Cancer-associated fibroblasts are the main contributors to epithelial-to-mesenchymal signatures in the tumor microenvironment.

Epithelial-to-mesenchymal transition (EMT) is associated with tumor initiation, metastasis, and drug resistance. However, the mechanisms underlying these associations are largely unknown. We studied several tumor types to identify the source of EMT gene expression signals and a potential mechanism of resistance to immuno-oncology treatment. Across tumor types, EMT-related gene expression was strongly associated with expression of stroma-related genes. Based on RNA sequencing of multiple patient-derived xenograft models, EMT-related gene expression was enriched in the stroma versus parenchyma. EMT-related markers were predominantly expressed by cancer-associated fibroblasts (CAFs), cells of mesenchymal origin which produce a variety of matrix proteins and growth factors. Scores derived from a 3-gene CAF transcriptional signature (COL1A1, COL1A2, COL3A1) were sufficient to reproduce association between EMT-related markers and disease prognosis. Our results suggest that CAFs are the primary source of EMT signaling and have potential roles as biomarkers and targets for immuno-oncology therapies.

Fc-Optimized Anti-CCR8 Antibody Depletes Regulatory T Cells in Human Tumor Models.

FOXP3+ regulatory T cells (Treg) play a critical role in mediating tolerance to self-antigens and can repress antitumor immunity through multiple mechanisms. Therefore, targeted depletion of tumor-resident Tregs is warranted to promote effective antitumor immunity while preserving peripheral homeostasis. Here, we propose the chemokine receptor CCR8 as one such optimal tumor Treg target. CCR8 was expressed by Tregs in both murine and human tumors, and unlike CCR4, a Treg depletion target in the clinic, CCR8 was selectively expressed on suppressive tumor Tregs and minimally expressed on proinflammatory effector T cells (Teff). Preclinical mouse tumor modeling showed that depletion of CCR8+ Tregs through an FcyR-engaging anti-CCR8 antibody, but not blockade, enabled dose-dependent, effective, and long-lasting antitumor immunity that synergized with PD-1 blockade. This depletion was tumor Treg-restricted, sparing CCR8+ T cells in the spleen, thymus, and skin of mice. Importantly, Fc-optimized, nonfucosylated (nf) anti-human CCR8 antibodies specifically depleted Tregs and not Teffs in ex vivo tumor cultures from primary human specimens. These findings suggest that anti-CCR8-nf antibodies may deliver optimal tumor-targeted Treg depletion in the clinic, providing long-term antitumor memory responses while limiting peripheral toxicities. SIGNIFICANCE: These findings show that selective depletion of regulatory T cells with an anti-CCR8 antibody can improve antitumor immune responses as a monotherapy or in combination with other immunotherapies. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/81/11/2983/F1.large.jpg.

Analyses of PD-L1 and Inflammatory Gene Expression Association with Efficacy of Nivolumab ± Ipilimumab in Gastric Cancer/Gastroesophageal Junction Cancer.

PURPOSE: In advanced gastric cancer/gastroesophageal junction cancer (GC/GEJC), there is a need to identify biomarkers of response to therapies, such as immune checkpoint inhibitors. PATIENTS AND METHODS: In post hoc exploratory analyses from CheckMate 032 (GC/GEJC cohort), we evaluated associations between nivolumab ± ipilimumab (NIVO ± IPI) efficacy and programmed death ligand 1 (PD-L1) expression, defined by tumor cells (% TC) or combined positive score (CPS; sum of PD-L1-staining TCs + immune cells, divided by total viable TCs, × 100) using the Dako PD-L1 IHC 28-8 pharmDx assay, or inflammatory gene expression. RESULTS: There was a trend toward increased efficacy (objective response and overall survival) when PD-L1 expression was determined by CPS compared with % TC at higher cutoffs of ≥5 and ≥10 in the pooled analysis of all treatment regimens. In this analysis, 19% and 26% of patients with PD-L1-positive tumors at a CPS cutoff of ≥5 and ≥10, respectively, had an objective response compared with 8% and 9% of patients at the equivalent % TC cutoffs. Longer survival was demonstrated in patients with PD-L1-positive (defined by CPS cutoffs of ≥5 and ≥10) versus PD-L1-negative status. Similar results were observed in the NIVO 1 mg/kg + IPI 3 mg/kg subgroup. Multiple inflammatory gene signatures/transcripts, including a signature consisting of four genes (CD274, CD8A, LAG3, and STAT1), showed associations with response to NIVO ± IPI. CONCLUSIONS: This study suggests a greater association of PD-L1 expression by CPS with NIVO ± IPI efficacy compared with % TC PD-L1 expression in patients with GC/GEJC. Inflammatory signatures were also associated with NIVO ± IPI response, warranting further investigation.See related commentary by Moutafi and Rimm, p. 3812.

