A genomic strategy to elucidate modules of oncogenic pathway signaling networks

miRNA551b-3p Activates an Oncostatin Signaling Module for the Progression of Triple-Negative Breast Cancer

Genomic amplification of 3q26.2 locus leads to the increased expression of microRNA 551b-3p (miR551b-3p) in triple-negative breast cancer (TNBC). Our results demonstrate that miR551b-3p translocates to the nucleus with the aid of importin-8 (IPO8) and activates STAT3 transcription. As a consequence, miR551b upregulates the expression of oncostatin M receptor (OSMR) and interleukin-31 receptor-α (IL-31RA) as well as their ligands OSM and IL-31 through STAT3 transcription. We defined this set of genes induced by miR551b-3p as the "oncostatin signaling module," which provides oncogenic addictions in cancer cells. Notably, OSM is highly expressed in TNBC, and the elevated expression of OSM associates with poor outcome in estrogen-receptor-negative breast cancer patients. Conversely, targeting miR551b with anti-miR551b-3p reduced the expression of the OSM signaling module and reduced tumor growth, as well as migration and invasion of breast cancer cells.

Beyond the Genomic Mutation: Rethinking the Molecular Biomarkers of K-RAS Dependency in Pancreatic Cancers

Oncogenic v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (K-RAS) plays a key role in the development and maintenance of pancreatic ductal adenocarcinoma (PDAC). The targeting of K-RAS would be beneficial to treat tumors whose growth depends on active K-RAS. The analysis of K-RAS genomic mutations is a clinical routine; however, an emerging question is whether the mutational status is able to identify tumors effectively dependent on K-RAS for tailoring targeted therapies. With the emergence of novel K-RAS inhibitors in clinical settings, this question is relevant. Several studies support the notion that the K-RAS mutation is not a sufficient biomarker deciphering the effective dependency of the tumor. Transcriptomic and metabolomic profiles of tumors, while revealing K-RAS signaling complexity and K-RAS-driven molecular pathways crucial for PDAC growth, are opening the opportunity to specifically identify K-RAS-dependent- or K-RAS-independent tumor subtypes by using novel molecular biomarkers. This would help tumor selection aimed at tailoring therapies against K-RAS. In this review, we will present studies about how the K-RAS mutation can also be interpreted in a state of K-RAS dependency, for which it is possible to identify specific K-RAS-driven molecular biomarkers in certain PDAC subtypes, beyond the genomic K-RAS mutational status.

Decomposing Oncogenic Transcriptional Signatures to Generate Maps of Divergent Cellular States

The systematic sequencing of the cancer genome has led to the identification of numerous genetic alterations in cancer. However, a deeper understanding of the functional consequences of these alterations is necessary to guide appropriate therapeutic strategies. Here, we describe Onco-GPS (OncoGenic Positioning System), a data-driven analysis framework to organize individual tumor samples with shared oncogenic alterations onto a reference map defined by their underlying cellular states. We applied the methodology to the RAS pathway and identified nine distinct components that reflect transcriptional activities downstream of RAS and defined several functional states associated with patterns of transcriptional component activation that associates with genomic hallmarks and response to genetic and pharmacological perturbations. These results show that the Onco-GPS is an effective approach to explore the complex landscape of oncogenic cellular states across cancers, and an analytic framework to summarize knowledge, establish relationships, and generate more effective disease models for research or as part of individualized precision medicine paradigms.

Concomitant dysregulation of the estrogen receptor and BRAF/MEK signaling pathways is common in colorectal cancer and predicts a worse prognosis

PURPOSE

Recurrence is a major cause of colorectal cancer (CRC)-related death. As yet, the accurate identification of CRC patients at high risk of recurrence is still a major clinical challenge. Previously, we found that an estrogen receptor (ER) pathway gene signature may predict disease recurrence in CRC patients. The aim of this study is to evaluate the potential application of additional pathway-specific gene signatures in the prediction of CRC recurrence.

METHODS

The activities of 26 cancer-related pathways in CRC were semi-quantified using gene signature-based Bayesian binary regression analysis, and putative associations of the pathways with cancer recurrence risk were assessed using survival analysis.

RESULTS

Among the 26 pathways tested, inactivation of the estrogen receptor (ER) pathway was found to be one of the most common events in CRC. Inactivation of this pathway was found to be frequently accompanied by over-activation of the BRAF/MEK pathway, and these two pathways were found to be associated with opposite effects on several clinicopathological CRC features, including microsatellite instability, subsite location, advanced stage and recurrence. Survival analysis of four independent CRC patient cohorts revealed that while the BRAF/MEK pathway was more strongly associated with recurrence than the ER pathway in mixed-stage CRCs, the ER pathway was a better predictor of recurrence than the BRAF/MEK pathway in stage II CRC. A combined use of these two pathways improved the prediction of CRC recurrence in both mixed stage CRC (n = 1122; overall HR: 2.518, 95% CI: 1.570-4.038, p < 0.001) and stage II CRC (n = 535; overall HR: 1.976, 95% CI: 1.306-2.989, p = 0.001).

CONCLUSIONS

Combined activity of the ER and BRAF/MEK pathways may represent a novel biomarker for CRC prognosis and clinical management.

Assessment of Functional Phosphatidylinositol 3-Kinase Pathway Activity in Cancer Tissue Using Forkhead Box-O Target Gene Expression in a Knowledge-Based Computational Model

The phosphatidylinositol 3-kinase (PI3K) pathway is commonly activated in cancer. Tumors are potentially sensitive to PI3K pathway inhibitors, but reliable diagnostic tests that assess functional PI3K activity are lacking. Because PI3K pathway activity negatively regulates forkhead box-O (FOXO) transcription factor activity, FOXO target gene expression is inversely correlated with PI3K activity. A knowledge-based Bayesian computational model was developed to infer PI3K activity in cancer tissue samples from FOXO target gene mRNA levels and validated in cancer cell lines treated with PI3K inhibitors. However, applied to patient tissue samples, FOXO was often active in cancer types with expected active PI3K. SOD2 was differentially expressed between FOXO-active healthy and cancer tissue samples, indicating that cancer-associated cellular oxidative stress alternatively activated FOXO. To enable correct interpretation of active FOXO in cancer tissue, threshold levels for normal SOD2 expression in healthy tissue were defined above which FOXO activity is oxidative stress induced and below which PI3K regulated. In slow-growing luminal A breast cancer and low Gleason score prostate cancer, FOXO was active in a PI3K-regulated manner, indicating inactive PI3K. In aggressive luminal B, HER2, and basal breast cancer, FOXO was increasingly inactive or actively induced by oxidative stress, indicating PI3K activity. We provide a decision tree that facilitates functional PI3K pathway activity assessment in tissue samples from patients with cancer for therapy response prediction and prognosis.

Contributions of the RhoA guanine nucleotide exchange factor Net1 to polyoma middle T antigen-mediated mammary gland tumorigenesis and metastasis

Background

The RhoA activating protein Net1 contributes to breast cancer cell proliferation, motility, and invasion in vitro, yet little is known about its roles in mammary gland tumorigenesis and metastasis.

