Methods: for studying pharmacogenetic profiles of combination chemotherapeutic drugs.

Review

1.

Introduction

2.

Before discussing the pharmacogenetic profiling

3.

Candidate genes and

Expert Opin. Drug Metab. Toxicol. Downloaded from informahealthcare.com by Nyu Medical Center on 06/17/15 For personal use only.

polymorphisms: how deep

Methods: for studying pharmacogenetic profiles of combination chemotherapeutic drugs Antonello Di Paolo†, Marialuisa Polillo, Marianna Lastella, Guido Bocci, Marzia Del Re & Romano Danesi University of Pisa, Department of Clinical and Experimental Medicine, 56126 Pisa, Italy

dive? 4.

Whatever else we need to know

5.

Conclusions

6.

Expert opinion

Introduction: Molecular and genetic analysis of tumors and individuals has led to patient-centered therapies, through the discovery and identification of genetic markers predictive of drug efficacy and tolerability. Present therapies often include a combination of synergic drugs, each of them directed against different targets. Therefore, the pharmacogenetic profiling of tumor masses and patients is becoming a challenge, and several questions may arise when planning a translational study. Areas covered: The review presents the different techniques used to stratify oncology patients and to tailor antineoplastic treatments according to individual pharmacogenetic profiling. The advantages of these methodologies are discussed as well as current limits. Expert opinion: Facing the rapid technological evolution for genetic analyses, the most pressing issues are the choice of appropriate strategies (i.e., from gene candidate up to next-generation sequencing) and the possibility to replicate study results for their final validation. It is likely that the latter will be the major obstacle in the future. However, the present landscape is opening up new possibilities, overcoming those hurdles that have limited result translation into clinical settings for years. Keywords: combination therapies, next-generation sequencing, oncology, pharmacogenetic profiling Expert Opin. Drug Metab. Toxicol. [Early Online]

1.

Introduction

In the modern era of antineoplastic chemotherapy, several strategies have been investigated to obtain the maximum therapeutic effect according to the clinical endpoint (i.e., overall survival [OS], disease-free survival or progression-free survival [PFS]) and depending on the initial stage and grade of tumors. Moreover, from a pathological and pharmacological perspective, tumor characteristics have been promptly enriched by genetic and mutational data, perceiving these aspects as fundamentals for predicting treatment outcome [1]. At the same time, sparing patients from unacceptable toxicities has been considered an additional goal that supports the analysis of individual’s genetic status to identify and develop predictive biomarkers. The aim of this review is to suggest answers and to discuss the role of different methods for predicting the activity of combination regimens, which represent the paradigm for a successful treatment of the modern onco-hematology. Therefore, a survey of published literature has been performed in Pubmed database using the following keywords in multiple combinations: antineoplastic agents, drug therapy, therapeutic use, humans, mutation, genetics, genome, neoplasms, tumor, marker, 10.1517/17425255.2015.1053460 © 2015 Informa UK, Ltd. ISSN 1742-5255, e-ISSN 1744-7607 All rights reserved: reproduction in whole or in part not permitted

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A. Di Paolo et al.

Article highlights. .

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Newest technologies (microarrays, genome-wide association studies and next-generation sequencing) are offering the possibility to tailor chemotherapy in each oncologic patient on the basis of genetic variants in nucleotide sequence or gene expression levels. Several new clinical trial designs have been planned to implement the use of biomarkers since the first phases of the clinical drug development. Moreover, alternative approaches (i.e., circulating plasma DNA and tumor cells, in vitro experiments on human cancer cell lines) have been successfully adopted. Some issues still impede a wide diffusion of these platforms in clinical trials and routine, namely the lack of replication studies, the quality of nucleic acids and costs. In the next years, some hurdles could be overcome by planning biomarker-directed clinical studies, together with further technical and bioinformatic developments.

Researchers should carefully stratify patients according to other possible confounding factors, such as other drugs and their possible interactions. Colony-stimulating factors like filgrastim and erythropoietin may mask treatment-induced myelotoxicity, while antidepressant drugs may inhibit CYP2D6-dependent tamoxifen activation, hence reducing therapeutic benefit for breast cancer patients [4]. Finally, newest technical platforms are offering huge amounts of results that need appropriate analysis instruments. As it will be discussed in the next paragraphs, many techniques and platforms may be used in pharmacogenomic studies thanks to their advantages and despite their limits. However, the clinical validation of the genetic signature in large prospective randomized trials remains the most severe hurdle [5,6]. Therefore, the clinical application of those signatures is still to come.

Candidate genes and polymorphisms: how deep dive?

3. This box summarizes key points contained in the article.

pharmacogenetics, pharmacogenomics, method, microarray, polymorphism, gene variation, gene expression, sequencing, clinical trial design. Additional information have been retrieved from the bibliography of selected articles. 2. Before discussing the pharmacogenetic profiling

Just before turning the reader’s attention towards the topics of this review, some important points of discussion should be stated. First of all, pharmacogenomic studies should address initial efforts to the most accurate definition of end points, namely phenotypes, endophenotypes and genetic variants (alterations in base sequence, gene expression, copy number, etc.) under investigation [2]. Clinical and pathological features of tumors, such as the hallmarks of neoplasms [3], belong to the first category. They should be identified and classified with particular attention because any error or missing information occurring in this phase may lead to negative or contradictory results when biomarkers are evaluated. Enzyme activities, protein and growth factor concentrations in tissues or drug plasma levels represent endophenotypes because they lie in the middle between clinical phenotypes and the genome. Moreover, gene polymorphisms (for example, single-nucleotide polymorphisms [SNPs]), mutations, gene copy number, mRNA expression levels and/or epigenetic characteristics are included in the third category. Interestingly, the initial hypothesis has to consider that a trait (i.e., the increased tumor metastatic potential or resistance to chemotherapy) may also depend on the interaction among genes. As a possible consequence, the analysis of candidate genes chosen on the basis of a mechanistic criterion does not guarantee a functional interpretation of results, nor their reproducibility across the studies. 2

