A simple and reproducible breast cancer prognostic test

Hepatic G Protein-Coupled Receptor 180 Deficiency Ameliorates High Fat Diet-Induced Lipid Accumulation via the Gi-PKA-SREBP Pathway

Single-nucleotide polymorphisms in G protein-coupled receptor 180 (GPR180) are associated with hypertriglyceridemia. The aim of this study was to determine whether hepatic GPR180 impacts lipid metabolism. Hepatic GPR180 was knocked down using two approaches: Gpr180-specific short hairpin (sh)RNA carried by adeno-associated virus 9 (AAV9) and alb-Gpr180-/- transgene established by crossbreeding albumin-Cre mice with Gpr180flox/flox animals, in which Gpr180 was specifically knocked down in hepatocytes. Adiposity, hepatic lipid contents, and proteins related to lipid metabolism were analyzed. The effects of GPR180 on triglyceride and cholesterol synthesis were further verified by knocking down or overexpressing Gpr180 in Hepa1-6 cells. Gpr180 mRNA was upregulated in the liver of HFD-induced obese mice. Deficiency of Gpr180 decreased triglyceride and cholesterol contents in the liver and plasma, ameliorated hepatic lipid deposition in HFD-induced obese mice, increased energy metabolism, and reduced adiposity. These alterations were associated with downregulation of transcription factors SREBP1 and SREBP2, and their target acetyl-CoA carboxylase. In Hepa1-6 cells, Gpr180 knockdown decreased intracellular triglyceride and cholesterol contents, whereas its overexpression increased their levels. Overexpression of Gpr180 significantly reduced the PKA-mediated phosphorylation of substrates and consequent CREB activity. Hence, GPR180 might represent a novel drug target for intervention of adiposity and liver steatosis.

Using biological constraints to improve prediction in precision oncology

Many gene signatures have been developed by applying machine learning (ML) on omics profiles, however, their clinical utility is often hindered by limited interpretability and unstable performance. Here, we show the importance of embedding prior biological knowledge in the decision rules yielded by ML approaches to build robust classifiers. We tested this by applying different ML algorithms on gene expression data to predict three difficult cancer phenotypes: bladder cancer progression to muscle-invasive disease, response to neoadjuvant chemotherapy in triple-negative breast cancer, and prostate cancer metastatic progression. We developed two sets of classifiers: mechanistic, by restricting the training to features capturing specific biological mechanisms; and agnostic, in which the training did not use any a priori biological information. Mechanistic models had a similar or better testing performance than their agnostic counterparts, with enhanced interpretability. Our findings support the use of biological constraints to develop robust gene signatures with high translational potential.

Cancer Cells Enter an Adaptive Persistence to Survive Radiotherapy and Repopulate Tumor

Repopulation of residual tumor cells impedes curative radiotherapy, yet the mechanism is not fully understood. It is recently appreciated that cancer cells adopt a transient persistence to survive the stress of chemo- or targeted therapy and facilitate eventual relapse. Here, it is shown that cancer cells likewise enter a "radiation-tolerant persister" (RTP) state to evade radiation pressure in vitro and in vivo. RTP cells are characterized by enlarged cell size with complex karyotype, activated type I interferon pathway and two gene patterns represented by CST3 and SNCG. RTP cells have the potential to regenerate progenies via viral budding-like division, and type I interferon-mediated antiviral signaling impaired progeny production. Depleting CST3 or SNCG does not attenuate the formation of RTP cells, but can suppress RTP cells budding with impaired tumor repopulation. Interestingly, progeny cells produced by RTP cells actively lose their aberrant chromosomal fragments and gradually recover back to a chromosomal constitution similar to their unirradiated parental cells. Collectively, this study reveals a novel mechanism of tumor repopulation, i.e., cancer cell populations employ a reversible radiation-persistence by poly- and de-polyploidization to survive radiotherapy and repopulate the tumor, providing a new therapeutic concept to improve outcome of patients receiving radiotherapy.

Distinct immune signature predicts progression of vestibular schwannoma and unveils a possible viral etiology

BACKGROUND

The management of sub-totally resected sporadic vestibular schwannoma (VS) may include observation, re-resection or irradiation. Identifying the optimal choice can be difficult due to the disease's variable progression rate. We aimed to define an immune signature and associated transcriptomic fingerprint characteristic of rapidly-progressing VS to elucidate the underpinnings of rapidly progressing VS and identify a prognostic model for determining rate of progression.

METHODS

We used multiplex immunofluorescence to characterize the immune microenvironment in 17 patients with sporadic VS treated with subtotal surgical resection alone. Transcriptomic analysis revealed differentially-expressed genes and dysregulated pathways when comparing rapidly-progressing VS to slowly or non-progressing VS.

RESULTS

Rapidly progressing VS was distinctly enriched in CD4⁺, CD8⁺, CD20⁺, and CD68⁺ immune cells. RNA data indicated the upregulation of anti-viral innate immune response and T-cell senescence. K - Top Scoring Pair analysis identified 6 pairs of immunosenescence-related genes (CD38-KDR, CD22-STAT5A, APCS-CXCR6, MADCAM1-MPL, IL6-NFATC3, and CXCL2-TLR6) that had high sensitivity (100%) and specificity (78%) for identifying rapid VS progression.

