Genomic treatment response prediction in schizophrenia

Chapter 34

Genomic treatment response prediction in schizophrenia Sophie E. Legge, Antonio F. Pardin˜as and James T.R. Walters MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom

1 Introduction Schizophrenia affects approximately 0.7% of the population (McGrath, Saha, Chant, & Welham, 2008). It is characterized by disturbances in cognition, perception, and thought and severely impacts both the quality of life and life expectancy (Owen, Sawa, & Mortensen, 2016). Antipsychotics are the primary treatment for schizophrenia and are effective in the majority of patients, although there is considerable heterogeneity between individuals in symptomatic response and the occurrence of adverse effects while taking these medications. Around 30% of individuals with schizophrenia will remain symptomatic and significantly impaired despite standard antipsychotic treatment, and are considered to have treatmentresistant (or treatment-refractory) schizophrenia (TRS) (Meltzer, 1997; Suzuki et al., 2012). TRS is defined by the National Institute for Health and Care Excellence (NICE) as a lack of adequate response to two sequential trials of antipsychotic treatment of sufficient dose and duration (National Collaborating Centre for Mental Health, 2014). A recent prospective study indicated that there might be two distinct subtypes of TRS: (i) the first group, representing the majority of TRS patients (70%), consists of those that fail to respond to antipsychotics from their onset of illness, and (ii) a smaller subset of patients that show initial response to antipsychotics but then delayed treatment resistance following periods of symptomatic relapse (Lally et al., 2016). It is as yet unclear whether TRS is better conceptualized as a form of illness at the severe end of a psychosis/schizophrenia spectrum or as a more biologically homogeneous subgroup of those with schizophrenia, although recent evidence has supported the latter hypothesis (Barnes & Dursun, 2008; Gillespie, Samanaite, Mill, Egerton, & MacCabe, 2017).

1.1 Burden of TRS TRS is one of the most disabling forms of mental illness and presents a major clinical management challenge (Conley & Kelly, 2001; Kennedy, Altar, Taylor, Degtiar, & Hornberger, 2014). Individuals with TRS have been shown to be more severely impaired compared to those with treatment-responsive schizophrenia across a range of clinical characteristics and outcomes, including a greater likelihood of a continuous course of illness, poorer cognitive functioning, a higher number of psychiatric inpatient admissions, a greater likelihood of deterioration from their premorbid level of functioning, more frequent detention under the Mental Health Act, and more severe positive and negative symptoms (Kennedy et al., 2014; Legge et al., 2019). Furthermore, patients with TRS have decreased life expectancy due to major physical health problems and increased suicide rates (Conley & Kelly, 2001). It should therefore come as no surprise that the annual direct costs of TRS treatment are higher than for patients with treatment-responsive schizophrenia (estimated at 3- to 11-fold higher), primarily driven by increased hospital admissions (Kennedy et al., 2014).

1.2 Clozapine treatment The only licensed medication with an indication for TRS is the antipsychotic clozapine. While the mechanism of action of clozapine is still not fully understood, it has been proposed that its efficacy in the context of TRS might be related to the biological underpinnings of treatment resistance, which in turn might implicate different neuronal systems to those affected in the majority of treatment-responsive cases. Clozapine is effective in around 60% of patients with TRS and improves the Personalized Psychiatry. https://doi.org/10.1016/B978-0-12-813176-3.00034-1 Copyright © 2020 Elsevier Inc. All rights reserved.

413

414

Personalized psychiatry

majority of morbidity and mortality indicators that have been examined (Tiihonen et al., 2017). Nonetheless, clozapine remains widely underprescribed, and for those who do receive clozapine treatment, substantial delays are commonplace (Howes et al., 2012), which is an important shortfall because such delays are associated with poorer long-term outcomes (Harris et al., 2005). When clozapine is not used, TRS patients are often treated with high-dose antipsychotics or polypharmacy. Both approaches have a very limited evidence base in TRS and can result in patients being exposed to substantial risk of adverse effects. Thus, ensuring that suitable patients have timely access to clozapine treatment is widely recognized as an important therapeutic goal in the management of people with schizophrenia (National Collaborating Centre for Mental Health, 2014).

2

Prediction of TRS

Personalized medicine refers to the use of genetic, biological, or wider individual factors to tailor therapeutic interventions, with the aim of achieving “the delivery of the right treatment to the right patient at the right time” (Hall, 1977). The identification of predictors of TRS has the potential to provide insights into the underlying etiology of the condition, and also offers the prospect of enabling clinicians to better identify and appropriately treat those with an increased risk of developing TRS when they first present with psychosis (Carbon & Correll, 2014). The ability to identify those at increased risk of developing TRS could also open the door to novel clinical trial designs to investigate alternative treatment pathways for those at high risk of developing TRS and limit the delays to effective treatments that currently exist. Research investigating TRS prediction has faced significant challenges, particularly as a result of limitations in suitable sample sizes and the use of diverse outcome measures and definitions, both issues that have historically plagued the field of pharmacogenetics (Gillespie et al., 2017). Characterizations of patient samples in studies of treatment resistance in schizophrenia have varied widely and generally have not conformed with established definitions, either from clinical guidelines (NICE; defined above) (National Collaborating Centre for Mental Health, 2014) or alternative proposed research definitions (Howes et al., 2017). Instead, it has been commonplace for studies to adopt a pragmatic definition for TRS such as “lifetime-ever clozapine treatment” because clozapine is the only licensed treatment for those with TRS in most countries (Nielsen et al., 2016), although such an approach will misclassify those with TRS who have not taken clozapine. An alternative approach has been to attempt to quantitatively assess response to antipsychotics, often using percentage change or threshold scores on symptom measures such as the Positive and Negative Syndrome Scale (PANSS) (Suzuki et al., 2012). Very few studies of TRS prediction have compared patients with strictly defined criteria for treatment resistance and/or treatment responsiveness. Such variations in methodology have limited comparability between studies, presenting a barrier to combining results across studies. That has led to a lack of independent replication of findings of TRS predictors. The agreement of international definitions and guidelines for TRS (Howes et al., 2017) should address some of the previous methodological shortcomings and enable the integration of research results to advance TRS prediction. Despite the challenges presented above for existing studies of TRS prediction, there have been consistent findings identifying clinical or demographic predictors of TRS. In particular, a younger age at onset of psychosis has been shown to be a significant and important clinical indicator of TRS (Lally, Gaughran, Timms, & Curran, 2016; Legge et al., 2019; Wimberley et al., 2016). Poor premorbid functioning has also been associated with TRS as well as poorer outcomes more widely in schizophrenia (Legge et al., 2019). Other demographic and environmental factors associated with TRS in previous studies include male sex, a longer duration of untreated psychosis, living in less urbanized areas, and comorbid substance-use disorders (Wimberley et al., 2016). The identification of clinical and environmental predictors specific for TRS supports the hypothesis that TRS is at least in part distinct from treatment-responsive schizophrenia.

