Next-generation Sequencing

A Powerful Tool for the Discovery of Molecular Markers in Breast Ductal Carcinoma In Situ

Hitchintan Kaur; Shihong Mao; Seema Shah; David H Gorski; Stephen A Krawetz; Bonnie F Sloane; Raymond R Mattingly

Disclosures

Expert Rev Mol Diagn. 2013;13(2):151-165. 

In This Article

Expert Commentary

A combinatorial systems approach that includes empirical and computational techniques is needed to gain a better understanding of the complex biology of DCIS. Rather than studying the contribution of individual genes or a single pathway to DCIS evolution or progression, the focus needs to shift toward identifying the crosstalk among multicomponent dysregulated pathways. The goal would be for NGS data to be leveraged to identify the key components in regulatory pathways and important gene targets in DCIS through the use of integrative approaches with tools such as Oncomine[147] to overlay gene expression data, including that from the Cancer Genome Atlas,[148] onto protein–protein interactions, signaling pathways and transcriptional regulatory networks.

Construction of networks based on genotype–phenotype linkage, gene-regulatory modules, functionally linked pathways and protein communication modules will help in elucidating the mechanisms responsible for DCIS progression. Computational systems biology approaches have revealed functional biological networks that could be targeted for therapy. This is shown by a recent network-based integrative study that identified distinct driver networks in ER+, Her-2+ and TNBC breast cancer subtypes .[149]

Further integration of genomic profiles with metabolomic, regulatory and signaling data will define functional networks and will aid in identifying candidate biomarkers in such networks. The NGS data can be explored to identify and analyze genes with similar expression patterns by exploring their promoter sequences to determine whether common promoter modules can be resolved. The common frameworks existing in the promoter regions, which can be verified by ChiP-Seq or resolved by DNase-1-Seq,[150] can decipher regulatory connections among the different genes and may explain the functional regulation of coexpressed genes that have no detectable sequence similarities.[151,152] With the continuing development of affordable NGS platforms, the construction of patient-specific signaling networks may become feasible. Analysis of those networks in individual patients could help in making risk predictions and treatment decisions that will transform current prognostic and treatment options (Figure 4).

Figure 4.

Integrative flowchart for discovery of knowledge to personalize ductal carcinoma in situ treatment therapy. The DCIS samples for NGS will come primarily from experimental models or clinical samples (FFPE or fresh biopsy). Genes with similar expression patterns could be identified by RNA-Seq and the common promoter frameworks verified by ChIP-Seq. Combining systems bionetwork modeling approaches with data from model systems, clinicopathological characteristics, molecular genotype and phenotype, NGS results will help to discover/predict personalized therapy for DCIS patients.
ChIP: Chromatin immunoprecipitation; DCIS: Ductal carcinoma in situ; FFPE: Formalin-fixed paraffin embedded; NGS: Next-generation sequencing.

Future advances in prognostic, predictive and risk biomarkers of DCIS could be based on molecular events pertaining to the oxidative and proapoptotic stress or altered metabolic processes in the cancer cells or on analysis of the subpopulations of heterogenous DCIS lesions to distinguish indolent and aggressive forms. Given the complexity of the disease, a single or a small panel of molecular markers is not likely to be sufficient. Comprehensive approaches such as whole-genome-based analyses that can identify gene expression and/or copy-number changes, mutations and epigenetic alterations could provide important information. The development of genetic and network-based biomarkers is needed to suitably predict the risk of progression in DCIS patients as well as their response to therapeutic regimens. All biomarkers, including those that will be derived from network analysis, require rigorous investigation to support their significance and applicability. Further validation of the predictive biomarkers that reflect specific mechanisms or aberrant signaling pathways may also lead to the development of targeted therapies like that of development of trastuzumab for Her-2.

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