CONCLUSION

Rapid progression of residual vestibular schwannoma following subtotal surgical resection has an underlying immune etiology that may be virally originating; and despite an abundant adaptive immune response, T-cell immunosenescence may be associated with rapid progression of VS. These findings provide a rationale for clinical trials evaluating immunotherapy in patients with rapidly progressing VS.

ZNF92, an unexplored transcription factor with remarkably distinct breast cancer over-expression associated with prognosis and cell-of-origin

Tumor phenotype is shaped both by transforming genomic alterations and the normal cell-of-origin. We identified a cell-of-origin associated prognostic gene expression signature, ET-9, that correlates with remarkably shorter overall and relapse free breast cancer survival, 8.7 and 6.2 years respectively. The genes associated with the ET-9 signature are regulated by histone deacetylase 7 (HDAC7) partly through ZNF92, a previously unexplored transcription factor with a single PubMed citation since its cloning in 1990s. Remarkably, ZNF92 is distinctively over-expressed in breast cancer compared to other tumor types, on a par with the breast cancer specificity of the estrogen receptor. Importantly, ET-9 signature appears to be independent of proliferation, and correlates with outcome in lymph-node positive, HER2+, post-chemotherapy and triple-negative breast cancers. These features distinguish ET-9 from existing breast cancer prognostic signatures that are generally related to proliferation and correlate with outcome in lymph-node negative, ER-positive, HER2-negative breast cancers. Our results suggest that ET-9 could be also utilized as a predictive signature to select patients for HDAC inhibitor treatment.

The current staging and classification systems of breast cancer and their pitfalls: Is it possible to integrate the complexity of this neoplasm into a unified staging system?

Breast cancer is one of the leading causes of cancer death in women worldwide due to its variable aggressiveness and high propensity to develop distant metastases. The staging can be performed clinically or pathologically, generating the stage stratification by the TNM (T - tumor size; N- lymph node metastasis; M - distant organ metastasis) system. However, cancers with virtually identical TNM characteristics can present highly contrasting behaviors due to the divergence of molecular profiles. This review focuses on the histopathological nuances and molecular understanding of breast cancer through the profiling of gene and protein expression, culminating in improvements promoted by the integration of this information into the traditional staging system. As a culminating point, it will highlight predictive statistical tools for genomic risks and decision algorithms as a possible solution to integrate the various systems because they have the potential to reduce the indications for such tests, serving as a funnel in association with staging and previous classification.

A top scoring pairs classifier for recent HIV infections

Accurate incidence estimation of HIV infection from cross-sectional biomarker data is crucial for monitoring the epidemic and determining the impact of HIV prevention interventions. A key feature of cross-sectional incidence testing methods is the mean window period, defined as the average duration that infected individuals are classified as recently infected. Two assays available for cross-sectional incidence estimation, the BED capture immunoassay, and the Limiting Antigen (LAg) Avidity assay, measure a general characteristic of antibody response; performance of these assays can be affected and biased by factors such as viral suppression, resulting in sample misclassification and overestimation of HIV incidence. As availability and use of antiretroviral treatment increase worldwide, algorithms that do not include HIV viral load and are not impacted by viral suppression are needed for cross-sectional HIV incidence estimation. Using a phage display system to quantify antibody binding to over 3300 HIV peptides, we present a classifier based on top scoring peptide pairs that identifies recent infections using HIV antibody responses alone. Based on plasma samples from individuals with known dates of seroconversion, we estimated the mean window period for our classifier to be 217 days (95% confidence interval 183 to 257 days), compared to the estimated mean window period for the LAg-Avidity protocol of 106 days (76 to 146 days). Moreover, each of the four peptide pairs correctly classified more of the recent samples than the LAg-Avidity assay alone at the same classification accuracy for non-recent samples.

Tumour profiling tests to guide adjuvant chemotherapy decisions in early breast cancer: a systematic review and economic analysis

BACKGROUND

Breast cancer and its treatment can have an impact on health-related quality of life and survival. Tumour profiling tests aim to identify whether or not women need chemotherapy owing to their risk of relapse.

OBJECTIVES

To conduct a systematic review of the effectiveness and cost-effectiveness of the tumour profiling tests oncotype DX^® (Genomic Health, Inc., Redwood City, CA, USA), MammaPrint^® (Agendia, Inc., Amsterdam, the Netherlands), Prosigna^® (NanoString Technologies, Inc., Seattle, WA, USA), EndoPredict^® (Myriad Genetics Ltd, London, UK) and immunohistochemistry 4 (IHC4). To develop a health economic model to assess the cost-effectiveness of these tests compared with clinical tools to guide the use of adjuvant chemotherapy in early-stage breast cancer from the perspective of the NHS and Personal Social Services.

DESIGN

A systematic review and health economic analysis were conducted.

REVIEW METHODS

The systematic review was partially an update of a 2013 review. Nine databases were searched in February 2017. The review included studies assessing clinical effectiveness in people with oestrogen receptor-positive, human epidermal growth factor receptor 2-negative, stage I or II cancer with zero to three positive lymph nodes. The economic analysis included a review of existing analyses and the development of a de novo model.

