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The Roles of Common Variation and Somatic Mutation in Cancer Pharmacogenomics

  • Hiu Ting Chan
  • Yoon Ming Chin
  • Siew-Kee LowEmail author
Open Access
Review

Abstract

Cancer pharmacogenomics is the science concerned with understanding genetic alterations and its effects on the pharmacokinetics and pharmacodynamics of anti-cancer drugs, with the aim to provide cancer patients with the precise medication that will achieve a good response and cause low/no incidence of adverse events. Advances in biotechnology and bioinformatics have enabled genomic research to evolve from the evaluation of alterations at the single-gene level to studies on the whole-genome scale using large-scale genotyping and next generation sequencing techniques. International collaborative efforts have resulted in the construction of databases to curate the identified genetic alterations that are clinically significant, and these are currently utilized in clinical sequencing and liquid biopsy screening/monitoring. Furthermore, countless clinical studies have accumulated sufficient evidence to match cancer patients to therapies by utilizing the information of clinical-relevant alterations. In this review we summarize the importance of germline alterations that act as predictive biomarkers for drug-induced toxicity and drug response as well as somatic mutations in cancer cells that function as drug targets. The integration of genomics into the medical field has transformed the era of cancer therapy from one-size-fits-all to cancer precision medicine.

Keywords

Cancer precision medicine Germline variants Next generation sequencing Pharmacogenomics Somatic mutations 

Introduction

Cancer pharmacogenomics studies play an important role in evaluating the relationship between genomic alterations and its effect on modulating the pharmacokinetics and pharmacodynamics of anti-cancer drugs. Genetic alterations in the human genome can be divided into two major categories: germline and somatic alterations. Germline alterations include highly penetrant susceptible mutations and common genetic variants that are inheritable from generation to generation. These type of variations, particularly single-nucleotide polymorphisms (SNPs), are useful as predictive biomarkers for drug-induced adverse events and drug response. Contrary, somatic mutations are acquired randomly following exposure to agents that have the potential to damage DNA in cells. In the context of cancer, these somatic mutations accumulate in the cancer cells and are commonly used as drug targets. For the past two decades, genomic technology has evolved from assessing a single mutation of a gene to the genome-wide perspective through large-scale genotyping and next generation sequencing (NGS). The emergence of abundant NGS data enables large-scale studies aimed at corroborating genomic sequencing and expression data to identify pathogenic germline variants that predispose to cancer [1].

The majority of germline variants are identified through candidate gene approaches or genome-wide association studies (GWAS), in which a GWAS is performed by genotyping up to millions of SNPs. Each approach has its own advantages and disadvantages. The candidate gene approach requires prior knowledge of the mechanism of action of the candidate gene and its target drug [2]. Despite the limitation of having a predefined gene set, candidate gene studies tend to have greater statistical power than GWAS to detect associations due to the lower number of multiple testing corrections performed [2]. The variants identified are relevant to the mode of action of a drug. This is in contrast to GWAS studies where large sample sizes are usually needed to confidently evaluate associations of thousands or even millions of variants in unison [3]. The advantage of GWAS studies is that they enable the identification of new and never before reported genes or variants with a potential effect on drug efficacy and toxicity. However, germline variants identified by both candidate gene studies and GWAS would require rigorous replication efforts to corroborate and confirm the associations [3].

The establishment of The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium has accelerated the identification of somatic mutations from cancer genomes by NGS. NGS is one of the key elements that has enabled the incorporation of genomic data into clinical practice. NGS utilizes the simultaneous sequencing of millions of DNA fragments to generate a large pool of genomic sequence data. The technique can be targeted to sequence a selected number of gene of interest (gene panel), the whole exome or the whole genome. With the constant advancement of technologies and bioinformatic pipelines, this procedure can now be conducted within an affordable cost and time frame [4, 5]. Numerous studies have been conducted to explore the mutational profile of different cancer types as a result of the availability of large-scale genomic sequencing via NGS. Large consortia and networks, such as COSMIC and GENIE, compile and collate somatic mutation data from various sources to deepen our understanding of the mutational landscape in cancer [6, 7]. These databases provide valuable knowledge on the possible associations of genomic information with different cancer subtypes, the development of metastasis and prognosis. More importantly, the increase in genomic knowledge of cancer also allows the identification of molecular targets which may enable the cancer patient to be started on an established targeted therapy or to be included into available clinical trials.

In this review, we have summarized the roles of both germline variants, particularly SNPs, and somatic mutations in cancer pharmacogenomics.

This article is based on previously conducted studies and does not contain any studies with human participants or animals performed by any of the authors.

Part I: Roles of Common Genetic Variations in Cancer Pharmacogenomics

In this section, we highlight a few key cancer drug germline targets that have been extensively reported and discuss the current status of these targets as a potential marker for efficacy and toxicity. A summary of all germline variants is found in Table 1.
Table 1

Germline cancer pharmacogenomic variants

Drug

Cancer type

Phenotype

Study type

Populationa

Discovery sample size

Replication sample size

SNP (P value; OR or HR)

Gene

Reference

Irinotecan (IROX)

Colon

Neutropenia

Candidate

North Americans

468

NA

UGT1A1*28 (P  = 0.003)

UGT1A1

[13]

Neutropenia

Candidate

North Americans

520

NA

UGT1A1*93 (P  = 0.004)

UGT1A1

[13]

Irinotecan and cisplatin

Cervical or Ovarian

Neutropenia

Candidate

Japanese

30

NA

UGT1A1*6 (P  = 0.04)

UGT1A1

[14]

Thrombocytopenia

Candidate

Japanese

30

NA

UGT1A1*6 (P  = 0.04)

UGT1A1

[14]

Diarrhea

Candidate

Japanese

30

NA

UGT1A1*6 (P  = 0.005)

UGT1A1

[14]

Irinotecan

Lung

Neutropenia

Candidate

Korean

81

NA

UGT1A1*6 (P  = 0.044)

UGT1A1

[15]

Lung

Diarrhea

Candidate

Korean

81

NA

UGT1A9*22 (P  = 0.037)

UGT1A9

[15]

Irinotecan, fluorouracil

Colon

Neutropenia

Candidate

Canadian and Italian

167

250

UGT1A6 p.T181A-UGT1A7 p.W208R-UGT1A9 c.-688 (P  = 0.03; OR 5.28; 95% CI 1.28–21.81)

UGT1A6-UGT1A7-UGT1A9

[16]

Irinotecan, S1, oxaliplatin

Colon

Vomitting

Candidate

Korean

43

NA

UGT1A6*2 (P  = 0.014)

UGT1A6

[17]

(TIROX)

Colon

Vomitting

Candidate

Korean

43

NA

UGT1A7*3 (P  = 0.014)

UGT1A7

[17]

Irinotecan (FOLFIRI)

Colon

Diarrhea

Candidate

Canadian

167

NA

rs3749438-T-rs10937158-C (P  = 0.001; OR 0.43)

ABCC5

[19]

Neutropenia

Candidate

Canadian

167

NA

rs225440-T-rs2292997-A (P  = 0.0002; OR 5.93)

ABCG1-ABCC5

[19]

Irinotecan

Acute lympho-blastic leukemia

PK

Candidate

Caucasian and Blacks

85

NA

rs6498588 (P  = 0.010; β  = 0.111; SE 0.042)

ABCC1

[20]

Acute lympho-blastic leukemia

PK

Candidate

Caucasian and Blacks

85

NA

rs1272006 (P  = 0.005; β  = − 0.204; SE 0.070)

ABCB1

[20]

Acute lympho-blastic leukemia

Neutropenia

Candidate

Caucasian and Blacks

85

NA

rs17501331 (P  = 0.019; β  = − 0.255; SE 0.106)

ABCC1

[20]

Acute lympho-blastic leukemia

Neutropenia

Candidate

Caucasian and Blacks

85

NA

rs12720066 (P  = 0.030; β  = 0.227; SE 0.102)

ABCB1

[20]

Irinotecan

Multiple

Diarrhea

GWAS

Japanese

53

NA

rs9351963 (P  = 0.03, OR 3.14, 95% CI 1.8–5.6)

KCNQ5

[21]

Irinotecan

Lung

Neutropenia

Candidate

Korean

107

NA

rs4149056 (P  = 0.007; OR 3.8; 95% CI 1.4–10.0)

SLCO1B1

[18]

Irinotecan

Lung

Neutropenia

Candidate

Korean

101

146

rs11979430 (P = 3.6 × 10−5; OR 3.1; 95% CI 1.8–5.5)

SEMA3C

[25]

Neutropenia

Candidate

Korean

101

146

rs7779029 (P = 2.8 × 10−5; OR 3.1; 95% CI 1.8–5.4)

SEMA3C

[25]

