Predicting lithium treatment response in bipolar patients using gender-specific gene expression biomarkers and machine learning [version 1; peer review: 1 approved with reservations]

Background: We sought to test the hypothesis that transcriptiome-level genes signatures are differentially expressed between male and female bipolar patients, prior to lithium treatment, in a patient cohort who later were clinically classified as lithium treatment responders. Methods: Gene expression study data was obtained from the Lithium Treatment-Moderate dose Use Study data accessed from the National Center for Biotechnology Information’s Gene Expression Omnibus via accession number GSE4548. Differential gene expression analysis was conducted using the Linear Models for Microarray and RNA-Seq (limma) package and the Random Forests machine learning algorithm in R. Results: In pre-treatment lithium responders, the following genes were found having a greater than 0.5 fold-change, and differentially expressed indicating a male bias: RBPMS2, SIDT2, CDH23, LILRA5, and KIR2DS5; while the female-biased genes were: HLA-H, RPS23, FHL3, RPL10A, NBPF14, PSTPIP2, FAM117B, CHST7, and ABRACL. Conclusions: Using machine learning, we developed a pre-treatment gender-and gene-expression-based predictive model selective for lithium responders with an ROC AUC of 0.92 for men and an ROC AUC of 1 for women.


Introduction
Lithium is the most well-established mood-stabilizer in the practice of psychiatry (Jermain et al., 1991;Landersdorfer et al., 2017).A recent propensity-score adjusted and matched longitudinal cohort-study evaluating the effectiveness of the newer mood stabilizers: olanzapine (n=1477), quetiapine (n=1376), and valproate (n=1670), in comparison to lithium (n=2148), found that patients treated with lithium experienced reduced rates of both unintentional injury and self-harm (Hayes et al., 2016).However, due to lithium's narrow window, 0.5-1.2mEq/mL, of maximal effectiveness and safety (i.e.therapeutic index), Therapeutic Drug Monitoring is the standard-of-care to ensure patient safety in medical practice (Hiemke et al., 2011).Further, divergent clinical response rates have been reported among male and female patients diagnosed with bipolar disorder and treated with lithium (Viguera et al., 2000).
In a 1986, Zetin and colleagues published the results of a study that evaluated four methods for predicting lithium daily dosages, and the final equation resulted in a 147.8mg/day increased dosage-adjustment for male patients (Zetin et al., 1986).Similarly, a later study by Lobeck and colleagues corroborated the 147.8 mg/day male increase dose requirement for the lithium maintenance dose in bipolar patients (Lobeck et al., 1987).However, neither do the current dosing guidelines recommend a gender-based dose adjustment via clinical pharmacometrics, to avoid toxicity, nor are gender-specific gene expression screening panels available to predict lithium efficacy currently available and implemented.
A recent large-scale meta-analysis of human body-tissue gene expression reported that the body organ with the most abundant gender-biased gene expression is the anterior cingulate cortex within the frontal cortex of the brain (Mayne et al., 2016).Thus, these findings suggest that therapeutic drug response may be influenced not only via drug absorption, distribution, metabolism, and elimination, but also within the underlying gene signatures across the human transcriptome and mechanisms of gene-gene interactions that regulate physiology.Beech and colleagues conducted a study to identify gene expression differences from the peripheral blood in patients classified as lithium responders and non-responders (Beech et al., 2014).However, the study reported that no significant gender-biased gene expression differences were found (p-value=0.941) in patients who were randomized to optimal therapy (control), defined as one FDA-approved mood stabilizer, versus patients treated with lithium plus optimal therapy (Beech et al., 2014).Despite these initially reported findings, a recent study by Labonté and colleagues, which used RNA-Seq to evaluate the transcriptome in patients diagnosed with major depressive disorder (MDD), concluded that gender dimorphism exists at the transcriptome-level in MDD patients and that gender-specific treatments should be investigated (Labonté et al., 2017).
Therefore, there is an urgent clinical need to improve behavioral healthcare by understanding gene expression variability that may lead to personalizing medicine in patients with mania.These findings may improve prediction of clinical drug response of lithium prior to initiating pharmacotherapy in patients with bipolar or schizoaffective disorders, who cannot risk drug inefficacy for obvious safety reasons.Therefore, the overall aim for our study is to define gender-specific transcriptional-level regulators of lithium treatment response that may influence treatment of bipolar or schizoaffective disorders.We will test the hypothesis that biologically plausible gene expression differences exist, prior to lithium treatment, in patients diagnosed with bipolar disorder in the following three patient subgroups: (1) male and female patients who were later clinically classified as lithium treatment responders; (2) male-responders versus male-non-responders; (3) female-responders versus female-non-responders.(Beech et al., 2014).From the original 120 peripheral blood samples used to generate probe and gene expression profiles, from patients diagnosed with bipolar disorder, the clinical phenotype of being either a treatment-responder or non-responder was assessed using the Clinical Global Impression Scale for Bipolar Disorder-Severity (CGI-BP-S) (Spearing et al., 1997).

