Molecular testing as a tool for the management of metastatic cancer patients

March 19, 2014

To earn CEUs, visit
Upon completion of this article, the reader will be able to:

  1. Identify characteristics of metastatic cancer.
  2. Define terms related to the molecular foundations of cancer.
  3. Describe molecular testing available for cancerous tumors.
  4. Describe the role of molecular diagnostics in treatment protocols for cancer.

The World Health Organization estimates that, by 2030, 21.6 million people per year will be diagnosed with cancer, and 13 million people will succumb to the disease globally.1,2 Metastatic disease, in particular, is responsible for the vast majority of cancer deaths. Thus, prevention, detection, and treatment of metastatic disease are critical aspects of patient care in a clinical setting. 

There are more than 200 types of cancer, and most have the potential to evolve into metastatic disease.3 The Centers for Disease Control and Prevention estimates that, in the United States, one-half of all colorectal and cervical cancers and one-third of all breast cancers are diagnosed in the late stages of disease.4 

The genomic alterations that give rise to cancer make it an ideal disease state for utilization of molecular diagnostic techniques. The fruition of more advanced molecular technologies has led to a paradigm shift in the industry from defining single causative agents in succession to combining several molecular techniques in parallel to identify signaling networks of metastatic disease.5 However, the key issue for implementation of molecular diagnostics in the context of metastatic disease is to correlate a tumor’s molecular profile to
effective therapy.

Figure 1. Accurate diagnosis followed by the generation of an actionable biomarker profile and final establishment of a rational treatment regimen are the key segments within the diagnostic continuum for metastatic patient care.

Molecular testing in metastatic disease allows physicians to evaluate individualized tumor-specific information and enhance the quality of patient care by establishing an accurate diagnosis, tailoring treatment regimens, monitoring response and resistance to medications, and accessing clinical decision support tools.6 Notably, molecular testing has made it possible for physicians to stratify some cancer patients into groups that are more likely to benefit from particular treatment regimens.7 The utilization of molecular testing is growing exponentially due to the ability to more accurately and expediently identify disease states during clinical evaluation.8 As the field moves forward, the combination of various molecular tools across the continuum of patient care must promote the rational and comprehensive management of metastatic disease (Figure 1).9 

This review will highlight how molecular tools have been integrated into the current diagnosis and treatment of metastatic cancer, and also discuss how technologies may evolve and advance personalized medicine in the future.

Molecular foundations
of cancer

Metastatic disease can result from several types and combinations of genomic alterations.10 Single nucleotide polymorphisms (SNPs), duplications, insertions and deletions of one or a few nucleotides, and larger structural variations including gene rearrangements and fusions, can result in amino acid changes, truncated or fusion proteins, and changes in overall expression and function of the protein.11,12 Cancer genomes harbor hundreds to thousands of mutations; however, not all of these play a role in disease progression.10 So called “driver mutations” are positively selected for during tumorigenesis due to conferred growth advantages, while “passenger mutations” are passively carried through during clonal expansion because they increase the probability of beneficial new mutations.5,10,13 

Due to the increasing volume of molecular data being generated, both academic and clinical decision support tools have been created to help oncology professionals research, understand, and interpret molecular data. For example, The Cancer Genome Atlas (TCGA) can amalgamate genomic findings within and among individual tumor types into functional and actionable information.14 TCGA assists users in understanding how molecular changes within and among cells lead to tumor growth and how targeted therapies can ameliorate corrupt cellular signaling pathways.

As the molecular underpinnings of cancer have come into focus in recent years, testing to identify the molecular characteristics of individual tumors has moved into clinical application. Molecular testing applications now include interrogation of alterations in DNA sequence and structure; alterations and quantification of RNA transcripts; quantification of protein and detection of post-translational modifications; and detection of various molecular analytes and metabolites during progression and treatment of metastatic disease. Some of these molecular approaches have an established clinical utility (as discussed below), while other technologies and assays must still be validated and/or establish actionable clinical utility. Despite continued advancements and the rapid pace of innovation, we are just at the beginning of understanding how to exploit all of the information gained from molecular testing. 

Molecular tools in metastatic cancer

The first step in managing a new patient with metastatic disease is to identify a single confirmed diagnosis of tumor type and tumor subtype. Typically, this diagnosis is made via standard pathologic techniques [morphologic examination and immunohistochemistry (IHC) review]; however, in a significant number of patients, the diagnosis is unknown or unclear following initial review. 

