Frequency of naevus cells in lymph nodes of melanoma and breast cancer patients.

INTRODUCTION: We aimed to study the frequency (prevalence) and histology of benign melanocytic naevus cells in regional lymph nodes in relation to age and sex and nodal location. MATERIAL AND METHODS: Histopathology reports of sentinel lymph node (SLN) biopsies from melanoma patients, 2002 - 2014, and from breast cancer patients, 2010- 2019, were obtained from records of a single hospital in England. All sections were similarly processed and examined. For standardisation, presence of naevus cells was assessed in a single node per patient: the first SLN biopsied (melanoma) or the node nearest the first SLN (breast cancer). RESULTS: Associations were tested using Fisher's exact test. Naevus cells were found in 10% (60/585) of melanoma patients' index SLNs. Frequency varied significantly by anatomic region: 13% in axillary to 0% cervical SLNs (p = 0.03), but not by sex or age. Within nodes, naevus cells were present in capsular or pericapsular tissue (93%), or trabeculae (7%). In breast cancer patients' index axillary nodes, 6% (11/196) contained naevus cells, all intracapsular. In the predominant 40-69 years age-group, prevalence was similar in breast cancer (7%) and female melanoma (9%) patients, but in those aged 70-100, prevalence was lower in breast cancer (2%) than in female melanoma (15%) patients (p = 0.05). CONCLUSIONS: Standard methods of assessment yielded no clear pattern of naevus cell frequency in lymph nodes by age or sex, but confirmed naevus cell location as mostly intracapsular.

Functional classification of DDOST variants of uncertain clinical significance in congenital disorders of glycosylation.

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.

Heritable defects in telomere and mitotic function selectively predispose to sarcomas.

Cancer genetics has to date focused on epithelial malignancies, identifying multiple histotype-specific pathways underlying cancer susceptibility. Sarcomas are rare malignancies predominantly derived from embryonic mesoderm. To identify pathways specific to mesenchymal cancers, we performed whole-genome germline sequencing on 1644 sporadic cases and 3205 matched healthy elderly controls. Using an extreme phenotype design, a combined rare-variant burden and ontologic analysis identified two sarcoma-specific pathways involved in mitotic and telomere functions. Variants in centrosome genes are linked to malignant peripheral nerve sheath and gastrointestinal stromal tumors, whereas heritable defects in the shelterin complex link susceptibility to sarcoma, melanoma, and thyroid cancers. These studies indicate a specific role for heritable defects in mitotic and telomere biology in risk of sarcomas.

Tumour infiltrating B cells discriminate checkpoint blockade-induced responses.

BACKGROUND: Immune cell-driven anti-cancer activity is paramount for effective responses to checkpoint inhibitors (ICB). However, the contribution of the different immune cell subsets in the circulation and within the tumour is poorly understood. MATERIALS AND METHODS: To elucidate the role of the different cell subsets in anti-tumour responses elicited by ICB, we performed single-cell analysis of the transcriptome and surface proteome of paired pre- and early on-treatment metastatic melanoma tumour biopsies and matched peripheral blood mononuclear cell samples. We next compared the survival of metastatic melanoma patients treated with ICB according to the abundance of pre-treatment tumour-infiltrating B cell clonotypes. RESULTS: We identified cell clusters associated with disease control or progression, defined differential expression of biological pathways likely involved in the immune awakening against the tumour and examined how cell-cell communication patterns between the tumour cell subsets change during treatment. Furthermore, we discovered that B cells (immunoglobulin expression and abundance of B cell clonotypes) discriminate the clinical response after ICB and propose that B cells likely contribute to anti-tumour immunity by antigen presentation through major histocompatibility complex molecules. Finally, we demonstrated that the abundance of tumour-infiltrating B cell clonotypes at baseline identifies two distinct risk groups, a finding that we confirmed in an independent cohort. CONCLUSIONS: Our exploratory translational study provides new insights on the mechanistic role of B cells in anti-melanoma immunity during treatment with ICB. Additionally, we support pre-treatment B cell tumour infiltration as a promising prognostic biomarker to be further validated as a tool for clinical risk stratification.

The T cell receptor repertoire of tumor infiltrating T cells is predictive and prognostic for cancer survival.

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.

PIKE-R2P: Protein-protein interaction network-based knowledge embedding with graph neural network for single-cell RNA to protein prediction.

BACKGROUND: 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. RESULTS: 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. CONCLUSION: 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.

Sunglasses to hide behind may also prevent melanoma of the eyes.

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.

Ultraviolet radiation drives mutations in a subset of mucosal melanomas.

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.

Ensemble learning models that predict surface protein abundance from single-cell multimodal omics data.

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.