RESULTS

A total of 153 studies were identified. Only one completed randomised controlled trial (RCT) using a tumour profiling test in clinical practice was identified: Microarray In Node-negative Disease may Avoid ChemoTherapy (MINDACT) for MammaPrint. Other studies suggest that all the tests can provide information on the risk of relapse; however, results were more varied in lymph node-positive (LN+) patients than in lymph node-negative (LN0) patients. There is limited and varying evidence that oncotype DX and MammaPrint can predict benefit from chemotherapy. The net change in the percentage of patients with a chemotherapy recommendation or decision pre/post test ranged from an increase of 1% to a decrease of 23% among UK studies and a decrease of 0% to 64% across European studies. The health economic analysis suggests that the incremental cost-effectiveness ratios for the tests versus current practice are broadly favourable for the following scenarios: (1) oncotype DX, for the LN0 subgroup with a Nottingham Prognostic Index (NPI) of > 3.4 and the one to three positive lymph nodes (LN1-3) subgroup (if a predictive benefit is assumed); (2) IHC4 plus clinical factors (IHC4+C), for all patient subgroups; (3) Prosigna, for the LN0 subgroup with a NPI of > 3.4 and the LN1-3 subgroup; (4) EndoPredict Clinical, for the LN1-3 subgroup only; and (5) MammaPrint, for no subgroups.

LIMITATIONS

There was only one completed RCT using a tumour profiling test in clinical practice. Except for oncotype DX in the LN0 group with a NPI score of > 3.4 (clinical intermediate risk), evidence surrounding pre- and post-test chemotherapy probabilities is subject to considerable uncertainty. There is uncertainty regarding whether or not oncotype DX and MammaPrint are predictive of chemotherapy benefit. The MammaPrint analysis uses a different data source to the other four tests. The Translational substudy of the Arimidex, Tamoxifen, Alone or in Combination (TransATAC) study (used in the economic modelling) has a number of limitations.

CONCLUSIONS

The review suggests that all the tests can provide prognostic information on the risk of relapse; results were more varied in LN+ patients than in LN0 patients. There is limited and varying evidence that oncotype DX and MammaPrint are predictive of chemotherapy benefit. Health economic analyses indicate that some tests may have a favourable cost-effectiveness profile for certain patient subgroups; all estimates are subject to uncertainty. More evidence is needed on the prediction of chemotherapy benefit, long-term impacts and changes in UK pre-/post-chemotherapy decisions.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42017059561.

FUNDING

The National Institute for Health Research Health Technology Assessment programme.

Characterization and Proteome of Circulating Extracellular Vesicles as Potential Biomarkers for NASH

Nonalcoholic fatty liver disease (NAFLD) is currently one of most common forms of chronic liver disease globally. NAFLD represents a wide spectrum of liver involvement from nonprogressive isolated steatosis to nonalcoholic steatohepatitis (NASH), characterized by liver necroinflammation and fibrosis and currently one of the top causes of end‐stage liver disease and hepatocellular carcinoma. At present, there is a lack of effective treatments, and a central barrier to the development of therapies is the requirement for an invasive liver biopsy for diagnosis of NASH. Discovery of reliable, noninvasive biomarkers are urgently needed. In this study, we tested whether circulating extracellular vesicles (EVs), cell‐derived small membrane‐surrounded structures with a rich cargo of bioactive molecules, may serve as reliable noninvasive “liquid biopsies” for NASH diagnosis and assessment of disease severity. Total circulating EVs and hepatocyte‐derived EVs were isolated by differential centrifugation and size‐exclusion chromatography from serum samples of healthy individuals, patients with precirrhotic NASH, and patients with cirrhotic NASH. EVs were further characterized by flow cytometry, electron microscopy, western blotting, and dynamic light scattering assays before performing a proteomics analysis. Our findings suggest that levels of total and hepatocyte‐derived EVs correlate with NASH clinical characteristics and disease severity. Additionally, using proteomics data, we developed understandable, powerful, and unique EV‐based proteomic signatures for potential diagnosis of advanced NASH. Conclusion: Our study shows that the quantity and protein constituents of circulating EVs provide strong evidence for EV protein–based liquid biopsies for NAFLD/NASH diagnosis.

Overview of Breast Cancer and Implications of Overtreatment of Early-Stage Breast Cancer: An Indian Perspective

The prevalence and mortality of breast cancer is increasing in Asian countries, including India. With advances in medical technology leading to better detection and characterization of the disease, it has been possible to classify breast cancer into various subtypes using markers, which helps predict the risk of distant recurrence, response to therapy, and prognosis using a combination of molecular and clinical parameters. Breast cancer and its therapy, mainly surgery, systemic therapy (anticancer chemotherapy, hormonal therapy, targeted therapy, and immunotherapy), and radiation therapy, are associated with significant adverse influences on physical and mental health, quality of life, and the economic status of the patient and her family. The fear of recurrence and its devastating effects often leads to overtreatment, with a toxic cost to the patient financially and physically in cases in which this is not required. This article discusses some aspects of a breast cancer diagnosis and its impact on the various facets of the life of the patient and her family. It further elucidates the role of prognostic factors, the currently available biomarkers and prognostic signatures, and the importance of ethnically validating biomarkers and prognostic signatures.

Predicting chromosome 1p/19q codeletion by RNA expression profile: a comparison of current prediction models

BACKGROUND

Chromosome 1p/19q codeletion is increasingly being recognized as the crucial genetic marker for glioma patients and have been included in WHO classification of glioma in 2016. Fluorescent in situ hybridization, a widely used method in detecting 1p/19q status, has some methodological limitations which might influence the clinical management for doctors. Here, we attempted to explore an RNA sequencing computational method to detect 1p/19q status.

