Hui Yu

Objectives: To evaluate basal‐luminal cell of origin subtyping using Prediction Analysis of Microarray of 50 genes (PAM50) genomic classification profiles for predicting disease progression in men undergoing active surveillance (AS) for prostate cancer (PCa). Patients and Methods: In the prospective Miami Active Surveillance Trial (MAST) trial, 205 men undergoing AS received serial multiparametric magnetic resonance imaging (MRI) and biopsies, including MRI‐targeted and systematic sampling. The highest‐grade core from each biopsy was sent for expression profiling using Decipher, a clinical‐grade transcriptome assay (Veracyte Inc., San Diego, CA, USA). Basal‐luminal subtyping was evaluated using PAM50 molecular subtype models. PCa grade progression was compared across subtypes, as were gene mutation signatures, prognostic indices, and pathway activities. Kaplan–Meier curves, log‐rank test, and multivariable Cox regression were used to assess association between PAM50 and grade progression. Heatmaps and volcano plots were rendered to illustrate potential mechanistical differences between PAM50 subtypes. Results: Of the 205 patients, 128 had transcriptome data for baseline basal‐luminal classification. PAM50 identified 46 Luminal A (LA), 26 Luminal B (LB), and 56 Basal subtypes. Decipher scores were lowest in LA, followed by Basal, and highest in LB. Grade progression‐free survival was worse in patients with the LB subtype (median 1.7 years) compared to those with LA and Basal subtypes (median 2.9 years; log‐rank P = 0.005); LB patients had grade progression‐free survival of 34% by 24 months of AS, compared to 63% for Basal or 68% for LA. Transcriptome analysis showed distinct enrichment profiles for each subtype, with LB strongly associated with SPOP and CHD1 mutations. Limitations include small sample size and single‐institution setting. Conclusion: The PAM50 basal‐luminal subtyping shows promise as a molecular classification tool for predicting progression risk in PCa. This is one of the only prospective studies evaluating PAM50 subtyping for predicting cancer progression in a cohort of men undergoing AS for PCa.

To investigate whether expression signatures from Grade Group 1 (GG1) biopsy cores can detect the presence of higher-grade cancer elsewhere in the prostate. We enrolled 205 men with low to favorable intermediate-risk prostate cancer undergoing active surveillance on a prospective protocol. All participants underwent MRI and confirmatory biopsy at enrollment, followed by annual biopsies for three more years. Select cores were sent for Decipher genomic classifier (DGC) testing, and derived Genomic Prostate Score (dGPS) and Cell Cycle Progression (dCCP) signatures were obtained. We compared genomic scores from GG1 biopsy cores with versus without coexisting GG2 and GG3 or higher cancer (GG2+ and GG3+, respectively). We repeated this comparison using only the highest-volume GG1 core from each biopsy, which is the current standard of care. Genomic profiling was successful in 141 of 205 patients (324 GG1 cores). There were no significant differences in DGC, dCCP, or dGPS scores between GG1 cores with versus without coexisting GG2+ cancer elsewhere. This remained true, when using the largest volume GG1 core from each biopsy. dGPS was higher among GG1 cores with coexisting GG3+ cancer compared to those without (0.24 vs 0.10, p=0.012); however, there was no difference between the groups on DGC or dCCP scores. Genomic classifiers in GG1 cores did not predict coexisting GG2+ cancer, while dGPS signatures showed some promise in detecting GG3+ cancer elsewhere in the gland. None of the signatures showed a difference between groups when using the highest volume GG1 core, which is the standard practice for genomic classifiers.

The coronavirus disease 2019 (COVID-19) pandemic has caused a dramatic loss of human life and devastated the worldwide economy. Numerous efforts have been made to mitigate COVID-19 symptoms and reduce the death rate. We conducted literature mining of more than 250 thousand published works and curated the 174 most widely used COVID-19 medications. Overlaid with the human protein–protein interaction (PPI) network, we used Steiner tree analysis to extract a core subnetwork that grew from the pharmacological targets of ten credible drugs ascertained by the CTD database. The resultant core subnetwork consisted of 34 interconnected genes, which were associated with 36 drugs. Immune cell membrane receptors, the downstream cellular signaling cascade, and severe COVID-19 symptom risk were significantly enriched for the core subnetwork genes. The lung mast cell was most enriched for the target genes among 1355 human tissue-cell types. Human bronchoalveolar lavage fluid COVID-19 single-cell RNA-Seq data highlighted the fact that T cells and macrophages have the most overlapping genes from the core subnetwork. Overall, we constructed an actionable human target-protein module that mainly involved anti-inflammatory/antiviral entry functions and highly overlapped with COVID-19-severity-related genes. Our findings could serve as a knowledge base for guiding drug discovery or drug repurposing to confront the fast-evolving SARS-CoV-2 virus and other severe infectious diseases.

