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Background: Treatment options for poorly differentiated (PDTC) and anaplastic (ATC) thyroid carcinoma are unsatisfactory and prognosis is generally poor. Lenvatinib (LEN), a multi-tyrosine kinase inhibitor targeting fibroblast growth factor receptors (FGFR) 1-4 is approved for advanced radioiodine refractory thyroid carcinoma, but response to single agent is poor in ATC. Recent reports of combining LEN with PD-1 inhibitor pembrolizumab (PEM) are promising. Materials and Methods: Primary ATC (n=93) and PDTC (n=47) tissue samples diagnosed 1997-2019 at five German tertiary care centers were assessed for PD-L1 expression by immunohistochemistry using Tumor Proportion Score (TPS). FGFR 1-4 mRNA was quantified in 31 ATC and 14 PDTC with RNAscope in-situ hybridization. Normal thyroid tissue (NT) and papillary thyroid carcinoma (PTC) served as controls. Disease specific survival (DSS) was the primary outcome variable. Results: PD-L1 TPS≥50% was observed in 42% of ATC and 26% of PDTC specimens. Mean PD-L1 expression was significantly higher in ATC (TPS 30%) than in PDTC (5%; p<0.01) and NT (0%, p<0.001). 53% of PDTC samples had PD-L1 expression ≤5%. FGFR mRNA expression was generally low in all samples but combined FGFR1-4 expression was significantly higher in PDTC and ATC compared to NT (each p<0.001). No impact of PD-L1 and FGFR 1-4 expression was observed on DSS. Conclusion: High tumoral expression of PD-L1 in a large proportion of ATCs and a subgroup of PDTCs provides a rationale for immune checkpoint inhibition. FGFR expression is low thyroid tumor cells. The clinically observed synergism of PEM with LEN may be caused by immune modulation.
Background: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, Major Depressive Disorder (MDD), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over-as well as underestimation of true model accuracy.
Methods: Addressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression.
Findings Training and testing a total of 2.4 million ML models, we find accuracies for diagnostic classification between 48.1% and 62.0%. Multimodal data integration of all neuroimaging modalities does not improve model performance. Similarly, training ML models on individuals stratified based on age, sex, or remission status does not lead to better classification. Even under simulated conditions of perfect reliability, performance does not substantially improve. Importantly, model error analysis identifies symptom severity as one potential target for MDD subgroup identification.
Interpretation: Although multivariate neuroimaging markers increase predictive power compared to univariate analyses, single-subject classification – even under conditions of extensive, best-practice Machine Learning optimization in a large, harmonized sample of patients diagnosed using state-of-the-art clinical assessments – does not reach clinically relevant performance. Based on this evidence, we sketch a course of action for Precision Psychiatry and future MDD biomarker research.