Precision Oncology, Artificial Intelligence, and Novel Therapeutic Advancements in the Diagnosis, Prevention, and Treatment of Cancer: Highlights from the 59th Irish Association for Cancer Research (IACR) Annual Conference.

Advancements in oncology, especially with the era of precision oncology, is resulting in a paradigm shift in cancer care. Indeed, innovative technologies, such as artificial intelligence, are paving the way towards enhanced diagnosis, prevention, and personalised treatments as well as novel drug discoveries. Despite excellent progress, the emergence of resistant cancers has curtailed both the pace and extent to which we can advance. By combining both their understanding of the fundamental biological mechanisms and technological advancements such as artificial intelligence and data science, cancer researchers are now beginning to address this. Together, this will revolutionise cancer care, by enhancing molecular interventions that may aid cancer prevention, inform clinical decision making, and accelerate the development of novel therapeutic drugs. Here, we will discuss the advances and approaches in both artificial intelligence and precision oncology, presented at the 59th Irish Association for Cancer Research annual conference.

Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer.

INTRODUCTION: Histological images contain phenotypic information predictive of patient outcomes. Due to the heavy workload of pathologists, the time-consuming nature of quantitatively assessing histological features, and human eye limitations to recognize spatial patterns, manually extracting prognostic information in routine pathological workflows remains challenging. Digital pathology has facilitated the mining and quantification of these features utilizing whole-slide image (WSI) scanners and artificial intelligence (AI) algorithms. AI algorithms to identify image-based biomarkers from the tumor microenvironment (TME) have the potential to revolutionize the field of oncology, reducing delays between diagnosis and prognosis determination, allowing for rapid stratification of patients and prescription of optimal treatment regimes, thereby improving patient outcomes. AREAS COVERED: In this review, the authors discuss how AI algorithms and digital pathology can predict breast cancer patient prognosis and treatment outcomes using image-based biomarkers, along with the challenges of adopting this technology in clinical settings. EXPERT OPINION: The integration of AI and digital pathology presents significant potential for analyzing the TME and its diagnostic, prognostic, and predictive value in breast cancer patients. Widespread clinical adoption of AI faces ethical, regulatory, and technical challenges, although prospective trials may offer reassurance and promote uptake, ultimately improving patient outcomes by reducing diagnosis-to-prognosis delivery delays.

Application of Artificial Intelligence Techniques to Predict Risk of Recurrence of Breast Cancer: A Systematic Review.

Breast cancer is the most common disease among women, with over 2.1 million new diagnoses each year worldwide. About 30% of patients initially presenting with early stage disease have a recurrence of cancer within 10 years. Predicting who will have a recurrence and who will not remains challenging, with consequent implications for associated treatment. Artificial intelligence strategies that can predict the risk of recurrence of breast cancer could help breast cancer clinicians avoid ineffective overtreatment. Despite its significance, most breast cancer recurrence datasets are insufficiently large, not publicly available, or imbalanced, making these studies more difficult. This systematic review investigates the role of artificial intelligence in the prediction of breast cancer recurrence. We summarise common techniques, features, training and testing methodologies, metrics, and discuss current challenges relating to implementation in clinical practice. We systematically reviewed works published between 1 January 2011 and 1 November 2021 using the methodology of Kitchenham and Charter. We leveraged Springer, Google Scholar, PubMed, and IEEE search engines. This review found three areas that require further work. First, there is no agreement on artificial intelligence methodologies, feature predictors, or assessment metrics. Second, issues such as sampling strategies, missing data, and class imbalance problems are rarely addressed or discussed. Third, representative datasets for breast cancer recurrence are scarce, which hinders model validation and deployment. We conclude that predicting breast cancer recurrence remains an open problem despite the use of artificial intelligence.

Mapping the Immune Landscape in Metastatic Melanoma Reveals Localized Cell-Cell Interactions That Predict Immunotherapy Response.