Although some exceptions (i.e., BCR-ABL tyrosine kinase inhibitors for chronic myeloid leukemia, breast adjuvant treatment with tamoxifen, sorafenib for hepatocellular carcinoma or sunitinib for renal cell carcinoma, 17-a-demethylase inhibitors for prostatic carcinoma), the majority of antitumoral treatments consists of two or more drugs. Therefore, the search for predictive biomarkers should be broadened towards several putative targets. From candidate genes to multi-gene platforms The candidate gene strategy represents the easiest approach for biomarker identification because the magnitude of drug effects depends on both drug concentrations at the site of action (pharmacokinetics) and expression of the molecular target (pharmacodynamics). The panel of investigated genes and polymorphisms may be enriched at one’s discretion until dozens of genes and variations may be included. In turn, this approach may offer suitable results even in small groups of patients and especially when pharmacokinetic data are available. Interestingly, good results may be attained in the presence of poor-quality nucleic acids, as those extracted from archived, formalin-fixed paraffin-embedded (FFPE) sections. Those results are possible thanks to newest techniques (i.e., droplet digital polymerase chain reaction [PCR]) [7]. Moreover, the wide availability of technical platforms (i.e., restriction fragment length polymorphisms PCR and quantitative PCR) and their relative low costs make this approach acceptable by the majority of laboratories [8]. Finally, the candidate gene strategy may benefit from the analysis of tag SNPs that are in linkage disequilibrium with other polymorphisms within a chromosomal region of variable length. Available databases and software allow the selection of these tag SNPs for every region of the genome [9]. Overall, the candidate gene approach has brought to interesting results (Table 1). Two polymorphisms in excision 3.1

Expert Opin. Drug Metab. Toxicol. (2015) 11(8)

Methods: for studying pharmacogenetic profiles of combination chemotherapeutic drugs

Table 1. Summary of gene-candidate studies in oncologic patients receiving polychemotherapies. Authors

Cancer type

Ruzzo et al. (2007)

mCRC

Braun et al. (2009)


Pander et al. (2011) Erculj et al. (2012) Loupakis et al. (2013)

N*

Treatment

166

FOLFOX-4

Targets

12 polymorphisms in TYMS, MTHFR, ERCC1, XRCC1, XPD, XRCC3, GSTPI, GSTMI, GSTTI mCRC 1188 5-FU )± IRI 10 polymorphisms in TYMS, or L-OHP DPYD, MTHFR, MLH1, UGT1A1, ABCB1, GSTP1, XRCC1, ERCC2 mCRC 279 CAPOX-B 17 polymorphisms in TYMS, MTHFR, ERCC1/2, XRCC1, GSTP1, KDR, VEGF malignant 97 PlatinumPolymorphisms in XPD, ERCC1, mesothelioma basedz GSTP1 and deletion of GSTM1 and GSTT1 mCRC 424 FOLFOXIRI- 9 polymorphisms in VEGF-A, B VEGFR1/2, EPAS

Source PB

FFPE-T

PB

Main results ERCC1 and XPD associated with PFS. XPD associated with neurotoxicity XRCC1, ERCC2 and GSTP1 predictive of IRI toxicity. TSER correlated with increased 5-FU/IRI toxicity TYMS and VEGF polymorphisms associated with PFS

Refs. [10]

[14]

[11]

FFPE-T, Significant associations between: [12] PB ERCC1 and PFS; ERCC1 + XPD and toxicity PB VEGFR2 polymorphism associated [13] with PFS§

*Number of enrolled patients. z (Cisplatin or carboplatin) plus (gemcitabine or pemetrexed or mitomycin C/vincristine). § Significance was lost after multiple testing correction. CAPOX-B: Capecitabine, oxaliplatin, bevacizumab; ERCC1: Excision repair cross complementing group 1; FFPE-T: Tumor formalin-fixed, paraffin-embedded specimens; FOLFOX-4: 5-FU, leucovorin, oxaliplatin; FOLFOXIRI-B: 5-FU, leucovorin, oxaliplatin, irinotecan, bevacizumab; IRI, irinotecan; L-OHP: Oxaliplatin; mCRC: Metastatic colorectal cancer; PB: Peripheral blood; TYMS: Thymidylate synthase; XPD: Xeroderma pigmentosum group D.

repair cross complementing group 1 and xeroderma pigmentosum group D were associated with an unfavorable PFS in 166 metastatic colorectal cancer (CRC) patients treated with 5-fluorouracil (5-FU) plus oxaliplatin [10]. Moreover, Pander et al. evaluated 17 polymorphisms in genes coding for drug targets, pathway molecules, metabolic and detoxification systems in 279 metastatic CRC patients treated with capecitabine, oxaliplatin and bevacizumab [11]. The investigated SNPs were not significantly associated with PFS, whereas the haplotype constituted by thymidylate synthase enhancer region (TSER) polymorphism and the +405G>C SNP of VEGF predicted PFS. Erculj and coworkers obtained similar results by investigating 10 DNA repair gene polymorphisms, efficacy and tolerability of gemcitabine-platinum chemotherapy in 109 patients affected by malignant mesothelioma [12]. Some of those polymorphisms predicted OS and treatment-related hematological toxicities. The alteration of a phenotype (i.e., increased proliferation or metastatic potential) could be dependent on a biochemical pathway (i.e., angiogenesis). Therefore, the pharmacogenomic study may include the evaluation of genes along that specific pathway. A prospective study investigated possible candidate SNPs along the VEGF pathway that could predict treatment efficacy in metastatic CRC patients receiving first-line FOLFIRI (L-leucovorin, 5-FU, and irinotecan [CPT-11]) plus bevacizumab [13]. However, final results failed to confirm a significant predictive role of VEGF-A variants for chemotherapy outcome. Although several advantages, the gene candidate strategy may suffer from some drawbacks. First of all, it is unlikely that one polymorphism could predict the relationship between a complex phenomenon and a specific drug (i.e., tumor

