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Glioblastoma is an aggressive cancer of the brain and spine. While analysis of glioblastoma ‘omics data has somewhat improved our understanding of the disease, it has not led to direct improvement in patient survival. Cancer survival is often characterized by differences in gene expression, but the mechanisms that drive these differences are generally unknown. We therefore set out to model the regulatory mechanisms associated with glioblastoma survival. We inferred individual patient gene regulatory networks using data from two different expression platforms from The Cancer Genome Atlas. We performed comparative network analysis between patients with long- and short-term survival. Seven pathways were identified as associated with survival, all of them involved in immune signaling; differential regulation of PD1 signaling was validated to correspond with outcome in an independent dataset from the German Glioma Network. In this pathway, transcriptional repression of genes for which treatment options are available was lost in short-term survivors; this was independent of mutational burden and only weakly associated with T-cell infiltration. Collectively, these results provide a new way to stratify patients with glioblastoma that uses network features as biomarkers to predict survival. They also identify new potential therapeutic interventions, underscoring the value of analyzing gene regulatory networks in individual patients with cancer. Significance: Genome-wide network modeling of individual glioblastomas identifies dysregulation of PD1 signaling in patients with poor prognosis, indicating this approach can be used to understand how gene regulation influences cancer progression.

Mucosal melanoma is a rare and poorly characterized subtype of human melanoma. Here we perform a cross-species analysis by sequencing tumor-germline pairs from 46 primary human muscosal, 65 primary canine oral and 28 primary equine melanoma cases from mucosal sites. Analysis of these data reveals recurrently mutated driver genes shared between species such as NRAS, FAT4, PTPRJ, TP53 and PTEN, and pathogenic germline alleles of BRCA1, BRCA2 and TP53. We identify a UV mutation signature in a small number of samples, including human cases from the lip and nasal mucosa. A cross-species comparative analysis of recurrent copy number alterations identifies several candidate drivers including MDM2, B2M, KNSTRN and BUB1B. Comparison of somatic mutations in recurrences and metastases to those in the primary tumor suggests pervasive intra-tumor heterogeneity. Collectively, these studies suggest a convergence of some genetic changes in mucosal melanomas between species but also distinctly different paths to tumorigenesis., Mucosal melanoma is a rare melanoma subtype that is poorly characterised. Here, the authors sequenced human, canine, and equine melanoma samples and performed a cross-species analysis, which revealed candidate driver genes, recurrent copy number alterations in regions syntenic between species, extensive intra-tumour heterogeneity and potential germline predisposing alleles

Glioblastoma is an aggressive cancer of the brain and spine. While analysis of glioblastoma ‘omics data has somewhat improved our understanding of the disease, it has not led to direct improvement in patient survival. Cancer survival is often characterized by differences in expression of particular genes, but the mechanisms that drive these differences are generally unknown. We therefore set out to model the regulatory mechanisms that associate with glioblastoma survival. We inferred individual patient gene regulatory networks using data from two different expression platforms from The Cancer Genome Atlas (n=522 and 431). We performed a comparative network analysis between patients with long- and short-term survival, correcting for patient age, sex, and neoadjuvant treatment status. We identified seven pathways associated with survival, all of which were involved in immune system signaling. Differential regulation of PD1 signaling was validated in an independent dataset from the German Glioma Network (n=70). We found that transcriptional repression of genes in this pathway—for which treatment options are available—was lost in short-term survivors and that this was independent of mutation burden and only weakly associated with T-cell infiltrate. These results provide a new way to stratify glioblastoma patients that uses network features as biomarkers to predict survival, and identify new potential therapeutic interventions, thus underscoring the value of analyzing gene regulatory networks in individual cancer patients.

Background Genome-wide association studies (GWASes) have identified many noncoding germline single-nucleotide polymorphisms (SNPs) that are associated with an increased risk of developing cancer. However, how these SNPs affect cancer risk is still largely unknown. Methods We used a systems biology approach to analyse the regulatory role of cancer-risk SNPs in thirteen tissues. By using data from the Genotype-Tissue Expression (GTEx) project, we performed an expression quantitative trait locus (eQTL) analysis. We represented both significant cis- and trans-eQTLs as edges in tissue-specific eQTL bipartite networks. Results Each tissue-specific eQTL network is organised into communities that group sets of SNPs and functionally related genes. When mapping cancer-risk SNPs to these networks, we find that in each tissue, these SNPs are significantly overrepresented in communities enriched for immune response processes, as well as tissue-specific functions. Moreover, cancer-risk SNPs are more likely to be ‘cores’ of their communities, influencing the expression of many genes within the same biological processes. Finally, cancer-risk SNPs preferentially target oncogenes and tumour-suppressor genes, suggesting that they may alter the expression of these key cancer genes. Conclusions This approach provides a new way of understanding genetic effects on cancer risk and provides a biological context for interpreting the results of GWAS cancer studies.