METHODS

We included 692 samples with 1p/19q status information from TCGA cohort as training set and 222 samples with 1p/19q status information from REMBRANDT cohort as validation set. We reviewed and compared five tools: TSPairs, GSVA, PAM, Caret, smoother, with respect to their accuracy, sensitivity and specificity.

RESULTS

In TCGA cohort, the GSVA method showed the highest accuracy (98.4%) in predicting 1p/19q status (sensitivity=95.5%, specificity=99.6%) and smoother method showed the second-highest accuracy (accuracy=97.8%, sensitivity=96.4%, specificity=98.3%). While in REMBRANDT cohort, smoother method exhibited the highest accuracy (98.6%) (sensitivity= 96.7%, specificity=98.9%) in 1p/19q status prediction.

CONCLUSIONS

Our independent assessment of five tools revealed that smoother method was selected as the most stable and accurate method in predicting 1p/19q status. This method could be regarded as a potential alternative method for clinical practice in future.

A qualitative transcriptional signature for predicting the biochemical recurrence risk of prostate cancer patients after radical prostatectomy

BACKGROUND

The qualitative transcriptional characteristics, the within-sample relative expression orderings (REOs) of genes, are highly robust against batch effects and sample quality variations. Hence, we develop a qualitative transcriptional signature based on REOs to predict the biochemical recurrence risk of prostate cancer (PCa) patients after radical prostatectomy.

METHODS

Gene pairs with REOs significantly correlated with the biochemical recurrence-free survival (BFS) were identified from 131 PCa samples in the training data set. From these gene pairs, we selected a qualitative transcriptional signature based on the within-sample REOs of gene pairs which could predict the recurrence risk of PCa patients after radical prostatectomy.

RESULTS

A signature consisting of 74 gene pairs, named 74-GPS, was developed for predicting the recurrence risk of PCa patients after radical prostatectomy based on the majority voting rule that a sample was assigned as high risk when at least 37 gene pairs of the 74-GPS voted for high risk; otherwise, low risk. The signature was validated in six independent datasets produced by different platforms. In each of the validation datasets, the Kaplan-Meier survival analysis showed that the average BFS of the low-risk group was significantly better than that of the high-risk group. Analyses of multiomics data of PCa samples from TCGA suggested that both the epigenomic and genomic alternations could cause the reproducible transcriptional differences between the two different prognostic groups.

CONCLUSIONS

The proposed qualitative transcriptional signature can robustly stratify PCa patients after radical prostatectomy into two groups with different recurrence risk and distinct multiomics characteristics. Hence, 74-GPS may serve as a helpful tool for guiding the management of PCa patients with radical prostatectomy at the individual level.

Oncotype DX Breast Cancer recurrence score resists inter-assay reproducibility with RT2-Profiler Multiplex RT-PCR

The Oncotype Dx assay is frequently used to test if breast cancer patients can be spared from chemotherapy without negative effects for their future clinical course. However, due to conflicting data on the assay utility, in the recent past its reimbursement situation in Germany was revised; due to continued requests by clinicians for predictive values, our group decided to implement an Oncotype Dx like alternative assay with the objective of obtaining quality and cost optimization. Customized RT²-Profiler assays covering the 21 gene panel of the Oncotype Dx assay were applied to a pilot cohort of breast cancer patients with known Oncotype Dx Recurrence Score (RS). The Ct values obtained with RT²-Profiler-assays were used to calculate the unscaled Recurrence Score (RSu) values and the thereon based RS according to the Oncotype DX assay rules if available. Despite consistent assay performance it was impossible to establish correlations between RT²-Profiler recurrence scores with the respective Oncotype DX values not to mention exact matches. By following the Oncotype DX assay and its interpretation as close as possible we faced several obstructions such as lack of information on RNA amount used, missing units in the single gene expression report, missing references cited in the original study that should explain the determination of the recurrence score formula, and vague information on the normalization of the gene expression impeding the reproduction of Oncotype Dx results in other laboratories. Unfortunately, the Oncotype Dx assay cannot be confirmed by the customized RT²-profiler assay, not least because of the fact that the individual gene measurements are not provided in the medical report, although these are mandatory for the RS calculation. In fact, the "single gene report" only contains unscaled scores of the ER, PR, and Her2 genes without any internationally accepted unit used to describe a transcript quantity. Therefore a direct comparison with the in-house measurement to evaluate its performance is impossible. With regard to our findings and the fact that the Oncotype RS represents a likelihood of the risk of relapse it thus remains impossible to assess the clinical necessity of this assay.

Identification of markers predictive for response to induction chemotherapy in patients with sinonasal undifferentiated carcinoma

OBJECTIVES

Sinonasal undifferentiated carcinoma (SNUC) is a rare, highly aggressive cancer. Despite aggressive multimodal therapy, its prognosis remains poor. Because of its locally advanced nature and high propensity for distant metastasis, we frequently use induction chemotherapy before definitive therapy in patients with SNUC. However, about 30% of patients do not respond to induction chemotherapy, and lack of response is associated with a poor survival rate. Therefore, in this study, we performed gene expression analysis of SNUC samples to identify prognostic markers for induction chemotherapy response.

