Predictive modeling of treatment resistant depression using data from STAR*D and an independent clinical study

<div><p>Identification of risk factors of treatment resistance may be useful to guide treatment selection, avoid inefficient trial-and-error, and improve major depressive disorder (MDD) care. We extended the work in predictive modeling of treatment resistant depression (TRD) via partition of the data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) cohort into a training and a testing dataset. We also included data from a small yet completely independent cohort RIS-INT-93 as an external test dataset. We used features from enrollment and level 1 treatment (up to week 2 response only) of STAR*D to explore the feature space comprehensively and applied machine learning methods to model TRD outcome at level 2. For TRD defined using QIDS-C₁₆ remission criteria, multiple machine learning models were internally cross-validated in the STAR*D training dataset and externally validated in both the STAR*D testing dataset and RIS-INT-93 independent dataset with an area under the receiver operating characteristic curve (AUC) of 0.70–0.78 and 0.72–0.77, respectively. The upper bound for the AUC achievable with the full set of features could be as high as 0.78 in the STAR*D testing dataset. Model developed using top 30 features identified using feature selection technique (k-means clustering followed by <i>χ</i>² test) achieved an AUC of 0.77 in the STAR*D testing dataset. In addition, the model developed using overlapping features between STAR*D and RIS-INT-93, achieved an AUC of > 0.70 in both the STAR*D testing and RIS-INT-93 datasets. Among all the features explored in STAR*D and RIS-INT-93 datasets, the most important feature was early or initial treatment response or symptom severity at week 2. These results indicate that prediction of TRD prior to undergoing a second round of antidepressant treatment could be feasible even in the absence of biomarker data.</p></div

Summary of cerebral amyloid deposition florbetapir PET quantitative traits—SNPs with uncorrected p-value less than 1x10^-6.

<p>Chr, chromosome; A₁, first allele code; A₂, second allele code</p><p>^a Representative SNPs with uncorrected p < 1x10^-6 in any of the Cerebral amyloid deposition GWAS Analyses</p><p>^b Build 37, assembly hg19</p><p>^c based on 2012 Apr release of 1000genome and all population</p><p>^d Fixed-effects meta-analysis p-value</p><p>^e Beta coefficient of for the SNP assuming additive genetic model</p><p>Top variants were clumped using parameters—clump-p1 0.000001—clump-p2 0.05—clump-r2 0.2—clump-range entrez.gene.map—clump-range-border 20.</p

Variations in the <i>FRA10AC1</i> Fragile Site and 15q21 Are Associated with Cerebrospinal Fluid Aβ_1-42 Level

<div><p>Proteolytic fragments of amyloid and post-translational modification of tau species in Cerebrospinal fluid (CSF) as well as cerebral amyloid deposition are important biomarkers for Alzheimer’s Disease. We conducted genome-wide association study to identify genetic factors influencing CSF biomarker level, cerebral amyloid deposition, and disease progression. The genome-wide association study was performed via a meta-analysis of two non-overlapping discovery sample sets to identify genetic variants other than <i>APOE</i> ε4 predictive of the CSF biomarker level (Aβ_1–42, t-Tau, p-Tau_181P, t-Tau:Aβ_1–42 ratio, and p-Tau_181P:Aβ_1–42 ratio) in patients enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. Loci passing a genome-wide significance threshold of <i>P</i> < 5 x 10⁻⁸ were followed-up for replication in an independent sample set. We also performed joint meta-analysis of both discovery sample sets together with the replication sample set. In the discovery phase, we identified variants in <i>FRA10AC1</i> associated with CSF Aβ_1–42 level passing the genome-wide significance threshold (directly genotyped SNV rs10509663 <i>P</i>_FE = 1.1 x 10⁻⁹, imputed SNV rs116953792 <i>P</i>_FE = 3.5 x 10⁻¹⁰), rs116953792 (<i>P</i>_one-sided = 0.04) achieved replication. This association became stronger in the joint meta-analysis (directly genotyped SNV rs10509663 <i>P</i>_FE = 1.7 x 10⁻⁹, imputed SNV rs116953792 <i>P</i>_FE = 7.6 x 10⁻¹¹). Additionally, we identified locus 15q21 (imputed SNV rs1503351 <i>P</i>_FE = 4.0 x 10⁻⁸) associated with CSF Aβ_1–42 level. No other variants passed the genome-wide significance threshold for other CSF biomarkers in either the discovery sample sets or joint analysis. Gene set enrichment analyses suggested that targeted genes mediated by miR-33, miR-146, and miR-193 were enriched in various GWAS analyses. This finding is particularly important because CSF biomarkers confer disease susceptibility and may be predictive of the likelihood of disease progression in Alzheimer’s Disease.</p></div

Inrich Analysis Results (P_corrected < 0.05).

<p>Inrich Analysis Results (P_corrected < 0.05).</p

Basic demographic information of the three ADNI CSF Sample Sets.

<p>Basic demographic information of the three ADNI CSF Sample Sets.</p

Regional Plot for the CSF Aβ_1–42 Loci.

<p><b>(A) <i>FRA10AC1</i> (B) 15q21</b> Association results (-log10 p) are plotted for all single nucleotide polymorphisms (SNPs) passing quality control. Chromosome position is plotted with reference to the NCBI build 37. Recombination rate as estimated from the HapMap Project is plotted in light blue. SNPs are color coded according to the LD measure (r²) with reference SNP based on the reference panel of CEU population from the 1000 Genome Project (March 2012 release). SNP annotation for all 1000GP SNPs are represented by the annotation categories: framestop (triangle), splice (triangle), non-synonymous (inverted triangle), synonymous (square), UTR (square), TFBScons (star), MCS44 Placental (square with diagonal lines) and none-of-the-above (filled circle).</p

Predictive modeling of treatment resistant depression using data from STAR*D and an independent clinical study - Fig 5

<p>variable of importance in statistical learning approaches for outcomes defined by (A) remission status (B) responder status. In both cases, the outcomes were defined using QIDS-C₁₆. SFHS: Short Form Health Survey (SF-12); WSAS: The Work and Social Adjustment Scale; *from PRISE: The Patient Rated Inventory of Side Effects, which collected symptoms one had experienced in the past week. Those symptoms may or may not have been caused by the treatment.</p

Receiver operating characteristic curves in training and test dataset (STAR*D) using the full set of features, top n features (n ~ 30), and the overlapping features where remission status was used to define TRD (STAR*D remission status was defined using QIDS-C₁₆ data, and RIS-INT-93 remission status was defined using HAM-D₁₇).

<p>Receiver operating characteristic curves in training and test dataset (STAR*D) using the full set of features, top n features (n ~ 30), and the overlapping features where remission status was used to define TRD (STAR*D remission status was defined using QIDS-C₁₆ data, and RIS-INT-93 remission status was defined using HAM-D₁₇).</p

Model performance (outcome defined by remission using QIDS-C₁₆) in the STAR*D testing dataset and RIS-INT-93.

<p>Model performance (outcome defined by remission using QIDS-C₁₆) in the STAR*D testing dataset and RIS-INT-93.</p

AUC for models containing top 2 to 75 representative predictors from k-means cluster (k = 75) was plotted against the number of predictors for each of the machine learning methods in the STAR*D training and test datasets, respectively.

<p>Remission status was used to define TRD using QIDS-C₁₆ data.</p