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`Germline pharmacogenomics in oncology: Decoding the patient for
`targeting therapy
`
`Peter H. O’Donnella,b,c,d,*, Mark J. Rataina,b,c,d
`
`aSection of Hematology/Oncology, Department of Medicine, The University of Chicago, 5841 S. Maryland Avenue, MC 2115,
`Chicago, IL 60637, USA
`bCommittee on Clinical Pharmacology and Pharmacogenomics, The University of Chicago, 5841 S. Maryland Avenue, MC 2115,
`Chicago, IL 60637, USA
`cComprehensive Cancer Center, The University of Chicago, 5841 S. Maryland Avenue, MC 2115, Chicago, IL 60637, USA
`dCenter for Personalized Therapeutics, The University of Chicago, 5841 S. Maryland Avenue, MC 2115, Chicago, IL 60637, USA
`
`A R T I C L E
`
`I N F O
`
`A B S T R A C T
`
`Article history:
`Received 20 September 2011
`Received in revised form
`14 December 2011
`Accepted 13 January 2012
`Available online 21 January 2012
`
`Keywords:
`Pharmacogenomics
`Targeted therapy
`Oncology
`
`Pharmacogenomics is the study of genetic factors determining drug response or toxicity.
`The use of pharmacogenomics is especially desirable in oncology because the therapeutic
`index of oncology drugs is often narrow, the need for favorable drug response is often
`acute, and the consequences of drug toxicity can be life-threatening. In this review, we ex-
`amine the state of pharmacogenomics in oncology, focusing only on germline pharmaco-
`genomic variants. We consider several critical points when assessing the quality of
`pharmacogenomic findings and their relevance to clinical use, and discuss potential con-
`founding factors limiting interpretation and implementation. Several of the most exten-
`sively studied drugegene pairs (irinotecan and UGT1A1; tamoxifen and CYP2D6; 5-
`fluorouracil and DPYD) are inspected in depth as illustrations of both the state of advance-
`mentdand the current limitations ofdpresent knowledge. We argue that there will likely
`soon be a critical mass of important germline pharmacogenomic biomarkers in oncology
`which deserve clinical implementation to provide optimal, personalized oncologic care.
`We conclude with a vision of how routine clinical testing of such germline markers could
`one day change the paradigm for cancer care.
`ª 2012 Federation of European Biochemical Societies.
`Published by Elsevier B.V. All rights reserved.
`
`1.
`
`Introduction
`
`Pharmacogenomics is the study of genetic factors determin-
`ing response to, or toxicity from, drugs. While the field origi-
`nally centered on the relationship between drugs and single
`
`genes (pharmacogenetics), pharmacogenomics now encom-
`passes information from the entire genome including germ-
`line variation (single nucleotide polymorphisms [SNPs], gene
`copy number alterations) and acquired changes (tumor muta-
`tions) as they relate to drug response or toxicity (Wang et al.,
`
`* Corresponding author. Section of Hematology/Oncology, Department of Medicine, The University of Chicago, 5841 S. Maryland Avenue,
`MC 2115, Chicago, IL 60637, USA. Tel.: þ1 773 702 7564.
`E-mail address: podonnel@medicine.bsd.uchicago.edu (P.H. O’Donnell).
`1574-7891/$ e see front matter ª 2012 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.
`doi:10.1016/j.molonc.2012.01.005
`
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`2011; Watson and McLeod, 2011). In contrast to disease genet-
`ics, pharmacogenomics focuses specifically on predictive ge-
`netic markers of outcome from pharmacologic interventions.
`The use of pharmacogenomic markers is perhaps espe-
`cially desirable in the field of oncology, where the therapeutic
`index of drugs is often narrow, and the consequences of drug
`toxicity can be life-threatening. However, since adverse drug
`reactions are reported to be the fifth leading cause of death
`in the United States, the risks are not specific to oncology
`drugs (Davies et al., 2007). At the same time, it is likely that
`we have failed to capitalize on the increased benefit that could
`be achieved with some therapies if we knew which patients
`were most likely to respond, or which patients required alter-
`native dosing. If we could better predict which individuals are
`at the greatest risk of suffering chemotherapy-related toxic-
`ities while simultaneously identifying those most likely to
`benefit, then the overall care of cancer patients could be
`greatly improved.