Methods

Net1 knockout (KO) mice were bred to mice with mammary gland specific expression of the polyoma middle T antigen (PyMT) oncogene. Mammary gland tumorigenesis and lung metastasis were monitored. Individual tumors were assessed for proliferation, apoptosis, angiogenesis, RhoA activation, and activation of PyMT-dependent signaling pathways. Primary tumor cells from wild-type and Net1 KO mice were transplanted into the mammary glands of wild-type, nontumor-bearing mice, and tumor growth and metastasis were assessed. Gene expression in wild-type and Net1 KO tumors was analyzed by gene ontology enrichment and for relative activation of gene expression signatures indicative of signaling pathways important for breast cancer initiation and progression. A gene expression signature indicative of Net1 function was identified. Human breast cancer gene expression profiles were screened for the presence of a Net1 gene expression signature.

Results

We show that Net1 makes fundamental contributions to mammary gland tumorigenesis and metastasis. Net1 deletion delays tumorigenesis and strongly suppresses metastasis in PyMT-expressing mice. Moreover, we observe that loss of Net1 reduces cancer cell proliferation, inhibits tumor angiogenesis, and promotes tumor cell apoptosis. Net1 is required for maximal RhoA activation within tumors and for primary tumor cell motility. Furthermore, the ability of PyMT to initiate oncogenic signaling to ERK1/2 and PI3K/Akt1 is inhibited by Net1 deletion. Primary tumor cell transplantation indicates that the reduction in tumor angiogenesis and lung metastasis observed upon Net1 deletion are tumor cell autonomous effects. Using a gene expression signature indicative of Net1 activity, we show that Net1 signaling is activated in 10% of human breast cancers, and that this correlates with elevated proliferation and PI3K pathway activity. We also demonstrate that human breast cancer patients with a high Net1 gene expression signature experience shorter distant metastasis-free survival.

Conclusions

These data indicate that Net1 is required for tumor progression in the PyMT mouse model and suggest that Net1 may contribute to breast cancer progression in humans.

Electronic supplementary material

The online version of this article (10.1186/s13058-018-0966-2) contains supplementary material, which is available to authorized users.

Effective personalized therapy for breast cancer based on predictions of cell signaling pathway activation from gene expression analysis

Current therapeutic outcomes for breast cancer underscore the complexity of treating a heterogeneous disease. Indeed, studies have shown that differences in gene expression among patients with the same subtype of breast cancer are correlated with the response to treatment. This strongly suggests that there is an urgent need to treat breast cancer with a personalized approach. Here we employed cell signaling pathway signatures to predict pathway activity in subtypes of MMTV-Myc mammary tumors. We then split tumors into subsets and developed individualized combinatorial treatments for two subtypes with distinct pathway activation patterns. Elevation of the EGFR, RAS and TGFβ pathways was observed in one subtype whereas these pathways were not predicted to be active in the other subtype that had high predicted activity of the Myc, Stat3 and Akt pathways. In a proof-of-principle experiment, treatment of these two subtypes with targeted therapies inhibited tumor growth only in the subtype of tumor where the therapy was designed to be active. We then analyzed gene expression profiles of human breast cancer patients and patient-derived xenograft (PDX) samples to predict pathway activity, and validated our approach of developing individualized treatments in mice with PDX tumors. Importantly, our combinatorial therapy resulted in tumor regression, including regression in PDX samples from triple-negative breast cancer. Together our data is a proof-of-principle experiment that demonstrates that cell signaling pathway signature-guided treatment for breast cancer is viable.

MIDAS: Mining differentially activated subpaths of KEGG pathways from multi-class RNA-seq data

Pathway based analysis of high throughput transcriptome data is a widely used approach to investigate biological mechanisms. Since a pathway consists of multiple functions, the recent approach is to determine condition specific sub-pathways or subpaths. However, there are several challenges. First, few existing methods utilize explicit gene expression information from RNA-seq. More importantly, subpath activity is usually an average of statistical scores, e.g., correlations, of edges in a candidate subpath, which fails to reflect gene expression quantity information. In addition, none of existing methods can handle multiple phenotypes. To address these technical problems, we designed and implemented an algorithm, MIDAS, that determines condition specific subpaths, each of which has different activities across multiple phenotypes. MIDAS utilizes gene expression quantity information fully and the network centrality information to determine condition specific subpaths. To test performance of our tool, we used TCGA breast cancer RNA-seq gene expression profiles with five molecular subtypes. 36 differentially activate subpaths were determined. The utility of our method, MIDAS, was demonstrated in four ways. All 36 subpaths are well supported by the literature information. Subsequently, we showed that these subpaths had a good discriminant power for five cancer subtype classification and also had a prognostic power in terms of survival analysis. Finally, in a performance comparison of MIDAS to a recent subpath prediction method, PATHOME, our method identified more subpaths and much more genes that are well supported by the literature information.

AVAILABILITY

http://biohealth.snu.ac.kr/software/MIDAS/.

Effective personalized therapy for breast cancer based on predictions of cell signaling pathway activation from gene expression analysis

Current therapeutic outcomes for breast cancer underscore the complexity of treating a heterogeneous disease. Indeed, studies have shown that differences in gene expression among patients with the same subtype of breast cancer are correlated with the response to treatment. This strongly suggests that there is an urgent need to treat breast cancer with a personalized approach. Here we employed cell signaling pathway signatures to predict pathway activity in subtypes of MMTV-Myc mammary tumors. We then split tumors into subsets and developed individualized combinatorial treatments for two subtypes with distinct pathway activation patterns. Elevation of the EGFR, RAS and TGFβ pathways was observed in one subtype whereas these pathways were not predicted to be active in the other subtype that had high predicted activity of the Myc, Stat3 and Akt pathways. In a proof-of-principle experiment, treatment of these two subtypes with targeted therapies inhibited tumor growth only in the subtype of tumor where the therapy was designed to be active. We then analyzed gene expression profiles of human breast cancer patients and patient-derived xenograft (PDX) samples to predict pathway activity, and validated our approach of developing individualized treatments in mice with PDX tumors. Importantly, our combinatorial therapy resulted in tumor regression, including regression in PDX samples from triple-negative breast cancer. Together our data is a proof-of-principle experiment that demonstrates that cell signaling pathway signature-guided treatment for breast cancer is viable.

A quinazoline-based HDAC inhibitor affects gene expression pathways involved in cholesterol biosynthesis and mevalonate in prostate cancer cells

Chronic inflammation can lead to the development of cancers and resolution of inflammation is an ongoing challenge. Inflammation can result from dysregulation of the epigenome and a number of compounds that modify the epigenome are in clinical use. In this study the anti-inflammatory and anti-cancer effects of a quinazoline epigenetic-modulator compound were determined in prostate cancer cell lines using a non-hypothesis driven transcriptomics strategy utilising the Affymetrix PrimeView® Human Gene Expression microarray. GATHER and IPA software were used to analyse the data and to provide information on significantly modified biological processes, pathways and networks. A number of genes were differentially expressed in both PC3 and DU145 prostate cancer cell lines. The top canonical pathways that frequently arose across both cell lines at a number of time points included cholesterol biosynthesis and metabolism, and the mevalonate pathway. Targeting of sterol and mevalonate pathways may be a powerful anticancer approach.

