Periodic Reporting for period 1 - ESTREA (Exome Sequencing in stages of Treatment REsistance to Antidepressants)
Reporting period: 2018-10-01 to 2020-03-31
Depression is the second-largest contributor to disability or health loss and a leading underlying cause for the 128,000 suicide deaths that occur each year in the European Region. The number of people with depressive disorders in the Region amounted to 40 million in 2015 (World Health Organization 2017).
More than 40 antidepressant drugs are available, however response rates to antidepressants are often unsatisfactory (complete symptom remission to the first treatment in about 1/3 of patients). No reliable predictors of response are available and both treatment choice and monitoring are guided by clinical observation only. Previous evidence suggests that antidepressant response has a genetic basis, therefore, the present project aimed to identify genetic variants associated with non-response and resistance to pharmacological treatments in patients with depression in order to provide an objective tool to identify patients at risk since the baseline evaluation. This is indeed the first step for the personalisation of treatments in depression and improvement of response rates. Compared to previous studies, this project investigated the role of not only common genetic variants (generally defined as those observed in > 1% of the population), but also rare variants in coding regions, which are those regions of our genome coding for proteins and therefore hypothetically critical in controlling the functioning of biological processes.
In detail, this project aimed to address the following research questions:
1) Is it possible to identify genetic predictors of non-response and resistance (failure of more than two treatments) to antidepressant medications?
2) Are these genetic predictors concentrated in some genes or groups of genes functionally related among each other?
3) How these genetic predictors interact with clinical risk factors of treatment resistance?
4) How can genetic predictors of antidepressant resistance be translated into clinical recommendations for the personalisation of treatment prescription?
More than 40 antidepressant drugs are available, however response rates to antidepressants are often unsatisfactory (complete symptom remission to the first treatment in about 1/3 of patients). No reliable predictors of response are available and both treatment choice and monitoring are guided by clinical observation only. Previous evidence suggests that antidepressant response has a genetic basis, therefore, the present project aimed to identify genetic variants associated with non-response and resistance to pharmacological treatments in patients with depression in order to provide an objective tool to identify patients at risk since the baseline evaluation. This is indeed the first step for the personalisation of treatments in depression and improvement of response rates. Compared to previous studies, this project investigated the role of not only common genetic variants (generally defined as those observed in > 1% of the population), but also rare variants in coding regions, which are those regions of our genome coding for proteins and therefore hypothetically critical in controlling the functioning of biological processes.
In detail, this project aimed to address the following research questions:
1) Is it possible to identify genetic predictors of non-response and resistance (failure of more than two treatments) to antidepressant medications?
2) Are these genetic predictors concentrated in some genes or groups of genes functionally related among each other?
3) How these genetic predictors interact with clinical risk factors of treatment resistance?
4) How can genetic predictors of antidepressant resistance be translated into clinical recommendations for the personalisation of treatment prescription?
Description of the work carried out for the four reported research questions:
1) and 2) Genetic markers of non-response (failure of one treatment) and resistance (failure of two or more treatments) to antidepressant medications were studied in a sample of about 1200 patients with major depressive disorder (MDD). Common genetic variants throughout the genome and rare variants in coding regions were obtained using genome-wide genotyping and whole exome sequencing. The distribution of variants predicted to have a functional impact on protein function/levels was compared between response groups, as well as more complex scores reflecting the burden of variants in specific regions, but no gene or group of functionally related genes (pathway) clearly emerged as associated with non-response or resistance. Therefore, the predictive performance of a combination of genes/pathways was tested using machine learning (Figure 1). This approach was able to correctly classify 61-66% of patients with treatment resistance and it showed improvement when considering only patients with genetic profiles in extremes percentiles.
3) Five clinical risk factors were identified as particularly relevant in the risk of antidepressant resistance (duration of the depressive episode, number of previous depressive episodes, suicidal ideation, pessimism and symptoms reflecting lack of interest and poor involvement in activities). The addition of these variables to the genetic predictors increased the proportion of patients correctly identified as treatment-resistant up to 75%.
4) The developed predictive models could be used to guide treatment prescription if effective and well tolerated therapeutic options are available as alternative to standard care (pharmacotherapy). The most suitable alternative to standard care was identified as combined antidepressant pharmacotherapy and psychotherapy such as cognitive-behavioural treatment, since it has good evidence of higher efficacy compared to pharmacotherapy alone and it is not associated with increased side effects, but it has limited availability because of higher costs compared to pharmacotherapy. The cost-effectiveness of using the developed genetic/clinical predictive model of treatment resistance in guiding the prescription of combined pharmacotherapy and psychotherapy vs. pharmacotherapy was tested using the obtained sensitivity/specificity and costs-utilities from publicly available databases; the comparator groups were standard care (pharmacotherapy to all patients) and prescription of combined pharmacotherapy and psychotherapy vs. pharmacotherapy guided by the identified clinical predictors only. The use of clinical predictors only was cost-effective compared to using clinical predictors combined with genetic variables: one quality-adjusted life year (QALY, a year lived in perfect health) improvement costed 2341 GBP in the group guided by clinical predictors and 3937 GBP in the group guided by clinical/genetic predictors (Figure 2). One major contributor to this result was the cost of whole exome sequencing; if the cost of genotyping/sequencing is 100 GBP or less the genetic/clinical model would become cost-effective compared to the clinical one.
The results were disseminated during eight conferences and through two peer-reviewed publications (doi: 10.1038/s41398-020-0738-5 and 10.1097/YIC.0000000000000305) one online preprint (10.1101/2020.03.31.20048538) social networks and the Pharmacogenomics Research Network (PGRN) (https://www.pgrn.org/featured-investigators/chiara-fabbri-phd(opens in new window)).