`In this review, we will examine the state of pharmacoge-
`nomics in the field of oncology. We will specifically restrict
`our considerations to germline genetic discoveries related to
`oncologic therapeutics; a discussion of the growing number
`of “molecularly-targeted” drugs based upon tumor pharmaco-
`genomics is beyond the scope of this current manuscript. To
`date, most germline oncology pharmacogenomic information
`has simply been cataloged, or, in a few instances, has led to
`FDA drug label changes. Therefore, we will also consider the
`barriers and means by which oncology pharmacogenomic
`informationdwhich is increasing every daydcan become
`more commonly integrated into the routine care of cancer pa-
`tients. We will posit that use of such patient-specific informa-
`tion should soon become the standard of care, rather than the
`exception.
`
`Major current pharmacogenomic findings in
`2.
`oncology
`
`The number of germline oncology drugegene pharmacoge-
`nomic pairs having high levels of evidentiary support is rela-
`tively small compared to other drugs. Perhaps the strongest
`examples are those for which the strength and scope of the
`data has resulted in FDA-mandated label changes so that pre-
`scribing clinicians are aware of well-characterized, pertinent
`germline pharmacogenomic information when prescribing
`(United States Food and Drug Administration, 2011). These
`highest level drug-variant pairs, along with several other of
`the most extensively studied oncology drug-variant pairs,
`are summarized in Table 1.
`As can be seen from Table 1, most of the existing described
`relationships have focused on genetic predictors of oncology
`drug-related toxicity phenotypes, rather than disease out-
`come phenotypes, although accumulating data suggest that
`germline polymorphisms might also affect treatment out-
`comes (see references in Table 1; and selected others (Huang
`et al., 2011; Wu et al., 2010; Ziliak et al., 2011; Yang et al.,
`2009)). Of the drugegene pairs in Table 1, the pharmacoge-
`nomic relationships between irinotecan and UGT1A1 (for neu-
`tropenia risk)
`(Innocenti and Ratain,
`2006), and 6-
`TPMT
`mercaptopurine/thioguanine
`and
`(for
`severe
`
`myelosuppression) (Relling et al., 2011) have the most consis-
`tent, strong supporting evidence in favor of their routine use.
`For UGT1A1 as an example, several prospective studies have
`demonstrated that patients with the high-risk genotypes
`(UGT1A1*28 and UGT1A1*6) are significantly more likely to ex-
`perience neutropenia, with two of these studies corroborating
`the relationship with pharmacokinetic supportive data
`(Innocenti et al., 2004; Minami et al., 2007). In the largest
`such study of 250 metastatic colorectal cancer patients, the
`odds ratio of risk of cycle 1 grade 3 or 4 neutropenia was w9,
`although the relationship did not persist for subsequent cycles
`(Toffoli et al., 2006). A meta-analysis of published studies on
`UGT1A1-irinotecan (including 821 patients) also confirmed
`the association for patients homozygous for the UGT1A1*28 al-
`lele who are receiving higher doses of irinotecan (150 mg/m2)
`(Hoskins et al., 2007). Including other risk alleles within UGT1A
`in a haplotype-based analysis may increase the predictive
`value of pharmacogenomic testing, since several other vari-
`ants in these genes have now also been shown to alter enzy-
`matic activity and impact
`irinotecan-related outcomes
`(Cecchin et al., 2009).
`There has also been significant interest in the relationship
`between tamoxifen and CYP2D6 (discussed further below).
`While the preponderance of the published data support the
`utility of CYP2D6 testing for tamoxifen use, there has not
`been a recommended pharmacogenomic FDA label change
`for this drug, and recent data presented in abstract form
`have been contradictory (Goetz et al., 2009; Rae et al., 2010;
`Leyland-Jones et al., 2010). There is also a large body of grow-
`ing evidence for many more oncology drug polymorphisms
`and various phenotypes. The best-performed studies of
`emerging pharmacogenomic associations now routinely in-
`clude replication testing upfront, and these drug-variant pairs
`deserve further examination for how they might be consid-
`ered in clinical utility investigations.
`Despite the existence of well-performed studies and vali-
`dation in many cases, some have still questioned whether
`any present germline oncology findings are currently clini-
`cally actionable without further prospective follow-up trials
`being performed (Coate et al., 2010). Certainly even the best-
`studied drugegene relationships have recognized limitations
`in applicability which must be considered (Lee and McLeod,
`2011). We believe that the clinical utility of each finding
`must be interpreted not only in light of the composite evi-
`dence describing a given relationship but also in the context
`of the clinical scenario in which the relative benefit versus
`risk must be considered. If a pharmacogenomic test could po-
`tentially mitigate risk without compromising efficacy, then we
`believe its practical value is high. We will discuss this topic
`further below. It is important to first consider interpretation
`of published pharmacogenomic findings as a starting point.
