QuantFusion: Novel Unified Methodology for Enhanced Coverage and Precision in Quantifying Global Proteomic Changes in Whole Tissues

Single quantitative platforms such as label-based or label-free quantitation (LFQ) present compromises in accuracy, precision, protein sequence coverage, and speed of quantifiable proteomic measurements. To maximize the quantitative precision and the number of quantifiable proteins or the quantifiable coverage of tissue proteomes, we have developed a unified approach, termed QuantFusion, that combines the quantitative ratios of all peptides measured by both LFQ and label-based methodologies. Here, we demonstrate the use of QuantFusion in determining the proteins differentially expressed in a pair of patient-derived tumor xenografts (PDXs) representing two major breast cancer (BC) subtypes, basal and luminal. Label-based in-spectra quantitative peptides derived from amino acid-coded tagging (AACT, also known as SILAC) of a non-malignant mammary cell line were uniformly added to each xenograft with a constant predefined ratio, from which Ratio-of-Ratio estimates were obtained for the label-free peptides paired with AACT peptides in each PDX tumor. A mixed model statistical analysis was used to determine global differential protein expression by combining complementary quantifiable peptide ratios measured by LFQ and Ratio-of-Ratios, respectively. With minimum number of replicates required for obtaining the statistically significant ratios, QuantFusion uses the distinct mechanisms to “rescue” the missing data inherent to both LFQ and label-based quantitation. Combined quantifiable peptide data from both quantitative schemes increased the overall number of peptide level measurements and protein level estimates. In our analysis of the PDX tumor proteomes, QuantFusion increased the number of distinct peptide ratios by 65%, representing differentially expressed proteins between the BC subtypes. This quantifiable coverage improvement, in turn, not only increased the number of measurable protein fold-changes by 8% but also increased the average precision of quantitative estimates by 181% so that some BC subtypically expressed proteins were rescued by QuantFusion. Thus, incorporating data from multiple quantitative approaches while accounting for measurement variability at both the peptide and global protein levels make QuantFusion unique for obtaining increased coverage and quantitative precision for tissue proteomes

The molecular portraits of breast tumors are conserved acress microarray platforms

Background Validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification. However, validation is often unconvincing because the size of the test set is typically small. To overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach to data fusion, in order to validate a new breast tumor intrinsic list. Results A 105-tumor training set containing 26 sample pairs was used to derive a new breast tumor intrinsic gene list. This intrinsic list contained 1300 genes and a proliferation signature that was not present in previous breast intrinsic gene sets. We tested this list as a survival predictor on a data set of 311 tumors compiled from three independent microarray studies that were fused into a single data set using Distance Weighted Discrimination. When the new intrinsic gene set was used to hierarchically cluster this combined test set, tumors were grouped into LumA, LumB, Basal-like, HER2+/ER-, and Normal Breast-like tumor subtypes that we demonstrated in previous datasets. These subtypes were associated with significant differences in Relapse-Free and Overall Survival. Multivariate Cox analysis of the combined test set showed that the intrinsic subtype classifications added significant prognostic information that was independent of standard clinical predictors. From the combined test set, we developed an objective and unchanging classifier based upon five intrinsic subtype mean expression profiles (i.e. centroids), which is designed for single sample predictions (SSP). The SSP approach was applied to two additional independent data sets and consistently predicted survival in both systemically treated and untreated patient groups. Conclusion This study validates the breast tumor intrinsic subtype classification as an objective means of tumor classification that should be translated into a clinical assay for further retrospective and prospective validation. In addition, our method of combining existing data sets can be used to robustly validate the potential clinical value of any new gene expression profile

The molecular portraits of breast tumors are conserved across microarray platforms