Study design
To assess for gender-specific differential gene signatures, in our first analysis we grouped patients based on gender alone and not on any other variables (i.e.optimal treatment versus lithium, or responder versus non-responder status).From the results of the genderspecific transcriptome signatures in first analysis, we set the top two-hundred and fifty genes as controls that would be excluded from all results that would be reported in subsequent gene expression analyses to identify genes with lithium-specific gene expression differences between genders associated with response to lithium treatment.In our second analysis, we only selected patients who were classified as lithium treatment-responders, at baseline, and the results from the gene expression differences are reported excluding the sex-specific control genes identified in the first experiment.In our third and fourth analyses, we compared: male-responders vs. male non-responders, and female-responders vs. female non-responders, respectively.

Gene expression analysis
Differential gene expression analysis of the microarray data was conducted using the Empirical Bayes method implemented within the limma package (version 3.34.5)and utilizes the Biobase package (version 2.38.0) which both run within the R for Statistical Programming environment (version 3.4.3;R Foundation for Statistical Computing, Vienna, Austria) (Ritchie et al., 2015;Team, 2013).Due to multiple testing of the peripheral blood transcriptome,the False-Discovery Rate was adjusted using the Benjamini-Hochberg method.The Decision Tree and Random Forests machine learning algorithms were used to assess gender using transcriptional signatures and for predictive modeling using the discovered microarray genes to select for the genderspecific lithium responders.A p-value of less 0.05 was considered to be statistically significant and a differential gene expression threshold of 0.5 was used and reported during the machine learning process.Further methods detailing the Random Forests decision processes for male-and female-responders are located in Supplementary File 1.

Results
Table 1 provides the patient age and sample sizes used during subgroup analyses.In our first analysis, which aimed to group patients based on gender alone and not based on clinical variables detailed in the original study, data-driven gene analytics identified four female-labeled patient samples with gene expression levels similar to that found in male patients for the following Y-chromosome genes: RPS4Y1, EIF1AY, KDM5D, RPS4Y2; and the XIST gene located on the X-chromosome.Therefore, all subsequent hypothesis-testing were analyzed with the updated male-gender classification for the following NCBI GEO patient samples: GSM1105526 (baseline lithium-non-responder), GSM1105528 (1-month lithium-non-responder), GSM1105546 (baseline lithium-non-responder), and GSM1105548 (1-month lithium-non-responder). Figure 1 illustrates the gene expression findings resulting in re-assignment for the aforementioned patient samples from females to males using a decision-tree approach that evaluated if the RPS4Y1 gene had an expression level of greater than or equal to 9.6 resulting in: yes=male (31%) and no=female (69%).After proceeding with the machine learning analysis of both the 'training' and 'validation' datasets, the final 'test' dataset resulted in the following diagnostic test evaluation parameters: Sensitivity=100% (95% C.I. 66.37%-100.00%),Specificity=100% (95% C.I. 78.20%-100.00%),and an area under the receiver operator characteristic (ROC) curve of 1. Figure 2 illustrates the variable importance plots used in the machine learning process.
Table 2 provides the results for the gender-specific differentially expressed genes from the entire study population using a fold-change (FC) threshold of 0.5.A total of five genes met the a priori FC requirements and were found to be RPS4Y1, EIF1AY, KDM5D, RPS4Y2, and EIF1AY.These five down-regulated malebiased genes were all found on the Y-chromosome.Contrastingly, a total of 10 upregulated female-biased genes were found to be: XIST, S100P, IFIT3, TNFAIP6, IFITM3, IFIT2, CHURC1, ANXA3, ADM, and PROK2.The RPS4Y1 gene in males  and the XIST gene (FC=1.7615,p=2.98E-36), found   on the X-chromosome, in females resulted in the greatest expression changes between genders.The male-favored genes resulted in a larger expression change than compared to the females.
Table 3 provides the results for the differentially expressed genes that were found between male and female responders prior to initiation of lithium and optimal therapy, meeting the FC criteria of at least 0.5.In male lithium responders, we found 5 differentially expressed and down-regulated genes while the RNA binding protein with multiple splicing 2 (RBPMS2) gene ranked with the greatest FC of -1.351 (unadjusted p=0.00111).Whereas, 9 up-regulated genes were associated with female lithium responders, with greatest expression change being the major histocompatibility complex class-1-H (HLA-H) at 1.602 (unadjusted p-value=0.00099).The neuroblastoma breakpoint family member-14 (NBPF14) gene met the Benjamani-Hochberg adjusted p-value criteria and resulted with an expression change of 0.586 (adjusted p=0.0462).Figure 3 illustrates the heat-map and dendrogram overview of the two-way unsupervised hierarchical cluster analysis of the reported differentially expressed genes among male and female responders to lithium therapy at baseline that correspond to values reported in Table 3.
Using the baseline blood sample microarray data, the predictive modeling results for identifying lithium-responders from the complete study population of male and female controls and treatment samples, resulted in a validation/test sample cohort for males of: Sensitivity=95.83%(95% C.I. 78.88%-99.89%),Specificity=not calculated due sample size of test dataset, and an ROC curve AUC = 0.92 using the RBPMS2 and LILRA5 genes.Likewise, in the test dataset for females: Sensitivity=91.67%(95% C.I. 61.52%-99.79%),Specificity= not calculated due sample size of test dataset, and an ROC curve AUC = 1 with the ABRACL and NBPF14 genes.Therefore, we developed a 2-gene predictive model for men and likewise for women predicting lithium response in bipolar patients from a general population of bipolar patients using transcriptional signatures at baseline.