While immune checkpoint-based immunotherapy (ICI) shows promising clinical results in patients with cancer, only a subset of patients responds favorably. Response to ICI is dictated by complex networks of cellular interactions between malignant and nonmalignant cells. Although insights into the mechanisms that modulate the pivotal antitumoral activity of cytotoxic T cells (Tcy) have recently been gained, much of what has been learned is based on single-cell analyses of dissociated tumor samples, resulting in a lack of critical information about the spatial distribution of relevant cell types. Here, we used multiplexed IHC to spatially characterize the immune landscape of metastatic melanoma from responders and nonresponders to ICI. Such high-dimensional pathology maps showed that Tcy gradually evolve toward an exhausted phenotype as they approach and infiltrate the tumor. Moreover, a key cellular interaction network functionally linked Tcy and PD-L1+ macrophages. Mapping the respective spatial distributions of these two cell populations predicted response to anti-PD-1 immunotherapy with high confidence. These results suggest that baseline measurements of the spatial context should be integrated in the design of predictive biomarkers to identify patients likely to benefit from ICI. SIGNIFICANCE: This study shows that spatial characterization can address the challenge of finding efficient biomarkers, revealing that localization of macrophages and T cells in melanoma predicts patient response to ICI. See related commentary by Smalley and Smalley, p. 3198.

1,4-dihydroxy quininib modulates the secretome of uveal melanoma tumour explants and a marker of oxidative phosphorylation in a metastatic xenograft model.

Uveal melanoma (UM) is an intraocular cancer with propensity for liver metastases. The median overall survival (OS) for metastatic UM (MUM) is 1.07 years, with a reported range of 0.84-1.34. In primary UM, high cysteinyl leukotriene receptor 1 (CysLT1) expression associates with poor outcomes. CysLT1 antagonists, quininib and 1,4-dihydroxy quininib, alter cancer hallmarks of primary and metastatic UM cell lines in vitro. Here, the clinical relevance of CysLT receptors and therapeutic potential of quininib analogs is elaborated in UM using preclinical in vivo orthotopic xenograft models and ex vivo patient samples. Immunohistochemical staining of an independent cohort (n = 64) of primary UM patients confirmed high CysLT1 expression significantly associates with death from metastatic disease (p = 0.02; HR 2.28; 95% CI 1.08-4.78), solidifying the disease relevance of CysLT1 in UM. In primary UM samples (n = 11) cultured as ex vivo explants, 1,4-dihydroxy quininib significantly alters the secretion of IL-13, IL-2, and TNF-α. In an orthotopic, cell line-derived xenograft model of MUM, 1,4-dihydroxy quininib administered intraperitoneally at 25 mg/kg significantly decreases ATP5B expression (p = 0.03), a marker of oxidative phosphorylation. In UM, high ATP5F1B is a poor prognostic indicator, whereas low ATP5F1B, in combination with disomy 3, correlates with an absence of metastatic disease in the TCGA-UM dataset. These preclinical data highlight the diagnostic potential of CysLT1 and ATP5F1B in UM, and the therapeutic potential of 1,4-dihydroxy quininib with ATP5F1B as a companion diagnostic to treat MUM.

Prognostic value of the 6-gene OncoMasTR test in hormone receptor-positive HER2-negative early-stage breast cancer: Comparative analysis with standard clinicopathological factors.

AIM: The aim of the study was to assess the prognostic performance of a 6-gene molecular score (OncoMasTR Molecular Score [OMm]) and a composite risk score (OncoMasTR Risk Score [OM]) and to conduct a within-patient comparison against four routinely used molecular and clinicopathological risk assessment tools: Oncotype DX Recurrence Score, Ki67, Nottingham Prognostic Index and Clinical Risk Category, based on the modified Adjuvant! Online definition and three risk factors: patient age, tumour size and grade. METHODS: Biospecimens and clinicopathological information for 404 Irish women also previously enrolled in the Trial Assigning Individualized Options for Treatment [Rx] were provided by 11 participating hospitals, as the primary objective of an independent translational study. Gene expression measured via RT-qPCR was used to calculate OMm and OM. The prognostic value for distant recurrence-free survival (DRFS) and invasive disease-free survival (IDFS) was assessed using Cox proportional hazards models and Kaplan-Meier analysis. All statistical tests were two-sided ones. RESULTS: OMm and OM (both with likelihood ratio statistic [LRS] P < 0.001; C indexes = 0.84 and 0.85, respectively) were more prognostic for DRFS and provided significant additional prognostic information to all other assessment tools/factors assessed (all LRS P ≤ 0.002). In addition, the OM correctly classified more patients with distant recurrences (DRs) into the high-risk category than other risk classification tools. Similar results were observed for IDFS. DISCUSSION: Both OncoMasTR scores were significantly prognostic for DRFS and IDFS and provided additional prognostic information to the molecular and clinicopathological risk factors/tools assessed. OM was also the most accurate risk classification tool for identifying DR. A concise 6-gene signature with superior risk stratification was shown to increase prognosis reliability, which may help clinicians optimise treatment decisions.

