A computational pipeline for identifying gene targets and signalling pathways in cancer cells to improve lymphocyte infiltration and immune checkpoint therapy efficacy.

BACKGROUND: Tumour-infiltrating lymphocytes (TILs) are crucial for effective immune checkpoint blockade (ICB) therapy in solid tumours. However, ∼70% of these tumours exhibit poor lymphocyte infiltration, rendering ICB therapies less effective. METHODS: We developed a bioinformatics pipeline integrating multiple previously unconsidered factors or datasets, including tumour cell immune-related pathways, copy number variation (CNV), and single tumour cell sequencing data, as well as tumour mRNA-seq data and patient survival data, to identify targets that can potentially improve T cell infiltration and enhance ICB efficacy. Furthermore, we conducted wet-lab experiments and successfully validated one of the top-identified genes. FINDINGS: We applied this pipeline in solid tumours of the Cancer Genome Atlas (TCGA) and identified a set of genes in 18 cancer types that might potentially improve lymphocyte infiltration and ICB efficacy, providing a valuable drug target resource to be further explored. Importantly, we experimentally validated SUN1, which had not been linked to T cell infiltration and ICB therapy previously, but was one of the top-identified gene targets among 3 cancer types based on the pipeline, in a mouse colon cancer syngeneic model. We showed that Sun1 KO could significantly enhance antigen presentation, increase T-cell infiltration, and improve anti-PD1 treatment efficacy. Moreover, with a single-cell multiome analysis, we identified subgene regulatory networks (sub-GRNs) showing Stat proteins play important roles in enhancing the immune-related pathways in Sun1-KO cancer cells. INTERPRETATION: This study not only established a computational pipeline for discovering new gene targets and signalling pathways in cancer cells that block T-cell infiltration, but also provided a gene target pool for further exploration in improving lymphocyte infiltration and ICB efficacy in solid tumours. FUNDING: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Breast Tumor Metastasis and Its Microenvironment: It Takes Both Seed and Soil to Grow a Tumor and Target It for Treatment.

Metastasis remains a major challenge in treating breast cancer. Breast tumors metastasize to organ-specific locations such as the brain, lungs, and bone, but why some organs are favored over others remains unclear. Breast tumors also show heterogeneity, plasticity, and distinct microenvironments. This contributes to treatment failure and relapse. The interaction of breast cancer cells with their metastatic microenvironment has led to the concept that primary breast cancer cells act as seeds, whereas the metastatic tissue microenvironment (TME) is the soil. Improving our understanding of this interaction could lead to better treatment strategies for metastatic breast cancer. Targeted treatments for different subtypes of breast cancers have improved overall patient survival, even with metastasis. However, these targeted treatments are based upon the biology of the primary tumor and often these patients' relapse, after therapy, with metastatic tumors. The advent of immunotherapy allowed the immune system to target metastatic tumors. Unfortunately, immunotherapy has not been as effective in metastatic breast cancer relative to other cancers with metastases, such as melanoma. This review will describe the heterogeneic nature of breast cancer cells and their microenvironments. The distinct properties of metastatic breast cancer cells and their microenvironments that allow interactions, especially in bone and brain metastasis, will also be described. Finally, we will review immunotherapy approaches to treat metastatic breast tumors and discuss future therapeutic approaches to improve treatments for metastatic breast cancer.

DVA: predicting the functional impact of single nucleotide missense variants.

