Wound Signature Research Articles

Phase-specific signatures of wound fibroblasts and matrix patterns define cancer-associated fibroblast subtypes

Healing wounds and cancers present remarkable cellular and molecular parallels, but the specific roles of the healing phases are largely unknown. We developed a bioinformatics pipeline to identify genes and pathways that define distinct phases across the time-course of healing. Their comparison to cancer transcriptomes revealed that a resolution phase wound signature is associated with increased severity in skin cancer and enriches for extracellular matrix-related pathways. Comparisons of transcriptomes of early- and late-phase wound fibroblasts vs skin cancer-associated fibroblasts (CAFs) identified an "early wound" CAF subtype, which localizes to the inner tumor stroma and expresses collagen-related genes that are controlled by the RUNX2 transcription factor. A "late wound" CAF subtype localizes to the outer tumor stroma and expresses elastin-related genes. Matrix imaging of primary melanoma tissue microarrays validated these matrix signatures and identified collagen- vs elastin-rich niches within the tumor microenvironment, whose spatial organization predicts survival and recurrence. These results identify wound-regulated genes and matrix patterns with prognostic potential in skin cancer.

Hspa8 and ICAM-1 as damage-induced mediators of γδ T cell activation.

Tissue-resident γδ T cells form the first line of defense at barrier surfaces where they survey host tissue for signs of stress or damage. Following recognition of injury, γδ T cells play a crucial role in the wound-healing response through the production of growth factors and cytokines that promote proliferation in surrounding epithelial cells. To initiate this response, γδ T cells require interactions with a variety of epithelial-expressed costimulatory molecules in addition to primary signaling through their TCR. In the epidermis these signals include the coxsackie and adenovirus receptor (CAR), histocompatibility antigen 60c (H60c), and plexin B2, which interact with γδ T cell-expressed junctional adhesion molecule-like protein (JAML), NKG2D, and CD100, respectively. Here we identify heat shock protein family A member 8 (Hspa8) and ICAM-1 as two additional keratinocyte-expressed costimulatory molecules for epidermal resident γδ T cells (termed DETC). These molecules were rapidly up-regulated in the epidermis following wounding in both mouse and human tissue. Both Hspa8 and ICAM-1 had a costimulatory effect on DETC, inducing proliferation, CD25 up-regulation, and IL-2 production. We also provide evidence that DETC can be activated through the potential ICAM-1 and Hspa8 receptors LFA-1 and CD316. Finally, knockdown of Hspa8 in keratinocytes reduced their ability to activate DETC in culture and ICAM-1-/- mice exhibited impaired rates of healing in skin-organ culture suggesting a role for these proteins in the DETC-mediated damage response. Together with previous work on CAR, H60c, and plexin B2, these results add to a picture of a complex keratinocyte wound signature that is required for efficient DETC activation.

γδ T cell activation in response to a keratinocyte wound signature

Abstract Epidermal resident γδ T cells are key players in the wound healing response, providing growth factors and cytokines that promote keratinocyte proliferation following recognition of damaged self. These cells are known to be unresponsive in chronic wounds and as such represent a novel target for new wound healing therapies. In this study we seek to modulate the γδ T cell response in chronic wounds through the identification, characterization, and delivery of keratinocyte-expressed proteins that comprise a “wound signature” allowing γδ T cells to recognize and respond to epithelial damage. We have identified a set of target genes though RNA-seq analysis which are upregulated following wounding in mice and correspond to membrane-bound and secreted proteins available for interactions with neighboring cells. Candidate gene expression was measured following wounding of human skin tissue, and ICAM1, CRISPLD2, and HSPA8 were shown to also be significantly upregulated in human skin wound samples. Hspa8 and ICAM-1 have been validated through in vitro assays to activate γδ T cells, and these proteins have been chosen for in vivo delivery. In order to deliver these candidate proteins into in vivo wound models we have developed a hydrogel-based delivery system with a highly tunable time frame of release. This system improves the retention of protein cargo at the wounds site post-wounding in mice and is being used to administer our candidate proteins into mouse models of non-healing wounds to assess effects on acceleration of tissue repair.