angiogenesis and bevacizumab) [13]. Furthermore, fixing the range of investigation towards candidate genes could ignore other potential targets and their reciprocal influence upon the same phenotype. In this view, final results of the FOCUS trial are paradigmatic [14]. Metastatic CRC patients were prospectively and randomly assigned to single-agent, sequential treatments or combination therapies that included 5-FU, CPT-11 or oxaliplatin. Genomic DNA was obtained from microdissected, paraffin-embedded normal tissues in 75% of 1188 patients, whereas in the remaining subjects DNA was extracted from tumors provided a strong concordance of genetic markers between those tissues. Results showed that XRCC1, ERCC2 and GSTP1 polymorphisms were predictive of CPT-11-associated toxicity, whereas UGT1A1*28 did not, despite it is one of the best investigated predictive markers of irinotecan tolerability. More interestingly, the investigated polymorphisms were not associated with outcome measures for incremental toxicity of combination regimens over 5-FU alone. The only exception was a weak correlation between TSER polymorphism and the augmented toxicity of 5-FUirinotecan regimens with respect to the fluoropyrimidine alone [14]. Different doses or drug combinations represent other pitfalls of candidate-gene studies because they may negatively influence the expected results and their validation in the following trials. Moreover, a large number of individuals are needed to obtain significant results: greater the population of enrolled subjects, more robust and significant the relationship between genetic variants and the phenotype [15]. The number of investigated genes is considerably increased in microarray platforms. Microarrays utilize a hybridization


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Table 2. Studies investigating gene expression profiling by microarray platforms in oncologic patients treated with drug combinations. Authors

Cancer type

N*

Treatment

Targets

Source

Kim et al. (2007)

Rectal cancer

46 (31/15)§ RX-CTX

Sotiriou et al. (2002)

Breast cancer

10

Doxorubicin + Gene ciclophosphamide expression (7,600)z

FNA-T{

Ayers et al. (2004)

Breast cancer

42

T/FAC

FNA-T

Naoi et al. (2011)

Breast cancer

84 (50 + 34)§/105#

P-FEC

Del Rio et al. (2007)

mCRC

40

FOLFIRI

Folgueira et al. (2005)

Breast cancer

51

Doxorubicin + ciclofosfamide

Friedman et al. (2009)

CLL

301

Chlorambucil or PCR

Gene expression

Gene expression (30,721)z Gene expression (14,500)z Gene expression (45,000)z Gene expression (4,608)z Gene expression (14,500)z

FF-T

Main results

Refs.

95-Gene classifier predicted complete response to radiochemotherapy in 26/31 and 13/15 patients of training and validation groups Gene expression profile associated with complete response. After the first cycle of chemotherapy, number of genes with changed expression was 10 greater in responding group 74-Gene classifier predicted pCR

[24]

CNB-T

70-Gene classifier predicted pCR and prognosis in lymph node negative patients

[28]

FF-T

14-Gene classifier predicted response**

[30]

FNA-T

3-Gene classifier predicted responsezz

[31]

PB

180- and 140-Gene classifiers predicted disease progression and resistance to chlorambucil or PCR, respectively

[29]

[26]

[27]

*

Number of enrolled patients. Numbers of target sequences. § Patients in training/validation groups. { Specimens were collected before and after treatment. # Lymph-node negative, estrogen receptor positive patients treated with adjuvant hormone therapy. ** According to WHO criteria. zz According to RECIST criteria. CLL: Chronic lymphocytic leukemia; CNB-T: Tumor core-needle biopsy; FF-T: Tumor fresh frozen specimens; FNA-T: Tumor fine needle aspiration; FOLFIRI: 5-FU, leucovorin, irinotecan; mCRC: Metastatic colorectal cancer; P-FEC: Paclitaxel then 5-FU, epirubicin, cyclcophosphamide; PB: Peripheral blood; PCR: Pentostatin/ cyclophosphamide/rituximab; pCR: Pathological complete response; RX-CTX: Radiotherapy plus chemotherapy (5-FU + leucovorin or capecitabine or capecitabine + irinotecan); T/FAC: Paclitaxel then 5-FU, doxorubicin, cyclophosphamide. z

technique between labeled, fluorescent fragments of cDNA or genomic DNA and allele-specific probes [16]. They have been formerly developed to investigate RNA levels of large gene sets in biological samples [17-19], but recent technical improvements have expanded the use of this platform to the evaluation of genetic variations, as well as gene polymorphisms, loss of heterozygosity and copy number [20-23]. Finally, the continuous decrease in cost over time has made this platform appealing to many laboratories. Several studies aimed at investigating gene expression have benefited from cDNA microarray in the recent past (Table 2). Kim and coworkers identified a tumor expression signature of 95 genes that was predictive of response to radiochemotherapy in 84% of 31 rectal cancer patients and in a following validation cohort of 15 individuals [24]. Tumor gene expression signatures predicted complete response to platinum/ paclitaxel-based chemotherapy or platinum sensitivity in more than 280 epithelial ovarian cancer patients [25]. In breast 4

cancer patients treated with different neoadjuvant regimens (including doxorubicin/cyclophosphamide, 5-FU, paclitaxel and epirubicin), 37-, 74- or 70-gene tumor signatures predicted chemotherapy outcome [26-28]. After the first cycle of chemotherapy, the expression of further 16 genes significantly changed with respect to baseline in a responding group of patients [26]. These genes could be further investigated as additional predictive biomarkers of efficacy because treatment was able to modulate their transcriptional levels in a significant manner. Finally, in CLL patients, Friedman et al. described the generation of a 180-gene classifier of disease progression [29]. A modified 140-gene signature was then successfully validated in two distinct cohorts of patients with regard to efficacy of chlorambucil or standard chemoimmunotherapy comprising pentostatin, cyclophosphamide and rituximab. In these examples, the signature was constituted by dozens of genes, likely reflecting the multi-factoriality of tumor