Discussion
The purpose of this investigation was to define gender-specific transcriptome-level regulators of lithium treatment response prior to the initiation of lithium treatment.We first established the genderrelevant transcriptional control genes across all study-participant blood samples and specifically to male-and female-responders using a differential gene expression threshold of 0.5.We found this to be adequate and corroborated with similar studies that used a similar threshold for establishing gene transcription signatures (Jansen et al., 2014;Mayne et al., 2016).However, when comparing the male-responders to male non-responders, as well as, the female responders to female non-responders, we set an inclusion fold-change threshold to 0.3.This approach is not unusual, since it is already established that both large and subtle expression changes produce to significant biological and physiological processes (Wurmbach et al., 2002).Our analysis is both hypothesis-generating, and establishes a computational methodology that provides insight to the importance of subgroup analysis in genomic medicine, irrespective of patient sample-sizes.The endgoal of such analyses serves as a testing methodology for establishing gene screening panels to improve personalized medicine in vulnerable and high-risk patient populations.In these patient populations, it is often not feasible to wait for weeks to determine whether a prescribed medication will work and in some cases manic patients are neither able to fully comprehend and be objectively assessed using the CGI-BP-S (Spearing et al., 1997).
When reviewing the heat-map and dendrogram hierarchical cluster analysis patterns, specifically the numerous non-responders clinically-labeled and illustrated in Figure 4, they suggest that the underlying etiology resulting in clinical symptoms (e.g.mania) that led to the diagnosis of bipolar disorder may need re-classification.Further, the subsequent treatments may need to be tailored in data-driven computational psychiatry approaches.
In Figure 4, for the females, the samples in the center cluster  illustrates that a group of patients are clear non-responders while the patients clustered in the far-right are partial-responders, from a molecular perspective.The natural questions that arise are: (1) How to best convert the non-and partial-responders to treatment-responders? (2) Is a behavioral intervention, in this select group of patients,for whom lithium is not effective, the best answer because the symptoms maybe of a different etiology?If indeed the symptoms are of a different etiology (e.g.inflammatory), from the lithium treatment-responders, then other diagnostic (e.g.electrophysiological neuroimaging) tools may be warranted and corresponding most efficacious treatments sought.
Computational psychiatry, as advocated by the National Institute of Mental Health's Research Domain Criteria (RDoC), may need data to drive the classification, diagnosis, and treatment response status, especially in patients with developmental delay, language difficulty, and condition of a potentially different etiology than traditionally taught (Clark et al., 2017;Eugene & Masiak, 2016).Ideally, in such cases, alternative FDA-approved mood stabilizers may be initially selected prior to any pharmacological intervention by simply using a blood test.Perhaps, a gene expression screening panel at baseline, prior to the initiation of lithium and/or other FDA-approved mood stabilizer, may be better in high-risk patient populations.
These findings suggest that when implementing genomic medicine, clinical research teams should move beyond the single-gene approach when screening for treatment responders or nonresponders.This approach is currently the standard when screening for patient toxicity at standard doses in poor or ultrarapid metabolizers; however, as more transcriptional factors are discovered that regulate the cytochrome (CYP) P-450 system of genes, multi-gene pharmacokinetic panels are inevitable and may be included in future Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines.Next, medical management of patients with mania and psychosis either with pharmacotherapy and/or behavioral intervention should be tailored to biological  et al., 2015).Further, as a result of lithium not being hepatically metabolized, but rather transported and renally excreted, as well as, the known myriad drug-drug interactions, patient dose selection may benefit from clinical pharmacometrics modeling by board-certified/eligible pharmacologists (Perera et al., 2014;Zetin et al., 1986).This approach may be implemented to ensure drug pharmacokinetic safety.
The limitations of our analysis and in most genetic studies are understandably due to multiple-comparison p-value adjustments and patient sample size (Dudoit et al., 2003).The fundamental aims of our research questions were designed to answer biological questions of gender and clinical response to lithium and not meant to be driven exclusively by multiple comparisons adjusted p-values.This approach has led to various successes in genomic medicine, specifically, in genome-wide association studies; however, understandably, the limitations are thoroughly acknowledged.In reference to patient sample sizes, 9 out of the 28 patients who received lithium and optimal therapy were classified as lithium treatment responders.Further, 30% of men and 33% of women, who were treated with lithium, were found to be responders at the respective gender categories (Beech et al., 2014).However, the strengths of our findings are in the gendergene screening ability for lithium treatment-responders in the general population of 60 patients at baseline, minus the tested responder group.Opportunities exist for prospective clinical trials and application of the methods outlined in this text for other therapeutic agents across several medical specialties.