Stroma remodeling and reduced cell division define durable response to PD-1 blockade in melanoma.

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.

Immune-awakening revealed by peripheral T cell dynamics after one cycle of immunotherapy.

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 TIE 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."

Bayesian Data Fusion of Gene Expression and Histone Modification Profiles for Inference of Gene Regulatory Network.

Accurately reconstructing gene regulatory networks (GRNs) from high-throughput gene expression data has been a major challenge in systems biology for decades. Many approaches have been proposed to solve this problem. However, there is still much room for the improvement of GRN inference. Integrating data from different sources is a promising strategy. Epigenetic modifications have a close relationship with gene regulation. Hence, epigenetic data such as histone modification profiles can provide useful information for uncovering regulatory interactions between genes. In this paper, we propose a method to integrate epigenetic data into the inference of GRNs. In particular, a dynamic Bayesian network (DBN) is employed to infer gene regulations from time-series gene expression data. Epigenetic data (histone modification profiles here) are integrated into the prior probability distribution of the Bayesian model. Our method has been validated on both synthetic and real datasets. Experimental results show that the integration of epigenetic data can significantly improve the performance of GRN inference. As more epigenetic datasets become available, our method would be useful for elucidating the gene regulatory mechanisms driving various cellular activities. The source code and testing datasets are available at https://github.com/Zheng-Lab/MetaGRN/tree/master/histonePrior.

Changes in plasma lipids predict pravastatin efficacy in secondary prevention.

BACKGROUNDStatins have pleiotropic effects on lipid metabolism. The relationship between these effects and future cardiovascular events is unknown. We characterized the changes in lipids upon pravastatin treatment and defined the relationship with risk reduction for future cardiovascular events.METHODSPlasma lipids (n = 342) were measured in baseline and 1-year follow-up samples from a Long-Term Intervention with Pravastatin in Ischaemic Disease (LIPID) study subcohort (n = 4991). The associations of changes in lipids with treatment and cardiovascular outcomes were investigated using linear and Cox regression. The effect of treatment on future cardiovascular outcomes was examined by the relative risk reduction (RRR).RESULTSPravastatin treatment was associated with changes in 206 lipids. Species containing arachidonic acid were positively associated while phosphatidylinositol species were negatively associated with pravastatin treatment. The RRR from pravastatin treatment for cardiovascular events decreased from 23.5% to 16.6% after adjustment for clinical risk factors and change in LDL-cholesterol (LDL-C) and to 3.0% after further adjustment for the change in the lipid ratio PI(36:2)/PC(38:4). Change in PI(36:2)/PC(38:4) mediated 58% of the treatment effect. Stratification of patients into quartiles of change in PI(36:2)/PC(38:4) indicated no benefit of pravastatin in the fourth quartile.CONCLUSIONThe change in PI(36:2)/PC(38:4) predicted benefit from pravastatin, independent of change in LDL-C, demonstrating its potential as a biomarker for monitoring the clinical benefit of statin treatment in secondary prevention.TRIAL REGISTRATIONAustralian New Zealand Clinical Trials Registry identifier ACTRN12616000535471.FUNDINGBristol-Myers Squibb; NHMRC grants 211086, 358395, and 1029754; NHMRC program grant 1149987; NHMRC fellowship 108026; and the Operational Infrastructure Support Program of the Victorian government of Australia.

Ultraviolet radiation-induced DNA damage is prognostic for outcome in melanoma.

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.

Publisher Correction: Ultraviolet radiation-induced DNA damage is prognostic for outcome in melanoma.

In the version of this article originally published, Extended Data Fig. 3 was incorrect. A duplicate of Extended Data Fig. 4 was uploaded in place of Extended Data Fig. 3. Extended Data Fig. 3 has now been uploaded. The error has been fixed in the PDF and HTML versions of this article.

Large-scale plasma lipidomic profiling identifies lipids that predict cardiovascular events in secondary prevention.