Single-Cell Transcriptomics of Traced Epidermal and Hair Follicle Stem Cells Reveals Rapid Adaptations during Wound Healing

Epithelial tissues, such as the skin, rely on cellular plasticity of stem cells (SCs) from different niches to restore tissue function after injury. How these molecularly and functionally diverse SC populations respond to injury remains elusive. Here, we genetically labeled Lgr5- or Lgr6-expressing cells from the hair follicle bulge and interfollicular epidermis (IFE), respectively, and monitored their individual transcriptional adaptations during wound healing using single-cell transcriptomics. Both Lgr5 and Lgr6 progeny rapidly induced a genetic wound signature that, for Lgr5 progeny, included the remodeling of receptors to permit interactions with the wound environment, a property that Lgr6 progeny possessed even before wounding. When contributing to re-epithelialization, Lgr5 progeny gradually replaced their bulge identity with an IFE identity, and this process started already before Lgr5 progeny left the bulge. Altogether, this study reveals how different SCs respond and adapt to a new environment, potentially explaining cellular plasticity of many epithelial tissues.

Abstract LB-311: Combinatorial logic over breast cancer control modules predicts survival and chemotherapy response of ISPY1 breast cancer patients (CALGB 150007/150012; ACRIN 6657)

Abstract Background: Breast cancer is a heterogeneous disease. Clinically useful predictors for breast cancer management may therefore need to consider the variety of molecular processes that can impact chemotherapy response and survival. With the growing database of gene expression data, it is now possible to deduce co-expression modules active in breast cancer subpopulations. Viewed from a combinatorial perspective supporting ‘OR’ and ‘AND’ reasoning, the activities of these modules might provide insights into breast cancer biology and form the basis for improved predictors. Methods: We assembled 74 breast-cancer related gene expression datasets containing ∼5,500 samples altogether. Per dataset, we identified genes with bimodal expression analyzed using mixture-model clustering to ultimately find gene groups that are consistently co-regulated across multiple datasets. We scored the AFFY U133 ISPY1 dataset (117 pts) for module expression, and explored the relationships between module state, chemotherapy response, patient outcome, receptor status, intrinsic subtype, and other signatures. To derive logical functions consisting of nested ‘AND’ and ‘OR’ gates relating module expression to clinical variables, we discretized the module scores and applied a methodology in the spirit of Karnaugh maps for digital circuit design. Results: Our meta-analysis identified 11 modules ranging in size from ∼5–200 genes. The expression level of some modules were associated with known molecular markers such as intrinsic subtype or wound signature scores, whereas others – three immune related modules, two ECM related modules, a mixed immune/ECM module, and a small module encoding histones – do not. Disease free survival of patients with triple negative disease is predicted (p=1.93e-08) by the logical function fDFS_TN=[ER_module>LOW] OR [TN_Immune_ECM=HIGH] OR [(AURK/PROLIF_module=HIGH) AND ((Histone_module>LOW) OR (TN_Immune_ECM=HIGH)], where TN_Immune_ECM is a composite function over the three immune modules and the immune/ECM hybrid module. The best predictor of chemo-sensitivity for triple negatives is a hybrid over subtype and module state, fRCB01_TN=Subtype_basal AND TN_immune_ECM, correctly classifying 95% of the RCB01 sensitives and 90% of the RCBIII resistants. Conclusion: In triple negatives, high proliferation can be rescued by immune/ECM upregulation, though neither immune rescue nor low RCB is necessary for survival if the estrogen module is even slightly upregulated or if the histone module is high. Similar analyses of ER/Pr+ and Her2+ patients demonstrate receptor-subtype specificity in the logic predicting response. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr LB-311. doi:10.1158/1538-7445.AM2011-LB-311

The use of a comprehensive tumour xenograft dataset to validate gene signatures relevant for radiation response