3

Genomic prediction of TRS

The main focus of this chapter is to provide an overview of the genomic prediction studies of TRS. The identified predictors of TRS will be summarized from studies investigating (i) candidate genes, (ii) common variants (typically over 1% population frequency) via genome-wide association studies (GWAS), (iii) genetic liability for schizophrenia via polygenic risk scores (PRS), and (iv) rare variants (<1% population frequency). We will then discuss the challenges and future directions for the field in genomic prediction of TRS. An overview of different genetic association study designs is given in Fig. 1.

3.1 Heritability of TRS It has been hypothesized that TRS may be more heritable than treatment-responsive schizophrenia, raising the possibility that there is a greater genetic liability for TRS either due to a larger number of risk alleles (greater polygenicity) or by these

Genomic treatment response prediction in schizophrenia Chapter

Population frequency

Study design

Genetic risk detected

Common variation

Rare variation

(>1% frequency)

(<1% frequency)

GWAS

Common variants of small-moderate effect

PRS

Candidate gene(s)

Cumulative estimate of common genetic liability to disorder/trait

Variants from candidate genes (any frequency and penetrance)

CNVs

Rare deletions and duplications of moderate-large effect

34

415

WGS/WES

SNVs (de novo or inherited) of moderatelarge effect

FIG. 1 Summary of genetic association study designs. A summary of commonly used genetic associations study designs. GWAS, genome-wide association study; PRS, polygenic risk score; CNVs, copy number variation; WGS/WES, whole genome sequencing/whole exome sequencing.

having stronger effects. Observational family studies have reported higher familial loading scores for schizophrenia in patients with TRS in comparison to healthy controls, whereas equivalent analyses in those with treatment-responsive schizophrenia failed to detect such differences ( Joober et al., 2005). In addition, a genetic contribution to TRS is supported by case reports of monozygotic twins showing similar responses to antipsychotic treatment (Mata, Madoz, Arranz, Sham, & Murray, 2001; Wehmeier et al., 2005). Despite these studies being suggestive of TRS having a genetic component, there have been no large-scale replicated evaluations and so no robust evidence of the specific heritability of TRS is available to date. It follows that the heritability of TRS, or indeed antipsychotic response, is yet to be quantified. This is perhaps due to the apparent challenges in conducting suitably powered twin or family studies that would include a requirement for two or more individuals with schizophrenia (within families) and accurate characterization of antipsychotic response or resistance. While recently developed statistical techniques can be used to ascertain heritability from molecular genetic data in casecontrol studies (“SNP-based heritability” Yang, Zeng, Goddard, Wray, & Visscher, 2017), these require the use of large cohorts of at least several thousand individuals in order to produce reliable estimates, a requirement that would not be fulfilled by any TRS study published to date.

3.2 Candidate studies Although there have been relatively few pharmacogenetic studies directly investigating TRS, studies of general antipsychotic response should offer insights into the genetic underpinnings of TRS. Previous candidate studies have primarily focused on either the neurotransmitters targeted by antipsychotic medications (chiefly dopamine and to a lesser extent serotonin) or genes encoding the enzymes responsible for drug metabolism (cytochrome P450 family). Among the dopaminerelated candidate gene studies, metaanalytic evidence indicates a potential role in antipsychotic response for the Val108Met polymorphism (rs4680) in the Catechol-O-Methyltranferase gene (COMT), which plays a key role in dopamine clearance (Huang et al., 2016). There is also some support from a metaanalysis that genetic variation in the dopamine D2 receptor (particularly the
141C Ins/Del polymorphism in DRD2 Zhang, Lencz, & Malhotra, 2010) may influence antipsychotic treatment response, which seems feasible given that this receptor is the therapeutic target of all currently licensed antipsychotics. Other studies have targeted genetic variation in serotonin system genes with suggestive evidence for the
1438G polymorphism in HRT2A (Ellingrod et al., 2003), and 5-HTTLPR, a degenerate repeat polymorphism in SLC6A4 (Dolzan et al., 2008), playing a putative role in antipsychotic response. SLC6A4 encodes the serotonin transporter and 5-HTTLPR has been demonstrated to affect the rate of serotonin uptake (Dolzan et al., 2008; Zhang & Malhotra, 2011). Despite these findings, we feel that the evidence can only be considered as preliminary, given that the constituent studies of these metaanalyses employed candidate gene approaches that the field has come to appreciate have important limitations, and none of the reported findings would reach what are now considered robust statistical thresholds for genome-wide significance. It follows that one of the largest systematically conducted studies in the field to date, the Clinical Antipsychotic Trials for

