The path to routine use of genomic biomarkers in the cancer clinic

Quantifying intratumoral biomarker heterogeneity in tubo-ovarian high-grade serous carcinoma to optimize clinical translation

Intratumoral heterogeneity (ITH) is spatial, phenotypic, or molecular differences within the same tumor that have important implications for accurate tumor classification and assessment of predictive biomarkers. The Canadian Ovarian Experimental Unified Resource (COEUR) has created a cohort of 437 FFPE tissue specimens from 108 tubo-ovarian high-grade serous carcinoma (HGSC) patients to quantify ITH across the anatomical sites and between primary and recurrence. We quantified the ITH of six clinically used immunohistochemical diagnostic and prognostic biomarkers (WT1, p53, p16, PR, CD8, and Ki67). Markers were stained on tissue microarrays and scored using a continuous or categorical interpretation of staining patterns. Two-way random effect and nested intraclass correlation were used to assess continuous markers, and Gwet's AC1 was used for categorical markers. All biomarkers showed at least substantial agreement over several spatial comparisons, with WT1, p53 and p16 showing almost perfect agreement for most spatial comparisons. Similarly, categorical WT1, p53 and p16 showed almost perfect agreement for temporal comparisons, while the agreement for primary versus recurrence for PR, CD8 and Ki67 was only fair. We provide power calculations to achieve reliability of > 0.60 and recommend testing emerging protein biomarkers to see whether they reach a clinically acceptable benchmark level of ITH.

A pathway-based computational framework for identification of a new modal of multi-omics biomarkers and its application in esophageal cancer

BACKGROUND

The pathway-based strategy has been recently proposed for identifying biomarkers with the advantages of higher biological interpretability and cross-data robustness than the conventional gene-based strategy. However, its utility in clinical applications has been limited due to the high computational complexity and ill-defined performance.

OBJECTIVE

The current study presents a machine learning-based computational framework using multi-omics data for identifying a new modal of biomarkers, called pathway-derived core biomarkers, which have the advantages of both gene-based and pathway-based biomarkers.

METHODS

Machine-learning methods and gene-pathway network were integrated to select the pathway-derived core biomarkers. Multiple machine-learning algorithms were used to construct and validate the diagnostic models of the biomarkers based on more than 1400 multi-omics clinical samples of esophageal squamous cell carcinoma (ESCC).

RESULTS

The results showed that the classifier models based on the new modal biomarkers achieved superior performance in the training datasets with an average AUC/accuracy of 0.98/0.95 and 0.89/0.81 for mRNAs and miRNA, respectively, higher than the currently known classifier models based on the conventional gene-based strategy and pathway-based strategy. In the testing cohorts, the AUC/accuracy increased by 6.1 %/7.3 % than the models based on the native gene-based biomarkers. The improved performance was further confirmed in independent validation cohorts. Specifically, the sensitivity/specificity increased by ∼3 % and the variance significantly decreased by ∼69 % compared with that of the native gene-based biomarkers. Importantly, the pathway-derived core biomarkers also recovered 45 % more previously reported biomarkers than the gene-based biomarkers and are more functionally relevant to the ESCC etiology (involved in 14 versus 7 pathways related with ESCC or other cancer), highlighting the cross-data robustness of this new modal of biomarkers via enhanced functional relevance.

CONCLUSIONS

The results demonstrated that the new modal of biomarkers not only have improved predicting performance and robustness, but also exhibit higher functional interpretability thus leading to the potential application in cancer diagnosis.

ssMutPA: single-sample mutation-based pathway analysis approach for cancer precision medicine

BACKGROUND

Single-sample pathway enrichment analysis is an effective approach for identifying cancer subtypes and pathway biomarkers, facilitating the development of precision medicine. However, the existing approaches focused on investigating the changes in gene expression levels but neglected somatic mutations, which play a crucial role in cancer development.