`
`Limitations to pharmacogenomic data
`3.
`interpretability
`
`As evidenced by the examples shown above, the most con-
`vincing drug-variant
`relationships are those identified
`through well-performed studies in which prospective pheno-
`type collection is performed and in which the potential for
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`253
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`Table 1 e Summary of the most extensively studied germline pharmacogenomic relationships for oncology drugs.
`
`Drug
`
`Phenotype(s)
`evaluated
`
`Genes
`
`Variants
`
`FDA label includes
`pharmacogenomic
`prescribing
`considerations?
`
`Important considerations
`
`Key references
`
`Irinotecan
`
`Neutropenia
`
`UGT1A1
`
`*28; plus others
`likely important
`
`6-mercaptopurine
`/thioguanine
`
`Myelosuppression
`
`TPMT
`
`Tamoxifen
`
`Disease recurrence
`
`CYP2D6
`
`*1, *2, *3A, *3B, *3C,
`*4, plus others
`
`Loss-of-function
`alleles:
`*3 (rs35742686);
`*4 (rs3892097);
`*5 (gene deletion);
`*6 (rs5030655);
`*7 (rs5030867)
`
`Decreased
`function alleles:
`*10 (rs1065852); *41
`(rs28371725); *9
`(rs5030656)
`Plus potentially others
`
`5-fluorouracil
`/capecitabine
`
`Neutropenia, stomatitis,
`diarrhea
`
`DPYD
`
`DPYD*2A (IVS14
`þ 1 G > A),
`plus others
`
`YES, but genetic
`variants are not
`mentioned; only
`functional DPD
`deficiency is included
`as a consideration
`
`Rituximab/cetuximab
`/trastuzumab
`
`Disease progression,
`response
`
`FcgRIIa,
`FcgRIIIa
`
`FcgRIIa-131H/R;
`FcgRIIIa-158 V/F
`
`NO
`
`YES
`
`YES
`
`NO
`
`1) Variants may only be predictive for
`patients receiving higher
`drug doses;
`2) Unclear if other irinotecan
`toxicities (like diarrhea) are similarly
`governed;
`3) Optimal strategy for treating
`*28/*28 patients is not defined
`1) Complementary clinical laboratory
`tests are available to
`functionally assess TPMT activity
`1) Some studies have been unable
`to reproduce the relationships;
`
`Innocenti et al., 2004;
`Minami et al., 2007; Toffoli
`et al., 2006; Hoskins et al., 2007;
`Cecchin et al., 2009; Innocenti
`and Ratain, 2006
`
`Relling et al., 2011
`
`Schroth et al., 2007, 2009;
`Nowell et al., 2005; Kiyotani
`et al., 2010; Jin et al., 2005;
`Ferraldeschi and Newman,
`2010; Rae, 2011
`
`2) Many studies have not included
`all of the known, main alleles;
`
`3) Genotyping (consideration
`of gene duplication) may be
`technically difficult which
`could confound results
`1) Sensitivity of best-studied
`DPYD variant is only w30%
`and has not been consistently
`reproducible;
`2) Results with other DPYD
`variants, or with variants in
`other genes (TYMS, MTHFR), have
`been inconsistent
`1) Some conflicting data; positive
`data mostly from small studies
`
`Yen and McLeod, 2007;
`van Kuilenburg, 2004
`
`Bibeau et al., 2009; Musolino
`et al., 2008; Kim et al., 2006;
`Weng and Levy, 2003; Carlotti
`et al., 2007
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`false discovery is minimized either (optimally) by inclusion of
`a replication set, or by conservative adjustment for multiple
`comparisons (Chanock et al., 2007; van den Oord, 2008). Stud-
`ies which are underpowered to adequately test less common
`variantsdvariants which in reality may be potentially impor-
`tant pharmacogenomic markersdcan have (falsely) negative
`results and can confuse the ability to understand conflicting
`data from several studies on a given drugegene pair. Inade-
`quate consideration of the potentially numerous different al-
`leles which may contribute to a given phenotype may also
`cause false negative results. This latter scenario may, in fact,
`be one of the causes of the recent conflicting data surrounding
`tamoxifen pharmacogenomics and CYP2D6 (Higgins and
`Stearns, 2011; Ferraldeschi and Newman, 2010; Rae, 2011).