The value of genomics in dissecting the RAS-network and in guiding therapeutics for RAS-driven cancers

The rise in genomic knowledge over the past decade has revealed the molecular etiology of many diseases, and has identified intricate signaling network activity in human cancers. Genomics provides the opportunity to determine genome structure and capture the activity of thousands of molecular events concurrently, which is important for deciphering highly complex genetic diseases such as cancer. In this review, we focus on genomic efforts directed towards one of cancer's most frequently mutated networks, the RAS pathway. Genomic tools such as gene expression signatures and assessment of mutations across the RAS network enable the capture of RAS signaling complexity. Due to this high level of interaction and cross-talk within the network, efforts to target RAS signaling in the clinic have generally failed, and we currently lack the ability to directly inhibit the RAS protein with high efficacy. We propose that the use of gene expression data can identify effective treatments that broadly inhibit the RAS network as this approach measures pathway activity independent of mutation status or any single mechanism of activation. Here, we review the genomic studies that map the complexity of the RAS network in cancer, and that show how genomic measurements of RAS pathway activation can identify effective RAS inhibition strategies. We also address the challenges and future directions for treating RAS-driven tumors. In summary, genomic assessment of RAS signaling provides a level of complexity necessary to accurately map the network that matches the intricacy of RAS pathway interactions in cancer.

Multimodal Assessment of Estrogen Receptor mRNA Profiles to Quantify Estrogen Pathway Activity in Breast Tumors

BACKGROUND

Molecular markers have transformed our understanding of the heterogeneity of breast cancer and have allowed the identification of genomic profiles of estrogen receptor (ER)-α signaling. However, our understanding of the transcriptional profiles of ER signaling remains inadequate. Therefore, we sought to identify the genomic indicators of ER pathway activity that could supplement traditional immunohistochemical (IHC) assessments of ER status to better understand ER signaling in the breast tumors of individual patients.

MATERIALS AND METHODS

We reduced ESR1 (gene encoding the ER-α protein) mRNA levels using small interfering RNA in ER⁺ MCF7 breast cancer cells and assayed for transcriptional changes using Affymetrix HG U133 Plus 2.0 arrays. We also compared 1034 ER⁺ and ER^- breast tumors from publicly available microarray data. The principal components of ER activity generated from these analyses and from other published estrogen signatures were compared with ESR1 expression, ER-α IHC, and patient survival.

RESULTS

Genes differentially expressed in both analyses were associated with ER-α IHC and ESR1 mRNA expression. They were also significantly enriched for estrogen-driven molecular pathways associated with ESR1, cyclin D1 (CCND1), MYC (v-myc avian myelocytomatosis viral oncogene homolog), and NFKB (nuclear factor kappa B). Despite their differing constituent genes, the principal components generated from these new analyses and from previously published ER-associated gene lists were all associated with each other and with the survival of patients with breast cancer treated with endocrine therapies.

CONCLUSION

A biomarker of ER-α pathway activity, generated using ESR1-responsive mRNAs in MCF7 cells, when used alongside ER-α IHC and ESR1 mRNA expression, could provide a method for further stratification of patients and add insight into ER pathway activity in these patients.

Gene signatures of estrogen and progesterone receptor pathways predict the prognosis of colorectal cancer

The associations of estrogen receptor (ER) and progesterone receptor (PR) pathways with the prognosis of colorectal cancer (CRC) are still controversial. The aim of this study was to readdress these issues by introducing a gene signature-based approach to semiquantitate pathway activity. In this approach, the ER and PR pathway activities in CRC were computed based on the expression profiles of the signature genes of ER and PR pathways, respectively. The results showed that the ER pathway activity was progressively significantly decreased from normal colorectal mucosa, colorectal adenoma to CRC. ER pathway signaling was a favorable factor for the presence of microsatellite stability (MSS) in CRC in seven cohorts tested, while was an unfavorable factor for cancer recurrence in all four CRC cohorts tested (n = 1122; overall HR: 0.311, 95% CI: 0.199-0.488, P < 0.001). Subset stratification in stage II patients showed that ER pathway remained significantly inversely associated with recurrence. PR pathway was also suppressed in colorectal tumors and inversely associated with recurrence of CRC, but to a much lesser extent than ER pathway. Moreover, the inverse association of PR pathway with cancer recurrence was more likely observed in CRC with high ER pathway activity, suggesting the interactions between the two pathways. PR pathway was not associated with MSS in CRC, but it was more significant than ER pathway associated with advance cancer stages and cancer response to adjuvant chemotherapy. These results suggested the potential application of the gene signatures of ER and PR pathways, especially the former, as novel markers for prognosis and management of CRC.

The ErbB2ΔEx16 splice variant is a major oncogenic driver in breast cancer that promotes a pro-metastatic tumor microenvironment

Amplification and over expression of erbB2/neu proto-oncogene is observed in 20–30% human breast cancer and is inversely correlated with the survival of the patient. Despite this, somatic activating mutations within erbB2 in human breast cancers are rare. However, we have previously reported that a splice isoform of erbB2, containing an in-frame deletion of exon 16 (herein referred to as ErbB2ΔEx16), results in oncogenic activation of erbB2 due to constitutive dimerization of the ErbB2 receptor. Here, we demonstrate that the ErbB2ΔEx16 is a major oncogenic driver in breast cancer that constitutively signals from the cell surface. We further show that inducible expression of the ErbB2Ex16 variant in mammary gland of transgenic mice results in the rapid development of metastatic multifocal mammary tumors. Genetic and biochemical characterization of the ErbB2ΔEx16 derived mammary tumors exhibit several unique features that distinguish it from the conventional ErbB2 models expressing the erbB2 proto-oncogene in mammary epithelium. Unlike the wild-type ErbB2 derived tumors that express luminal keratins, ErbB2ΔEx16 derived tumors exhibit high degree of intra-tumoral heterogeneity co-expressing both basal and luminal keratins. Consistent with these distinct pathological features, the ErbB2ΔEx16 tumors exhibited distinct signaling and gene expression profiles that correlated with activation of number of key transcription factors implicated in breast cancer metastasis and cancer stem cell renewal.

A systematic study on drug-response associated genes using baseline gene expressions of the Cancer Cell Line Encyclopedia

We have studied drug-response associated (DRA) gene expressions by applying a systems biology framework to the Cancer Cell Line Encyclopedia data. More than 4,000 genes are inferred to be DRA for at least one drug, while the number of DRA genes for each drug varies dramatically from almost 0 to 1,226. Functional enrichment analysis shows that the DRA genes are significantly enriched in genes associated with cell cycle and plasma membrane. Moreover, there might be two patterns of DRA genes between genders. There are significantly shared DRA genes between male and female for most drugs, while very little DRA genes tend to be shared between the two genders for a few drugs targeting sex-specific cancers (e.g., PD-0332991 for breast cancer and ovarian cancer). Our analyses also show substantial difference for DRA genes between young and old samples, suggesting the necessity of considering the age effects for personalized medicine in cancers. Lastly, differential module and key driver analyses confirm cell cycle related modules as top differential ones for drug sensitivity. The analyses also reveal the role of TSPO, TP53, and many other immune or cell cycle related genes as important key drivers for DRA network modules. These key drivers provide new drug targets to improve the sensitivity of cancer therapy.

Denoising perturbation signatures reveal an actionable AKT-signaling gene module underlying a poor clinical outcome in endocrine-treated ER+ breast cancer

Background

Databases of perturbation gene expression signatures and drug sensitivity provide a powerful framework to develop personalized medicine approaches, by helping to identify actionable genomic markers and subgroups of patients who may benefit from targeted treatments.