Irinotecan

Colorectal

hematological toxicities

Candidate

Unknown

109

NA

rs10934498 (P = 0.009, OR 0.17, 95% CI 0.04–0.08)

NR1I2

[26]

Irinotecan and cisplatin

Lung

Overall survival

GWAS

Korean

334

NA

rs16950650 (OS 2.5 months; 95% CI 0–5.9)

ABCC4

[27]

Overall survival

GWAS

Korean

334

NA

rs17574269- (OS 12.2 months; 95% CI 10.9–13.5)

DCBID1

[27]

Irinotecan (FOLFIRI)

Colon

Overall survival

Candidate

Spaniard

74

NA

rs11942466 (PFS 8.4 months, 95% CI 6.6–9.4, P = 0.006)

AREG

[29]

PFS and OS

Candidate

Spaniard

74

NA

rs712829 (PFS 6.4 months, 95% CI 5.1–9.4, P = 0.03)

EGFR

[29]

Mercaptopurine

Leukemia

6-MP clearance

GWAS

Mixed1

1026

NA

rs1142345-A > G (TPMT*3C) (P = 8.6 × 10−61)

TPMT

[33]

Leukemia

6-MP clearance

GWAS

Mixed1

1026

NA

rs1800460-G > A (TPMT*3A) (P = 2.0 × 10−44)

TPMT

[33]

Mercaptopurine

Acute lymphoblastic leukemia

GI toxicity

GWAS

European

87 HapMap CEU LCL

286

rs2413739 (P  = 0.04; OR 2.09; 95% CI 1.0–4.6)

PACSIN2

[35]

Mercaptopurine

Acute lymphoblastic leukemia

6-MP dosage

GWAS

Mixed2

657

371

rs116855232 (P = 8.8 × 10−9; -TT 6.22 mg/m2/day; -TC 47.25 mg/m2/day; -CC 62.63 mg/m2/day)

NUDT15

[40]

Mercaptopurine

Acute lymphoblastic leukemia

6-MP dosage

Candidate

Taiwan

404

NA

rs116855232 (P < 1.0 × 10−4; -TT 9.4 mg/m2/day; -TC 30.7 mg/m2/day; -CC 44.1 mg/m2/day

NUDT15

[41]

Mercaptopurine

Acute lymphoblastic leukemia

6-MP dosage

Candidate

Mixed3

270

NA

rs116855232 (P = 4.45 × 10−8; Effect size = − 11.5)

NUDT15

[42]

Tamoxifen

Breast cancer

Recurrence-free survival

GWAS

Japanese

240

R1 = 105

R2 = 117

rs10509373 (P  = 6.29 × 10−29; HR 4.51; 95% CI 2.72–7.51)

C10orf11

[48]

Breast cancer

Endoxifen sensitivity

GWAS

Caucasian

60 HapMap CEU LCL

NA

rs478437 (P < 0.05)

USP7

[49]

Erlotinib

Malignant brain tumor, head and neck carcinoma

PK-PD

Candidate

Europe

88

NA

CYP3A5*1 (P < 0.001; 42% increase in CL)

CYP3A5

[51]

Malignant brain tumor, head and neck carcinoma

PK-PD

Candidate

Europe

88

NA

ABCB1 2677G > T/A (P < 0.001; 19% decrease in CL)

ABCB1

[51]

Erlotinib and gefitinib

Lung cancer

Overall survival

Candidate

Chinese

100

NA

ABCG2 34-GG (OS = 18 months; 95% CI 14.9-21.1 months)

ABCG2

[52]

Erlotinib and gefitinib

Lung cancer

Skin rash and diarrhea

GWAS

Chinese

226

NA

rs884225-TT (P = 0.001)

EGFR

[53]

Erlotinib and gefitinib

Lung cancer

Progression free survival

GWAS

Chinese

128

R1 = 198, R2 = 153

rs3805383 (P < 10−8; HR  >  4)

NMU

[54]

Erlotinib

Lung cancer

Progression free survival

Candidate

Chinese

60

134

rs1042640 (P = 0.009; OR 1.7; 95% CI 0.7–4.1)

UGT1A10

[55]

Lung cancer

Progression free survival

Candidate

Chinese

60

134

rs1060463 (P = 0.001; OR 0.2; 95% CI 0.07–0.5)

CYP4F11

[55]

Lung cancer

Progression free survival

Candidate

Chinese

60

134

rs1064796 (P = 0.013; OR 3.1; 95% CI 1.2–7.8)

CYP4F11

[55]

Lung cancer

Progression free survival

Candidate

Chinese

60

134

rs2074900 (P = 0.001; OR 5.8; 95% CI 1.8–18.5)

CYP4F2

[55]

Lapatinib

Breast cancer

Liver injury

Candidate

European

323

179

HLA-DQA1*02:01 (P < 0.001; OR 9; 95% CI 3.2–27.4)

HLA-DQA1

[57]

Lapatinib

Breast cancer

Liver injury

GWAS

Unknown

844

NA

HLA-DRB1*07:01

(P = 2.0 × 10−18)

HLA-DRB1

[58]

Sunitinib

Renal cell carcinoma

Thrombo-cytopenia

Candidate

Japanese

219

NA

ABCG2 421C > A/rs2231142 (P = 8.41 × 10−3, OR 1.86, 95% CI 1.17–2.94)

ABCG2

[68]

Sunitinib

Renal cell carcinoma

Thrombo-cytopenia

Candidate

Korean

65

NA

ABCG2 421C > A/rs2231142 (P = 0.04, OR 9.90, 95% CI 1.16-infinity)

ABCG2

[70]

Anastrazole or exemestane

Breast cancer

Bone fractures

GWAS

Mixed4

1070

NA

rs10485828; P = 2.56 × 10−7

CTSZ-SLMO2-ATP5E

[74]

     

rs6901146; P = 1.15 × 10−6

TRAM2-TMEM14A

[74]

     

rs4550690; P = 2.89 × 10−6

MAP4K4

[74]

Anastrazole or exemestane

Breast cancer

MSAE

Candidate

Chinese

208

NA

rs7984870 (P = 2.19 × 10−4; OR 3.259; 95% CI 1.843–5.763)

RANKL

[75]

     

rs2073618 (P = 7.95 × 10−4; OR 2.931; 95% CI 1.624–5.288)

OPG

[75]

Exemestane

Breast cancer

MSAE

Candidate

Dutch

737

NA

rs934635 (P = 0.007; OR 5.08; 95% CI 1.8–14.3)

CYP19A1

[79]

VM

Candidate

Dutch

737

NA

rs934635 (P = 0.044; OR 2.78; 95% CI 1.02–7.56)

CYP19A1

[79]

Exemestane

Breast cancer

VM

Candidate

Unknown

1967

NA

rs10046 (P = 0.03; OR 0.78; 95% CI 0.63–0.97)

CYP19A1

[80]

Letrozole

Breast cancer

Bone loss

Candidate

Unknown

122

NA

rs4870061 (P = 3.0 × 10−4; VT/VT%BMD change = − 10.94%; WT/WT,WT/VT%BMD change = − 3.76%)

ESR1

[81]

     

rs10140457 (P = 3.0 × 10−4;WT/VT  %BMD change = 3.08%; WT/WT%BMD change = − 3.43%)

ESR2

[81]

Letrozole

Breast cancer

Bone loss, fracture, osteoporosis

Candidate

Unknown

4861

NA

rs936308-CC (HR 1.37; 95% CI 1.01–1.85)

CYP19A1

[82]

Anastrazole, letrozole, exemestane

Breast cancer

Bone loss

Candidate

Mixed5

97

NA

rs700518 (P = 0.03)

CYP19A1

[83]

BMD Bone mineral density, CEU Utah residents with Northern and Western European ancestry, CI confidence interval, GI toxicity Grade 3–4 mucositis and diarrhea, GWAS genome-wide association studies, HR hazards ratio, LCL lymphoblastoid cell line, MSAE musculoskeletal adverse events, NA not applicable OR odds ratio, OS overall survival, PFS progression free survival, PK–PD pharmacokinetics–pharmacodynamics, SE standard error, SNP single-nucleotide polymorphism, VM vasomotor, VT variant type, WT wild type

aMixed1: White, Black, Hispanic, Asian, others. Mixed2: White, Black, Hispanic, Asian. Mixed3: Singapore, Guatemala, Japan. Mixed4: Asian, Black, Hawaiian, White, Unknown. Mixed5: Caucasians, African Americans, Asian

Irinotecan

Irinotecan is a campthotecin analog constantly used in the treatment of lung and colorectal cancer [8, 9]. It functions as a topoisomerase inhibitor and is activated to its active form SN-38 by carboxylesterases CES1 and CES2 [10]. SN-38 binds to the topoisomerase complex I, preventing the rewinding of the DNA double helix and eventually leading to DNA damage and cell death [11]. The active SN-38 is subsequently inactivated through glucuronidation by the uridine diphosphate-glucuronosyltransferase (UGT) family [11]. Diarrhea and neutropenia are the most common symptoms of irinotecan toxicity due to the accumulation of SN-38 [11].