BACKGROUND: In the past decade, single nucleotide variants (SNVs) have been identified as having a significant relationship with the development and treatment of diseases. Among them, prioritizing missense variants for further functional impact investigation is an essential challenge in the study of common disease and cancer. Although several computational methods have been developed to predict the functional impacts of variants, the predictive ability of these methods is still insufficient in the Mendelian and cancer missense variants. RESULTS: We present a novel prediction method called the disease-related variant annotation (DVA) method that predicts the effect of missense variants based on a comprehensive feature set of variants, notably, the allele frequency and protein-protein interaction network feature based on graph embedding. Benchmarked against datasets of single nucleotide missense variants, the DVA method outperforms the state-of-the-art methods by up to 0.473 in the area under receiver operating characteristic curve. The results demonstrate that the proposed method can accurately predict the functional impact of single nucleotide missense variants and substantially outperforms existing methods. CONCLUSIONS: DVA is an effective framework for identifying the functional impact of disease missense variants based on a comprehensive feature set. Based on different datasets, DVA shows its generalization ability and robustness, and it also provides innovative ideas for the study of the functional mechanism and impact of SNVs.

Application of Single-Cell Assay for Transposase-Accessible Chromatin with High Throughput Sequencing in Plant Science: Advances, Technical Challenges, and Prospects.

The Single-cell Assay for Transposase-Accessible Chromatin with high throughput sequencing (scATAC-seq) has gained increasing popularity in recent years, allowing for chromatin accessibility to be deciphered and gene regulatory networks (GRNs) to be inferred at single-cell resolution. This cutting-edge technology now enables the genome-wide profiling of chromatin accessibility at the cellular level and the capturing of cell-type-specific cis-regulatory elements (CREs) that are masked by cellular heterogeneity in bulk assays. Additionally, it can also facilitate the identification of rare and new cell types based on differences in chromatin accessibility and the charting of cellular developmental trajectories within lineage-related cell clusters. Due to technical challenges and limitations, the data generated from scATAC-seq exhibit unique features, often characterized by high sparsity and noise, even within the same cell type. To address these challenges, various bioinformatic tools have been developed. Furthermore, the application of scATAC-seq in plant science is still in its infancy, with most research focusing on root tissues and model plant species. In this review, we provide an overview of recent progress in scATAC-seq and its application across various fields. We first conduct scATAC-seq in plant science. Next, we highlight the current challenges of scATAC-seq in plant science and major strategies for cell type annotation. Finally, we outline several future directions to exploit scATAC-seq technologies to address critical challenges in plant science, ranging from plant ENCODE(The Encyclopedia of DNA Elements) project construction to GRN inference, to deepen our understanding of the roles of CREs in plant biology.

Distinct immune escape and microenvironment between RG-like and pri-OPC-like glioma revealed by single-cell RNA-seq analysis.

The association of neurogenesis and gliogenesis with glioma remains unclear. By conducting single-cell RNA-seq analyses on 26 gliomas, we reported their classification into primitive oligodendrocyte precursor cell (pri-OPC)-like and radial glia (RG)-like tumors and validated it in a public cohort and TCGA glioma. The RG-like tumors exhibited wild-type isocitrate dehydrogenase and tended to carry EGFR mutations, and the pri-OPC-like ones were prone to carrying TP53 mutations. Tumor subclones only in pri-OPC-like tumors showed substantially down-regulated MHC-I genes, suggesting their distinct immune evasion programs. Furthermore, the two subgroups appeared to extensively modulate glioma-infiltrating lymphocytes in distinct manners. Some specific genes not expressed in normal immune cells were found in glioma-infiltrating lymphocytes. For example, glial/glioma stem cell markers OLIG1/PTPRZ1 and B cell-specific receptors IGLC2/IGKC were expressed in pri-OPC-like and RG-like glioma-infiltrating lymphocytes, respectively. Their expression was positively correlated with those of immune checkpoint genes (e.g., LGALS33) and poor survivals as validated by the increased expression of LGALS3 upon IGKC overexpression in Jurkat cells. This finding indicated a potential inhibitory role in tumor-infiltrating lymphocytes and could provide a new way of cancer immune evasion.

SPIDE: A single cell potency inference method based on the local cell-specific network entropy.