BACKGROUND: Validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification. However, validation is often unconvincing because the size of the test set is typically small. To overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach to data fusion, in order to validate a new breast tumor intrinsic list. RESULTS: A 105-tumor training set containing 26 sample pairs was used to derive a new breast tumor intrinsic gene list. This intrinsic list contained 1300 genes and a proliferation signature that was not present in previous breast intrinsic gene sets. We tested this list as a survival predictor on a data set of 311 tumors compiled from three independent microarray studies that were fused into a single data set using Distance Weighted Discrimination. When the new intrinsic gene set was used to hierarchically cluster this combined test set, tumors were grouped into LumA, LumB, Basal-like, HER2+/ER-, and Normal Breast-like tumor subtypes that we demonstrated in previous datasets. These subtypes were associated with significant differences in Relapse-Free and Overall Survival. Multivariate Cox analysis of the combined test set showed that the intrinsic subtype classifications added significant prognostic information that was independent of standard clinical predictors. From the combined test set, we developed an objective and unchanging classifier based upon five intrinsic subtype mean expression profiles (i.e. centroids), which is designed for single sample predictions (SSP). The SSP approach was applied to two additional independent data sets and consistently predicted survival in both systemically treated and untreated patient groups. CONCLUSION: This study validates the "breast tumor intrinsic" subtype classification as an objective means of tumor classification that should be translated into a clinical assay for further retrospective and prospective validation. In addition, our method of combining existing data sets can be used to robustly validate the potential clinical value of any new gene expression profile

Non–Small Cell Lung Cancer: Prognostic Importance of Positive FDG PET Findings in the Mediastinum for Patients with N0–N1 Disease at Pathologic Analysis

In non-small cell lung cancer, preoperative evaluation of the mediastinum with PET may complement the surgical and histologic findings

Inelastic Black Hole Scattering from Charged Scalar Amplitudes

We explain how the lowest-order classical gravitational radiation produced during the inelastic scattering of two Schwarzschild black holes in General Relativity can be obtained from a tree scattering amplitude in gauge theory coupled to scalar fields. The gauge calculation is related to gravity through the double copy. We remove unwanted scalar forces which can occur in the double copy by introducing a massless scalar in the gauge theory, which is treated as a ghost in the link to gravity. We hope these methods are a step towards a direct application of the double copy at higher orders in classical perturbation theory, with the potential to greatly streamline gravity calculations for phenomenological applications.Comment: 28 pages, 6 figure

CD40-Activated B Cells Can Efficiently Prime Antigen-Specific Naïve CD8+ T Cells to Generate Effector but Not Memory T cells

Background: The identification of the signals that should be provided by antigen-presenting cells (APCs) to induce a CD8 + T cell response in vivo is essential to improve vaccination strategies using antigen-loaded APCs. Although dendritic cells have been extensively studied, the ability of other APC types, such as B cells, to induce a CD8 + T cell response have not been thoroughly evaluated. Methodology/Principal Findings: In this manuscript, we have characterized the ability of CD40-activated B cells, stimulated or not with Toll-like receptor (TLR) agonists (CpG or lipopolysaccharide) to induce the response of mouse naïve CD8 + T cells in vivo. Our results show that CD40-activated B cells can directly present antigen to naïve CD8 + T cells to induce the generation of potent effectors able to secrete cytokines, kill target cells and control a Listeria monocytogenes infection. However, CD40-activated B cell immunization did not lead to the proper formation of CD8 + memory T cells and further maturation of CD40-activated B cells with TLR agonists did not promote the development of CD8 + memory T cells. Our results also suggest that inefficient generation of CD8 + memory T cells with CD40-activated B cell immunization is a consequence of reduced Bcl-6 expression by effectors and enhanced contraction of the CD8 + T cell response. Conclusions: Understanding why CD40-activated B cell immunization is defective for the generation of memory T cells and gaining new insights about signals that should be provided by APCs are key steps before translating the use of CD40-B cel

