The Role of the Extracellular Matrix in Mammary Tumor Metastasis
By Patrick Magahis
The extracellular matrix (ECM) is the set of acellular components that fill interstitial spaces and provide structural support for tissues. ECM molecules provide regulatory cues during tissue development and influence cell type, differentiation, and function.1 A key event in breast cancer progression is invasion of carcinoma cells beyond the basement membrane and their exposure to, and interaction with, the mammary stroma. The mammary stroma is a vastly different microenvironment primarily comprising of fibroblasts, adipocytes, and ECM molecules such as Collagen I. The tumor stroma is characterized by significant changes in stromal ECM composition and an altered cellular landscape.2 Tumor-associated alterations in stromal ECM composition appear to be an important driving factor in the metastatic progression of breast cancer.3
Notably, tumor-associated ECM molecules restrict immune cell tumor infiltration, alter immune cell tumor reactivity, and confer resistance to radiation therapy and chemotherapy agents.4 While a dramatic change in tumor stroma ECM composition is well-studied, the mechanistic basis for ECM-associated enhanced tumorigenic properties is not yet fully known. Moreover, methods that specifically target tumor-associated changes in ECM may provide opportunities for therapeutic intervention of metastatic progression.
Our recent studies conducted at the Dr. Stuart Calderwood Lab at Beth Israel Medical Center/Harvard Medical School have identified a novel connection between the stress-inducible HSP70 family member HSPA1A/HSPA1B and expression of tumor-associated ECM genes. Analysis of MMT Hsp70-/- tumors indicated that ECM genes were the common major alteration when compared to MMT WT tumors of higher metastatic potential, further supporting the notion that ECM composition has an important modulatory role in mammary tumor metastasis.
Several properties of HSP70 make it an attractive therapeutic target in breast cancer. Firstly, HSP70 is not expressed in unstressed normal tissues but exists at high levels in cancer cells- offering specificity as a tumor target.5 In addition, although HSP70 family members share highly conserved structures, relatively specific drugs have been developed showing the feasibility of targeting HSP70 selectively. One of the most appealing properties of HSP70 inhibition in cancer is that HSP70 supports multiple tumorigenic processes, and its inactivation thereby perturbs multiple oncogenic processes simultaneously. Our research proposal aims to determine whether HSP70 regulation of ECM genes contributes to its role in breast cancer metastasis. Completion of our research objectives would both further determine the potential of HSP70 as a therapeutic target and identify a novel method for combating pro-metastatic signals emanating from the tumor stroma. Importantly, our study also aims to identify the molecular basis for how HSP70 regulates ECM genes.
HSP70 is elevated in human breast cancer and predicts for clinical outcome
Human breast tumors with higher HSP70 mRNA expression exhibit poorer relapse-free survival (RFS) and overall survival (OS) as measured by high (red) and low (black) microarray probe signals specific to both HSPA1A and HSPA1B across 3951 (RFS) and 1402 (OS) patient samples (Fig. 1).6 Consistent with HSP70 activity having a modulatory role in human breast cancer, HSP70 mRNA is significantly increased in human breast tumors (Tu) compared to normal mammary tissue (N) (Fig. 2). The encoding gene for HSP70’s transcriptional regulator, HSF1, is also significantly increased in human breast tumors (Fig. 2). Fig. 2 represents analysis of TCGA (The Cancer Genome Atlas) read-count data of 177 human breast tumors and 102 normal-mammary tissue samples sourced from the National Cancer Institute GDC Data portal. Raw feature counts were normalized for sample library size and sample RNA composition using the trimmed mean of log expression ratios (TMM normalization) method. The edgeR QLF model was used to determine differentially expressed genes with the q-value (FDR-values) represented.7
Gene expression of Extracellular Matrix (ECM) components are highly sensitive to perturbation of Hsp70 activity.
