for Toll-like Receptor 9 (TLR9), and Toll-like Receptor 4 (TLR4). In this system, higher amounts of CTLs in the liver only occurred when TLR9 or TLR4 ligands were given, indicating that this stimulation is responsible for CTL proliferation².

Huang et al. also noted that these proliferating cells were not uniformly dispersed inside the liver and, when tested with mice that had knocked out Tlr9, the adoptively transferred CTLs did not proliferate. Thus, Huang et al. posited that the activation of TLR9 must occur in cells that were not CTLs. Since macrophage are susceptible to TLR9 stimulation, the authors abrogated the macrophage population and saw a decrease in CTL proliferation in the liver ². Furthermore, these monocytes showed clustering after TLR9 stimulation, which coincided with proliferative T-cell phenotype.

In order to show that iMATES are a population that, when stimulated, will facilitate the expansion of CTLs by iMATES, Huang et al. demonstrated CTL proliferation using transferred CD90.1⁺CTLs in CD90.2⁺ mice, assaying for changes in CD8 (CTL maker),CD45.1 (B Cell Marker) and CD11b (Innate Immune Cell marker) expression levels using intravital microscopy, immunohistochemical analysis, and electron microscopy. Data showed that CTL proliferation was localized to the liver site and iMATE formation only required TLR9 stimulation. Furthermore, the authors demonstrated that TLR9 treated mice had higher amounts of resident moncoyte cells (Kupffer cells) and non-resident cells of a monocytic origin. Moreover, monocyte derived-Dendritic Cells (DCs), and not conventional DCs, were found in the TLR9-mediated inflammatory site, which were uniquely able to induce CTL proliferation without antigen presentation².

The correlation of iMATE formation with CTL proliferation begs the question which mechanism is responsible for iMATE-induced CTL proliferation. To assay for this, the authors looked at the iMATE population for changes in costimulatory receptors and found the receptors CD80, CD86, and OX40L to be upregulated. Furthermore, OX40L was not found in parenchymal liver tissue, thus the authors believed that OX40L/OX40-stimulation may play a significant role in iMATE-induced proliferation of CTLs. Huang et al. demonstrated that TLR9-induction of CTL proliferation only occurred when OX40 receptor was expressed on CTLs and only occurred at the liver site ². Furthermore, they demonstrated that while OX40L may enhance survival of CTLS, it did not play a role in CTL proliferation. Thus, the major function of iMATES may be to increase survivability of entering CTLs through an antigen-independent pathway.

Finally, the authors assayed whether the increased CTL populations via iMATE stimulation may increase the ability of viral infection in the hepatic environment in mice with an inflammatory response by treatment with TLR9 ligand. They demonstrated, in two different virally infected mouse models and one human model, that acute inflammation may cause iMATE formation and increase CTL populations, whereas chronic inflammation did not. Furthermore, Huang et al. observed lower amounts of exhaustion, via PD1⁺ and CD69⁺ expression, in TLR9-stimulated mice. When used along-side vaccination, there was an increase in activated, non-exhausted CTLs that were restricted to the vaccination target ². Finally, using a vaccination regiment in combination with TLR9 to form iMATES, there was clearance of infected hepatocytes in a mouse model ².

In conclusion, these data may be taken together to indicate that stimulation of iMATES may increase endogenous CTL-mediated anti-viral immune response, thus leading to better outcome of immunotherapeutic treatment of chronic viral infection in the liver. However, whether this phenomenon is found in other virally-infected sites, or other pathologies, remains unknown ¹. Furthermore, it is not known whether this phenomenon is unique to ex-vivo expanded and activated CTLs, or whether endogenously-activated CTLs will also undergo this same phenotype. Nonetheless, a paradigm of changing the inflammatory milieu of a viral site of infection before adoptive cell therapy to increase its efficacy is promising. With the use of TLR9 stimulation-based therapy already in clinical trials for other pathologies, combining these two techniques of immunotherapy may lead to promising advancements in the immunotherapeutic treatment of virally-infected pathologies.

References:

1. Crispe, I. N. & Pierce, R. H. Killer T cells find meaningful encounters through iMATEs. Nature immunology 14, 533-534, doi:10.1038/ni.2620 (2013).

2. Huang, L. R. et al. Intrahepatic myeloid-cell aggregates enable local proliferation of CD8(+) T cells and successful immunotherapy against chronic viral liver infection. Nature immunology 14, 574-583, doi:10.1038/ni.2573 (2013).

July 10, 2013July 10, 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.

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”.

References:

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).