sensitivity/resistance to anticancer drugs. In other studies, the signature was made by a smaller number of tumor target genes, as a 14-gene signature in CRC patients who were treated with a standard FOLFIRI regimen [30]. In breast cancer patients treated with doxorubicin plus cyclophosphamide, a classifier of three genes was able to distinguish between sensitive and resistant tumors in more of 95% of the samples [31]. The discrepancy in gene numbers among signatures could be related to several factors, as well as tumor biology (CRC, ovarian and breast carcinomas, leukemia), their stage, grade and treatment regimens (platinum-, paclitaxel- or anthracycline-based chemotherapy, FOLFIRI, chemo-immunotherapy). However, the possibility to stratify tumors and patients according to several genes (or variants) is an advantage of the microarray with respect to the candidate gene strategy. Genome-wide association studies The need to investigate a greater number of target gene variants to capture any possible relationship between phenotypes and polymorphisms promptly led to the development of genome-wide association studies (GWAS) [32]. This approach does not rely on a hypothesis-driven search strategy because the investigation is performed on several hundreds of thousands of polymorphisms distributed across the whole genome. Therefore, the probability to find relationships among phenotypes (i.e., drug effects in terms of efficacy and toxicity) and genetic variants in both tumors and healthy tissues is increased [32]. GWAS produces a large mass of data and the identification of the causal gene(s) needs appropriate analyses. For example, an iCHAV is a set of variants associated with a specific phenotype and the causal polymorphism belongs to the iCHAV itself [2]. Furthermore, the use of tag SNPs in GWAS makes more affordable a whole-genome analysis because only a subset of possible variants is genotyped through a customizable array. Also in these cases, a meaningful biological interpretation of collected data could not be found. GWAS identified polymorphisms associated with both treatment efficacy [33,34] and tolerability [35-37] of single-agent chemotherapies and combination regimens (Table 3). For example, some polymorphisms predicted OS in more than 1700 NSCLC patients treated with platinum-based chemotherapies [38-40]. In one of those trials, polymorphisms were predictive of treatment outcome in two distinct Chinese groups of patients (535 and 340 subjects) [40]. Interestingly, two of those five polymorphisms were positively validated in a following independent Caucasian cohort of 409 patients. These results suggested that GWAS studies may have the possibility to overcome problems related to individuals’ ancestry. GWAS may play a role also in deciphering the relationship between genetic variants and toxicities induced by combination regimens. An interesting example is the study of Takahashi and coworkers who used a 109,365-SNP chip to identify possible markers predictive of CPT-11-induce diarrhea in 3.2

168 CRC patients [41]. The analysis of genomic DNA extracted from peripheral blood showed that a variant in potassium voltage-gated channel subfamily KQT member 5 was predictive of irinotecan-induced diarrhea. Furthermore, a huge trial has been performed in Japanese patients who received 17 different subsets of chemotherapies, including combination regimens based on platinum complexes, antimetabolites (5-FU and gemcitabine), anthracyclines (doxorubicin and epirubicin) or topoisomerase inhibitors (irinotecan and topotecan) [42]. Patients’ blood was obtained from a biobank and genotyped for 733,202 SNPs, but none of the variants was predictive of toxic effects in a statistical significant manner (i.e., p < 5.0 10-8) [43], probably because the ‘insufficient statistical power [of the study] and complex clinical features’ [42]. However, a weighted genetic risk score, based on six risk alleles and their effect size, predicted neutropenia and/or leucopenia in patients receiving carboplatin plus paclitaxel. GWAS studies display some limitations, such as the inadequate sample size, the need of replication studies and the difficulty to investigate rare variants [44,45]. Other possible issues are different drug combinations and dose deviations from standard protocols according to patient’s performance status and toxic effects). Furthermore, the retrospective design of the study does not ensure the set up of complete databases and sample collection [42]. Next-generation (or massive parallel) sequencing Newest technological platforms for next-generation or massive parallel sequencing ([NGS] or [MPS], respectively) are aimed at finding both known and rare genetic variants possibly associated with the phenotype of interest [46]. They are applied to analyze the entire genome (whole-genome sequencing, WGS), the exome (whole-exome sequencing, WES), the transcriptome or more limited portion of genome [47,48]. The technical characteristics of different NGS platforms are described elsewhere [49]. The amount of retrieved information differs among these approaches according to the extent of the investigated portion of the genome, and the costs for these analyses are proportional to extension and coverage. However, the increased technical efficiency and the reduced turnaround time have diminished costs over time [50]. Beyond the quantity of collected data, different approaches led to different possibilities. For example, WGS is characterized by an increased sensitivity with respect to WES and may investigate DNA variants outside the exome or those variations (i.e., duplications) that may be identified by analyzing the adjacent non-coding sequences [51]. The high throughput of these techniques is based on parallel, massive sequencing of short nucleic acid fragments (called reads), and the following reassembling through the alignment according to known reference sequences. In these analyses, the coverage is the fraction of polymorphisms of the whole genome that is tagged by (or in linkage disequilibrium with) SNPs on the array: higher the coverage (i.e., approaching to 3.3


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Table 3. Summary of genome-wide association studies and next-generation sequencing studies in oncologic patient receiving polychemotherapies.


Authors

Cancer type

N*

Treatment

Targets‡

Source z

Wu et al. (2011) Sato et al. (2011)

advanced-stage NSCLC advanced-stage NSCLC

1062 (327/ PlatinumGWAS (307,260) 735)§ based ± radiation 105 Carboplatin + GWAS (110,000)z docetaxel

Hu et al. (2012)

advanced-stage NSCLC

1284 (535/ Platinum-based 749)§

GWAS (906,703)z

Takahashi et al. (2014)

Various types

168

Irinotecan-based

GWAS (109,365)z

Low et al. (2013)

Various types

5609

Various regimens GWAS (733,202)z

Ciardiello et al. (2014)

mCRC

182

FOLFIRIcetuximab

NGS: mutations in 22 genes

Zardavas et al. (2014)

Metastatic breast cancer

1300{

At physician’s discretion

NGS: mutations in 411 genes + WES

Saal et al. (2015)