MATERIALS AND METHODS

Formalin-fixed, paraffin-embedded SNUC tumor samples from previously untreated patients harvested before induction chemotherapy were used. Gene expression was performed using an oncology gene expression panel.

RESULTS

We identified 34 differentially expressed genes that distinguish the responders from the non-responders. Pathway analysis using these genes revealed alteration of multiple pathways between the two groups. Of these 34 genes, 24 distinguished between these two groups. Additionally, 16 gene pairs were associated with response to induction therapy.

CONCLUSION

We identified genes predictive of SNUC response to induction chemotherapy and pathways potentially associated with treatment outcome. This is the first report of identification of predictive biomarkers for response of SNUC to induction chemotherapy, and it may help us develop therapeutic strategies to improve the treatment outcomes of non-responders.

A biological function based biomarker panel optimization process

Implementation of multi-gene biomarker panels identified from high throughput data, including microarray or next generation sequencing, need to be adapted to a platform suitable in a clinical setting such as quantitative polymerase chain reaction. However, technical challenges when transitioning from one measurement platform to another, such as inconsistent measurement results can affect panel development. We describe a process to overcome the challenges by replacing poor performing genes during platform transition and reducing the number of features without impacting classification performance. This approach assumes that a diagnostic panel reflects the effect of dysregulated biological processes associated with a disease, and genes involved in the same biological processes and coordinately affected by a disease share a similar discriminatory power. The utility of this optimization process was assessed using a published sepsis diagnostic panel. Substitution of more than half of the genes and/or reducing genes based on biological processes did not negatively affect the performance of the sepsis diagnostic panel. Our results suggest a systematic gene substitution and reduction process based on biological function can be used to alleviate the challenges associated with clinical development of biomarker panels.

Digitizing omics profiles by divergence from a baseline

Technological advances enable increasingly comprehensive profiling of the molecular landscapes of cells, and these data can inform the personalized treatment of complex diseases. Two major obstacles are the complexity of these data and the high degree of person-to-person heterogeneity. We develop a highly simplified, personalized data representation by comparing the profile of an individual to the range of landscapes in a baseline population, thereby mimicking basic clinical diagnostic testing for departures of selected variables from normal levels. Moreover, our method can be applied to any data modality and at any level of granularity, from single features to any subset of features treated as a single entity, for example the gene expression levels in a pathway. Experiments involve both healthy human tissues and various cancer subtypes.

Data collected from omics technologies have revealed pervasive heterogeneity and stochasticity of molecular states within and between phenotypes. A prominent example of such heterogeneity occurs between genome-wide mRNA, microRNA, and methylation profiles from one individual tumor to another, even within a cancer subtype. However, current methods in bioinformatics, such as detecting differentially expressed genes or CpG sites, are population-based and therefore do not effectively model intersample diversity. Here we introduce a unified theory to quantify sample-level heterogeneity that is applicable to a single omics profile. Specifically, we simplify an omics profile to a digital representation based on the omics profiles from a set of samples from a reference or baseline population (e.g., normal tissues). The state of any subprofile (e.g., expression vector for a subset of genes) is said to be “divergent” if it lies outside the estimated support of the baseline distribution and is consequently interpreted as “dysregulated” relative to that baseline. We focus on two cases: single features (e.g., individual genes) and distinguished subsets (e.g., regulatory pathways). Notably, since the divergence analysis is at the individual sample level, dysregulation can be analyzed probabilistically; for example, one can estimate the probability that a gene or pathway is divergent in some population. Finally, the reduction in complexity facilitates a more “personalized” and biologically interpretable analysis of variation, as illustrated by experiments involving tissue characterization, disease detection and progression, and disease–pathway associations.

Cloud computing for genomic data analysis and collaboration

Next-generation sequencing has made major strides in the past decade. Studies based on large sequencing data sets are growing in number, and public archives for raw sequencing data have been doubling in size every 18 months. Leveraging these data requires researchers to use large-scale computational resources. Cloud computing, a model whereby users rent computers and storage from large data centres, is a solution that is gaining traction in genomics research. Here, we describe how cloud computing is used in genomics for research and large-scale collaborations, and argue that its elasticity, reproducibility and privacy features make it ideally suited for the large-scale reanalysis of publicly available archived data, including privacy-protected data.

Selective analysis of cancer-cell intrinsic transcriptional traits defines novel clinically relevant subtypes of colorectal cancer

Stromal content heavily impacts the transcriptional classification of colorectal cancer (CRC), with clinical and biological implications. Lineage-dependent stromal transcriptional components could therefore dominate over more subtle expression traits inherent to cancer cells. Since in patient-derived xenografts (PDXs) stromal cells of the human tumour are substituted by murine counterparts, here we deploy human-specific expression profiling of CRC PDXs to assess cancer-cell intrinsic transcriptional features. Through this approach, we identify five CRC intrinsic subtypes (CRIS) endowed with distinctive molecular, functional and phenotypic peculiarities: (i) CRIS-A: mucinous, glycolytic, enriched for microsatellite instability or KRAS mutations; (ii) CRIS-B: TGF-β pathway activity, epithelial–mesenchymal transition, poor prognosis; (iii) CRIS-C: elevated EGFR signalling, sensitivity to EGFR inhibitors; (iv) CRIS-D: WNT activation, IGF2 gene overexpression and amplification; and (v) CRIS-E: Paneth cell-like phenotype, TP53 mutations. CRIS subtypes successfully categorize independent sets of primary and metastatic CRCs, with limited overlap on existing transcriptional classes and unprecedented predictive and prognostic performances.