Purpose To investigate the use of xenograft models in a novel gene signature validation method using gene expression microarrays. Materials and methods Gene expression profiles of ten human Head and Neck squamous cell carcinomas (HNSCCs) were obtained. Several published prognostic gene expression signatures were evaluated within this set. These consisted of different radiotherapy relevant signatures (i.e. for hypoxia, proliferation and ‘stemness’). Signatures were correlated with various endpoints that have been determined in the ten different xenograft models. These include immunohistochemical measures for hypoxia and proliferation, volume doubling time (VDT) and local tumour control after fractionated irradiation or after single dose irradiation under clamp hypoxia. Results We found several significant correlations between the published gene expression signatures and tumour parameters. Several signatures, like the proliferation and wound signature correlated with BrdU labelling index. Further a ‘stemness’-related gene signature showed a strong negative correlation with hypoxic fraction. Conclusions Simultaneous assessment of immunohistochemistry, in vivo tumour properties and gene expression profiling in a comprehensive set of xenograft models can be used to validate and potentially infer biological information about prognostic gene signatures.

Time dependence of prognostic values of gene-expression signatures for breast cancer

11085 Background: Gene-expression signatures were shown to be prognostic and predictive for breast cancer but their performances over time have not been investigated. Methods: Recurrence score (RS) of oncotype, 70-gene profile (SGP) of Mammaprint, intrinsic subtype (IS) and wound signature (WS) were analyzed using a single dataset of 295 samples from Fan et al. (Fan et al. N Engl J of Med 2006; 355:560–9). Time-varying coefficient Cox models were used to estimate the time-dependent hazard ratios (HR) for relapse-free survival (RFS). Annualized RFS hazard rates were estimated using cubic splines. Concordance between RS and SGP was calculated for different intrinsic subtypes in all 295 samples and was evaluated in a subset of 173 ER+ samples not in the training set of SGP for unbiased comparison. Results: For the high risk groups of RS, SGP, IS and WS, hazard rates peaked around the 2nd year from surgery and decreased rapidly over time. For the low risk groups, hazard rates also peaked around the 2nd yr but remained fairly constant over time. Hazard ratios of high vs. low risk groups of RS at yr 2, 5 and 10 were 17.0 (8.4–34.0), 4.0 (2.0–8.0), 1.7 (0.83–3.4). For intermediate vs. low risk groups of RS, the corresponding HRs were 2.7 (1.1–7.0), 5.0 (1.1–7.4), 1.6 (0.63–4.2). Poor vs. good groups of SVP had HRs of 7.4 (4.5–12.1), 3.6 (2.2–5.9), 2.0 (1.2–3.3) at yr 2, 5, 10. HRs of Luminal B (LB), Basal-like (BL), HER2/ER- (HER2), Normal-like (NL) vs. Luminal A (LA) of Intrinsic Subtypes were 6.8 (4.0–11.5), 5.7 (3.2–10.0), 7.6 (4.0–14.1) and 1.7 (0.9–3.3) at yr 2. HRs at yr 5 were 4.0 (2.3–6.8), 0.7 (0.4–1.2), 1.1 (0.6–2.0) and 2.3 (1.2–4.4) respectively. For WS, HRs at yr 2, 5, 10 were 6.3 (3.3–11.9), 2.9 (1.6–5.6) and 2.2 (1.2–4.1) respectively. Concordance rates of RS and SVP were 1.0, 0.91, 0.86, 0.72 and 0.62 for BL, HER2, LB, LA, NL of Intrinsic Subtypes. In 173 ER+ samples not present in the SGP training set, concordance rate between RS and SVP was 0.77 (Cramer's V = 0.56); the same as in the whole set of 225 ER+ patients (Fan et al. N Engl J of Med 2006; 355:560–9). Conclusions: Prognostic values of gene-expression signatures decrease over time. They can best predict breast cancer relapse during the first 5 years. Concordance between RS and SPV is highest in the basal-like tumors and lowest in the Luminal B and Normal-like subtypes. No significant financial relationships to disclose.

A comprehensive analysis of nine prognostic signatures reveals the high classification instability in breast cancer.