The most extensively reported marker linked to irinotecan toxicity is UGT1A1. UGT1A1 is an important enzyme of the metabolic pathway for hepatic bilirubin glucuronidation [12]. Polymorphisms reported to be associated with irinotecan toxicity include UGT1A1 SNPs and UGT1A1 alleles [11]. Colorectal cancer patients carrying the homozygous UGT1A1*28 and UGT1A1*93 allele have shown an increased risk for neutropenia as compared to non-carriers (P  = 0.003 and P  = 0.004, respectively) [13]. Masashi and colleagues found that patients carrying UGT1A1*28 and *6 alleles had an increased frequency of neutropenia, thrombocytopenia and diarrhea [14]. In another study involving patients with advanced non-small-cell lung cancer (NSCLC), homozygous carriers of UGT1A1*6 were linked to higher risk for severe neutropenia and lower progression-free survival [15]; UGT1A1*6/*6 carriers also had a lower tumor response [15]. UGT1A9*22 has also been linked to a higher chance of diarrhea but not to tumor response [15]. Levesque and colleagues reported the haplotype combination of UGT1A6 p.T181A–UGT1A7 p.W208R–UGT1A9 c.-688 to be the strongest predictor of severe neutropenia (P = 0.03; odds ratio (OR) 5.28; (95% confidence interval [CI] 1.28–21.81) [16]. In chemo-naïve metastatic colorectal cancer patients, patients carrying UGT1A6*2 and UGT1A7*3 show a higher tendency to vomit when treated using the combination TIROX treatment (S-1, irinotecan and oxaliplatin) [17]. In a separate study, NSCLC patients carrying the UGT1A9 rs3832043 del/del genotype showed an increased risk of severe diarrhea [18].

Hepatic drug transporters are also known to play an important role in irinotecan toxicity [11]. Chen and colleagues evaluated the ABC transporter genes ABCB1, ABCC1, ABCC2, ABCC5, ABCG1 and ABCG2 as well as the solute carrier organic anion transporter SLCO1B1 in metastatic colorectal cancer patients [19]. Their findings revealed that patients with the ABCC5-rs3749438-T-rs10937158-C haplotype had decreased risk of severe diarrhea (P  = 0.001; OR 0.43) and those patients with the ABCG1-rs225440T-ABCC5-rs2292997A haplotype had an increased risk of severe neutropenia (P  < 0.0001; OR 7.68) [19]. In a more recent study by Li and colleagues, cancer patients treated with monotherapy of irinotecan demonstrated an association of the ABCC1 rs6498588 (P  = 0.010; β = 0.111; standard error [SE] 0.042) and ABCB1 (P  = 0.005; β = − 0.204; SE 0.070) SNPs to increased SN-38 exposure [20]. In addition, the ABCC1 rs17501331 SNP (P  = 0.019; β = − 0.255; SE 0.106) and ABCB1 gene (P = 0.030; β = 0.227; SE 0.102) were linked to increased risk of neutropenia [20]. A GWAS study evaluating the relationship between SNPs and irinotecan toxicity in Japanese cancer patients identified SNP rs9351963 in potassium voltage-gated channel subfamily KQT member 5 (KCNQ5) to be associated to an increased risk of diarrhea [21]. KCNQ5 has been linked to irritable bowel syndrome and could be a possible predictor of irinotecan-induced diarrhea.

SLCO1B1 encodes the hepatic protein OATP1B1 whose function is to transport compounds from the blood to the liver where they will be metabolized and cleared from the body [22]. In patients with advanced NSCLC receiving irinotecan treatment, carriers of SLCO1B1 rs4149056-TC or -CC are associated with a higher incidence of neutropenia (P  = 0.007; OR 3.8; 95% CI 1.4–10.0) [18].

SEMA3C is a protein involved in cell survival [23]. SEMA3C variants have been linked to serum bilirubin levels, suggesting a possible link to irinotecan-induced neutropenia [24]. In a GWAS study in patients with advanced NSCLC, Han and colleagues identified SEMA3C SNPs rs11979430 (P  = 3.6 × 10−5; OR 3.1; 95% CI 1.8–5.5) and rs7779029 (P  = 2.8 × 10−5; OR 1.8; 95% CI 1.8–5.4) to have a marginal association to severe neutropenia [25].

In a recent study on metastatic colorectal cancer patients, the xenobiotic sensing receptor NR1I2 SNP rs10934498-A genotype was associated with increased degradation of SN-38 as well as increased risk for irinotecan-induced toxicity [26]. The authors of this study deemed the association of NR1I2 to be independent from UGT1A1*28 after adjusting for effects of the corresponding variant [26].

Genomic markers associated with irinotecan efficacy have been less extensively studied than toxicity. A GWAS study evaluating the survival of patients with SCLC receiving combination therapy of irinotecan + cisplatin observed a decreased overall survival (OS) in patients with the ABCC4 SNP rs16950650-CT and the DCBlD1 rs17574269-AG genotype [27]. Patients carrying the ABCC4 SNP rs16950650-CT showed a median OS of 2.5 months (95% CI 0.0–5.9) while patients carrying -CC genotype showed median OS of 12.2 months (95% CI 10.9–13.5) [27]. Patients carrying the DCBlD1 rs17574269-AG genotype showed median OS of 5.6 months (95% CI 3.4–7.8) while patients carrying the AA genotype had median OS of 12.7 months (95% CI 11.1–14.3) [27].

In metastatic colorectal patients treated with FOLFIRI regimen, patients with the ABCG2 rs7699188-GG genotype show decreased tumor response [28]. In a separate study, metastatic colorectal patients carrying AREG rs11942466 C > A and rs9996584 C > T were associated with OS while those carrying EGFR rs712829 G > T were associated with progression-free survival (PFS) and OS [29]. For AREG rs11942466 C > A, patients carrying the C/C or C/A genotypes had a median PFS of 8.4 months (95% CI 6.6–9.4), while patients carrying an A/A genotype showed a median PFS of 3.0 months [29]. For patients carrying EGFR rs712829 G > T, the median PFS was 6.4 months (95% CI 5.1–9.4) for patients with a G/G genotype, 9 months (95% CI 6.6–9.9) for patients with a G/T genotype and 11.6 months for patients carrying the T/T genotype [29].

Mercaptopurine

6-Mercaptopurine (6-MP) is used to treat acute lymphocytic leukemia (ALL) and chronic myeloid leukemia (CML) [30]. 6-MP has a similar structure to purine bases in the DNA. When incorporated into the DNA structure, it prevents cell division and inhibits DNA synthesis [31]. The clearance of 6-MP from the human body is highly dependent on the function of the enzyme thiopurine S-methyltransferase (TPMT) [32]. TPMT inactivates 6-MP through methylation. The side-effects of 6-MP toxicity include myelosuppression and pancreatitis [32]. A GWAS study conducted by Liu and colleagues in children with leukemia identified top TPMT SNPs rs1142345 or 719A  >  G (P  = 8.6 × 10−61) and rs1800460 (P  = 2.0 × 10−44) to be associated with TPMT activity [33]. The TPMT genotypes were also correlated with mercaptopurine clearance, as reduced TPMT activity would result in an accumulation of 6-MP [33]. The median dose intensities in TPMT heterozygotes who carried one *2, *3A or *3C allele was 63, 59 and 72%, respectively, which were lower than in those who carried the *1/*1 genotype (median 86%) [33]. The association of TPMT SNPs rs1800462 (G > C), rs1142345 (A > G) and rs1800460 (G > A) and their corresponding TPMT alleles TPMT*2, TPMT*3A and TPMT*3C, respectively, was also observed in a separate study [34].

A separate GWAS of children with ALL treated with mercaptopurine identified a new variant, PACSIN2 SNP rs2413739, to be associated with gastrointestinal (GI) toxicity with increased GI toxicity for carriers of the PACSIN2-rs2413739-T allele [35]. This SNP is deemed to be independent of the effects of TPMT as the association was retained after adjusting for effects of TPMT SNPs [35]. In addition, PACSIN2 was shown to be able to modulate TPMT activity through an effect on TPMT mRNA levels and/or TPMT protein degradation [35]. PACSIN2 plays a role in autophagy that may be involved in the degradation of the TPMT protein expressed by variant TPMT∗3A and to a lesser extent by wild-type (WT) TPMT∗1 [36].

