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  1. The potential of clinical cancer genomics
  2. Use of High-Throughput DNA Microarrays to Identify Biomarkers for Bladder Cancer
  3. Gene Expression Detection Assay for Cancer Clinical Use
  4. Molecular Diagnostics: Current Research and Applications

Single-gene markers have the immense advantage of simplicity, both in terms of genomics interrogation and in terms of data analysis.

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However, the biology of a tumor can be extremely complex, especially when considering endpoints like prognosis: No single molecule can fully capture all the determinants of the processes of tumor initiation, progression, or metastasis. Indeed, classically 6—10 distinct molecular or biochemical functions have been identified as associated with these processes Hanahan and Weinberg , Independent of whether or not domain knowledge is used, these complex models can use tens to thousands of genes, transcripts, or proteins Monzon et al.

To better reflect the nonlinearities of biological pathways, this large number of genes is often weighted using mathematical models such as support vector machines, random forests, and network models. Despite the potential greater predictive accuracy introduced by the better fit between true biology and these types of mathematical models, another challenge is introduced: that of interpretability.

Patients and their caregivers need to be ready to interpret the results of genomic tests. When these tests involve complex multigene models or sophisticated statistical terminology, that communication can be challenging and can limit uptake. At least four major changes are likely to occur in this area over the next decade. First, new generations of clinicians are much more familiar with and better trained in genomic techniques, which will facilitate interpretation of final models. Second, patients will become more comfortable with genomics and genomic techniques and be more capable of conversing with their clinicians in this area.

Third, standardization of genomic approaches across multiple areas of medicine will create more familiarity and consistency. Fourth, ongoing work by many groups in visualization and communication will provide technical solutions.

The potential of clinical cancer genomics

At times it seems inevitable to those doing genomic research that multimodal -omic biomarkers will become prevalent in routine clinical practice over the next 25 years. However, the path to move from current targeted sequencing panels of specific, carefully selected point mutations to genome-wide assays at multiple levels is unclear.

It will require significant advances in genomics and computational biology. The seminal paper demonstrating that gene expression can predict outcome in breast cancer was published 13 years ago van't Veer et al. In part, this is a function of incomplete clinical annotation of many cohorts with genomic data, particularly with regard to long-term outcomes and response to treatment. This will change as the raw data sets underpinning biomarker discovery and application improve, with more consistent genomic data, better access to and sharing of clinical trial-linked data as proposed in the next iteration of the ICGC , challenge-based methods assessments, and more frequent assessment of spatial heterogeneity within a tumor.

These changes in the raw data will be complemented by improvements in data analysis, particularly in handling heterogeneity, incorporating prior biological knowledge, and in scoring large-scale genomic phenomena. Finally, these improvements in genomics and computational biology will reach their full potential as large numbers of new, targeted therapies continue to be developed, providing the clinical need to drive the development and application of genomic biomarkers for the cancer clinic.

I thank Renasha Small-O'Connor for editing support. View all The path to routine use of genomic biomarkers in the cancer clinic Paul C. Previous Section Next Section. Stable biomarkers In many fields, a very large number of biomarkers have been developed. Reproducibility of analyses Clinical application of genomic techniques requires that the resulting tests are highly accurate and highly reproducible.

Defining complex phenomena Some recently uncovered genomic abnormalities are highly complex. Integrating multiple levels of data Interrogation of any single type of genomic data may provide limited predictive accuracy: Several groups have tested large numbers of random biomarkers to evaluate the probable upper limit of prediction accuracies Boutros et al.

Pharmaco-economics of genomic tests A genomic biomarker may have good accuracy and reproducibility across a range of independent validation data sets. Explainability Even if a biomarker is demonstrated to be accurate and economic, this is not always sufficient to guarantee its routine use; that requires adoption and interpretation by clinicians and patients.

Use of High-Throughput DNA Microarrays to Identify Biomarkers for Bladder Cancer

Previous Section. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5 : Medline Google Scholar. Signatures of mutational processes in human cancer. Nature : — CrossRef Medline Google Scholar. Deciphering signatures of mutational processes operative in human cancer.