`For that drugegene pair, multiple studies have demon-
`strated that patients with poor metabolizer genotypes are
`more likely to have worse outcomes. This is due to suboptimal
`conversion (primarily via CYP2D6) of tamoxifen into the more
`potent, active antiestrogenic metabolites, endoxifen and 4-
`hydroxytamoxifen (Higgins and Stearns, 2010), a relationship
`which is supported by pharmacokinetic data showing that pa-
`tients with these genotypes have lower levels of endoxifen
`(Borges et al., 2006). In one study, 206 tamoxifen-treated pa-
`tients receiving the drug in the adjuvant setting were com-
`pared based upon genotype groups (Schroth et al., 2007) for
`disease-related outcomes. Patients with poor metabolizer
`CYP2D6 genotypes were significantly more likely to experience
`recurrence of breast cancer, had shorter times to relapse, and
`worse event-free survival compared with patients having
`functional alleles (Schroth et al., 2007). Importantly, this study
`also examined genotypes for an identical control group of
`women not treated with adjuvant tamoxifen, and genotype
`had no bearing on disease-related outcomes. A 1325-patient
`international consortium study confirmed these findings
`(Schroth et al., 2009). A smaller prior study (also including
`a control group) had failed to demonstrate the association of
`three loss-of-function CYP2D6 genotypes (CYP2D6*3, *4, and
`*6) with reduced tamoxifen-related survival benefit, but im-
`portantly, this study did not test for any of the other now
`known loss-of-function and reduced-function alleles (Nowell
`et al., 2005). The recent data presented only in abstract form
`(Goetz et al., 2009) from the International Tamoxifen Pharma-
`cogenomics Consortium study on >2800 patients receiving ad-
`juvant tamoxifen did not show an association with survival
`outcomes, however, a number of patients was excluded
`from the analysis because of incomplete genotypic or clinical
`data, including lack of information about concomitant medi-
`cation use (Ferraldeschi and Newman, 2010). Two other, re-
`cent large prospective trials (both also only presented in
`abstract form thus far) which examined CYP2D6 genotypes
`with outcomes in patients receiving tamoxifen also failed to
`show associations (Rae et al., 2010; Leyland-Jones et al.,
`2010). The
`apparent
`importance of
`considering
`co-
`administered drugsdincluding simply whether the antineo-
`plastic drug being studied is being given as monotherapy or
`as part of a larger combination regimendhas been elegantly
`illustrated by Kiyotani et al. (2010). These authors showed
`that, for multiple studies (including theirs) where tamoxifen
`was given as part of a combination chemotherapy regimen,
`analyses were unable to demonstrate a positive relationship
`
`between CYP2D6 genotype and disease outcomes. However,
`in their study and in seven of eight other prior published stud-
`ies of patients receiving tamoxifen as monotherapy, the rela-
`tionship between CYP2D6 genotype and tamoxifen response
`was positive (Kiyotani et al., 2010).
`This drugegene example is instructive for three reasons.
`First, for genes where genotyping may be difficult or complex,
`inaccurate or incomplete genotyping can be a significant bar-
`rier to pharmacogenomic interpretation. CYP2D6 is known to
`be frequently duplicated, which can confound interpretation
`of genotyping results if duplication is not well-characterized.