FINDINGS

In this study, we proposed a novel single-sample mutation-based pathway analysis approach (ssMutPA) to infer individualized pathway activities by integrating somatic mutation data and the protein-protein interaction network. For each sample, ssMutPA first uses local and global weighted strategies to evaluate the effects of genes from mutations according to the network topology and then calculates a single-sample mutation-based pathway enrichment score (ssMutPES) to reflect the accumulated effect of mutations of each pathway. To illustrate the performance of ssMutPA, we applied it to 33 cancer cohorts from The Cancer Genome Atlas database and revealed patient stratification with significantly different prognosis in each cancer type based on the ssMutPES profiles. We also found that the identified characteristic pathways with high overlap across different cancers could be used as potential prognosis biomarkers. Moreover, we applied ssMutPA to 2 melanoma cohorts with immunotherapy and identified a subgroup of patients who may benefit from therapy.

CONCLUSIONS

We provided evidence that ssMutPA could infer mutation-based individualized pathway activity profiles and complement the current individualized pathway analysis approaches focused on gene expression data, which may offer the potential for the development of precision medicine. ssMutPA is available at https://CRAN.R-project.org/package=ssMutPA.

Advances in transcriptomics and proteomics in differentiated thyroid cancer: An updated perspective (Review)

Thyroid cancer (TC) is a broad classification of neoplasms that includes differentiated thyroid cancer (DTC) as a common histological subtype. DTC is characterized by an increased mortality rate in advanced stages, which contributes to the overall high mortality rate of DTC. This progression is mainly attributed to alterations in molecular driver genes, resulting in changes in phenotypes such as invasion, metastasis and dedifferentiation. Clinical management of DTC is challenging due to insufficient diagnostic and therapeutic options. The advent of-omics technology has presented a promising avenue for the diagnosis and treatment of DTC. Identifying molecular markers that can predict the early progression of DTC to a late adverse outcome is essential for precise diagnosis and treatment. The present review aimed to enhance our understanding of DTC by integrating big data with biological systems through-omics technology, specifically transcriptomics and proteomics, which can shed light on the molecular mechanisms underlying carcinogenesis.

The clonal expression genes associated with poor prognosis of liver cancer

The extensive spatial genomic intratumor heterogeneity (ITH) in liver cancer hindered treatment development and limited biomarker design. Early events that drive tumor malignant transformation in tumor founder cells are clonally present in all tumor cell populations, which provide stable biomarkers for the localization of tumor cells and patients’ prognosis. In the present study, we identified the recurrently clonal somatic mutations and copy number alterations (CNAs) (893 clonal somatic mutations and 6,617 clonal CNAs) in 353 liver cancer patients from The Cancer Genome Atlas (TCGA) and evaluated their prognosis potential. We showed that prognosis-related clonal alterations might play essential roles in tumor evolution. We identified 32 prognosis related clonal alterations differentially expressed between paired normal and tumor samples, that their expression was cross-validated by three independent cohorts (50 paired samples in TCGA, 149 paired samples in GSE76297, and 9 paired samples in SUB6779164). These clonal expression alterations were also significantly correlated with clinical phenotypes. Using stepwise regression, we identified five (UCK2, EFNA4, KPAN2, UBE2T, and KIF14) and six (MCM10, UCK2, IQGAP3, EFNA4, UBE2T, and KPNA2) clonal expression alterations for recurrence and survival model construction, respectively. Furthermore, in 10 random repetitions, we showed strong applicability of the multivariate Cox regression models constructed based on the clonal expression genes, which significantly predicted the outcomes of the patients in all the training and validation sets. Taken together, our work may provide a new avenue to overcome spatial ITH and refine biomarker design across cancer types.

ENDORSE: a prognostic model for endocrine therapy in estrogen-receptor-positive breast cancers

Advanced and metastatic estrogen receptor-positive (ER+ ) breast cancers are often endocrine resistant. However, endocrine therapy remains the primary treatment for all advanced ER+ breast cancers. Treatment options that may benefit resistant cancers, such as add-on drugs that target resistance pathways or switching to chemotherapy, are only available after progression on endocrine therapy. Here we developed an endocrine therapy prognostic model for early and advanced ER+ breast cancers. The endocrine resistance (ENDORSE) model is composed of two components, each based on the empirical cumulative distribution function of ranked expression of gene signatures. These signatures include a feature set associated with long-term survival outcomes on endocrine therapy selected using lasso-regularized Cox regression and a pathway-based curated set of genes expressed in response to estrogen. We extensively validated ENDORSE in multiple ER+ clinical trial datasets and demonstrated superior and consistent performance of the model over clinical covariates, proliferation markers, and multiple published signatures. Finally, genomic and pathway analyses in patient data revealed possible mechanisms that may help develop rational stratification strategies for endocrine-resistant ER+ breast cancer patients.