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Cell Rep 3 : — Multifocal endometriotic lesions associated with cancer are clonal and carry a high mutation burden. J Pathol : — Punctuated evolution of prostate cancer genomes. Cell : — Gene expression profiling in cervical cancer: an exploration of intratumor heterogeneity. Clin Cancer Res 12 : — Distinct evolutionary trajectories of primary high-grade serous ovarian cancers revealed through spatial mutational profiling.

  • The path to routine use of genomic biomarkers in the cancer clinic.
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  • J Pathol : 21 — The genomic complexity of primary human prostate cancer. Prognostic gene signatures for non-small-cell lung cancer. Proc Natl Acad Sci : — Global optimization of somatic variant identification in cancer genomes with a global community challenge. Nat Genet 46 : — Toward better benchmarking: challenge-based methods assessment in cancer genomics.

    Gene Expression Detection Assay for Cancer Clinical Use

    Genome Biol 15 : Spatial genomic heterogeneity within localized, multifocal prostate cancer. Nat Genet 47 : — A comprehensive multicenter comparison of whole genome sequencing pipelines using a uniform tumor-normal sample pair. Evaluation of DNA microarray results with quantitative gene expression platforms. Nat Biotechnol 24 : — The Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Comprehensive molecular portraits of human breast tumours. Nature : 61 — Comprehensive genomic characterization of head and neck squamous cell carcinomas.

    Comprehensive genomic characterization defines human glioblastoma genes and core pathways. SeqControl: process control for DNA sequencing. Nat Methods 11 : — Analysis of the genetic phylogeny of multifocal prostate cancer identifies multiple independent clonal expansions in neoplastic and morphologically normal prostate tissue. Pathway and network analysis of cancer genomes. Nat Methods 12 : — The genomic and transcriptomic architecture of 2, breast tumours reveals novel subgroups.

    Daley T , Smith AD. Predicting the molecular complexity of sequencing libraries.

    Biomarker detection technologies and future directions

    Nat Methods 10 : — Spatial and temporal diversity in genomic instability processes defines lung cancer evolution. Science : — PhyloWGS: reconstructing subclonal composition and evolution from whole-genome sequencing of tumors. Genome Biol 16 : The shaping and functional consequences of the microRNA landscape in breast cancer.

    Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution. Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection. Stromal gene expression predicts clinical outcome in breast cancer.

    Nat Med 14 : — Ensemble analyses improve signatures of tumour hypoxia and reveal inter-platform differences. BMC Bioinformatics 15 : Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med : — Mahendran, Edward C. Keystone, Roman J. Krawetz, Kun Liang, Eleftherios P.

    Molecular Diagnostics: Current Research and Applications

    Diamandis and Vinod Chandran. Most recent articles RSS.

    View all articles. Most accessed articles RSS. In , Clinical Proteomics converted from a subscription publication to a fully open access journal. The journal's back content can be viewed on SpringerLink. Clinical Proteomics encompasses all aspects of translational proteomics. Special emphasis will be placed on the application of proteomic technology to all aspects of clinical research and molecular medicine. The journal is committed to rapid scientific review and timely publication of submitted manuscripts. In particular, Rouzier et al. Presumably due to the high correlation between molecular subtype and ER status, these subtypes were not independently associated with CR, as the microarray-based signature provided limited additional information in a multivariate analysis Rouzier et al.

    Whether genes selected in these studies will turn out to be useful not only in the neoadjuvant setting but also for predicting long-term therapeutic response is still not known. Neither do we know whether these gene signatures could be informative for patients in other cohorts and for patients at different stages of the disease. However, the idea of being able to determine the optimal treatment for a patient from an FNA sample of a tumor taken before surgery is obviously very attractive.

    Several additional class prediction studies have addressed whether prognostic profiles correlate with a specific adjuvant treatment Bertucci et al. The latter studies report successful predictors for prognosis after treatment progression-free survival in patients with tamoxifen-resistant breast carcinomas. Again, few genes overlapped in related studies, highlighting the likely impact of unresolved confounding issues such as differing sets of genes included in the studies, patient cohorts having different characteristics, and different analytical methodologies used.

    It appears that not only the identity of genes used for predictors is a crucial factor, but their performance is also impacted by the way in which they are used in the analysis. Perhaps, more focus should be placed on how the different genes are weighted in the analysis, possibly even including conditional dependence between different sets of genes in the algorithm Paik et al.