416

Personalized psychiatry

Intervention Effectiveness (CATIE) study (n ¼ 738 cases), failed to identify any variants in 74 candidate genes that were significantly associated with discontinuation of olanzapine, quetiapine, risperidone, ziprasidone, or perphenazine (a proxy for nonresponse and/or tolerability) (Need et al., 2009). More recent candidate studies have directly investigated TRS. A study of 74 candidate genes in 89 patients treated with clozapine (and thus TRS) and 190 schizophrenia patients found that variants within BDNF, including the Val66Met (rs6265) polymorphism, were associated with TRS (Zhang et al., 2013). BDNF, which interacts with multiple neurotransmitters including dopamine and serotonin, is also implicated in synaptic plasticity, and has been previously associated with therapeutic response in schizophrenia (Krebs et al., 2000). However, the association of variants within BDNF was not replicated in subsequent longitudinal studies or a metaanalysis (Cargnin, Massarotti, & Terrazzino, 2016). A significant association between TRS and DISC1 has been reported (Mouaffak et al., 2011), although this was not replicated in a study of Japanese patients (Hotta et al., 2011). No significant associations have been reported from candidate gene studies specifically relating to serotoninergic genes and TRS ( Ji, Takahashi, Branko, et al., 2008; Ji, Takahashi, Saito, et al., 2008), although nominal associations with the 5HT2A T102C polymorphism have been reported (Anttila et al., 2007; Joober et al., 1999). In 2012, a study of 384 candidate markers from 46 genes failed to identify any variants significantly associated with TRS (Teo et al., 2012). As with candidate gene approaches in wider neuropsychiatry, candidate pharmacogenetic studies of antipsychotic response have produced few consistent or replicated findings that are at the levels of statistical significance required in modern psychiatric genomics. This is likely due to inadequate statistical power from relatively limited sample sizes, and the considerable variability in experimental design, particularly in definitions of treatment response (van den Oord, Chan, & Aberg, 2018). Advocates of these methodological approaches would argue, perhaps with some justification, that more is known about the metabolism and therapeutic action of antipsychotics than the pathophysiology of neuropsychiatric disorders and hence a more hypothesis-driven (gene-based) rather than a data-driven (genome-wide) approach is appropriate. It has also been argued that sample sizes for pharmacogenetic studies do not need to be as large as case control studies of complex disorders or traits, given the larger predicted effects of pharmacogenetic variants on treatment response (Maranville & Cox, 2015). Whether this observation is generally applicable, the current sample sizes in antipsychotic pharmacogenetic studies are rarely above 1000 cases and have failed to produce genome-wide significant results, which would seem to be a prerequisite to advance the field as far as gene discovery is concerned. Furthermore, it has become clear from the field of schizophrenia genetics that biology-driven candidate gene studies are unlikely to yield robust insights into disease etiology (Farrell et al., 2015) and thus large, well-powered GWAS studies are required to validate and extend the findings that have been reported from candidate gene pharmacogenetic studies of antipsychotic response to date. Indeed, GWAS is being employed productively in other areas of psychiatric pharmacogenetics, such as antidepressant response, yielding promising findings ( Jukic Marin, Haslemo, Molden, & Ingelman-Sundberg, 2018) and encouraging initial findings are being reported from such approaches in schizophrenia antipsychotic response (Yu et al., 2018).

3.3 GWAS A GWAS is a systematic evaluation of polymorphisms throughout the genome (most commonly single nucleotide polymorphisms or SNPs) that aims to evaluate their statistical association to traits or disease. GWAS genotyping arrays rely on an informative backbone of tag SNPs that capture most of the common genetic variation in the genome, and have made large-scale whole-genome genotyping affordable (Visscher, Brown, McCarthy, & Yang, 2012). As with candidate gene studies, there have been few GWAS reports investigating TRS directly. One of the first GWAS studies for antipsychotic response came from the CATIE study in 738 individuals, which identified an intergenic variant on chromosome 4p15 that predicted the effect of ziprasidone on positive symptoms (McClay et al., 2011), although this did not reach the now accepted genome-wide significance level of P < 5  10
8 (see the review of Sham and Purcell (2014) for a discussion on statistical significance in the GWAS framework). Two further GWAS studies for response to iloperidone (Lavedan et al., 2009) and lurasidone (Li, Yoshikawa, Brennan, Ramsey, & Meltzer, 2018) failed to identify any genetic variants at the genome-wide significant level. One of the largest GWAS to date was recently reported from a randomized controlled trial of antipsychotic response to olanzapine, risperidone, quetiapine, aripiprazole, ziprasidone, and haloperidol or perphenazine in a total of 3792 individuals with Han Chinese ancestry (Yu et al., 2018). Yu and colleagues identified five novel genetic associations with general antipsychotic response (MEGF10, SLC1A1, PCDH7, CNTNAP5, and TNIK) and three associations with drug-specific treatment responses (CACNA1C for olanzapine, SLC1A1 for risperidone, and CNTN4 for aripiprazole) (Yu et al., 2018). The authors report that these genes are related to synaptic function and neurotransmitter receptors, and support the hypothesis that implicated pharmacogenetic risk variants have a similar effect on response to several different antipsychotics.

Genomic treatment response prediction in schizophrenia Chapter

34

417

There have been three GWAS studies published to date investigating TRS directly, none of which have identified any variants that were significantly associated at the accepted genome-wide significant level, although all sample sizes were small (Koga et al., 2017; Li & Meltzer, 2014; Liou et al., 2012). Adequate sample size is a critical aspect for statistical power in GWAS in order to detect genetic variants that do not typically have large effects because in most polygenic traits, the usual odds-ratios (ORs) of individual SNPs range between 1.05 and 1.3 (Visscher et al., 2017). Association studies in other disorders have demonstrated that as sample sizes increase, so do the number of genetic loci that are detected. For example, the first schizophrenia GWAS from the Psychiatric Genomics Consortium (50,000 samples) (Ripke et al., 2011) identified seven susceptibility loci compared to 108 loci when the sample size was increased to 150,000 samples (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014). There seems no reason to think that the same shouldn’t be true for TRS and antipsychotic treatment response. Thus, it is critical for further research to be undertaken in larger sample sizes in order to reliably detect genetic loci associated with TRS.

