• R35 GM122547 NIGMS NIH HHS
• T15 LM009451 NLM NIH HHS

• Circulating DNA
• Circulating tumor cells
• Exosomes
• Microfluidics technologies
• Precision oncology

Due to cancer heterogeneity, only some patients can benefit from drug therapy. The personalized drug usage is important for improving the treatment response rate of cancer patients. The value of the transcriptome of patients has been recently demonstrated in guiding personalized drug use, and the Connectivity Map (CMAP) is a reliable computational approach for drug recommendation. However, there is still no personalized drug recommendation tool based on transcriptomic profiles of patients and CMAP. To fill this gap, here, we proposed such a feasible workflow and a user-friendly R package—Cancer-Personalized Drug Recommendation (CPDR). CPDR has three features. 1) It identifies the individual disease signature by using the patient subgroup with transcriptomic profiles similar to those of the input patient. 2) Transcriptomic profile purification is supported for the subgroup with high infiltration of non-cancerous cells. 3) It supports in silico drug efficacy assessment using drug sensitivity data on cancer cell lines. We demonstrated the workflow of CPDR with the aid of a colorectal cancer dataset from GEO and performed the in silico validation of drug efficacy. We further assessed the performance of CPDR by a pancreatic cancer dataset with clinical response to gemcitabine. The results showed that CPDR can recommend promising therapeutic agents for the individual patient. The CPDR R package is available at https://github.com/AllenSpike/CPDR.

• National Key Research and Development Program of China
• National Natural Science Foundation of China

• COVID-19
• SARS-CoV-2
• biomarkers
• mass spectrometry
• proteomics
• public health

• hepatoblastoma
• wnt/β-catenin
• hippo
• nrf2

• Adult cancers
• Clinical trials
• Immune checkpoint inhibitors
• Immunotherapy
• Pediatric cancers

Breast cancer (BC) is one of the most widespread cancers globally. Understanding the etiology of BC may help in determining the various risk factors involved in its malignancy. Certain genetic mutations are considered to play a key role in increasing the risk of BC.

In this study, we explored the correlation between a variable number tandem repeat (VNTR) polymorphism in the IL-4 gene and BC.

PCR and subsequent gel electrophoresis were used to genotype this variant in 360 Jordanian women (180 BC patients and 180 controls). In addition, phenotype–genotype analysis was carried out.

Our findings illustrate that there is no significant relationship between the variant genotypes in the IL-4 gene and BC among Jordanian females. Other than body mass index and tumor differentiation (p< 0.05), none of the clinical and pathological parameters of BC patients exhibited any association with the variant genotypes.

From this study, we propose that the IL-4 genetic variant does not impact BC development and progression but that it could influence the disease prognosis.

• IL-4
• breast
• cancer
• genetic variation
• prognosis

• Biomarkers
• Drug discovery
• Integrative genomics
• RPPA
• Reverse-phase protein array
• Technology

• Precision Medicine/instrumentation
• Precision Medicine/trends
• Protein Array Analysis/standards
• Protein Array Analysis/trends
• Proteomics
• Research/instrumentation
• Research/trends

• R21 AG057218 NIA NIH HHS
• R21 AI099851 NIAID NIH HHS

Background: Adrenocortical carcinoma (ACC) is a limited endocrine fatality with a minor diagnosis and rare remedial options. The progressive and predictive meaning of message RNA (mRNA) expression oddity in ACC has been studied extensively in recent years. However, differences in measurement platforms and lab protocols as well as small sample sizes can render gene expression levels incomparable.

Methods: An extensive study of GEO datasets was conducted to define potential mRNA biomarkers for ACC. The study compared the mRNA expression profiles of ACC tissues and neighboring noncancerous adrenal tissues in the pair. The study covered a sum of 165 tumors and 36 benign control samples. Hub genes were identified through a protein-protein interaction (PPI) network and Robust Rank Aggregation method. Then the Cancer Genome Atlas (TCGA) and Oncomine database were used to perform the validation of hub genes. 4 ACC tissues and 4 normal tissues were collected and then Polymerase Chain Reaction (PCR), Western-blot and immunofluorescence were conducted to validate the expression of five hub genes.