Results

Here we use a perturbation expression signature database encompassing perturbations of over 90 cancer genes, in combination with a large breast cancer expression dataset and a novel statistical denoising algorithm, to help discern cancer perturbations driving most of the variation in breast cancer gene expression. Clustering estrogen receptor positive cancers over the perturbation activity scores recapitulates known luminal subtypes. Analysis of individual activity scores enables identification of a novel cancer subtype, defined by a 31-gene AKT-signaling module. Specifically, we show that activation of this module correlates with a poor prognosis in over 900 endocrine-treated breast cancers, a result we validate in two independent cohorts. Importantly, breast cancer cell lines with high activity of the module respond preferentially to PI3K/AKT/mTOR inhibitors, a result we also validate in two independent datasets. We find that at least 34 % of the downregulated AKT module genes are either mediators of apoptosis or have tumor suppressor functions.

Conclusions

The statistical framework advocated here could be used to identify gene modules that correlate with prognosis and sensitivity to alternative treatments. We propose a randomized clinical trial to test whether the 31-gene AKT module could be used to identify estrogen receptor positive breast cancer patients who may benefit from therapy targeting the PI3K/AKT/mTOR signaling axis.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-015-0630-4) contains supplementary material, which is available to authorized users.

Pathway and network approaches for identification of cancer signature markers from omics data

The advancement of high throughput omic technologies during the past few years has made it possible to perform many complex assays in a much shorter time than the traditional approaches. The rapid accumulation and wide availability of omic data generated by these technologies offer great opportunities to unravel disease mechanisms, but also presents significant challenges to extract knowledge from such massive data and to evaluate the findings. To address these challenges, a number of pathway and network based approaches have been introduced. This review article evaluates these methods and discusses their application in cancer biomarker discovery using hepatocellular carcinoma (HCC) as an example.

Impact of upstream and downstream constraints on a signaling module's ultrasensitivity

Much work has been done on the study of the biochemical mechanisms that result in ultrasensitive behavior of simple biochemical modules. However, in a living cell, such modules are embedded in a bigger network that constrains the range of inputs that the module will receive as well as the range of the module's outputs that network will be able to detect. Here, we studied how the effective ultrasensitivity of a modular system is affected by these restrictions. We use a simple setup to explore to what extent the dynamic range spanned by upstream and downstream components of an ultrasensitive module impact on the effective sensitivity of the system. Interestingly, we found for some ultrasensitive motifs that dynamic range limitations imposed by downstream components can produce effective sensitivities much larger than that of the original module when considered in isolation.

ADAM17-dependent c-MET-STAT3 signaling mediates resistance to MEK inhibitors in KRAS mutant colorectal cancer

There are currently no approved targeted therapies for advanced KRAS mutant (KRASMT) colorectal cancer (CRC). Using a unique systems biology approach, we identified JAK1/2-dependent activation of STAT3 as the key mediator of resistance to MEK inhibitors in KRASMT CRC in vitro and in vivo. Further analyses identified acute increases in c-MET activity following treatment with MEK inhibitors in KRASMT CRC models, which was demonstrated to promote JAK1/2-STAT3-mediated resistance. Furthermore, activation of c-MET following MEK inhibition was found to be due to inhibition of the ERK-dependent metalloprotease ADAM17, which normally inhibits c-MET signaling by promoting shedding of its endogenous antagonist, soluble "decoy" MET. Most importantly, pharmacological blockade of this resistance pathway with either c-MET or JAK1/2 inhibitors synergistically increased MEK-inhibitor-induced apoptosis and growth inhibition in vitro and in vivo in KRASMT models, providing clear rationales for the clinical assessment of these combinations in KRASMT CRC patients.

Genomic classification of the RAS network identifies a personalized treatment strategy for lung cancer

Better approaches are needed to evaluate a single patient's drug response at the genomic level. Targeted therapy for signaling pathways in cancer has met limited success in part due to the exceedingly interwoven nature of the pathways. In particular, the highly complex RAS network has been challenging to target. Effectively targeting the pathway requires development of techniques that measure global network activity to account for pathway complexity. For this purpose, we used a gene-expression-based biomarker for RAS network activity in non-small cell lung cancer (NSCLC) cells, and screened for drugs whose efficacy was significantly highly correlated to RAS network activity. Results identified EGFR and MEK co-inhibition as the most effective treatment for RAS-active NSCLC amongst a panel of over 360 compounds and fractions. RAS activity was identified in both RAS-mutant and wild-type lines, indicating broad characterization of RAS signaling inclusive of multiple mechanisms of RAS activity, and not solely based on mutation status. Mechanistic studies demonstrated that co-inhibition of EGFR and MEK induced apoptosis and blocked both EGFR-RAS-RAF-MEK-ERK and EGFR-PI3K-AKT-RPS6 nodes simultaneously in RAS-active, but not RAS-inactive NSCLC. These results provide a comprehensive strategy to personalize treatment of NSCLC based on RAS network dysregulation and provide proof-of-concept of a genomic approach to classify and target complex signaling networks.

Reconstructing targetable pathways in lung cancer by integrating diverse omics data

Global 'multi-omics' profiling of cancer cells harbours the potential for characterizing the signalling networks associated with specific oncogenes. Here we profile the transcriptome, proteome and phosphoproteome in a panel of non-small cell lung cancer (NSCLC) cell lines in order to reconstruct targetable networks associated with KRAS dependency. We develop a two-step bioinformatics strategy addressing the challenge of integrating these disparate data sets. We first define an 'abundance-score' combining transcript, protein and phospho-protein abundances to nominate differentially abundant proteins and then use the Prize Collecting Steiner Tree algorithm to identify functional sub-networks. We identify three modules centred on KRAS and MET, LCK and PAK1 and β-Catenin. We validate activation of these proteins in KRAS-dependent (KRAS-Dep) cells and perform functional studies defining LCK as a critical gene for cell proliferation in KRAS-Dep but not KRAS-independent NSCLCs. These results suggest that LCK is a potential druggable target protein in KRAS-Dep lung cancers.

Potential biomarkers for sensitivity of gallbladder cancer cells to gemcitabine

Gemcitabine (Gem)-based chemotherapies are the main therapeutic regimens for patients with unresectable advanced or metastatic gallbladder cancer (GBC). However, the modest ORR and mild benefit on survival demonstrates the need for finding biomarkers for sensitivity to Gem and hence improving the therapy. In present work, two GBC cell lines with vast difference in sensitivity to Gem were subjected to DNA microarray analysis. Dramatic expression difference was found in protein kinase A signaling, P2Y purigenic receptor signaling, ErbB signaling and p70S6K signaling. Predicted low expression of KRAS and inactivation of AKT/ERK signaling in Gem-resistant GBC cells was validated by quantitative PCR and immunoblotting, respectively. However, p70S6K, p38MAPK and NF-κB signaling was probably activated in Gem-resistant GBC cells, which deserves further investigation in more GBC cell lines and tissues. Our work provides potential pathway signatures for Gem sensitivity of GBC patients.

Separate enrichment analysis of pathways for up- and downregulated genes

Two strategies are often adopted for enrichment analysis of pathways: the analysis of all differentially expressed (DE) genes together or the analysis of up- and downregulated genes separately. However, few studies have examined the rationales of these enrichment analysis strategies. Using both microarray and RNA-seq data, we show that gene pairs with functional links in pathways tended to have positively correlated expression levels, which could result in an imbalance between the up- and downregulated genes in particular pathways. We then show that the imbalance could greatly reduce the statistical power for finding disease-associated pathways through the analysis of all-DE genes. Further, using gene expression profiles from five types of tumours, we illustrate that the separate analysis of up- and downregulated genes could identify more pathways that are really pertinent to phenotypic difference. In conclusion, analysing up- and downregulated genes separately is more powerful than analysing all of the DE genes together.