3.4 Genetic liability to schizophrenia Neuropsychiatric traits such as schizophrenia are polygenic in nature, meaning that genetic liability arises through the cumulative effects of many genetic variants, each of a relatively small effect size. The individual SNPs identified by GWAS can be combined into a single continuous variable called a polygenic risk score (PRS), which can be thought of as a measure of the genetic liability (arising from common variants) to that disorder or trait (Lewis & Vassos, 2017). The PRS is calculated using the GWAS SNP results from an independent “training” sample to derive a score for each individual in the target sample, which represents their cumulative genetic risk by summing the number of risk alleles carried by the individual, weighted by the effect size from the training sample (Wray et al., 2014). Given the explanatory power of PRS for susceptibility to schizophrenia, it is reasonable to investigate whether these scores can be informative regarding clinical heterogeneity within the disorder with the aim of stratification. There have been mixed reports regarding the association of schizophrenia PRS with TRS. A study by Frank and colleagues found that the PRS for schizophrenia was increased in 434 patients treated with clozapine in comparison to 370 patients with no history of clozapine treatment (Frank et al., 2015). A small study investigating response to lurasidone reported a significant association between schizophrenia PRS and improvement in positive symptoms (Li et al., 2018). Zhang and colleagues recently reported the first longitudinal study from a first episode sample (total n ¼ 510) and found that patients with a low schizophrenia PRS were more likely to be treatment responders after 12 weeks of antipsychotic treatment than patients with a high PRS.(Zhang et al., 2018) However, three other independent studies, encompassing a similar range of experimental designs and TRS definitions, have found no evidence for an association between schizophrenia PRS and TRS (Legge et al., 2019; Martin & Mowry, 2015; Wimberley et al., 2017). Whether schizophrenia PRS is increased in individuals with TRS is an important question, given that the results could provide insights into whether TRS should be conceptualized as a form of illness at the severe end of a psychosis/schizophrenia spectrum, or as a more biologically homogeneous subgroup of those with schizophrenia, that is, not influenced by liability to schizophrenia beyond the requirement to have schizophrenia (Barnes & Dursun, 2008; Gillespie et al., 2017). As was the case in pharmacogenetic studies, all PRS-based studies to date have had a total sample size smaller than 1000 individuals and thus larger samples are needed to provide answers to this question. Nonetheless, even those studies that reported a significant association found that the schizophrenia PRS explained only a small amount of the variance in TRS (maximum value reported r2 < 3.7% (Zhang et al., 2018)), suggesting that factors other than broad genetic liability to schizophrenia are likely to provide greater insights into the genetic architecture of TRS.

3.5 Rare variant studies Compared to healthy controls, individuals with schizophrenia have an increased burden of rare copy number variations (CNVs), which are chromosomal deletions and duplications that range in size from a few kilobases to several megabases of DNA (Rees et al., 2014). Most of the CNVs associated with schizophrenia have been shown to also increase risk for other neurodevelopmental disorders, such as autism spectrum disorders, intellectual deficit, developmental delay, and epilepsy (Kirov et al., 2014). Martin and Mowry reported an increased burden of genome-wide rare copy number duplications in 277 patients with TRS compared to 385 individuals with schizophrenia (Martin & Mowry, 2015). However, this was not replicated by a later study that reported no difference in the burden of rare pathogenic CNVs previously associated with schizophrenia or intellectual disability in individuals with TRS compared to those with treatment-responsive schizophrenia (Legge et al., 2019).

418

Personalized psychiatry

Other studies of rare variations in schizophrenia have focused on single nucleotide variations (SNVs) derived from nextgeneration sequencing analyses, which may either be inherited or occur as de novo mutations. Ruderfer and colleagues found that patients taking clozapine did not have an enrichment of rare disruptive (protein-altering) variants in genes previously associated with schizophrenia, but did report an excess of rare disruptive variants in gene targets of antipsychotics in those taking clozapine, compared to schizophrenia patients who had not taken clozapine (Ruderfer et al., 2016). In an RCT assessing treatment response to seven antipsychotic medications, Wang and colleagues found that a set of genes associated with reduced N-methyl-D-aspartate (NMDA)—mediated synaptic currents was enriched for rare damaging variants in patients with low treatment response (Wang et al., 2018). This finding was replicated in an independent sample and suggests that glutamatergic signaling at NMDA receptors may play a role in mediating antipsychotic efficacy (Wang et al., 2018).

4

Glutamate hypothesis of TRS

Dysregulation of the neurotransmitters dopamine and glutamate are the two primary neurochemical hypotheses of schizophrenia, and additionally it has been hypothesized that glutamate dysfunction may be particularly relevant for TRS (Moghaddam & Javitt, 2012). Neuroimaging studies have reported evidence that in comparison with schizophrenia patients who are responsive to treatment, those with TRS demonstrate reduced striatal dopamine synthesis (Mouchlianitis, McCutcheon, & Howes, 2016) and elevated anterior cingulate cortex glutamate levels (Demjaha et al., 2014). Reduced striatal dopamine synthesis may explain why TRS patients show at best a partial response to the D2 dopamine receptor blockade that is the target of conventional antipsychotic treatment. Clozapine has been shown to interact with glutamatergic-based signaling mechanisms via actions at the NMDA/glycine receptor (Schwieler, Linderholm, NilssonTodd, Erhardt, & Engberg, 2008). Preliminary evidence suggests that high-dose glycine, an NMDA agonist, may reduce negative and cognitive symptoms when used to augment olanzapine or risperidone treatment (Heresco-Levy, Ermilov, Lichtenberg, Bar, & Javitt, 2004). This suggests that other atypical antipsychotics may have different effects on NMDA receptor-mediated neurotransmission compared with clozapine. The development of more comprehensive genetic studies of TRS might help to illuminate this issue, given recent advances in the field of single-cell sequencing, which have already been used to pinpoint specific neuronal subtypes implicated in psychiatric disorders (Skene et al., 2018).