Future of biomarker evaluation in the realm of artificial intelligence algorithms: application in improved therapeutic stratification of patients with breast and prostate cancer.

Clinical workflows in oncology depend on predictive and prognostic biomarkers. However, the growing number of complex biomarkers contributes to costly and delayed decision-making in routine oncology care and treatment. As cancer is expected to rank as the leading cause of death and the single most important barrier to increasing life expectancy in the 21st century, there is a major emphasis on precision medicine, particularly individualisation of treatment through better prediction of patient outcome. Over the past few years, both surgical and pathology specialties have suffered cutbacks and a low uptake of pathology specialists means a solution is required to enable high-throughput screening and personalised treatment in this area to alleviate bottlenecks. Digital imaging in pathology has undergone an exponential period of growth. Deep-learning (DL) platforms for hematoxylin and eosin (H&E) image analysis, with preliminary artificial intelligence (AI)-based grading capabilities of specimens, can evaluate image characteristics which may not be visually apparent to a pathologist and offer new possibilities for better modelling of disease appearance and possibly improve the prediction of disease stage and patient outcome. Although digital pathology and AI are still emerging areas, they are the critical components for advancing personalised medicine. Integration of transcriptomic analysis, clinical information and AI-based image analysis is yet an uncultivated field by which healthcare professionals can make improved treatment decisions in cancer. This short review describes the potential application of integrative AI in offering better detection, quantification, classification, prognosis and prediction of breast and prostate cancer and also highlights the utilisation of machine learning systems in biomarker evaluation.

Feature fusion of Raman chemical imaging and digital histopathology using machine learning for prostate cancer detection.

The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient's quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained Digital Histopathology (DP) and unstained RCI. The approach was developed and tested on a set of 178 clinical samples from 32 patients, containing a range of non-cancerous, Gleason grade 3 (G3) and grade 4 (G4) tissue microarray samples. For each histological sample, there is a pathologist labelled DP-RCI image pair. The hypothesis tested was whether multimodal image models can outperform single modality baseline models in terms of diagnostic accuracy. Binary non-cancer/cancer models and the more challenging G3/G4 differentiation were investigated. Regarding G3/G4 classification, the multimodal approach achieved a sensitivity of 73.8% and specificity of 88.1% while the baseline DP model showed a sensitivity and specificity of 54.1% and 84.7% respectively. The multimodal approach demonstrated a statistically significant 12.7% AUC advantage over the baseline with a value of 85.8% compared to 73.1%, also outperforming models based solely on RCI and mean and median Raman spectra. Feature fusion of DP and RCI does not improve the more trivial task of tumour identification but does deliver an observed advantage in G3/G4 discrimination. Building on these promising findings, future work could include the acquisition of larger datasets for enhanced model generalization.

High Cysteinyl Leukotriene Receptor 1 Expression Correlates with Poor Survival of Uveal Melanoma Patients and Cognate Antagonist Drugs Modulate the Growth, Cancer Secretome, and Metabolism of Uveal Melanoma Cells.