Individualized pathway activity algorithm identifies oncogenic pathways in pan-cancer analysis

Background

Accumulative evidences have shown that dysregulation of biological pathways contributed to the initiation and progression of malignant tumours. Several methods for pathway activity measurement have been proposed, but they are restricted to making comparisons between groups or sensitive to experimental batch effects.

Methods

We introduced a novel method for individualized pathway activity measurement (IPAM) that is based on the ranking of gene expression levels in individual sample. Taking advantage of IPAM, we calculated the pathway activity of 318 pathways from KEGG database in the 10528 tumour/normal samples of 33 cancer types from TCGA to identify characteristic dysregulated pathways among different cancer types.

Findings

IPAM precisely quantified the level of activity of each pathway in pan-cancer analysis and exhibited better performance in cancer classification and prognosis prediction over five widely used tools. The average ROC-AUC of cancer diagnostic model using tumour-educated platelets (TEPs) reached 92.84%, suggesting the potential of our algorithm in early diagnosis of cancer. We identified several pathways significantly deregulated and associated with patient survival in a large fraction of cancer types, such as tyrosine metabolism, fatty acid degradation, cell cycle, p53 signalling pathway and DNA replication. We also confirmed the dominant role of metabolic pathways in cancer pathway dysregulation and identified the driving factors of specific pathway dysregulation, such as PPARA for branched-chain amino acid metabolism and NR1I2, NR1I3 for fatty acid metabolism.

Interpretation

Our study will provide novel clues for understanding the pathological mechanisms of cancer, ultimately paving the way for personalized medicine of cancer.

Funding

A full list of funding can be found in the Acknowledgements section.

Identification of human gene research articles with wrongly identified nucleotide sequences

Nucleotide sequence reagents underpin molecular techniques that have been applied across hundreds of thousands of publications. We have previously reported wrongly identified nucleotide sequence reagents in human research publications and described a semi-automated screening tool Seek & Blastn to fact-check their claimed status. We applied Seek & Blastn to screen >11,700 publications across five literature corpora, including all original publications in Gene from 2007 to 2018 and all original open-access publications in Oncology Reports from 2014 to 2018. After manually checking Seek & Blastn outputs for >3,400 human research articles, we identified 712 articles across 78 journals that described at least one wrongly identified nucleotide sequence. Verifying the claimed identities of >13,700 sequences highlighted 1,535 wrongly identified sequences, most of which were claimed targeting reagents for the analysis of 365 human protein-coding genes and 120 non-coding RNAs. The 712 problematic articles have received >17,000 citations, including citations by human clinical trials. Given our estimate that approximately one-quarter of problematic articles may misinform the future development of human therapies, urgent measures are required to address unreliable gene research articles.

Mechanism-Centric Approaches for Biomarker Detection and Precision Therapeutics in Cancer

Biomarker discovery is at the heart of personalized treatment planning and cancer precision therapeutics, encompassing disease classification and prognosis, prediction of treatment response, and therapeutic targeting. However, many biomarkers represent passenger rather than driver alterations, limiting their utilization as functional units for therapeutic targeting. We suggest that identification of driver biomarkers through mechanism-centric approaches, which take into account upstream and downstream regulatory mechanisms, is fundamental to the discovery of functionally meaningful markers. Here, we examine computational approaches that identify mechanism-centric biomarkers elucidated from gene co-expression networks, regulatory networks (e.g., transcriptional regulation), protein-protein interaction (PPI) networks, and molecular pathways. We discuss their objectives, advantages over gene-centric approaches, and known limitations. Future directions highlight the importance of input and model interpretability, method and data integration, and the role of recently introduced technological advantages, such as single-cell sequencing, which are central for effective biomarker discovery and time-cautious precision therapeutics.

