Biology subjects

Ott, P.

Publications and source records attributed to Ott, P..

3 recordsLinked to original sources

bioRxiv2025

Mechanisms of tumor persistence in metastatic melanoma following successful immunotherapy

Tumor dormancy is thought to enable cancer recurrence, but the settings and mechanisms of dormancy in patients are poorly characterized. Both immunogenic tumor mass dormancy, involving continued tumor cell proliferation and balanced immune-mediated cell death, and cell-level quiescence have been observed in preclinical models. Here, we demonstrate the existence of mass dormancy rather than quiescence in long-term stable residual lesions in melanoma patients treated with immune checkpoint inhibitors (ICI). In a large stable lesion subjected to detailed spatial profiling, the proportion of proliferating tumor cells is similar to that in site-matched tumors from patients progressing after ICI. Residual stable lesions from other patients, judged to contain only scar tissue upon clinical pathology review, also contained nests of dividing tumor cells surrounded by active immune cells. These findings demonstrate the presence of viable tumor cells and mass dormancy in persistent stable lesions, a finding with implications for disease monitoring and management.

cancer biology↗

bioRxiv2019

Benchmarking Computational Methods for Estimating the Pathogenicity of Wilson’s Disease Mutations

Genetic variations in the gene encoding the copper-transport protein ATP7B are the primary cause of Wilsons disease. Controversially, clinical prevalence seems much smaller than prevalence estimated by genetic screening tools, causing fear that many people are undiagnosed although early diagnosis and treatment is essential. To address this issue, we benchmarked 16 state-of-the-art computational disease-prediction methods against established data of missense ATP7B mutations. Our results show that the quality of the methods vary widely. We show the importance of optimizing the threshold of the methods used to distinguish pathogenic from non-pathogenic mutations against data of clinically confirmed pathogenic and non-pathogenic mutations. We find that most methods use thresholds that predict too many ATP7B mutations to be pathogenic. Thus, our findings explain the current controversy on Wilsons disease prevalence, because meta analysis and text search methods include many computational estimates that lead to higher disease prevalence than clinically observed. Since proteins differ widely, a one-size-fits-all threshold for all proteins cannot distinguish efficiently pathogenic and non-pathogenic mutations, as shown here. We also show that amino acid changes with small evolutionary substitution probability, mainly due to amino acid volume, are more associated with disease, implying a pathological effect on the conformational state of the protein, which could affect copper transport or ATP recognition and hydrolysis. These findings may be a first step towards a more quantitative genotype-phenotype relationship of Wilsons disease.

biochemistry↗

bioRxiv2019

On the development of sleep states in the first weeks of life

Human newborns spend up to 18 hours sleeping. The organization of their sleep differs immensely from adult sleep, and its quick maturation and fundamental changes correspond to the rapid cortical development at this age. Manual sleep classification is specifically challenging in this population given major body movements and frequent shifts between vigilance states; in addition various staging criteria co-exist. In the present study we utilized a machine learning approach and investigated how EEG complexity and sleep stages evolve during the very first weeks of life. We analyzed 42 full-term infants which were recorded twice (at week two and five after birth) with full polysomnography. For sleep classification EEG signal complexity was estimated using multi-scale permutation entropy and fed into a machine learning classifier. Interestingly the babys brain signal complexity (and spectral power) revealed huge developmental changes in sleep in the first 5 weeks of life, and were restricted to NREM (\"quiet\") and REM (\"active sleep\") states with little to no changes in state wake. Data demonstrate that our classifier performs well over chance (i.e., >33% for 3-class classification) and reaches almost human scoring accuracy (60% at week-2, 73% at week-5). Altogether, these results demonstrate that characteristics of newborn sleep develop rapidly in the first weeks of life and can be efficiently identified by means of machine learning techniques.\n\nAuthor summaryThe organization of newborn sleep differs from adult sleep, and its ongoing maturation over time corresponds with cortical development. However, sleep scoring in this population is challenging given frequent artifacts in polysomnography (PSG) and absence of established staging criteria which results in low inter-scorer reliability. To investigate changes in the early brain activity, we analyzed large sample of newborn data at week 2 and 5 after birth. First we evaluated sleep that was previously scored visually, in terms of both entropy and oscillatory power. Next we accessed the performance of automatic sleep scoring based on machine learning compared with conventional, manual scoring. We observed clear developmental changes on the brain-level in the first 5 weeks of life in human newborns. These changes were limited to \"quiet\" NREM and \"active\" REM sleep. Also our classifier data demonstrated that we can classify well above chance and similar to human scorers using multi-scale permutation entropy (and just 6 EEG and 5 physiological channels).

neuroscience↗