Replication and extension of the results of this project is planned as part of already funded studies using independent samples of patients with depression (UK Biobank (https://www.ukbiobank.ac.uk(opens in new window)) and a new sample in phase of recruitment by the European Group for the Study of Resistant Depression (GSRD)).
1) and 2) Genetic markers of non-response (failure of one treatment) and resistance (failure of two or more treatments) to antidepressant medications were studied in a sample of about 1200 patients with major depressive disorder (MDD). Common genetic variants throughout the genome and rare variants in coding regions were obtained using genome-wide genotyping and whole exome sequencing. The distribution of variants predicted to have a functional impact on protein function/levels was compared between response groups, as well as more complex scores reflecting the burden of variants in specific regions, but no gene or group of functionally related genes (pathway) clearly emerged as associated with non-response or resistance. Therefore, the predictive performance of a combination of genes/pathways was tested using machine learning (Figure 1). This approach was able to correctly classify 61-66% of patients with treatment resistance and it showed improvement when considering only patients with genetic profiles in extremes percentiles.
3) Five clinical risk factors were identified as particularly relevant in the risk of antidepressant resistance (duration of the depressive episode, number of previous depressive episodes, suicidal ideation, pessimism and symptoms reflecting lack of interest and poor involvement in activities). The addition of these variables to the genetic predictors increased the proportion of patients correctly identified as treatment-resistant up to 75%.
4) The developed predictive models could be used to guide treatment prescription if effective and well tolerated therapeutic options are available as alternative to standard care (pharmacotherapy). The most suitable alternative to standard care was identified as combined antidepressant pharmacotherapy and psychotherapy such as cognitive-behavioural treatment, since it has good evidence of higher efficacy compared to pharmacotherapy alone and it is not associated with increased side effects, but it has limited availability because of higher costs compared to pharmacotherapy. The cost-effectiveness of using the developed genetic/clinical predictive model of treatment resistance in guiding the prescription of combined pharmacotherapy and psychotherapy vs. pharmacotherapy was tested using the obtained sensitivity/specificity and costs-utilities from publicly available databases; the comparator groups were standard care (pharmacotherapy to all patients) and prescription of combined pharmacotherapy and psychotherapy vs. pharmacotherapy guided by the identified clinical predictors only. The use of clinical predictors only was cost-effective compared to using clinical predictors combined with genetic variables: one quality-adjusted life year (QALY, a year lived in perfect health) improvement costed 2341 GBP in the group guided by clinical predictors and 3937 GBP in the group guided by clinical/genetic predictors (Figure 2). One major contributor to this result was the cost of whole exome sequencing; if the cost of genotyping/sequencing is 100 GBP or less the genetic/clinical model would become cost-effective compared to the clinical one.
The results were disseminated during eight conferences and through two peer-reviewed publications (doi: 10.1038/s41398-020-0738-5 and 10.1097/YIC.0000000000000305) one online preprint (10.1101/2020.03.31.20048538) social networks and the Pharmacogenomics Research Network (PGRN) (https://www.pgrn.org/featured-investigators/chiara-fabbri-phd(opens in new window)).
Replication and extension of the results of this project is planned as part of already funded studies using independent samples of patients with depression (UK Biobank (https://www.ukbiobank.ac.uk(opens in new window)) and a new sample in phase of recruitment by the European Group for the Study of Resistant Depression (GSRD)).
This project provides a significant contribution to the state of the art and it has potential impact on future research and clinical practice:
1) The results suggested that there are no individual genes/pathways playing a major role in the risk of antidepressant resistance, but the combined effect from different genomic units plays a role.
2) Predictive models based on genetic variables only do not currently have enough discriminative ability to be translated into clinical applications. Possible approaches to obtain meaningful applications are in patients with no clinical risk factors (who still may develop treatment-resistant depression but there is no clinical clue) and in those having a genetic risk profile in extreme percentiles (high burden of genetic variants in the genes/pathways predictive of resistance).
3) Prediction of the risk of antidepressant resistance based on a combination of genetic and clinical risk variables provides potential clinical application when a more effective treatment option with no increased risk of side effects is available but access is limited because of cost, such as combined pharmacotherapy and psychotherapy.
4) The current cost of genotyping/sequencing is still a limitation in the applicability of genetic biomarkers in clinical practice. At present, clinical predictors of antidepressant resistance are cost-effective compared to genetic/clinical predictors. If the cost of genotyping is 100 GBP or less, the use of genetic predictors would become cost-effective.
1) The results suggested that there are no individual genes/pathways playing a major role in the risk of antidepressant resistance, but the combined effect from different genomic units plays a role.
2) Predictive models based on genetic variables only do not currently have enough discriminative ability to be translated into clinical applications. Possible approaches to obtain meaningful applications are in patients with no clinical risk factors (who still may develop treatment-resistant depression but there is no clinical clue) and in those having a genetic risk profile in extreme percentiles (high burden of genetic variants in the genes/pathways predictive of resistance).
3) Prediction of the risk of antidepressant resistance based on a combination of genetic and clinical risk variables provides potential clinical application when a more effective treatment option with no increased risk of side effects is available but access is limited because of cost, such as combined pharmacotherapy and psychotherapy.
4) The current cost of genotyping/sequencing is still a limitation in the applicability of genetic biomarkers in clinical practice. At present, clinical predictors of antidepressant resistance are cost-effective compared to genetic/clinical predictors. If the cost of genotyping is 100 GBP or less, the use of genetic predictors would become cost-effective.