Stromal cells contribute to the gene expression profiles based on which colorectal cancer (CRC) molecular subtypes are classified. Here, patient-derived xenografts enable the authors to obtain cancer cell-specific transcriptomes by excluding transcripts from murine stromal cells, based on which they define CRC intrinsic subtypes (CRIS) and evaluate their prognostic and predictive potential.

Incorporating prior biological knowledge for network-based differential gene expression analysis using differentially weighted graphical LASSO

Background

Conventional differential gene expression analysis by methods such as student’s t-test, SAM, and Empirical Bayes often searches for statistically significant genes without considering the interactions among them. Network-based approaches provide a natural way to study these interactions and to investigate the rewiring interactions in disease versus control groups. In this paper, we apply weighted graphical LASSO (wgLASSO) algorithm to integrate a data-driven network model with prior biological knowledge (i.e., protein-protein interactions) for biological network inference. We propose a novel differentially weighted graphical LASSO (dwgLASSO) algorithm that builds group-specific networks and perform network-based differential gene expression analysis to select biomarker candidates by considering their topological differences between the groups.

Results

Through simulation, we showed that wgLASSO can achieve better performance in building biologically relevant networks than purely data-driven models (e.g., neighbor selection, graphical LASSO), even when only a moderate level of information is available as prior biological knowledge. We evaluated the performance of dwgLASSO for survival time prediction using two microarray breast cancer datasets previously reported by Bild et al. and van de Vijver et al. Compared with the top 10 significant genes selected by conventional differential gene expression analysis method, the top 10 significant genes selected by dwgLASSO in the dataset from Bild et al. led to a significantly improved survival time prediction in the independent dataset from van de Vijver et al. Among the 10 genes selected by dwgLASSO, UBE2S, SALL2, XBP1 and KIAA0922 have been confirmed by literature survey to be highly relevant in breast cancer biomarker discovery study. Additionally, we tested dwgLASSO on TCGA RNA-seq data acquired from patients with hepatocellular carcinoma (HCC) on tumors samples and their corresponding non-tumorous liver tissues. Improved sensitivity, specificity and area under curve (AUC) were observed when comparing dwgLASSO with conventional differential gene expression analysis method.

Conclusions

The proposed network-based differential gene expression analysis algorithm dwgLASSO can achieve better performance than conventional differential gene expression analysis methods by integrating information at both gene expression and network topology levels. The incorporation of prior biological knowledge can lead to the identification of biologically meaningful genes in cancer biomarker studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1515-1) contains supplementary material, which is available to authorized users.

Meta-analytic support vector machine for integrating multiple omics data

BACKGROUND

Of late, high-throughput microarray and sequencing data have been extensively used to monitor biomarkers and biological processes related to many diseases. Under this circumstance, the support vector machine (SVM) has been popularly used and been successful for gene selection in many applications. Despite surpassing benefits of the SVMs, single data analysis using small- and mid-size of data inevitably runs into the problem of low reproducibility and statistical power. To address this problem, we propose a meta-analytic support vector machine (Meta-SVM) that can accommodate multiple omics data, making it possible to detect consensus genes associated with diseases across studies.

RESULTS

Experimental studies show that the Meta-SVM is superior to the existing meta-analysis method in detecting true signal genes. In real data applications, diverse omics data of breast cancer (TCGA) and mRNA expression data of lung disease (idiopathic pulmonary fibrosis; IPF) were applied. As a result, we identified gene sets consistently associated with the diseases across studies. In particular, the ascertained gene set of TCGA omics data was found to be significantly enriched in the ABC transporters pathways well known as critical for the breast cancer mechanism.

CONCLUSION

The Meta-SVM effectively achieves the purpose of meta-analysis as jointly leveraging multiple omics data, and facilitates identifying potential biomarkers and elucidating the disease process.

Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Biochemically-inspired Machine Learning

Genomic aberrations and gene expression-defined subtypes in the large METABRIC patient cohort have been used to stratify and predict survival. The present study used normalized gene expression signatures of paclitaxel drug response to predict outcome for different survival times in METABRIC patients receiving hormone (HT) and, in some cases, chemotherapy (CT) agents. This machine learning method, which distinguishes sensitivity vs. resistance in breast cancer cell lines and validates predictions in patients; was also used to derive gene signatures of other HT  (tamoxifen) and CT agents (methotrexate, epirubicin, doxorubicin, and 5-fluorouracil) used in METABRIC. Paclitaxel gene signatures exhibited the best performance, however the other agents also predicted survival with acceptable accuracies. A support vector machine (SVM) model of paclitaxel response containing genes  ABCB1, ABCB11, ABCC1, ABCC10, BAD, BBC3, BCL2, BCL2L1, BMF, CYP2C8, CYP3A4, MAP2, MAP4, MAPT, NR1I2, SLCO1B3, TUBB1, TUBB4A, and TUBB4B was 78.6% accurate in predicting survival of 84 patients treated with both HT and CT (median survival ≥ 4.4 yr). Accuracy was lower (73.4%) in 304 untreated patients. The performance of other machine learning approaches was also evaluated at different survival thresholds. Minimum redundancy maximum relevance feature selection of a paclitaxel-based SVM classifier based on expression of genes  BCL2L1, BBC3, FGF2, FN1, and  TWIST1 was 81.1% accurate in 53 CT patients. In addition, a random forest (RF) classifier using a gene signature ( ABCB1, ABCB11, ABCC1, ABCC10, BAD, BBC3, BCL2, BCL2L1, BMF, CYP2C8, CYP3A4, MAP2, MAP4, MAPT, NR1I2,SLCO1B3, TUBB1, TUBB4A, and TUBB4B) predicted >3-year survival with 85.5% accuracy in 420 HT patients. A similar RF gene signature showed 82.7% accuracy in 504 patients treated with CT and/or HT. These results suggest that tumor gene expression signatures refined by machine learning techniques can be useful for predicting survival after drug therapies.