5

Challenges and future directions

There have been relatively few pharmacogenetic studies directly investigating TRS, despite the potential importance of this research. Previous candidate gene studies have failed to produce replicated findings at now established levels of statistical significance. GWAS studies are yet to produce robustly replicated risk loci for TRS, and there has only been one study to date that has been powered enough to identify variants at the genome-wide significant level, although this study targeted general antipsychotic response as an outcome rather than TRS specifically (Yu et al., 2018). A small number of rare variant studies have reported enrichments in gene sets related to antipsychotic targets and NMDA-mediated synaptic currents, but these findings are yet to be replicated. There have been mixed reports regarding the association of schizophrenia PRS with TRS but there is no convincing evidence for a meaningful enrichment of schizophrenia polygenic signal in TRS, suggesting that factors other than genetic susceptibility to broad schizophrenia are likely to provide greater insights into the genetic architecture of TRS. Box 1 gives a summary of the identified risk factors for TRS. The field of genomic prediction of TRS faces significant challenges related to the likely causal heterogeneity of TRS and the lack of well-characterized samples, problems that have inhibited progress in wider psychiatric pharmacogenomics. The majority of studies to date have been underpowered as a result of small sample sizes and the variability in TRS definition has limited the comparability between studies. There have been relatively few studies comparing patients with strictly defined treatment resistance and treatment responsiveness, a lack of prospective studies, and little independent replication of significant findings. This is partly due to the often prohibitive costs associated with carrying out long-term followup of therapeutic drug trials and the lack of prospective studies with consistent and reliable characterization of treatment response or resistance that also have available genetic data. An encouraging development in this respect involves combining population-based genetic studies with electronic medical records (Wimberley et al., 2016) and targeted recruitment strategies of very large numbers of TRS cases, as exemplified in the CLOZUK study (Pardin˜as et al., 2018). In addition to the limitations of existing research described above, most studies of TRS have been performed in populations of European ancestry, together with the vast majority of complex disorder genomic research. This situation is gradually changing, given the potential scientific advantages of genetic research in non-European populations, but also as a vital

Genomic treatment response prediction in schizophrenia Chapter

34

419

BOX 1 Summary of risk factors for TRS Clinical indicators  Earlier age of onset of psychosis; this finding has been replicated in several independent studies  Poor premorbid functioning  Male sex  Longer duration of untreated psychosis  Comorbid substance use disorders Genetic risk factors  Val66Met (rs6265) polymorphism in BDNF; suggestive evidence from candidate gene studies only and not replicated in metaanalysis.





  

Missense variant (rs3738401) in DISC1; suggestive evidence from candidate gene studies only and not yet replicated. No genome-wide significant variants from three GWAS published to date; all studies were underpowered to detect variants of small effect. An increased polygenic risk for schizophrenia; this finding has not been replicated in independent studies An increased burden of CNVs; this findings has not been replicated in independent studies Enrichment of rare disruptive SNVs in gene targets of antipsychotics; this finding has not yet been replicated

step in determining the generalizability or specificity of risk variant findings across populations (Chan, Jin, Loh, & Brunham, 2015). Box 2 provides an indication of the future direction of TRS studies. Future insights may be gained from investigating the role of genetics of the antipsychotic metabolism ( Jukic et al., 2017) and nongenetic factors. For example, the age of onset of psychosis and poor premorbid functioning have been consistently identified as predictors of TRS, suggesting that genetic factors influencing these traits could have relevance to TRS. Also, given its wide use in TRS treatment, improvements in the characterization of the mechanism of action of clozapine might reveal insights into TRS, work that would open up hypotheses for pharmacogenomic and drug-repurposing studies (Gaspar & Breen, 2017).

BOX 2 Future directions for genetic studies of TRS   

Adequately powered studies with large sample sizes An agreed definition of TRS that is replicable across study populations GWAS and next generation sequencing studies

 

Studies in non-European populations Prospective, longitudinal cohort studies

6 Conclusions In conclusion, TRS is an important area of need for patients, their relatives, and health services, and it presents the potential for advances in stratification and personalized psychiatry. However, while there is suggestive evidence that TRS may be heritable, this has not been confirmed in large family or molecular genetic studies. Similarly, no robustly replicated risk loci for TRS have been identified to date, largely due to the fact that studies have been underpowered, conducted in relatively small samples, and with limitations in their clinical phenotypic characterization. In order for the field to advance and for further assessment of the clinical utility of genomics in this area of medicine, further well-designed and sufficiently powered research is required before risk variants can be identified with confidence and in order to improve prediction of TRS.

REFERENCES Anttila, S., Kampman, O., Illi, A., Rontu, R., Lehtimaki, T., & Leinonen, E. (2007). Association between 5-HT2A, TPH1 and GNB3 genotypes and response to typical neuroleptics: A serotonergic approach. BMC Psychiatry, 7. Barnes, T. R., & Dursun, S. (2008). Treatment resistance in schizophrenia. Psychiatry, 7, 487–490. Carbon, M., & Correll, C. U. (2014). Clinical predictors of therapeutic response to antipsychotics in schizophrenia. Dialogues in Clinical Neuroscience, 16, 505–524. Cargnin, S., Massarotti, A., & Terrazzino, S. (2016). BDNF Val66Met and clinical response to antipsychotic drugs: A systematic review and metaanalysis. European Psychiatry, 33, 45–53. Chan, S. L., Jin, S., Loh, M., & Brunham, L. R. (2015). Progress in understanding the genomic basis for adverse drug reactions: A comprehensive review and focus on the role of ethnicity. Pharmacogenomics, 16, 1161–1178.