For a given single cell RNA-seq data, it is critical to pinpoint key cellular stages and quantify cells' differentiation potency along a differentiation pathway in a time course manner. Currently, several methods based on the entropy of gene functions or PPI network have been proposed to solve the problem. Nevertheless, these methods still suffer from the inaccurate interactions and noises originating from scRNA-seq profile. In this study, we proposed a cell potency inference method based on cell-specific network entropy, called SPIDE. SPIDE introduces the local weighted cell-specific network for each cell to maintain cell heterogeneity and calculates the entropy by incorporating gene expression with network structure. In this study, we compared three cell entropy estimation models on eight scRNA-Seq datasets. The results show that SPIDE obtains consistent conclusions with real cell differentiation potency on most datasets. Moreover, SPIDE accurately recovers the continuous changes of potency during cell differentiation and significantly correlates with the stemness of tumor cells in Colorectal cancer. To conclude, our study provides a universal and accurate framework for cell entropy estimation, which deepens our understanding of cell differentiation, the development of diseases and other related biological research.

A Self-attention Graph Convolutional Network for Precision Multi-tumor Early Diagnostics with DNA Methylation Data.

DNA methylation-based precision tumor early diagnostics is emerging as state-of-the-art technology that could capture early cancer signs 3 ~ 5 years in advance, even for clinically homogenous groups. Presently, the sensitivity of early detection for many tumors is ~ 30%, which needs significant improvement. Nevertheless, based on the genome-wide DNA methylation data, one could comprehensively characterize tumors' entire molecular genetic landscape and their subtle differences. Therefore, novel high-performance methods must be modeled by considering unbiased information using excessively available DNA methylation data. To fill this gap, we have designed a computational model involving a self-attention graph convolutional network and multi-class classification support vector machine to identify the 11 most common cancers using DNA methylation data. The self-attention graph convolutional network automatically learns key methylation sites in a data-driven way. Then, multi-tumor early diagnostics is realized by training a multi-class classification support vector machine based on the selected methylation sites. We evaluated our model's performance through several data sets of experiments, and our results demonstrate the effectiveness of the selected key methylation sites, which are highly relevant for blood diagnosis. The pipeline of the self-attention graph convolutional network based computational framework.

Deciphering gene contributions and etiologies of somatic mutational signatures of cancer.

Somatic mutational signatures (MSs) identified by genome sequencing play important roles in exploring the cause and development of cancer. Thus far, many such signatures have been identified, and some of them do imply causes of cancer. However, a major bottleneck is that we do not know the potential meanings (i.e. carcinogenesis or biological functions) and contributing genes for most of them. Here, we presented a computational framework, Gene Somatic Genome Pattern (GSGP), which can decipher the molecular mechanisms of the MSs. More importantly, it is the first time that the GSGP is able to process MSs from ribonucleic acid (RNA) sequencing, which greatly extended the applications of both MS analysis and RNA sequencing (RNAseq). As a result, GSGP analyses match consistently with previous reports and identify the etiologies for a number of novel signatures. Notably, we applied GSGP to RNAseq data and revealed an RNA-derived MS involved in deficient deoxyribonucleic acid mismatch repair and microsatellite instability in colorectal cancer. Researchers can perform customized GSGP analysis using the web tools or scripts we provide.

Comparing the associations between host and tumor factors with survival outcomes with anti-PD-1 immunotherapy in metastatic melanoma.