Metastatic uveal melanoma (UM) is a rare, but often lethal, form of ocular cancer arising from melanocytes within the uveal tract. UM has a high propensity to spread hematogenously to the liver, with up to 50% of patients developing liver metastases. Unfortunately, once liver metastasis occurs, patient prognosis is extremely poor with as few as 8% of patients surviving beyond two years. There are no standard-of-care therapies available for the treatment of metastatic UM, hence it is a clinical area of urgent unmet need. Here, the clinical relevance and therapeutic potential of cysteinyl leukotriene receptors (CysLT1 and CysLT2) in UM was evaluated. High expression of CYSLTR1 or CYSLTR2 transcripts is significantly associated with poor disease-free survival and poor overall survival in UM patients. Digital pathology analysis identified that high expression of CysLT1 in primary UM is associated with reduced disease-specific survival (p = 0.012; HR 2.76; 95% CI 1.21-6.3) and overall survival (p = 0.011; HR 1.46; 95% CI 0.67-3.17). High CysLT1 expression shows a statistically significant (p = 0.041) correlation with ciliary body involvement, a poor prognostic indicator in UM. Small molecule drugs targeting CysLT1 were vastly superior at exerting anti-cancer phenotypes in UM cell lines and zebrafish xenografts than drugs targeting CysLT2. Quininib, a selective CysLT1 antagonist, significantly inhibits survival (p < 0.0001), long-term proliferation (p < 0.0001), and oxidative phosphorylation (p < 0.001), but not glycolysis, in primary and metastatic UM cell lines. Quininib exerts opposing effects on the secretion of inflammatory markers in primary versus metastatic UM cell lines. Quininib significantly downregulated IL-2 and IL-6 in Mel285 cells (p < 0.05) but significantly upregulated IL-10, IL-1ß, IL-2 (p < 0.0001), IL-13, IL-8 (p < 0.001), IL-12p70 and IL-6 (p < 0.05) in OMM2.5 cells. Finally, quininib significantly inhibits tumour growth in orthotopic zebrafish xenograft models of UM. These preclinical data suggest that antagonism of CysLT1, but not CysLT2, may be of therapeutic interest in the treatment of UM.

Advances in tissue-based imaging: impact on oncology research and clinical practice.

INTRODUCTION: Tissue-based imaging has emerged as a critical tool in translational cancer research and is rapidly gaining traction within a clinical context. Significant progress has been made in the digital pathology arena, particularly in respect of brightfield and fluorescent imaging. Critically, the cellular context of molecular alterations occurring at DNA, RNA, or protein level within tumor tissue is now being more fully appreciated. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumor microenvironment, including the potential interplay between various cell types. AREAS COVERED: This review summarizes the recent developments within the field of tissue-based imaging, centering on the application of these approaches in oncology research and clinical practice. EXPERT OPINION: Significant advances have been made in digital pathology during the last 10 years. These include the use of quantitative image analysis algorithms, predictive artificial intelligence (AI) on large datasets of H&E images, and quantification of fluorescence multiplexed tissue imaging data. We believe that new methodologies that can integrate AI-derived histologic data with omic data, together with other forms of imaging data (such as radiologic image data), will enhance our ability to deliver better diagnostics and treatment decisions to the cancer patient.

Triple Combination of Ascorbate, Menadione and the Inhibition of Peroxiredoxin-1 Produces Synergistic Cytotoxic Effects in Triple-Negative Breast Cancer Cells.

Triple-negative breast cancer (TNBC) is an aggressive form of mammary malignancy currently without satisfactory systemic treatment options. Agents generating reactive oxygen species (ROS), such as ascorbate (Asc) and menadione (Men), especially applied in combination, have been proposed as an alternative anticancer modality. However, their effectiveness can be hampered by the cytoprotective effects of elevated antioxidant enzymes (e.g., peroxiredoxins, PRDX) in cancer. In this study, PRDX1 mRNA and protein expression were assessed in TNBC tissues by analysis of the online RNA-seq datasets and immunohistochemical staining of tissue microarray, respectively. We demonstrated that PRDX1 mRNA expression was markedly elevated in primary TNBC tumors as compared to non-malignant controls, with PRDX1 protein staining intensity correlating with favorable survival parameters. Subsequently, PRDX1 functionality in TNBC cell lines or non-malignant mammary cells was targeted by genetic silencing or chemically by auranofin (AUR). The PRDX1-knockdown or AUR treatment resulted in inhibition of the growth of TNBC cells in vitro. These cytotoxic effects were further synergistically potentiated by the incubation with a combination of the prooxidant agents, Asc and Men. In conclusion, we report that the PRDX1-related antioxidant system is essential for maintaining redox homeostasis in TNBC cells and can be an attractive therapeutic target in combination with ROS-generating agents.

Low expression of pro-apoptotic proteins Bax, Bak and Smac indicates prolonged progression-free survival in chemotherapy-treated metastatic melanoma.