Abstract Abstract #2026 To determine the prognosis of breast cancer patients, clinical and pathological factors are currently employed. Gene expression micro-arrays offer new opportunities to determine individual prognosis. Publications have raised concerns about micro-arrays studies who have the potential to preclude their use in clinical routine. To improve the understanding of gene-expression classifiers we addressed the following issues: 1) Is the performance similar between independent classifiers? 2) Is proliferation a common biological theme that represents various signatures? 3) Are there other enriched pathways among signatures with prognostic ability?
 Methods:
 On 6 public datasets we applied the 76-gene signature; the Molecular subtypes; the Chromosomal Instability Signature; the Wound Signature; the Invasiveness Gene Signature; the Molecular Prognosis Index; and the Genomic Grade Index. Survival, predictive accuracy and overlap analyses were performed. We created enlarged signatures by including all probes with significant correlation to at least one of the genes in the original signatures. We gathered a collection of gene sets from four databases (GO, KEGG, Reactome, MSDB). For each signature, we evaluated whether specific gene sets (modules) are overrepresented. We tested the prognosis ability of each of them.
 Results:
 The survival and predictive accuracy analyses gave similar results for each of the 9 signatures. They all added significant information to a multivariate model including standard pathological and clinical criteria. Nevertheless, we showed that none of these signatures were able to identify good and poor prognosis patients when applied to samples with intrinsically poor prognosis features (Positive Lymph Node, Negative Estrogen Receptor, High Grade). Conversely they identified good and poor prognosis patients when applied to samples with intrinsically good prognosis features (Negative LN, Positive ER Low Grade). The overlap analysis showed a low agreement between the signatures. 50% of the samples had almost one discordant classification result out of the 9 classifiers tested. The intersection of the signatures revealed a set of proliferation genes. The signatures were build on 10 different gene ontology modules with prognostic ability.
 Conclusion:
 This study underlines the need of large prospective validation studies of gene expression signatures. Further computational intelligence and system biology studies would be held to determine the best way to use these classifiers in clinical routine. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 2026.

Revealing Targeted Therapy for Human Cancer by Gene Module Maps

A major goal of cancer research is to match specific therapies to molecular targets in cancer. Genome-scale expression profiling has identified new subtypes of cancer based on consistent patterns of variation in gene expression, leading to improved prognostic predictions. However, how these new genetic subtypes of cancers should be treated is unknown. Here, we show that a gene module map can guide the prospective identification of targeted therapies for genetic subtypes of cancer. By visualizing genome-scale gene expression in cancer as combinations of activated and deactivated functional modules, gene module maps can reveal specific functional pathways associated with each subtype that might be susceptible to targeted therapies. We show that in human breast cancers, activation of a poor-prognosis "wound signature" is strongly associated with induction of both a mitochondria gene module and a proteasome gene module. We found that 3-bromopyruvic acid, which inhibits glycolysis, selectively killed breast cells expressing the mitochondria and wound signatures. In addition, inhibition of proteasome activity by bortezomib, a drug approved for human use in multiple myeloma, abrogated wound signature expression and selectively killed breast cells expressing the wound signature. Thus, gene module maps may enable rapid translation of complex genomic signatures in human disease to targeted therapeutic strategies.

In the pursuit of complexity: Systems medicine in cancer biology

Adler et al., in a paper appearing in Nature Genetics, exploited the intersect of genetic information from expression profiles with that from array comparative genomic hybridization in human breast cancers to identify genes that may induce the transcription of the prognostic "wound response" expression signature. The amplification of two genes, MYC and CSN5, appeared to be correlated with the wound response cassette. In vitro validation showed that the wound signature could be induced in MCF10A cells only when MYC and CSN5 were coexpressed. This work shows that the intersect analysis of gene amplification and transcriptional expression on a genome-wide scale can uncover complex conditional interactions embedded in the systems map of transcriptional regulation.

Genetic regulators of large-scale transcriptional signatures in cancer

Gene expression signatures encompassing dozens to hundreds of genes have been associated with many important parameters of cancer, but mechanisms of their control are largely unknown. Here we present a method based on genetic linkage that can prospectively identify functional regulators driving large-scale transcriptional signatures in cancer. Using this method we show that the wound response signature, a poor-prognosis expression pattern of 512 genes in breast cancer, is induced by coordinate amplifications of MYC and CSN5 (also known as JAB1 or COPS5). This information enabled experimental recapitulation, functional assessment and mechanistic elucidation of the wound signature in breast epithelial cells.