Molecular prognosis markers hold promise for improved prediction of patient survival, and a pathway or gene set may add mechanistic interpretation to their prognostic prediction power. In this study, we demonstrated a novel strategy to identify prognosis-relevant gene sets in cancers. Our study consists of a first round of gene-level analyses and a second round of gene-set-level analyses, in which the Composite Gene Expression Score critically summarizes a surrogate expression value at gene set level and a permutation procedure is exerted to assess prognostic significance of gene sets. An optional differential coexpression module is appended to the two phases of survival analyses to corroborate and refine prognostic gene sets. Our strategy was demonstrated in 33 cancer types across 32,234 gene sets. We found oncogenic gene sets accounted for an increased proportion among the final gene sets, and genes involved in DNA replication and DNA repair have ubiquitous prognositic value for multiple cancer types. In summary, we carried out the largest gene set based prognosis study to date. Compared to previous similar studies, our approach offered multiple improvements in design and methodology implementation. Functionally relevant gene sets of ubiquitous prognostic significance in multiple cancer types were identified.

Somatic mutations are one of the most important factors in tumorigenesis and are the focus of most cancer-sequencing efforts. The co-occurrence of multiple mutations in one tumor has gained increasing attention as a means of identifying cooperating mutations or pathways that contribute to cancer. Using multi-omics, phenotypical, and clinical data from 29,559 cancer subjects and 1747 cancer cell lines covering 78 distinct cancer types, we show that co-mutations are associated with prognosis, drug sensitivity, and disparities in sex, age, and race. Some co-mutation combinations displayed stronger effects than their corresponding single mutations. For example, co-mutation TP53:KRAS in pancreatic adenocarcinoma is significantly associated with disease specific survival (hazard ratio = 2.87, adjusted p-value = 0.0003) and its prognostic predictive power is greater than either TP53 or KRAS as individually mutated genes. Functional analyses revealed that co-mutations with higher prognostic values have higher potential impact and cause greater dysregulation of gene expression. Furthermore, many of the prognostically significant co-mutations caused gains or losses of binding sequences of RNA binding proteins or micro RNAs with known cancer associations. Thus, detailed analyses of co-mutations can identify mechanisms that cooperate in tumorigenesis.

Background: While most differential coexpression (DC) methods are bound to quantify a single correlation value for a gene pair across multiple samples, a newly devised approach under the name Correlation by Individual Level Product (CILP) revolutionarily projects the summary correlation value to individual product correlation values for separate samples. CILP greatly widened DC analysis opportunities by allowing integration of non-compromised statistical methods.Methods: Here, we performed a study to verify our hypothesis that conditional relationships, i.e., gene pairs of remarkable differential coexpression, may be sought as quantitative prognostic markers for human cancers. Alongside the seeking of prognostic gene links in a pan-cancer setting, we also examined whether a trend of global expression correlation loss appeared in a wide panel of cancer types and revisited the controversial subject of mutual relationship between the DE approach and the DC approach.Results: By integrating CILP with classical univariate survival analysis, we identified up to 244 conditional gene links as potential prognostic markers in five cancer types. In particular, five prognostic gene links for kidney renal papillary cell carcinoma tended to condense around cancer geneESPL1, and the transcriptional synchrony betweenESPL1andPTTG1tended to be elevated in patients of adverse prognosis. In addition, we extended the observation of global trend of correlation loss in more than ten cancer types and empirically proved DC analysis results were independent of gene differential expression in five cancer types.Conclusions: Combining the power of CILP and the classical survival analysis, we successfully fetched conditional transcriptional relationships that conferred prognosis power for five cancer types. Despite a general trend of global correlation loss in tumor transcriptomes, most of these prognosis conditional links demonstrated stronger expression correlation in tumors, and their stronger coexpression was associated with poor survival.

Transcriptomic studies of mental disorders using the human brain tissues have been limited, and gene expression signatures in schizophrenia (SCZ) remain elusive. In this study, we applied three differential co-expression methods to analyze five transcriptomic datasets (three RNA-Seq and two microarray datasets) derived from SCZ and matched normal postmortem brain samples. We aimed to uncover biological pathways where internal correlation structure was rewired or inter-coordination was disrupted in SCZ. In total, we identified 60 rewired pathways, many of which were related to neurotransmitter, synapse, immune, and cell adhesion. We found the hub genes, which were on the center of rewired pathways, were highly mutually consistent among the five datasets. The combinatory list of 92 hub genes was generally multi-functional, suggesting their complex and dynamic roles in SCZ pathophysiology. In our constructed pathway crosstalk network, we found “Clostridium neurotoxicity” and “signaling events mediated by focal adhesion kinase” had the highest interactions. We further identified disconnected gene links underlying the disrupted pathway crosstalk. Among them, four gene pairs (PAK1:SYT1, PAK1:RFC5, DCTN1:STX1A, and GRIA1:MAP2K4) were normally correlated in universal contexts. In summary, we systematically identified rewired pathways, disrupted pathway crosstalk circuits, and critical genes and gene links in schizophrenia transcriptomes.