420

Personalized psychiatry

Conley, R. R., & Kelly, D. L. (2001). Management of treatment resistance in schizophrenia. Biological Psychiatry, 50, 898–911. Demjaha, A., Egerton, A., Murray, R. M., Kapur, S., Howes, O. D., Stone, J. M., et al. (2014). Antipsychotic treatment resistance in schizophrenia associated with elevated glutamate levels but normal dopamine function. Biological Psychiatry, 75, e11–e13. Dolzan, V., Serretti, A., Mandelli, L., Koprivsek, J., Kastelic, M., & Plesnicar, B. K. (2008). Acute antipyschotic efficacy and side effects in schizophrenia: Association with serotonin transporter promoter genotypes. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 32, 1562–1566. Ellingrod, V. L., Lund, B. C., Miller, D., Fleming, F., Perry, P., Holman, T. L., et al. (2003). 5-HT2A receptor promoter polymorphism, -1438G/A and negative symptom response to olanzapine in schizophrenia. Psychopharmacology Bulletin, 37, 109–112. Farrell, M. S., Werge, T., Sklar, P., Owen, M. J., Ophoff, R. A., O’Donovan, M. C., et al. (2015). Evaluating historical candidate genes for schizophrenia. Molecular Psychiatry, 20, 555–562. Frank, J., Lang, M., Witt, S. H., Strohmaier, J., Rujescu, D., Cichon, S., et al. (2015). Identification of increased genetic risk scores for schizophrenia in treatment-resistant patients. Molecular Psychiatry, 20, 150–151. Gaspar, H. A., & Breen, G. (2017). Drug enrichment and discovery from schizophrenia genome-wide association results: An analysis and visualisation approach. Scientific Reports, 7, 12460. Gillespie, A. L., Samanaite, R., Mill, J., Egerton, A., & MacCabe, J. H. (2017). Is treatment-resistant schizophrenia categorically distinct from treatmentresponsive schizophrenia? a systematic review. BMC Psychiatry, 17, 12. Hall, B.L. Patient placement in a long-term care. California State University (1977). Harris, M. G., Henry, L. P., Harrigan, S. M., Purcell, R., Schwartz, O. S., Farrelly, S. E., et al. (2005). The relationship between duration of untreated psychosis and outcome: An eight-year prospective study. Schizophrenia Research, 79, 85–93. Heresco-Levy, U., Ermilov, M., Lichtenberg, P., Bar, G., & Javitt, D. C. (2004). High-dose glycine added to olanzapine and risperidone for the treatment of schizophrenia. Biological Psychiatry, 55, 165–171. Hotta, Y., Ohnuma, T., Hanzawa, R., Shibata, N., Maeshima, H., Baba, H., et al. (2011). Association study between disrupted-in-schizophrenia-1 (DISC1) and Japanese patients with treatment-resistant schizophrenia (TRS). Progress in Neuro-Psychopharmacology & Biological Psychiatry, 35, 636–639. Howes, O. D., McCutcheon, R., Agid, O., de Bartolomeis, A., van Beveren, N. J. M., Birnbaum, M. L., et al. (2017). Treatment-resistant schizophrenia: Treatment response and resistance in psychosis (TRRIP) working group consensus guidelines on diagnosis and terminology. The American Journal of Psychiatry, 174, 216–229. Howes, O. D., Vergunst, F., Gee, S., McGuire, P., Kapur, S., & Taylor, D. (2012). Adherence to treatment guidelines in clinical practice: Study of antipsychotic treatment prior to clozapine initiation. The British Journal of Psychiatry, 201, 481–485. Huang, E., Zai, C. C., Lisoway, A., Maciukiewicz, M., Felsky, D., Tiwari, A. K., et al. (2016). Catechol-O-methyltransferase Val158Met polymorphism and clinical response to antipsychotic treatment in schizophrenia and schizo-affective disorder patients: A meta-analysis. The International Journal of Neuropsychopharmacology, 19, pyv132. Ji, X., Takahashi, N., Branko, A., Ishihara, R., Nagai, T., Mouri, A., et al. (2008). An association between serotonin receptor 3B gene (HTR3B) and treatment-resistant schizophrenia (TRS) in a Japanese population. Nagoya Journal of Medical Science, 70, 11–17. Ji, X., Takahashi, N., Saito, S., Ishihara, R., Maeno, N., Inada, T., et al. (2008). Relationship between three serotonin receptor subtypes (HTR3A, HTR2A and HTR4) and treatment-resistant schizophrenia in the Japanese population. Neuroscience Letters, 435, 95–98. Joober, R., Benkelfat, C., Brisebois, K., Toulouse, A., Turecki, G., Lal, S., et al. (1999). T102C polymorphism in the 5HT2A gene and schizophrenia: Relation to phenotype and drug response variability. Journal of Psychiatry & Neuroscience, 24, 141–146. Joober, R., Rouleau, G. A., Lal, S., Bloom, D., Lalonde, P., Labelle, A., et al. (2005). Increased prevalence of schizophrenia spectrum disorders in relatives of neuroleptic-nonresponsive schizophrenic patients. Schizophrenia Research, 77, 35–41. Jukic Marin, M., Haslemo, T., Molden, E., & Ingelman-Sundberg, M. (2018). Impact of CYP2C19 genotype on escitalopram exposure and therapeutic failure: A retrospective study based on 2,087 patients. The American Journal of Psychiatry, 175, 463–470. Jukic, M. M., Opel, N., Strom, J., Carrillo-Roa, T., Miksys, S., Novalen, M., et al. (2017). Elevated CYP2C19 expression is associated with depressive symptoms and hippocampal homeostasis impairment. Molecular Psychiatry, 22, 1224. Kennedy, J. L., Altar, C. A., Taylor, D. L., Degtiar, I., & Hornberger, J. C. (2014). The social and economic burden of treatment-resistant schizophrenia: A systematic literature review. International Clinical Psychopharmacology, 29, 63–76. Kirov, G., Rees, E., Walters, J. T., Escott-Price, V., Georgieva, L., Richards, A. L., et al. (2014). The penetrance of copy number variations for schizophrenia and developmental delay. Biological Psychiatry, 75, 378–385. Koga, A., Bani-Fatemi, A., Hettige, N., Borlido, C., Zai, C., Strauss, J., et al. (2017). GWAS analysis of treatment resistant schizophrenia: Interaction effect of childhood trauma. Pharmacogenomics, 18, 663–671. Krebs, M. O., Guillin, O., Bourdell, M. C., Schwartz, J. C., Olie, J. P., Poirier, M. F., et al. (2000). Brain derived neurotrophic factor (BDNF) gene variants association with age at onset and therapeutic response in schizophrenia. Molecular Psychiatry, 5, 558–562. Lally, J., Ajnakina, O., Di Forti, M., Trotta, A., Demjaha, A., Kolliakou, A., et al. (2016). Two distinct patterns of treatment resistance: Clinical predictors of treatment resistance in first-episode schizophrenia spectrum psychoses. Psychological Medicine, 46, 3231–3240. Lally, J., Gaughran, F., Timms, P., & Curran, S. R. (2016). Treatment-resistant schizophrenia: Current insights on the pharmacogenomics of antipsychotics. Pharmgenomics Pers Med, 9, 117–129. Lavedan, C., Licamele, L., Volpi, S., Hamilton, J., Heaton, C., Mack, K., et al. (2009). Association of the NPAS3 gene and five other loci with response to the antipsychotic iloperidone identified in a whole genome association study. Molecular Psychiatry, 14, 804–819. Legge, S. E., Dennison, C. A., Pardin˜as, A. F., Rees, E., Lynham, A. J., Hopkins, L., et al. (2019). Clinical indicators of treatment-resistant psychosis. The British Journal of Psychiatry. https://doi.org/10.1192/bjp.2019.120.