Over the past decade, several RNA expression-based technologies have been developed to aid in determining tumor type and subtype. These assays work by comparing the gene expression profile from a patient’s tumor to a reference gene expression database from known tumor types. For example, one 92-gene assay classifies a tumor as one of 50 distinct tumor types.15-17 This assay had an overall sensitivity of 87% in a multi-institutional validation study, and was found to be superior to IHC in difficult-to-diagnose metastatic cases.16,18 When patients were treated with available molecularly-targeted and site-specific chemotherapies according to the assay’s molecular diagnosis, overall survival was shown to increase by more than 30% across 26 tumor types.19

Table 1. Biomarkers included in FDA-approved oncology drug labeling.

Once a diagnosis is established, determining the predictive biomarker profile is the next step for developing a comprehensive treatment plan for metastatic patients. The FDA has approved 41 medications indicated in oncology with genetic biomarker information in their labeling, for which there are 52 associated biomarkers (Table 1).9 These include KRAS mutations in metastatic colorectal cancer (mCRC), EGFR mutations and ALK rearrangements in advanced NSCLC, BRAF mutations in metastatic melanoma, and HER2 testing in breast cancer. Testing for these biomarkers—within these specific tumor types—has been incorporated into various clinical practice guidelines.20-23

The importance of accurate diagnosis of tumor type with respect to the rational application and interpretation of information garnered from molecular diagnostics is becoming increasingly clear with regard to metastatic disease. Mutations are not actionable in every tumor type. For example, HER2/neu amplification is indicative of trastuzumab efficacy in breast and gastric cancers, but not in NSCLC.24-26 Similarly, BRAF V600E mutations are associated with vemurafenib benefit in metastatic melanoma, but not in colorectal cancer.27,28 Even within a particular tumor type, the presence of different mutations within a driver gene carries different weight with respect to disease progression and resistance to therapy. For example, KRAS G13D (codon 13) mutations are less deleterious than codon 12 mutations (G12D) in colorectal cancer with respect to resistance mechanisms.22,29 

Thus, in current standard of care, mutational profiles must be interpreted within the cellular context of the tumor. Individual genomic alterations may or may not be actionable with currently available targeted molecular therapies. With this in mind, comprehensive and personalized patient care begins with an accurate diagnosis and is complemented by biomarker profiling. As Martini and colleagues noted in a recent review, “We have learned that neoplasms arising in a given tissue—such as the colon or breast—that seemed histologically indistinguishable, are highly heterogeneous at the molecular level and that this translates into variable prognosis and clinical outcomes and potentially indicates different personalized therapies….The pattern of cancer mutations is tumor type-specific, and mutations within a given gene may have different phenotypic effects.”30

A more recently developed tool for biomarker profiling is next generation sequencing (NGS).  NGS allows for the interrogation of thousands of variants from multiple genes within a given tumor sample at the same time. These data will continue to expand our knowledge of the complex genomic relationships that lead to tumorigenesis and therapeutic resistance mechanisms. NGS can be customized for gene coverage and sequence depth, allowing physicians to assess variable quantities of genes and capture information on rare transcripts.  For these reasons, NGS is particularly useful when identifying mutations within the genomically heterogeneous cell population of most tumors.31 

However, NGS is not without its limitations, as both interpretation and actionability of the complex findings from NGS present clinical practice challenges. Furthermore, NGS must be integrated with other test methodologies (e.g., FISH, RNA expression, protein expression) to give a complete picture of genomic alterations. Currently, NGS is being used in metastatic cancer to “systematically catalogue” cancer genomes to assess both common and rare mutations, in the hope of identifying targeted treatment regimens or to enroll patients in
clinical trials.32,33

Innovations in molecular diagnostics

Several newer technologies may become increasingly important within the framework of metastatic disease. Circulating tumor cell (CTC) assays have been developed as prognostic and predictive markers in metastatic disease. For example, quantification of CTCs has been correlated with survival, genetic mutations in CTCs provide insight into disease severity when compared with the primary tumor, and changes within CTCs during treatment are associated with treatment efficacy.34 Another new technological innovation—analysis of cell-free DNA (cf-DNA) from tumors, which may arise from tumor necrosis, apoptosis, or secretion—is being investigated as a marker of acquired resistance in patients before disease spread can be confirmed visually.35 Continued development of these technologies and others promises to expedite clinical decision-making in metastatic disease, and ultimately may help to monitor or slow disease progression.