`Moreover, over 100 different alleles of CYP2D6 have been
`reported (Higgins and Stearns, 2010; Bradford, 2002), with at
`least five of these variants well-characterized as loss-of-func-
`tion alleles, and another three well-described as associated
`with decreased enzymatic function (Becquemont et al.,
`2011). None of the above studies comprehensively included
`all of the common functional variants. The lack of standard-
`ized inclusion of all of the various known functional variants
`in clinical studies may therefore be a source of inconsistency
`in reported response outcomes. Secondly, the presence of
`concomitant medications may be important when interrogat-
`ing pharmacogenomic relationships. For tamoxifen, co-
`administered inhibitors of CYP2D6 can functionally “cause”
`the poor metabolizer phenotype (Jin et al., 2005) and confound
`genetic influences. Or, as just mentioned, even the presence of
`drugs not known to be directly acting via the same pathway as
`the antineoplastic of interest (including other concomitant
`antineoplastics) may mask the “penetrance” of pharmacoge-
`nomic risk alleles. The reduced penetrance could be due to di-
`rect effects of the other drugs, plus potentially the reduced
`effect on the drug of interest, especially if there was dose re-
`duction. This issue has now been suggested to be important
`for both the tamoxifen (Kiyotani et al., 2010) and irinotecan ex-
`amples (Hoskins et al., 2007). Third, one of the common prob-
`lems confounding oncology pharmacogenomics is that
`evaluated studies often lack a control group (the relatively
`well-performed study referenced above on tamoxifendwhich
`diddis an exception). Especially when the phenotype of inter-
`est is progression-free survival or overall survival, without
`such a group, or without an intermediate phenotype which re-
`lates the ultimate outcome to drug response, it can be difficult
`to determine whether an associated variant is actually predic-
`tive of treatment effect (truly pharmacogenomic) rather than
`simply prognostic (i.e., a marker for disease severity). This
`consideration can be especially relevant when the gene(s) be-
`ing studied could theoretically be related to not only drug re-
`sponse, but also disease propensity or severity (like, for
`example, genes in DNA repair pathways). All three of these
`are important points to consider when assessing the quality
`of pharmacogenomic findings and their relevance to clinical
`use, and their confounding nature has tempered clinical
`implementation of some results.
`Separately, racial/ethnic differences in genetic variation
`must be considered. The example of dihydropyrimidine dehy-
`drogenase (DPD) deficiency and 5-fluorouracil (5-FU) toxicity
`exemplifies this point. DPD catabolizes >80% of 5-FU into fluo-
`rinated b-alanine (Heggie et al., 1987). A causative link be-
`tween DPD deficiency and severe toxicity in response to 5-
`FU treatment has been repeatedly shown (Milano et al.,
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`1999; van Kuilenburg et al., 2000; Johnson et al., 1999). While
`clinical assays of enzymatic DPD activity are available, they
`are not always easy to obtain, and there has been a substantial
`effort to characterize causative genetic variants within the
`DPYD gene relating to the DPD deficient phenotype (Yen and
`McLeod, 2007; Van Kuilenburg et al., 1999). In fact, over 40
`SNPs and deletion mutations have been identified within
`DPYD, but most have been shown to have no functional conse-
`quences on enzymatic activity (Yen and McLeod, 2007). The
`the IVS14 þ 1 G > A variant
`best-studied of
`these SNPs,
`(DPYD*2A), has been found in up to 40e50% of people with par-
`tial or complete DPD deficiency (van Kuilenburg, 2004). Yet
`a recent summary of the data on DPYD*2A, including multiple
`studies of this variant alone or in combination with other
`common variants, showed a performance sensitivity (the per-
`centage of actual patients with severe toxicity who were cor-
`rectly identified by the allele) ranging between 6.3 and 83%,
`with a median sensitivity of 30% (Yen and McLeod, 2007).
`Even more importantly, despite the fact that the prevalence
`of functional enzymatic DPD deficiency is higher in African
`Americans (Mattison et al., 2006), the DPYD*2A variant is not
`even present in African Americans (van Kuilenburg, 2004),
`making such testing of limited generalizability and utility. Di-
`rect to consumer genetic testing services like 23andMe fail to
`convey these nuances: 23andMe Inc (2011) advertises genetic
`testing for 5-FU sensitivity, but their testing consists only of
`genotyping of the DPYD*2A variant, and it is not mentioned di-
`rectly on their website that there is likely to be no relevant in-
`formation about 5-FU susceptibility for certain ethnic groups
`like African Americans. This notwithstanding, the data on
`DPYD testing overall is insufficient to support routine pharma-
`cogenomic testing for 5-FU, in our opinion.
`Finally, even the results of well-performed studies which
`include replication may be of limited utility because of the op-
`posite problem: it might be unclear how to assimilate a larger
`number of different variantsdeach of which might have
`a small (but real) impact on the phenotype of interestdinto
`one coherent pharmacogenomic model, let alone a model
`which might also include clinical factors. The very novel find-
`ing of 102 different variants associated with treatment out-
`come in pediatric acute lymphoblastic leukemiadidentified
`through a very well-conducted analysis of two independent
`cohortsdmight beg that question (Yang et al., 2009). Even if
`a clinician could test for all these variants, how would he or
`she assimilate information about all the variants in composite
`when determining treatment options? These types of ques-
`tions are becoming more relevant as pharmacogenomic dis-
`coveries increase and as the field moves into tackling the
`issues not of discovery, but of clinical implementation.