6-MP-induced toxicities also occur in patients with WT TPMT variants, thus suggesting additional germ line variants contributing to 6-MP toxicity [37, 38]. Nudix hydrolase 15 (NUDT15) is another enzyme involved in 6-MP metabolism. It prevents the incorporation of thiopurine active metabolites thioguanine triphosphate (TGTP) and thioguanine diphosphate (TdGTP) into DNA by dephosphorylating them, thereby preventing the cytotoxic effects of 6-MP. In the presence of NUDT15 variants or defective alleles, there will be an excess of thiopurine active metabolites TGTP and TdGTP, and this accumulation will lead to 6-MP toxicity [39].

One specific variant is rs116855232 (c.415C > T) in NUDT15; this variant shows a distinct population distribution, with a particularly higher occurrence of the rare allele rs116855232-T in East Asians (10%) compared to Hispanics, Europeans and Africans (http://www.internationalgenome.org/1000-genomes-browsers). A GWAS study on children with ALL reported an association of rs116855232 in NUDT15 (P  = 8.8 × 10−9) to mercaptopurine sensitivity in only East Asian patients, with a lower tolerance to mercaptopurine resulting in hematologic toxicities [40]. Patients carrying the rs116855232-TT genotype were less tolerant, with an average dose intensity of 8.3%, compared with those with TC and CC genotypes, who tolerated 63 and 83.5% of the planned dose of 75 mg/m2 per day [40].

An association of rs116855232 to mercaptopurine sensitivity was also detected in a Taiwanese population through the candidate approach study (P  < 1.0 × 10−4) [41]. The tolerable daily doses of mercaptopurine were 9.4 mg/m2 per day for patients carrying rs116855232-TT, 30.7 mg/m2 per day for those carrying rs116855232-TC and 44.1 mg/m2 per day for those carrying rs116855232-CC. Moriyama and colleagues reported an association between NUDT15 variant rs116855232 (c.415C > T) and increased 6-MP toxicity due to loss-of-function in the NUDT15-TT genotype (P  = 4.45 × 10−8, effect size = − 11.5) based on the results of their meta-analysis combining data on children with ALL from Guatemala, Singapore and Japanese populations [42].

Tamoxifen

Tamoxifen is constantly used in the treatment of the estrogen receptor (ER+) subtype of breast cancer [43]. Tamoxifen itself has no affinity towards the estrogen receptor. It is a prodrug, requiring activation after being metabolized by cytochrome P450 (CYP) isoform CYP2D6 and CYP3A4 into its active form 4-hydroxytamoxifen (4-OHT) (afimoxifene) and N-desmethyl-4-hydroxytamoxifen (endoxifene) [44]. The active forms of tamoxifen show markedly greater affinity for the estrogen receptors than does the parent drug tamoxifen [44]. The association of CYP2D6 alleles towards efficacy and toxicity of tamoxifen have been well documented. Several studies have shown that women who are homozygous carriers of CYP2D6*4 (poor metabolizers) have a higher risk of breast cancer relapse and a decreased disease-free survival, and they also experience less severe hot flashes [45, 46, 47]. In a GWAS study evaluating the effects of polymorphisms on the clinical outcomes of breast cancer patients receiving tamoxifen treatment, the rs10509373 SNP in the C10orf11 gene was found to be associated with recurrence-free survival (P  = 1.26 × 10−10), with carriers of the rs10509373-C allele linked to poorer recurrence-free survival (hazards ratio [HR] 4.51; 95% CI 2.72–7.51; P  = 6.29 × 10−29) [48]. Weng and colleagues used a multi-platform approach and identified SNPs in the USP7 (ubiquitin carboxyl-terminal hydrolase 7) gene that were associated with tamoxifen sensitivity [49]. Initial screening using HapMap lymphoblastoid cell lines identified an association between SNP rs478437 and USP7 expression, with rs478437-T linked to lower USP7 expression. Lower expression of USP7 resulted in a higher resistance to endoxifen [49].

Erlotinib

Erlotinib is used in the treatment of several types of cancers, in particular NSCLC and pancreatic cancer. It is a receptor tyrosine kinase inhibitor (TKI) that specifically acts on the epidermal growth factor receptor (EGFR) [50]. Erlotinib binds to EGFR, preventing the formation of EGFR homodimers that are needed to activate subsequent signaling cascades in the nucleus or other biochemical processes [50]. Koning and colleagues (2011) compared ABCB1, ABCG2 and CYP3A5 SNPs for their effects on erlotinib pharmacokinetics in both adults and children [51]. Their results indicate that CYP3A5*1 and ABCB1 (2677G > T/A) were associated with erlotinib clearance (P  < 0.001), with ABCB1 2677G > T/A being associated with a 19% decrease in erlotinib clearance and the CYP3A5*1 allele being linked to a 42% increase in erlotinib clearance [51].

In a study of advanced NSCLC Chinese patients receiving gefitinib or erlotinib treatment, Chen and colleagues identified ABCG2 34-GG carriers to have shorter OS (18 months; 95% CI 14.9–21.1 months) compared to carriers of the -GA or -AA genotype (OS 31 months; 95% CI 22.9–39.1 months) (P  < 0.05) [52]. Patients undergoing erlotinib treatment are also prone to adverse drug reactions (ADRs) such as skin rash and diarrhea. A study investigating EGFR polymorphisms and theirs link to erlotinib ADRs in NSCLC patients identified rs884225-TC and -CC carriers to have a lower risk for erlotinib ADRs than did carriers of the WT rs884225-TT (P  = 0.001) [53]. A GWAS study evaluating NSCLC patients receiving first-line EGFR-TKIs treatment of gefitinib or erlotinib identified SNPs at 4q12 to be associated with PFS at genome-wide significance (P  < 10−8), with an estimated HR of  >  4. In particular, functional analyses of rs3805383 showed a positive correlation between SNPs and EGFR expression levels (P  = 0.04; β = 0.279) [54].

Wang and colleagues performed targeted sequencing to evaluate the link between EGFR and EGFR-linked pathway gene SNPs with EGFR-TKI response and ADRs in patients with advanced NSCLC [55]. They identified rs1042640 in UGT1A10, rs1060463 and rs1064796 in CYP4F11 and rs2074900 in CYP4F2 as being associated with erlotinib treatment response, with improved an median PFS of 12.57 months compared to that of non-responders (median PFS 3.55 months) [55]. SNP rs1064796 in CYP4F11 and SNP rs10045685 in UGT3A1 were also linked to ADRs, with carriers of CYP4F11-rs1064796-C and UGT3A1-G showing an increased risk for skin rash or digestive track injury [55].

Lapatinib

Lapatinib is a human EGFR inhibitor administered to metastatic breast cancer patients found to be overexpressing EGFR [56]. Spraggs and colleagues conducted a candidate approach study to identify genetic variants associated with lapatinib-induced liver injury and identified the HLA-DQA1*02:01 allele as being associated with liver enzyme alanine aminotransferase adverse effects (P  < 0.001; OR 9; CI 3.2–27.4) [57]. They also reported that HLA-DQA1*02:01 had negative and positive predictive values of 0.97 (95% CI 0.95–0.99) and 0.17 (95% CI 0.10–0.26), respectively, for liver risk. In a separate study, Parham and colleagues performed a GWAS study to identify genetic variants associated with liver injury [58]. The results identified HLA-DRB1*07:01 to be associated with elevated levels of ALT (P  = 2.0 × 10−18) [58].

Sunitinib

Sunitinib is a TKI and used as the first-line treatment for advanced renal cell carcinoma (RCC) as well as imatinib-resistant GI stromal tumor (GIST) [59]. It inhibits cellular signaling by targeting platelet-derived growth factors and vascular EGFRs, reducing tumor vascularization and subsequently causing cancer cell apoptosis and tumor shrinkage [60]. Sunitinib also inhibits CD117 (c-KIT) [61], the receptor tyrosine kinase that drives the majority of GISTs [62]. Patients receiving sunitinib exhibit a varying response to treatment, with several common sunitinib-induced adverse reactions reported, such as thrombocytopenia, hypertension, hand–foot syndrome, leucopenia and neutropenia [63, 64, 65, 66, 67].

The most commonly reported variant associated with sunitinib-induced adverse response is ABCG2 421C > A (rs2231142). According to the 1000 Genomes Project database, this variant is more common in Asians (Japanese, 32%; Chinese Han Southern China, 26%; Chinese Han Beijing, 31%) than in Caucasians (Utah residents of European descent, 12%; British, 14%; Iberian in Spain, 7%) (http://www.internationalgenome.org/1000-genomes-browsers). This ethnic difference in allele frequency could explain the ethnic difference in sunitinib toxicity.