Despite the introduction of novel targeted therapies, chemotherapy still remains the primary treatment for metastatic melanoma in poorly funded healthcare environments or in case of disease relapse, with no reliable molecular markers for progression-free survival (PFS) available. As chemotherapy primarily eliminates cancer cells by apoptosis, we here evaluated if the expression of key apoptosis regulators (Bax, Bak, Bcl-2, Bcl-xL, Smac, Procaspase-9, Apaf-1, Procaspase-3 and XIAP) allows prognosticating PFS in stage III/IV melanoma patients. Following antibody validation, marker expression was determined by automated and manual scoring of immunohistochemically stained tissue microarrays (TMAs) constructed from treatment-naive metastatic melanoma biopsies. Interestingly and counter-intuitively, low expression of the pro-apoptotic proteins Bax, Bak and Smac indicated better prognosis (log-rank p < 0.0001, p = 0.0301 and p = 0.0227 for automated and p = 0.0422, p = 0.0410 and p = 0.0073 for manual scoring). These findings were independently validated in the cancer genome atlas (TCGA) metastatic melanoma cohort (TCGA-SKCM) at transcript level (log-rank p = 0.0004, p = 0.0104 and p = 0.0377). Taking expression heterogeneity between the markers in individual tumour samples into account allowed defining combinatorial Bax, Bak, Smac signatures that were associated with significantly increased PFS (p = 0.0002 and p = 0.0028 at protein and transcript level, respectively). Furthermore, combined low expression of Bax, Bak and Smac allowed predicting prolonged PFS (> 12 months) on a case-by-case basis (area under the receiver operating characteristic curve (ROC AUC) = 0.79). Taken together, our results therefore suggest that Bax, Bak and Smac jointly define a signature with potential clinical utility in chemotherapy-treated metastatic melanoma.

A Machine Learning Platform to Optimize the Translation of Personalized Network Models to the Clinic.

PURPOSE: Dynamic network models predict clinical prognosis and inform therapeutic intervention by elucidating disease-driven aberrations at the systems level. However, the personalization of model predictions requires the profiling of multiple model inputs, which hampers clinical translation. PATIENTS AND METHODS: We applied APOPTO-CELL, a prognostic model of apoptosis signaling, to showcase the establishment of computational platforms that require a reduced set of inputs. We designed two distinct and complementary pipelines: a probabilistic approach to exploit a consistent subpanel of inputs across the whole cohort (Ensemble) and a machine learning approach to identify a reduced protein set tailored for individual patients (Tree). Development was performed on a virtual cohort of 3,200,000 patients, with inputs estimated from clinically relevant protein profiles. Validation was carried out in an in-house stage III colorectal cancer cohort, with inputs profiled in surgical resections by reverse phase protein array (n = 120) and/or immunohistochemistry (n = 117). RESULTS: Ensemble and Tree reproduced APOPTO-CELL predictions in the virtual patient cohort with 92% and 99% accuracy while decreasing the number of inputs to a consistent subset of three proteins (40% reduction) or a personalized subset of 2.7 proteins on average (46% reduction), respectively. Ensemble and Tree retained prognostic utility in the in-house colorectal cancer cohort. The association between the Ensemble accuracy and prognostic value (Spearman ρ = 0.43; P = .02) provided a rationale to optimize the input composition for specific clinical settings. Comparison between profiling by reverse phase protein array (gold standard) and immunohistochemistry (clinical routine) revealed that the latter is a suitable technology to quantify model inputs. CONCLUSION: This study provides a generalizable framework to optimize the development of network-based prognostic assays and, ultimately, to facilitate their integration in the routine clinical workflow.

Apelin: A putative novel predictive biomarker for bevacizumab response in colorectal cancer.

Bevacizumab (bvz) is currently employed as an anti-angiogenic therapy across several cancer indications. Bvz response heterogeneity has been well documented, with only 10-15% of colorectal cancer (CRC) patients benefitting in general. For other patients, clinical efficacy is limited and side effects are significant. This reinforces the need for a robust predictive biomarker of response. To identify such a biomarker, we performed a DNA microarray-based transcriptional profiling screen with primary endothelial cells (ECs) isolated from normal and tumour colon tissues. Thirteen separate populations of tumour-associated ECs and 10 of normal ECs were isolated using fluorescence-activated cell sorting. We hypothesised that VEGF-induced genes were overexpressed in tumour ECs; these genes could relate to bvz response and serve as potential predictive biomarkers. Transcriptional profiling revealed a total of 2,610 differentially expressed genes when tumour and normal ECs were compared. To explore their relation to bvz response, the mRNA expression levels of top-ranked genes were examined using quantitative PCR in 30 independent tumour tissues from CRC patients that received bvz in the adjuvant setting. These analyses revealed that the expression of MMP12 and APLN mRNA was significantly higher in bvz non-responders compared to responders. At the protein level, high APLN expression was correlated with poor progression-free survival in bvz-treated patients. Thus, high APLN expression may represent a novel predictive biomarker for bvz unresponsiveness.