Advances in rheumatology: new targeted therapeutics

Treatment of inflammatory arthritides - including rheumatoid arthritis, ankylosing spondylitis, and psoriatic arthritis - has seen much progress in recent years, partially due to increased understanding of the pathogenesis of these diseases at the cellular and molecular levels. These conditions share some common mechanisms. Biologic therapies have provided a clear advance in the treatment of rheumatological conditions. Currently available TNF-targeting biologic agents that are licensed for at east one of the above-named diseases are etanercept, infliximab, adalimumab, golimumab, and certolizumab. Biologic agents with a different mechanism of action have also been approved in rheumatoid arthritis (rituximab, abatacept, and tocilizumab). Although these biologic agents are highly effective, there is a need for improved management strategies. There is also a need for education of family physicians and other healthcare professionals in the identification of early symptoms of inflammatory arthritides and the importance of early referral to rheumatologists for diagnosis and treatment. Also, researchers are developing molecules - for example, the Janus kinase inhibitor CP-690550 (tofacitinib) and the spleen tyrosine kinase inhibitor R788 (fostamatinib) - to target other aspects of the inflammatory cascade. Initial trial results with new agents are promising, and, in time, head-to-head trials will establish the best treatment options for patients. The key challenge is identifying how best to integrate these new, advanced therapies into daily practice

Validation of Interobserver Agreement in Lung Cancer Assessment: Hematoxylin-Eosin Diagnostic Reproducibility for Non–Small Cell Lung Cancer: The 2004 World Health Organization Classification and Therapeutically Relevant Subsets

Precise subtype diagnosis of non–small cell lung carcinoma is increasingly relevant, based on the availability of subtype-specific therapies, such as bevacizumab and pemetrexed, and based on the subtype-specific prevalence of activating epidermal growth factor receptor mutations

Septicaemia models using Streptococcus pneumoniae and Listeria monocytogenes: understanding the role of complement properdin

Streptococcus pneumoniae and Listeria monocytogenes, pathogens which can cause severe infectious disease in human, were used to infect properdin-deficient and wildtype mice. The aim was to deduce a role for properdin, positive regulator of the alternative pathway of complement activation, by comparing and contrasting the immune response of the two genotypes in vivo. We show that properdin-deficient and wildtype mice mounted antipneumococcal serotype-specific IgM antibodies, which were protective. Properdin-deficient mice, however, had increased survival in the model of streptococcal pneumonia and sepsis. Low activity of the classical pathway of complement and modulation of FcγR2b expression appear to be pathogenically involved. In listeriosis, however, properdin-deficient mice had reduced survival and a dendritic cell population that was impaired in maturation and activity. In vitro analyses of splenocytes and bone marrow-derived myeloid cells support the view that the opposing outcomes of properdin-deficient and wildtype mice in these two infection models is likely to be due to a skewing of macrophage activity to an M2 phenotype in the properdin-deficient mice. The phenotypes observed thus appear to reflect the extent to which M2- or M1-polarised macrophages are involved in the immune responses to S. pneumoniae and L. monocytogenes. We conclude that properdin controls the strength of immune responses by affecting humoral as well as cellular phenotypes during acute bacterial infection and ensuing inflammation

Amplifying IFN-γ Signaling in Dendritic Cells by CD11c-Specific Loss of SOCS1 Increases Innate Immunity to Infection while Decreasing Adaptive Immunity.

Although prophylactic vaccines provide protective humoral immunity against infectious agents, vaccines that elicit potent CD8 T cell responses are valuable tools to shape and drive cellular immunity against cancer and intracellular infection. In particular, IFN-γ-polarized cytotoxic CD8 T cell immunity is considered optimal for protective immunity against intracellular Ags. Suppressor of cytokine signaling (SOCS)1 is a cross-functional negative regulator of TLR and cytokine receptor signaling via degradation of the receptor-signaling complex. We hypothesized that loss of SOCS1 in dendritic cells (DCs) would improve T cell responses by accentuating IFN-γ-directed immune responses. We tested this hypothesis using a recombinan