Cross-platform prediction of gene expression signatures

Gene expression signatures can predict the activation of oncogenic pathways and other phenotypes of interest via quantitative models that combine the expression levels of multiple genes. However, as the number of platforms to measure genome-wide gene expression proliferates, there is an increasing need to develop models that can be ported across diverse platforms. Because of the range of technologies that measure gene expression, the resulting signal values can vary greatly. To understand how this variation can affect the prediction of gene expression signatures, we have investigated the ability of gene expression signatures to predict pathway activation across Affymetrix and Illumina microarrays. We hybridized the same RNA samples to both platforms and compared the resultant gene expression readings, as well as the signature predictions. Using a new approach to map probes across platforms, we found that the genes in the signatures from the two platforms were highly similar, and that the predictions they generated were also strongly correlated. This demonstrates that our method can map probes from Affymetrix and Illumina microarrays, and that this mapping can be used to predict gene expression signatures across platforms.

Latent factor analysis facilitates modelling of oncogenic genes for colon adenocarcinoma

Identification of oncogenic genes from a large sample number of genomic data is a challenge. In this study, a well-established latent factor model, Bayesian factor and regression model, are applied to predict unknown colon cancer related genes from colon adenocarcinoma genomic data. Four important latent factors were addressed by the latent factor model, focusing on characterisation of heterogeneity of expression patterns of specific oncogenic genes by using microarray data of 174 colon cancer patients. Based on the fact that variables included in the same latent factor have some common characteristics and known cancer related genes in Online Mendelian Inheritance in Man, the authors found that the four latent factors can be employed to predict unknown colon cancer related genes that were never reported in the literature. The authors validated 15 identified genes by checking their somatic mutations of the same patients from DNA sequencing data.

Functional comparison between genes dysregulated in ulcerative colitis and colorectal carcinoma

Background

Patients with ulcerative colitis (UC) are predisposed to colitis-associated colorectal cancer (CAC). However, the transcriptional mechanism of the transformation from UC to CAC is not fully understood.

Methodology

Firstly, we showed that CAC and non-UC-associated CRC were very similar in gene expression. Secondly, based on multiple datasets for UC and CRC, we extracted differentially expressed (DE) genes in UC and CRC versus normal controls, respectively. Thirdly, we compared the dysregulation directions (upregulation or downregulation) between DE genes of UC and CRC in CRC-related functions overrepresented with the DE genes of CRC, and proposed a regulatory model to explain the CRC-like dysregulation of genes in UC. A case study for “positive regulation of immune system process” was done to reveal the functional implication of DE genes with reversal dysregulations in these two diseases.

Principal Findings

In all the 44 detected CRC-related functions except for “viral transcription”, the dysregulation directions of DE genes in UC were significantly similar with their counterparts in CRC, and such CRC-like dysregulation in UC could be regulated by transcription factors affected by pro-inflammatory stimuli for colitis. A small portion of genes in each CRC-related function were dysregulated in opposite directions in the two diseases. The case study showed that genes related to humoral immunity specifically expressed in B cells tended to be upregulated in UC but downregulated in CRC.

Conclusions

The CRC-like dysregulation of genes in CRC-related functions in UC patients provides hints for understanding the transcriptional basis for UC to CRC transition. A small portion of genes with distinct dysregulation directions in each of the CRC-related functions in the two diseases implicate that their reversal dysregulations might be critical for UC to CRC transition. The cases study indicates that the humoral immune response might be inhibited during the transformation from UC to CRC.

Lung tumor NF-κB signaling promotes T cell-mediated immune surveillance

NF-κB is constitutively activated in many cancer types and is a potential key mediator of tumor-associated inflammation, tumor growth, and metastasis. We investigated the role of cancer cell NF-κB activity in T cell-mediated antitumor responses. In tumors rendered immunogenic by model antigen expression or following administration of antitumor vaccines, we found that high NF-κB activity leads to tumor rejection and/or growth suppression in mice. Using a global RNA expression microarray, we demonstrated that NF-κB enhanced expression of several T cell chemokines, including Ccl2, and decreased CCL2 expression was associated with enhanced tumor growth in a mouse lung cancer model. To investigate NF-κB function in human lung tumors, we identified a gene expression signature in human lung adenocarcinoma cell lines that was associated with NF-κB activity level. In patient tumor samples, overall lung tumor NF-κB activity was strongly associated with T cell infiltration but not with cancer cell proliferation. These results therefore indicate that NF-κB activity mediates immune surveillance and promotes antitumor T cell responses in both murine and human lung cancer.

What does systems biology mean for biomarker discovery?

IMPORTANCE OF THE FIELD

The global, integrated analysis of large-scale data sets encoding different levels of biological information opens up new possibilities to discover new biomarkers and elucidate complex mechanisms driving health and disease.

AREAS COVERED IN THIS REVIEW

This article reviews fundamental systems approaches and applications for biomarker discovery in different biomedical domains. It introduces key challenges and requirements for the development of advanced computational techniques, resources and applications. It discusses how these approaches can fill in some of the current gaps in traditional biomarker discovery and disease classification.

WHAT THE READER WILL GAIN

The reader will be introduced to recent advances, techniques and applications of systems approaches to biomarker discovery and disease classification. The reader will learn fundamental research principles and tasks required in the implementation of these approaches and applications. The reader will gain a better understanding of the role of systems biology, as well as of potential opportunities and advances.

TAKE HOME MESSAGE

Systems approaches to biomarker discovery may contribute to the discovery of more accurate and robust predictors of disease and clinical responses. Moreover, they can provide new and deeper clues of potential causal mechanisms underpinning physiological and pathological conditions.

Gene expression-based classifiers identify Staphylococcus aureus infection in mice and humans

Staphylococcus aureus causes a spectrum of human infection. Diagnostic delays and uncertainty lead to treatment delays and inappropriate antibiotic use. A growing literature suggests the host’s inflammatory response to the pathogen represents a potential tool to improve upon current diagnostics. The hypothesis of this study is that the host responds differently to S. aureus than to E. coli infection in a quantifiable way, providing a new diagnostic avenue. This study uses Bayesian sparse factor modeling and penalized binary regression to define peripheral blood gene-expression classifiers of murine and human S. aureus infection. The murine-derived classifier distinguished S. aureus infection from healthy controls and Escherichia coli-infected mice across a range of conditions (mouse and bacterial strain, time post infection) and was validated in outbred mice (AUC>0.97). A S. aureus classifier derived from a cohort of 94 human subjects distinguished S. aureus blood stream infection (BSI) from healthy subjects (AUC 0.99) and E. coli BSI (AUC 0.84). Murine and human responses to S. aureus infection share common biological pathways, allowing the murine model to classify S. aureus BSI in humans (AUC 0.84). Both murine and human S. aureus classifiers were validated in an independent human cohort (AUC 0.95 and 0.92, respectively). The approach described here lends insight into the conserved and disparate pathways utilized by mice and humans in response to these infections. Furthermore, this study advances our understanding of S. aureus infection; the host response to it; and identifies new diagnostic and therapeutic avenues.

