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Background <p>Genetic interactions, including synthetic lethality (SL) and synthetic viability (SV), are crucial for understanding tumor-specific vulnerabilities and mechanisms of drug resistance. However, predicting drug response at single-cell resolution based on SL and SV remains challenging.</p> Methods <p>Here, we construct a large-scale atlas of cell-type-specific SL and SV networks across 14 human cancers using scRNA-seq datasets. Based on this atlas, we develop DISCERN (Drug response Inference from Single-Cell gEnetic inteRactioNs), a novel computational framework designed to infer single-cell drug sensitivity by utilizing malignant cell-specific genetic interactions. We also establish CellGIdb, an interactive portal that provides the cell-type-specific genetic interaction networks.</p> Results <p>We reconstruct cell-type-specific genetic interaction networks across cancers, revealing both shared and distinct patterns among cell types. Notably, SL and SV interactions derived from malignant cells and T cells exhibit prognostic value and correlate with response to immunotherapy. DISCERN effectively infers tumor cell-specific drug sensitivity in scRNA-seq datasets from lung and breast cancers. DISCERN demonstrates improved predictive performance compared with existing computational methods. CellGIdb provides user-friendly analytical tools to facilitate the exploration of genetic interactions’ roles in drug response and immunotherapy.</p> Conclusions <p>Collectively, this study provides a comprehensive atlas and a novel computational framework, DISCERN, for interpreting drug responses in the context of genetic interactions at single-cell resolution. The publicly available CellGIdb (<a href="https://biodata.hrbmu.edu.cn/CellGIdb/index.html">https://biodata.hrbmu.edu.cn/CellGIdb/index.html</a>) resource will support further exploration of cell-type-specific vulnerabilities in cancer therapy.</p>

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DISCERN: inferring drug sensitivity from single-cell transcriptomes using cell-type-specific genetic interaction networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月16日

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  • Mingyue Liu,
  • Yu Tian,
  • Yuchao Jia,
  • Jiewei Zhang,
  • Xi Yi,
  • Shaocong Sang,
  • Nan Zhang,
  • Kaidong Liu,
  • Yunyi Peng,
  • Yuncong Wang,
  • Xin Li,
  • Bo Chen,
  • Haihai Liang,
  • Yunyan Gu

摘要

Background

Genetic interactions, including synthetic lethality (SL) and synthetic viability (SV), are crucial for understanding tumor-specific vulnerabilities and mechanisms of drug resistance. However, predicting drug response at single-cell resolution based on SL and SV remains challenging.

Methods

Here, we construct a large-scale atlas of cell-type-specific SL and SV networks across 14 human cancers using scRNA-seq datasets. Based on this atlas, we develop DISCERN (Drug response Inference from Single-Cell gEnetic inteRactioNs), a novel computational framework designed to infer single-cell drug sensitivity by utilizing malignant cell-specific genetic interactions. We also establish CellGIdb, an interactive portal that provides the cell-type-specific genetic interaction networks.

Results

We reconstruct cell-type-specific genetic interaction networks across cancers, revealing both shared and distinct patterns among cell types. Notably, SL and SV interactions derived from malignant cells and T cells exhibit prognostic value and correlate with response to immunotherapy. DISCERN effectively infers tumor cell-specific drug sensitivity in scRNA-seq datasets from lung and breast cancers. DISCERN demonstrates improved predictive performance compared with existing computational methods. CellGIdb provides user-friendly analytical tools to facilitate the exploration of genetic interactions’ roles in drug response and immunotherapy.

Conclusions

Collectively, this study provides a comprehensive atlas and a novel computational framework, DISCERN, for interpreting drug responses in the context of genetic interactions at single-cell resolution. The publicly available CellGIdb (https://biodata.hrbmu.edu.cn/CellGIdb/index.html) resource will support further exploration of cell-type-specific vulnerabilities in cancer therapy.