Using molecular profiling diagnostics to identify predictive biomarkers in metastatic cancer

Sept. 20, 2014

Despite recent advances in cancer drug development, most chemotherapeutic agents eventually fail in the metastatic setting. While some patients’ tumors respond to certain agents, the limited response rate in most patients places increasing importance on tailoring chemotherapeutic regimens for optimal response. Fewer than half of all patients respond to agents commonly used in the metastatic setting, and this fact has significant economic as well as medical ramifications (Figure 1).1,2 In addition, our incomplete understanding of tumor heterogeneity has made treating metastatic cancer difficult. 

Figure 1. High cost of non-responders in the metastatic setting

In the near future, insurers and other stakeholders will want to see evidence that proposed regimens are likely to work in individual patients. At the same time, identifying regimens that are not likely to work can simplify the selection of agents to use in certain patients. Laboratory professionals can make use of advanced molecular profiling technologies to help clinicians identify biomarkers in patients’ tumors that are predictive of response (or non-response) to specific chemotherapeutic regimens.

Value of molecular profiling in detecting protein biomarkers

While DNA sequencing for known mutations is highly useful in therapeutic decision-making for lung cancer and other solid tumors, the power of this technology is limited by the lack of targeted agents known to work against aberrant sequencing events. Sole reliance on sequencing technologies denies the lab professional the opportunity to detect predictive and prognostic protein biomarkers in patients’ tumors. The true strength of these biomarkers derives from their negative predictive value (i.e., the ability to predict the likelihood of treatment failure). The presence of a biomarker associated with non-response to a specific chemotherapeutic agent may preclude use of that agent against a tumor lacking that biomarker. For example, except for the rare activating mutations in HER2, the absence of HER2 expression correlates with lack of response to trastuzumab and lapatinib.3 Furthermore, though we accept the protein biomarkers estrogen receptor (ER), progesterone receptor (PR), and HER2 for their response data in breast cancer, their possible application in other tumor sites is often overlooked. 

As DNA sequencing has attracted increasing attention, many oncologists have drifted from focusing on the protein biomarkers to merely performing DNA sequencing. This trend leads to a loss of prognostication, as there is a limited choice of drugs for use with DNA mutations. Revisiting protein biomarkers in conjunction with DNA sequencing can yield a more complete molecular profile of an individual’s cancer, with significant implications for treatment and clinical trial selection. 

Molecular profiling can help the lab professional identify protein biomarkers in tumor types for which their associations have not been well studied. For example, although HER2 amplification and combined TOP2A amplification and deletion are potentially valuable in predicting responsiveness to anthracycline-based adjuvant chemotherapy in patients with early breast cancer, findings from a recent meta-analysis do not support the use of anthracyclines only in patients with HER2-amplified or TOP2A-aberrated tumors.4 In addition, whereas the negative predictive value of BRAF is well-established in non-small-cell lung cancer (NSCLC), melanoma, and colorectal cancer, the presence of mutated BRAF genes makes patients less likely to respond to EGFR-targeted monoclonal antibodies and more likely to respond to BRAF inhibition.5 Finally, druggable mutations that are known to be common in certain tumor types may also be common in others; for example, BRAF mutations, commonly associated with melanoma, have now been reported in malignant peripheral nerve sheath tumors and are associated with response to vemurafinib.6,7  

Choosing profiling techniques wisely

It behooves the oncologist to obtain a full molecular profile of the patient’s tumor. Table 1 lists several profiling technologies that can be used to detect and interrogate each biomarker in a tumor. When combined with an exhaustive, evidence-based literature review, the results of these profiling tests can help to individualize anticancer treatment.

  Immunohistochemistry (IHC): determines level of protein expression
 • Fluorescence/chromogenic in situ hybridization (FISH/CISH): detects gene deletions, amplifications, translocations, and fusions
 • Next-generation sequencing (NGS): rapidly examines and more   broadly detects somatic mutations across hundreds of hotspots in cancer genomes by determining the DNA sequence
 • Quantitative polymerase chain reaction (qPCR): amplifies and quantifies a targeted DNA molecule
Table 1. Molecular profiling technologies

In the metastatic setting, combining DNA sequencing, fluorescence in situ hybridization (FISH), and immunohistochemistry (IHC) is useful for identification of phase I clinical trials, as these technologies can help clinicians identify targets that can increase the chances of finding an active agent. However, although treatment guidelines for NSCLC,8 colorectal cancer,9,10 and melanoma11 document a clear benefit for DNA sequencing, such a benefit is lacking for sarcomas and other less common cancers. DNA sequencing is also of limited benefit in breast cancer, which is still heavily reliant on the “classic” protein biomarkers for treatment decision-making. The medical consumer must therefore be savvy about ordering certain profiling technologies. Relying solely on sequencing is not in the patient’s best interest, given the lack of available drugs to target aberrant sequences.

Future directions

In the future, the use of molecular profiling will likely expand to incorporate immunologic markers to predict response to cancer immunotherapy. The parameters for studying such markers, as well as their predictive and prognostic value, will need to be defined by clinical trials.

Additionally, biomarker data and treatment selection will increasingly be informed by results from so-called “basket” trials. These are studies that evaluate a drug targeting a specific molecular abnormality (e.g., a PI3KCA mutation) in patients with different tumor types, and group patients into separate study arms or “baskets” based on their tumor type. The “basket” trial design enables separate analyses of responses in tumor-specific patient cohorts as well as an overall analysis of response in all patients as a group.

Ultimately, molecular profiling will be used to prove a drug will not fail before it is administered to the patient. Such an approach will save significant resources due to the avoidance of ineffective therapies and unnecessary toxicities, without the loss of response.

Pinpointing gene changes in breast cancer cells

In the accompanying article, Dr. Van Tine comments that “the true strength of [tumor] biomarkers derives from their negative predictive value (i.e., the ability to predict the likelihood of treatment failure).” Research recently reported in the online journal PLOS Computational Biology adds a new perspective to this interesting and important area of inquiry. Computer scientists at Carnegie Mellon University (CMU), working with high-throughput data generated by breast cancer biologists at Lawrence Berkeley National Laboratory, have devised a computational method to determine how gene networks are rewired as normal breast cells turn malignant and as they respond to potential cancer therapy agents. The method could provide new insights into cancer and identify the most promising molecular targets for drug therapy. In their study, for instance, the researchers were able to show how changes in these gene networks led breast cancer cells to develop resistance to several different agents. “With our system, pharmaceutical developers wouldn’t need to go to expensive clinical trials to discover that a drug isn’t going to work,” said Wei Wu, associate research professor in CMU’s Lane Center for Computational Biology. The approach also might be used to detect differences in gene regulation between individuals, helping physicians select which treatment will be most effective for each patient, she added. The researchers investigated whether distinctly different gene regulatory networks could be identified within cells as normal cells become malignant and as the malignant cells respond to various drug treatments. They studied these breast cancer cells using a 3D cell-culturing technique In this way, the researchers were able to identify different signaling networks with just three microarrays for each of five cell states—normal, malignant, and three types of reverted cells. Though the reverted cells looked normal in culture, Wu says their signaling pathways differed not only from malignant cells, but also from normal cells. In fact, each had different signaling pathways depending on what drug had been used to treat them, as each compensated for the effects of the drugs in different ways. —MLO Editors

Brian A. Van Tine, MD, PhD, is a medical oncologist specializing in sarcomas and rare tumors at Barnes Jewish Hospital and Assistant Professor of Medicine at the Washington University School of Medicine in St. Louis.


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