Tracking Cancer Evolution through the Disease Course

During cancer evolution, constituent tumor cells compete under dynamic selection pressures. Phenotypic variation can be observed as intratumor heterogeneity, which is propagated by genome instability leading to mutations, somatic copy-number alterations, and epigenomic changes. TRACERx was set up in 2014 to observe the relationship between intratumor heterogeneity and patient outcome. By integrating multiregion sequencing of primary tumors with longitudinal sampling of a prospectively recruited patient cohort, cancer evolution can be tracked from early- to late-stage disease and through therapy. Here we review some of the key features of the studies and look to the future of the field. SIGNIFICANCE: Cancers evolve and adapt to environmental challenges such as immune surveillance and treatment pressures. The TRACERx studies track cancer evolution in a clinical setting, through primary disease to recurrence. Through multiregion and longitudinal sampling, evolutionary processes have been detailed in the tumor and the immune microenvironment in non-small cell lung cancer and clear-cell renal cell carcinoma. TRACERx has revealed the potential therapeutic utility of targeting clonal neoantigens and ctDNA detection in the adjuvant setting as a minimal residual disease detection tool primed for translation into clinical trials.

A fluorogenic probe for dynamic tracking of lipid droplets’ polarity during the evolution of cancer

Exploring the changes in the polarity of intracellular lipid droplets (LDs) during the evolution of cancer is important for cancer detection and treatment.

Identification of the Mutational Landscape of Gynecological Malignancies

Background: Cancer is a complex disease that arises from the accumulation of multiple genetic and non-genetic changes. Advances in sequencing technologies have allowed unbiased and global analysis of patient-derived tumor samples and the discovery of genetic and transcriptional changes in key genes and oncogenic pathways. That in turn has facilitated a better understanding of the underlying causes of cancer initiation and progression, resulting in new therapeutic targets. Methods: In our study, we have analyzed the mutational landscape of gynecological malignancies using datasets from The Cancer Genome Atlas (TCGA). We have also analyzed Oncomine datasets to establish the impact of their alteration on disease recurrence and survival of patients. Results: In this study, we analyzed a series of different gynecological malignancies for commonly occurring genetic and non-genetic alterations. These studies show that white women have higher incidence of gynecological malignancies. Furthermore, our study identified 16 genes that are altered at a frequency >10% among all of the gynecological malignancies and tumor suppressor TP53 is the most altered gene in these malignancies (>50% of the cases). The top 16 genes fall into the categories of either tumor suppressor or oncogenes and a subset of these genes are associated with poor prognosis, some affecting recurrence and survival of ovarian cancer patients. Conclusion: In sum, our study identified 16 major genes that are broadly mutated in a large majority of gynecological malignancies and in some cases predict survival and recurrence in patients with gynecological malignancies. We predict that the functional studies will determine their relative role in the initiation and progression of gynecological malignancies and also establish if some of them represents drug targets for anti-cancer therapy.

BATCAVE: calling somatic mutations with a tumor- and site-specific prior

Detecting somatic mutations withins tumors is key to understanding treatment resistance, patient prognosis and tumor evolution. Mutations at low allelic frequency, those present in only a small portion of tumor cells, are particularly difficult to detect. Many algorithms have been developed to detect such mutations, but none models a key aspect of tumor biology. Namely, every tumor has its own profile of mutation types that it tends to generate. We present BATCAVE (Bayesian Analysis Tools for Context-Aware Variant Evaluation), an algorithm that first learns the individual tumor mutational profile and mutation rate then uses them in a prior for evaluating potential mutations. We also present an R implementation of the algorithm, built on the popular caller MuTect. Using simulations, we show that adding the BATCAVE algorithm to MuTect improves variant detection. It also improves the calibration of posterior probabilities, enabling more principled tradeoff between precision and recall. We also show that BATCAVE performs well on real data. Our implementation is computationally inexpensive and straightforward to incorporate into existing MuTect pipelines. More broadly, the algorithm can be added to other variant callers, and it can be extended to include additional biological features that affect mutation generation.

Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology

In the past decade, advances in precision oncology have resulted in an increased demand for predictive assays that enable the selection and stratification of patients for treatment. The enormous divergence of signalling and transcriptional networks mediating the crosstalk between cancer, stromal and immune cells complicates the development of functionally relevant biomarkers based on a single gene or protein. However, the result of these complex processes can be uniquely captured in the morphometric features of stained tissue specimens. The possibility of digitizing whole-slide images of tissue has led to the advent of artificial intelligence (AI) and machine learning tools in digital pathology, which enable mining of subvisual morphometric phenotypes and might, ultimately, improve patient management. In this Perspective, we critically evaluate various AI-based computational approaches for digital pathology, focusing on deep neural networks and 'hand-crafted' feature-based methodologies. We aim to provide a broad framework for incorporating AI and machine learning tools into clinical oncology, with an emphasis on biomarker development. We discuss some of the challenges relating to the use of AI, including the need for well-curated validation datasets, regulatory approval and fair reimbursement strategies. Finally, we present potential future opportunities for precision oncology.

A clonal expression biomarker associates with lung cancer mortality

An aim of molecular biomarkers is to stratify patients with cancer into disease subtypes predictive of outcome, improving diagnostic precision beyond clinical descriptors such as tumor stage1. Transcriptomic intratumor heterogeneity (RNA-ITH) has been shown to confound existing expression-based biomarkers across multiple cancer types2-6. Here, we analyze multi-region whole-exome and RNA sequencing data for 156 tumor regions from 48 patients enrolled in the TRACERx study to explore and control for RNA-ITH in non-small cell lung cancer. We find that chromosomal instability is a major driver of RNA-ITH, and existing prognostic gene expression signatures are vulnerable to tumor sampling bias. To address this, we identify genes expressed homogeneously within individual tumors that encode expression modules of cancer cell proliferation and are often driven by DNA copy-number gains selected early in tumor evolution. Clonal transcriptomic biomarkers overcome tumor sampling bias, associate with survival independent of clinicopathological risk factors, and may provide a general strategy to refine biomarker design across cancer types.

Cytogenetics and Cytogenomics Evaluation in Cancer

The availability of cytogenetics and cytogenomics technologies improved the detection and identification of tumor molecular signatures as well as the understanding of cancer initiation and progression. The use of large-scale and high-throughput cytogenomics technologies has led to a fast identification of several cancer candidate biomarkers associated with diagnosis, prognosis, and therapeutics. The advent of array comparative genomic hybridization and next-generation sequencing technologies has significantly improved the knowledge about cancer biology, underlining driver genes to guide targeted therapy development, drug-resistance prediction, and pharmacogenetics. However, few of these candidate biomarkers have made the transition to the clinic with a clear benefit for the patients. Technological progress helped to demonstrate that cellular heterogeneity plays a significant role in tumor progression and resistance/sensitivity to cancer therapies, representing the major challenge of precision cancer therapy. A paradigm shift has been introduced in cancer genomics with the recent advent of single-cell sequencing, since it presents a lot of applications with a clear benefit to oncological patients, namely, detection of intra-tumoral heterogeneity, mapping clonal evolution, monitoring the development of therapy resistance, and detection of rare tumor cell populations. It seems now evident that no single biomarker could provide the whole information necessary to early detect and predict the behavior and prognosis of tumors. The promise of precision medicine is based on the molecular profiling of tumors being vital the continuous progress of high-throughput technologies and the multidisciplinary efforts to catalogue chromosomal rearrangements and genomic alterations of human cancers and to do a good interpretation of the relation genotype-phenotype.

The Possibility of Systematic Research Fraud Targeting Under-Studied Human Genes: Causes, Consequences, and Potential Solutions

A major reason for biomarker failure is the selection of candidate biomarkers based on inaccurate or incorrect published results. Incorrect research results leading to the selection of unproductive biomarker candidates are largely considered to stem from unintentional research errors. The additional possibility that biomarker research may be actively misdirected by research fraud has been given comparatively little consideration. This review discusses what we believe to be a new threat to biomarker research, namely, the possible systematic production of fraudulent gene knockdown studies that target under-studied human genes. We describe how fraudulent papers may be produced in series by paper mills using what we have described as a 'theme and variations' model, which could also be considered a form of salami slicing. We describe features of these single-gene knockdown publications that may allow them to evade detection by journal editors, peer reviewers, and readers. We then propose a number of approaches to facilitate their detection, including improved awareness of the features of publications constructed in series, broader requirements to post submitted manuscripts to preprint servers, and the use of semi-automated literature screening tools. These approaches may collectively improve the detection of fraudulent studies that might otherwise impede future biomarker research.