Genome-wide association analysis identifies genetic correlates of immune infiltrates in solid tumors.

Therapeutic options for the treatment of an increasing variety of cancers have been expanded by the introduction of a new class of drugs, commonly referred to as checkpoint blocking agents, that target the host immune system to positively modulate anti-tumor immune response. Although efficacy of these agents has been linked to a pre-existing level of tumor immune infiltrate, it remains unclear why some patients exhibit deep and durable responses to these agents while others do not benefit. To examine the influence of tumor genetics on tumor immune state, we interrogated the relationship between somatic mutation and copy number alteration with infiltration levels of 7 immune cell types across 40 tumor cohorts in The Cancer Genome Atlas. Levels of cytotoxic T, regulatory T, total T, natural killer, and B cells, as well as monocytes and M2 macrophages, were estimated using a novel set of transcriptional signatures that were designed to resist interference from the cellular heterogeneity of tumors. Tumor mutational load and estimates of tumor purity were included in our association models to adjust for biases in multi-modal genomic data. Copy number alterations, mutations summarized at the gene level, and position-specific mutations were evaluated for association with tumor immune infiltration. We observed a strong relationship between copy number loss of a large region of chromosome 9p and decreased lymphocyte estimates in melanoma, pancreatic, and head/neck cancers. Mutations in the oncogenes PIK3CA, FGFR3, and RAS/RAF family members, as well as the tumor suppressor TP53, were linked to changes in immune infiltration, usually in restricted tumor types. Associations of specific WNT/beta-catenin pathway genetic changes with immune state were limited, but we noted a link between 9p loss and the expression of the WNT receptor FZD3, suggesting that there are interactions between 9p alteration and WNT pathways. Finally, two different cell death regulators, CASP8 and DIDO1, were often mutated in head/neck tumors that had higher lymphocyte infiltrates. In summary, our study supports the relevance of tumor genetics to questions of efficacy and resistance in checkpoint blockade therapies. It also highlights the need to assess genome-wide influences during exploration of any specific tumor pathway hypothesized to be relevant to therapeutic response. Some of the observed genetic links to immune state, like 9p loss, may influence response to cancer immune therapies. Others, like mutations in cell death pathways, may help guide combination therapeutic approaches.

An immune-active tumor microenvironment favors clinical response to ipilimumab.

PURPOSE: Ipilimumab, a fully human monoclonal antibody specific to CTLA-4, has been shown to improve overall survival in metastatic melanoma patients. As a consequence of CTLA-4 blockade, ipilimumab treatment is associated with proliferation and activation of peripheral T cells. To better understand various tumor-associated components that may influence the clinical outcome of ipilimumab treatment, gene expression profiles of tumors from patients treated with ipilimumab were characterized. EXPERIMENTAL DESIGN: Gene expression profiling was performed on tumor biopsies collected from 45 melanoma patients before and 3 weeks after the start of treatment in a phase II clinical trial. RESULTS: Analysis of pre-treatment tumors indicated that patients with high baseline expression levels of immune-related genes were more likely to respond favorably to ipilimumab. Furthermore, ipilimumab appeared to induce two major changes in tumors from patients who exhibited clinical activity: genes involved in immune response showed increased expression, whereas expression of genes for melanoma-specific antigens and genes involved in cell proliferation decreased. These changes were associated with the total lymphocyte infiltrate in tumors, and there was a suggestion of association with prolonged overall survival in these patients. Many IFN-γ-inducible genes and Th1-associated markers showed increased expression after ipilimumab treatment, suggesting an accumulation of this particular type of T cell at the tumor sites, which might play an important role in mediating the antitumor activity of ipilimumab. CONCLUSIONS: These results support the proposed mechanism of action of ipilimumab, suggesting that cell-mediated immune responses play an important role in the antitumor activity of ipilimumab.

SDRS--an algorithm for analyzing large-scale dose-response data.