Invasive breast cancer

3979{

At physician’s discretion

Mutational (NGS + microarray) and expression (NGS) analysis

PB

Main results

A SNP in CMKLR1 predicted OS PB Three SNPs in EIF4E2, ETS2 and DSCAM genes predicted OS n.a. Five and 2 SNPs predicted OS in the entire population and only in Caucasian patients, respectively PB A SNP in KQNC5 gene was associated with irinotecaninduced diarrhea PB No significant associations between neutropenia/ leucopenia and SNPs were found FFPE-T OS and PFS were reduced in patients with turmors harbouring mutated KRAS, NRAS, BRAF or PIK3CA genes FFPE-T, Prospective study FNA-T, PB FF-T, Prospective study PB

Ref. [38] [39]

[40]

[41]

[42]

[63]

[64]

[65]

*

Number of enrolled patients. Numbers of target polymorphisms. § Patients in training/validation groups. { Studies are ongoing, enrolment at the time of publication. FFPE-T: Tumor formalin-fixed, paraffin-embedded specimens; FF-T: Tumor fresh frozen specimens; FNA-T: Tumor fine needle aspiration; FOLFIRI: 5-FU, leucovorin, irinotecan; GWAS: Genome-wide association study; Mcrc: Metastatic colorectal cancer; NGS: Next generation sequencing; NSCLC: Non-small cell lung cancer; OS: Overall survival; PB: Peripheral blood; PFS: Progression-free survival; SNP: Single-nucleotide polymorphism; WES: Whole-exome sequencing. z

the unit), greater the number of SNPs on the array. Alternatively, the coverage refers to the number of sequences that include each base, thus a 10 or 100 coverage means that the base is covered by 10 or 100 reads, respectively. NGS may be directed to transcriptome analysis (or other RNA sources, such as miRNA or tRNA) to investigate gene expression, alternative splicing transcripts, SNPs and posttranscription single-nucleotide variants. In these cases, the coverage and replicates depend on both aims of the study and variability in repeated measures [52]. In agreement with what happened after the introduction of microarrays in laboratory practice, NGS has brought to a) a significant reduction of analysis costs, b) the diffusion of the platform across laboratories, c) an increased sensitivity and d) a greater variety of genome alterations that can be detected (mutations, insertions/deletions, copy number variations and rearrangements) [50,53]. Therefore, NGS studies tend to identify hundreds of variants associated with the trait. The vast majority of these SNPs do not lie in the exome, but they tag functional or causal polymorphisms located elsewhere. 6

Interestingly, the rarity of found variants is inversely correlated with the number of analyzed samples, hence rarest SNPs are identified in large groups [54]. However, most common variants may be detected in few samples, and this increases the opportunities to perform an NGS. In some occasions, the coverage is suboptimal (approximately 0.5 -- 0.6) to identify rare variants, but imputation techniques may estimate the probability of a certain genotype for an SNP on the basis of large polymorphism databases (such as HapMap Project or 1000 Genomes Project) [55] and statistical methods [7]. For example, Pasaniuc et al. have recently demonstrated that NGS is feasible and returns compelling results even when the sequencing was performed at a coverage rate of about 0.1 -- 0.5 [56]. Although recent progresses have been achieved in genome investigation, some steps of these novel approaches still remain critical. In particular, NGS suffers from poor-quality nucleic acids such as those obtained from FFPE sections [57] because regions to be amplified should be long at least 170-bp. Moreover, short-length fragments may render




difficult the de novo assembling of a genome [58] while a remarkable expertise in performing the analyses and turnaround time are not negligible [1,50,57]. For example, the AmpliSeq NGS service recently launched by UK National Health Service has demonstrated to provide ‘highly sensitive and quantitative mutation detection for most of the genes’ from FFPE human CRC specimens. However, independent validation tests suggest caution in data interpretation for some discordant results [59]. Partial solutions to these problems may come from direct sequencing and analysis of single molecules or fragments of DNA [50], while the adoption of quality control measures, as well as orthogonal re-sequencing, may be of further help [60]. MPS was successfully adopted to investigate the possible correlation among tumor biology, genetic status and response to polychemotherapies (Table 3). NGS was able to shed light on breast cancer biology and its relationships with the efficacy of selective estrogen receptor modulators (tamoxifen and raloxifene) and aromatase inhibitors (anastrozole, exemestane and letrozole) [61]. Another study in 29 adrenocortical carcinoma patients identified possible targets for chemotherapy in relapsed and metastatic forms of the disease that have a poor prognosis [62]. In particular, several genes belonging to cyclin-dependent or receptor tyrosine kinases, mammalian target of rapamycin (mTOR) and Ras pathways were altered in tumor samples. These results suggested the use of specific targeted therapies (i.e., imatinib, sorafenib, sunitinib, mTOR inhibitors, etc.) only in some patients. Moreover, in 182 CRC patients who received FOLFIRI plus cetuximab NGS was directed to 87 hotspot regions of 22 genes involved in colon cancer, as well as TP53, KRAS, BRAF, ALK and EGFR [63]. Results demonstrated that those individuals carrying mutated KRAS, NRAS, BRAF and PIK3CA genes in their tumor samples had a worse outcome after chemotherapy with respect to ‘quadruple wild-type patients’. Two interesting prospective studies, focused on breast cancer and launched in the last years, are addressed to the implementation of genomic analyses in clinical routine [64,65]. The aims are the dissection of tumor molecular characteristics, the identification of new predictive biomarkers and the setting up of new therapeutic regimens. The AURORA and SCAN-B initiatives are collecting primary tumor tissues and blood samples. Gene expression and mutational analyses are performed by RNA sequencing (RNA-seq), and up-to-now more than 5000 patients have been enrolled. Interestingly, patients who participate to the AURORA study receive standard treatments or targeted therapies according to the individual molecular profile. Furthermore, a subsequent deeper molecular characterization (WES) will be carried out in ‘exceptional responders or rapid progressors’ [64]. It is likely that these two prospective studies will bring more important information about the biology of breast cancer and its sensitivity to different chemotherapeutic agents.

