Tumor infiltration by T cells is paramount for effective anti-cancer immune responses. We hypothesized that the T cell receptor (TCR) repertoire of tumor infiltrating T lymphocytes could therefore be ...indicative of the functional state of these cells and determine disease course at different stages in cancer progression. Here we show that the diversity of the TCR of tumor infiltrating T cell at baseline is prognostic in various cancers, whereas the TCR clonality of T cell infiltrating metastatic melanoma pre-treatment is predictive for activity and efficacy of PD1 blockade immunotherapy.
BACKGROUND:Clinical lipid measurements do not show the full complexity of the altered lipid metabolism associated with diabetes mellitus or cardiovascular disease. Lipidomics enables the assessment ...of hundreds of lipid species as potential markers for disease risk.
METHODS:Plasma lipid species (310) were measured by a targeted lipidomic analysis with liquid chromatography electrospray ionization–tandem mass spectrometry on a case-cohort (n=3779) subset from the ADVANCE trial (Action in Diabetes and Vascular DiseasePreterax and Diamicron-MR Controlled Evaluation). The case-cohort was 61% male with a mean age of 67 years. All participants had type 2 diabetes mellitus with ≥1 additional cardiovascular risk factors, and 35% had a history of macrovascular disease. Weighted Cox regression was used to identify lipid species associated with future cardiovascular events (nonfatal myocardial infarction, nonfatal stroke, and cardiovascular death) and cardiovascular death during a 5-year follow-up period. Multivariable models combining traditional risk factors with lipid species were optimized with the Akaike information criteria. C statistics and NRIs were calculated within a 5-fold cross-validation framework.
RESULTS:Sphingolipids, phospholipids (including lyso- and ether- species), cholesteryl esters, and glycerolipids were associated with future cardiovascular events and cardiovascular death. The addition of 7 lipid species to a base model (14 traditional risk factors and medications) to predict cardiovascular events increased the C statistic from 0.680 (95% confidence interval CI, 0.678–0.682) to 0.700 (95% CI, 0.698–0.702; P<0.0001) with a corresponding continuous NRI of 0.227 (95% CI, 0.219–0.235). The prediction of cardiovascular death was improved with the incorporation of 4 lipid species into the base model, showing an increase in the C statistic from 0.740 (95% CI, 0.738–0.742) to 0.760 (95% CI, 0.757–0.762; P<0.0001) and a continuous net reclassification index of 0.328 (95% CI, 0.317–0.339). The results were validated in a subcohort with type 2 diabetes mellitus (n=511) from the LIPID trial (Long-Term Intervention With Pravastatin in Ischemic Disease).
CONCLUSIONS:The improvement in the prediction of cardiovascular events, above traditional risk factors, demonstrates the potential of plasma lipid species as biomarkers for cardiovascular risk stratification in diabetes mellitus.
CLINICAL TRIAL REGISTRATION:URLhttps://clinicaltrials.gov. Unique identifierNCT00145925.
Recent advances in simultaneous measurement of RNA and protein abundances at single-cell level provide a unique opportunity to predict protein abundance from scRNA-seq data using machine learning ...models. However, existing machine learning methods have not considered relationship among the proteins sufficiently. We formulate this task in a multi-label prediction framework where multiple proteins are linked to each other at the single-cell level. Then, we propose a novel method for single-cell RNA to protein prediction named PIKE-R2P, which incorporates protein-protein interactions (PPI) and prior knowledge embedding into a graph neural network. Compared with existing methods, PIKE-R2P could significantly improve prediction performance in terms of smaller errors and higher correlations with the gold standard measurements. The superior performance of PIKE-R2P indicates that adding the prior knowledge of PPI to graph neural networks can be a powerful strategy for cross-modality prediction of protein abundances at the single-cell level.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
•Machine learning models can predict single-cell protein abundance from RNA-seq data.•Multimodal omics data of CITE-seq and REAP-seq have been used to train models.•Among 6 methods compared, Random ...Forest performed the best overall.•Data denoising and parameter tuning are crucial for prediction performance.
