Multi-Biomarker Profiling for Precision Diagnosis of Lung Cancer.

Multi-biomarker analysis can enhance the accuracy of the single-biomarker analysis by reducing the errors caused by genetic and environmental differences. For this reason, multi-biomarker analysis shows higher accuracy in early and precision diagnosis. However, conventional analysis methods have limitations for multi-biomarker analysis because of their long pre-processing times, inconsistent results, and large sample requirements. To solve these, a fast and accurate precision diagnostic method is introduced for lung cancer by multi-biomarker profiling using a single drop of blood. For this, surface-enhanced Raman spectroscopic immunoassay (SERSIA) is employed for the accurate, quick, and reliable quantification of biomarkers. Then, it is checked the statistical relation of the multi-biomarkers to differentiate between healthy controls and lung cancer patients. This approach has proven effective; with 20 µL of blood serum, lung cancer is diagnosed with 92% accuracy. It also accurately identifies the type and stage of cancer with 87% and 85%, respectively. These results show the importance of multi-biomarker analysis in overcoming the challenges posed by single-biomarker diagnostics. Furthermore, it markedly improves multi-biomarker-based analysis methods, illustrating its important impact on clinical diagnostics.

Effect of 1 H-NMR serum lipoproteins on immunotherapy response in advanced triple-negative breast cancer patients.

We previously reported the results of a phase II trial of anti-PD-1 antibody plus anti-vascular endothelial growth factor receptor 2 inhibitors and eribulin in heavily pretreated advanced triple-negative breast cancer with a favorable objective response rate (ORR) of 37.0% (NCT04303741). Here we report updated survival outcomes and serum metabolite changes of the study. Proton nuclear magnetic resonance spectroscopy was used to detect metabolite dynamics and explore biomarkers for response. We found that treatment-sensitive patients had higher very low-density lipoprotein-related metabolite expression at baseline. A lipid proteomics model consisting of six metabolites predicted ORR and progression-free survival at 6 months with area under the receiver operating characteristic curves of 0.88 and 0.87, respectively. Serum asparagine and sarcosine concentrations were significantly higher after treatment in treatment-resistant patients. In conclusion, we constructed a model consisting of six metabolites to identify patients who benefit more from the triplet treatment, and asparagine and sarcosine may be associated with treatment resistance.

Integrating Nutrient Biomarkers, Cognitive Function, and Structural MRI Data to Build Multivariate Phenotypes of Healthy Aging.

BACKGROUND: Research in the emerging field of nutritional cognitive neuroscience demonstrates that many aspects of nutrition-from entire diets to specific nutrients-affect cognitive performance and brain health. OBJECTIVES: Although previous research has primarily examined the bivariate relationship between nutrition and cognition or nutrition and brain health, this study sought to investigate the joint relationship between these essential and interactive elements of human health. METHODS: We applied a state-of-the-art data fusion method, coupled matrix tensor factorization, to characterize the joint association between measures of nutrition (52 nutrient biomarkers), cognition (Wechsler Abbreviated Test of Intelligence and Wechsler Memory Scale), and brain health (high-resolution MRI measures of structural brain volume) within a cross-sectional sample of 111 healthy older adults, with an average age of 69.1 y, 62% being female, and an average body mass index of 26.0 kg/m2. RESULTS: Data fusion uncovered latent factors that capture the joint association between specific nutrient profiles, cognitive measures, and cortical volumes, demonstrating the respects in which these health domains are coupled. A hierarchical cluster analysis further revealed systematic differences between a subset of variables contributing to the underlying latent factors, providing evidence for multivariate phenotypes that represent high and low levels of performance across multiple health domains. The primary features that distinguish between each phenotype were as follows: 1) nutrient biomarkers for monounsaturated and polyunsaturated fatty acids; 2) cognitive measures of immediate, auditory, and delayed memory; and 3) brain volumes within frontal, temporal, and parietal cortexes. CONCLUSIONS: By incorporating innovations in nutritional epidemiology (nutrient biomarker analysis), cognitive neuroscience (high-resolution structural brain imaging), and statistics (data fusion), this study provides an interdisciplinary synthesis of methods that elucidate how nutrition, cognition, and brain health are integrated through lifestyle choices that affect healthy aging.