To identify the mechanistic basis for how Hsp70 modulates mammary tumor metastasis we performed RNA-seq analysis on RNA extracted from non-necrotic, age-matched four WT MMT and five Hsp70-/-MMT mammary tumor cells (Fig. 3). The cDNA libraries generated from these nine samples were sequenced on an Illumina® HiSeq 2500 platform to give approximately 15-20 million reads per sample. Reads were checked for quality using FastQC and aligned to annotated mouse genes (UCSC mm10) using TopHat2.8 HTseq-counts21 were then used as input for differential gene expression (DGE) analysis. DGE analysis was performed in R using the Bioconductor package edgeR. This method uses TMM normalized read counts and fisher’s exact test with Benjamini Hochberg correction for multiple testing to identify significantly altered genes with a false discovery rate (FDR) less than 0.05. Consistent with a robust effect upon metastasis, Hsp70 knockout significantly altered a cohort of mRNAs (Fig. 3). Examination of the DGE list of genes revealed a marked prevalence for extracellular matrix (ECM) –related genes to be reduced in Hsp70 -/- MMT tumors compared to MMT WT tumors. These mRNAs included Bgn, Col1a1, Col1a2, Sparc, and Col5a1. Human homologs of these genes are up-regulated in human breast tumor (Tu) RNA-seq data samples deposited at the TCGA compared to normal mammary tissue (N) (Fig. 3). To identify descriptive summaries of Hsp70 gene functions, we performed gene ontology (GO) analysis using the MMT WT v MMT Hsp70-/- DGE list. The DGE list was used as input for the GOseq Bioconductor R package.9 Extracellular matrix-related terms were heavily represented within the most significant GO terms (Fig. 3). These findings were consistent with the allosteric HSP70 inhibitor JG98,10 inhibiting the expression Collagen I gene and protein expression in treated Hs578T human breast cancer cells in vitro (Fig 4).
In conclusion, our proposed investigation builds upon our studies that have identified a novel relationship between HSP70, mammary tumor metastasis, and the expression of tumor-associated ECM genes. The study has potential to identify a novel method to target pro-metastatic cues derived from the tumor-associated ECM as well as identify new functions of HSP70 in breast cancer progression and thereby further inform its potential as a therapeutic target.
References
1. Bissell MJ, Rizki A, Mian IS. Tissue architecture: the ultimate regulator of breast epithelial function. Current Opinion in Cell Biology. 2003;15(6):753-62. doi: 10.1016/j.ceb.2003.10.016.
2. Gascard P, Tlsty TD. Carcinoma-associated fibroblasts: orchestrating the composition of malignancy. Genes Dev. 2016;30:1002-19. doi: 10.1101/gad.279737.
3. Provenzano PP, Inman DR, Eliceiri KW, Knittel JG, Yan L, Rueden CT, White JG, Keely PJ. Collagen density promotes mammary tumor initiation and progression. BMC Med. 2008;6:11. Epub 2008/04/30. doi: 10.1186/1741-7015- 6-11. PubMed PMID: 18442412; PMCID: PMC2386807
4. Kaplan G. In vitro differentiation of human monocytes. J Exp Med. 1983;157(6):2061–72.
5. Hunt CR, Dix DJ, Sharma GG, Pandita RK, Gupta A, Funk M, Pandita TK. Genomic Instability and Enhanced Radiosensitivity in Hsp70.1- and Hsp70.3-Deficient Mice. Molecular and Cellular Biology. 2003;24(2):899-911. doi: 10.1128/mcb.24.2.899-911.2004.
6. Gyorffy B, Lanczky A, Eklund A, Denkert C, Budczies J, Li Q, Szallasi Z. 2010. Breast Cancer Res Treatment. An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1809 patients;123(3):725-31.
7. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139-40. Epub 2009/11/17. doi: 10.1093/bioinformatics/btp616. PubMed PMID: 19910308; PMCID: PMC2796818.
8. Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009;25(9):1105-11. Epub 2009/03/18. doi: 10.1093/bioinformatics/btp120. PubMed PMID: 19289445; PMCID: PMC2672628.
9. Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 2010;11(2):R14. Epub 2010/02/06. doi: 10.1186/gb-2010-11-2-r14. PubMed PMID: 20132535; PMCID: PMC2872874.
