Biology subjects

Kortuem, K.

Publications and source records attributed to Kortuem, K..

3 recordsLinked to original sources

bioRxiv2026

Profound CD4+ T-Cell Reprogramming by Melphalan-Driven Oxidative Stress in High-Risk Multiple Myeloma

T cell-based immunotherapies have become central to the treatment of multiple myeloma (MM), yet their efficacy depends on the functionality of endogenous T cells. How cumulative treatment exposure, particularly high-dose melphalan, together with disease-intrinsic high-risk features shapes T-cell composition and immune competence remains incompletely understood. Here, we analyzed T cell composition and function in bone marrow (BM) and peripheral blood (PB) samples from MM patients across different stages of their treatment journey using flow cytometry (BM, n=162; PB, n=1,733), single-cell RNA sequencing (n=19), and cytotoxicity assays (n=20). We reveal reduced overall T cell frequencies and CD4+/CD8+ T cell ratio, associated with lines of therapy and driven in part by depletion of naive CD4+ T cells in gene-expression defined high risk (HR) disease. Among therapeutic agents, melphalan exerted the strongest effects on T cell populations and induced pronounced redox stress in both T cells and myeloma cell lines. This oxidative stress signature was enriched in HR patients and was reversible with N-acetyl-L-cysteine treatment. Together, these findings identify immune dysregulation as a defining feature of HR MM that extends beyond tumor-intrinsic genomic alterations and is further shaped by treatment-induced remodeling of the BM microenvironment. Given the association between higher CD4+ T cell numbers and improved CAR-T cell outcomes, our data highlight the translational importance of treatment sequencing, particularly in HR MM. One Sentence SummaryMelphalan-induced redox stress depletes CD4+ naive T cells, particularly in patients with high-risk multiple myeloma.

Cancer Biology↗

bioRxiv2019

Self-supervised retinal thickness prediction enables deep learning from unlabeled data to boost classification of diabetic retinopathy

Access to large, annotated samples represents a considerable challenge for training accurate deep-learning models in medical imaging. While current leading-edge transfer learning from pre-trained models can help with cases lacking data, it limits design choices, and generally results in the use of unnecessarily large models. We propose a novel, self-supervised training scheme for obtaining high-quality, pre-trained networks from unlabeled, cross-modal medical imaging data, which will allow for creating accurate and efficient models. We demonstrate this by accurately predicting optical coherence tomography (OCT)-based retinal thickness measurements from simple infrared (IR) fundus images. Subsequently, learned representations outperformed advanced classifiers on a separate diabetic retinopathy classification task in a scenario of scarce training data. Our cross-modal, three-staged scheme effectively replaced 26,343 diabetic retinopathy annotations with 1,009 semantic segmentations on OCT and reached the same classification accuracy using only 25% of fundus images, without any drawbacks, since OCT is not required for predictions. We expect this concept will also apply to other multimodal clinical data-imaging, health records, and genomics data, and be applicable to corresponding sample-starved learning problems.

bioinformatics↗

bioRxiv2019

The Moorfields AMD Database Report 2 - Fellow Eye Involvement with Neovascular Age-related Macular Degeneration

Background/AimsNeovascular age-related macular degeneration (nAMD) is frequently bilateral, and previous reports on fellow eyes have assumed sequential treatment after a period of treatment of the first eye only. The aim of our study was to analyse baseline characteristics and visual acuity (VA) outcomes of fellow eye involvement with nAMD, specifically differentiating between sequential and non-sequential (due to macular scarring in the first eye) anti-vascular endothelial growth factor treatment and timelines for fellow eye involvement.\n\nMethodsRetrospective, electronic medical record database study of the Moorfields AMD database of 8174 eyes/120,756 single entries with data extracted between October 21, 2008 and August 9, 2018. The dataset for analysis consisted of 1180 sequential, 413 nonsequential, and 1110 unilateral eyes.\n\nResultsMean VA of sequentially treated fellow eyes at baseline was significantly higher (62{+/-}13), VA gain over two years lower (0.65{+/-}14), and proportion of eyes with good VA ([≥]20/40 or 70 letters) higher (46%) than the respective first eyes (baseline VA 54{+/-}16, VA gain at two years 5.6{+/-}15, percentage of eyes with good VA 38%). Non-sequential fellow eyes showed baseline characteristics and VA outcomes similar to first eyes. Fellow eye involvement rate was 32% at two years, and median time interval to fellow eye involvement was 71 (IQR 27-147) weeks.\n\nConclusionThis reports shows sequentially treated nAMD fellow eyes have better baseline and final VA than non-sequentially treated eyes after 2 years of treatment. Sequentially treated eyes also had a greater proportion with good VA after 2 years of treatment.\n\nPRECISDepending on age, fellow eye involvement occurs in 32% of patients with neovascular AMD by two years. Fellow eyes generally maintain better vision, except in cases where late-stage disease in the first eye was untreated.

epidemiology↗