-Omics and cancer biomarkers: link to the biological truth or bear the consequences

The impact of ultraviolet- and infrared-based laser microdissection technology on phosphoprotein detection in the laser microdissection-reverse phase protein array workflow

Reversible protein phosphorylation represents a key mechanism by which signals are transduced in eukaryotic cells. Dysregulated phosphorylation is also a hallmark of carcinogenesis and represents key drug targets in the precision medicine space. Thus, methods that preserve phosphoprotein integrity in the context of clinical tissue analyses are crucially important in cancer research. Here we investigated the impact of UV laser microdissection (UV LMD) and IR laser capture microdissection (IR LCM) on phosphoprotein abundance of key cancer signaling protein targets assessed by reverse-phase protein microarray (RPPA). Tumor epithelial cells from consecutive thin sections obtained from four high-grade serous ovarian cancers were harvested using either UV LMD or IR LCM methods. Phosphoprotein abundances for ten phosphoproteins that represent important drug targets were assessed by RPPA and revealed no significant differences in phosphoprotein integrity from those obtained using higher-energy UV versus the lower-energy IR laser methods.

miR-9 induces cell arrest and apoptosis of oral squamous cell carcinoma via CDK 4/6 pathway

Oral cancer remains a major public concern with considerable socioeconomic impact in the world, especially in southeast Asia. Despite substantial advancements have been made in treating oral cancer, the five-year survival rate for OSCC remained undesirable, and 35-55% patients developed recurrence within two years even with multimodality therapeutic strategies. Hence, identification of novel biomarkers for diagnosis and evaluating clinical outcome is of vital importance. MicroRNAs are 22-24 nt non-coding RNAs that could bind to 3' UTR of target mRNAs, thereby inducing degradation of mRNA or inhibiting translation. Due to its implication in regulation of post-transcriptional processes, the role of miRNAs in malignancies has been extensively studied, among which the discovery of functional miR-9 in oral squamous cell carcinoma (OSCC) remained to be elucidated. We first demonstrated that miR-9 was down-regulated in 21 OSCC patients, and we further found that forced expression of miR-9 could result in deterred cell proliferation and decreased ability to migrate and form colonies. Flow cytometry displayed cell-cycle arrested at G0/G1 phase. We next used Targetscan to predict target genes of miR-9. CDK6, a protein highly implicated in cell cycle control, was chosen for verification. Down-regulation of CDK6 and Cyclin D1 in Tca8113 transfected with miR-9 mimics indicate that the complex formed by both proteins may be the effector of the antiproliferative function of miR-9 in OSCCs. Considering small molecules are developed to target CDK4/6, our finding may provide valuable scientific evidence for research and development of therapies and diagnostic methodology in OSCCs.

An individualized gene expression signature for prediction of lung adenocarcinoma metastases

Our laboratory previously reported an individual‐level signature consisting of nine gene pairs, named 9‐GPS. This signature was developed by training on microarray expression data and validated using three independent integrated microarray data sets, with samples of stage I non‐small‐cell lung cancer after complete surgical resection. In this study, we first validated the cross‐platform robustness of 9‐GPS by demonstrating that 9‐GPS could significantly stratify the overall survival of 213 stage I lung adenocarcinoma (LUAD) patients detected with RNA‐sequencing platform in The Cancer Genome Atlas (TCGA; log‐rank P = 0.0318, C‐index = 0.55). Applying 9‐GPS to all the 423 stage I‐IV LUAD samples in TCGA, the predicted high‐risk samples were significantly enriched with clinically diagnosed metastatic samples (Fisher's exact test, P = 0.0015). We further modified the voting rule of 9‐GPS and found that the modified 9‐GPS had a better performance in predicting metastasis states (Fisher's exact test, P < 0.0001). With the aid of the modified 9‐GPS for reclassifying the metastasis states of patients with LUAD, the reclassified metastatic samples presented clearer transcriptional and genomic characteristics compared to the reclassified nonmetastatic samples. Finally, regulator network analysis identified TP53 and IRF1 with frequent genomic aberrations in the reclassified metastatic samples, indicating their key roles in driving tumor metastasis. In conclusion, 9‐GPS is a robust signature for identifying early‐stage LUAD patients with potential occult metastasis. This occult metastasis prediction was associated with clear transcriptional and genomic characteristics as well as the clinical diagnoses.