Considerations when implementing molecular tools

Patients with metastatic cancer are particularly challenging because they often present with poorly differentiated tumors, exhibit atypical presentation and/or have undefined primary sites, and may not respond to first- or second-line therapies. Given the increasing number of molecular diagnostic tests available and recommended for patients with metastatic cancer, it is imperative that pathologists carefully consider the quantity of biopsy tissue available for molecular studies.36 Additionally, physicians must collaboratively determine what molecular tests are critical for patient care and prioritize the order in which they are employed. In recent years, biopsy specimens with limited tissue availability such as fine needle aspirates (FNA), peritoneal effusions, and pleural effusions have become more prevalent; however, expectations for the amount of information provided by these specimens continue to grow. With this in mind, novel molecular diagnostics should be tissue-sparing: examples include the 92-gene assay for tumor classification and NGS for mutational profiling. 

One of the paradoxical features of the knowledge of tumor mutational status is that few therapies retain their efficacy for an extended period of time.37 Patients often exhibit resistance to molecularly targeted therapies during the course of disease treatment, leaving clinicians to wonder about the next rational step in treatment. Given that tumor genomes can and do evolve to dampen the efficacy of first- and second-line therapies, the subsequent logical step for treatment is a combinatorial approach to selection of targeted therapies in an effort to bypass these resistance mechanisms.10 This approach was initially demonstrated with combination chemotherapy in the treatment of lymphoma.37 A necessary component of this combinatorial treatment regimen would be the identification of tumor-carrying multiple actionable mutations with available targeted therapies.29

Rapid technological advancements in molecular testing have empowered physicians to come closer to the goal of personalized medicine for metastatic disease. However, a number of key challenges remain. The data generated by the various technological approaches (e.g., tumor sequencing, gene expression profiling, protein expression) must be integrated and, importantly, the actionability of the results must continue to be defined with respect to available therapeutic interventions. In addition, diagnostic algorithms must be developed to conserve and optimally utilize precious biopsy tissue. As the translational and clinical research continues to mature, collaboration among pathologists, oncologists, and technology developers will be fundamental to bring the best available personalized care to patients with metastatic cancer. 

Karen Ann Cherkis, PhD, serves as medical science liaison for bioTheranostics, Inc., a San Diego-based provider of molecular diagnostic solutions for metastatic cancer. Brock E. Schroeder, PhD, is director, medical & scientific affairs, for bioTheranostics.


