Biology subjects

Qin, L.-X.

Publications and source records attributed to Qin, L.-X..

4 recordsLinked to original sources

bioRxiv2026

Contextual Evaluation of MicroRNA Sequencing Data Harmonization: Performance in Sample Clustering

Reliable translation of microRNA sequencing data depends on effective harmonization to mitigate artifacts from variable experimental handling. Although many harmonization methods exist, prior evaluations have focused mainly on differential expression analysis, leaving the impact on subgroup discovery understudied. We present a framework for evaluating harmonization in the context of sample clustering, which integrates AI-augmented datasets, statistical evaluation pipelines, and accessible software tools, enabling systematic comparisons across diverse signal-to-artifact ratios and cluster composition settings. Using this framework, we show that harmonization can, often partially, restore clustering accuracy lost to artifacts, especially at moderate signal-to-artifact ratios, with the level of gains depending on the specific harmonization method, the paired clustering technique, and the cluster composition setting. We further confirm these findings by analyzing reconstructed cohorts from The Cancer Genome Atlas breast cancer microRNA sequencing data. Collectively, the results underscore the need for tailored harmonization to support reliable subgroup discovery and highlight the broader importance of context-specific workflows in translational genomics.

bioinformatics↗

bioRxiv2025

Revisiting Tumor Mutational Burden Cutoff: Multi-Study Replicability in Immunotherapy

PURPOSETumor Mutational Burden (TMB) is a crucial biomarker for predicting the effectiveness of cancer immunotherapy. However, ongoing debates about the optimal cutoff significantly impact the clinical application of immunotherapy. The purpose of our study is to comprehensively evaluate TMB cutoffs for predicting immunotherapy outcomes across multiple studies, considering both cancer type and outcome endpoint, using statistically principled approaches. METHODSWe analyzed data from PredictIO, curated through a PubMed search for studies involving immune checkpoint blockade treatment in the adjuvant setting, excluding those involving combinations with chemotherapy, targeted treatment, or radiation. The included studies, published between January 2015 and June 2022, required tumor sequencing data from whole exomes or targeted gene panels for TMB assessment. Outcome endpoints included clinical benefit rate (CBR), progression-free survival (PFS), and overall survival (OS). CBR was defined as the rate of complete response, partial response, or stable disease lasting at least six months, according to RECIST 1.1 criteria. OS and PFS were defined as the interval from treatment initiation to death and disease progression, respectively. TMB was uniformly derived from tumor sequencing data, representing the count of nonsynonymous mutations relative to the target sequencing size. TMB cutoffs were evaluated for outcome associations using study replicability analysis, alongside individual-study analysis and random-effects meta-analysis for comparison. RESULTSThe data provided sufficient evidence of replicable outcome associations for specific TMB cutoffs in melanoma, lung cancer, and bladder cancer. The FDA-recommended cutoff of 10 mutations per megabase showed replicable associations in melanoma for OS (p-value < 0.01) and CBR (p-value < 0.01), though more replicable cutoffs were identified for the latter. Lower cutoffs of 4 and 2 were found to be replicable in lung cancer for CBR (p-value = 0.04) and in bladder cancer for OS (p-value < 0.01), respectively. No cutoff was deemed replicable for the other cancer type and outcome combinations, due to no association, inadequate power, or insufficient data. CONCLUSIONA pan-cancer cutoff of 10 mutations per megabase may not be optimal for predicting immunotherapy outcomes. Further studies are needed to determine appropriate cutoffs specific to cancer types and outcomes through statistically principled replicability analyses.

cancer biology↗

bioRxiv2019

A Chemical Probe of CARM1 Alters Epigenetic Plasticity against Breast Cancer Cell Invasion

CARM1 is a cancer-relevant protein arginine methyltransferase that regulates many aspects of transcription. Its pharmacological inhibition is a promising anti-cancer strategy. Here SKI-73 is presented as a CARM1 chemical probe with pro-drug properties. SKI-73 can rapidly penetrate cell membranes and then be processed into active inhibitors, which are retained intracellularly with 10-fold enrichment for days. These compounds were characterized for their potency, selectivity, modes of action, and on-target engagement. SKI-73 recapitulates the effect of CARM1 knockout against breast cancer cell invasion. Single-cell RNA-seq analysis revealed that the SKI-73-associated reduction of invasiveness act via altering epigenetic plasticity and suppressing the invasion-prone subpopulation. Interestingly, SKI-73 and CARM1 knockout alter the epigenetic plasticity with remarkable difference, arguing distinct modes of action between the small-molecule and genetic perturbation. We therefore discovered a CARM1-addiction mechanism of cancer metastasis and developed a chemical probe to target this process.

biochemistry↗

bioRxiv2019

Multi-omic analysis supports a developmental hierarchy of molecular subtypes in high-grade serous ovarian carcinoma

Multiple studies have identified transcriptome subtypes of high-grade serous ovarian carcinoma (HGSOC), but these have yet to impact clinical practice. Interpretation and translation of HGSOC subtypes are complicated by tumor evolution and polyclonality accompanied by accumulation of somatic aberrations, varying cell type admixtures, and different tissues of origin. The chronology of HGSOC subtype evolution was examined in the context of these factors by a novel integrative analysis of bulk absolute somatic copy number analysis and gene expression in The Cancer Genome Atlas, complemented by single-cell RNA-seq analysis of six independent tumors. The approach was validated by contrast to soft-tissue sarcoma. Genomic lesions associated with HGSOC subtypes tend to be subclonal, implying subtype divergence at later stages of tumor evolution. Subclonality of recurrent HGSOC alterations is particularly evident for proliferative tumors, characterized by extreme genomic instability, absence of immune infiltration, and greater patient age. In contrast, differentiated tumors are characterized by largely intact genome integrity, high immune infiltration, and younger patient age. We propose an alternative model to discrete subtypes of HGSOC, in which tumors develop from an early differentiated spectrum to a late proliferative spectrum, along a timeline characterized by increasing genomic instability and subclonal expansion. The proposed methods provide a new approach to investigating tumor evolution through multi-omic analysis. Statement of SignificanceThis study proposes a method to infer whether transcriptome-based groupings of tumors differentiate early in carcinogenesis and are therefore potentially appropriate targets for therapy, and demonstrates that this is not the case for high-grade serous ovarian carcinoma (HGSOC). Significant findings for HGSOC include: O_LITumor purity, ploidy, and subclonality can be reliably inferred from different genomic platforms and show marked differences between subtypes C_LIO_LIRecurrent DNA alterations are associated with subtypes and tend to occur more frequently in subclones C_LIO_LISingle-cell sequencing of 42,000 tumor cells reveals widespread heterogeneity in tumor cell type composition that drives bulk subtype calls, but demonstrates a lack of intrinsic subtypes among tumor epithelial cells C_LIO_LIFindings prompt the dismissal of discrete transcriptome subtypes for HGSOC and replacement by a more realistic model of continuous tumor development that includes mixtures of subclones, accumulation of somatic aberrations, infiltration of immune and stromal cells in proportions correlated with tissue of origin and tumor stage, and evolution between properties previously associated with discrete subtypes C_LI

cancer biology↗