Advanced tumours are often heterogeneous, consisting of subclones with various genetic alterations and functional roles. The precise molecular features that characterize the contributions of multiscale intratumour heterogeneity to malignant progression, metastasis, and poor survival are largely unknown. Here, we address these challenges in breast cancer by defining the landscape of heterogeneous tumour subclones and their biological functions using radiogenomic signatures. Molecular heterogeneity is identified by a fully unsupervised deconvolution of gene expression data. Relative prevalence of two subclones associated with cell cycle and primary immunodeficiency pathways identifies patients with significantly different survival outcomes. Radiogenomic signatures of imaging scale heterogeneity are extracted and used to classify patients into groups with distinct subclone compositions. Prognostic value is confirmed by survival analysis accounting for clinical variables. These findings provide insight into how a radiogenomic analysis can identify the biological activities of specific subclones that predict prognosis in a noninvasive and clinically relevant manner.
Motivation Complex biological tissues are often a heterogeneous mixture of several molecularly distinct cell subtypes. Both subtype compositions and subtype-specific expressions can vary across biological conditions. Computational deconvolution aims to dissect patterns of bulk tissue data into subtype compositions and subtype-specific expressions. Existing deconvolution methods can only estimate averaged subtype-specific expressions in a population, while many downstream analyses such as inferring co-expression networks in particular subtypes require subtype expression estimates in individual samples. However, individual-level deconvolution is a mathematically underdetermined problem because there are more variables than observations. Results We report a sample-wise Convex Analysis of Mixtures (swCAM) method that can estimate subtype proportions and subtype-specific expressions in individual samples from bulk tissue transcriptomes. We extend our previous CAM framework to include a new term accounting for between-sample variations and formulate swCAM as a nuclear-norm and ℓ2,1-norm regularized matrix factorization problem. We determine hyperparameter values using cross-validation with random entry exclusion and obtain a swCAM solution using an efficient alternating direction method of multipliers. Experimental results on realistic simulation data show that swCAM can accurately estimate subtype-specific expressions in individual samples and successfully extract co-expression networks in particular subtypes that are otherwise unobtainable using bulk data. In two real-world applications, swCAM analysis of bulk RNASeq data from brain tissue of cases and controls with bipolar disorder or Alzheimer’s disease identified significant changes in cell proportion, expression pattern and co-expression module in patient neurons. Comparative evaluation of swCAM versus peer methods is also provided. Availability The R Scripts of swCAM are freely available at https://github.com/Lululuella/swCAM. A user’s guide and a vignette are provided. Supplementary information Supplementary data are available at Bioinformatics online.
Psychiatric disorders are highly heritable yet polygenetic, potentially involving hundreds of risk genes. Genome-wide association studies (GWAS) have identified hundreds of genomic susceptibility loci for psychiatric disorders, but how these loci contribute to the underlying psychopathology and etiology remains elusive. Here we generated a deep human brain proteome by quantifying 11,672 proteins across 288 subjects using 11-plex tandem mass tag (TMT) coupled with two-dimensional liquid chromatography-tandem mass spectrometry (LC/LC-MS/MS). We identified 788 cis-acting protein quantitative trait loci (cis-pQTLs) associated with 883 proteins at a genome-wide false discovery rate (FDR) < 5%. In contrast to expression at transcript level and complex diseases that are found to be mainly influenced by noncoding variants, we found protein expression level tends to be regulated by non-synonymous variants. We also provided evidence of 487 shared regulatory signals between gene expression (i.e., eQTL) and protein abundance (i.e., pQTLs). Mediation analysis revealed that for most (64%) of the colocalized genes, the expression level of their corresponding proteins are regulated by cis-pQTLs via gene transcription. Causality analysis by Mendelian Randomization (MR) revealed 4 cis-pQTLs and 19 cis-eQTLs causally controlling schizophrenia (SCZ) GWAS loci, respectively. We further integrated multiple omic data together with network analysis to prioritize candidate genes for SCZ GWAS loci. Collectively, our results underscore the potential of proteome-wide linkage analysis for mechanistic understanding of psychiatric disorders.
BACKGROUND The longitudinal personal health record (LPHR) is a foundation for managing patients’ health, but we do not have such a system in the US except for the patients in the Veterans Affairs (VA) Health Care service. The fact that individual health records are scattered in multiple health care facilities without any standards make it very difficult to build such system. In addition, patients have been raising privacy and ethical concerns related to consent and granular control of LPHR. Consent is desired to be specific. However, the current consent in the industry is not that granular. At most, there is an opt-in or opt-out choice. “A scalable and interoperable LPHR is desired with patient-controlled privacy and confidentiality that preserves patients’ health information integrity and availability” [1]. To patients, consent, data security and privacy are translated to trustfulness. Patients want to be engaged and ensure only authorized people can view their personal health records with patient-managed granularity. Solving this challenge of patient-controlled consent granularity on LPHR is an important step in making LPHR useful for patient care. OBJECTIVE This research aims to design a secure LPHR with patient-controlled consent granularity, data security and privacy that both patients and providers can trust in the United States. METHODS Built upon our prior work of the blockchain-enabled next generation access control (BeNGAC) model, we designed a blockchain-enabled personalized health record (BEPHR) sharing platform with patient-controlled consent granularity capability. We implemented the construct for a patient’s LPHR with a Web-based application prototype consisting of two health care organizations with their EHRs. RESULTS In this work, we proposed a BEPHR model trusted by patients and health care providers and implemented a Web-based BEPHR sharing platform with patient-controlled consent granularity. Consent, security and privacy of BEPHR are ensured by the merits of the BeNGAC model. The instantiation of the designed model suggested the feasibility of combining emerging blockchain technology with next generation access control model to tackle a longstanding health care LPHR problem CONCLUSIONS Our BEPHR solution provides patients with granularity, security and privacy they can trust and strengthens informed consent process. Jointly, the blockchain technology and NGAC offer security, privacy and confidentiality, data integrity, auditability, scalability, distributedness, patient consent autonomy, and zero-trust capabilities. The always-validate next generation access control model prevents the insider threats. A Fast Healthcare Interoperability Resources (FHIR) interface is incorporated to show readiness of LPHR interoperability and integration.
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