Low and colleagues conducted a candidate approach study on adverse reactions of sunitinib treatment in Japanese patients with RCC and reported the association of ABCG2 421C > A with severe thrombocytopenia (P  = 8.41 × 10−3; OR 1.86; 95% CI 1.17–2.94) [68]. The ABCG2 functions as a half transporter of sunitinib. ABCG2 421C > A encodes ABCG2 Q141K, a variant associated with lower expression of ABCG2; this lower expression may in turn affect the oral absorption and/or elimination of sunitinib, thereby increasing patient toxicity to sunitinib [69]. Similar findings were also reported by Kim and colleagues, who found ABCG2 421C > A to be associated with severe thrombocytopenia in Korean patients with metastatic RCC (P  = 0.04; OR 9.90; 95% CI 1.16–∞) [70]. Kim and colleagues also reported the association of ABCG2 421C > A with neutropenia (P  = 0.02; OR 18.20; 95% CI 1.49–222.09) as well as hand–foot syndrome (P  = 0.01, OR 28.46, 95% CI 2.22–364.94) [70]. ABCG2 421C > A has also been associated with sunitinib-induced neutropenia, with ABCG2 421-AA linked with poorer clearance of sunitinib (P  = 0.03; OR 0.3; 95% CI 0.1–0.9) [71].

Aromatase Inhibitor

In contrast to tamoxifen that requires activation through a metabolic process, third-generation aromatase inhibitors (AIs) are active in their parent form, with metabolism resulting in inactivation of the drug. Common adverse effects associated with AI treatment include bone loss, musculoskeletal adverse events (MSAEs), such as arthralgia, osteoporosis, and bone fractures, as well as vasomotor symptoms (VMSs) such as hot flashes and night sweats [72, 73].

Ingle and colleagues performed a GWAS to identify genomic variants associated with AI-induced bone fractures in postmenopausal ER+ breast cancer patients [74]. After initial GWAS discovery and imputation, the study identified SNPs near CTSZ-SLMO2-ATP5E (rs10485828; P = 2.56 × 10−7), TRAM2-TMEM14A (rs6901146; P = 1.15 × 10−6) and MAP4K4 (rs4550690; P = 2.89 × 10−6) that were moderately associated with risk for bone fracture [74]. These associations did not overcome the genome-wide significance threshold (P  < 5.0 × 10−8). It is possible that these SNPs are involved in SNP-dependent and estrogen-dependent regulation of the corresponding genes, with possible downstream influence on the RANK/RANKL/OPG genes related to osteoporosis [74].

Wang and colleagues evaluated the association of RANKL/RANK/OPG gene polymorphisms with AI-induced MSAEs in early-stage, hormone-sensitive breast cancer patients [75]. Patients received either letrozole or anastrazole treatment. The RANKL/RANK/OPG signaling pathway plays an important role in bone health [76, 77]. Wang and colleagues found that the RANKL SNP rs7984870 (P  = 2.19 × 10−4; OR 3.259; 95% CI 1.843–5.763) and OPG SNP rs2073618 (P  = 7.95 × 10−4; OR 2.931; 95% CI 1.624–5.288) were associated with an increased risk of AI-related MSAEs [75].

In a separate study involving patients from the B-ABLE study, Garcia-Gira and colleagues investigated the association of SNPs with AI-induced arthralgia and found that SNPs in the CYP17A1 and VDR genes showed significant association (P  < 0.01) [78]. In a study involving hormone receptor-positive early-breast cancer patients from the TEAM trial, patients receiving exemestane treatment experienced known side-effects of AIs, such as MSAEs and VMSs. Patients carrying the aromatase gene variant CYP19A1 rs934635-AA were associated with a significantly higher odds of having MSAEs (P  = 0.007; OR 5.08; 95% CI 1.8–14.3) and VMSs (P  = 0.044; OR 2.78; 95% CI 1.02–7.56) [79].

In the TEXT trial study involving premenopausal hormone receptor (HR)-positive breast cancer patients, the aromatase gene variant CYP19A1 rs10046-TT was associated with a reduced incidence of hot flashes/sweating in patients receiving exemestane treatment (P  = 0.03; OR 0.78; 95% CI 0.63–0.97). A stronger association was observed in the combination treatment of exemestane and suppression of ovarian function (TT vs. CT/CC: OR 0.65; 95% CI 0.48–0.89) [80].

In a randomized trial comparing exemestane and letrozole in early-stage HR-positive breast cancer patients, patients in the letrozole treatment arm of the study showed significantly greater bone loss than did those receiving exemestane [81]. Letrozole-treated patients carrying variant genotype ESR1 rs4870061 (P  = 3.0 × 10−4; VT/VT%BMD change − 10.94%; WT/WT,WT/VT%BMD change − 3.76%) and ESR2 rs10140457 (P  = 3.0 × 10−4; WT/VT  %BMD change 3.08%; WT/WT%BMD change − 3.43%) were associated with decreased BMD [81].

In the BIG1-98 trial, which compared tamoxifen and letrozole treatment, letrozole-treated patients carrying the CYP19A1 rs936308-CC genotype showed a higher risk of bone adverse effects (bone fractures, osteoporosis, arthralgia, and myalgia) (HR 1.37; 95% CI 1.01–1.85) [82].

Napoli and colleagues reported the association of CYP19A1 variant rs700518 with bone loss in postmenopausal women with ER+ breast cancer treated with the third-generation AIs anastrazole, letrozole and exemestane [83]. Carriers of the rs700518-AA genotype developed significant bone loss at the lumbar spine and total hip at 12 months when compared to carriers of the WT GA/GG genotypes (P  = 0.03) [83].

Part II: Roles of Somatic Mutations in Cancer Pharmacogenomics

The use of genomic data to facilitate the development of molecularly targeted therapy was first demonstrated in the use of imatinib in CML patients. CML is characterized by the presence of the BCR-ABL fusion gene, which leads to the formation of a constitutively active tyrosine kinase, resulting in uncontrolled cell proliferation and malignant transformation. Imatinib, a first-generation ABL1 TKI, was approved by the U.S. Food and Drug Administration (FDA) in 2001 with the indication of CML for both newly diagnosed patients and for those with a failed interferon-alpha (IFα) response [84]. Imatinib was soon considered to be the first-line therapy for CML as it achieved a better response rate and tolerability among patients than did the existing therapy at the time, IFα [85]. More importantly, imatinib has dramatically improved the prognosis of CML, with a sustained OS rate and PFS of  >  80% after 10 years of treatment [86]. The success of imatinib opened the era of molecular-targeted therapy for cancer. In recent years, the availability of NGS for in-depth genomic characterization has allowed the increasing identification of genomic biomarkers that could be targeted by FDA-approved therapies (Table 2) [87, 88]. Some of the most extensively studied genomic markers, EGFR, ALK, BRAF and MEK, will be further reviewed in this paper.
Table 2

U.S. Food and Drug Administration-approved molecular-targeted drugs

Molecular targetsa

Drugs

Cancer typeb

Specific mutations approved for patient selection

References

ALK

Alectinib

NSCLC

ALK mutation

[120]

Brigatinib

NSCLC

ALK mutation

[122]

Ceritinib

NSCLC

ALK mutation

[119]

ALK, MET, ROS1

Crizotinib

NSCLC

ALK fusion or ROS1 fusion

[113]

BCR-ABL1

Bosutinib

CML

BCR-ABL1 fusion

[159]

Dasatinib

CML

BCR-ABL1 fusion

[160]

Imatinib

CML and ALL

BCR-ABL1 fusion

[161, 162]

Nilotinib

CML

BCR-ABL1 fusion

[163]

Ponatinib

CML

BCR-ABL1 fusion

[164]

BRAF

Dabrafenib

NSCLC, melanoma, anaplastic thyroid cancer

BRAF V600/K

[130, 131, 132]

CDK4/6

Abemaciclib

ER+ HER2− breast cancer

 

[139]

Palbociclib

HR+ HER2− breast cancer

 

[165]

Ribociclib

HR+ HER2− breast cancer

 

[137, 166]

EGFR

Cetuximab

Colorectal cancer, squamous head and neck cancer

EGFR expressing and KRAS wild type for colorectal cancer

[167]

Erlotinib

NSCLC, pancreatic cancer

EGFR (exon 19 deletions/L858R) for NSCLC

[98, 168, 169]

Gefitinib

NSCLC

EGFR (exon 19 deletions/L858R)

[170]

Necitumumab

Squamous NSCLC

 

[104]

Osimertinib

NSCLC

EGFR (T790M/exon 19 deletions/L858R)

[171, 172, 173]

Panitumumab

Colorectal cancer

KRAS and NRAS wild type

[174, 175]

EGFR/ERBB2

Afatinib

NSCLC

EGFR (exon 19 deletions/L858R/S768I/L861Q/G719X)

[96]

ERBB2

Ado-Trastuzumab Emtansine

HER2+ breast cancer

 

[176, 177]

Trastuzumab

HER2+ breast cancer, HER2+ gastric cancer

 

[178, 179, 180]

Pertuzumab

HER2+ breast cancer

 

[181]