Spatial and molecular resolution of diffuse malignant mesothelioma heterogeneity by integrating label-free FTIR imaging, laser capture microdissection and proteomics

Diffuse malignant mesothelioma (DMM) is a heterogeneous malignant neoplasia manifesting with three subtypes: epithelioid, sarcomatoid and biphasic. DMM exhibit a high degree of spatial heterogeneity that complicates a thorough understanding of the underlying different molecular processes in each subtype. We present a novel approach to spatially resolve the heterogeneity of a tumour in a label-free manner by integrating FTIR imaging and laser capture microdissection (LCM). Subsequent proteome analysis of the dissected homogenous samples provides in addition molecular resolution. FTIR imaging resolves tumour subtypes within tissue thin-sections in an automated and label-free manner with accuracy of about 85% for DMM subtypes. Even in highly heterogeneous tissue structures, our label-free approach can identify small regions of interest, which can be dissected as homogeneous samples using LCM. Subsequent proteome analysis provides a location specific molecular characterization. Applied to DMM subtypes, we identify 142 differentially expressed proteins, including five protein biomarkers commonly used in DMM immunohistochemistry panels. Thus, FTIR imaging resolves not only morphological alteration within tissue but it resolves even alterations at the level of single proteins in tumour subtypes. Our fully automated workflow FTIR-guided LCM opens new avenues collecting homogeneous samples for precise and predictive biomarkers from omics studies.

Proteomic Profiling of Serial Prediagnostic Serum Samples for Early Detection of Colon Cancer in the U.S. Military

Background: Serum proteomic biomarkers offer a promising approach for early detection of cancer. In this study, we aimed to identify proteomic profiles that could distinguish colon cancer cases from controls using serial prediagnostic serum samples.Methods: This was a nested case-control study of active duty military members. Cases consisted of 264 patients diagnosed with colon cancer between 2001 and 2009. Controls were matched to cases on age, gender, race, serum sample count, and collection date. We identified peaks that discriminated cases from controls using random forest data analysis with a 2/3 training and 1/3 validation dataset. We then included epidemiologic data to see whether further improvement of model performance was obtainable. Proteins that corresponded to discriminatory peaks were identified.Results: Peaks with m/z values of 3,119.32, 2,886.67, 2,939.23, and 5,078.81 were found to discriminate cases from controls with a sensitivity of 69% and a specificity of 67% in the year before diagnosis. When smoking status was included, sensitivity increased to 76% while histories of other cancer and tonsillectomy raised specificity to 76%. Peaks at 2,886.67 and 3,119.32 m/z were identified as histone acetyltransferases while 2,939.24 m/z was a transporting ATPase subunit.Conclusions: Proteomic profiles in the year before cancer diagnosis have the potential to discriminate colon cancer patients from controls, and the addition of epidemiologic information may increase the sensitivity and specificity of discrimination.Impact: Our findings indicate the potential value of using serum prediagnostic proteomic biomarkers in combination with epidemiologic data for early detection of colon cancer. Cancer Epidemiol Biomarkers Prev; 26(5); 711-8. ©2016 AACR.

Epidemiology: Then and Now

Twenty-five years ago, on the 75th anniversary of the Johns Hopkins Bloomberg School of Public Health, I noted that epidemiologic research was moving away from the traditional approaches used to investigate "epidemics" and their close relationship with preventive medicine. Twenty-five years later, the role of epidemiology as an important contribution to human population research, preventive medicine, and public health is under substantial pressure because of the emphasis on "big data," phenomenology, and personalized medical therapies. Epidemiology is the study of epidemics. The primary role of epidemiology is to identify the epidemics and parameters of interest of host, agent, and environment and to generate and test hypotheses in search of causal pathways. Almost all diseases have a specific distribution in relation to time, place, and person and specific "causes" with high effect sizes. Epidemiology then uses such information to develop interventions and test (through clinical trials and natural experiments) their efficacy and effectiveness. Epidemiology is dependent on new technologies to evaluate improved measurements of host (genomics), epigenetics, identification of agents (metabolomics, proteomics), new technology to evaluate both physical and social environment, and modern methods of data collection. Epidemiology does poorly in studying anything other than epidemics and collections of numerators and denominators without specific hypotheses even with improved statistical methodologies.

