Predictive markers

Definition

Predictive markers are biological characteristics that are objectively measured and evaluated to predict the course of a disease or a response to a therapeutic intervention. Examples include the presence of a particular gene variant, patterns of gene expression or levels of a particular protein in body fluids.

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    Major depressive disorder is heritable and a leading cause of disability. Cognitive behavior therapy is an effective treatment for major depression. By quantifying genetic risk scores based on common genetic variants, the aim of this report was to explore the utility of psychiatric and cognitive trait genetic risk scores, for predicting the response of 894 adults with major depressive disorder to cognitive behavior therapy. The participants were recruited in a psychiatric setting, and the primary outcome score was measured using the Montgomery Åsberg Depression Rating Scale-Self Rated. Single-nucleotide polymorphism genotyping arrays were used to calculate the genomic risk scores based on large genetic studies of six phenotypes: major depressive disorder, bipolar disorder, attention-deficit/hyperactivity disorder, autism spectrum disorder, intelligence, and educational attainment. Linear mixed-effect models were used to test the relationships between the six genetic risk scores and cognitive behavior therapy outcome. Our analyses yielded one significant interaction effect (B = 0.09, p < 0.001): the autism spectrum disorder genetic risk score correlated with Montgomery Åsberg Depression Rating Scale-Self Rated changes during treatment, and the higher the autism spectrum disorder genetic load, the less the depressive symptoms decreased over time. The genetic risk scores for the other psychiatric and cognitive traits were not related to depressive symptom severity or change over time. Our preliminary results indicated, as expected, that the genomics of the response of patients with major depression to cognitive behavior therapy were complex and that future efforts should aim to maximize sample size and limit subject heterogeneity in order to gain a better understanding of the use of genetic risk factors to predict treatment outcome.

    • Evelyn Andersson
    • , James J. Crowley
    • , Nils Lindefors
    • , Brjánn Ljótsson
    • , Erik Hedman-Lagerlöf
    • , Julia Boberg
    • , Samir El Alaoui
    • , Robert Karlsson
    • , Yi Lu
    • , Manuel Mattheisen
    • , Anna K. Kähler
    • , Cecilia Svanborg
    • , David Mataix-Cols
    • , Simon Mattsson
    • , Erik Forsell
    • , Viktor Kaldo
    • , Martin Schalling
    • , Catharina Lavebratt
    • , Patrick F. Sullivan
    •  & Christian Rück
  • News and Views |

    In a cohort of 100 patients with neuroendocrine cancer, the use of NETest enabled earlier prediction of tumour progression and resulted in a reduction in the frequency of follow-up procedures. These outcomes are exciting and promising, but limited in value by the heterogeneity of the study cohort and by suboptimal assay sensitivity and specificity.

    • Guido Rindi
    •  & Bertram Wiedenmann