BACKGROUNDWith the onset of next generation sequencing technologies, we have made great progress in identifying recurrent mutational drivers of cancer. As cancer tissues are now frequently screened for specific sets of mutations, a large amount of samples has become available for analysis. Classification of patients with similar mutation profiles may help identifying subgroups of patients who might benefit from specific types of treatment. However, classification based on somatic mutations is challenging due to the sparseness and heterogeneity of the data.METHODSHere, we describe a new method to de-sparsify somatic mutation data using biological pathways. We applied this method to 23 cancer types from The Cancer Genome Atlas, including samples from 5, 805 primary tumors.RESULTSWe show that, for most cancer types, de-sparsified mutation data associates with phenotypic data. We identify poor prognostic subtypes in three cancer types, which are associated with mutations in signal transduction pathways for which targeted treatment options are available. We identify subtype-drug associations for 14 additional subtypes. Finally, we perform a pan-cancer subtyping analysis and identify nine pan-cancer subtypes, which associate with mutations in four overarching sets of biological pathways.CONCLUSIONSThis study is an important step towards understanding mutational patterns in cancer.

One of the biggest challenges in cancer is predicting its initiation and course of progression. In this issue of Cancer Research, Rockne and colleagues use state transition theory to predict how peripheral mononuclear blood cells in mice transition from a healthy state to acute myeloid leukemia. They found that critical transcriptomic perturbations could predict initiation and progression of the disease. This is an important step toward accurately predicting cancer evolution, which may eventually facilitate early diagnosis of cancer and disease recurrence, and which could potentially inform on timing of therapeutic interventions. See related article by Rockne et al., 3157

Based on morphology it is often challenging to distinguish between the many different soft tissue sarcoma subtypes. Moreover, outcome of disease is highly variable even between patients with the same disease. Machine learning on transcriptome sequencing data could be a valuable new tool to understand differences between and within entities. Here we used machine learning analysis to identify novel diagnostic and prognostic markers and therapeutic targets for soft tissue sarcomas. Gene expression data was used from the Cancer Genome Atlas, the Genotype-Tissue Expression project and the French Sarcoma Group. We identified three groups of tumors that overlap in their molecular profiles as seen with unsupervised t-Distributed Stochastic Neighbor Embedding clustering and a deep neural network. The three groups corresponded to subtypes that are morphologically overlapping. Using a random forest algorithm, we identified novel diagnostic markers for soft tissue sarcoma that distinguished between synovial sarcoma and MPNST, and that we validated using qRT-PCR in an independent series. Next, we identified prognostic genes that are strong predictors of disease outcome when used in a k-nearest neighbor algorithm. The prognostic genes were further validated in expression data from the French Sarcoma Group. One of these, HMMR, was validated in an independent series of leiomyosarcomas using immunohistochemistry on tissue micro array as a prognostic gene for disease-free interval. Furthermore, reconstruction of regulatory networks combined with data from the Connectivity Map showed, amongst others, that HDAC inhibitors could be a potential effective therapy for multiple soft tissue sarcoma subtypes. A viability assay with two HDAC inhibitors confirmed that both leiomyosarcoma and synovial sarcoma are sensitive to HDAC inhibition. In this study we identified novel diagnostic markers, prognostic markers and therapeutic leads from multiple soft tissue sarcoma gene expression datasets. Thus, machine learning algorithms are powerful new tools to improve our understanding of rare tumor entities., Author summary Soft-tissue sarcomas are a group of rare cancers that can be challenging to diagnose and treat. The morphology of the different soft-tissue sarcoma subtypes can overlap and the prognosis differs significantly between, and also within, the different subtypes. Moreover, targeted therapies are often not available. In this study we used transcriptome sequencing data from The Cancer Genome Atlas, containing 206 soft-tissue sarcoma samples which we analyzed using different machine learning algorithms to gain novel insights. When possible, we verified our findings in independent datasets or in cell lines. First, we found that both synovial sarcomas and malignant peripheral nerve sheath tumors show the largest overlap with normal tissue derived from the nervous system. This link with neural differentiation for synovial sarcoma was not well established until now. Second, genes were identified whose expression could be used to differentiate between the different soft-tissue sarcomas where the morphology overlaps. Third, novel prognostic genes were identified for separate subtypes. One gene, HMMR, which we found as a strong prognostic gene for leiomyosarcoma, was verified with immunohistochemistry on samples from our archives. Last, using a network analysis new potential therapies were identified. HDAC inhibitors were identified as a potential strong therapy for sarcomas, including leiomyosarcomas, which we verified in cell lines.

Despite pronounced sex differences in cancer incidence, severity, and response to treatment, most current approaches to clinical management, as well as therapeutics development and selection, are sex-independent. For most cancer types, including liver cancer, males have a higher risk of developing the disease and a lower survival rate than women. However, the molecular features that drive these sex differences are poorly understood. We inferred patient-specific regulatory networks of liver hepatocellular carcinoma using data from TCGA. By comparing the female and male networks, we found marked sex differences in transcriptional regulatory processes relevant to disease development, progression, and response to therapy. We found that oncogenes have significantly higher regulatory targeting in males, while tumor suppressor genes have significantly higher targeting in females. Many “hallmark” cancer pathways, including the HEDGEHOG, WNT, and TGF-β signaling pathways, were significantly more highly targeted in males, while drug metabolism and immune related pathways were enriched for genes highly targeted in females. We also evaluated sex-biased somatic mutation patterns using mutation and copy number alteration data in TCGA. By summarizing mutations found in genes into pathway mutation scores, we found sex-biased mutation profiles for many pathways, providing additional support for biological sex differences associated with WNT, NOTCH, TGF-β, and HEDGEHOG signaling pathways. Our analysis uncovered patterns of gene regulation that differentiate male and female liver cancer and may be associated with sex differences in prognosis and treatment response. These findings provide insight into the mechanisms that drive clinically observed sex differences and underscore the importance of considering sex as a factor influencing disease etiology and in developing and prescribing therapies. Our network approach can provide insights into why some therapies have differential effect in males and females, and suggest new ways to optimize drug response in each sex. Citation Format: Camila M. Lopes-Ramos, Marieke Kuijjer, Kimberly Glass, Dawn DeMeo, John Quackenbush. Regulatory networks of liver carcinoma reveal sex specific patterns of gene regulation [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6569.