4.

Whatever else we need to know

The goal of microarrays, GWAS or NGS for pharmacogenetic profiling is the identification of a genetic signature that will be applied a) to stratify patients according to a risk probability of treatment resistance and/or toxicity and b) to tailor combination therapies in every patient. The results from a pharmacogenomic study are followed by validation (i.e., the set up of a test) and finally by the application of the test in the real-life world, but all these steps may suffer from several conditions (Figure 1). Some of these conditions have already identified in the previous paragraphs, such as the use of archived DNA. They could be considered as covariates that can be identified, recorded and then used for stratification purposes. In other cases, those variables may be analyzed together with pharmacogenomic results to reduce variability and uncertainty. Furthermore, genetic studies may be influenced by subjects’ ancestry that may be inferred by appropriate methods (i.e., principal component analysis) [66] and included within the analyses [67]. Other possible confounding factors are represented by drug daily and cumulative doses, comedications, tumor type, stage and source of DNA (i.e., primary tumors, synchronous or metachronous metastatic lesions). All these problems delay the validation of the genetic signatures and their definitive application in clinical settings. Pharmacogenetic profiling of mono- versus poly-chemotherapies

4.1

A challenge in pharmacogenetic profiling is represented by combination regimens, when drugs are used in concomitant or sequential schedules by i.v. boluses, prolonged infusions or chronic oral treatments [68]. Moreover, the administration of several dosages could contaminate statistical analyses, hence increasing study variability. A cytotoxic drug could be well tolerated by patients even in the presence of polymorphisms in a gene that is responsible for drug catabolism. For example, patients carrying a low-activity allele for uridine diphosphate glucuronosyl transferase may tolerate CPT-11 when the infusion rate is very low [68], despite this is not considered a standard regimen. Therefore, it is likely that the evaluation of drug concentrations (an endophenotype) in mathematical models may help in deciphering interindividual variability [35,69]. A further question regards the possibility that a drug may influence transcriptional levels of genes involved in the pharmacokinetics and pharmacodynamics of a second drug. In this case, sequential and concomitant exposures could lead to different results, both in terms of in vitro experiment sensitivity and clinical outcome. For example, the significant association between CYP2D6 SNPs and treatment outcome observed when patients receive the sole tamoxifen [70] was lost in combination regimens [71], as a proof that drugs could affect the level of correlation between genotypes and clinical


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DNA

mRNA Trait, phenotype

… Polymorphisms Mutations Copy number variations Chromosomic alterations Epigenetic variations

Gene expression Alternative splicing transcripts Polymorphisms Post-transcription single nucleotide variants miRNA, tRNA

Limits


Microarray Polymorphisms Hundreds → thousands targets

Gene expression Hundreds → thousands targets

Access and costs of custom microarrays Quantity and quality of mRNA Need of replication studies Different results with different tissues Requires microdissection

GWAS Polymorphisms Hundreds of thousands targets Increased number of possible targets across the genome Identifies common variants with small effect size

Sample size, statistical analysis, bioinformatics Multistage design and replication studies Custom arrays are expensive Influence of ancestry Rarest genetic variants NGS

Genome sequencing (WGS) Polymorphisms De novo sequencing Epigenome characterisation

Transcriptome sequencing (RNA-seq) Gene expression Fusion genes

Sample size, statistical analysis, bioinformatics Quantity and quality of nucleic acids Multistage design and replication studies Expensive De novo sequencing may be troublesome

Figure 1. Main characteristics of microarrays, whole-genome association studies (WGAS) and next generation sequencing platforms. Microarrays are intended to investigate gene expression, but in some cases they are used to evaluate DNA polymorphisms. Interestingly, next-generation sequencing seems to cover the widest variety of application, but some hurdles limit its use (i.e., quality of nucleic acids, replication studies, short reads). WGS: Whole-genome sequencing.

results. Another potential drug--drug interaction has been described for 5-FU and oxaliplatin [72] that are often administered as a combination therapy to CRC patients. In some pharmacogenomic studies, there is the possibility that the effect size of genetic variants could be too small to have a significant utility in clinical settings [2,73]. A compelling solution could be to perform genetic analyses in those individuals that lie in the distribution tails of the investigated trait (the so-called extreme discordant phenotypes) [74], as recently applied in a large multinational, prospective study [64]. A further interesting area of research is the evaluation of gene expression before and after drug administration, in order to investigate which pathways are significantly modulated by pharmacological agents [75]. For example, in 25 breast cancer patients, tumor expression of 24 genes significantly differed before and 24 h after the first cycle of a neoadjuvant, anthracycline-based chemotherapy [31,76]. It is worth noting that these particular signatures, based on the modulation of 8

transcriptional activity, may accelerate the identification of biomarkers. Finally, tumor cell sensitivity to drugs could depend on the previous exposure to antineoplastic agents [77], and this may explain the heterogeneity observed among studies. Who or what should be genotyped? The source of genomic DNA to investigate polymorphisms or mRNA for gene expression represents an important issue in pharmacogenetic profiling for combination therapies. Tissue source depends on whether the study is aimed at investigating drug efficacy with respect to molecular targets, resistance mechanisms and modulation of cellular biochemical pathways. In these cases, nucleic acids are extracted from tumor tissues, and the harvesting of a sufficient quantity of goodquality DNA or RNA is the main issue. Otherwise, the trial may be addressed to the identification of biomarkers predictive of treatment-associated toxicities and changes in drug pharmacokinetics. Germline DNA variations may be 4.2



