Pharmacogenomics in oncology care
Cancer pharmacogenomics have contributed a number of important discoveries to current cancer treatment, changing the paradigm of treatment decisions. Both somatic and germline mutations are utilized to better understand the underlying biology of cancer growth and treatment response. The level of evidence required to fully translate pharmacogenomic discoveries into the clinic has relied heavily on randomized control trials. In this review, the use of observational studies, as well as, the use of adaptive trials and next generation sequencing to develop the required level of evidence for clinical implementation are discussed.
In the last decade, the use of genomics in oncology significantly impacted treatment decisions for many patients. Oncologists have a variety of treatment options; however one patient may experience serious adverse events whereas another patient receives no therapeutic effect. Within a population, substantial variation exists resulting in unpredictable responses. Pharmacogenomics, the study of the interaction between the genome and clinical drug response (Monte et al., 2012), evaluates the associations between drug efficacy and toxicity and variation in drug metabolizing enzymes, receptors, transporters, and drug targets (Crews et al., 2012). The main priority of pharmacogenomics is to optimize treatment by understanding the underlying biological mechanisms and utilizing genomic contributions to treatment response to predict and individualize therapy and improve treatment outcomes. Unlike other diseases, cancer genetics must take into account both acquired (somatic) and inherited (germline) variation, both of which contribute to the efficacy and safety of a drug. However to date, integrated studies of germline and somatic variation have been limited. Somatic mutations are often attributed to treatment efficacy, whereas germline mutations are used to identify patients at highest risk of developing serious adverse events (Gillis et al., 2014). In this review, we will discuss examples of pharmacogenomic markers and differences in the evidence level required for implementation into clinical care. In addition, several mechanisms for developing evidence for clinical implementation and new technologies entering oncology practice will be discussed.
Somatic Mutations and Targeted Therapy
Somatic mutations have highlighted the importance of understanding the underlying biology of cancer with discoveries elucidating the primary genetic changes driving tumorigenesis providing molecular drug targets. Prospective tumor sequencing is being increasingly utilized, changing the paradigm of cancer treatment from site specific cytotoxic treatment, to molecularly targeted treatment (MacConaill et al., 2011; Ong et al., 2012). Many drugs are being developed for defined molecular targets (Simon, 2013), one such example is the use of crizotinib in anaplastic lymphoma kinase (ALK) positive non-small cell lung cancer (NSCLC). Tumor DNA sequencing identified two patients with NSCLC harboring novel ALK rearrangements as crizotinib, a tyrosine kinase inhibitor, moved into clinical trials. Both patients showed marked response to crizotinib prompting protocol amendments to prospectively test for ALK rearrangements throughout clinical development (Ou, 2011; Ou et al., 2012). The fortuitous discovery of ALK rearrangements during phase I trials of crizotinib restricted development to a subset of patients relying heavily on rigorous randomized controlled trials with an appropriate companion diagnostic to select patients. Predictive testing for biomarkers like ALK reduces unnecessary treatment in patients that will not respond and helps avoid potentially toxic effects of treatment (Ong et al., 2012). Molecularly targeted therapies like crizotinib have replaced cytotoxic therapy as standard of care in several cancer types including breast cancer, NSCLC, and melanoma (Ong et al., 2012; Gillis et al., 2014). Randomized clinical trials (RCTs) have been important to modern medicine, however the shift away from average treatment effects within a whole population to molecularly defined sub-populations is new to clinical trial design, and will be discussed later in this review.