Lapatinib

HER2+ breast cancer

 

[182]

Neratinib

HER2+ breast cancer

 

[183, 184]

KIT

Imatinib

Aggressive systemic mastocytosis

Lack of D816V c-Kit mutation

[185]

Gastrointestinal stromal tumors

Kit (CD117) positive

[186]

MEK

Trametinib

NSCLC, melanoma, anaplastic thyroid cancer

BRAF V600E/K

[130, 131]

mTOR

Everolimus

HR+ HER2− breast cancer; renal cell carcinoma; pancreatic, gastrointestinal or lung origin of neuroendocrine tumor; subependymal giant cell astrocytoma

 

[187, 188, 189, 190, 191]

PDGFR

Imatinib

Myelodysplastic/myeloproliferative disorders

PDGFR gene rearrangements

[192]

Dermatofibrosarcoma protuberans

COL1A1-PDGFB fusion

[193]

Hypereosinophilic syndrome and Eosinophilic leukemia

FIP1L1-PDGFRA fusion

[194]

VEGF

Bevacizumab

NSCLC; colorectal cancer; cervical cancer; glioblastoma; ovarian epithelial, fallopian tube or primary peritoneal cancer; renal cell carcinoma

 

[195, 196]

VEGFR2

Ramucirumab

NSCLC, metastasized colorectal cancer, advanced gastric or gastro-esophageal junction adenocarcinoma

 

[197]

aALK Anaplastic lymphoma kinase, BRAF Proto-oncogene B-Raf, CDK4/6 Cyclin-dependent kinase 4/6, EGFR Epidermal growth factor receptor, ERBB2 Erb-B2 Receptor Tyrosine Kinase 2, KIT Tyrosine-protein kinase Kit, MEK Mitogen-activated protein kinase kinase, mTOR Mammalian target of rapamycin, MET Tyrosine-protein kinase Met, PDGFR Platelet-derived growth factor receptors, ROS1 Proto-oncogene tyrosine-protein kinase, VEGF Vascular endothelial growth factor, VEGFR2 Vascular Endothelial Growth Factor Receptor 2

bALL Acute lymphocytic leukemia, CML chronic myeloid leukemia, ER+ estrogen receptor subtype of breast cancer, HER human epidermal growth factor receptor 2, HR hormone receptor, NSCLC non-small-cell lung cancer

Epidermal Growth Factor Receptor

The superiority of personalized, genomic-based targeted therapy is most evident in the use of EGFR inhibitors in NSCLC treatments. Somatic mutations in EGFR are some of the most extensively studied targets due to their high prevalence (15–50%) in NSCLC patients. The two most common EGFR mutations are exon 19 deletions and exon 20 substitution (-L858R), together accounting for up to 90% of total EGFR mutations. These alterations result in a mutated tyrosine kinase that is under constant phosphorylation and activates downstream signals (RAS/MAPK and P13K/Akt), leading to tumorigenesis. More importantly, these two mutations, also recognized as sensitizing mutations, were found to predict the response rate of TKIs in NSCLC patients [89, 90]. Data from several clinical trials have shown that up to 67% of NSCLC patients who harbored a sensitizing mutation achieved objective response from erlotinib or gefitinib (FDA-approved first-generation TKIs) [91]. Similarly, the superiority of TKIs over traditional chemotherapy in response rate and PFS was also only observed in patients who harbored a sensitizing mutation [92, 93]. These positive results have secured the role of TKIs as the first-line treatment for metastatic NSCLC patients who harbor EGFR sensitizing mutations. Although TKIs achieved a remarkable initial response, up to 50% of the patients acquired resistance after 1 year of treatment, with the secondary -T790M mutation accounting for the majority of the resistance cases [94]. Second-generation TKIs, such as afatinib and dacomitinib, were initially designed to overcome the resistance by increasing the inhibition potency. Unfortunately, they did not overcome the resistance caused by the -T790M mutation [95]; instead, afatinib was found to be effective against three other rare EGFR mutations, namely, - S768I/L861Q/G719X, with 78% of the patients who harbored one of these three mutations having an objective response [96]. Osimertinib, a third-generation TKI has recently been approved for advanced NSCLC patients with a T790 M mutation. The approval was based on the promising results from a phase II clinical trial that assessed the efficacy of osimertinib in patients who harbored either an intrinsic or acquired EGFR-T790M mutation. Up to 70% of the patients achieved an objective response with manageable side effects [97]. In addition to being a treatment for NSCLC, erlotinib is also currently approved as a combination therapy with gemcitabine for metastatic pancreatic cancer patients based on findings showing that this combination therapy achieved an improved PFS and disease control rate [98].

In addition to EGFR inhibitors, anti-EGFR monoclonal antibodies have also been developed to inhibit EGFR auto-phosphorylation and further downstream signaling. Anti-EGFR monoclonal antibodies are most commonly used for treating metastatic colorectal cancer [99]. Cetuximab and panitumumab are currently approved to be used either as combination treatment with irinotecan or as monotherapy for advanced EGFR-positive colorectal cancer patients due to their superior response rate, disease control rate and longer PFS compared to existing chemotherapy treatment [100, 101, 102]. In more recent years, a second-generation anti-EGFR monoclonal antibody, necitumumab, has been approved for use in combination with gemcitabine and cisplatin as the first-line treatment for patients with metastatic squamous NSCLC [103]. The approval was based on the promising results obtained from a large randomized, multicenter study that involved 1093 patients with squamous NSCLC across 26 countries [104]. These patients were divided into two treatment arms: gemcitabine + cisplatin with or without necitumumab. Significant improvement in the OS and PFS rates was observed in patients who were treated with necitumumab [104]. Anti-EGFR monoclonal antibodies have shown clinical benefits over traditional chemotherapy in NSCLC and colorectal cancer patients; however, unlike EGFR inhibitors, the predictive biomarker for such clinical response is still unclear. Several clinical trials have shown EGFR-positive patients (identified using fluorescence in situ hybridization [FISH] or immunohistochemistry [IHC]) to have a more favorable outcome from necitumumab and cetuximab than those who did not express EGFR; however, this difference was found to be non-significant [105, 106]. The limited sensitivity of FISH and IHC to detect EGFR expression also questions the predictivity of EGFR expression for treatment response to anti-EGFR monoclonal antibodies. Future studies using assays with higher sensitivity, such as NGS, are required to verify and quantify such a relationship. In contrast to EGFR expression, KRAS mutation status has been well established as a predictor of response to cetuximab and panitumumab [99]. KRAS mutations result in a constitutively active guanine nucleotide-binding (GTP)-binding protein, allowing the tumor to escape from the inhibition effect of EGFR-targeted therapies. Such effects have been demonstrated in several clinical trials where the improved PFS and response rate of anti-EGFR monoclonal antibodies were only observed in KRAS WT groups [107, 108]. Recently, NRAS and BRAF mutation status were found to predict the response rate of cetuximab and panitumumab in colorectal cancer patients. Cumulative evidence shows that patients whose tumor harbors mutations in NRAS exons 2, 3 and 4 and BRAF-V600E are unlikely to respond to anti-EGFR monoclonal antibodies [109]. RAS mutation testing is currently mandatory before the initiation of cetuximab and panitumumab treatment as they are not indicated for patients whose tumor harbor somatic mutations in exon 2, 3 and 4 of either KRAS or NRAS.

In recent years, in vitro studies have been conducted to explore the possibility of targeting EGFR in NSCLC patients using immunotherapy. In a pre-clinical study, the authors developed an adoptive T-cell treatment with chimeric antigen receptor (CAR) that targets EGFR. The modified CAR T cells showed great anti-cancer efficacy with significant regression of EGFR-positive human lung cancer xenografts [110]. Future clinical studies are required to confirm their efficacy in humans. The feasibility of CAR T cells that target EGFR in patients with metastatic colorectal cancer is currently under a phase I/II clinical trial (ClinicalTrials.gov Identifier: NCT03152435).