Lauriston S. Taylor Lecture on radiation protection and measurements: what makes particle radiation so effective?

The scientific basis for the physical and biological effectiveness of particle radiations has emerged from many decades of meticulous basic research. A diverse array of biologically relevant consequences at the molecular, cellular, tissue, and organism level have been reported, but what are the key processes and mechanisms that make particle radiation so effective, and what competing processes define dose dependences? Recent studies have shown that individual genotypes control radiation-regulated genes and pathways in response to radiations of varying ionization density. The fact that densely ionizing radiations can affect different gene families than sparsely ionizing radiations, and that the effects are dose- and time-dependent, has opened up new areas of future research. The complex microenvironment of the stroma and the significant contributions of the immune response have added to our understanding of tissue-specific differences across the linear energy transfer (LET) spectrum. The importance of targeted versus nontargeted effects remains a thorny but elusive and important contributor to chronic low dose radiation effects of variable LET that still needs further research. The induction of cancer is also LET-dependent, suggesting different mechanisms of action across the gradient of ionization density. The focus of this 35th Lauriston S. Taylor Lecture is to chronicle the step-by-step acquisition of experimental clues that have refined our understanding of what makes particle radiation so effective, with emphasis on the example of radiation effects on the crystalline lens of the human eye.

Mass spectrometry-based proteomics and network biology

In the life sciences, a new paradigm is emerging that places networks of interacting molecules between genotype and phenotype. These networks are dynamically modulated by a multitude of factors, and the properties emerging from the network as a whole determine observable phenotypes. This paradigm is usually referred to as systems biology, network biology, or integrative biology. Mass spectrometry (MS)-based proteomics is a central life science technology that has realized great progress toward the identification, quantification, and characterization of the proteins that constitute a proteome. Here, we review how MS-based proteomics has been applied to network biology to identify the nodes and edges of biological networks, to detect and quantify perturbation-induced network changes, and to correlate dynamic network rewiring with the cellular phenotype. We discuss future directions for MS-based proteomics within the network biology paradigm.

Ras-driven transcriptome analysis identifies aurora kinase A as a potential malignant peripheral nerve sheath tumor therapeutic target

PURPOSE

Patients with neurofibromatosis type 1 (NF1) develop malignant peripheral nerve sheath tumors (MPNST), which are often inoperable and do not respond well to current chemotherapies or radiation. The goal of this study was to use comprehensive gene expression analysis to identify novel therapeutic targets.

EXPERIMENTAL DESIGN

Nerve Schwann cells and/or their precursors are the tumorigenic cell types in MPNST because of the loss of the NF1 gene, which encodes the RasGAP protein neurofibromin. Therefore, we created a transgenic mouse model, CNP-HRas12V, expressing constitutively active HRas in Schwann cells and defined a Ras-induced gene expression signature to drive a Bayesian factor regression model analysis of differentially expressed genes in mouse and human neurofibromas and MPNSTs. We tested functional significance of Aurora kinase overexpression in MPNST in vitro and in vivo using Aurora kinase short hairpin RNAs (shRNA) and compounds that inhibit Aurora kinase.

RESULTS

We identified 2,000 genes with probability of linkage to nerve Ras signaling of which 339 were significantly differentially expressed in mouse and human NF1-related tumor samples relative to normal nerves, including Aurora kinase A (AURKA). AURKA was dramatically overexpressed and genomically amplified in MPNSTs but not neurofibromas. Aurora kinase shRNAs and Aurora kinase inhibitors blocked MPNST cell growth in vitro. Furthermore, an AURKA selective inhibitor, MLN8237, stabilized tumor volume and significantly increased survival of mice with MPNST xenografts.

CONCLUSION

Integrative cross-species transcriptome analyses combined with preclinical testing has provided an effective method for identifying candidates for molecular-targeted therapeutics. Blocking Aurora kinases may be a viable treatment platform for MPNST.

Identification of aberrant pathways and network activities from high-throughput data

Many complex diseases such as cancer are associated with changes in biological pathways and molecular networks rather than being caused by single gene alterations. A major challenge in the diagnosis and treatment of such diseases is to identify characteristic aberrancies in the biological pathways and molecular network activities and elucidate their relationship to the disease. This review presents recent progress in using high-throughput biological assays to decipher aberrant pathways and network activities. In particular, this review provides specific examples in which high-throughput data have been applied to identify relationships between diseases and aberrant pathways and network activities. The achievements in this field have been remarkable, but many challenges have yet to be addressed.

Genomics and bioinformatics of Parkinson's disease

Within the last two decades, genomics and bioinformatics have profoundly impacted our understanding of the molecular mechanisms of Parkinson's disease (PD). From the description of the first PD gene in 1997 until today, we have witnessed the emergence of new technologies that have revolutionized our concepts to identify genetic mechanisms implicated in human health and disease. Driven by the publication of the human genome sequence and followed by the description of detailed maps for common genetic variability, novel applications to rapidly scrutinize the entire genome in a systematic, cost-effective manner have become a reality. As a consequence, about 30 genetic loci have been unequivocally linked to the pathogenesis of PD highlighting essential molecular pathways underlying this common disorder. Herein we discuss how neurogenomics and bioinformatics are applied to dissect the nature of this complex disease with the overall aim of developing rational therapeutic interventions.

Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks

Background

High-throughput measurement technologies produce data sets that have the potential to elucidate the biological impact of disease, drug treatment, and environmental agents on humans. The scientific community faces an ongoing challenge in the analysis of these rich data sources to more accurately characterize biological processes that have been perturbed at the mechanistic level. Here, a new approach is built on previous methodologies in which high-throughput data was interpreted using prior biological knowledge of cause and effect relationships. These relationships are structured into network models that describe specific biological processes, such as inflammatory signaling or cell cycle progression. This enables quantitative assessment of network perturbation in response to a given stimulus.

Results

Four complementary methods were devised to quantify treatment-induced activity changes in processes described by network models. In addition, companion statistics were developed to qualify significance and specificity of the results. This approach is called Network Perturbation Amplitude (NPA) scoring because the amplitudes of treatment-induced perturbations are computed for biological network models. The NPA methods were tested on two transcriptomic data sets: normal human bronchial epithelial (NHBE) cells treated with the pro-inflammatory signaling mediator TNFα, and HCT116 colon cancer cells treated with the CDK cell cycle inhibitor R547. Each data set was scored against network models representing different aspects of inflammatory signaling and cell cycle progression, and these scores were compared with independent measures of pathway activity in NHBE cells to verify the approach. The NPA scoring method successfully quantified the amplitude of TNFα-induced perturbation for each network model when compared against NF-κB nuclear localization and cell number. In addition, the degree and specificity to which CDK-inhibition affected cell cycle and inflammatory signaling were meaningfully determined.

Conclusions

The NPA scoring method leverages high-throughput measurements and a priori literature-derived knowledge in the form of network models to characterize the activity change for a broad collection of biological processes at high-resolution. Applications of this framework include comparative assessment of the biological impact caused by environmental factors, toxic substances, or drug treatments.