  1. Bray F, Ren JS, Masuyer E, Ferlay J. Global estimates of cancer prevalence for 27 sites in the adult population in 2008. Int J Cancer. 2013;132:1133-1145.
  2. Ferlay SI, Ervik M, Dikshit R, et al. GLOBOCAN 2012: Estimated  Cancer Incidence, Mortality and Prevalence Worldwide. International Agency for Research on Cancer. Accessed Jan. 29, 2014.
  3. Types of cancer. National Cancer Institute. Accessed Jan. 29, 2014. 
  4. Henley SJ, King JB, German RR, Richardson LC, Plescia M. Surveillance of screening-detected cancers (colon and rectum, breast, and cervix)—United States 2004–2006. MMWR Surveillance Summaries. 2010; 59(9):1-25.
  5. Salk JJ, Fox EJ, Loeb LA. Mutational heterogeneity in human cancers: origin and consequences. Annu Rev Pathol. 2010;5:51-75.
  6. Dancey JE, Bedard PL, Onetto N, Hudson TJ. The genetic basis for cancer treatment decisions. Cell. 2012;148:409-420.
  7. Dancey, J. Genomics, personalized medicine and cancer practice. Clinical Biochemistry. 2012;45:379-381.
  8. Wagle N. Berger MF, Davis MJ, et al. High-throughput detection of actionable genomic alterations in clinical tumor samples by targeted, massively parallel sequencing. Cancer Discov. 2012;2:82-93.
  9. Table of Pharmacogenomic Biomarkers in Drug Labeling. U.S. Food and Drug Administration. Accessed Jan. 29, 2014
  10. Podlaha O, Riester M, De S, Michor F. Evolution of the cancer genome. Trends Genet. 2012;28:155-163.
  11. Lobo I. Chromosome abnormalities and cancer cytogenetics. Nature Education. 2008;1:68.
  12. Syvanen AC. Accessing genetic variation: genotyping single nucleotide polymorphisms. Nat Rev Gen. 2001;2:930-942.
  13. Bignell GR, Greenman CD, Davies H, et al. Signatures of mutation and selection in the cancer genome. Nature. 2010;463:893-898.
  14. The Cancer Genome Atlas (TCGA). National Cancer Institute. Accessed Jan. 29, 2014.
  15. Erlander MG, Ma XJ, Kesty NC, Bao L, Salunga R, Schnabel CA. Performance and clinical evaluation of the 92-gene real-time PCR assay for tumor classification. J Mol Diagn. 2011;13:493-503.
  16. Kerr SE, Schnabel CA, Sullivan PS, et al. Multisite validation study to determine performance characteristics of a 92-gene molecular cancer classifier. Clin Cancer Res. 2012;18:3952-3960.
  17. Ma XJ, Patel R, Wang X, et al. Molecular classification of human cancers using a 92-gene real-time quantitative polymerase chain reaction assay. Arch Pathol Lab Med. 2006;130:465-473.
  18. Weiss LM, Chu P, Schroeder BE, et al. Blinded comparator study of immunohistochemical analysis versus a 92-gene cancer classifier in the diagnosis of the primary site in metastatic tumors. J Mol Diagn. 2013:15;263-269.
  19. Hainsworth JD, Rubin MS, Spigel DR, et al. Molecular gene expression profiling to predict the tissue of origin and direct site-specific therapy in patients with carcinoma of unknown primary site: a prospective trial of the Sarah Cannon Research Institute. J Clin Oncol. 2013;31:217-223.
  20. NCCN Guidelines. National Comprehensive Cancer Network. Accessed Jan. 29, 2014.
  21. CAP Guidelines. College of American Pathologists.{actionForm.contentReference}=reference%2Fguidelines.html&_state=maximized&_pageLabel=cntvwr. Accessed Jan. 29, 2014.
  22. Gonzalez de Castro D, Clarke PA, Al-Lazikani B, et al. Personalized cancer medicine: molecular diagnostics, predictive biomarkers, and drug resistance. Clin Pharmacol Ther. 2013;93:252-259.
  23. Ong FS, Das K, Wang J, et al. Personalized medicine and pharmacogenetic biomarkers: progress in molecular oncology testing. Expert Rev Mol Diagn. 2012;12:593-602.
  24. Arnould L, Arveux P, Couturier J, et al. Pathologic complete response to trastuzumab-based neoadjuvant therapy is related to the level of HER-2 amplification. Clin Cancer Res. 2007;13:6404-6409.
  25. Gatzemeier U, Groth G, Butts C, et al. Randomized phase II trial of gemcitabine-cisplatin with or without trastuzumab in HER2-positive non-small-cell lung cancer. Ann Oncol. 2004;15:19-27.
  26. Gomez-Martin C, Plaza JC, Pazo-Cid R, et al. Level of HER2 gene amplification predicts response and overall survival in HER2-positive advanced gastric cancer treated with trastuzumab. J Clin Oncol. 2013;31:4445-4452.
  27. Bollag G, Hirth P, Tsai J, et al. Clinical efficacy of a RAF inhibitor needs broad target blockade in BRAF-mutant melanoma. Nature. 2010;467:596-599.
  28. Prahallad A, Sun C, Huang S, et al. Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR. Nature. 2012;483:100-103.
  29. Vogelstein B, Papadopoulos N, Velculescu VE, et al. Cancer genome landscapes. Science. 2013;339:1546-1558.
  30. Martini M, Vecchione L, Siena S, et al. Targeted therapies: how personal should we go? Nat Rev Clinical Oncol. 2012;9:87-97.
  31. De Sousa EMF, Vermeulen L, Fessler E, et al. Cancer heterogeneity—a multifaceted view. EMBO Rep. 2013;14:686-695.
  32. Roukos DH, Ku CS. Clinical cancer genome and precision medicine. Ann Surg Oncol. 2012;19:3646-3650.
  33. Simon R, Roychowdhury S. Implementing personalized cancer genomics in clinical trials. Nat Rev Drug Discov. 2013;12:358-369.
  34. Williams SC. Circulating tumor cells. Proc Natl Acad Sci USA. 2013;110:4861.
  35. Kin C, Kidess E, Poultsides GA, et al. Colorectal cancer diagnostics: biomarkers, cell-free DNA, circulating tumor cells and defining heterogeneous populations by single-cell analysis. Expert Rev Mol Diagn. 2013;13:581-599.
  36. Lindeman NI, Cagle PT, Beasley MB, et al. Molecular testing guideline for selection of lung cancer patients for EGFR and ALK tyrosine kinase inhibitors: guideline from the College of American Pathologists, International Association for the Study of Lung Cancer, and Association for Molecular Pathology. Arch Pathol Lab Med. 2013;137:828-860.
  37. Garraway LA, Janne, PA. Circumventing cancer drug resistance in the era of personalized medicine. Cancer Discov. 2012;2:214-226.