Results: We identified five statistically significant genes (TOP2A, NDC80, CEP55, CDKN3, CDK1) corrected with clinical features. The expression of five hub genes in TCGA and Oncomine database were significantly overexpressed in ACC compared with normal ones. Among all the TCGA ACC cases, the strong expression of TOP2A (logrank p=1.4e-04, HR=4.7), NDC80 (logrank p=8.8e-05, HR=4.9), CEP55 (logrank p=5.2e-07, HR=8.6), CDKN3 (log rank p=2.3e-06, HR=7.6) and CDK1 (logrank p=7e-08, HR=11) were correlated with low comprehensive survival, disease free survival (logrank p < 0.001), pathology stage and pathology T stage (FDR < 0.001). PCR results showed that the transcriptional levels of these five genes were significantly higher in ACC tissues than in normal tissues. The western blotting results also showed that the translational level of TOP2A was significantly higher in tumor tissues than in normal tissues. The results of immunofluorescence showed that TOP2A was abundantly observed in the adrenal cortical cell membrane and nucleus and its expression in ACC tissues was significantly higher than that in normal tissues.

Conclusions: The distinguished five genes may be utilized to form a board of progressive and predictive biomarkers for ACC for clinical purpose.

• adrenocortical carcinoma
• biomarker
• protein-protein interaction (PPI)
• robust rank analysis

• LFSAs
• PDMS
• high-throughput screening
• lateral flow strip assays
• microfluidic devices
• point of care
• polydimethylsiloxane
• single cell analysis
• µPADs

• Dimethylpolysiloxanes/chemistry
• Equipment Design
• Humans
• Lab-On-A-Chip Devices
• Microfluidic Analytical Techniques/methods
• Microfluidics/methods
• Point-of-Care Systems
• Single-Cell Analysis

• CBET1509713 Division of Chemical, Bioengineering, Environmental, and Transport Systems

Genetic risk to cancer is a knowledge largely confined to experts and the more educated sectors of the developed western countries. The perception of genetic susceptibility to cancer among the masses is fragmented, particularly in developing countries. As cancer diseases affect developing countries as much as developed nations, it is imperative to study perception and reception of genetic risk to cancer in Southeast Asia. Here, we report on a novel case study to gauge the awareness and attitudes towards genetic determination of cancer among the undergraduates of a Malaysian public university. A total of 272 university undergraduate students completed an online questionnaire. On causes of cancer, the respondents believed that cancer is caused by lifestyle and environmental factors, but those with science background were more likely to associate it with genetic factors. The results on awareness of genetic profiling of cancer risk showed that there are significant differences between those with science and nonscience background but there are no significant differences for gender and socioeconomic background. As for attitudes towards cancer risk, female respondents, those from middle socioeconomic status and science background, are more likely to believe in genetic determinism of cancer. The findings have implications on target population segmentation in strategic health communication on cancer.

• Cholesterol
• GSEA
• Gene expression analysis
• HMGCR
• Prostate cancer
• RNA-Seq
• Stroma
• Tissue heterogeneity

• Biosynthetic Pathways/genetics
• Cholesterol/biosynthesis
• Cohort Studies
• Down-Regulation
• Gene Expression Regulation, Enzymologic
• Gene Expression Regulation, Neoplastic
• Humans
• Lipid Metabolism/genetics
• Male
• Models, Biological
• Prostatic Neoplasms/genetics
• Prostatic Neoplasms/metabolism
• Prostatic Neoplasms/pathology
• Reproducibility of Results
• Stromal Cells/metabolism
• Stromal Cells/pathology
• Transcription, Genetic

• 758306 European Research Council
• R01 CA132874 NCI NIH HHS
• grant100792-2013 Kreftforeningen
• P50 CA069568 NCI NIH HHS
• U01 CA111275 NCI NIH HHS
• Samarbeidsorganet mellom Helse Midt-Norge RHF og NTNU
• PhD position from strategic funding ISB, Norwegian University of Science and Technology NTNU
• PhD position from Enabling Technologies, Norwegian University of Science and Technology (NTNU)

• R25 CA154015 NCI NIH HHS
• R33 CA177462 NCI NIH HHS

• Acetyltransferases/genetics
• Antigens, CD/genetics
• Breast Neoplasms/chemistry
• Breast Neoplasms/genetics
• CD24 Antigen
• DNA-Binding Proteins/genetics
• Early Growth Response Transcription Factors
• Female
• Gene Expression Profiling
• Histone Acetyltransferases
• Humans
• Hyaluronan Receptors/genetics
• Kruppel-Like Transcription Factors
• Membrane Glycoproteins
• Neuropilin-1/genetics
• Receptors, Estrogen/analysis
• Receptors, Retinoic Acid/genetics
• Retinoid X Receptors
• Reverse Transcriptase Polymerase Chain Reaction
• Transcription Factors/genetics

• Biomarkers
• immunotherapy
• non-small cell lung cancer (NSCLC)

• 6-gene predictor score
• diffuse large B-cell lymphoma (DLBCL)
• formalin fixed paraffin embedded tissue (FFPE)