Conclusion
We explored the Lithium Treatment-Moderate dose Use Study clinical trial gene expression data with the aim of identifying gender-specific transcriptome-level regulators of lithium treatment response.Using machine learning, we successfully developed a pre-treatment gender-and gene-expressionspecific predictive model selective for lithium responders with an ROC AUC of 0.92 for men and an ROC AUC of 1 for women.Further, by using well-established Bayesian statistical methods, to identify differentially expressed genes and then machine learning, we discovered 5-genes selective for men and 9-genes that are selective for women that will inform the physicians and clinical staff of whether the patient will respond to lithium prior to being prescribed the drug.With the small number of patient responders from the clinical trial, our results should be confirmed.Lastly, in an overall context, our results suggest that the methodology used in this analysis may be extended to other therapeutic drug classes and provides insight to the gender-based gene transcriptome differences influencing lithium pharmacodynamics.
The authors stated that their predictive model for lithium responders with an ROC AUC 0.92 for men, and 1 for women.If the prediction accuracy is so significant, what are the potential biological mechanisms beyond these genes?More discussion regarding the biology of those genes should be included in the paper.Once again, if the prediction accuracy is so significant, it is needed a replication study using different data sets?In summary, the authors claimed the prediction model with very high accuracy; it should be included either functional validation of those genes or a replication study population.

2.
Specific comments: Methods -study design, it might be better to use a flow chart to demonstrate the study design. 1.
Methods -study design, please clarify the rationale of filtering out "250" genes.2.

3.
Figure 2: please elaborate the data presented in Figure 2. The key results for each of the four panels should be summarized in Results.

4.
Table 2 and Table 4, the log FC threshold of 0.5 or 0.3 might be too low.The changes in gene expression are very subtle in Table 4.
Limitations of the study should be addressed in Discussion.7. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Figure 1 .
Figure 1.Gene expression levels for the Ribosomal protein S4, Y-linked 1 (RPS4Y1) gene illustrating 4 patient samples as labeled as female and were re-assigned to the male patient gender group.Males (n=41) and Females (n=39).

Figure 2 .
Figure 2. Variable importance ratings of genes selective (above) male lithium responders versus the entire population of treated and untreated patient men and women; and (below) female lithium responders versus the entire population of treated and untreated men and women.

Figure 3 .
Figure 3. Heat-map and dendrogram overview of the two-way unsupervised hierarchical cluster analysis of differentially expressed genes in male (n=3) and female (n=6) lithium responders after filtering out the top 250 differentially expressed genes found gender biased genes.
the work clearly and accurately presented and does it cite the current literature?Partly Is the study design appropriate and is the work technically sound?Partly Are sufficient details of methods and analysis provided to allow replication by others?Partly If applicable, is the statistical analysis and its interpretation appropriate?Partly Are all the source data underlying the results available to ensure full reproducibility?Partly Are the conclusions drawn adequately supported by the results?Partly Competing Interests: No competing interests were disclosed.
DNA microarray data analyzed in this study are originally referenced from the Lithium Treatment-Moderate dose Use Study placed in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) via accession number GSE45484 with the Illumina HumanHT12 V4.0 expression Beadchip GPL10558 platform file to associate gene names and descriptions.The original multisite clinical study recruited patients from Case Western Reserve University, Massachusetts General Hospital, Stanford University, Yale University, and the Universities of: Pittsburgh, Texas Health Science Center at San Antonio, and Pennsylvania

Table 1 . Patient age and sample sizes used during subgroup analyses. Lithium treated patient population
*General mood stabilizers patient population Baseline Mean age S.D. Sample size (n) Gender Mean age S.D. Sample size (n) *Note: United States Food and Drug Administration approved Mood Stabilizers.

Table 3 . Differentially expressed genes between male and female responders prior to Lithium pharmacotherapy with a log fold- change threshold of 0.5. Genes downregulated in male lithium responders
Notes: **The NBPF14 gene reached the Benjamani-Hochberg adjusted p-value.