Transcriptional signatures of Ral GTPase are associated with aggressive clinicopathologic characteristics in human cancer

RalA and RalB are small GTPases that support malignant development and progression in experimental models of bladder, prostate, and squamous cancer. However, demonstration of their clinical relevance in human tumors remains lacking. Here, we developed tools to evaluate Ral protein expression, activation, and transcriptional output and evaluated their association with clinicopathologic parameters in common human tumor types. To evaluate the relevance of Ral activation and transcriptional output, we correlated RalA and RalB activation with the mutational status of key human bladder cancer genes. We also identified and evaluated a transcriptional signature of genes that correlates with depletion of RalA and RalB in vivo. The Ral transcriptional signature score, but not protein expression as evaluated by immunohistochemistry, predicted disease stage, progression to muscle invasion, and survival in human bladder cancers and metastatic and stem cell phenotypes in bladder cancer models. In prostate cancer, the Ral transcriptional signature score was associated with seminal vesicle invasion, androgen-independent progression, and reduced survival. In squamous cell carcinoma, this score was decreased in cancer tissues compared with normal mucosa, validating the experimental findings that Ral acts as a tumor suppressor in this tumor type. Together, our findings show the clinical relevance of Ral in human cancer and provide a rationale for the development of Ral-directed therapies.

Suppressive effect of maslinic acid on PMA-induced protein kinase C in human B-lymphoblastoid cells

Protein kinase C (PKC) has been implicated in carcinogenesis and displays variable expression profiles during cancer progression. Studies of dietary phytochemicals on cancer signalling pathway regulation have been conducted to search for potent signalling regulatory agents. The present study was designed to evaluate any suppressive effect of maslinic acid on PKC expression in human B-lymphoblastoid cells (Raji cells), and to identify the PKC isoforms expressed. Effects of maslinic acid on PKC activity were determined using a PepTag assay for non-radioactive detection of PKC. The highest expression in Raji cells was obtained at 20 nM PMA induced for 6 hours. Suppressive effects of maslinic acid were compared with those of four PKC inhibitors (H- 7, rottlerin, sphingosine, staurosporine) and two triterpenes (oleanolic acid and ursolic acid). The IC₅₀ values achieved for maslinic acid, staurosporine, H-7, sphingosine, rottlerin, ursolic acid and oleanolic acid were 11.52, 0.011, 0.767, 2.45, 5.46, 27.93 and 39.29 μM, respectively. Four PKC isoforms, PKC βI, βII, δ, and ζ, were identified in Raji cells via western blotting. Maslinic acid suppressed the expression of PKC βI, δ, and ζ in a concentration-dependent manner. These preliminary results suggest promising suppressive effects of maslinic acid on PKC activity in Raji cells. Maslinic acid could be a potent cancer chemopreventive agent that may be involved in regulating many downstream signalling pathways that are activated through PKC receptors.

A novel method of transcriptional response analysis to facilitate drug repositioning for cancer therapy

Little research has been done to address the huge opportunities that may exist to reposition existing approved or generic drugs for alternate uses in cancer therapy. In addition, there has been little work on strategies to reposition experimental cancer agents for testing in alternate settings that could shorten their clinical development time. Progress in each area has lagged, in part, because of the lack of systematic methods to define drug off-target effects (OTE) that might affect important cancer cell signaling pathways. In this study, we addressed this critical gap by developing an OTE-based method to repurpose drugs for cancer therapeutics, based on transcriptional responses made in cells before and after drug treatment. Specifically, we defined a new network component called cancer-signaling bridges (CSB) and integrated it with a Bayesian factor regression model (BFRM) to form a new hybrid method termed CSB-BFRM. Proof-of-concept studies were conducted in breast and prostate cancer cells and in promyelocytic leukemia cells. In each system, CSB-BFRM analysis could accurately predict clinical responses to more than 90% of drugs approved by the U.S. Food and Drug Administration and more than 75% of experimental clinical drugs that were tested. Mechanistic investigation of OTEs for several high-ranking drug-dose pairs suggested repositioning opportunities for cancer therapy, based on the ability to enforce retinoblastoma-dependent repression of important E2F-dependent cell-cycle genes. Together, our findings establish new methods to identify opportunities for drug repositioning or to elucidate the mechanisms of action of repositioned drugs.

Sarcomas induced in discrete subsets of prospectively isolated skeletal muscle cells

Soft-tissue sarcomas are heterogeneous cancers that can present with tissue-specific differentiation markers. To examine the cellular basis for this histopathological variation and to identify sarcoma-relevant molecular pathways, we generated a chimeric mouse model in which sarcoma-associated genetic lesions can be introduced into discrete, muscle-resident myogenic and mesenchymal cell lineages. Expression of Kirsten rat sarcoma viral oncogene [Kras(G12V)] and disruption of cyclin-dependent kinase inhibitor 2A (CDKN2A; p16p19) in prospectively isolated satellite cells gave rise to pleomorphic rhabdomyosarcomas (MyoD-, Myogenin- and Desmin-positive), whereas introduction of the same oncogenetic hits in nonmyogenic progenitors induced pleomorphic sarcomas lacking myogenic features. Transcriptional profiling demonstrated that myogenic and nonmyogenic Kras; p16p19(null) sarcomas recapitulate gene-expression signatures of human rhabdomyosarcomas and identified a cluster of genes that is concordantly up-regulated in both mouse and human sarcomas. This cluster includes genes associated with Ras and mechanistic target of rapamycin (mTOR) signaling, a finding consistent with activation of the Ras and mTOR pathways both in Kras; p16p19(null) sarcomas and in 26-50% of human rhabdomyosarcomas surveyed. Moreover, chemical inhibition of Ras or mTOR signaling arrested the growth of mouse Kras; p16p19(null) sarcomas and of human rhabdomyosarcoma cells in vitro and in vivo. Taken together, these data demonstrate the critical importance of lineage commitment within the tumor cell-of-origin in determining sarcoma histotype and introduce an experimental platform for rapid dissection of sarcoma-relevant cellular and molecular events.

SIGNATURE: a workbench for gene expression signature analysis

Background

The biological phenotype of a cell, such as a characteristic visual image or behavior, reflects activities derived from the expression of collections of genes. As such, an ability to measure the expression of these genes provides an opportunity to develop more precise and varied sets of phenotypes. However, to use this approach requires computational methods that are difficult to implement and apply, and thus there is a critical need for intelligent software tools that can reduce the technical burden of the analysis. Tools for gene expression analyses are unusually difficult to implement in a user-friendly way because their application requires a combination of biological data curation, statistical computational methods, and database expertise.

Results

We have developed SIGNATURE, a web-based resource that simplifies gene expression signature analysis by providing software, data, and protocols to perform the analysis successfully. This resource uses Bayesian methods for processing gene expression data coupled with a curated database of gene expression signatures, all carried out within a GenePattern web interface for easy use and access.

Conclusions

SIGNATURE is available for public use at http://genepattern.genome.duke.edu/signature/.

Predictive and prognostic molecular markers for cancer medicine

Over the last 10 years there has been an explosion of information about the molecular biology of cancer. A challenge in oncology is to translate this information into advances in patient care. While there are well-formed routes for translating new molecular information into drug therapy, the routes for translating new information into sensitive and specific diagnostic, prognostic and predictive tests are still being developed. Similarly, the science of using tumor molecular profiles to select clinical trial participants or to optimize therapy for individual patients is still in its infancy. This review will summarize the current technologies for predicting treatment response and prognosis in cancer medicine, and outline what the future may hold. It will also highlight the potential importance of methods that can integrate molecular, histopathological and clinical information into a synergistic understanding of tumor progression. While these possibilities are without doubt exciting, significant challenges remain if we are to implement them with a strong evidence base in a widely available and cost-effective manner.