• Aged
• Asia
• Biomarkers, Tumor/genetics
• Biopsy
• Disease-Free Survival
• Europe
• Female
• Fixatives/chemistry
• Formaldehyde/chemistry
• Gene Expression Profiling/methods
• Gene Expression Profiling/standards
• Genetic Predisposition to Disease
• Humans
• Kaplan-Meier Estimate
• Lymphoma, Large B-Cell, Diffuse/drug therapy
• Lymphoma, Large B-Cell, Diffuse/genetics
• Lymphoma, Large B-Cell, Diffuse/mortality
• Lymphoma, Large B-Cell, Diffuse/pathology
• Male
• Middle Aged
• Paraffin Embedding
• Phenotype
• Predictive Value of Tests
• Proportional Hazards Models
• Real-Time Polymerase Chain Reaction/standards
• Reproducibility of Results
• Reverse Transcriptase Polymerase Chain Reaction/standards
• South America
• Time Factors
• Tissue Fixation/methods
• Transcriptome

• Adverse outcome pathway
• Chemotherapy sensitivity and resistance assays
• Microenvironment
• Personalized medicine
• Targeted therapy

• Antineoplastic Agents/adverse effects
• Antineoplastic Agents/therapeutic use
• Biomarkers, Tumor/genetics
• Biomarkers, Tumor/metabolism
• Cell Separation/methods
• Drug Resistance, Neoplasm
• Drug Screening Assays, Antitumor/methods
• Humans
• Neoplasms/drug therapy
• Neoplasms/genetics
• Neoplasms/metabolism
• Neoplasms/pathology
• Patient Selection
• Precision Medicine/methods
• Predictive Value of Tests
• Primary Cell Culture
• Signal Transduction/drug effects
• Treatment Outcome
• Tumor Cells, Cultured
• Tumor Microenvironment

• R01 CA159578 NCI NIH HHS
• P30 CA014520 NCI NIH HHS
• R01 CA186134 NCI NIH HHS
• T32 CA157322 NCI NIH HHS
• T32 ES007015 NIEHS NIH HHS

Molecular analysis of patient tissue samples is essential to characterize the in vivo variability in human cancers which are not accessible in cell-lines or animal models. This applies particularly to studies of tumor metabolism. The challenge is, however, the complex mixture of various tissue types within each sample, such as benign epithelium, stroma and cancer tissue, which can introduce systematic biases when cancers are compared to normal samples. In this study we apply a simple strategy to remove such biases using sample selections where the average content of stroma tissue is balanced between the sample groups. The strategy is applied to a prostate cancer patient cohort where data from MR spectroscopy and gene expression have been collected from and integrated on the exact same tissue samples. We reveal in vivo changes in cancer-relevant metabolic pathways which are otherwise hidden in the data due to tissue confounding. In particular, lowered levels of putrescine are connected to increased expression of SRM, reduced levels of citrate are attributed to upregulation of genes promoting fatty acid synthesis, and increased succinate levels coincide with reduced expression of SUCLA2 and SDHD. In addition, the strategy also highlights important metabolic differences between the stroma, epithelium and prostate cancer. These results show that important in vivo metabolic features of cancer can be revealed from patient data only if the heterogeneous tissue composition is properly accounted for in the analysis.

Liver cancer, of which hepatocellular carcinoma (HCC) is by far the most common type, is the second most deadly cancer (746,000 deaths in 2012). Currently, the only curative treatment for HCC is surgery to remove the malignancy (resection) or to remove the entire diseased liver followed by transplantation of healthy liver tissue. Given the shortage of healthy livers, it is crucial to provide transplants to patients that have the best chance of long-term survival. Currently, transplantation is determined via the Milan criteria—patients within Milan (single tumor < 5 cm or 2–3 tumors < 3 cm with no extrahepatic spread nor intrahepatic vascular invasion) are typically eligible for transplantation. However, combining microRNA expression profiling with the Milan criteria can improve prediction of recurrence.

HCC often presents with multiple distinct tumor foci arising from local spread of a primary tumor or from the oncogenic predisposition of the diseased liver. Substantial genomic heterogeneity between tumor foci within a single patient has been reported; therefore, biomarker development must account for the possibility of highly heterogeneous genomic profiles from the same individual.

MicroRNA profiling was performed on 180 HCC tumor samples from 89 patients who underwent liver transplantation at the University of Rochester Medical Center. The primary outcome was recurrence-free survival time, and patients were observed for 3 years post-transplantation.