The extracellular matrix (ECM) is the set of acellular components that fill interstitial spaces and provide structural support for tissues. ECM molecules provide regulatory cues during tissue development and influence cell type, differentiation, and function.1 A key event in breast cancer progression is invasion of carcinoma cells beyond the basement membrane and their exposure to, and interaction with, the mammary stroma. The mammary stroma is a vastly different microenvironment primarily comprising of fibroblasts, adipocytes, and ECM molecules such as Collagen I. The tumor stroma is characterized by significant changes in stromal ECM composition and an altered cellular landscape.2 Tumor-associated alterations in stromal ECM composition appear to be an important driving factor in the metastatic progression of breast cancer.3
Notably, tumor-associated ECM molecules restrict immune cell tumor infiltration, alter immune cell tumor reactivity, and confer resistance to radiation therapy and chemotherapy agents.4 While a dramatic change in tumor stroma ECM composition is well-studied, the mechanistic basis for ECM-associated enhanced tumorigenic properties is not yet fully known. Moreover, methods that specifically target tumor-associated changes in ECM may provide opportunities for therapeutic intervention of metastatic progression.
Our recent studies conducted at the Dr. Stuart Calderwood Lab at Beth Israel Medical Center/Harvard Medical School have identified a novel connection between the stress-inducible HSP70 family member HSPA1A/HSPA1B and expression of tumor-associated ECM genes. Analysis of MMT Hsp70-/- tumors indicated that ECM genes were the common major alteration when compared to MMT WT tumors of higher metastatic potential, further supporting the notion that ECM composition has an important modulatory role in mammary tumor metastasis.
Several properties of HSP70 make it an attractive therapeutic target in breast cancer. Firstly, HSP70 is not expressed in unstressed normal tissues but exists at high levels in cancer cells- offering specificity as a tumor target.5 In addition, although HSP70 family members share highly conserved structures, relatively specific drugs have been developed showing the feasibility of targeting HSP70 selectively. One of the most appealing properties of HSP70 inhibition in cancer is that HSP70 supports multiple tumorigenic processes, and its inactivation thereby perturbs multiple oncogenic processes simultaneously. Our research proposal aims to determine whether HSP70 regulation of ECM genes contributes to its role in breast cancer metastasis. Completion of our research objectives would both further determine the potential of HSP70 as a therapeutic target and identify a novel method for combating pro-metastatic signals emanating from the tumor stroma. Importantly, our study also aims to identify the molecular basis for how HSP70 regulates ECM genes.
HSP70 is elevated in human breast cancer and predicts for clinical outcome
Human breast tumors with higher HSP70 mRNA expression exhibit poorer relapse-free survival (RFS) and overall survival (OS) as measured by high (red) and low (black) microarray probe signals specific to both HSPA1A and HSPA1B across 3951 (RFS) and 1402 (OS) patient samples (Fig. 1).6 Consistent with HSP70 activity having a modulatory role in human breast cancer, HSP70 mRNA is significantly increased in human breast tumors (Tu) compared to normal mammary tissue (N) (Fig. 2). The encoding gene for HSP70’s transcriptional regulator, HSF1, is also significantly increased in human breast tumors (Fig. 2). Fig. 2 represents analysis of TCGA (The Cancer Genome Atlas) read-count data of 177 human breast tumors and 102 normal-mammary tissue samples sourced from the National Cancer Institute GDC Data portal. Raw feature counts were normalized for sample library size and sample RNA composition using the trimmed mean of log expression ratios (TMM normalization) method. The edgeR QLF model was used to determine differentially expressed genes with the q-value (FDR-values) represented.7
Gene expression of Extracellular Matrix (ECM) components are highly sensitive to perturbation of Hsp70 activity.