BACKGROUND: Anti-programmed death-1 (PD-1) immunotherapy has drastically improved survival for metastatic melanoma; however, 50% of patients have progression within 6 months despite treatment. In this study, we investigated host, and tumor factors for metastatic melanoma patients treated with anti-PD-1 immunotherapy. METHODS: Patients treated with the anti-PD-1 immunotherapy between 2014 and 2017 were identified in Alberta, Canada. All patients had Stage IV melanoma. Patient characteristics, investigations, treatment, and clinical outcomes were obtained from electronic medical records. RESULTS: We identified 174 patients treated with anti-PD-1 immunotherapy. At 37.1 months median follow-up time 135 (77.6%) individuals had died and 150 (86.2%) had progressed. An elevated lactate dehydrogenase (LDH) had a response rate of 21.0% versus 41.0% for those with a normal LDH (p = 0.017). Host factors associated with worse median progression-free survival (mPFS) and median overall survival (mOS) included liver metastases, >3 sites of disease, elevated LDH, thrombocytosis, neutrophilia, anemia, lymphocytopenia, and an elevated neutrophil/lymphocyte ratio. Primary ulcerated tumors had a worse mOS of 11.8 versus 19.3 months (p = 0.042). We identified four prognostic subgroups in advanced melanoma patients treated with anti-PD-1 therapy. (1) Normal LDH with <3 visceral sites, (2) normal LDH with ≥3 visceral sites, (3) LDH 1-2x upper limit of normal (ULN), (4) LDH ≥2x ULN. The mPFS each group was 14.0, 6.5, 3.3, and 1.9 months, while the mOS for each group was 33.3, 15.7, 7.9, and 3.4 months. CONCLUSION: Our study reports that host factors measuring the general immune function, markers of systemic inflammation, and tumor burden and location are the most prognostic for survival.

An ensemble prediction model for COVID-19 mortality risk.

Background: It's critical to identify COVID-19 patients with a higher death risk at early stage to give them better hospitalization or intensive care. However, thus far, none of the machine learning models has been shown to be successful in an independent cohort. We aim to develop a machine learning model which could accurately predict death risk of COVID-19 patients at an early stage in other independent cohorts. Methods: We used a cohort containing 4711 patients whose clinical features associated with patient physiological conditions or lab test data associated with inflammation, hepatorenal function, cardiovascular function, and so on to identify key features. To do so, we first developed a novel data preprocessing approach to clean up clinical features and then developed an ensemble machine learning method to identify key features. Results: Finally, we identified 14 key clinical features whose combination reached a good predictive performance of area under the receiver operating characteristic curve 0.907. Most importantly, we successfully validated these key features in a large independent cohort containing 15 790 patients. Conclusions: Our study shows that 14 key features are robust and useful in predicting the risk of death in patients confirmed SARS-CoV-2 infection at an early stage, and potentially useful in clinical settings to help in making clinical decisions.

TAIGET: A small-molecule target identification and annotation web server.

Background: Accurate target identification of small molecules and downstream target annotation are important in pharmaceutical research and drug development. Methods: We present TAIGET, a friendly and easy to operate graphical web interface, which consists of a docking module based on AutoDock Vina and LeDock, a target screen module based on a Bayesian-Gaussian mixture model (BGMM), and a target annotation module derived from >14,000 cancer-related literature works. Results: TAIGET produces binding poses by selecting ≤5 proteins at a time from the UniProt ID-PDB network and submitting ≤3 ligands at a time with the SMILES format. Once the identification process of binding poses is complete, TAIGET then screens potential targets based on the BGMM. In addition, three medical experts and 10 medical students curated associations among drugs, genes, gene regulation, cancer outcome phenotype, 2,170 cancer cell types, and 73 cancer types from the PubMed literature, with the aim to construct a target annotation module. A target-related PPI network can be visualized by an interactive interface. Conclusion: This online tool significantly lowers the entry barrier of virtual identification of targets for users who are not experts in the technical aspects of virtual drug discovery. The web server is available free of charge at http://www.taiget.cn/.

The clinical implication of psychiatric illnesses in patients with alcoholic liver disease: an analysis of US hospitals.