Anaplastic Lymphoma Kinase

In addition to EGFR, several other targeted inhibitors have been approved for NSCLC patients who harbor alterations in the gene for anaplastic lymphona kinase (ALK). ALK mutations, are found in 3–7% of patients with NSCLC, which include gene fusion, point mutation and amplification, with gene fusion accounting for majority of these cases. Several fusion partners of ALK have been identified, and the gene that encodes EML4 has been found to be most abundant in NSCLC. Other fusion partners include KIF5B, TFG, DCTN1, SQSTM1, Nucleoprotein TPR, CRIM1, STRN, HIP1, PTPN3, KLC1, CLTC and FBX036 [111]. ALK rearrangements lead to the formation of a constitutively active oncogenic fusion protein that activates downstream signaling pathways, such as the mitogen-activated protein kinase (MAPK) or JAK-STAT pathways [112]. Several ALK inhibitors are now available, and these are considered to be first-line therapy for NSCLC patients who harbor ALK mutations due to their superior efficacy. In a randomized phase III clinical trial, the median PFS in NSCLC patients who were treated with crizotinib, a first-generation ALK/ROS1/MET inhibitor, was significantly longer than that in patients treated with the standard chemotherapy (7.7 vs. 3.0 months, respectively) [113]. Also, the objective response rate was 46% higher in the crizotinib treatment arm than in the chemotherapy arm [113]. Unfortunately, similar to EGFR inhibitors, the majority of the patients treated with crizotinib experienced relapse within 12–24 months, with the resistance mechanisms being either ALK dependent or ALK independent [114]. Around 30% of the resistance cases developed due to the presence of a secondary mutation in the ALK tyrosine kinase domain that led to reactivation of the fusion protein [114]. Some of the reported secondary point mutations include L1196M, C1156Y, F1174L and F1174V [115]. Second- and third-generation ALK inhibitors (ceritinib, alectinib and brigatinib) have been designed to overcome such resistance by increasing the potency and selectivity to ALK fusion proteins [116]. In two studies, both ceritinib and alectinib demonstrated an overall response rate of  >  50% in patients who progressed or were intolerant to crizotinib [117, 118]. Furthermore, these agents have also been shown to have superior efficacy in ALK inhibitor-naïve patients compared to chemotherapy; this efficacy led to their recent approval as first-line treatment for NSCLC patients carrying the ALK mutation [119, 120]. In a pre-clinical study, brigatinib, a recently FDA-approved ALK inhibitor, showed inhibition activity against several ALK acquired resistance mutations, such as ALK C1156Y, I1171S/T, V1180L, L1196M, L1152R/P, E1210K and G1269A [121]. This finding was confirmed in a phase II clinical trial, where the overall response rate to brigatinib was observed to be  >  53% in ALK-positive, crizotinib-treated patients with metastatic NSCLC [122].

Serine–Threonine Protein Kinase and Mitogen-Activated Protein Kinase

Approximately half of advanced melanomas harbor a serine–threonine protein kinase BRAF mutation, with V600E being the most common mutation (90% of total BRAF mutations). The mutated BRAF phosphorylates and activates MEK proteins, which in turn activates the MAPK pathway, leading to an uncontrolled cell growth and proliferation [123]. The high prevalence of the BRAF mutation in melanoma makes it a perfect candidate for targeted therapies for melanoma patients. Dabrafenib and vemurafenib, both BRAF inhibitors, were both found to exhibit improved PFS or OS compared to the empirical chemotherapy agent dacarbazine in patients with BRAF-V600 metastatic melanoma [124, 125]. Unfortunately, the majority of the patients who received monotherapy with the BRAF inhibitor developed resistance within 6–7 months of treatment [126]. Resistant melanoma cells may arise from either preexisting resistant clones that were undetected in the single biopsy specimen or acquired through secondary mutation [127]. Some of the known acquired resistance mutations for BRAF inhibitors include alternative splicing of BRAF, BRAF copy number amplification, NRAS mutations (Q61, Q12 and Q13 on codon 12 or 61) and alterations in PI3K. The secondary mutations allow reactivation of the MAPK pathway, thus permitting the melanoma cells to escape BRAF inhibition [128]. In addition to BRAF inhibitors, the MEK inhibitor trametinib has also been approved to intervene the amended MAPK pathway in melanoma patients with BRAF-V600 mutations. In one study, trametinib demonstrated improved OS at 6 months compared to the empirical chemotherapy agents (81% and 67%, respectively); however, resistance quickly emerged with a median PFS of 4.8 months [129]. More importantly, combination therapy of trametinib + dabrafenib has shown superior clinical effects with durable benefit which overcomes the frequent occurrence of resistance that was observed in monotherapies [130]. Combination targeted therapy has led to significant improvement in the 3-year PFS (22 vs. 12%) and 3-year OS (44 vs. 32%) compared to dabrafenib monotherapy [130]. Combination therapies of trametinib + dabrafenib or of vemurafenib + cobimetinib are currently approved for the treatment of advanced melanoma that harbors the BRAF V600E or V600K mutation. Combination therapy of dabrafenib + trametinib has also shown superior efficacy in NSCLC and anaplastic thyroid cancer with BRAF V600 mutations [131, 132].

Other Molecular Targets: c-Met Tyrosine Kinase and Cyclin Dependent Kinases 4/6

In addition to the approved biomarkers mentioned above, which can be utilized for treatment selection, several other targets have also been validated to expand treatment availability for cancer patients. However, further studies are warranted to identify the specific genomic markers for selecting sensitive patients. c-Met tyrosine kinase (MET) dysregulations, in particular MET exon 14 splice site mutations, are detected in approximately 3–4% of lung adenocarcinomas [112]. MET amplification has also been reported in cases with acquired resistance to EGFR inhibitors in NSCLC patients [133]. The possibility of MET inhibition as a treatment option was suggested in several early clinical trials that tested the effectiveness of MET inhibitor in combination therapies. Cabozantinib and onartuzumab in combination with erlotinib, an EGFR TKI, showed significantly improved PFS compared to monotherapy with erlotinib [134, 135]. Unfortunately, these promising results could not be replicated in a phase III clinical trial where superior clinical benefits of MET inhibitor were not observed in NSCLC patients who harbored the MET mutation [136]. However, it should be noted that the patients participating in this trial were selected based on the tumor overexpressing MET, as determined by IHC. It is unclear whether IHC is a sufficiently sensitive selection tool to identify MET-positive patients. Further studies with accurate molecular profiling using NGS are warranted to validate the implication of MET in NSCLC patients.

The importance of cyclin dependent kinases 4/6 (CDK4 and CDK6, respectively) in cell division and their hyperactivation in multiple cancer types has been well established. Great efforts have been expended to develop drugs targeting CDK activity, and three of these are now approved for ER+ advanced or metastatic breast cancer in combination with an AI. The addition of a CDK4/6 inhibitor (ribociclib, palbociclib and abemaciclib) to the hormone therapy has successfully increased PFS when compared to AI monotherapy [137, 138, 139]. Despite their superior efficacy in cancer treatments, the determinants of sensitivity of CDK4/6 inhibitors are still unknown. The association of cyclin D1 amplification and the response to CDK4/6 have been extensively studied in both pre-clinical and clinical trials across different cancer types. Patients with mantle cell lymphoma whose tumor harbors cyclin D1 deregulation were found to be sensitive to CDK4/6 inhibitors; however, such a specific response was not observed in breast cancer patients [140, 141]. It should be noted that the frequency of cyclin D1 genetic alteration is strongly disease specific; therefore, CDK4/6 inhibitors are most likely to be sensitive in tumors with a strong dependence on cyclin D1 alterations [142]. Similarly, the association of CDK4/6 amplification with the sensitivity of CDK4/6 inhibitors has been assessed in several tumors. In a phase II clinical trial, palbociclib was found to be sensitive in patients with CDK4 amplified liposarcoma [143]; however, this study was not powered to assess whether CDK4 amplification can be used as a biomarker to identify sensitive patients due to the lack of a control arm. Furthermore, contradicting results have been reported in pre-clinical studies where CDK4 and CDK6 amplifications were associated with acquired resistance of CDK4/6 inhibitors in breast cancer and renal cancer [144, 145]. Several clinical trials are currently underway to confirm the association of these genetic markers with the efficacy of CDK4/6 inhibitors (ClinicalTrials.gov Identifier: NCT03310879 and NCT02187783).

Efficacy of Genomic-Based Therapies

The increased availability of targeted drugs and the increased use of genomic-based therapies have driven the urgency to conduct trials to assess whether genomic-based molecular targeted therapies are clinically superior to empirical therapies. SHIVA, conducted in 2012–2014, was the first prospective, randomized phase II trial to assess the efficacy of molecularly targeted therapies in refractory solid tumors based on the patient’s genomic data compared to clinician’s choice [146]. In this trial, the genomic DNA of 741 patients were screened using the AmpliSeq cancer panel (Thermo Fisher Scientific, Waltham, MA, USA). Alterations in the gene copy number were assessed using the CytoScan® HD cytogenetic microarray system (Thermo Fisher Scientific), and expression of the hormone receptors was assessed using IHC. Of the screened patients, 40% contained at least one molecular alteration that could be targeted by one of the 11 pre-selected molecularly targeted agents. 26% of the patients were randomly assigned into one of the two treatment arms: molecularly targeted therapy or clinician’s choice. Interestingly, no differences in PFS was observed between the two arms. However, it should be noted the main aim of the study was to assess the efficacy of the off-label use of marketed drugs in patients harboring invalidated genomic alteration in multiple cancer types. Furthermore, the study was not powered to assess the efficacy of a specific drug in a specific subgroup of patients with certain molecular alteration patterns. The negative results from this study prompted the need for more narrowly focused studies that would evaluate the efficacy of one molecularly targeted drug in one specific subgroup of patients compared to a non-targeted empirical treatment. In contrast to the results obtained from SHIVA, genomic-based therapies assessed in a recent retrospective trial showed improved treatment outcome [147]. This trial (IMPACT/COMPACT) involved 1640 patients with advanced solid tumors who were molecular profiled in the study; overall response rate was observed to be higher in patients with the genotyped-matched treatment than in those with the genotype-unmatched treatment (19 vs. 9%, respectively) [147]. Similar benefits were also observed in several other smaller trials: the iCat study [148] and the NEXT-1 study [149]. In addition to the trials mentioned above, NCI-MATCH, an-ongoing clinical trial has attracted much attention as this is to date one of the largest precision medicine trial based on molecular alteration targeted therapies. This phase II clinical trial is being conducted across 1173 sites for patients with relapsed/refractory solid tumors, lymphomas and myelomas. Enrolled patients are assigned to one of the 30 treatment arms based on their tumor molecular alterations. The results from this study would provide a better understanding of the feasibility and efficacy of genomic-based therapies.

