Biology subjects

Qing, T.

Publications and source records attributed to Qing, T..

6 recordsLinked to original sources

bioRxiv2024

Small molecule activation of the tumor suppressor kinase LKB1

The ability to identify and target oncogenic signals has transformed clinical oncology. Drug development for targeted therapies has historically focused on the inhibition of oncogenic kinases and GTPases. However, many cancer patients do not benefit from targeted approaches because their tumors lack targetable mutations. Therapeutic augmentation of tumor suppressive signaling could be a viable alternative but poses challenges. Specifically, designing compounds capable of stimulating kinase activity is more structurally challenging than inhibitor design, and most kinases lack targetable allosteric pockets. Inactivation of the liver kinase B1 (LKB1) tumor suppressor kinase is associated with poor prognosis and therapeutic resistance. Thus, augmented LKB1 function could be beneficial for cancer patients whose tumors retain intact copies of the gene. LKB1 signals as part of an obligate trimer including the scaffolding protein Mouse protein-25 (MO25) and the pseudokinase (PSK) STE20-related kinase adapter protein (STRAD). As STRAD binds but does not metabolize ATP, it provides the opportunity for a novel activation strategy. We have developed STRAD-binding compounds capable of activating LKB1 and demonstrate the therapeutic benefits of LKB1 activation in a target-dependent manner within cancer cell lines.

biochemistry↗

bioRxiv2022

Ratio-based quantitative multiomics profiling using universal reference materials empowers data integration

Multiomics profiling is a powerful tool to characterize the same samples with complementary features orchestrating the genome, epigenome, transcriptome, proteome, and metabolome. However, the lack of ground truth hampers the objective assessment of and subsequent choice from a plethora of measurement and computational methods aiming to integrate diverse and often enigmatically incomparable omics datasets. Here we establish and characterize the first suites of publicly available multiomics reference materials of matched DNA, RNA, proteins, and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters, providing built-in truth defined by family relationship and the central dogma. We demonstrate that the "ratio"-based omics profiling data, i.e., by scaling the absolute feature values of a study sample relative to those of a concurrently measured universal reference sample, were inherently much more reproducible and comparable across batches, labs, platforms, and omics types, thus empower the horizontal (within-omics) and vertical (cross-omics) data integration in multiomics studies. Our study identifies "absolute" feature quantitation as the root cause of irreproducibility in multiomics measurement and data integration, and urges a paradigm shift from "absolute" to "ratio"-based multiomics profiling with universal reference materials.

systems biology↗

bioRxiv2022

Quartet RNA reference materials and ratio-based reference datasets for reliable transcriptomic profiling

As an indispensable tool for transcriptome-wide analysis of differential gene expression, RNA sequencing (RNAseq) has demonstrated great potential in clinical applications. However, the lack of multi-group RNA reference materials of biological relevance and the corresponding reference datasets for assessing the reliability of RNAseq hampers its wide clinical applications wherein the underlying biological differences among study groups are often small. As part of the Quartet Project for quality control and data integration of multiomic profiling, we established four RNA reference materials derived from immortalized B-lymphoblastoid cell lines from four members of a monozygotic twin family. Additionally, we constructed ratio-based transcriptome-wide reference datasets using multi-batch RNAseq datasets, providing "ground truth" for benchmarking. Moreover, Quartet-sample-based quality metrics were developed for assessing reliability of RNAseq technology in terms of intra-batch proficiency and cross-batch reproducibility. The small intrinsic biological differences among the Quartet samples enable sensitive assessment of performance of transcriptomic measurements. The Quartet RNA reference materials combined with the reference datasets can be served as unique resources for assessing data quality and improving reliability of transcriptomic profiling.

genetics↗

bioRxiv2021

A Network Propagation Approach to Prioritize Long Tail Genes in Cancer

IntroductionThe diversity of genomic alterations in cancer pose challenges to fully understanding the etiologies of the disease. Recent interest in infrequent mutations, in genes that reside in the "long tail" of the mutational distribution, uncovered new genes with significant implication in cancer development. The study of these genes often requires integrative approaches with multiple types of biological data. Network propagation methods have demonstrated high efficacy in uncovering genomic patterns underlying cancer using biological interaction networks. Yet, the majority of these analyses have focused their assessment on detecting known cancer genes or identifying altered subnetworks. In this paper, we introduce a network propagation approach that entirely focuses on long tail genes with potential functional impact on cancer development. ResultsWe identify sets of often overlooked, rarely to moderately mutated genes whose biological interactions significantly propel their mutation-frequency-based rank upwards during propagation in 17 cancer types. We call these sets "upward mobility genes" (UMGs, 28-83 genes per cancer type) and hypothesize that their significant rank improvement indicates functional importance. We report new cancer-pathway associations based on UMGs that were not previously identified using driver genes alone, validate UMGs role in cancer cell survival in vitro--alone and compared to other network methods--using extensive genome-wide RNAi and CRISPR data repositories, and further conduct in vitro functional screenings resulting the validation of 8 previously unreported genes. ConclusionOur analysis extends the spectrum of cancer relevant genes and identifies novel potential therapeutic targets.

bioinformatics↗

bioRxiv2021

Cancer relevance of human genes

BackgroundIt is unclear how many of genes contribute to the biology of cancer. We hypothesize that genes that interact with core cancer gene (CCG) in a protein-protein interaction network (PPI) may have functional importance. MethodsWe categorized genes into 1- (n=6791), 2- (n=7724), 3- (n=1587), and >3-steps (n=362) removed from the nearest CCG in the STRING PPI and demonstrate that the cancer-biology related functional contribution of the genes in these different neighborhood categories decreases as their distance from the CCGs increases. ResultsGenes closer to cancer genes manifest greater connectedness in the network, show greater importance in maintaining cell viability in a broad range of cancer cells in vitro, are also under greater negative germline selection pressure in the healthy populations, and have higher somatic mutation frequency and cancer effect. ConclusionsApproximately 70% of human genes are 1 or 2 steps removed from cancer genes in protein network and show functional importance in cancer-biology. These results suggest that the universe of cancer-relevant genes extends to thousands of genes that can contribute functional effects when dysregulated.

genomics↗

bioRxiv2020

Cancer-specific associations of driver genes with immunotherapy outcome

Genomic features such as microsatellite instability (MSI) and tumor mutation burden (TMB) are predictive of immune checkpoint inhibitor (ICI) response. However, they do not account for the functional effects of specific driver gene mutations, which may alter the immune microenvironment and influence immunotherapy outcomes. By analyzing a multi-cancer cohort of 1,525 ICI-treated patients, we identified 12 driver genes in 6 cancer types associated with treatment outcomes, including genes involved in oncogenic signaling pathways (NOTCH, WNT, FGFR) and chromatin remodeling. Mutations of PIK3CA, PBRM1, SMARCA4, and KMT2D were associated with worse outcomes across multiple cancer types. In comparison, genes showing cancer-specific associations--such as KEAP1, BRAF, and RNF43--harbored distinct variant types and variants, some of which were individually associated with outcomes. In colorectal cancer, a common RNF43 indel was a putative neoantigen associated with higher immune infiltration and favorable ICI outcomes. Finally, we showed that selected mutations were associated with PD-L1 status and could further stratify patient outcomes beyond MSI or TMB, highlighting their potential as biomarkers for immunotherapy.

genomics↗