Genome-wide associations studies (GWASes) have identified many germline genetic variants that are associated with an increased risk of developing cancer. However, how these single nucleotide polymorphisms (SNPs) alter biological function in a way that increases cancer risk is still largely unknown. We used a systems biology approach to analyze the regulatory role and functional associations of cancer-risk SNPs in thirteen distinct tissues. Using data from the Genotype-Tissue Expression (GTEx) project, we performed an expression quantitative trait locus (eQTL) analysis, keeping both cis- and trans-eQTLs, and representing those significant associations as edges in tissue-specific eQTL bipartite networks. We find that each network is organized into highly modular communities that group sets of SNPs together with functionally-related collections of genes. We mapped cancer-risk SNPs to each tissue-specific eQTL network. Although we find in each tissue that cancer-risk SNPs are distributed across the network, they are not uniformly distributed. Rather they are significantly over-represented in a small number of communities. This includes communities enriched for immune response processes as well as communities representing tissue-specific functions. Moreover, cancer-risk SNPs are over-represented in the central “cores” of communities, meaning they are more likely to influence the expression of many genes within the same community, thus affecting biological processes. And finally, we find that cancer-risk SNPs preferentially target oncogenes and tumor suppressor genes, suggesting non-genic mutations may still alter the effects of these key cancer-associated genes. This bipartite eQTL network approach provides a new way of understanding genetic effects on cancer risk and provides a biological context for interpreting the results of GWAS cancer studies.

This paper presents a new algorithm, Reinforced and Informed Network-based Clustering(RINC), for finding unknown groups of similar data objects in sparse and largely non-overlapping feature space where a network structure among features can be observed. Sparse and non-overlapping unlabeled data become increasingly common and available especially in text mining and biomedical data mining. RINC inserts a domain informed model into a modelless neural network. In particular, our approach integrates physically meaningful feature dependencies into the neural network architecture and soft computational constraint. Our learning algorithm efficiently clusters sparse data through integrated smoothing and sparse auto-encoder learning. The informed design requires fewer samples for training and at least part of the model becomes explainable. The architecture of the reinforced network layers smooths sparse data over the network dependency in the feature space. Most importantly, through back-propagation, the weights of the reinforced smoothing layers are simultaneously constrained by the remaining sparse auto-encoder layers that set the target values to be equal to the raw inputs. Empirical results demonstrate that RINC achieves improved accuracy and renders physically meaningful clustering results.

Significant sex differences are observed in colon cancer, and understanding these differences is essential to advance disease prevention, diagnosis, and treatment. Males have a higher risk of developing colon cancer and a lower survival rate than women. However, the molecular features that drive these sex differences are poorly understood. We used both transcript-based and gene regulatory network methods to analyze RNA-Seq data from The Cancer Genome Atlas for 445 patients with colon cancer. We compared gene expression between tumors in men and women and found no significant sex differences except for sex-chromosome genes. We then inferred patient-specific gene regulatory networks, and found significant regulatory differences between males and females, with drug and xenobiotics metabolism via cytochrome P450 pathways more strongly targeted in females. This finding was validated in a dataset that included 1,193 patients from five independent studies. While targeting of the drug metabolism pathway did not change the overall survival for males treated with adjuvant chemotherapy, females with greater targeting had an increase in 10-year overall survival probability, with 89% (95% CI: 78%-100%) survival compared to 61% (95% CI: 45%-82%) for women with lower targeting, respectively (p=0.034). Our network analysis uncovered patterns of transcriptional regulation that differentiate male and female colon cancer. Most importantly, targeting of the drug metabolism pathway was predictive of survival in women who received adjuvant chemotherapy. This network-based approach can be used to investigate the molecular features that drive sex differences in other cancers and complex diseases.

Understanding sex differences in colon cancer is essential to advance disease prevention, diagnosis, and treatment. Males have a higher risk of developing colon cancer and a lower survival rate than women. However, the molecular features that drive these sex differences are poorly understood. In this study, we use both transcript-based and gene regulatory network methods to analyze RNA-seq data from The Cancer Genome Atlas for 445 patients with colon cancer. We compared gene expression between tumors in men and women and observed significant sex differences in sex chromosome genes only. We then inferred patient-specific gene regulatory networks and found significant regulatory differences between males and females, with drug and xenobiotics metabolism via cytochrome P450 pathways more strongly targeted in females. This finding was validated in a dataset of 1,193 patients from five independent studies. While targeting, the drug metabolism pathway did not change overall survival for males treated with adjuvant chemotherapy, females with greater targeting showed an increase in 10-year overall survival probability, 89% [95% confidence interval (CI), 78–100] survival compared with 61% (95% CI, 45–82) for women with lower targeting, respectively (P = 0.034). Our network analysis uncovers patterns of transcriptional regulation that differentiate male and female colon cancer and identifies differences in regulatory processes involving the drug metabolism pathway associated with survival in women who receive adjuvant chemotherapy. This approach can be used to investigate the molecular features that drive sex differences in other cancers and complex diseases. Significance: A network-based approach reveals that sex-specific patterns of gene targeting by transcriptional regulators are associated with survival outcome in colon cancer. This approach can be used to understand how sex influences progression and response to therapies in other cancers. Cancer Res; 78(19); 5538–47. ©2018 AACR.