BACKGROUND: Plasma lipidomic measures may enable improved prediction of cardiovascular outcomes in secondary prevention. The aim of this study is to determine the association of plasma lipidomic measurements with cardiovascular events and assess their potential to predict such events. METHODS: Plasma lipids (n = 342) were measured in a retrospective subcohort (n = 5,991) of the LIPID study. Proportional hazards regression was used to identify lipids associated with future cardiovascular events (nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death) and cardiovascular death. Multivariable models adding lipid species to traditional risk factors were created using lipid ranking established from the Akaike information criterion within a 5-fold cross-validation framework. The results were tested on a diabetic case cohort from the ADVANCE study (n = 3,779). RESULTS: Specific ceramide species, sphingolipids, phospholipids, and neutral lipids containing omega-6 fatty acids or odd-chain fatty acids were associated with future cardiovascular events (106 species) and cardiovascular death (139 species). The addition of 7 lipid species to a base model (11 conventional risk factors) resulted in an increase in the C-statistics from 0.629 (95% CI, 0.628-0.630) to 0.654 (95% CI, 0.653-0.656) for prediction of cardiovascular events and from 0.673 (95% CI, 0.671-0.675) to 0.727 (95% CI, 0.725-0.728) for prediction of cardiovascular death. Categorical net reclassification improvements for cardiovascular events and cardiovascular death were 0.083 (95% CI, 0.081-0.086) and 0.166 (95% CI, 0.162-0.170), respectively. Evaluation on the ADVANCE case cohort demonstrated significant improvement on the base models. CONCLUSIONS: The improvement in the prediction of cardiovascular outcomes, above conventional risk factors, demonstrates the potential of plasma lipidomic profiles as biomarkers for cardiovascular risk stratification in secondary prevention. FUNDING: Bristol-Myers Squibb, the National Health and Medical Research Council of Australia (grants 211086, 358395, and 1029754), and the Operational Infrastructure Support Program of the Victorian government of Australia.

Changes in plasma lipidome following initiation of antiretroviral therapy.

INTRODUCTION: HIV and antiretroviral therapy (ART) have been associated with increased cardiovascular disease and important changes in lipid metabolism. Advances in mass-spectrometry technology allow for the detailed assessment of individual lipid species which may illuminate the mechanisms underlying increased cardiovascular risk. We describe the change in plasma lipidome with initiation of antiretroviral therapy and compare these by regimen. METHODS: Plasma lipid profiling (by electrospray isonisation-tandem mass spectrometry) was performed on ARV-naive HIV positive participants randomised to one of three regimens; tenofovir/emtricitabine with efavirenz, ritonavir-boosted atazanavir (atazanavir/r) or zidovudine/abacavir. Participants (n = 115) who remained on their randomised regimen with complete samples available at baseline, week 12 and 48 were included. 306 lipid species from 22 lipid classes were analysed. RESULTS: Initiation of ART led to significant changes in lipidome which were partly dependent on the randomised regimen received. This led to significant differences in 72 lipid species and 7 classes (cholesterol ester, free cholesterol, phosphatidylcholine, GM3 ganglioside, trihexosylceramide, monohexosylceramide, and ceramides) by arm at week 48. Consistently higher lipid concentrations were seen with efavirenz compared with atazanavir/r or zidovudine/abacavir. Twelve of the lipid species and two lipid classes (cholesterol esters and ceramides) that were significantly increased in the efavirenz arm compared with the atazanavir/r or zidovudine/abacavir arms have previously been associated with future cardiovascular events in HIV positive patients. Change in HIV viral load was predictive of change in 3 lipid species. CONCLUSIONS: Initiation of ART lead to significant changes in the plasma lipidome that were greatest in those receiving efavirenz.

Weight Loss and Exercise Alter the High-Density Lipoprotein Lipidome and Improve High-Density Lipoprotein Functionality in Metabolic Syndrome.

OBJECTIVE: High-density lipoprotein (HDL) lipid composition and function may better reflect cardiovascular risk than HDL cholesterol concentration. This study characterized the relationships between HDL composition, metabolism, and function in metabolic syndrome (MetS) patients and how changes in composition after weight loss (WL) and exercise treatments are related to function. APPROACH AND RESULTS: Plasma samples from MetS patients (n=95) and healthy individuals (n=40) were used in this study. Subsets of the MetS group underwent 12 weeks of no treatment (n=17), WL (n=19), or WL plus exercise (WLEX; n=17). HDL was isolated using density-gradient ultracentrifugation. The HDL lipidome was analyzed by mass spectrometry, and particle size determined by nuclear magnetic resonance. Cholesteryl ester transfer protein activity and ex vivo HDL cholesterol efflux capacity (CEC) were assessed. The HDL lipidome in the MetS patients was substantially different from that in healthy individuals, mean particle size was smaller, and CEC was lower. Several HDL phospholipid and sphingolipid species were associated with HDL diameter and CEC. The HDL lipidome and particle size were modified toward the healthy individuals after WL and WLEX treatments, with greater effects observed in the latter group. Cholesteryl ester transfer protein activity was reduced after WL and WLEX, and CEC was improved after WLEX. CONCLUSIONS: WLEX treatment in MetS patients normalizes the HDL lipidome and particle size profile and enhances CEC. HDL lipids associated with diminished CEC may represent novel biomarkers for early prediction of HDL dysfunction and disease risk and may represent potential therapeutic targets for future HDL therapies. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00163943.