MicroRNA expression profiles were used to develop a biomarker that distinguishes HCC patients at greater risk of recurrence post-transplantation. Unsupervised clustering uncovered two distinct subgroups with vast differences in standard transplantation selection criteria and recurrence-free survival times. These subgroups were subsequently used to identify microRNAs strongly associated with HCC recurrence. Our results show that reduced expression of five specific microRNAs is significantly associated with HCC recurrence post-transplantation.

MicroRNA profiling of distinct tumor foci, coupled with methods that address within-subject tumor heterogeneity, has the potential to significantly improve prediction of HCC recurrence post-transplantation. The development of a clinically applicable HCC biomarker would inform treatment options for patients and contribute to liver transplant selection criteria for practitioners.

The online version of this article (doi:10.1186/s12920-016-0179-4) contains supplementary material, which is available to authorized users.

• Biomarker
• Hepatocellular carcinoma (HCC)
• Liver transplantation
• Recurrence
• microRNA

• CMA
• NGS
• colon
• lung
• profiling

• Cell Line, Tumor
• Computer Simulation
• Female
• Genetic Variation
• High-Throughput Nucleotide Sequencing/methods
• Humans
• Polymorphism, Single Nucleotide/genetics
• ROC Curve
• Risk Factors
• Software

• U19 AI100627 NIAID NIH HHS
• U19 AII00627 PHS HHS

• Biocompatibility
• Cumulative release
• Hydrophilicity
• Kinetics
• cis-Dichlorodiamminoplatinum (II)

• Cytokine
• Diagnostic
• Immunoassay
• Optical biosensor
• Silicon photonics

• Biomarkers/blood
• Biosensing Techniques
• Chemokine CCL2/blood
• Fibromyalgia/blood
• Fibromyalgia/diagnosis
• Humans
• Limit of Detection
• Myocardial Ischemia/blood
• Myocardial Ischemia/diagnosis
• Neoplasms/blood
• Neoplasms/diagnosis
• Photons

• R33 CA177462 NCI NIH HHS
• R33-CA177462-01 NCI NIH HHS

Clinical specimens are each inherently unique, limited and non-renewable. As such, small samples such as tissue biopsies are often completely consumed after a limited number of analyses. Here we present a method that enables fast and reproducible conversion of a small amount of tissue (approximating the quantity obtained by a biopsy) into a single, permanent digital file representing the mass spectrometry-measurable proteome of the sample. The method combines pressure cycling technology (PCT) and SWATH mass spectrometry (MS), and the resulting proteome maps can be analyzed, re-analyzed, compared and mined in silico to detect and quantify specific proteins across multiple samples. We used this method to process and convert 18 biopsy samples from 9 renal cell carcinoma patients into SWATH-MS fragment ion maps. From these proteome maps we detected and quantified more than 2,000 proteins with a high degree of reproducibility across all samples. The identified proteins clearly separated tumorous kidney tissues from healthy tissue, and differentiated distinct histomorphological kidney cancer subtypes.

Muscle diseases have been associated with changes in the expression of proteins involved in energy metabolism. To this aim we have developed a number of monoclonal antibodies against proteins of energy metabolism.

Herein, we have used Reverse Phase Protein Microarrays (RPMA), a high throughput technique, to investigate quantitative changes in protein expression with the aim of identifying potential biomarkers in rare neuromuscular diseases. A cohort of 73 muscle biopsies that included samples from patients diagnosed of Duchenne (DMD), Becker (BMD), symptomatic forms of DMD and BMD in female carriers (Xp21 Carriers), Limb Girdle Muscular Dystrophy Type 2C (LGMD2C), neuronal ceroid lipofuscinosis (NCL), glycogenosis type V (Mc Ardle disease), isolated mitochondrial complex I deficiency, intensive care unit myopathy and control donors were investigated. The nineteen proteins of energy metabolism studied included members of the mitochondrial oxidation of pyruvate, the tricarboxylic acid cycle, β-oxidation of fatty acids, electron transport and oxidative phosphorylation, glycogen metabolism, glycolysis and oxidative stress using highly specific antibodies.

The results indicate that the phenotype of energy metabolism offers potential biomarkers that could be implemented to refine the understanding of the biological principles of rare diseases and, eventually, the management of these patients.

We suggest that some biomarkers of energy metabolism could be translated into the clinics to contribute to the improvement of the clinical handling of patients affected by rare diseases.

The online version of this article (doi:10.1186/s12967-015-0424-1) contains supplementary material, which is available to authorized users.