Master Transcriptional Regulators in Cancer: Discovery via Reverse Engineering Approaches and Subsequent Validation.

Reverse engineering of transcriptional networks using gene expression data enables identification of genes that underpin the development and progression of different cancers. Methods to this end have been available for over a decade and, with a critical mass of transcriptomic data in the oncology arena having been reached, they are ever more applicable. Extensive and complex networks can be distilled into a small set of key master transcriptional regulators (MTR), genes that are very highly connected and have been shown to be involved in processes of known importance in disease. Interpreting and validating the results of standardized bioinformatic methods is of crucial importance in determining the inherent value of MTRs. In this review, we briefly describe how MTRs are identified and focus on providing an overview of how MTRs can and have been validated for use in clinical decision making in malignant diseases, along with serving as tractable therapeutic targets. Cancer Res; 77(9); 2186-90. ©2017 AACR.

Induction of immunomodulatory miR-146a and miR-155 in small intestinal epithelium of Vibrio cholerae infected patients at acute stage of cholera.

The potential immunomodulatory role of microRNAs in small intestine of patients with acute watery diarrhea caused by Vibrio cholerae O1 or enterotoxigenic Escherichia coli (ETEC) infection was investigated. Duodenal biopsies were obtained from study-participants at the acute (day 2) and convalescent (day 21) stages of disease, and from healthy individuals. Levels of miR-146a, miR-155 and miR-375 and target gene (IRAK1, TRAF6, CARD10) and 11 cytokine mRNAs were determined by qRT-PCR. The cellular source of microRNAs in biopsies was analyzed by in situ hybridization. The ability of V. cholerae bacteria and their secreted products to cause changes in microRNA- and mRNA levels in polarized tight monolayers of intestinal epithelial cells was investigated. miR-146a and miR-155 were expressed at significantly elevated levels at acute stage of V. cholerae infection and declined to normal at convalescent stage (P<0.009 versus controls; P = 0.03 versus convalescent stage, pairwise). Both microRNAs were mainly expressed in the epithelium. Only marginal down-regulation of target genes IRAK1 and CARD10 was seen and a weak cytokine-profile was identified in the acute infected mucosa. No elevation of microRNA levels was seen in ETEC infection. Challenge of tight monolayers with the wild type V. cholerae O1 strain C6706 and clinical isolates from two study-participants, caused significant increase in miR-155 and miR-146a by the strain C6706 (P<0.01). One clinical isolate caused reduction in IRAK1 levels (P<0.05) and none of the strains induced inflammatory cytokines. In contrast, secreted factors from these strains caused markedly increased levels of IL-8, IL-1ß, and CARD10 (P<0.001), without inducing microRNA expression. Thus, miR-146a and miR-155 are expressed in the duodenal epithelium at the acute stage of cholera. The inducer is probably the V. cholerae bacterium. By inducing microRNAs the bacterium can limit the innate immune response of the host, including inflammation evoked by its own secreted factors, thereby decreasing the risk of being eliminated.

Garbage in, garbage out: a critical evaluation of strategies used for validation of immunohistochemical biomarkers.

The use of immunohistochemistry (IHC) in clinical cohorts is of paramount importance in determining the utility of a biomarker in clinical practice. A major bottleneck in translating a biomarker from bench-to-bedside is the lack of well characterized, specific antibodies suitable for IHC. Despite the widespread use of IHC as a biomarker validation tool, no universally accepted standardization guidelines have been developed to determine the applicability of particular antibodies for IHC prior to its use. In this review, we discuss the technical challenges faced by the use of immunohistochemical biomarkers and rigorously explore classical and emerging antibody validation technologies. Based on our review of these technologies, we provide strict criteria for the pragmatic validation of antibodies for use in immunohistochemical assays.

Syntaxin-4 is essential for IgE secretion by plasma cells.