investigated in peripheral blood, whereas gene expression profiling is performed in healthy tissues from which the tumor has been developed, such as the normal gut mucosa for CRC [78,79]. Source and quality of nucleic acids for analyses Since the beginning of the genetic investigation, much attention has been paid to obtain microdissected tumor tissue from FFPE sections, in order to avoid the isolation of contaminating healthy cells together with neoplastic ones [80]. Analogously, the clonal nature of a tumor requires the biopsy of different portions of the neoplastic mass [81,82], but the quality of nucleic acids remains a critical point. In order to address these issues, several studies have been aimed at investigating whether different characteristics of fixation protocols (i.e., times, reagents and techniques) could influence both the quality of DNA and RNA [83] and results from different instrumental platforms. For example, formalin fixation may affect RNA integrity in a time-dependent manner, especially for longest RNA sequences, and it may decrease the efficiency of cDNA synthesis [84]. As a consequence, both qRT-PCR results and microarray gene expression data show major changes with respect to the analyses performed in fresh-frozen (FF) tissues. Conversely, other studies comparing FFPE and FF samples from the endometrium and renal cell carcinoma showed that collected RNA performed successfully on whole-genome cDNA array and NGS [85,86]. Finally, Sinicropi et al. demonstrated that a whole transcriptome profiling based on longer RNA molecules ‘has a sufficient precision and sensitivity for biomarker discovery’ [87]. Several studies have demonstrated that DNA extracted from FFPE samples could be used in NGS-based analyses [88,89], while results were in good correlation with those obtained by RT-PCR [90]. Of note, a recent work successfully performed NGS on FFPE samples stored for up to 20 years [91], despite the quality of reads could be decreased.


4.2.1

Circulating tumor cells and circulating tumor DNA

4.2.2

In the case of germinal polymorphisms, their investigation may be performed through the collection of easily accessible healthy tissues, such as epithelial buccal cells for hematological malignancies and peripheral blood for solid tumors [92]. The latter source deserves particular interest because more recent approaches allow the isolation of circulating tumor DNA (ctDNA) and circulating tumor cells (CTCs) [93,94], whose detectable presence in peripheral blood is considered a prognostic marker per se [95]. The quantity and the quality of isolated nucleic acids seem to be the major limits in microarray or NGS experiments [93,94]. Furthermore, the antitumor treatment decreases the burden of CTC, while white blood cells may contaminate the harvest of circulating neoplastic cells [96]. These issues could generate discrepancies in results among archived tissues, ctDNA and CTC [93]. However, in some recent studies, genetic analyses performed in CTC or

ctDNA were in good concordance with data obtained from corresponding archived tumor samples [96,97] or bioptic tissues [98]. In particular, the quantity of DNA from plasma and CTC was sufficient to perform NGS mutational analyses of TP53 and EGFR in triple-negative breast cancer and NSCLC patients, respectively [97,99]. Furthermore, in order to personalize therapeutic regimens [100] and to anticipate the occurrence of drug resistance [101], the tumor mutational status may be investigated by NGS on CTC or ctDNA over time. For example, the increasing number of mutant alleles or the occurrence of gene amplification as resulted from ctDNA was associated with the emergence of drug resistance in patients affected by solid tumors [102,103]. Interestingly, the ex vivo cultivation of CTC allowed a comprehensive analysis of mutational status by NGS and the evaluation of tumor cell sensitivity towards several drugs (used alone or in combination) [100]. Cell lines and in vitro studies The in vitro pharmacogenetic profiling associated with the investigation of drug sensitivity in large numbers of human cancer cell lines may overcome some problems experienced by NGS, GWAS or microarrays [104]. In the very recent past, two interesting articles published on Nature journal report gene expression analyses and gene copy number evaluations in large panels of human tumor cell lines by using two different NGS platforms [105,106]. Furthermore, a number of those cell lines were exposed to anticancer drugs and in vitro sensitivity results were matched with genetic data. Both studies identified several biomarkers of drug sensitivity and unexpected relationships between drugs and genetic status of tumors, but some discrepancies did exist [107]. In particular, the comparison of drug sensitivity analyses performed in 471 cell lines exposed to 15 drugs revealed ‘poor correspondence between studies’. The different pharmacological assays and the range of tested drug concentrations could explain those discrepancies. Finally, human lymphoblastoid and breast cancer cell lines have been adopted as in vitro models [53,108] because a high concordance rate of WES has been demonstrated with whole blood analyses [109]. The in vitro drug evaluation suffers from evident differences with respect to human studies. Cell line models do not take into account the extracellular milieu and the interactions among tumor, healthy cells and the immune system, nor the balance between efficacy and toxicity that characterizes antineoplastic drug [110]. Therefore, in vitro studies are conducted on dozens of drugs, hundreds of human cell lines and thousands of genes, but some authors suggest that sensitivity assays could be more appropriate for hypothesis generation and validation ‘rather than for formal statistical prediction’ [110,111]. 4.2.3

Software and data mining The increasing data collected in GWAS and NGS studies have prompted the elaboration of algorithms and strategies that could manage the huge amount of information with the 4.3


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lowest false prediction rate [112]. Several computational tools are available to analyze GWAS and NGS results [113-115], and they may be integrated in frameworks together with large genetic databases [116,117]. One of these tools is the system biology approach, which is aimed at the exploration of biological systems [118] and at the identification of predictive markers by joining highthroughput techniques to computational modeling [119]. Another strategy is a knowledge-based algorithm combined with a two-stage screening and a permutation test [41] that has been used to distinguish a subgroup of polymorphisms predictive of CPT-11-induced diarrhea. Moreover, Allen et al. analyzed microarray data in an unsupervised fashion through principal component analysis to identify those genes likely associated with resistance to 5-FU and SN-38, the active metabolite of CPT-11 [120]. A similar approach, termed Rapid Learning for Precision Oncology, adopts panomic technologies and statistical methods to establish which deranged networks sustain tumor growth [121].

Clinical trials may be also based on a ‘hybrid design’ when there is a preliminary evidence of treatment efficacy in selected patients [126]. All subjects are screened for marker status to decide which ones will be randomized to investigational regimens (biomarker positive) or will receive standard treatment (biomarker negative). Specimens and follow-up data are collected for future testing in all patients. In general, a major advantage of biomarker-driven trials is the need of ‘fewer randomized patients compared to the untargeted design’ [125] and this certainly reduces the costs for the inclusion of genetic investigations into the study. However, biomarker designs may suffer from some disadvantages: a) the possibility to choose among treatment options; b) the identification of patients’ subgroup could not be completely correct; and c) treatment efficacy may be similar in biomarker-positive patients receiving the experimental treatment and in biomarker-negative subjects treated with standard therapy [127]. 5.