Next Generation Sequencing in Clinical Oncology: Applications, Challenges and Promises: A Review Article

BACKGROUND

The aim of this mini-review is to highlight the potential applications of next-generation sequencing technology to the field of clinical oncology with respect to genetic diagnosis, cancer classification, predictive biomarkers and personalized medicine.

METHODS

Scientific databases were searched to collect relative data.

RESULTS

Effective systematic analysis of whole-genome sequence and whole-exome sequence of tumors, targeted genome profiling, transcriptome sequencing and tumor-normal comparisons can be performed using NGS in order to diagnosis of several types of cancer.

CONCLUSION

NGS technology can be powerful enough to discover new and infrequent gene alterations, identify hereditary cancer mutation carriers and provide a reliable molecular portrait of wide range of cancers in a quick and cost-effective manner.

Prediction of early breast cancer patient survival using ensembles of hypoxia signatures

Background

Biomarkers are a key component of precision medicine. However, full clinical integration of biomarkers has been met with challenges, partly attributed to analytical difficulties. It has been shown that biomarker reproducibility is susceptible to data preprocessing approaches. Here, we systematically evaluated machine-learning ensembles of preprocessing methods as a general strategy to improve biomarker performance for prediction of survival from early breast cancer.

Results

We risk stratified breast cancer patients into either low-risk or high-risk groups based on four published hypoxia signatures (Buffa, Winter, Hu, and Sorensen), using 24 different preprocessing approaches for microarray normalization. The 24 binary risk profiles determined for each hypoxia signature were combined using a random forest to evaluate the efficacy of a preprocessing ensemble classifier. We demonstrate that the best way of merging preprocessing methods varies from signature to signature, and that there is likely no ‘best’ preprocessing pipeline that is universal across datasets, highlighting the need to evaluate ensembles of preprocessing algorithms. Further, we developed novel signatures for each preprocessing method and the risk classifications from each were incorporated in a meta-random forest model. Interestingly, the classification of these biomarkers and its ensemble show striking consistency, demonstrating that similar intrinsic biological information are being faithfully represented. As such, these classification patterns further confirm that there is a subset of patients whose prognosis is consistently challenging to predict.

Conclusions

Performance of different prognostic signatures varies with pre-processing method. A simple classifier by unanimous voting of classifications is a reliable way of improving on single preprocessing methods. Future signatures will likely require integration of intrinsic and extrinsic clinico-pathological variables to better predict disease-related outcomes.

Novel urinary biomarkers for the detection of bladder cancer: A systematic review

BACKGROUND

Urinary biomarkers for the diagnosis of bladder cancer represents an area of considerable research which has been tested in both patients presenting with haematuria and non-muscle invasive bladder cancer patients requiring surveillance cystoscopy. In this systematic review, we identify and appraise the diagnostic sensitive and specificity of reported novel biomarkers of different 'omic' class and highlight promising biomarkers investigated to date.

METHODS

A MEDLINE/Pubmed systematic search was performed between January 2013 and July 2017 using the following keywords: (bladder cancer OR transitional cell carcinoma OR urothelial cell carcinoma) AND (detection OR diagnosis) AND urine AND (biomarker OR assay). All studies had a minimum of 20 patients in both bladder cancer and control arms and reported sensitivity and/or specificity and/or receiver operating characteristics (ROC) curve. QUADAS-2 tool was used to assess risk of bias and applicability of studies. The search protocol was registered in the PROSPERO database (CRD42016049918).

RESULTS

Systematic search yielded 115 reports were included for analysis. In single target biomarkers had a sensitivity of 2-94%, specificity of 46-100%, positive predictive value (PPV) of 47-100% and negative predictive value (NPV) of 21-94%. Multi-target biomarkers achieved a sensitivity of 24-100%, specificity of 48-100%, PPV of 42-95% and NPV of 32-100%. 50 studies achieved a sensitivity and specificity of ≥80%. Protein (n = 59) and transcriptomic (n = 21) biomarkers represents the most studied biomarkers. Multi-target biomarker panels had a better diagnostic accuracy compared to single biomarker targets. Urinary cytology with urinary biomarkers improved the diagnostic ability of the biomarker. The sensitivity and specificity of biomarkers were higher for primary diagnosis compared to patients in the surveillance setting. Most studies were case control studies and did not have a predefined threshold to determine a positive test result indicating a possible risk of bias.