SUMMARY: Dose-response information is critical to understanding drug effects, yet analytical methods for dose-response assays cannot cope with the dimensionality of large-scale screening data such as the microarray profiling data. To overcome this limitation, we developed and implemented the Sigmoidal Dose Response Search (SDRS) algorithm, a grid search-based method designed to handle large-scale dose-response data. This method not only calculates the pharmacological parameters for every assay, but also provides built-in statistic that enables downstream systematic analyses, such as characterizing dose response at the transcriptome level. AVAILABILITY: Bio::SDRS is freely available from CPAN (www.cpan.org). CONTACTS: ruiruji@gmail.com; bruc@acm.org SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.

Exome sequencing reveals comprehensive genomic alterations across eight cancer cell lines.

It is well established that genomic alterations play an essential role in oncogenesis, disease progression, and response of tumors to therapeutic intervention. The advances of next-generation sequencing technologies (NGS) provide unprecedented capabilities to scan genomes for changes such as mutations, deletions, and alterations of chromosomal copy number. However, the cost of full-genome sequencing still prevents the routine application of NGS in many areas. Capturing and sequencing the coding exons of genes (the "exome") can be a cost-effective approach for identifying changes that result in alteration of protein sequences. We applied an exome-sequencing technology (Roche Nimblegen capture paired with 454 sequencing) to identify sequence variation and mutations in eight commonly used cancer cell lines from a variety of tissue origins (A2780, A549, Colo205, GTL16, NCI-H661, MDA-MB468, PC3, and RD). We showed that this technology can accurately identify sequence variation, providing ∼95% concordance with Affymetrix SNP Array 6.0 performed on the same cell lines. Furthermore, we detected 19 of the 21 mutations reported in Sanger COSMIC database for these cell lines. We identified an average of 2,779 potential novel sequence variations/mutations per cell line, of which 1,904 were non-synonymous. Many non-synonymous changes were identified in kinases and known cancer-related genes. In addition we confirmed that the read-depth of exome sequence data can be used to estimate high-level gene amplifications and identify homologous deletions. In summary, we demonstrate that exome sequencing can be a reliable and cost-effective way for identifying alterations in cancer genomes, and we have generated a comprehensive catalogue of genomic alterations in coding regions of eight cancer cell lines. These findings could provide important insights into cancer pathways and mechanisms of resistance to anti-cancer therapies.

Transcriptional profiling of the dose response: a more powerful approach for characterizing drug activities.

The dose response curve is the gold standard for measuring the effect of a drug treatment, but is rarely used in genomic scale transcriptional profiling due to perceived obstacles of cost and analysis. One barrier to examining transcriptional dose responses is that existing methods for microarray data analysis can identify patterns, but provide no quantitative pharmacological information. We developed analytical methods that identify transcripts responsive to dose, calculate classical pharmacological parameters such as the EC50, and enable an in-depth analysis of coordinated dose-dependent treatment effects. The approach was applied to a transcriptional profiling study that evaluated four kinase inhibitors (imatinib, nilotinib, dasatinib and PD0325901) across a six-logarithm dose range, using 12 arrays per compound. The transcript responses proved a powerful means to characterize and compare the compounds: the distribution of EC50 values for the transcriptome was linked to specific targets, dose-dependent effects on cellular processes were identified using automated pathway analysis, and a connection was seen between EC50s in standard cellular assays and transcriptional EC50s. Our approach greatly enriches the information that can be obtained from standard transcriptional profiling technology. Moreover, these methods are automated, robust to non-optimized assays, and could be applied to other sources of quantitative data.

Gene set enrichment analysis.

Set enrichment analytical methods have become commonplace tools applied to the analysis and interpretation of biological data. The statistical techniques are used to identify categorical biases within lists of genes, proteins, or metabolites. The goal is to discover the shared functions or properties of the biological items represented within the lists. Application of these methods can provide great biological insight, including the discovery of participation in the same biological activity or pathway, shared interacting genes or regulators, common cellular compartmentalization, or association with disease. The methods require ordered or unordered lists of biological items as input, understanding of the reference set from which the lists were selected, categorical classifiers describing the items, and a statistical algorithm to assess bias of each classifier. Due to the complexity of most algorithms and the number of calculations performed, computer software is almost always used for execution of the algorithm, as well as for presentation of the results. This chapter will provide an overview of the statistical methods used to perform an enrichment analysis. Guidelines for assembly of the requisite information will be presented, with a focus on careful definition of the sets used by the statistical algorithms. The need for multiple test correction when working with large libraries of classifiers is emphasized, and we outline several options for performing the corrections. Finally, interpreting the results of such analysis will be discussed along with examples of recent research utilizing the techniques.

