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LUMING洛铭BIOMEDICAL DATA SCIENCE CONSULTING

SELECTED COLLABORATION

Selected Publications

From computational signal to translational evidence: the PLT012 anti-CD36 antibody study in Cancer Discovery 2025 and other co-authored work.

SELECTED COLLABORATION

From computational signal to translational evidence

Selected publications co-authored by our team members. For each one, we list only the computational and analytical contribution.

IF 29.5 (JCR 2025; subject to the journal's official figure)

Authors: Tzeng S-F, Yu Y-R, …, Chiu L-T, …, Ho P-C.

PLT012, a humanized anti-CD36 antibody, to release anti-tumor immunity in liver cancer and liver metastasis

Background

PLT012 is a humanized anti-CD36 antibody. The study set out to release suppressed anti-tumor immunity in liver cancer and liver metastasis. Our partner was a Swiss clinical-stage immunotherapy startup, and the paper is co-authored.

Computational contribution

The author contributions credit L.-T. Chiu with data curation, formal analysis and methodology.

The authors' contribution was on the computational side: curating and analyzing signals from public data, and connecting them with preclinical and human HCC ex vivo results into one mechanistic line of evidence.

Methods

  • TCGA pan-cancer: public tumor molecular data across cancer types.
  • CIBERSORT: estimates the proportions of immune cell types in a tissue from expression data.
  • scRNA-seq: expression analysis at single-cell resolution.
  • GSEA (gene set enrichment analysis): tests whether a set of functionally related genes shifts up or down together.
  • Combined with preclinical studies and ex vivo validation in human HCC (hepatocellular carcinoma).

Why it matters

The study supported proof of concept for CD36-targeted immunotherapy. For our partner, the value went beyond a paper: it is mechanistic evidence a third party can check, where computational signals from public data line up with experimental validation.

Note

Journal impact factors are taken from JCR 2025 and are subject to the journal's official figures.

Need to explain a mechanism to someone outside your team?

Fundraising, licensing and publication all call for mechanistic evidence that holds up to questions.