INDEED: Integrated differential expression and differential network analysis of omic data for biomarker discovery

Differential expression (DE) analysis is commonly used to identify biomarker candidates that have significant changes in their expression levels between distinct biological groups. One drawback of DE analysis is that it only considers the changes on single biomolecule level. Recently, differential network (DN) analysis has become popular due to its capability to measure the changes on biomolecular pair level. In DN analysis, network is typically built based on correlation and biomarker candidates are selected by investigating the network topology. However, correlation tends to generate over-complicated networks and the selection of biomarker candidates purely based on network topology ignores the changes on single biomolecule level. In this paper, we propose a novel approach, INDEED, that builds sparse differential network based on partial correlation and integrates DE and DN analyses for biomarker discovery. We applied this approach on real proteomic and glycomic data generated by liquid chromatography coupled with mass spectrometry for hepatocellular carcinoma (HCC) biomarker discovery study. For each omic data, we used one dataset to select biomarker candidates, built a disease classifier and evaluated the performance of the classifier on an independent dataset. The biomarker candidates, selected by INDEED, were more reproducible across independent datasets, and led to a higher classification accuracy in predicting HCC cases and cirrhotic controls compared with those selected by separate DE and DN analyses. INDEED also identified some candidates previously reported to be relevant to HCC, such as intercellular adhesion molecule 2 (ICAM2) and c4b-binding protein alpha chain (C4BPA), which were missed by both DE and DN analyses. In addition, we applied INDEED for survival time prediction based on transcriptomic data acquired by analysis of samples from breast cancer patients. We selected biomarker candidates and built a regression model for survival time prediction based on a gene expression dataset and patients' survival records. We evaluated the performance of the regression model on an independent dataset. Compared with the biomarker candidates selected by DE and DN analyses, those selected through INDEED led to more accurate survival time prediction.

Genomic and clinical predictors for improving estimator precision in randomized trials of breast cancer treatments

Background

The hope that genomic biomarkers would dramatically and immediately improve care for common, complex diseases has been tempered by slow progress in their translation beyond bioinformatics. We propose a novel use of genomic information where the goal is to improve estimator precision in a randomized trial. We analyze the potential precision gains from the popular MammaPrint genomic signature and clinical variables in simulations of randomized trials analyzed using covariate adjustment.

Methods

We apply an estimator for the average treatment effect in the trial that adjusts for prognostic baseline variables to improve precision [1]. This precision gain can be translated directly into sample size reduction and corresponding cost savings. We conduct simulation studies based on resampling genomic and clinical data of breast cancer patients from four publicly available observational studies.

Results

Separate simulation studies were conducted based on each of the four data sets, with sample sizes ranging from 115 to 307. Adjusting only for clinical variables provided gains of −1%, 5%, 6%, 17%, compared to the unadjusted estimator. Adjusting only for the MammaPrint genomic signature provided gains of 2%, 4%, 4%, 5%. Simultaneously adjusting for clinical variables and the genomic signature provided gains of 2%, 6%, 7%, 16%. The differences between precision gains from adjusting for genomic plus clinical variables, versus only clinical variables, were −1%, 0%, 1%, 3%.

Conclusions

Adjusting only for clinical variables led to substantial precision gains (at least 5%) in three of the four data sets, with a 1% precision loss in the remaining data set. These gains were unchanged or increased when sample sizes were doubled in our simulations. The precision gains due to incorporating genomic information, beyond the gains from adjusting for clinical variables, were not substantial.

MetaKTSP: a meta-analytic top scoring pair method for robust cross-study validation of omics prediction analysis

MOTIVATION

Supervised machine learning is widely applied to transcriptomic data to predict disease diagnosis, prognosis or survival. Robust and interpretable classifiers with high accuracy are usually favored for their clinical and translational potential. The top scoring pair (TSP) algorithm is an example that applies a simple rank-based algorithm to identify rank-altered gene pairs for classifier construction. Although many classification methods perform well in cross-validation of single expression profile, the performance usually greatly reduces in cross-study validation (i.e. the prediction model is established in the training study and applied to an independent test study) for all machine learning methods, including TSP. The failure of cross-study validation has largely diminished the potential translational and clinical values of the models. The purpose of this article is to develop a meta-analytic top scoring pair (MetaKTSP) framework that combines multiple transcriptomic studies and generates a robust prediction model applicable to independent test studies.