Conventional high-grade osteosarcoma is the most common primary bone cancer with relatively high incidence in young people. Recurrent and metastatic tumors are difficult to treat. We performed a kinase inhibitor screen in two osteosarcoma cell lines, which identified MEK1/2 inhibitors. These inhibitors were further validated in a panel of six osteosarcoma cell lines. Western blot analysis was performed to assess ERK activity and efficacy of MEK inhibition. A 3D culture system was used to validate results from 2D monolayer cultures. Gene expression analysis was performed to identify differentially expressed gene signatures in sensitive and resistant cell lines. Activation of the AKT signaling network was explored using Western blot and pharmacological inhibition. In the screen, Trametinib, AZD8330 and TAK-733 decreased cell viability by more than 50%. Validation in six osteosarcoma cell lines identified three cell lines as resistant and three as sensitive to the inhibitors. Western blot analysis of ERK activity revealed that sensitive lines had high constitutive ERK activity. Treatment with the three MEK inhibitors in a 3D culture system validated efficacy in inhibition of osteosarcoma viability. MEK1/2 inhibition represents a candidate treatment strategy for osteosarcomas displaying high MEK activity as determined by ERK phosphorylation status.

In the original version of [this article][1] ([1][2]), in the Disclosure of Potential Conflicts of Interest section, Dr. Charles S. Fuchs did not report consulting work with the biotechnology and pharmaceutical industry focused on cancer drug development. With respect to an analysis of molecular

Ionizing radiation (IR) and germline mutations in the retinoblastoma tumor suppressor gene (RB1) are the strongest risk factors for developing osteosarcoma. Recapitulating the human predisposition, we found that Rb1+/- mice exhibited accelerated development of IR-induced osteosarcoma, with a latency of 39 weeks. Initial exposure of osteoblasts to carcinogenic doses of IR in vitro and in vivo induced RB1-dependent senescence and the expression of a panel of proteins known as senescence-associated secretory phenotype (SASP), dominated by IL-6. RB1 expression closely correlated with that of the SASP cassette in human osteosarcomas, and low expression of both RB1 and the SASP genes was associated with poor prognosis. In vivo, IL-6 was required for IR-induced senescence, which elicited NKT cell infiltration and a host inflammatory response. Mice lacking IL-6 or NKT cells had accelerated development of IR-induced osteosarcomas. These data elucidate an important link between senescence, which is a cell-autonomous tumor suppressor response, and the activation of host-dependent cancer immunosurveillance. Our findings indicate that overcoming the immune response to senescence is a rate-limiting step in the formation of IR-induced osteosarcoma.

High-grade osteosarcoma is a tumor with a complex genomic profile, occurring primarily in adolescents with a second peak at middle age. The extensive genomic alterations obscure the identification of genes driving tumorigenesis during osteosarcoma development. To identify such driver genes, we integrated DNA copy number profiles (Affymetrix SNP 6.0) of 32 diagnostic biopsies with 84 expression profiles (Illumina Human-6 v2.0) of high-grade osteosarcoma as compared with its putative progenitor cells, i.e., mesenchymal stem cells (n ¼ 12) or osteoblasts (n ¼ 3). In addition, we performed paired analyses between copy number and expression profiles of a subset of 29 patients for which both DNA and mRNA profiles were available. Integrative analyses were performed in Nexus Copy Number software and statistical language R. Paired analyses were performed on all probes detecting significantly differentially expressed genes in corresponding LIMMA analyses. For both nonpaired and paired analyses, copy number aberration frequency was set to >35%. Nonpaired and paired integrative analyses resulted in 45 and 101 genes, respectively, which were present in both analyses using different control sets. Paired analyses detected >90% of all genes found with the corresponding nonpaired analyses. Remarkably, approximately twice as many genes as found in the corresponding nonpaired analyses were detected. Affected genes were intersected with differentially expressed genes in osteosarcoma cell lines, resulting in 31 new osteosarcoma driver genes. Cell division related genes, such as MCM4 and LATS2, were overrepresented and genomic instability was predictive for metastasis-free survival, suggesting that deregulation of the cell cycle is a driver of osteosarcomagenesis. V C 2012 Wiley Periodicals, Inc.

Purpose: High-grade osteosarcoma is a malignant primary bone tumor with a peak incidence in adolescence. Overall survival (OS) of patients with resectable metastatic disease is approximately 20%. The exact mechanisms of development of metastases in osteosarcoma remain unclear. Most studies focus on tumor cells, but it is increasingly evident that stroma plays an important role in tumorigenesis and metastasis. We investigated the development of metastasis by studying tumor cells and their stromal context. Experimental Design: To identify gene signatures playing a role in metastasis, we carried out genome-wide gene expression profiling on prechemotherapy biopsies of patients who did (n = 34) and patients who did not (n = 19) develop metastases within 5 years. Immunohistochemistry (IHC) was performed on pretreatment biopsies from 2 additional cohorts (n = 63 and n = 16) and corresponding postchemotherapy resections and metastases. Results: A total of 118/132 differentially expressed genes were upregulated in patients without metastases. Remarkably, almost half of these upregulated genes had immunological functions, particularly related to macrophages. Macrophage-associated genes were expressed by infiltrating cells and not by osteosarcoma cells. Tumor-associated macrophages (TAM) were quantified with IHC and associated with significantly better overall survival (OS) in the additional patient cohorts. Osteosarcoma samples contained both M1- (CD14/HLA-DRα positive) and M2-type TAMs (CD14/CD163 positive and association with angiogenesis). Conclusions: In contrast to most other tumor types, TAMs are associated with reduced metastasis and improved survival in high-grade osteosarcoma. This study provides a biological rationale for the adjuvant treatment of high-grade osteosarcoma patients with macrophage activating agents, such as muramyl tripeptide. Clin Cancer Res; 17(8); 2110–9. ©2011 AACR.