To identify the mechanistic basis for how Hsp70 modulates mammary tumor metastasis we performed RNA-seq analysis on RNA extracted from non-necrotic, age-matched four WT MMT and five Hsp70-/-MMT mammary tumor cells (Fig. 3). The cDNA libraries generated from these nine samples were sequenced on an Illumina® HiSeq 2500 platform to give approximately 15-20 million reads per sample. Reads were checked for quality using FastQC and aligned to annotated mouse genes (UCSC mm10) using TopHat2.8 HTseq-counts21 were then used as input for differential gene expression (DGE) analysis. DGE analysis was performed in R using the Bioconductor package edgeR. This method uses TMM normalized read counts and fisher’s exact test with Benjamini Hochberg correction for multiple testing to identify significantly altered genes with a false discovery rate (FDR) less than 0.05. Consistent with a robust effect upon metastasis, Hsp70 knockout significantly altered a cohort of mRNAs (Fig. 3). Examination of the DGE list of genes revealed a marked prevalence for extracellular matrix (ECM) –related genes to be reduced in Hsp70 -/- MMT tumors compared to MMT WT tumors. These mRNAs included Bgn, Col1a1, Col1a2, Sparc, and Col5a1. Human homologs of these genes are up-regulated in human breast tumor (Tu) RNA-seq data samples deposited at the TCGA compared to normal mammary tissue (N) (Fig. 3). To identify descriptive summaries of Hsp70 gene functions, we performed gene ontology (GO) analysis using the MMT WT v MMT Hsp70-/- DGE list. The DGE list was used as input for the GOseq Bioconductor R package.9 Extracellular matrix-related terms were heavily represented within the most significant GO terms (Fig. 3). These findings were consistent with the allosteric HSP70 inhibitor JG98,10 inhibiting the expression Collagen I gene and protein expression in treated Hs578T human breast cancer cells in vitro (Fig 4).
In conclusion, our proposed investigation builds upon our studies that have identified a novel relationship between HSP70, mammary tumor metastasis, and the expression of tumor-associated ECM genes. The study has potential to identify a novel method to target pro-metastatic cues derived from the tumor-associated ECM as well as identify new functions of HSP70 in breast cancer progression and thereby further inform its potential as a therapeutic target.
References
1. Bissell MJ, Rizki A, Mian IS. Tissue architecture: the ultimate regulator of breast epithelial function. Current Opinion in Cell Biology. 2003;15(6):753-62. doi: 10.1016/j.ceb.2003.10.016.
2. Gascard P, Tlsty TD. Carcinoma-associated fibroblasts: orchestrating the composition of malignancy. Genes Dev. 2016;30:1002-19. doi: 10.1101/gad.279737.
3. Provenzano PP, Inman DR, Eliceiri KW, Knittel JG, Yan L, Rueden CT, White JG, Keely PJ. Collagen density promotes mammary tumor initiation and progression. BMC Med. 2008;6:11. Epub 2008/04/30. doi: 10.1186/1741-7015- 6-11. PubMed PMID: 18442412; PMCID: PMC2386807
4. Kaplan G. In vitro differentiation of human monocytes. J Exp Med. 1983;157(6):2061–72.
5. Hunt CR, Dix DJ, Sharma GG, Pandita RK, Gupta A, Funk M, Pandita TK. Genomic Instability and Enhanced Radiosensitivity in Hsp70.1- and Hsp70.3-Deficient Mice. Molecular and Cellular Biology. 2003;24(2):899-911. doi: 10.1128/mcb.24.2.899-911.2004.
6. Gyorffy B, Lanczky A, Eklund A, Denkert C, Budczies J, Li Q, Szallasi Z. 2010. Breast Cancer Res Treatment. An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1809 patients;123(3):725-31.
7. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139-40. Epub 2009/11/17. doi: 10.1093/bioinformatics/btp616. PubMed PMID: 19910308; PMCID: PMC2796818.
8. Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009;25(9):1105-11. Epub 2009/03/18. doi: 10.1093/bioinformatics/btp120. PubMed PMID: 19289445; PMCID: PMC2672628.
9. Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 2010;11(2):R14. Epub 2010/02/06. doi: 10.1186/gb-2010-11-2-r14. PubMed PMID: 20132535; PMCID: PMC2872874.