Compared to the conventional differential expression approach, differential coexpression analysis represents a different yet complementary perspective into diseased transcriptomes. In particular, global loss of transcriptome correlation was previously observed in aging mice, and a most recent study found genetic and environmental perturbations on human subjects tended to cause universal attenuation of transcriptome coherence. While methodological progresses surrounding differential coexpression have helped with research on several human diseases, there has not been an investigation of coexpression disruptions in chronic kidney disease (CKD) yet. RNA-seq was performed on total RNAs of kidney tissue samples from 140 CKD patients. A combination of differential coexpression methods were employed to analyze the transcriptome transition in CKD from the early, mild phase to the late, severe kidney damage phase. We discovered a global expression correlation attenuation in CKD progression, with pathway Regulation of nuclear SMAD2/3 signaling demonstrating the most remarkable intra-pathway correlation rewiring. Moreover, the pathway Signaling events mediated by focal adhesion kinase displayed significantly weakened crosstalk with seven pathways, including Regulation of nuclear SMAD2/3 signaling. Well-known relevant genes, such as ACTN4, were characterized with widespread correlation disassociation with partners from a wide array of signaling pathways. Altogether, our analysis reported a global expression correlation attenuation within and between key signaling pathways in chronic kidney disease, and presented a list of vanishing hub genes and disrupted correlations within and between key signaling pathways, illuminating on the pathophysiological mechanisms of CKD progression.

Somatic mutation and gene expression dysregulation are considered two major tumorigenesis factors. While independent investigations of either factor pervade, studies of associations between somatic mutations and gene expression changes have been sporadic and nonsystematic. Utilizing genomic data collected from 11 315 subjects of 33 distinct cancer types, we constructed MutEx, a pan-cancer integrative genomic database. This database records the relationships among gene expression, somatic mutation and survival data for cancer patients. MutEx can be used to swiftly explore the relationship between these genomic/clinic features within and across cancer types and, more importantly, search for corroborating evidence for hypothesis inception. Our database also incorporated Gene Ontology and several pathway databases to enhance functional annotation, and elastic net and a gene expression composite score to aid in survival analysis. To demonstrate the usability of MutEx, we provide several application examples, including top somatic mutations associated with the most extensive expression dysregulation in breast cancer, differential mutational burden downstream of DNA mismatch repair gene mutations and composite gene expression score-based survival difference in breast cancer. MutEx can be accessed at http://www.innovebioinfo.com/Databases/Mutationdb_About.php.

Very short tandem repeats bear substantial genetic, evolutional, and pathological significance in genome analyses. Here, we compiled a census of tandem mono-nucleotide/di-nucleotide/tri-nucleotide repeats (MNRs/DNRs/TNRs) in GRCh38, which we term “polytracts” in general. Of the human genome, 144.4 million nucleotides (4.7%) are occupied by polytracts, and 0.47 million single nucleotides are identified as polytract hinges, i.e., break-points of tandem polytracts. Preliminary exploration of the census suggested polytract hinge sites and boundaries of AAC polytracts may bear a higher mapping error rate than other polytract regions. Further, we revealed landscapes of polytract enrichment with respect to nearly a hundred genomic features. We found MNRs, DNRs, and TNRs displayed noticeable difference in terms of locational enrichment for miscellaneous genomic features, especially RNA editing events. Non-canonical and C-to-U RNA-editing events are enriched inside and/or adjacent to MNRs, while all categories of RNA-editing events are under-represented in DNRs. A-to-I RNA-editing events are generally under-represented in polytracts. The selective enrichment of non-canonical RNA-editing events within MNR adjacency provides a negative evidence against their authenticity. To enable similar locational enrichment analyses in relation to polytracts, we developed a software Polytrap which can handle 11 reference genomes. Additionally, we compiled polytracts of four model organisms into a Track Hub which can be integrated into USCS Genome Browser as an official track for convenient visualization of polytracts. Short tandem repeats in the human genome are frequently used as genetic markers in population studies and they are also associated with genetic diseases. Nevertheless, accurate localization and mapping of short tandem repeats in the reference genome have not been well addressed, and there is a lack of systematic catalog of short tandem repeats in the most updated human reference genome. Here, we compiled an updated census of mono-nucleotide/di-nucleotide/tri-nucleotide repeats (MNRs/DNRs/TNRs) from the human reference genome GRCh38, and collectively termed them polytracts. The resultant polytract dataset encompasses TNRs, the presumably more biologically significant repeat species, as well as the under-studied species MNRs and DNRs. With such a composition, the polytract dataset can provide a negative control for genome analyses which are potentially confounded with polytract regions, and it can also be used as a discovery tool to screen for MNR/DNR/TNR-characteristic genomic features. We integrated the polytract dataset with genome coordinates of RNA-editing sites, and found significant enrichment of C-to-U and non-canonical RNA-editing events in adjacency of MNR polytracts, especially break-points of tandem polytracts. The same phenomenon was not observed for the canonical A-to-I RNA editing type. This distinct enrichment patterns between canonical and non-canonical RNA-editing events provides a negative evidence against the authenticity of non-canonical RNA-editing events. Similarly, we examined locations of enhancer sequences relative to polytracts, and found varied degree of locational enrichment among subtypes of polytracts. In practice, different researchers may be interested in different genomic features and/or different organisms, so we developed a tool Polytrap and a Public Track Hub to assist with general locational enrichment analysis of polytracts with respect to localizable genomic features.