Genomic treatment response prediction in schizophrenia Chapter

34

421

Lewis, C. M., & Vassos, E. (2017). Prospects for using risk scores in polygenic medicine. Genome Medicine, 9, 96. Li, J., & Meltzer, H. Y. (2014). A genetic locus in 7p12.2 associated with treatment resistant schizophrenia. Schizophrenia Research, 159, 333–339. Li, J., Yoshikawa, A., Brennan, M. D., Ramsey, T. L., & Meltzer, H. Y. (2018). Genetic predictors of antipsychotic response to lurasidone identified in a genome wide association study and by schizophrenia risk genes. Schizophrenia Research, 192, 194–204. Liou, Y. J., Wang, H. H., Lee, M. T., Wang, S. C., Chiang, H. L., Chen, C. C., et al. (2012). Genome-wide association study of treatment refractory schizophrenia in Han Chinese. PLoS One, 7, e33598. Maranville, J. C., & Cox, N. J. (2015). Pharmacogenomic variants have larger effect sizes than genetic variants associated with other dichotomous complex traits. The Pharmacogenomics Journal, 16, 388. Martin, A. K., & Mowry, B. (2015). Increased rare duplication burden genomewide in patients with treatment-resistant schizophrenia. Psychological Medicine, 1–8. Mata, I., Madoz, V., Arranz, M. J., Sham, P., & Murray, R. M. (2001). Olanzapine: Concordant response in monozygotic twins with schizophrenia. The British Journal of Psychiatry, 178, 86. McClay, J. L., Adkins, D. E., Aberg, K., Stroup, S., Perkins, D. O., Vladimirov, V. I., et al. (2011). Genome-wide pharmacogenomic analysis of response to treatment with antipsychotics. Molecular Psychiatry, 16, 76–85. McGrath, J., Saha, S., Chant, D., & Welham, J. (2008). Schizophrenia: A concise overview of incidence, prevalence, and mortality. Epidemiologic Reviews, 30, 67–76. Meltzer, H. Y. (1997). Treatment-resistant schizophrenia—The role of clozapine. Current Medical Research and Opinion, 14, 1–20. Moghaddam, B., & Javitt, D. (2012). From revolution to evolution: The glutamate hypothesis of schizophrenia and its implication for treatment. Neuropsychopharmacology, 37, 4–15. Mouaffak, F., Kebir, O., Chayet, M., Tordjman, S., Vacheron, M. N., Millet, B., et al. (2011). Association of disrupted in schizophrenia 1 (DISC1) missense variants with ultra-resistant schizophrenia. The Pharmacogenomics Journal, 11, 267–273. Mouchlianitis, E., McCutcheon, R., & Howes, O. D. (2016). Brain-imaging studies of treatment-resistant schizophrenia: A systematic review. Lancet Psychiatry, 3, 451–463. National Collaborating Centre for Mental Health. (2014). Psychosis and schizophrenia in adults: Treatment and management. . Need, A. C., Keefe, R. S., Ge, D., Grossman, I., Dickson, S., McEvoy, J. P., et al. (2009). Pharmacogenetics of antipsychotic response in the CATIE trial: A candidate gene analysis. European Journal of Human Genetics, 17, 946–957. Nielsen, J., Young, C., Ifteni, P., Kishimoto, T., Xiang, Y.-T., Schulte, P. F. J., et al. (2016). Worldwide differences in regulations of clozapine use. CNS Drugs, 30, 149–161. Owen, M. J., Sawa, A., & Mortensen, P. B. (2016). Schizophrenia. Lancet, 388, 86–97. Pardin˜as, A. F., Holmans, P., Pocklington, A. J., Escott-Price, V., Ripke, S., Carrera, N., et al. (2018). Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nature Genetics, 50, 381–389. Rees, E., Walters, J. T., Georgieva, L., Isles, A. R., Chambert, K. D., Richards, A. L., et al. (2014). Analysis of copy number variations at 15 schizophreniaassociated loci. The British Journal of Psychiatry, 204, 108–114. Ripke, S., Sanders, A. R., Kendler, K. S., Levinson, D. F., Sklar, P., Holmans, P. A., et al. (2011). Genome-wide association study identifies five new schizophrenia loci. Nature Genetics, 43, 969. Ruderfer, D. M., Charney, A. W., Readhead, B., Kidd, B. A., Kahler, A. K., Kenny, P. J., et al. (2016). Polygenic overlap between schizophrenia risk and antipsychotic response: A genomic medicine approach. The Lancet Psychiatry, 3, 350–357. Schizophrenia Working Group of the Psychiatric Genomics Consortium. (2014). Biological insights from 108 schizophrenia-associated genetic loci. Nature, 511, 421–427. Schwieler, L., Linderholm, K. R., Nilsson-Todd, L. K., Erhardt, S., & Engberg, G. (2008). Clozapine interacts with the glycine site of the NMDA receptor: Electrophysiological studies of dopamine neurons in the rat ventral tegmental area. Life Sciences, 83, 170–175. Sham, P. C., & Purcell, S. M. (2014). Statistical power and significance testing in large-scale genetic studies. Nature Reviews Genetics, 15, 335–346. Skene, N. G., Bryois, J., Bakken, T. E., Breen, G., Crowley, J. J., Gaspar, H. A., et al. (2018). Genetic identification of brain cell types underlying schizophrenia. Nature Genetics, 50, 825–833. Suzuki, T., Remington, G., Mulsant, B. H., Uchida, H., Rajji, T. K., Graff-Guerrero, A., et al. (2012). Defining treatment-resistant schizophrenia and response to antipsychotics: A review and recommendation. Psychiatry Research, 197, 1–6. Teo, C., Zai, C., Borlido, C., Tomasetti, C., Strauss, J., Shinkai, T., et al. (2012). Analysis of treatment-resistant schizophrenia and 384 markers from candidate genes. Pharmacogenetics and Genomics, 22, 807–811. Tiihonen, J., Mittendorfer-Rutz, E., Majak, M., Mehtala, J., Hoti, F., Jedenius, E., et al. (2017). Real-world effectiveness of antipsychotic treatments in a nationwide cohort of 29823 patients with schizophrenia. JAMA Psychiatry, 74, 686–693. van den Oord, E. J. C. G., Chan, R. F., & Aberg, K. A. (2018). Successes and challenges in precision medicine in psychiatry successes and challenges in precision medicine in psychiatry research. JAMA Psychiatry, 75, 1269–1270. Visscher, P. M., Brown, M. A., McCarthy, M. I., & Yang, J. (2012). Five years of GWAS discovery. American Journal of Human Genetics, 90, 7–24. Visscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A., et al. (2017). 10 years of GWAS discovery: Biology, function, and translation. American Journal of Human Genetics, 101, 5–22. Wang, Q., Man Wu, H., Yue, W., Yan, H., Zhang, Y., Tan, L., et al. (2018). Effect of damaging rare mutations in synapse-related gene sets on response to short-term antipsychotic medication in Chinese patients with schizophrenia: A randomized clinical trial. JAMA Psychiatry, 75, 1261–1269.