Analysis of Circulating DNA to Assess Prognoses for Metastatic Colorectal Cancer Patients Treated with Regorafenib Dose-Escalation Therapy: A Retrospective, Exploratory Analysis of the RECC Trial.

INTRODUCTION: Regorafenib is a multi-kinase inhibitor approved for patients with metastatic colorectal cancer (mCRC) who were previously treated with standard therapies. A few reports showed the impact of KRAS mutation on therapeutic efficacy of regorafenib. Only one study reported poor prognoses for patients treated with regorafenib who had large amounts of circulating cell-free DNA (cfDNA). In the present study, we evaluated the impact of KRAS mutations in tissue or plasma and amounts of cfDNA on prognoses of mCRC patients treated with regorafenib. METHOD: This is a biomarker investigation of the RECC study, which evaluated efficacy of regorafenib dose-escalation therapy. Plasma samples were obtained just before initiation of treatment with regorafenib. KRAS mutations were evaluated using tissue and plasma samples. cfDNA was extracted from plasma samples and quantified. RESULTS: Forty-five patients were enrolled in this biomarker study. Median progression-free survival (PFS) and overall survival (OS) of patients without KRAS mutations in tissues were 1.9 months (95% confidence interval [CI] 1.7-2.0) and 8.9 months (95% CI: 6.5-11.2), and those of patients with KRAS mutations were 1.4 months (95% CI: 1.3-1.5) and 6.8 months (95% CI: 5.0-8.5). Median PFS and OS of patients with plasma KRAS mutations were 1.9 months (95% CI: 1.8-1.9) and 7.0 months (95% CI: 5.3-8.7), respectively. Median PFS and OS of patients without plasma KRAS mutations were 1.7 months (95% CI: 1.1-2.3) and 8.9 months (95% CI: 6.7-11.2), respectively. Prior to administration of regorafenib, KRAS mutations were detected in 6 of 16 (37.5%) patients who had no tissue KRAS mutations. Median OS of patients with high cfDNA concentration (>median) was significantly poorer than that of patients with low cfDNA. CONCLUSION: KRAS mutations in the tissue or plasma have no impact on efficacy of regorafenib. KRAS emerging mutations were observed in quite a few patients. Large amounts of cfDNA may indicate poorer prognoses for patients receiving late-line regorafenib chemotherapy.

Cluster analysis of extracellular matrix biomarkers predicts the development of impaired systolic function within 1 year of acute myocardial infarction.

The clinical utility of combining extracellular matrix (ECM) biomarkers to predict the development of impaired systolic function following acute myocardial infarction (AMI) remains largely undetermined. A combination of ELISA and multiplexing assays were performed to measure matrix metalloproteinase (MMP)-2, MMP-3, MMP-8, MMP-9, periostin, N-terminal type I procollagen (PINP) and tissue inhibitor of matrix metalloproteinase-1 (TIMP-1) in plasma samples from 120 AMI patients. All patients had an echocardiogram within 1 year of AMI, and were divided into impaired (n = 37, LVEF < 50%) and preserved (n = 83, LVEF ≥ 50%) systolic function groups. Exploratory factor analysis was performed on log-transformed biomarkers using principle axis analysis with Oblimin rotation. Cluster analysis was performed on log-transformed and normalised biomarkers using Ward's method of minimum variance and the squared Euclidean distance metric. Upon univariate analysis, current smoking, prescription of ACE inhibitors at discharge, peak hsTnT > 610 ng/L (median), MMP-8 levels, Factor 1 scores and Cluster One assignment were predictive of impaired systolic function. Upon multivariate analysis, Cluster One assignment (odds ratio [95% CI], 2.74 [1.04-7.23], p = 0.04) remained an independent predictor of systolic dysfunction in combination with clinical variables. These observations support the usefulness of combining ECM biomarkers using cluster analysis for predicting the development of impaired systolic function in AMI patients.

Biomarkers of response to camrelizumab combined with apatinib: an analysis from a phase II trial in advanced triple-negative breast cancer patients.