Incorporating pharmacogenetic profiling and biomarker evaluation in clinical trials

4.4

Pharmacogenetic profiling may be included in both preclinical and clinical phases of drug development, but study aims differ according to the phase [122]. For example, the analysis of biomarkers in Phase I studies may be considered an exploratory evaluation and useful to support the choice of drug dosages. In Phase II clinical trials, the working hypothesis (i.e., the relationship between the predictive marker and therapy outcome) needs to be tested while the reliability of the assay should be evaluated [123]. Finally, Phase III trials validate the biomarker and may justify its entry into clinical use. Therefore, WGS and NGS platforms could be adopted to guide the early phases of drug development, while less expensive techniques, such as RT-PCR or microarrays, may be used in Phase II/III clinical trials. In general, trial design may differ according to biomarker importance and role in study activities. In ‘unselected’ clinical studies, every patient has the same probability to receive any of the investigated treatments regardless the individual biomarker status. Then biomarkers are used a posteriori to stratify patients’ responses and to assess any possible association between markers and therapy benefit (or toxicity). On the contrary, biomarkers may guide patients’ selection across different treatment arms in a prospective way (the so-called targeted designs) [124]. The ‘enrichment design’ is adopted to compare standard or ‘targeted’ therapies in patients who harbor the biomarker. A ‘biomarker-strategy design’ is adopted when a control arm will receive standard therapy without the investigation of biomarker status. Biomarker-guided designs are the most appropriate when therapies have a modest effect on unselected patients, biomarker assay is accurate (high specificity and sensitivity) and marker prevalence within the population is high enough [125]. 10

Conclusions

Modern techniques offer several possibilities to investigate human genome variations (in their widest definition) for the identification of predictive biomarkers of drug efficacy and tolerability. The increased availability and reduced costs of microarrays, GWAS and NGS platforms make the pharmacogenetic profiling an appealing area of investigation, as demonstrated by the set up of specific genomic classifiers in breast and CRC patients [128,129]. Hopefully, the future application of results in clinical settings will ensure an efficient and effective stratification of patients while drug use will become more appropriate by adapting doses and combinations to individual genetic signature. This will bring to the development of companion diagnostics that may revolutionize cancer chemotherapy [57]. However, some challenges limit the validation and the clinical application of genetic profiling. The turnaround time is not negligible and still represents a technical limit of these platforms [1]. Prospective replication studies represent another challenge [43]: the enrolment of a matched population of patients may be difficult, antineoplastic chemotherapies are rapidly changing over time, and negative results in the first study may impede subsequent replication trials for ethical concerns [92]. A possible solution may come from a robust planned framework. The latter begins with the set up of biobanks through the collection of data and biospecimens, and it ends with the involvement of stakeholders in generating recognized and approved guidelines [126,130]. Therefore, prospective clinical trials and following replication/validation studies will ensure the translation of genetic signatures into clinical routine practice. 6.

Expert opinion

At present, current available strategies allow the identification of a genomic signature that could help physicians to choose




the most appropriate therapeutic regimen for the patient and his/her disease. However, this is true for single-drug predictive biomarkers (i.e., dihydropyrimidine dehydrogenase activity and fluoropyrimidines), whereas the identification and the validation of markers for combination regimens are more difficult. A practical solution to this problem is represented by a clear-cut definition of genetic and phenotypic endpoints of the study, which will help in patients’ enrolment and data analyses. Moreover, replication studies will confirm (or reject) initial hypotheses or generate other ones. Finally, an integrated ‘omic’ approach (i.e., genotype, endophenotype and phenotype) that also considers possible covariates and Bibliography Papers of special note have been highlighted as either of interest () or of considerable interest () to readers. 1.

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Affiliation

Antonello Di Paolo† MD PhD, Marialuisa Polillo PharmD, Marianna Lastella BSc, Guido Bocci MD PhD, Marzia Del Re PharmD & Romano Danesi MD PhD † Author for correspondence University of Pisa, Department of Clinical and Experimental Medicine, Via Roma 55, 56126 Pisa, Italy Tel: +39 050 2218755; Fax: +39 050 2218758; E-mail: [email protected]

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Methods for studying oviductal physiology.

Rational combination therapy of vintafolide (EC145) with commonly used chemotherapeutic drugs.

Case-base methods for studying vaccination safety.

Social media methods for studying rare diseases.

Methods for studying the yeast vacuole.

Use of numerical profiles for studying bacterial diversity.

Transgenic plants as low-cost platform for chemotherapeutic drugs screening.

Methods for Studying Movement of Molecules Within Cilia.

Analysis methods for studying the 3D architecture of the genome.

Radiological methods of studying the orbit.

Preliminary evaluation of prostate-targeted radiotherapy using (131) I-MIP-1095 in combination with radiosensitising chemotherapeutic drugs.

ADR cells.

Tools and methods for studying Notch signaling in Drosophila melanogaster.

Methods for Studying Autophagy Within the Tumor Microenvironment.

Quantitative methods for studying hemostasis in zebrafish larvae.

STAT pathway.

Potential carriers of chemotherapeutic drugs: matrix based nanoparticulate polymeric systems.

Local bacteria affect the efficacy of chemotherapeutic drugs.

Selecting combinations of chemotherapeutic drugs to maximize dose intensity.

Interferon-alpha and -gamma in combination with chemotherapeutic drugs: in vitro sensitivity studies in four human mesothelioma cell lines.

Perineal versus anal submucosal injection of chemotherapeutic drugs.

Studying the impact of patient drug profiles in an HMO.

Nanopotentiated combination cancer therapy: Chemotherapeutic and chemosensitizer (2C approach).

Epigenetic targeting drugs potentiate chemotherapeutic effects in solid tumor therapy.