The humoral immune system provides a crucial first defense against the invasion of microbial pathogens via the secretion of antigen specific immunoglobulins (Ig). The secretion of Ig is carried out by terminally differentiated B-lymphocytes called plasma cells. Despite the key role of plasma cells in the immune response, the mechanisms by which they constitutively traffic large volumes of Ig out of the cell is poorly understood. The involvement of Soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) proteins in the regulation of protein trafficking from cells has been well documented. Syntaxin-4, a member of the Qa SNARE syntaxin family has been implicated in fusion events at the plasma membrane in a number of cells in the immune system. In this work we show that knock-down of syntaxin-4 in the multiple myeloma U266 human plasma cell line results in a loss of IgE secretion and accumulation of IgE within the cells. Furthermore, we show that IgE co-localises with syntaxin-4 in U266 plasma cells suggesting direct involvement in secretion at the plasma membrane. This study demonstrates that syntaxin-4 plays a critical role in the secretion of IgE from plasma cells and sheds some light on the mechanisms by which these cells constitutively traffic vesicles to the surface for secretion. An understanding of this machinery may be beneficial in identifying potential therapeutic targets in multiple myeloma and autoimmune disease where over-production of Ig leads to severe pathology in patients.

Antimicrobial peptides in the duodenum at the acute and convalescent stages in patients with diarrhea due to Vibrio cholerae O1 or enterotoxigenic Escherichia coli infection.

Patients with acute watery diarrhea caused by Vibrio cholerae O1 or enterotoxigenic Escherichia coli (ETEC) were analyzed for innate immune factors produced by the epithelium during the disease process. Duodenal biopsies were obtained from study participants at the acute (day 2) and convalescent (day 21) stages of disease. Levels of α-defensin (HD-5 and -6), ß-defensin (hBD-1-4), and cathelicidin (LL-37) mRNAs were determined by real-time qRT-PCR. hBD-2, HD-5, LL-37 peptides were analyzed in duodenal epithelium by immunomorphometry. Concentration of hBD-2 in stool was determined by ELISA. Specimens from healthy controls were also analyzed. hBD-2 mRNA levels were significantly increased at acute stage of diarrhea; hBD-2 peptide was detected in fecal specimens but barely in duodenal epithelium at acute stage. Immunomorphometry analysis showed that Paneth cells contain significantly higher amounts of HD-5 pre/propeptide at convalescence (P<0.01) and in healthy controls (P<0.001) compared to acute stage, LL-37 peptide levels also decreased at acute stage while mRNA levels remained unchanged. mRNA expression levels of the other antimicrobial peptides remained unchanged with higher levels of α-defensins than ß-defensins. V. cholerae induced an innate immune response at the acute stage of disease characterized by increased expression of hBD-2, and continued expression of hBD-1, HD-5-6, and LL-37.

A conjugated linoleic acid-enriched beef diet attenuates lipopolysaccharide-induced inflammation in mice in part through PPARgamma-mediated suppression of toll-like receptor 4.

Conjugated linoleic acid (CLA) is a PUFA found in beef and dairy products that has immunoregulatory properties. The level of CLA in beef can be enhanced by feeding cattle fresh grass rather than concentrates. This study determined the effect of feeding a high-CLA beef diet on inflammation in an in vivo model of septic shock. Mice were fed a high-CLA beef (4.3% total fatty acid composition) or low-CLA beef diet (0.84% total fatty acid composition) for 6 wk. Lipopolysaccharide (LPS; 3 microg) or sterile PBS was injected i.v. and serum was harvested 6 h after injection. Serum interleukin (IL)-1beta, IL-12p70, IL-12p40, and interferon-gamma concentrations were significantly reduced in response to the LPS challenge in the high-CLA beef diet group. Bone marrow-derived dendritic cells (BMDC) from the high-CLA beef diet group had significantly less IL-12 and more IL-10 in response to ex vivo LPS stimulation. Furthermore, toll-like receptor 4 (TLR4) and CD14 protein and mRNA expression on BMDC was significantly attenuated in the high-CLA compared with the low-CLA beef diet group. Complimentary in vitro experiments to determine the specificity of the effect showed that synthetic cis9, trans11-CLA suppressed surface expression of CD14 and TLR4 on BMDC. Treatment with the PPARgamma inhibitor GW9662 partially reversed TLR4 expression in immature BMDC. The results of this study demonstrate that feeding a diet enriched in high-beef CLA exerts profound antiinflammatory effects in vivo within the context of LPS-induced sepsis. In addition, downregulation of BMDC TLR4 is mediated through induction of PPARgamma.