PURPOSE: We recently reported results of a phase II trial that camrelizumab plus apatinib induced an objective response rate (ORR) at 43.3% in advanced triple-negative breast cancer (TNBC). This study presents analysis of potential biomarkers. METHODS: TILs, CD8+ T cells and PD-1/PD-L1 expression were evaluated in tumor samples by immunohistochemistry. 59 Cytokines/chemokines, growth factors, or checkpoint-related proteins, blood immune cell subpopulations were analyzed in blood samples by multiplexed bead immunoassays or flow cytometry. Correlation between biomarkers and clinical outcomes including ORR, progression-free survival (PFS), and overall survival (OS) was analyzed. RESULTS: 28 Patients had biopsies and blood collected. Baseline TILs were significantly associated with longer PFS (P = 0.035). An increase of tumor-infiltrating CD8+ T cells > 15% during therapy was associated with higher ORR (P = 0.040). Patients with lower baseline plasma levels of HGF or IL-8 were more likely to respond to treatment (P = 0.005 or 0.001, respectively), and showed a longer PFS and OS. Patients with a decrease of IL-8, or an increase of TIM-3 or CD152 during treatment responded more to treatment (P = 0.008, 0.040, or 0.014, respectively). Responders had a higher baseline CD4+ T cells and B cell proportions in blood than non-responders (P = 0.002 and 0.030, respectively). CONCLUSION: Higher baseline TILs or a greater increase of tumor-infiltrating CD8+ T cells during therapy, lower baseline plasma HGF/IL-8, a decrease of plasma IL-8, an increase of plasma TIM-3/CD152 during therapy, higher baseline CD4+ T cells or B cells proportion in blood are potential biomarkers for combinational anti-angiogenesis and immunotherapy in advanced TNBC patients.

Study on the daily Ad Libitum smoking habits of active Korean smokers and their effect on urinary smoking exposure and impact biomarkers.

INTRODUCTION: Understanding interactions of smoking topography with biomarkers of exposure to tobacco is essential for accurate smoking risk assessments. METHODS: In this study, the smoking topography and the levels of tobacco smoke exposure urinary biomarkers of a sample of active Korean smokers were quantified and measured. The results were used to investigate the effect of daily activities and smoking time on the smoking topography. Moreover, correlations between the smoking topography parameters and biomarkers were assessed. RESULTS: No significant effect of either the daily activities or time on the smoking topography of the subjects were observed. Synchronic correlations of the cigarette consumption per day (CPD) and the average flow per puff with both urinary cotinine and trans-3'-hydroxycotinine were significant. For the urinary nicotine metabolites, the peak levels appeared when the CPD was over 19 cigarettes per day and the average puff velocity was between 35 and 45 ml/s. Nevertheless, when the average flow was over 60 ml/s, the levels of cotinine and trans-3'-hydroxycotinine significantly dropped. CONCLUSIONS: The findings of this study may be beneficial for further smoking risk assessments with contributions of both the smoking topography and biomarkers to provide current smokers with applicable cession programs.Clinical significanceSmoking habits and levels of urinary biomarkers of Korean smokers are investigated.People with a higher dependency on nicotine smoke cigarettes with slower puffs.Effects of daily activities or time on smoking topography were not significant.Correlations between smoking topography and urinary biomarkers were significant.Peak biomarker levels were observed under certain smoking topography conditions.

Korean male active smokers: quantifying their smoking habits and the transformation factor among biomarkers in urine and blood.

OBJECTIVES: The aim of the study was to investigate the correlations within the levels of biomarkers in different biological matrices, along with smoking topography variables, among active male smokers in Korea. Accordingly, we defined a transformation factor to convert level of tobacco smoke exposure and impact biomarkers from different biometrics. METHODS: We examined smoking topography of recruited volunteers using a self-reporting survey. The level of tobacco smoke exposure and impact biomarkers in subjects' urine and blood were analysed. Results were used to assess the correlations between the topography survey items with biomarkers in biological matrices. The relationship between the biomarkers in urine and blood was analysed. Accordingly, we defined a transformation factor as the ratio of different biomarkers in urine and blood matrices. RESULTS: Significant correlations among smoking topography variables and biomarkers were found. Besides, a strong significant association was found among urine and blood cotinine (ρ = 0.817) and NMR (ρ = 0.905). Urine vs blood cotinine and NMR transformation factors were calculated to be 6.17 L-Blood/g-Creatinine and 10.2, respectively. CONCLUSIONS: The validated transformation factor connects epidemiological cohort studies with tobacco smoking exposure risk assessment. Hence, this study might be beneficial for further habit-based smoking risk assessments to obtain successful regional cession policies.

