Future Cancer Care: Anticipating “Panomics” with Bioinformatics


The pathology report of the future will be an interactive software tool for clinical teams to design a customized care regimen and monitor its efficacy during treatment.

The pathology report of the future will be an interactive software tool for clinical teams to design a customized care regimen and monitor its efficacy during treatment.

Originally posted on GEN

In 2009 Mark Boguski and colleagues published a paper entitled “Customized care 2020: how medical sequencing and network biology will enable personalized medicine.” In the paper the authors described a model incorporating these pathways, annotation of disease networks and drug targets, and simulation of therapeutic interventions with virtual drugs or with combinations of them. The pathology report of the future, the authors said, will provide precision diagnoses that are at the core of personalized medicine and will be an interactive software tool for clinical teams to design a customized care regimen and monitor its efficacy during treatment.


Beyond cancer, noted Zhu et al. in their 2007 paper in Plos Computational Biology, molecular dissection of diseases such as obesity and diabetes will require a systematic approach to show how genes interact with one another, and with genetic and environmental factors.

With respect to cancer, completion of the human genome sequence has led to a postgenomic era that includes detailed exploration of the DNA sequence (the genome), DNA sequences that are transcribed into mRNA (the transcriptome), and the translated and post-translationally modified protein sequences (the proteome). The new paradigm in cancer, researchers say, encompasses analysis of entire gene and protein sets to identify a “reasonable” drug target. Multiple experimental and data analysis methodologies are being used to achieve this goal, and bioinformatics support is an essential component of this research effort.

As a discipline, according to Abeloff’s Clinical Oncology, “bioinformatics sits at the interface where biology and medicine meet a confluence of quantitative sciences, including computer science, mathematics, and statistics.” While we are still a long way off from the pathology report of the future these analyses can been used to “sift through omics datasets to identify biomarkers and molecular signatures that can be used to predict clinically relevant outcomes.” These include technologies focused on a particular class of biological molecules: genomics for sequence-based studies of DNA, epigenomics for DNA modifications, transcriptomics for RNA, proteomics for proteins, and metabolomics for small molecules.

Clinically Actionable
But how will all this information be transformed into clinically actionable reports? And how will it change the fact that while worldwide spending on cancer drugs hit about $80 billion in 2012, “The average cancer drug only works about 25 percent of the time,” says Randy Scott, executive chairman of the molecular diagnostics company Genomic Health, which markets its gene OncotypeDx assays for breast cancer to risk stratify early-stage estrogen receptor (ER)-positive breast cancer.

“That means as a society we’re spending $60 billion on drugs that don’t work,” he said.

But multiple enterprises are developing complex bioinformatics solutions to move toward the pathology report of the future. The question is whether faster genomic tests, and tests that pile genomic information higher and deeper, will impact cancer treatment favorably and help patients and clinicians make treatment decisions.

Foundation Medicine provides its clinical assays, FoundationOne™ for solid tumors and FoundationOne Heme for hematologic malignancies, sarcomas, and pediatric cancers. Each test provides a fully informative genomic profile to identify a patient’s individual molecular alterations and matches them with relevant targeted therapies and clinical trials. By accurately decoding cancer genes, Foundation says, it uncovers not only the most commonly seen mutations but also rare ones that might give doctors additional clues.

“You can see how it will get very expensive, if not impossible, to test for each individual marker separately,” says Foundation Medicine’s COO, Kevin Krenitsky. A more complete study “switches on all the lights in the room.”

The company says thousands of patients globally have had their tumors analyzed and recent company announcements have stated that the test has been able “to identify actionable alterations in 76.4% of clinical tumor specimens profiled.” About 1,350 of its tests in 2012 were used by pharmaceutical companies, and 1,750 of them were done for physicians, the company said.
Appistry, which offers high-performance computing and analytics solutions for next-generation medicine, and N-of-One, which provides molecular interpretation and therapeutic strategies for personalized medicine in cancer care, formed a partnership this past February to jointly provide genetic sequencing data analysis and molecular interpretation services.

This partnership will bring together, the companies said, actionable components that will enable clinicians to use genomic sequencing to better understand the molecular profile of a patient’s tumor and select appropriate targeted therapeutic options. Under the terms of the agreement, N-of-One and Appistry will jointly market and sell N-of-One’s interpretation services and content together with Appistry’s bioinformatics next-generation sequencing analysis solutions and services to enable a complete workflow that is expected to yield therapeutic insights and options from raw sequencing data.

But has information sifted through by bioinformatics proven useful to clinicians? Currently, researchers say, the genomics era has changed the clinicopathologic paradigm, to some extent, of selecting patients for adjuvant cytotoxic chemotherapy. Sufficiently powered prospective studies are under way that may establish these molecular assays as elements of standard clinical practice in breast cancer treatment.