Development of Basket Trial

One of the biggest limitations of genomic-based therapies is the limited availability of validated genomic markers. The American Association for Cancer Research (AACR) project, GENIE, is one of the biggest consortium that explores the linkage of cancer genomic data with clinical outcomes. It contains over 19,000 genomic and clinical records that were obtained from multiple international institutes. Of these, 7.3% of tumors harbored a level 1 or 2A molecular alteration that is indicative of treatment with an FDA-approved drug or standard care in the same disease type. An additional 6.4% tumors contained Level 3A alterations, which are those with clinical evidence for response to investigational therapies in the same disease type; and a further 17.8% of tumors had level 2B (FDA-approved target in another disease type) or 3B alterations (target with clinical evidence in another disease type). In total, up to 30% of the patients harbored at least one potential actionable target [7]. As observed from these figures, through the expansion of targeted therapies across different disease types, an additional 20% of patients may benefit from such therapies. This gives rise to the necessity of basket trials. Basket trials incorporate different tumor types with the same genetic alteration into one study. These trials are extremely beneficial for studying low prevalent mutations and diseases for which it is often difficult to recruit sufficient patients to collate clinically meaningful data. The successful targeting of BRAF inhibitors and EGFR inhibitors across different cancer types reinforces the importance of validating biomarkers on other patient groups who share the same molecular alterations. This may broaden the number of patients who can benefit from genomic-based therapy.

The AKT1-E17K mutation is found in a broad range of tumor types, but it is infrequent in all individual tumor lineages. This makes the testing of AKT inhibitors in this patient population in one tumor type difficult, which makes AKT1-E17K mutation an ideal candidate for a basket trial. A total of 52 patients with advanced solid tumors who carried the AKT1 mutation were recruited in this trial and were treated with AZD5363, an ATP-competitive pan-AKT kinase inhibitor. Durable responses and tumor regression were observed across a variety of tumor types harboring the E17K mutation: breast cancer, both ER+ and ER-triple negative; endometrial cancer; cervical cancer; and lung cancer. Furthermore, patients carrying AKT1-Q79K (a rare mutation) also responded to AZD5363 [150].

Basket trials also allow exploration of the underlying biology of diverse but rare mutations, such as HER2 and HER3 alterations. The efficacy of neratinib, a pan-HER kinase inhibitor, in HER2+ breast cancer has been well established, however, due to the diversity of HER2 and HER3 mutations and its low prevalence in one tumor type, little is known about the therapeutic importance of these genomic alterations in the efficacy of pan-HER inhibitors. In a basket trial conducted by Hyman et al., neratinib showed the greatest activity in breast, cervical and biliary cancers where the tumors contained HER2 kinase domain missense mutations [151].

Future Directions

The development and availability of NGS has allowed genomic profiling through somatic mutation identification to be more easily achieved. However, several factors should be taken into consideration when using this information for the selection of genomic-based targeted therapies.

One of the major drawbacks with the identification of somatic mutations using single biopsy specimen is the underdetection of the clonal heterogeneity of cancer. Biomarker sampling in a single tumor region may underdetect or even not detect at all some biomarkers that may be crucial for targeted therapy selection. Undetected heterogeneity may result in potential escaping mechanisms being overlooked, possibly accounting for the resistance that is commonly observed in targeted therapies. Furthermore, improved understanding of the clonal heterogeneity may allow the development of combination target therapies to reduce the incidences of resistance. The success of the combination therapy using BRAF and MEK inhibitors to reduce disease recurrence for melanoma patients reinforces the potential importance of combining targeted therapies. Current understanding of the clonal heterogeneity of tumors may be enhanced by the use of liquid biopsy where it reflects the global, both primary and metastatic sites, molecular status of the patient. However, further studies are required to confirm the concordance rates of the mutation profile obtained using liquid biopsy and tumor tissue, respectively [152].

The current standard for somatic variant calling is achieved through the alignment of genomic information from the tumor with either the reference DNA or the paired-blood/normal sample. However, recent studies have found that alignment with the reference DNA may incorrectly identify a germline mutation, in particular clonal hematopoiesis (CH), as a somatic mutation. CH is the somatic acquisition of genomic alterations in hematopoietic stem and/or progenitor cells that leads to clonal expansion. They are usually associated with aging, smoking and radiation therapy. When NGS is carried out in an unpaired setting, CH can potentially be incorrectly identified as somatic mutations, as illustrated in a recent large retrospective study in which NGS, using the MSK-IMPACT platform, was performed on matched tumor and blood samples from 17,469 patients across 69 cancer types [153]. Up to 14.1% of the CH-associated mutations identified were also detectable in the matched tumor. Without matched blood samples, 5.2% of the patients would have at least one CH-associated mutation mistakenly considered to be a tumor-derived somatic mutation [153]. Similar results were reported in a smaller retrospective study where up to 8% of the identified clonal hematopoiesis-related genes from tumor samples were also identified in the paired blood sample. Some of these identified mutations were also considered to be actionable mutations [154]. These results highlight the importance of using paired tumor and blood samples to prevent incorrect target identification, which in turn may lead to inappropriate clinical management.

To date, only a limited number of studies have been conducted to explore the variable frequency of somatic mutations across different ethnic groups. In a study conducted by Nagahashi et al., ERBB2, APC, TP53 and NRAS mutations were found to be significantly higher in Japanese colorectal cancer patients than in the data obtained from TCGA which were based on the U.S. population [155]. The same study also found that close to 50% of BRAF mutations occurred outside the hotspot V600E, which is the most common BRAF mutation in the Western population [155]. Similarly, EGFR mutationd were more frequently observed in the non-smoking Asian population than in the Western population. Up to 59.7% of East Asian never or light smokers had tumors harboring an EGFR mutation while only 11% of the Western patients with lung adenocarcinoma possessed an EGFR mutation [156, 157]. The variable frequencies of certain variants between different ethnic groups bring into question the applicability of the same gene panels to be used across all populations. This challenge may be met by using a customized panel of genes, increasing specific genes that are more applicable for the situation.

The recent development of liquid biopsy to detect circulating tumor cells or cell-free tumor DNA from patients’ blood further enhances the utilization of genomic information to improve patient outcome. The minimal invasiveness of liquid biopsy in combination with NGS allow ongoing monitoring of disease progression, drug response and resistance development [158]. Overall, NGS has allowed the translation of genomic information into clinical practice and the development of cancer precision therapy.

Conclusion

The integration of genomics into the medical field has transformed the era from one-size-fits-all to cancer precision medicine, in which cancer precision medicine aims to provide the right dose of the right drug, to the right patient, at the right time. In particular, clinical trials now request the pre-screening of genetic mutations in individual medical institutions to refine patients’ selection before enrolment, enhancing the pivotal role of genetic mutation in the clinical settings. Also, the establishment of basket trials that evaluate the same genetic alteration in different tumor types provide the possibility of drug repurposing to treat cancer patients based on the mutation status in the near future. It is hopeful that the incorporation of genetic information could improve treatment precision, leading to a better quality of life for cancer patients.

Notes

Acknowledgements

Funding

No funding or sponsorship was received for this work or publication of this article.

Authorship

All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published.

Disclosures

Ting Chan, Yoon Ming Chin and Siew-Kee Low have nothing to disclose.

Compliance with Ethics Guidelines

This article is based on previously conducted studies and does not contain any studies with human participants or animals performed by any of the authors.

Open Access

This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Copyright information

© The Author(s) 2018

Authors and Affiliations

  1. 1.Cancer Precision Medicine CenterJapanese Foundation for Cancer ResearchTokyoJapan

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