CONCLUSION

This comprehensive systematic review provides an update on urinary biomarkers of different 'omic' class and highlights promising biomarkers. Few biomarkers achieve a high sensitivity and negative predictive value. Such biomarkers will require external validation in a prospective observational setting before adoption in clinical practice.

Placing RNA in context and space - methods for spatially resolved transcriptomics

Single-cell transcriptomics provides us with completely new insights into the molecular diversity of different cell types and the different states they can adopt. The technique generates inventories of cells that constitute the building blocks of multicellular organisms. However, since the method requires isolation of discrete cells, information about the original location within tissue is lost. Therefore, it is not possible to draw detailed cellular maps of tissue architecture and their positioning in relation to other cells. In order to better understand the cellular and tissue function of multicellular organisms, we need to map the cells within their physiological, morphological, and anatomical context and space. In this review, we will summarize and compare the different methods of in situ RNA analysis and the most recent developments leading to more comprehensive and highly multiplexed spatially resolved transcriptomic approaches. We will discuss their highlights and advantages as well as their limitations and challenges and give an outlook on promising future applications and directions both within basic research as well as clinical integration.

Cancer molecular markers: A guide to cancer detection and management

Cancer is generally caused by the molecular alterations which lead to specific mutations. Advances in molecular biology have provided an impetus to the study of cancers with valuable prognostic and predictive significance. Over the hindsight various attempts have been undertaken by scientists worldwide, in the management of cancer; where, we have witnessed a number of molecular markers which allow the early detection of cancers and lead to a decrease in its mortality rate. Recent advances in oncology have led to the discovery of cancer markers that has allowed early detection and targeted therapy of tumors. In this context, current review provides a detail outlook on various molecular markers for diagnosis, prognosis and management of therapeutic response in cancer patients.

Biomarkers of Duchenne muscular dystrophy: current findings

Numerous biomarkers have been unveiled in the rapidly evolving biomarker discovery field, with an aim to improve the clinical management of disorders. In rare diseases, such as Duchenne muscular dystrophy, this endeavor has created a wealth of knowledge that, if effectively exploited, will benefit affected individuals, with respect to health care, therapy, improved quality of life and increased life expectancy. The most promising findings and molecular biomarkers are inspected in this review, with an aim to provide an overview of currently known biomarkers and the technological developments used. Biomarkers as cells, genetic variations, miRNAs, proteins, lipids and/or metabolites indicative of disease severity, progression and treatment response have the potential to improve development and approval of therapies, clinical management of DMD and patients’ life quality. We highlight the complexity of translating research results to clinical use, emphasizing the need for biomarkers, fit for purpose and describe the challenges associated with qualifying biomarkers for clinical applications.

Circular RNAs as Biomarkers for Cancer

As a type of novel noncoding RNAs, circular RNAs (circRNAs) have attracted great interest due to its different characteristics from linear RNAs. They are abundantly and stably present in the transcriptome of eukaryotic cells, with development stage specificity and high conservatism. Because circRNAs are not easily degraded by exonuclease RNase R, they can exist more stably in body fluids than linear RNAs. Based on these unique conditions, circRNAs have great potential value as clinical diagnostic and prognostic markers. As the research deepens, more and more evidences suggest that circRNAs may be closely associated with many diseases, especially cancer. Numerous studies have demonstrated the abnormal expression of circRNAs in cancer, and they can regulate the occurrence and progression of cancer by targeting key genes. Abundant circRNAs in tissues and cells can be released into saliva and blood. It is undeniable that circRNAs are a class of promising future biomarkers for cancer diagnosis and prognosis. Here we summarize the researches on circRNAs and cancer over the past few years. We expect this summary to be a stepping stone to further exploration of possible circRNAs as cancer biomarkers.