Electrochemical Paper-Based Biosensor Devices for Rapid Detection of Biomarkers.

In healthcare, new diagnostic tools that help in the diagnosis, prognosis, and monitoring of diseases rapidly and accurately are in high demand. For in-situ measurement of disease or infection biomarkers, point-of-care devices provide a dramatic speed advantage over conventional techniques, thus aiding clinicians in decision-making. During the last decade, paper-based analytical devices, combining paper substrates and electrochemical detection components, have emerged as important point-of-need diagnostic tools. This review highlights significant works on this topic over the last five years, from 2015 to 2019. The most relevant articles published in 2018 and 2019 are examined in detail, focusing on device fabrication techniques and materials applied to the production of paper fluidic and electrochemical cell architectures as well as on the final device assembly. Two main approaches were identified, that are, on one hand, those ones where the fabrication of the electrochemical cell is done on the paper substrate, where the fluidic structures are also defined, and, on the other hand, the fabrication of those ones where the electrochemical cell and liquid-driving paper component are defined on different substrates and then heterogeneously assembled. The main limitations of the current technologies are outlined and an outlook on the current technology status and future prospects is given.

Systemic and Local Metabolic Alterations in Sleep-Deprivation-Induced Stress: A Multiplatform Mass-Spectrometry-Based Lipidomics and Metabolomics Approach.

Sleep deprivation (SD) is known to be associated with metabolic disorders and chronic diseases. Complex metabolic alterations induced by SD at omics scale and the associated biomarker candidates have been proposed. However, in vivo systemic and local metabolic shift patterns of the metabolome and lipidome in acute and chronic partial SD models remain to be elucidated. In the present study, the serum, hypothalamus, and hippocampus CA1 of sleep-deprived rats (SD rats) from acute and chronic sleep restriction models were analyzed using three different omics platforms for the discovery and mechanistic assessment of systemic and local SD-induced dysregulated metabolites. We found a similar pattern of systemic metabolome alterations between two models, for which the area under the curve (AUC) of receiver operating characteristic curves was AUC = 0.847 and 0.930 with the pseudotargeted and untargeted metabolomics approach, respectively. However, SD-induced systemic lipidome alterations were significantly different and appeared to be model-dependent (AUC = 0.374). Comprehensive pathway analysis of the altered lipidome and metabolome in the hypothalamus indicated the abnormal behavior of eight metabolic and lipid metabolic pathways. The metabolic alterations of the hippocampus CA1 was subtle in two SD models. Collectively, these results extend our understanding of the quality of sleep and suggest metabolic targets in developing diagnostic biomarkers for better SD control.

Pollen-borne microbes shape bee fitness.

Teeming within pollen provisions are diverse communities of symbiotic microbes, which provide a variety of benefits to bees. Microbes themselves may represent a major dietary resource for developing bee larvae. Despite their apparent importance in sustaining bee health, evidence linking pollen-borne microbes to larval health is currently lacking. We examined the effects of microbe-deficient diets on the fitness of larval mason bees. In a series of diet manipulations, microbe-rich maternally collected pollen provisions were replaced with increasing fractions of sterilized, microbe-deficient pollen provisions before being fed to developing larvae. Convergent findings from amino acid and fatty acid trophic biomarker analyses revealed that larvae derived a substantial amount of nutrition from microbial prey and occupied a significantly higher trophic position than that of strict herbivores. Larvae feeding on increasingly sterile diets experienced significant adverse effects on growth rates, biomass and survivorship. When completely deprived of pollen-borne microbes, larvae consistently exhibited marked decline in fitness. We conclude that microbes associated with aged pollen provisions are central to bee health, not only as nutritional mutualists, but also as a major dietary component. In an era of global bee decline, the conservation of such bee-microbe interactions may represent an important facet of pollinator protection strategies.

Tumour parcellation and quantification (TuPaQ): a tool for refining biomarker analysis through rapid and automated segmentation of tumour epithelium.