BACKGROUND: In this study, we evaluate the clinical impact of psychiatric illnesses (PI) on the hospital outcomes of patients admitted with alcoholic liver disease (ALD). METHODS: From the National Inpatient Sample from 2012-2017, patients with alcoholic cirrhosis or alcoholic hepatitis were selected and stratified using the presence/absence of PI (which was a composite of psychiatric conditions). The cases were propensity score-matched to PI-absent controls and were compared to the following endpoints: mortality, death due to suicide, length of stay (LOS), hospitalization charges, and hepatic complications. RESULTS: After matching, there were 122,907 PI with and 122,907 without PI. Those with PI were younger (51.8 vs. 51.9 years p = 0.02) and more likely to be female (39.2 vs. 38.7% p = 0.01); however, there was no difference in race. Patients with PI had lower rates of alcoholic cirrhosis but higher rates of alcoholic hepatitis/alcoholic hepatic steatosis. In multivariate, patients with PI had lower rates of all-cause mortality (aOR 0.51 95%CI 0.49-0.54); however, they experienced higher rates of deaths due to suicide (aOR 3.00 95%CI 1.56-5.78) and had longer LOS (aOR 1.02 95%CI 1.01-1.02). CONCLUSION: Presence of PI in ALD patients is associated with prolonged hospital stay and higher rates deaths due to suicide.

Unique presentation of recurrent subdural effusions and subsequent abdominal lymphadenopathy in a patient diagnosed with Kikuchi-Fujimoto disease.

Kikuchi Fujimoto Disease, originally discovered in 1972, is a rare lymphoproliferative disorder traditionally characterized by cervical lymphadenopathy, fevers, parotid gland enlargement, and several other nonspecific manifestations. Differentials include lymphoma, other viral diseases such as Epstein-Bar Virus, as well as other autoimmune conditions such as Systemic Lupus Erythematosus. Central nervous system involvement is exceptionally rare, with manifestations including meningitis as well as subdural effusions, as presented in this case. This review will summarize a case of a 24-year-old man with recurrent subdural effusions requiring intervention, subsequent relapse with abdominal lymphadenopathy, and possible IgG4 related disease. The background epidemiology, radiology, and potential pathophysiology will be reviewed.

A comparison on predicting functional impact of genomic variants.

Single-nucleotide polymorphism (SNPs) may cause the diverse functional impact on RNA or protein changing genotype and phenotype, which may lead to common or complex diseases like cancers. Accurate prediction of the functional impact of SNPs is crucial to discover the 'influential' (deleterious, pathogenic, disease-causing, and predisposing) variants from massive background polymorphisms in the human genome. Increasing computational methods have been developed to predict the functional impact of variants. However, predictive performances of these computational methods on massive genomic variants are still unclear. In this regard, we systematically evaluated 14 important computational methods including specific methods for one type of variant and general methods for multiple types of variants from several aspects; none of these methods achieved excellent (AUC ≥ 0.9) performance in both data sets. CADD and REVEL achieved excellent performance on multiple types of variants and missense variants, respectively. This comparison aims to assist researchers and clinicians to select appropriate methods or develop better predictive methods.

Malnutrition as a risk factor of adverse postoperative outcomes in patients undergoing hepatic resection: analysis of US hospitals.

Patients with liver cancer or space-occupying cysts suffer from malnutrition due to compression of gastric and digestive structures, liver and cancer-mediated dysmetabolism, and impaired nutrient absorption. As proportion of these patients requires removal of lesions through hepatic resection, it is important to evaluate the effects of malnutrition on post-hepatectomy outcomes. In our study approach, 2011-2017 National Inpatient Sample was used to isolate in-hospital hepatectomy cases, which were stratified using malnutrition (composite of malnutrition, sarcopenia and weight loss/cachexia). The malnutrition-absent controls were matched to cases using nearest neighbour propensity score matching method and compared with the following endpoints: mortality, length of stay, hospitalisation costs and postoperative complications. There were 2531 patients in total who underwent hepatectomy with matched number of controls from the database; following the match, malnutrition cohort (compared with controls) was more likely to experience in-hospital death (6·60 % v. 5·25 % P < 0·049, OR 1·27, 95 % CI 1·01, 1·61) and was more likely to have higher length of stay (18·10 d v. 9·32 d, P < 0·001) and hospitalisation costs ($278 780 v. $150 812, P < 0·001). In terms of postoperative complications, malnutrition cohort was more likely to experience bleeding (6·52 % v. 3·87 %, P < 0·001, OR 1·73, 95 % CI 1·34, 2·24), infection (6·64 % v. 2·49 %, P < 0·001, OR 2·79, 95 % CI 2·07, 3·74), wound complications (4·5 % v. 1·38 %, P < 0·001, OR 3·36, 95 % CI 2·29, 4·93) and respiratory failure (9·40 % v. 4·11 %, P < 0·001, OR 2·42, 95 % CI 1·91, 3·07). In multivariate analysis, malnutrition was associated with higher mortality (P < 0·028, adjusted OR 1·3, 95 % CI 1·03, 1·65). Thus, we conclude that malnutrition is a risk factor of postoperative mortality in patients undergoing hepatectomy.