`
`Clinical implementation of pharmacogenomics
`4.
`into oncology practice
`
`In 2001, when the first draft sequence of the human genome
`was released (Lander et al., 2001; Venter et al., 2001), there
`was significant public expectation that this information would
`be quickly utilized to individualize medical care (Ratain and
`Relling, 2001). However, work during the past decade has,
`within oncology, focused mostly on tumor-specific changes
`
`rather than germline variation as the keys to advancement
`in clinical care. The two disciplinesdtumor versus germline
`variationdare of course very different. The former explains
`variability in disease, which can usually be associated with
`differences in natural history and/or etiology, and occasion-
`ally in treatment response. On the other hand, germline vari-
`ation explains variability in the patient, which does affect
`both pharmacokinetics and pharmacodynamics, as well as
`potentially disease risk (even risk for specific mutations (Liu
`et al., 2011)). Some might argue that especially for the latter
`group of drug-related germline variants, the list of the most
`extensively studied within oncology (summarized in Table 1)
`and especially the list of those that has become routinely clin-
`ically tested remains relatively small.
`Implementation into routine practice has been hindered by
`lack of knowledge about such information (on the part of both
`patients and physicians), uncertainty about how to order such
`tests, and reimbursement, and timeliness of results. We be-
`lieve that we are now at a point where SNP genotyping has be-
`come so widely available and inexpensive that this should no
`longer be the barrier. Indeed, whole genome sequencing is
`itself likely to quickly surmount these same barriers in the
`very near future. And it is also likely that in the very near fu-
`ture, we will have a critical mass of information regarding
`germline pharmacogenomic biomarkers in oncology which
`deserve clinical implementation to provide optimal (personal-
`ized) oncologic care. Before discussing the ways to bring this
`goal to fruition, it is worthwhile to examine the question of
`whether prospective, randomized data need to be demon-
`strated for a drug-variant pair before clinical implementation
`can be considered.
`
`5.
`
`Necessity of prospective validation?
`
`Pharmacogenomic findings even from a well-performed sin-
`gle study require validation in a separate patient population
`to confirm that such results are reproducible (Chanock et al.,
`2007). Successful reproducibility in a separate cohort provides
`considerable confidence that the original findings were not
`false positives and were not misleading due to some unique
`phenotypic or measurement characteristics of the original
`population. Outside of the oncology realm, however, even
`two of the most prominent drugs with repeatedly reproduc-
`ible pharmacogenomic informationdwarfarin and clopidog-
`reldhave not yet seen widespread clinical implementation
`of genomic prescribing. It has been felt that prospective, ran-
`domized studies for each of these drugs (the ongoing Clarifica-
`tion of Optimal Anticoagulation through Genetics [COAG] trial
`for warfarin (French et al., 2010); and the proposed and funded
`Pharmacogenomics of Anti-Platelet
`Intervention [PAPI-2]
`study for clopidogrel (United States Department of Health
`and Human Services, 2011)) are needed to demonstrate the
`clear utility of the pharmacogenomic information. Skeptics
`of pharmacogenomics will argue that this type of prospective
`randomized validation (ideally double-blind) might be neces-
`sary for any pharmacogenomic discovery before it is clinically
`implemented, including those for oncology drugs. In contrast,
`we, like others (Altman, 2011; Frueh, 2009), argue that this will
`not only be practically infeasible given the burgeoning
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`
`number of pharmacogenomic discoveries that continue to be
`reported, but also potentially unnecessary given the diminish-
`ing costs of genotyping, the idea that in many cases a pharma-
`cogenomically-informed prescribing decision will at least be
`non-inferior to the alternatives (Altman, 2011) (and potentially
`highly advantageous to the individual patient being consid-
`ered), and the idea that comparative effectiveness research
`will
`likely continue to validate pharmacogenomically-
`informed prescribing in real practice without needing to
`wait for the randomized controlled trial (Epstein et al., 2010).
`We propose instead that using a broad pre-emptive genotyp-
`ing model, described further below, which eliminates mar-
`ginal
`costs,
`that
`the
`plausible
`benefit
`of
`using
`pharmacogenomics for any given patient should only be
`weighed against the plausible risk.
`
`6.