HBEGF, SRA1, and IK: Three cosegregating genes as determinants of cardiomyopathy.

Human dilated cardiomyopathy (DCM), a disorder of the cardiac muscle, causes considerable morbidity and mortality and is one of the major causes of sudden cardiac death. Genetic factors play a role in the etiology and pathogenesis of DCM. Disease-associated genetic variations identified to date have been identified in single families or single sporadic patients and explain a minority of the etiology of DCM. We show that a 600-kb region of linkage disequilibrium (LD) on 5q31.2-3, harboring multiple genes, is associated with cardiomyopathy in three independent Caucasian populations (combined P-value = 0.00087). Functional assessment in zebrafish demonstrates that at least three genes, orthologous to loci in this LD block, HBEGF, IK, and SRA1, result independently in a phenotype of myocardial contractile dysfunction when their expression is reduced with morpholino antisense reagents. Evolutionary analysis across multiple vertebrate genomes suggests that this heart failure-associated LD block emerged by a series of genomic rearrangements across amphibian, avian, and mammalian genomes and is maintained as a cluster in mammals. Taken together, these observations challenge the simple notion that disease phenotypes can be traced to altered function of a single locus within a haplotype and suggest that a more detailed assessment of causality can be necessary.

Nearest Neighbor Networks: clustering expression data based on gene neighborhoods.

BACKGROUND: The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both individual biological pathways and the integrated workings of the cell. However, translating this amount of data into biological insight remains a daunting task. An important initial step in the analysis of microarray data is clustering of genes with similar behavior. A number of classical techniques are commonly used to perform this task, particularly hierarchical and K-means clustering, and many novel approaches have been suggested recently. While these approaches are useful, they are not without drawbacks; these methods can find clusters in purely random data, and even clusters enriched for biological functions can be skewed towards a small number of processes (e.g. ribosomes). RESULTS: We developed Nearest Neighbor Networks (NNN), a graph-based algorithm to generate clusters of genes with similar expression profiles. This method produces clusters based on overlapping cliques within an interaction network generated from mutual nearest neighborhoods. This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered. We compared the clusters generated by NNN with those generated by eight other clustering methods. NNN was particularly successful at generating functionally coherent clusters with high precision, and these clusters generally represented a much broader selection of biological processes than those recovered by other methods. CONCLUSION: The Nearest Neighbor Networks algorithm is a valuable clustering method that effectively groups genes that are likely to be functionally related. It is particularly attractive due to its simplicity, its success in the analysis of large datasets, and its ability to span a wide range of biological functions with high precision.

Structural and functional characterization of CFE88: evidence that a conserved and essential bacterial protein is a methyltransferase.

CFE88 is a conserved essential gene product from Streptococcus pneumoniae. This 227-residue protein has minimal sequence similarity to proteins of known 3D structure. Sequence alignment models and computational protein threading studies suggest that CFE88 is a methyltransferase. Characterization of the conformation and function of CFE88 has been performed by using several techniques. Backbone atom and limited side-chain atom NMR resonance assignments have been obtained. The data indicate that CFE88 has two domains: an N-terminal domain with 163 residues and a C-terminal domain with 64 residues. The C-terminal domain is primarily helical, while the N-terminal domain has a mixed helical/extended (Rossmann) fold. By aligning the experimentally observed elements of secondary structure, an initial unrefined model of CFE88 has been constructed based on the X-ray structure of ErmC' methyltransferase (Protein Data Bank entry 1QAN). NMR and biophysical studies demonstrate binding of S-adenosyl-L-homocysteine (SAH) to CFE88; these interactions have been localized by NMR to the predicted active site in the N-terminal domain. Mutants that target this predicted active site (H26W, E46R, and E46W) have been constructed and characterized. Overall, our results both indicate that CFE88 is a methyltransferase and further suggest that the methyltransferase activity is essential for bacterial survival.

Cephalosporin prodrugs of paclitaxel for immunologically specific activation by L-49-sFv-beta-lactamase fusion protein.

Paclitaxel conjugates of 7-phenylacetamidocephalosporanic acid were prepared as prodrugs for site specific activation by targeted beta-lactamase. Immunologically specific activation of the prodrug 5 containing 3,3-dimethyl-4-amino-butyric acid as linker in combination with the fusion protein L-49-sFv-beta-lactamase was demonstrated in vitro on 3677 melanoma cells.