Background In vitro expanded mesenchymal stromal cells (MSCs) are increasingly used as experimental cellular therapy. However, there have been concerns regarding the safety of their use, particularly with regard to possible oncogenic transformation. MSCs are the hypothesized precursor cells of high-grade osteosarcoma, a tumor with often complex karyotypes occurring mainly in adolescents and young adults. Methods To determine if MSCs from osteosarcoma patients could be predisposed to malignant transformation we cultured MSCs of nine osteosarcoma patients and five healthy donors for an average of 649 days (range 601–679 days). Also, we compared MSCs derived from osteosarcoma patients at diagnosis and from healthy donors using genome wide gene expression profiling. Results Upon increasing passage, increasing frequencies of binucleate cells were detected, but no increase in proliferation suggestive of malignant transformation occurred in MSCs from either patients or donors. Hematopoietic cell specific Lyn substrate 1 (HLCS1) was differentially expressed (fold change 0.25, P value 0.0005) between MSCs of osteosarcoma patients (n = 14) and healthy donors (n = 9). Conclusions This study shows that although HCLS1 expression was downregulated in MSCs of osteosarcoma patients and binucleate cells were present in both patient and donor derived MSCs, there was no evidence of neoplastic changes to occur during long-term culture. Electronic supplementary material The online version of this article (doi:10.1186/s13569-015-0031-1) contains supplementary material, which is available to authorized users.

An increased level of interleukin-6 (IL-6) in epithelial ovarian cancer (EOC) is correlated with a worse prognosis. IL-6 stimulates tumor-growth and inflammation. We investigated the intricate interaction between the IL-6 signaling pathway and tumor-infiltrating myeloid cells (TIMs) to determine their prognostic impact in EOC. 160 EOC samples were analyzed for the expression of IL-6, its receptor (IL-6R) and downstream signaling via pSTAT3 by immunohistochemistry. Triple color immunofluorescence confocal microscopy was used to identify myeloid cell populations by CD14, CD33, and CD163. The relationship between these markers, tumor-infiltrating immune cells, clinical-pathological characteristics and survival was investigated. EOC displayed a dense infiltration with myeloid cells, in particular of the CD163(+) type. The distribution pattern of all myeloid subtypes was comparable among the different histological subtypes. Analysis of the tumor cells revealed a high expression of IL-6R in 15% and of IL-6 in 23% of patients. Interestingly, tumors expressing IL-6 or IL-6R formed two different groups. Tumors with a high expression of IL-6R displayed low mature myeloid cell infiltration and a longer disease-specific survival (DSS), especially in late stage tumors. High expression of IL-6R was an independent prognostic factor for survival by multivariate analyses (hazard ratio = 0.474, p = 0.011). In contrast, tumors with high epithelial IL-6 expression displayed a dense infiltration of mature myeloid cells and were correlated with a shorter DSS. Furthermore, in densely CD8(+) T-cell infiltrated tumors, the ratio between these lymphoid cells and CD163(+) myeloid cells was predictive for survival. Thus, IL-6 and IL-6R are opposite markers for myeloid cell infiltration and survival.

Background In osteosarcoma, the presence of tumor-infiltrating macrophages positively correlates with patient survival in contrast to the negative effect of tumor-associated macrophages in patients with other tumors. Liposome-encapsulated muramyl tripeptide (L-MTP-PE) has been introduced in the treatment of osteosarcoma patients, which may enhance the potential anti-tumor activity of macrophages. Direct anti-tumor activity of human macrophages against human osteosarcoma cells has not been described so far. Hence, we assessed osteosarcoma cell growth after co-culture with human macrophages. Methods Monocyte-derived M1-like and M2-like macrophages were polarized with LPS + IFN-γ, L-MTP-PE +/− IFN-γ or IL-10 and incubated with osteosarcoma cells. Two days later, viable tumor cell numbers were analyzed. Antibody-dependent effects were investigated using the therapeutic anti-EGFR antibody cetuximab. Results M1-like macrophages inhibited osteosarcoma cell growth when activated with LPS + IFN-γ. Likewise, stimulation of M1-like macrophages with liposomal muramyl tripeptide (L-MTP-PE) inhibited tumor growth, but only when combined with IFN-γ. Addition of the tumor-reactive anti-EGFR antibody cetuximab did not further improve the anti-tumor activity of activated M1-like macrophages. The inhibition was mediated by supernatants of activated M1-like macrophages, containing TNF-α and IL-1β. However, specific blockage of these cytokines, nitric oxide or reactive oxygen species did not inhibit the anti-tumor effect, suggesting the involvement of other soluble factors released upon macrophage activation. While LPS + IFN-γ–activated M2-like macrophages had low anti-tumor activity, IL-10–polarized M2-like macrophages were able to reduce osteosarcoma cell growth in the presence of the anti-EGFR cetuximab involving antibody-dependent tumor cell phagocytosis. Conclusion This study demonstrates that human macrophages can be induced to exert direct anti-tumor activity against osteosarcoma cells. Our observation that the induction of macrophage anti-tumor activity by L-MTP-PE required IFN-γ may be of relevance for the optimization of L-MTP-PE therapy in osteosarcoma patients.