Reverse phase protein arrays: mapping the path towards personalized medicine

Reverse phase protein array (RPPA) technology evolved from the advent of miniaturized immunoassays and gene microarray technology. Reverse phase protein arrays provide either a low throughput or high throughput methodology for quantifying proteins and their post-translationally modified forms in both cellular and non-cellular samples. As the demand for patient tailored therapies increases so does the need for precise and sensitive technology to accurately profile the molecular circuitry driving an individual patient's disease. RPPAs are currently utilized in clinical trials for profiling and comparing the functional state of protein signaling pathways, either temporally within tumors, between patients, or within the same patients before/after treatment. RPPAs are generally employed for quantifying large numbers of samples on one array, under identical experimental conditions. However, the goal of personalized cancer medicine is to design therapies based on the molecular portrait of a patient's tumor, which in turn result in more efficacious treatments with less toxicity. Therefore, RPPAs are also being validated for low throughput assays of individual patient samples. This review explores RPPA technology in the cancer research field, concentrating on its role as a fundamental tool for deciphering protein signaling networks and its emerging role in personalized medicine.

Translational cancer research: balancing prevention and treatment to combat cancer globally

Cancer research is drawing on the human genome project to develop new molecular-targeted treatments. This is an exciting but insufficient response to the growing, global burden of cancer, particularly as the projected increase in new cases in the coming decades is increasingly falling on developing countries. The world is not able to treat its way out of the cancer problem. However, the mechanistic insights from basic science can be harnessed to better understand cancer causes and prevention, thus underpinning a complementary public health approach to cancer control. This manuscript focuses on how new knowledge about the molecular and cellular basis of cancer, and the associated high-throughput laboratory technologies for studying those pathways, can be applied to population-based epidemiological studies, particularly in the context of large prospective cohorts with associated biobanks to provide an evidence base for cancer prevention. This integrated approach should allow a more rapid and informed translation of the research into educational and policy interventions aimed at risk reduction across a population.

Proteogenomic convergence for understanding cancer pathways and networks

During the past several decades, the understanding of cancer at the molecular level has been primarily focused on mechanisms on how signaling molecules transform homeostatically balanced cells into malignant ones within an individual pathway. However, it is becoming more apparent that pathways are dynamic and crosstalk at different control points of the signaling cascades, making the traditional linear signaling models inadequate to interpret complex biological systems. Recent technological advances in high throughput, deep sequencing for the human genomes and proteomic technologies to comprehensively characterize the human proteomes in conjunction with multiplexed targeted proteomic assays to measure panels of proteins involved in biologically relevant pathways have made significant progress in understanding cancer at the molecular level. It is undeniable that proteomic profiling of differentially expressed proteins under many perturbation conditions, or between normal and "diseased" states is important to capture a first glance at the overall proteomic landscape, which has been a main focus of proteomics research during the past 15-20 years. However, the research community is gradually shifting its heavy focus from that initial discovery step to protein target verification using multiplexed quantitative proteomic assays, capable of measuring changes in proteins and their interacting partners, isoforms, and post-translational modifications (PTMs) in response to stimuli in the context of signaling pathways and protein networks. With a critical link to genotypes (i.e., high throughput genomics and transcriptomics data), new and complementary information can be gleaned from multi-dimensional omics data to (1) assess the effect of genomic and transcriptomic aberrations on such complex molecular machinery in the context of cell signaling architectures associated with pathological diseases such as cancer (i.e., from genotype to proteotype to phenotype); and (2) target pathway- and network-driven changes and map the fluctuations of these functional units (proteins) responsible for cellular activities in response to perturbation in a spatiotemporal fashion to better understand cancer biology as a whole system.

The promise and challenge of systems biology in translational medicine

Abnormalities in clinical parameters and routine laboratory tests are frequently detected at late stages of many diseases (too late to cure or to prevent life-threatening complications). Hence earlier diagnostic and prognostic markers are needed for decision making and improving therapeutic outcome. In the present issue of Clinical Science, Rudiger and co-workers report findings from a transcriptomic study, which revealed that changes in transcripts involved in amino-sugar metabolism, p53-dependent cell-cycle arrest, β-adrenergic signalling and intracellular calcium cycling in cardiac tissue of rats with early sepsis could discriminate survivors from non-survivors. These findings underscore the great potential of systems biology in translational medicine. However, further investigations should be done to make the benchside results more feasible for routine clinical practice.