`
`Pre-emptive genotyping
`
`It is our vision that the implementation of pharmacogenomics
`in the clinic will require a transformation from the current
`paradigm of genotyping as a laboratory test, to a future para-
`digm of genotyping conducted at a single time, and perpetu-
`ally available as part of the “genetic examination”. Whether
`this will involve whole genome sequencing or multiplexed
`genotyping is irrelevant, as the key concept is that the genetic
`examination will be performed in anticipation of future med-
`ical needs, as opposed to the current paradigm where geno-
`typing is performed when clinically indicated (like all other
`current laboratory tests). Thus, we envision that future physi-
`cians will utilize a “genomic prescribing system” at every pre-
`scribing encounter (Ratain, 2007),
`including if and when
`a patient might be diagnosed with cancer. Others have argued
`for a similar approach (Relling et al., 2010). In this model, pa-
`tients could be examined with a single blood sample, used
`for genotyping all polymorphisms of potential pharmacoge-
`nomic significance. If the same panel is used for all patients,
`and if patients are genotyped in large batches, this would
`also reduce the costs of genotyping, creating feasibility for
`the approach.
`This approach would address one of the key barriers to
`pharmacogenomic implementationdthat physicians are re-
`luctant to wait for pharmacogenomic results before prescrib-
`ing. This might be especially true for oncologists and for
`their patients diagnosed with cancer, since there is often
`(much warranted) urgency on the part of oncologists, and pa-
`tients, to begin treatment quickly.
`Successful implementation of this type of model (Ratain,
`2007) will require the training of physicians (and related
`health care providers) with expertise in pharmacogenomics,
`genetic testing, and informatics. It will also require the devel-
`opment of medical records systems which can accommodate
`large-scale, patient-specific genotypic information and deliver
`such information in a usable, succinct format to busy
`clinicians.
`We at the University of Chicago (www.clinicaltrials.gov
`study identifier NCT01280825) and others (e.g. Ohio State-
`Coriell Personalized Medicine Study (Coriell, 2011); Vander-
`bilt’s VESPA project
`(Snyder, 2009; St.
`Jude Children’s
`Research Hospital, 2011)) currently have pharmacogenomic
`
`implementation efforts underway which are beginning to
`studydand realizedthe goal of routine incorporation of phar-
`macogenomics in patient care,
`including oncology care.
`Implementation of the best-evidence variants should be the
`starting point for such studies. Equally important will be si-
`multaneous pharmacogenomic discovery research and ongo-
`ing confirmatory studies of contradictory drugegene pairs, in
`order to properly define the scope of which variants to include
`and to increase the number of drugegene pairs having ade-
`quate clinical evidence to warrant implementation.
`
`Special considerations in implementing oncology
`7.
`pharmacogenomics
`
`Knowledge about pharmacogenomic susceptibility (either re-
`sponse or toxicity prediction) may be most obviously usable
`to inform treatment choices in disease settings where several
`equivalent therapies exist. This would allow the physician to
`potentially choose one therapy over another if specific toxicity
`risks were high for one of the given drugs, or, alternatively, to
`select a therapy if the expected likelihood of response was
`higher. In settings where more than one treatment choice
`may not exist, information about a drug may allow the physi-
`cian to weigh toxicity risks of that treatment versus potential
`benefits, and such questions are of course even more relevant
`when treatments are being used in palliative settings. The
`third, different, scenario where implementation questions
`can arise is that of potential patient-specific dose modifica-
`tions based upon pharmacogenomic knowledge of toxicity
`risk. In other words, if a patient is known to be at high (genetic)
`risk of toxicity from a drug at standard treatment doses, could
`the clinician still use that drug, but at a lower dose? Specific
`dose-reduction pharmacogenomic prescribing recommenda-
`tions are, to our knowledge, not available for any oncology
`drugs except for the above-mentioned TPMT substrates
`(Relling et al., 2011). For irinotecan, a genotype-driven dose-
`finding study using pharmacogenomics showed that the rec-
`ommended 180 mg/m2 dose for irinotecan in the FOLFIRI reg-
`imen is considerably lower than what can be actually
`tolerated if patients with high-risk UGT1A1 genotypes are ex-
`cluded (Toffoli et al., 2010).
`A final potential concern surrounding implementation is
`that, especially when considering toxicity pharmacogenom-
`ics, one must consider the additional question of whether
`the same variants governing toxicity susceptibility might
`also govern antitumor response. If that were known to be
`true, the question arises whether dose reduction (to avoid tox-
`icity) would still be appropriate. In the absence of prospect