One of the hallmarks of cancer is the influx of myeloid cells. In our study, we investigated the constitution of tumor-infiltrating myeloid cells and their relationship to other tumor-infiltrating immune cells, tumor characteristics and the disease-specific survival of patients with cervical cancer (CxCa). Triple-color immunofluorescence confocal microscopy was used to locate, identify and quantify macrophages (CD14), their maturation status (CD33) and their polarization (CD163) in a cohort of 86 patients with cervical carcinoma. Quantification of the numbers of myeloid cells revealed that a strong intraepithelial infiltration of CD14+ cells, and more specifically the population of CD14+CD33-CD163- matured M1 macrophages, is associated with a large influx of intraepithelial T lymphocytes (p = 0.008), improved disease-specific survival (p = 0.007) and forms an independent prognostic factor for survival (p = 0.033). The intraepithelial CD8+ T-cell and regulatory T-cell (Treg) ratio also forms an independent prognostic factor (p = 0.010) and combination of these two factors reveals a further increased benefit in survival for patients whose tumor displays a dense infiltration with intraepithelial matured M1 macrophages and a high CD8 T-cell/Treg ratio, indicating that both populations of immune cells simultaneously improve survival. Subsequently, we made a heatmap including all known immune parameters for these patients, whereby we were able to identify different immune signatures in CxCa. These results indicate that reinforcement and activation of the intratumoral M1 macrophages may form an attractive immunotherapeutic option in CxCa.

Background High-grade osteosarcoma is an aggressive tumor most often developing in the long bones of adolescents, with a second peak in the 5th decade of life. Better knowledge on cellular signaling in this tumor may identify new possibilities for targeted treatment. Methods We performed gene set analysis on previously published genome-wide gene expression data of osteosarcoma cell lines (n=19) and pretreatment biopsies (n=84). We characterized overexpression of the insulin-like growth factor receptor (IGF1R) signaling pathways in human osteosarcoma as compared with osteoblasts and with the hypothesized progenitor cells of osteosarcoma – mesenchymal stem cells. This pathway plays a key role in the growth and development of bone. Since most profound differences in mRNA expression were found at and upstream of the receptor of this pathway, we set out to inhibit IR/IGF1R using OSI-906, a dual inhibitor for IR/IGF1R, on four osteosarcoma cell lines. Inhibitory effects of this drug were measured by Western blotting and cell proliferation assays. Results OSI-906 had a strong inhibitory effect on proliferation of 3 of 4 osteosarcoma cell lines, with IC50s below 100 nM at 72 hrs of treatment. Phosphorylation of IRS-1, a direct downstream target of IGF1R signaling, was inhibited in the responsive osteosarcoma cell lines. Conclusions This study provides an in vitro rationale for using IR/IGF1R inhibitors in preclinical studies of osteosarcoma.

High-grade osteosarcoma is an extremely genomically unstable tumor. This, together with other challenges, such as the heterogeneity within and between tumor samples, and the rarity of the disease, renders it difficult to study this tumor on a genome-wide level. Now that most laboratories change from genome-wide microarray experiments to Next-Generation Sequencing it is important to discuss the lessons we have learned from microarray studies. In this review, we discuss the challenges of high-grade osteosarcoma data analysis. We give an overview of microarray studies that have been conducted so far on both osteosarcoma tissue samples and cell lines. We discuss recent findings from integration of different data types, which is particularly relevant in a tumor with such a complex genomic profile. Finally, we elaborate on the translation of results obtained with bioinformatics into functional studies, which has lead to valuable findings, especially when keeping in mind that no new therapies with a significant impact on survival have been developed in the past decades.

High-grade conventional osteosarcoma is a malignant tumour predominantly affecting adolescents and, despite multimodal intensive therapy, lethal for one third of the patients. Although there is currently detailed knowledge of normal skeletal development, this has not been integrated into research on the genesis of osteosarcoma. Recently we showed that the canonical Wnt pathway is not active in osteosarcoma and that its reactivation is disadvantageous to osteosarcoma cells. Since Wnt is regulating normal skeletogenesis together with other pathways, here we report on the activities of the bone morphogenic protein (BMP), the transforming growth factor beta (TGFβ) and the hedgehog (Hh) pathways in osteosarcoma. Human osteosarcoma samples (n=210), benign bone tumours of osteoblastic lineage called osteoblastoma (n=25) and osteosarcoma cell lines (n=19) were examined. For pathway activity luciferase transcriptional reporter assays and gene and protein expression analyses were performed. Immunohistochemical analysis of phosphorylated Smad1 and Smad2, the intracellular effectors of BMP and TGFβ, respectively, showed nuclear expression of both proteins in 70% of the osteosarcoma samples at levels comparable to osteoblastoma. Interestingly cases with lower expression showed significantly worse disease free survival. This may imply that drugs restoring impaired signalling pathways in osteosarcoma might change the tumour’s aggressive clinical course, however targeted pathway modulation in vitro did not affect cell proliferation.