BACKGROUND AND AIMS: Immunohistochemistry (IHC) is an essential component of biomarker research in cancer. Automated biomarker quantification is hampered by the failure of computational algorithms to discriminate 'negative' tumour cells from 'negative' stromal cells. We sought to develop an algorithm for segmentation of tumour epithelium in colorectal cancer (CRC), irrespective of the biomarker expression in the cells. METHODS AND RESULTS: We developed tumour parcellation and quantification (TuPaQ) to segment tumour epithelium and parcellate sections into 'epithelium' and 'non-epithelium'. TuPaQ comprises image pre-processing, extraction of regions of interest (ROIs) and quantification of tumour epithelium (total area occupied by epithelium and number of nuclei in the occupied area). A total of 286 TMA cores from CRC were manually annotated and analysed using the commercial halo software to provide ground truth. The performance of TuPaQ was evaluated against the ground truth using a variety of metrics. The image size of each core was 7000 × 7000 pixels and each core was analysed in a matter of seconds. Pixel × pixel analysis showed a sensitivity of 84% and specificity of 95% in detecting epithelium. The mean tumour area obtained by TuPaQ was very close to the area quantified after manual annotation (r = 0.956, P < 0.001). Moreover, quantification of tumour nuclei by TuPaQ correlated very strongly with that of halo (r = 0.891, P < 0.001). CONCLUSION: TuPaQ is a very rapid and accurate method of separating the epithelial and stromal compartments of colorectal tumours. This will allow more accurate and objective analysis of immunohistochemistry.

A prospective, multicenter pilot study to investigate the feasibility and safety of a 1-year controlled exercise training after adjuvant chemotherapy in colorectal cancer patients.

INTRODUCTION: Despite advances in adjuvant chemotherapy, 20-30% of patients in stages II-III colorectal cancer will eventually relapse. Observational studies showed a reduction in relapse rate, colon cancer-specific mortality, and overall mortality by physical activity. Results from prospective randomized interventional studies to confirm these observational data are lacking. The aims of this prospective single-arm multicenter pilot study are to evaluate feasibility and safety of exercise training after adjuvant chemotherapy in colorectal cancer patients. PATIENTS AND METHODS: The training was performed three times per week for 1 year and was increased gradually in three phases until reaching 18 metabolic equivalent task hours per week. RESULTS: Overall, 30 patients were included. The planned training intensity could be achieved in all three phases. Patients experienced a performance increase of median 35.5 watt, a weight-loss of a median of 3.0 kg, and a reduction in body fat content of median 1.0% during this exercise training. The analysis showed early study termination due to non-compliance in 10/30 patients (33.3%), disease progression in 4 patients (13.3%), and serious adverse events in 2 patients (6.7%). About half of patients (46.7%) completed the pilot study as planned. Biomarker analysis from 20 patients showed a non-significant reduction in insulin-like growth factor 1 (IGF-1), insulin-like growth factor 2 (IGF-2) and insulin-like growth factor binding protein 3 (IGF-BP3) levels, significant increases in adiponectin and leptin levels, and a non-significant increase in C-peptide levels. CONCLUSION: Exercise training is feasible in patients with colorectal cancer after completion of adjuvant chemotherapy. The main problem encountered during the study was compliance. To improve compliance of exercise training, several measures were adapted for the upcoming prospective randomized ABCSG C08 Exercise II study.

Protocol of the PANCALYZE trial: a multicenter, prospective study investigating the tumor biomarkers CXCR4, SMAD4, SOX9 and IFIT3 in patients with resected pancreatic adenocarcinoma to predict the pattern of recurrence of the disease.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies today with an urgent need for novel therapeutic strategies. Biomarker analysis helps to better understand tumor biology and might emerge as a tool to develop personalized therapies. The aim of the study is to investigate four promising biomarkers to predict the clinical course and particularly the pattern of tumor recurrence after surgical resection. DESIGN: Patients undergoing surgery for PDAC can be enrolled into the PANCALYZE trial. Biomarker expression of CXCR4, SMAD4, SOX9 and IFIT3 will be prospectively assessed by immunohistochemistry and verified by rt.-PCR from tumor and adjacent healthy pancreatic tissue of surgical specimen. Immunohistochemistry expression pattern of all four biomarkers will be combined into a single score. Beginning with the hospital stay clinical data from enrolled patients will be collected and followed. Different adjuvant chemotherapy protocols will be used to create subgroups. The combined biomarker expression score will be correlated with the further clinical course of the patients to test the hypothesis if CXCR4 positive, SMAD4 negative, SOX9 positive, IFIT3 positive tumors will predominantly develop metastatic spread. DISCUSSION: Pancreatic cancer is associated with different patterns of progression requiring personalized therapeutic strategies. Biomarker expression analysis might be a tool to predict the pattern of tumor recurrence and discriminate patients that develop systemic metastatic disease from those with tumors that rather develop local recurrence over time. This data might lead to personalized adjuvant treatment decisions as patients with tumors that stay localized might benefit from adjuvant local therapies like radiochemotherapy as compared to those with systemic recurrence who would benefit exclusively from chemotherapy. Moreover, the pattern of propagation might be a predefined characteristic of pancreatic cancer determined by the genetic signature of the tumor. In the future, biomarker expression analysis could be performed on tumor biopsies to develop personalized therapeutic pathways right after diagnosis of cancer. TRIAL REGISTRATION: German Clinical Trials Register, DRKS00006179 .