Gene expression profiling, the authors note, such as that provided by the OncotypeDx assay and Agendia’s 70-gene MammaPrint microarray can be used to provide prognostic information beyond standard clinical assessment. But the purpose of their study, the authors said, was “To improve on current standards for breast cancer prognosis and prediction of chemotherapy benefit by developing a risk model that incorporates the gene expression-based intrinsic subtypes luminal A, luminal B, HER2-enriched, and basal-like.”

In a 2009, Parker et al. published a paper entitled “Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes.” The authors noted that breast cancer heterogeneity in terms of molecular alterations, cellular composition, and clinical outcome creates a “challenge in developing tumor classifications that are clinically useful with respect to prognosis or prediction.”
The scientists developed a 50-gene expression assay based on microarray and quantitative RT-PCR called PAM50 by analyzing 189 FFPE breast tumor samples to separate them into four known intrinsic molecular breast cancer subtypes (basal-like, HER-2/neu positive, luminal A, and luminal B).

In the validation study, tumor analysis was performed on 761 samples from patients with early-stage, node-negative breast cancer not treated with chemotherapy and 133 samples that came from patients who received taxane and anthracycline containing neoadjuvant chemotherapy. Patients with luminal A subtype as assessed by PAM50 had better prognosis in contrast to the other three intrinsic types. Two “risk of relapse” (ROR) scores were created, one using subtype correlation only (ROR-S) and the other one using subtype correlation in addition to tumor size (ROS-C). In multivariate analysis that controlled for known clinical risk factors, these scores correlated well with good prognosis.

Genomic Portraits of Human Breast Tumors
In 2012, the Cancer Genome Atlas Network published its comprehensive molecular portraits of human breast tumors, using six different technology platforms, including genomic DNA copy number arrays, DNA methylation, exome sequencing, messenger RNA arrays, microRNA sequencing, and reverse-phase protein arrays. The authors noted that their ability to integrate information across platforms provided key insights into previously defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity.

In their discussion, the authors noted that their integrated molecular analyses of breast carcinomas helped produce a comprehensive catalog of likely genomic drivers of the most common breast cancer subtypes. Their novel observation, they said, that diverse genetic and epigenetic alterations converge phenotypically into four main breast cancer classes, is consistent with convergent evolution of gene circuits, as seen across multiple organisms, but also with models of breast cancer clonal expansion and in vivo cell selection proposed to explain the phenotypic heterogeneity observed.

Comparison of basal-like breast tumors’ with high-grade serous ovarian tumors showed many molecular commonalities, indicating a related etiology and similar therapeutic opportunities. The biological finding of the four main breast cancer subtypes caused by different subsets of genetic and epigenetic abnormalities raises the hypothesis that much of the clinically observable plasticity and heterogeneity occurs within, and not across, these major biological subtypes of breast cancer, they noted.

Writing in an editorial in a 2011 issue of the Journal of the National Cancer Institute, Van Tine and Ellis, noted “Our understanding of the cancer biology associated with these genes sets often remains obscure and, to add to the confusion, the same biology, for example, poor outcome on adjuvant tamoxifen therapy, can be predicted by different gene expression profiles with only partial overlap.”

Nonetheless, data published by these authors and their colleagues used whole-genome sequencing to gain insight into the mutational landscape of tissue samples from patients with estrogen-receptor-positive (ER+) breast cancer treated with a neoadjuvant aromatase inhibitor. Specifically, the investigators conducted massively parallel sequencing (MPS) on 77 samples accrued from two neoadjuvant aromatase inhibitor clinical trials. Forty-six cases underwent whole-genome sequencing (WGS) and 31 cases underwent exome sequencing, followed by extensive analysis for somatic alterations and their association with aromatase inhibitor response. Case selection for discovery was based on the levels of the tumor proliferation marker Ki67 in the surgical specimen, because high cellular proliferation despite aromatase inhibitor treatment identifies poor prognosis tumors exhibiting estrogen-independent growth.

The authors identified disease-linked mutations that specifically correlated with tumor-cell histology, proliferation rates, and response to treatment. Such information, Nature commented, could potentially be used to determine which patients will benefit from aromatase-inhibitor therapy.
But authors concluded that the accrual of large numbers of patients and the use of comprehensive sequencing and gene expression approaches will be required because of the extreme genomic heterogeneity documented by this investigation

And as they commented in the 2011 article, “The scale of the battlefield is expanding rapidly as cancer genomes are documented at a single nucleotide level. In the next several years, many entire breast cancer genomes will be published, revealing for the first time the enormous scale of the disruption of the human genome associated with the development of this disease.”

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