In oncology, germline mutations play a significant role in the treatment response to both chemotherapy and targeted anti-cancer agents. These mutations are often associated with the pharmacokinetics of a drug contributing to treatment related adverse events experienced by patients (Hertz and McLeod, 2013; Gillis et al., 2014). In this regard, germline pharmacogenomic markers can identify patients at highest risk of developing serious adverse events that could subsequently lead to treatment discontinuation and failure like musculoskeletal pain after treatment with aromatase inhibitors. Severe musculoskeletal pain has been reported in up to half of women treated with aromatase inhibitors contributing to a treatment discontinuation rate of about 10% (Crew et al., 2007; Henry et al., 2008; Ingle et al., 2010). Ingle et al. found four single nucleotide polymorphisms (SNPs) mapping to the T-cell leukemia 1A (TCL1A) gene were associated with the development of musculoskeletal adverse events in patients receiving adjuvant aromatase inhibitors (Ingle et al., 2010). Subsequent functional studies revealed that TCL1A was induced by estrogen with higher levels of expression in cells with the variant alleles for these SNPs. Further results suggested an estrogen dependent, TCL1A SNP-dependent regulation of cytokines, cytokine receptors, and NF-κB transcriptional activity. These SNP-dependent changes may help to elucidate the pathway involved in musculoskeletal pain following aromatase inhibitor mediated estrogen deprivation (Liu et al., 2012). The strategy of discovering genetic variants and studying the underlying biology of the association is central in pharmacogenomic studies. It outlines a strong biological basis for the genetic association and provides mechanistic insight into the biology of the event that could lead to new drug targets to prevent the toxicity. Pharmacogenomic markers like TCL1A are extremely important when taken into context with not only the large number of women that could be exposed to aromatase inhibitors, but the fact that many of those women will have long term survival after receiving aromatase inhibitors and may experience decreased quality of life due to musculoskeletal pain. However, like many pharmacogenomic markers, TCL1A may never be used in clinical practice because a large randomized clinical trial will never be completed to study the association, even though other treatment options are available and with the understanding of the biology prevention strategies could be developed.
In addition to adverse events and pharmacokinetics of a drug, germline mutations may influence drug efficacy. Recently a germline mutation in the proapoptotic gene BIM was associated with the resistance to tyrosine kinase inhibitors in chronic myeloid leukemia (CML) and epidermal growth factor receptor (EGFR) mutant NSCLC. Identification of this mutation not only explains some of the poor response seen in patients with CML treated with imatinib, but also provides biological insight into different strategies to overcome the resistance that are currently in preclinical testing (Cheng and Sawyers, 2012; Ng et al., 2012). Although still in development, BIM is an important reminder that only focusing on somatic or germline variation investigators can miss key mutations that affect treatment outcomes.
One of the most well known pharmacogenomic markers is the association of thiopurine-S-methyltransferase (TPMT) and mercaptopurine (6-MP). Mercaptopurine is an important component of pediatric acute lymphoblastic leukemia (ALL) treatment, and is used in the treatment of some nonmalignant diseases (Paugh et al., 2011). A variant in the TPMT gene reduces the function of the enzyme leading to excessive levels of cytotoxic thioguanine nucleotides (6-TGNs) subsequently leading to an increased risk of severe myelosuppression (Paugh et al., 2011). Although a randomized clinical trial has never been done, the Food and Drug Administration (FDA) agreed that the evidence was sufficient to mention testing for TPMT deficiency, thus allowing identification of safe doses of mercaptopurine without compromising efficacy (Relling et al., 2011). Ample evidence, including in vitro and retrospective analyses provide support for the use of TPMT testing in patients receiving mercaptopurine to prevent serious treatment induced myelosuppression (Relling et al., 2011), but the consistent and widespread use of pre-treatment TPMT testing has not been universally accepted.
Strategies for Developing Pharmacogenomic Evidence
The field of pharmacogenomics has uncovered an abundance of actionable and clinically relevant markers including both somatic (Table 1) and germline (Table 2) mutations. Prospective screening for predictive pharmacogenomic markers, like TPMT, may enhance treatment response by reducing the risk of toxicity from systemic drug concentrations while maintaining the anti-cancer activity of the drug. However, implementation into clinical practice has been slow, in part due to contradictory professional guidelines and recommendations, and differing thresholds for evidence (Gillis et al., 2014). RCTs have been the gold standard for evidence in treatment response and genetic testing and for pharmacogenomic markers like ALK that were discovered during the drug development process RCTs are feasible. Randomization in experimental studies attempts to control for biases by balancing factors that affect outcomes across study groups. However, clinical trials are not always the most feasible approach for developing evidence due to cost, time constraints, and the large sample size necessary to complete a trial (Gillis et al., 2014). Observational studies are more prone to a number of biases and the potential confounding which may lead to incorrect results. But these studies offer some advantages over clinical trials including larger numbers of subjects at an affordable cost, ability to examine meaningful genomic subgroups, longer follow-up times, and ability to examine drugs and their interactions with the genome that are used off-label (Dreyer et al., 2010). Rigorously designed high-quality observational studies can play a particularly important role in developing evidence for decision making in cancer pharmacogenomics because they can relatively quickly generate results that are applicable to real-world situations and examine long-term risk and benefits. Below we discuss the use of epidemiologic studies for developing evidence to support the use of pharmacogenomic tests where RCTs are not an option.