Ollier disease and Maffucci syndrome are non-hereditary skeletal disorders characterized by multiple enchondromas (Ollier disease) combined with spindle cell hemangiomas (Maffucci syndrome). We report somatic heterozygous mutations in IDH1 (c.394C>T encoding an R132C substitution and c.395G>A encoding an R132H substitution) or IDH2 (c.516G>C encoding R172S) in 87% of enchondromas (benign cartilage tumors) and in 70% of spindle cell hemangiomas (benign vascular lesions). In total, 35 of 43 (81%) subjects with Ollier disease and 10 of 13 (77%) with Maffucci syndrome carried IDH1 (98%) or IDH2 (2%) mutations in their tumors. Fourteen of 16 subjects had identical mutations in separate lesions. Immunohistochemistry to detect mutant IDH1 R132H protein suggested intraneoplastic and somatic mosaicism. IDH1 mutations in cartilage tumors were associated with hypermethylation and downregulated expression of several genes. Mutations were also found in 40% of solitary central cartilaginous tumors and in four chondrosarcoma cell lines, which will enable functional studies to assess the role of IDH1 and IDH2 mutations in tumor formation.

Background High-grade osteosarcoma is a primary malignant bone tumor mostly occurring in adolescents and young adults, with a second peak at middle age. Overall survival is approximately 60%, and has not significantly increased since the introduction of neoadjuvant chemotherapy in the 1970s. The genomic profile of high-grade osteosarcoma is complex and heterogeneous. Integration of different types of genome-wide data may be advantageous in extracting relevant information from the large number of aberrations detected in this tumor. Methods We analyzed genome-wide gene expression data of osteosarcoma cell lines and integrated these data with a kinome screen. Data were analyzed in statistical language R, using LIMMA for detection of differential expression/phosphorylation. We subsequently used Ingenuity Pathways Analysis to determine deregulated pathways in both data types. Results Gene set enrichment indicated that pathways important in genomic stability are highly deregulated in these tumors, with many genes showing upregulation, which could be used as a prognostic marker, and with kinases phosphorylating peptides in these pathways. Akt and AMPK signaling were identified as active and inactive, respectively. As these pathways have an opposite role on mTORC1 signaling, we set out to inhibit Akt kinases with the allosteric Akt inhibitor MK-2206. This resulted in inhibition of proliferation of osteosarcoma cell lines U-2 OS and HOS, but not of 143B, which harbors a KRAS oncogenic transformation. Conclusions We identified both overexpression and hyperphosphorylation in pathways playing a role in genomic stability. Kinome profiling identified active Akt signaling, which could inhibit proliferation in 2/3 osteosarcoma cell lines. Inhibition of PI3K/Akt/mTORC1 signaling may be effective in osteosarcoma, but further studies are required to determine whether this pathway is active in a substantial subgroup of this heterogeneous tumor.

BackgroundOsteosarcomas are the most common primary malignant tumors of bone and show multiple and complex genomic aberrations. miRNAs are non-coding RNAs capable of regulating gene expression at the post transcriptional level, and miRNAs and their target genes may represent novel therapeutic targets or biomarkers for osteosarcoma. In order to investigate the involvement of miRNAs in osteosarcoma development, global microarray analyses of a panel of 19 human osteosarcoma cell lines was performed.Principal findingsWe identified 177 miRNAs that were differentially expressed in osteosarcoma cell lines relative to normal bone. Among these, miR-126/miR-126*, miR-142-3p, miR-150, miR-223, miR-486-5p and members of the miR-1/miR-133a, miR-144/miR-451, miR-195/miR-497 and miR-206/miR-133b clusters were found to be downregulated in osteosarcoma cell lines. All miRNAs in the paralogous clusters miR-17-92, miR-106b-25 and miR-106a-92 were overexpressed. Furthermore, the upregulated miRNAs included miR-9/miR-9*, miR-21*, miR-31/miR-31*, miR-196a/miR-196b, miR-374a and members of the miR-29 and miR-130/301 families. The most interesting inversely correlated miRNA/mRNA pairs in osteosarcoma cell lines included miR-9/TGFBR2 and miR-29/p85α regulatory subunit of PI3K. PTEN mRNA correlated inversely with miR-92a and members of the miR-17 and miR-130/301 families. Expression profiles of selected miRNAs were confirmed in clinical samples. A set of miRNAs, miR-1, miR-18a, miR-18b, miR-19b, miR-31, miR-126, miR-142-3p, miR-133b, miR-144, miR-195, miR-223, miR-451 and miR-497 was identified with an intermediate expression level in osteosarcoma clinical samples compared to osteoblasts and bone, which may reflect the differentiation level of osteosarcoma relative to the undifferentiated osteoblast and fully differentiated normal bone.SignificanceThis study provides an integrated analysis of miRNA and mRNA in osteosarcoma, and gives new insight into the complex genetic mechanisms of osteosarcoma development and progression.