422

Personalized psychiatry

Wehmeier, P. M., Gebhardt, S., Schmidtke, J., Remschmidt, H., Hebebrand, J., & Theisen, F. M. (2005). Clozapine: Weight gain in a pair of monozygotic twins concordant for schizophrenia and mild mental retardation. Psychiatry Research, 133, 273–276. Wimberley, T., Gasse, C., Meier, S. M., Agerbo, E., MacCabe, J. H., & Horsdal, H. T. (2017). Polygenic risk score for schizophrenia and treatmentresistant schizophrenia. Schizophrenia Bulletin, 43, 1064–1069. Wimberley, T., Stovring, H., Sorensen, H. J., Horsdal, H. T., MacCabe, J. H., & Gasse, C. (2016). Predictors of treatment resistance in patients with schizophrenia: A population-based cohort study. Lancet Psychiatry, 3, 358–366. Wray, N. R., Lee, S. H., Mehta, D., Vinkhuyzen, A. A., Dudbridge, F., & Middeldorp, C. M. (2014). Research review: Polygenic methods and their application to psychiatric traits. Journal of Child Psychology and Psychiatry, 55, 1068–1087. Yang, J., Zeng, J., Goddard, M. E., Wray, N. R., & Visscher, P. M. (2017). Concepts, estimation and interpretation of SNP-based heritability. Nature Genetics, 49, 1304. Yu, H., Yan, H., Wang, L., Li, J., Tan, L., Deng, W., et al. (2018). Five novel loci associated with antipsychotic treatment response in patients with schizophrenia: A genome-wide association study. Lancet Psychiatry, 5, 327–338. Zhang, J. P., Lencz, T., Geisler, S., DeRosse, P., Bromet, E. J., & Malhotra, A. K. (2013). Genetic variation in BDNF is associated with antipsychotic treatment resistance in patients with schizophrenia. Schizophrenia Research, 146, 285–288. Zhang, J. P., Lencz, T., & Malhotra, A. K. (2010). D2 receptor genetic variation and clinical response to antipsychotic drug treatment: A meta-analysis. The American Journal of Psychiatry, 167, 763–772. Zhang, J. P., & Malhotra, A. K. (2011). Pharmacogenetics and antipsychotics: Therapeutic efficacy and side effects prediction. Expert Opinion on Drug Metabolism & Toxicology, 7, 9–37. Zhang, J. P., Robinson, D., Yu, J., Gallego, J., Fleischhacker, W. W., Kahn, R. S., et al. (2018). Schizophrenia polygenic risk score as a predictor of antipsychotic efficacy in first-episode psychosis. The American Journal of Psychiatry. https://doi.org/10.1176/appi.ajp.2018.17121363.