Gene Expression Programs in Response to Hypoxia: Cell Type Specificity and Prognostic Significance in Human Cancers

BackgroundInadequate oxygen (hypoxia) triggers a multifaceted cellular response that has important roles in normal physiology and in many human diseases. A transcription factor, hypoxia-inducible factor (HIF), plays a central role in the hypoxia response; its activity is regulated by the oxygen-dependent degradation of the HIF-1α protein. Despite the ubiquity and importance of hypoxia responses, little is known about the variation in the global transcriptional response to hypoxia among different cell types or how this variation might relate to tissue- and cell-specific diseases.Methods and FindingsWe analyzed the temporal changes in global transcript levels in response to hypoxia in primary renal proximal tubule epithelial cells, breast epithelial cells, smooth muscle cells, and endothelial cells with DNA microarrays. The extent of the transcriptional response to hypoxia was greatest in the renal tubule cells. This heightened response was associated with a uniquely high level of HIF-1α RNA in renal cells, and it could be diminished by reducing HIF-1α expression via RNA interference. A gene-expression signature of the hypoxia response, derived from our studies of cultured mammary and renal tubular epithelial cells, showed coordinated variation in several human cancers, and was a strong predictor of clinical outcomes in breast and ovarian cancers. In an analysis of a large, published gene-expression dataset from breast cancers, we found that the prognostic information in the hypoxia signature was virtually independent of that provided by the previously reported wound signature and more predictive of outcomes than any of the clinical parameters in current use.ConclusionsThe transcriptional response to hypoxia varies among human cells. Some of this variation is traceable to variation in expression of the HIF1A gene. A gene-expression signature of the cellular response to hypoxia is associated with a significantly poorer prognosis in breast and ovarian cancer.

Advances in the Treatment of Breast Cancer

The biennial Madrid Breast Cancer Conference has become one of the leading summer meetings dedicated to reviewing and discussing the most relevant advances in breast cancer research. Seminal, practice-changing results have been presented and reviewed in different sessions and symposia, embracing various aspects from molecular genetics to advances in imaging, staging and treatment in the different settings of the disease. Oncologists need improved tools to select treatments for individual patients. Most cancer treatments benefit only a minority of the patients to whom they are administered. Prognostic and predictive factors play important roles in the treatment of breast cancer, but very few such factors are used in clinical practice and prognostic relevance remains unclear. The ability to predict which patients are most likely to benefit from a given treatment would not only save patients from unnecessary toxicity and inconvenience, but might also facilitate their receiving drugs that are more likely to help. The current standard tools used to test new therapies in early breast cancer are Phase III randomized adjuvant trials. Such trials are large, expensive and take years to achieve their outcome. Neoadjuvant or primary systemic therapy (PST) trials might offer the eventual possibility of a more rapid and less resource-demanding alternative, at least for some therapies, if short-term surrogate clinical, pathologic or biologic end points could be identified that might predict for long-term outcome in adjuvant trials. Genome-wide monitoring of gene expression using DNA microarrays makes it possible to study thousands of genes in a tumor sample with a single experiment. By looking for an association between the gene expression pattern and tumor behavior, it should be possible to identify new prognostic and predictive factors. Dr M Van de Vijver (Rotterdam, The Netherlands) and his team have previously identified a 70gene expression profile (GEP) associated with an increased risk for developing distant metastases within 5 years. More recently, they identified consistent features in the transcriptional response of normal fibroblasts to serum and used this wound-response signature to reveal links between wound healing and cancer progression in the same tumors. By combining the 70-GEP (good-prognosis and poor-prognosis tumors) and the wound signature (activated and quiescent tumors), subgroups of patients with markedly different prognoses can be identified [1]. One serious limitation of microarray expression profiling, however, is that it is an RNA assay that requires fresh or well-preserved frozen tissue for extraction of viable RNA. Consequently, some investigators use microarrays to screen the genes for those factors associated with outcome and then develop classifiers for clinical applications that are based on subsequent studies using reverse transcriptionpolymerase chain reaction (RT-PCR) of the selected genes. This was the approach used and reviewed in detail by Dr S Paik (PA, USA) in developing the Oncotype Dx® risk score for patients with lymph node-negative, estrogen