Experimental design and quantitative analysis of microbial community multiomics

Studies of the microbiome have become increasingly sophisticated, and multiple sequence-based, molecular methods as well as culture-based methods exist for population-scale microbiome profiles. To link the resulting host and microbial data types to human health, several experimental design considerations, data analysis challenges, and statistical epidemiological approaches must be addressed. Here, we survey current best practices for experimental design in microbiome molecular epidemiology, including technologies for generating, analyzing, and integrating microbiome multiomics data. We highlight studies that have identified molecular bioactives that influence human health, and we suggest steps for scaling translational microbiome research to high-throughput target discovery across large populations.

Quantification of mutant SPOP proteins in prostate cancer using mass spectrometry-based targeted proteomics

BACKGROUND

Speckle-type POZ protein (SPOP) is an E3 ubiquitin ligase adaptor protein that functions as a potential tumor suppressor, and SPOP mutations have been identified in ~10% of human prostate cancers. However, it remains unclear if mutant SPOP proteins can be utilized as biomarkers for early detection, diagnosis, prognosis or targeted therapy of prostate cancer. Moreover, the SPOP mutation sites are distributed in a relatively short region with multiple lysine residues, posing significant challenges for bottom-up proteomics analysis of the SPOP mutations.

METHODS

To address this issue, PRISM (high-pressure, high-resolution separations coupled with intelligent selection and multiplexing)-SRM (selected reaction monitoring) mass spectrometry assays have been developed for quantifying wild-type SPOP protein and 11 prostate cancer-derived SPOP mutations.

RESULTS

Despite inherent limitations due to amino acid sequence constraints, all the PRISM-SRM assays developed using Arg-C digestion showed a linear dynamic range of at least two orders of magnitude, with limits of quantification ranged from 0.1 to 1 fmol/μg of total protein in the cell lysate. Applying these SRM assays to analyze HEK293T cells with and without expression of the three most frequent SPOP mutations in prostate cancer (Y87N, F102C or F133V) led to confident detection of all three SPOP mutations in corresponding positive cell lines but not in the negative cell lines. Expression of the F133V mutation and wild-type SPOP was at much lower levels compared to that of F102C and Y87N mutations; however, at present, it is unknown if this also affects the biological activity of the SPOP protein.

CONCLUSIONS

In summary, PRISM-SRM enables multiplexed, isoform-specific detection of mutant SPOP proteins in cell lysates, providing significant potential in biomarker development for prostate cancer.

Docking of CDK1 with antibiotic drugs revealed novel therapeutic value in breast ductal cancer in situ

The aim of our research is to identify potential genes associated with Ductal carcinoma in situ (DCIS) through microarrays. The microarray dataset GS54665 were downloaded from the GEO(Gene Expression Omnibus) database. Dysregulated genes were screened and their associations with DCIS was analyzed by comprehensive bioinformatics tools. A total of 649 differential expression genes were identified between normal and DCIS samples, including 224 up-regulated genes and 425 down-regulated genes. Biological process annotation and pathway enrichment analysis identified several DCIS-related signaling pathways. Finally, PPI network was constructed with String website in order to get the hub codes involved in Ductal carcinoma in situ. We thus concluded that Five genes: CDK1, CCNB2, MAD2L1, PPARG, ACACB were finally identified to participate in the regulation and serve as potential diagnosis signatures in in Ductal carcinoma in situ. Finally, complmentarity between CDK1 and three drugs, Aminophenazone, Pomalidomide and the Rosoxacin, implies novel pharmacological value of those drugs in breast cancer.

Variation-preserving normalization unveils blind spots in gene expression profiling

RNA-Seq and gene expression microarrays provide comprehensive profiles of gene activity, but lack of reproducibility has hindered their application. A key challenge in the data analysis is the normalization of gene expression levels, which is currently performed following the implicit assumption that most genes are not differentially expressed. Here, we present a mathematical approach to normalization that makes no assumption of this sort. We have found that variation in gene expression is much larger than currently believed, and that it can be measured with available assays. Our results also explain, at least partially, the reproducibility problems encountered in transcriptomics studies. We expect that this improvement in detection will help efforts to realize the full potential of gene expression profiling, especially in analyses of cellular processes involving complex modulations of gene expression.