Tutorial in biostatistics: data-driven subgroup identification and analysis in clinical trials.

It is well known that both the direction and magnitude of the treatment effect in clinical trials are often affected by baseline patient characteristics (generally referred to as biomarkers). Characterization of treatment effect heterogeneity plays a central role in the field of personalized medicine and facilitates the development of tailored therapies. This tutorial focuses on a general class of problems arising in data-driven subgroup analysis, namely, identification of biomarkers with strong predictive properties and patient subgroups with desirable characteristics such as improved benefit and/or safety. Limitations of ad-hoc approaches to biomarker exploration and subgroup identification in clinical trials are discussed, and the ad-hoc approaches are contrasted with principled approaches to exploratory subgroup analysis based on recent advances in machine learning and data mining. A general framework for evaluating predictive biomarkers and identification of associated subgroups is introduced. The tutorial provides a review of a broad class of statistical methods used in subgroup discovery, including global outcome modeling methods, global treatment effect modeling methods, optimal treatment regimes, and local modeling methods. Commonly used subgroup identification methods are illustrated using two case studies based on clinical trials with binary and survival endpoints. Copyright © 2016 John Wiley & Sons, Ltd.

Measurement of downstream kinase activity modulation as indicator of epidermal growth factor receptor inhibitor efficacy.

The acoustic membrane micro particle (AMMP) technology has been used to quantify single analytes out of multiple sample types. In this study the technology is used to reveal molecular interactions of components of kinase pathways. Specifically, the downstream kinase activity of the EGFR receptor in the presence or absence of EGFR inhibitors is investigated. These experiments substantiate that EGFR stimulation predominantly activates the MEK/ERK pathway. The EGFR inhibitors tested had varying effectiveness at preventing phosphorylation at the EGFR or downstream kinase activity. These experiments reveal the use of the AMMP technology for observing multiple signaling pathways in a single experiment.

Evaluating marker-guided treatment selection strategies.

A potential venue to improve healthcare efficiency is to effectively tailor individualized treatment strategies by incorporating patient level predictor information such as environmental exposure, biological, and genetic marker measurements. Many useful statistical methods for deriving individualized treatment rules (ITR) have become available in recent years. Prior to adopting any ITR in clinical practice, it is crucial to evaluate its value in improving patient outcomes. Existing methods for quantifying such values mainly consider either a single marker or semi-parametric methods that are subject to bias under model misspecification. In this article, we consider a general setting with multiple markers and propose a two-step robust method to derive ITRs and evaluate their values. We also propose procedures for comparing different ITRs, which can be used to quantify the incremental value of new markers in improving treatment selection. While working models are used in step I to approximate optimal ITRs, we add a layer of calibration to guard against model misspecification and further assess the value of the ITR non-parametrically, which ensures the validity of the inference. To account for the sampling variability of the estimated rules and their corresponding values, we propose a resampling procedure to provide valid confidence intervals for the value functions as well as for the incremental value of new markers for treatment selection. Our proposals are examined through extensive simulation studies and illustrated with the data from a clinical trial that studies the effects of two drug combinations on HIV-1 infected patients.

Advantages of automated immunostain analyses for complex membranous immunostains: An exemplar investigating loss of E-cadherin expression in colorectal cancer.