• Breast Neoplasms/genetics
• Breast Neoplasms/metabolism
• Breast Neoplasms/mortality
• Carcinoma/genetics
• Carcinoma/metabolism
• Carcinoma/mortality
• Chromatin Immunoprecipitation
• E2F4 Transcription Factor/genetics
• E2F4 Transcription Factor/metabolism
• Female
• Gene Expression Profiling
• Gene Expression Regulation, Neoplastic
• Humans
• Prognosis
• Proportional Hazards Models
• Survival Rate
• Transcriptome

• P20 GM103534 NIGMS NIH HHS
• P30 CA023108 NCI NIH HHS
• GM103534 NIGMS NIH HHS

• anti-cancer bioactive peptide (acbp)
• cisplatin
• tumor growth
• quality of life
• gastric cancer

• Animals
• Disease Models, Animal
• Gene Expression Profiling
• Genomics/methods
• Humans
• Neoplasm Metastasis/diagnosis
• Neoplasm Metastasis/genetics
• Neoplasm Metastasis/pathology
• Prognosis

• Chemokines/metabolism
• Graft Rejection/diagnosis
• Graft Rejection/prevention & control
• Heart Transplantation/methods
• Humans
• Immunosuppressive Agents/pharmacology
• Kidney Transplantation/methods
• Lung Transplantation/methods
• Matrix Metalloproteinases/metabolism
• Molecular Diagnostic Techniques
• Nucleic Acid Hybridization
• Oligonucleotide Array Sequence Analysis/methods
• T-Lymphocytes/metabolism

• Animals
• Biological Evolution
• Disease
• Drug Design
• History, 20th Century
• History, 21st Century
• Humans
• Microarray Analysis/history
• Microarray Analysis/methods
• Models, Biological
• Technology

• DNA damage response
• Digital-image-analysis
• P-Chk2
• automated quantitative analysis
• immunofluorescence
• ionizing radiation
• melanoma
• γH2AX

Like other solid tumors, colorectal cancer (CRC) is a genomic disorder in which various types of genomic alterations, such as point mutations, genomic rearrangements, gene fusions, or chromosomal copy number alterations, can contribute to the initiation and progression of the disease. The advent of a new DNA sequencing technology known as next-generation sequencing (NGS) has revolutionized the speed and throughput of cataloguing such cancer-related genomic alterations. Now the challenge is how to exploit this advanced technology to better understand the underlying molecular mechanism of colorectal carcinogenesis and to identify clinically relevant genetic biomarkers for diagnosis and personalized therapeutics. In this review, we will introduce NGS-based cancer genomics studies focusing on those of CRC, including a recent large-scale report from the Cancer Genome Atlas. We will mainly discuss how NGS-based exome-, whole genome- and methylome-sequencing have extended our understanding of colorectal carcinogenesis. We will also introduce the unique genomic features of CRC discovered by NGS technologies, such as the relationship with bacterial pathogens and the massive genomic rearrangements of chromothripsis. Finally, we will discuss the necessary steps prior to development of a clinical application of NGS-related findings for the advanced management of patients with CRC.

• Next-generation sequencing
• Cancer genomics
• Colorectal cancers
• Personalized medicine
• The cancer genome atlas

• Biomarkers, Tumor/genetics
• Colorectal Neoplasms/diagnosis
• Colorectal Neoplasms/genetics
• Colorectal Neoplasms/therapy
• Gene Expression Regulation, Neoplastic
• Genetic Predisposition to Disease
• Genetic Testing/methods
• High-Throughput Nucleotide Sequencing
• Humans
• Molecular Targeted Therapy
• Patient Selection
• Phenotype
• Precision Medicine
• Predictive Value of Tests
• Prognosis
• Risk Factors

Study of normal cell physiology and disease pathogenesis heavily relies on untangling the complexity of intracellular molecular mechanisms and pathways. To achieve this goal, comprehensive molecular profiling of individual cells within the context of microenvironment is required. Here we report the development of a multicolour multicycle in situ imaging technology capable of creating detailed quantitative molecular profiles for individual cells at the resolution of optical imaging. A library of stoichiometric fluorescent probes is prepared by linking target-specific antibodies to a universal quantum dot-based platform via protein A in a quick and simple procedure. Surprisingly, despite the potential for multivalent binding between protein A and antibody and the intermediate affinity of this non-covalent bond, fully assembled probes do not aggregate or exchange antibodies, facilitating highly multiplexed parallel staining. This single-cell molecular profiling technology is expected to open new opportunities in systems biology, gene expression studies, signalling pathway analysis and molecular diagnostics.

Multiplexed labelling of individual cells allows the direct observation of intracellular molecular composition, but is difficult to achieve with existing techniques. Here, self-assembled fluorescent nanoparticle probes and multicolour multicycle staining are used for the simultaneous evaluation of multiple biomolecules at subcellular resolution.