High-grade osteosarcoma is a tumor with a complex genomic profile, occurring primarily in adolescents. The extensive genomic alterations obscure the identification of genes driving tumorigenesis in osteosarcoma progenitor cells. In order to identify such driver genes, we integrated DNA copy number profiles (Affymetrix SNP 6.0) of 32 diagnostic biopsies with 84 expression profiles (Illumina Human-6 v2.0) of osteosarcoma as compared with its putative progenitor cells, i.e. mesenchymal stem cells (n=12) or osteoblasts (n=3). In addition, we performed paired analyses between copy number and expression profiles of a subset of 29 patients for which we had both DNA and mRNA profiles. Integrative analyses were performed in Nexus Copy Number Software and statistical language R. Paired analyses were performed on all probes which were significantly differentially expressed in corresponding LIMMA analyses. For both non-paired and paired analyses, copy number aberration frequency was set to >35%. Non-paired integrative analyses resulted in 45 genes that were present in both analyses using different control sets, i.e. MSCs and osteoblasts. 101 genes overlapped between the two paired integrative analyses. Paired analyses detected >90% of all genes found with the corresponding non-paired analyses. Remarkably, approximately twice as many genes as found in the corresponding non-paired analyses were detected. Affected genes were compared with differential expression in osteosarcoma cell lines. An overrepresentation of altered cell division related genes was found, such as MCM4 and LATS2, suggesting that deregulation of the cell cycle is an initial driver of osteosarcomagenesis. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5128. doi:1538-7445.AM2012-5128

Background Conventional high-grade osteosarcoma is a primary malignant bone tumor, which is most prevalent in adolescence. Survival rates of osteosarcoma patients have not improved significantly in the last 25 years. Aiming to increase this survival rate, a variety of model systems are used to study osteosarcomagenesis and to test new therapeutic agents. Such model systems are typically generated from an osteosarcoma primary tumor, but undergo many changes due to culturing or interactions with a different host species, which may result in differences in gene expression between primary tumor cells, and tumor cells from the model system. We aimed to investigate whether gene expression profiles of osteosarcoma cell lines and xenografts are still comparable to those of the primary tumor. Methods We performed genome-wide mRNA expression profiling on osteosarcoma biopsies (n = 76), cell lines (n = 13), and xenografts (n = 18). Osteosarcoma can be subdivided into several histological subtypes, of which osteoblastic, chondroblastic, and fibroblastic osteosarcoma are the most frequent ones. Using nearest shrunken centroids classification, we generated an expression signature that can predict the histological subtype of osteosarcoma biopsies. Results The expression signature, which consisted of 24 probes encoding for 22 genes, predicted the histological subtype of osteosarcoma biopsies with a misclassification error of 15%. Histological subtypes of the two osteosarcoma model systems, i.e. osteosarcoma cell lines and xenografts, were predicted with similar misclassification error rates (15% and 11%, respectively). Conclusions Based on the preservation of mRNA expression profiles that are characteristic for the histological subtype we propose that these model systems are representative for the primary tumor from which they are derived.

Sarcomas comprise a collection of highly heterogeneous malignancies that can be grossly grouped in the categories of sarcomas with simple or complex genomes. Since the outcome for most sarcoma patients has barely improved in the last decades, there is an urgent need for improved therapies. Immunotherapy, and especially T cell checkpoint blockade, has recently been a game-changer in cancer therapy as it produced significant and durable treatment responses in several cancer types. Currently, only a small fraction of sarcoma patients benefit from immunotherapy, supposedly due to a general lack of somatically mutated antigens (neoantigens) and spontaneous T cell immunity in most cancers. However, genomic events resulting from chromosomal instability are frequent in sarcomas with complex genomes and could drive immunity in those tumors. Improving our understanding of the mechanisms that shape the immune landscape of sarcomas will be crucial to overcoming the current challenges of sarcoma immunotherapy. This review focuses on what is currently known about the tumor microenvironment in sarcomas and how this relates to their genomic features. Moreover, we discuss novel therapeutic strategies that leverage the tumor microenvironment to increase the clinical efficacy of immunotherapy, and which could provide new avenues for the treatment of sarcomas.

Osteosarcoma is the most frequent bone cancer in children and young adults. The outcome of patients with advanced disease is dismal. Exploitation of tumor-immune cell interactions may provide novel therapeutic approaches. CD70-CD27 interactions are important for the regulation of adaptive immunity. CD70 expression has been reported in some solid cancers and implicated in tumor escape from immunosurveillance. In this study, expression of CD70 and CD27 was analyzed in osteosarcoma cell lines and tumor specimens. CD70 protein was expressed on most osteosarcoma cell lines (5/7) and patient-derived primary osteosarcoma cultures (4/6) as measured by flow cytometry. In contrast, CD70 was detected on few Ewing sarcoma cell lines (5/15) and was virtually absent from neuroblastoma (1/7) and rhabdomyosarcoma cell lines (0/5). CD70(+) primary cultures were derived from CD70(+) osteosarcoma lesions. CD70 expression in osteosarcoma cryosections was heterogeneous, restricted to tumor cells and not attributed to infiltrating CD3(+) T cells as assessed by immunohistochemistry/immunofluorescence. CD70 was detected in primary (1/5) but also recurrent (2/4) and metastatic (1/3) tumors. CD27, the receptor for CD70, was neither detected on tumor cells nor on T cells in CD70(+) or CD70(-) tumors, suggesting that CD70 on tumor cells is not involved in CD27-dependent tumor-immune cell interactions in osteosarcoma. CD70 gene expression in diagnostic biopsies of osteosarcoma patients did not correlate with the occurrence of metastasis and survival (n = 70). Our data illustrate that CD70 is expressed in a subset of osteosarcoma patients. In patients with CD70(+) tumors, CD70 may represent a novel candidate for antibody-based targeted immunotherapy.