RESULTS

We proposed two frameworks, by averaging TSP scores or by combining P-values from individual studies, to select the top gene pairs for model construction. We applied the proposed methods in simulated data sets and three large-scale real applications in breast cancer, idiopathic pulmonary fibrosis and pan-cancer methylation. The result showed superior performance of cross-study validation accuracy and biomarker selection for the new meta-analytic framework. In conclusion, combining multiple omics data sets in the public domain increases robustness and accuracy of the classification model that will ultimately improve disease understanding and clinical treatment decisions to benefit patients.

AVAILABILITY AND IMPLEMENTATION

An R package MetaKTSP is available online. (http://tsenglab.biostat.pitt.edu/software.htm).

CONTACT

ctseng@pitt.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Proteomics of pediatric heart failure: from traditional biomarkers to new discovery strategies

Heart failure in children is a complex clinical syndrome with multiple aetiologies. The underlying disorders that lead to heart failure in children differ significantly from those in adults. Some clinical biomarkers for heart failure status and prognosis appear to be useful in both age groups. This review outlines the use and the present status of biomarkers for heart failure in paediatric cardiology. Furthermore, clinical scenarios in which development of new biomarkers might address management or prognosis are discussed. Finally, strategies for proteomic discovery of novel biomarkers and application to practice are described.

Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration

The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org.

Learning dysregulated pathways in cancers from differential variability analysis

Analysis of gene sets can implicate activity in signaling pathways that is responsible for cancer initiation and progression, but is not discernible from the analysis of individual genes. Multiple methods and software packages have been developed to infer pathway activity from expression measurements for set of genes targeted by that pathway. Broadly, three major methodologies have been proposed: over-representation, enrichment, and differential variability. Both over-representation and enrichment analyses are effective techniques to infer differentially regulated pathways from gene sets with relatively consistent differentially expressed (DE) genes. Specifically, these algorithms aggregate statistics from each gene in the pathway. However, they overlook multivariate patterns related to gene interactions and variations in expression. Therefore, the analysis of differential variability of multigene expression patterns can be essential to pathway inference in cancers. The corresponding methodologies and software packages for such multivariate variability analysis of pathways are reviewed here. We also introduce a new, computationally efficient algorithm, expression variation analysis (EVA), which has been implemented along with a previously proposed algorithm, Differential Rank Conservation (DIRAC), in an open source R package, gene set regulation (GSReg). EVA inferred similar pathways as DIRAC at reduced computational costs. Moreover, EVA also inferred different dysregulated pathways than those identified by enrichment analysis.

switchBox: an R package for k-Top Scoring Pairs classifier development

k-Top Scoring Pairs (kTSP) is a classification method for prediction from high-throughput data based on a set of the paired measurements. Each of the two possible orderings of a pair of measurements (e.g. a reversal in the expression of two genes) is associated with one of two classes. The kTSP prediction rule is the aggregation of voting among such individual two-feature decision rules based on order switching. kTSP, like its predecessor, Top Scoring Pair (TSP), is a parameter-free classifier relying only on ranking of a small subset of features, rendering it robust to noise and potentially easy to interpret in biological terms. In contrast to TSP, kTSP has comparable accuracy to standard genomics classification techniques, including Support Vector Machines and Prediction Analysis for Microarrays. Here, we describe 'switchBox', an R package for kTSP-based prediction.

AVAILABILITY

The 'switchBox' package is freely available from Bioconductor: http://www.bioconductor.org.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Increased expression of mdig predicts poorer survival of the breast cancer patients

Breast cancer is the most common cancer and the second leading cause of cancer death among women of all races and Hispanic origin populations in the United States. In the present study, we reported that the survival time of the breast cancer patients is influenced by the expression level of mdig, a previously identified lung cancer-associated oncogene encoding a JmjC-domain protein. By checking the expression levels of mRNA and protein of mdig through both RT-PCR and immunohistochemistry in samples from 204 patients, we noticed that about 30% of breast cancer samples showed increased expression of mdig. Correlation of the mdig expression levels with the survival time of the breast cancer patients indicated a clear inverse relationship between mdig expression and patient survival, including poorer overall survival, distant metastasis free survival, relapse free survival, and post-progression survival. Taken together, these data suggest that an increased expression of mdig is an important prognostic factor for poorer survival time of the breast cancer patients.

A simple and reproducible breast cancer prognostic test

Background

A small number of prognostic and predictive tests based on gene expression are currently offered as reference laboratory tests. In contrast to such success stories, a number of flaws and errors have recently been identified in other genomic-based predictors and the success rate for developing clinically useful genomic signatures is low. These errors have led to widespread concerns about the protocols for conducting and reporting of computational research. As a result, a need has emerged for a template for reproducible development of genomic signatures that incorporates full transparency, data sharing and statistical robustness.

Results

Here we present the first fully reproducible analysis of the data used to train and test MammaPrint, an FDA-cleared prognostic test for breast cancer based on a 70-gene expression signature. We provide all the software and documentation necessary for researchers to build and evaluate genomic classifiers based on these data. As an example of the utility of this reproducible research resource, we develop a simple prognostic classifier that uses only 16 genes from the MammaPrint signature and is equally accurate in predicting 5-year disease free survival.

Conclusions

Our study provides a prototypic example for reproducible development of computational algorithms for learning prognostic biomarkers in the era of personalized medicine.