The impact of frailty on the postoperative outcomes of patients undergoing cholecystectomy: propensity score matched analysis of 2011-2017 US hospitals.

BACKGROUND: Frailty is an aggregate variable that encompasses debilitating geriatric conditions, which potentially affects postoperative outcomes. In this study, we evaluate the relationship between clinical frailty and post-cholecystectomy outcomes using a national registry of hospitalized patients. METHODS: 2011-2017 National Inpatient Sample database was used to identify patients who underwent cholecystectomy. Patients were stratified using the Johns Hopkins ACG frailty definition into binary (frailty and no-frailty) and tripartite frailty (frailty, prefrailty, no-frailty) indicators. The controls were matched to study cohort using 1:1 propensity score-matching and postoperative outcomes were compared. RESULTS: Post-match, using the binary term, frail patients (n = 40,067) had higher rates of mortality (OR 2.07 95%CI 1.90-2.25), length of stay, costs, and complications. In multivariate, frailty was associated with higher mortality (aOR 2.06 95%CI 1.89-2.24). When using tripartite frailty term, prefrail (n = 35,595) and frail (n = 4472) patients had higher mortality (prefrailty: OR 2.04 95%CI 1.86-2.23; frailty: OR 2.49 95%CI 1.99-3.13), length of stay, costs, and complications. In multivariate, prefrailty and frailty were associated with higher mortality (prefrailty: aOR 2.02 95%CI 1.84-2.21; frailty: aOR 2.54 95%CI 2.02-3.19). CONCLUSION: This study shows the presence of frailty (and prefrailty) is an independent risk factor of adverse postoperative outcomes in patients undergoing cholecystectomy.

Examining the etiology of early-onset breast cancer in the Canadian Partnership for Tomorrow's Health (CanPath).

PURPOSE: Breast cancer incidence among younger women (under age 50) has increased over the past 25 years, yet little is known about the etiology among this age group. The objective of this study was to investigate relationships between modifiable and non-modifiable risk factors and early-onset breast cancer among three prospective Canadian cohorts. METHODS: A matched case-control study was conducted using data from Alberta's Tomorrow Project, BC Generations Project, and the Ontario Health Study. Participants diagnosed with breast cancer before age 50 were identified through provincial registries and matched to three control participants of similar age and follow-up. Conditional logistic regression was used to examine the association between factors and risk of early-onset breast cancer. RESULTS: In total, 609 cases and 1,827 controls were included. A body mass index ≥ 30 kg/m2 was associated with a lower risk of early-onset breast cancer (OR 0.65; 95% CI 0.47-0.90), while a waist circumference ≥ 88 cm was associated with an increased risk (OR 1.58; 95% CI 1.18-2.11). A reduced risk was found for women with ≥ 2 pregnancies (OR 0.76; 95% CI 0.59-0.99) and a first-degree family history of breast cancer was associated with an increased risk (OR 1.95; 95% CI 1.47-2.57). CONCLUSIONS: In this study, measures of adiposity, pregnancy history, and familial history of breast cancer are important risk factors for early-onset breast cancer. Evidence was insufficient to conclude if smoking, alcohol intake, fruit and vegetable consumption, and physical activity are meaningful risk factors. The results of this study could inform targeted primary and secondary prevention for early-onset breast cancer.