As pathology moves towards digitisation, biomarker profiling through automated image analysis provides potentially objective and time-efficient means of assessment. This study set out to determine how a complex membranous immunostain, E-cadherin, assessed using an automated digital platform fares in comparison to manual evaluation in terms of clinical correlations and prognostication. Tissue microarrays containing 1000 colorectal cancer samples, stained with clinical E-cadherin antibodies were assessed through both manual scoring and automated image analysis. Both manual and automated scores were correlated to clinicopathological and survival data. E-cadherin data generated through digital image analysis was superior to manual evaluation when investigating for clinicopathological correlations in colorectal cancer. Loss of membranous E-cadherin, assessed on automated platforms, correlated with: right sided tumours (p = <0.001), higher T-stage (p = <0.001), higher grade (p = <0.001), N2 nodal stage (p = <0.001), intramural lymphovascular invasion (p = 0.006), perineural invasion (p = 0.028), infiltrative tumour edge (p = 0.001) high tumour budding score (p = 0.038), distant metastasis (p = 0.035), and poorer 5-year (p= 0.042) survival status. Manual assessment was only correlated with higher grade tumours, though other correlations become apparent only when assessed for morphological expression pattern (circumferential, basolateral, parallel) irrespective of intensity. Digital assessment of E-cadherin is effective for prognostication of colorectal cancer and may potentially offer benefits of improved objectivity, accuracy, and economy of time. Incorporating tools to assess patterns of staining may further improve such digital assessment in the future.

Evaluation of Collection and Processing Conditions for Gene Expression Analysis Using Human Myeloid Cells.

Background: The population of blast cells among peripheral blood mononuclear cells (PBMCs) obtained from patients is a desirable specimen for analyzing gene expression in diseases including acute myeloid leukemia. Although the enrichment of blast cells often needs to be performed at a central laboratory, acceptable conditions for sample transport from clinical sites remain to be established. Methods: We evaluated storage temperature, duration, and tube type before initiating sample processing for the analysis of cluster of differentiation (CD)33+ myeloid cells among PBMCs as an alternative to CD34+/CD33+ blast cells. Results: CD33+ myeloid cells were successfully purified by MACS. The cell viability and the RNA integrity were sustained during storage up to 48 hours before sample processing. Storage at 4°C had minimal effects on gene expression, whereas storage at room temperature induced the senescence pathway, characterized by the expression of stress-inducible genes. A CPT tube was also better than an ethylenediaminetetraacetic acid tube for minimizing gene expression change. Conclusions: Our study provided important clues for establishing a sample handling approach for gene expression analysis with purified cell fractions from human PBMCs. To keep the variation of gene expression to a minimum, samples should be delivered at 4°C within 48 hours before processing.

Construction of immune-related molecular diagnostic and predictive models of hepatocellular carcinoma based on machine learning.

Background: To exploit hepatocellular carcinoma (HCC) diagnostic substances, we identify potential predictive markers based on machine learning and to explore the significance of immune cell infiltration in this pathology. Method: Three HCC gene expression datasets were used for weighted gene co-expression network analysis (WGCNA) and differential expression analysis. Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest were applied to identify candidate biomarkers. The diagnostic value of HCC diagnostic gene biomarkers was further assessed by the area under the ROC curve observed in the validation dataset. CIBERSORT was used to analyze 22 immune cell fractions from HCC patients and to analyze their correlation with diagnostic markers. In addition, the prognostic value of the markers and the sensitivity of the drugs were analyzed. Result: WGCNA and differential expression analysis were used to screen 396 distinct gene signatures in HCC tissues. They were mostly engaged in cytoplasmic fusion and the cell division cycle, according to gene enrichment analyses. Five genes were shown to have a high diagnostic value for use as diagnostic biomarkers for HCC, including EFHD1 (AUC = 0.77), KIF4A (AUC = 0.97), UBE2C (AUC = 0.96), SMYD3 (AUC = 0.91), and MCM7 (AUC = 0.93). T cells, NK cells, macrophages, and dendritic cells were found to be related to diagnostic markers in HCC tissues by immune cell infiltration analysis, indicating that these cells are intimately linked to the onset and spread of HCC. Concurrently, these five genes and their constructed models have considerable prognostic value. Conclusion: These five genes (EFHD1, KIF4A, UBE2C, SMYD3, and MCM7) may serve as new candidate molecular markers for HCC, providing new insights for future diagnosis, prognosis, and molecular therapy of HCC.