Biology subjects

Wang, R. H.

Publications and source records attributed to Wang, R. H..

3 recordsLinked to original sources

bioRxiv2026

Deep-learning-driven tumor microenvironment profiling improves immunotherapy response prediction

Immunotherapy has transformed the management of many cancers, such as clear cell renal cell carcinoma (ccRCC), yet durable clinical benefit is achieved in only a subset of patients. Immunotherapy outcomes are governed by the multicellular tumor microenvironment (TME), through the structured and directional interplay of regulatory programs and ligand-receptor communications. Yet most existing methods for immunotherapy response prediction rely on independent signatures or cell types and fail to model the structured and directional signaling network within the TME. To address this gap, we introduce EcoNet, a computational framework that first models ecosystems consisting of co-occurring cell states, then constructs intercellular and intracellular signaling networks based on the ecosystems, and applies a graph attention network to derive embeddings from the signaling network for immunotherapy response prediction. Pretrained on 2,532 pan-cancer immunotherapy samples across multiple cohorts, EcoNet outperformed existing prediction methods by 7.5-19.9% and marker-based signatures by 4.4-21.2% in AUC, and its high-attention genes were enriched among those required for T-cell-mediated tumor killing in an independent CRISPR screen; applied to ccRCC with disease-specific fine-tuning, it stratified an independent ccRCC cohort by clinical outcome (P=0.024). Biologically, high-attention genes capture an interferon-responsive program that enhances antigen presentation and immune infiltration. To validate these findings, we curated a ccRCC clinical cohort profiled with CosMx single-cell spatial transcriptomics (46 samples, ~450,000 cells) and found the response-associated TME configuration identified by EcoNet localized to immune-enriched niches, supporting the response stratification. These results establish EcoNet as a generalizable framework for TME-informed therapeutic response prediction, readily extensible to other cancer types and therapeutic contexts.

bioinformatics↗

bioRxiv2026

DeSpotX: Identifiability-Based Decontamination for Spatial Transcriptomics

Spatial transcriptomics (ST) at single-cell resolution profiles gene expression in its native spatial context, but a substantial fraction of transcripts contaminate neighboring cells, compromising downstream biological analyses. Existing decontamination methods rely on heuristic priors and either ignore the spatial structure of contamination or aggregate over neighbors without separating contamination from native expression, leaving the decomposition ambiguous. To resolve this ambiguity, we introduce DeSpotX, a deep generative model that uses anchor genes, defined as genes not natively expressed in a given cell cluster, to constrain the contamination decomposition and make it identifiable. DeSpotX further uses spatial information to estimate contamination locally through a cluster-masked, distance-weighted average over neighboring cells, and prevents over-correction of low-expression signal through a learned diffusion prior. On spike-in simulations across five datasets and four ST platforms, DeSpotX achieves AUROC > 0.94 on every dataset, with gains of 0.02 to 0.12 over the best baseline, and remains robust to inaccuracies in the cell-cluster annotation and in anchor gene construction. On real tissues, we show that the decontaminated counts produce improved marker-gene specificity, more spatially coherent expression, and cell-cell communication networks consistent with known biology. We further show that iterating decontamination and cell-cluster annotation refines these outcomes, reassigning ligand-receptor signaling to the expected source cells in mouse brain and breast cancer tissues.

bioinformatics↗

bioRxiv2021

Coupled protein synthesis and ribosome-guided piRNA processing on mRNAs

PIWI-interacting small RNAs (piRNAs) protect the germline genome and are essential for fertility. Previously, we showed that ribosomes guide the biogenesis of piRNAs from long non-coding RNAs (lncRNAs) after translating the short open reading frames (ORFs) near their 5 cap. It remained unclear, however, how ribosomes proceed downstream of ORFs and how piRNA precursors distinguish from other RNAs. It is thus important to test whether a short ORF length is required for substrate recognition for ribosome guided-piRNA biogenesis. Here, we characterized a poorly understood class of piRNAs that originate from the 3 untranslated regions (3UTRs) of protein coding genes in mice and chickens. We demonstrate that their precursors are full-length mRNAs and that post-termination 80S ribosomes guide piRNA production on 3UTRs after translation of upstream long ORFs. Similar to non-sense mediated decay (NMD), piRNA biogenesis degrades mRNA right after pioneer rounds of translation and fine-tunes protein production from mRNAs. Interestingly, however, we found that NMD, along with other surveillance pathways for ribosome recycling are temporally sequestered during the pachytene stage to allow for robust piRNA production. Although 3UTR piRNA precursor mRNAs code for distinct proteins in mice and chickens, they all harbor embedded transposable elements (TEs) and produce piRNAs that cleave TEs, suggesting that TE suppression, rather than the function of proteins, is the primary evolutionary force maintaining a subset of mRNAs as piRNA precursors. Altogether, we discover a function of the piRNA pathway in fine-tuning protein production and reveal a conserved, general piRNA biogenesis mechanism that recognizes translating RNAs regardless of their ORF length in amniotes.

molecular biology↗