The relationship between mast cell activation syndrome, postural tachycardia syndrome, and Ehlers-Danlos syndrome.

Background: Postural tachycardia syndrome (POTS), hypermobile Ehlers-Danlos syndrome (EDS), and mast cell activation syndrome (MCAS) can occur in the same patient. In this study, we investigated the relationship among these three syndromes. Objective: To establish the relationship of MCAS in patients diagnosed with POTS and hypermobile EDS as well as characterize the demographics of the patients affected by these syndromes. Methods: A total of 195 medical records of patients by using a diagnostic codes data base search for disorders of autonomic dysfunction were identified. The demographics of the patients and diagnoses of POTS, EDS, or MCAS were recorded. Confidence intervals of the proportion of patients MCAS within a population of patients with POTS and EDS were compared with the proportion of patients with MCAS and without POTS and EDS. Odds ratios were also calculated within these groups. Results: The percentage of MCAS within the group of POTS and EDS was 31% in comparison with 2% within the non-POTS and EDS group. The 95% confidence interval calculated for the MCAS in the POTS and EDS group did not overlap with 2%, which showed a statistically significant result. The odds ratio between the two groups was found to be 32.46. Conclusion: There was a marked percentage of MCAS among the patients with diagnoses of POTS and EDS.

Integrative Modeling of Gene Expression and Metabolic Networks of Arabidopsis Embryos for Identification of Seed Oil Causal Genes.

Fatty acids in crop seeds are a major source for both vegetable oils and industrial applications. Genetic improvement of fatty acid composition and oil content is critical to meet the current and future demands of plant-based renewable seed oils. Addressing this challenge can be approached by network modeling to capture key contributors of seed metabolism and to identify underpinning genetic targets for engineering the traits associated with seed oil composition and content. Here, we present a dynamic model, using an Ordinary Differential Equations model and integrated time-course gene expression data, to describe metabolic networks during Arabidopsis thaliana seed development. Through in silico perturbation of genes, targets were predicted in seed oil traits. Validation and supporting evidence were obtained for several of these predictions using published reports in the scientific literature. Furthermore, we investigated two predicted targets using omics datasets for both gene expression and metabolites from the seed embryo, and demonstrated the applicability of this network-based model. This work highlights that integration of dynamic gene expression atlases generates informative models which can be explored to dissect metabolic pathways and lead to the identification of causal genes associated with seed oil traits.

eTumorMetastasis: A Network-based Algorithm Predicts Clinical Outcomes Using Whole-exome Sequencing Data of Cancer Patients.

Continual reduction in sequencing cost is expanding the accessibility of genome sequencing data for routine clinical applications. However, the lack of methods to construct machine learning-based predictive models using these datasets has become a crucial bottleneck for the application of sequencing technology in clinics. Here, we develop a new algorithm, eTumorMetastasis, which transforms tumor functional mutations into network-based profiles and identifies network operational gene (NOG) signatures. NOG signatures model the tipping point at which a tumor cell shifts from a state that doesn't favor recurrence to one that does. We show that NOG signatures derived from genomic mutations of tumor founding clones (i.e., the 'most recent common ancestor' of the cells within a tumor) significantly distinguish the recurred and non-recurred breast tumors as well as outperform the most popular genomic test (i.e., Oncotype DX). These results imply that mutations of the tumor founding clones are associated with tumor recurrence and can be used to predict clinical outcomes. As such, predictive tools could be used in clinics to guide treatment routes. Finally, the concepts underlying the eTumorMetastasis pave the way for the application of genome sequencing in predictions for other complex genetic diseases. eTumorMetastasis pseudocode and related data used in this study are available at https://github.com/WangEdwinLab/eTumorMetastasis.