A pathway-based approach to identify molecular biomarkers in cancer

Many examples highlight the power of gene expression profiles, or signatures, to provide an understanding of biological phenotypes. This is best seen in the context of cancer, where expression signatures have tremendous power to identify new cancer subtypes and to predict clinical outcomes. Gene expression profiles have been developed to personalize medicine, accurately predicting disease recurrence and tumor response to therapy. The use of these signatures as surrogate phenotypes allows us to link diverse experimental systems, which dissect the complexity of biological systems, with the in vivo setting in a way that was not previously feasible. Taken together, these new genomic tools provide the opportunity to develop rational strategies for treating the individual cancer patient.

Molecular pattern discovery based on penalized matrix decomposition

A reliable and precise identification of the type of tumors is crucial to the effective treatment of cancer. With the rapid development of microarray technologies, tumor clustering based on gene expression data is becoming a powerful approach to cancer class discovery. In this paper, we apply the penalized matrix decomposition (PMD) to gene expression data to extract metasamples for clustering. The extracted metasamples capture the inherent structures of samples belong to the same class. At the same time, the PMD factors of a sample over the metasamples can be used as its class indicator in return. Compared with the conventional methods such as hierarchical clustering (HC), self-organizing maps (SOM), affinity propagation (AP) and nonnegative matrix factorization (NMF), the proposed method can identify the samples with complex classes. Moreover, the factor of PMD can be used as an index to determine the cluster number. The proposed method provides a reasonable explanation of the inconsistent classifications made by the conventional methods. In addition, it is able to discover the modules in gene expression data of conterminous developmental stages. Experiments on two representative problems show that the proposed PMD-based method is very promising to discover biological phenotypes.

DART: Denoising Algorithm based on Relevance network Topology improves molecular pathway activity inference

BACKGROUND

Inferring molecular pathway activity is an important step towards reducing the complexity of genomic data, understanding the heterogeneity in clinical outcome, and obtaining molecular correlates of cancer imaging traits. Increasingly, approaches towards pathway activity inference combine molecular profiles (e.g gene or protein expression) with independent and highly curated structural interaction data (e.g protein interaction networks) or more generally with prior knowledge pathway databases. However, it is unclear how best to use the pathway knowledge information in the context of molecular profiles of any given study.

RESULTS

We present an algorithm called DART (Denoising Algorithm based on Relevance network Topology) which filters out noise before estimating pathway activity. Using simulated and real multidimensional cancer genomic data and by comparing DART to other algorithms which do not assess the relevance of the prior pathway information, we here demonstrate that substantial improvement in pathway activity predictions can be made if prior pathway information is denoised before predictions are made. We also show that genes encoding hubs in expression correlation networks represent more reliable markers of pathway activity. Using the Netpath resource of signalling pathways in the context of breast cancer gene expression data we further demonstrate that DART leads to more robust inferences about pathway activity correlations. Finally, we show that DART identifies a hypothesized association between oestrogen signalling and mammographic density in ER+ breast cancer.

CONCLUSIONS

Evaluating the consistency of prior information of pathway databases in molecular tumour profiles may substantially improve the subsequent inference of pathway activity in clinical tumour specimens. This de-noising strategy should be incorporated in approaches which attempt to infer pathway activity from prior pathway models.

A quantitative proteomic approach of the different stages of colorectal cancer establishes OLFM4 as a new nonmetastatic tumor marker

Expression profiles represent new molecular tools that are useful to characterize the successive steps of tumor progression and the prediction of recurrence or chemotherapy response. In this study, we have used quantitative proteomic analysis to compare different stages of colorectal cancer. A combination of laser microdissection, OFFGEL separation, iTRAQ labeling, and MALDI-TOF/TOF MS was used to explore the proteome of 28 colorectal cancer tissues. Two software packages were used for identification and quantification of differentially expressed proteins: Protein Pilot and iQuantitator. Based on ∼1,190,702 MS/MS spectra, a total of 3138 proteins were identified, which represents the largest database of colorectal cancer realized to date and demonstrates the value of our quantitative proteomic approach. In this way, individual protein expression and variation have been identified for each patient and for each colorectal dysplasia and cancer stage (stages I-IV). A total of 555 proteins presenting a significant fold change were quantified in the different stages, and this differential expression correlated with immunohistochemistry results reported in the Human Protein Atlas database. To identify a candidate biomarker of the early stages of colorectal cancer, we focused our study on secreted proteins. In this way, we identified olfactomedin-4, which was overexpressed in adenomas and in early stages of colorectal tumors. This early stage overexpression was confirmed by immunohistochemistry in 126 paraffin-embedded tissues. Our results also indicate that OLFM4 is regulated by the Ras-NF-κB2 pathway, one of the main oncogenic pathways deregulated in colorectal tumors.

Double-blind, placebo-controlled, randomized phase 2 study of the proapoptotic agent AT-101 plus docetaxel, in second-line non-small cell lung cancer

BACKGROUND

AT-101 is an inhibitor of Bcl-2 family proteins including Bcl-2, Bcl-xL, Mcl-1, and Bcl-w. In vivo and in vitro studies have exhibited broad activity of AT-101, including synergy with docetaxel in non-small cell lung cancer tumor models.

METHODS

We conducted a prospective, randomized (1:1), double-blind, placebo-controlled phase 2 study. Eligible patients must have received one prior chemotherapeutic regimen for advanced or metastatic non-small cell lung cancer and may also have received therapy with an epidermal growth factor receptor inhibitor. Patients received AT-101 (40 mg b.i.d. × 3 days) or placebo in combination with docetaxel (75 mg/m on day 1) every 21 days. The primary endpoint was progression-free survival (PFS) as determined by independent review; other endpoints include overall survival and PFS by investigator determination. Approximately 102 patients were planned to provide 70 events (80% power, hazard ratio [HR] of 0.6, one-sided alpha of 0.1).

RESULTS

: One hundred six patients were assigned to treatment and 105 patients received at least one dose of AT-101 or placebo. Baseline factors were balanced between treatment groups: median age 59 years; 77% men, and 79% current or former smokers. Ninety-three percent of patients had distant metastatic disease at randomization and 56% squamous histology. The most frequently reported adverse events were fatigue (18%), anemia (18%), and dyspnea (18%). No statistically significant differences in serious adverse events were observed between AT-101 and placebo; grade 1/2 headaches appeared more frequently with AT-101 (9% versus 0%) and neutropenia was reported more frequently in the docetaxel plus placebo arm compared with docetaxel plus AT-101 (17% versus 8%). Unlike trials with continuous daily dosing of AT-101, no cases of small bowel obstruction were reported. The response rate and median PFS were not different between the arms by independent review, PFS 7.5 weeks for docetaxel plus AT-101 and 7.1 weeks for docetaxel plus placebo arms (HR, 1.04; p = 0.57). The median overall survival was 7.8 months for docetaxel plus AT-101 versus 5.9 months for docetaxel plus placebo (HR, 0.82; p = 0.21).

CONCLUSIONS

The primary endpoint of improved PFS for AT-101 plus docetaxel was not met. AT-101 plus docetaxel was well tolerated with an adverse event profile indistinguishable from the base docetaxel regimen. AT-101 is the first oral, pan Bcl-2 family inhibitor to exhibit a possible survival benefit in a randomized study.