Single-cell protein abundance is a fundamental type of information to characterize cell states. Due to high cost and technical barriers, however, direct quantification of proteins is difficult. Single-cell RNA sequencing (scRNA-seq) data, serving as a cost-effective substitute of single-cell proteomics, may not accurately reflect protein expression levels due to measurement error, noise, post-transcriptional and translational regulation, etc. The recently emerging single-cell multimodal omics data, e.g. CITE-seq and REAP-seq, can simultaneously profile RNA and protein abundances in single cells, providing labeled data for predictive modeling in a supervised learning framework. Deep neural network-based transfer learning method has been applied to imputation of surface protein abundances from single-cell transcriptomic data. However, it is unclear if the artificial neural network is the best model, and it is desirable to improve the prediction performance (e.g. accuracy, interpretability) of machine learning models. In this paper, we compared several tree-based ensemble learning methods with neural network models, and found that ensemble learning often performed better than neural network, and Random Forest (RF) performed the best overall. Moreover, we used the feature importance scores from RF to interpret biological mechanisms underlying the prediction. Our study demonstrates the effectiveness of ensemble learning for reliable protein abundances prediction using single-cell multimodal omics data, and paves the way for knowledge discovery by mining single-cell multi-omics data in large scale.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Congenital disorders of glycosylation (CDG) are rare genetic disorders with a spectrum of clinical manifestations caused by abnormal N-glycosylation of secreted and cell surface proteins. Over 130 ...genes are implicated and next generation sequencing further identifies potential disease drivers in affected individuals. However, functional testing of these variants is challenging, making it difficult to distinguish pathogenic from non-pathogenic events. Using proximity labelling, we identified OST48 as a protein that transiently interacts with lysyl oxidase (LOX), a secreted enzyme that cross-links the fibrous extracellular matrix. OST48 is a non-catalytic component of the oligosaccharyltransferase (OST) complex, which transfers glycans to substrate proteins. OST48 is encoded by DDOST, and 43 variants of DDOST are described in CDG patients, of which 34 are classified as variants of uncertain clinical significance (VUS). We developed an assay based on LOX N-glycosylation that confirmed two previously characterised DDOST variants as pathogenic. Notably, 39 of the 41 remaining variants did not have impaired activity, but we demonstrated that p.S243F and p.E286del were functionally impaired, consistent with a role in driving CDG in those patients. Thus, we describe a rapid assay for functional testing of clinically relevant CDG variants to complement genome sequencing and support clinical diagnosis of affected individuals.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
In 1967, Sandy Posey pronounced that sunglasses are essential beachwear ( https://www.youtube.com/watch?v=4HVBEb-GA1Y ). Now, whole-genome sequencing reveals that ultraviolet radiation (UVR) can ...contribute to melanomas in the iris and conjunctiva, data that provide a molecular explanation for why it is important to protect our eyes from exposure to UVR.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Although identified as the key environmental driver of common cutaneous melanoma, the role of ultraviolet radiation (UVR)-induced DNA damage in mucosal melanoma is poorly defined. We analyze 10 ...mucosal melanomas of conjunctival origin by whole genome sequencing and our data shows a predominance of UVR-associated single base substitution signature 7 (SBS7) in the majority of the samples. Our data shows mucosal melanomas with SBS7 dominance have similar genomic patterns to cutaneous melanomas and therefore this subset should not be excluded from treatments currently used for common cutaneous melanoma.
Our understanding of how checkpoint inhibitors (CPI) affect T cell evolution is incomplete, limiting our ability to achieve full clinical benefit from these drugs. Here we analyzed peripheral T cell ...populations after one cycle of CPI and identified a dynamic awakening of the immune system revealed by T cell evolution in response to treatment. We sequenced T cell receptors (TCR) in plasma cell-free DNA (cfDNA) and peripheral blood mononuclear cells (PBMC) and performed phenotypic analysis of peripheral T cell subsets from metastatic melanoma patients treated with CPI. We found that early peripheral T cell turnover and TCR repertoire dynamics identified which patients would respond to treatment. Additionally, the expansion of a subset of immune-effector peripheral T cells we call T
cells correlated with response. These events are prognostic and occur within 3 weeks of starting immunotherapy, raising the potential for monitoring patients responses using minimally invasive liquid biopsies."
The melanoma genome is dominated by ultraviolet radiation (UVR)-induced mutations. Their relevance in disease progression is unknown. Here we classify melanomas by mutation signatures and identify ...ten recurrently mutated UVR signature genes that predict patient survival. We validate these findings in primary human melanomas; in mice we show that this signature is imprinted by short-wavelength UVR and that four exposures to UVR are sufficient to accelerate melanomagenesis.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Although immune checkpoint inhibitors (ICIs) have achieved unprecedented results in melanoma, the biological features of the durable responses initiated by these drugs remain unknown. Here we show ...the genetic and phenotypic changes induced by treatment with programmed cell death-1 (PD-1) blockade in a genetically engineered mouse model of melanoma driven by oncogenic BRAF. In this controlled system anti-PD-1 treatment yields responses in ~35% of the tumors, and prolongs survival in ~27% of the animals. We identify increased stroma remodeling and reduced expression of proliferation markers as features associated with prolonged response. These traits are corroborated in two independent early on-treatment anti-PD-1 melanoma patient cohorts. These insights into the biological responses of tumors to ICI provide a strategy for identification of durable response early during the course of treatment and could improve patient stratification for checkpoint inhibitory drugs.
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