ACSCeND: AI Framework to Detect Cancer Stem-like Cells

13 Aug 2026

Subject GS 03: Science and Technology

Context: The Department of Science and Technology highlighted a new AI framework, ACSCeND, that identifies hidden cancer stem-like cells linked to tumour recurrence, metastasis and treatment resistance.

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About ACSCeND

ACSCeND (AI-based Cancer Stem-like Cell Profiler and Neoplasm Deconvoluter) is an AI framework designed to identify hidden cancer stem-like cell populations within tumour samples.
Developed By: The research was led by S. N. Bose National Centre for Basic Sciences (SNBNCBS), an autonomous institute under DST, in collaboration with Ashoka University.
Key Features
- It combines knowledge from single-cell sequencing with deep learning to analyse conventional bulk tumour RNA sequencing data.
- Unlike conventional approaches that assign a single stemness score, ACSCeND distinguishes three developmental states of cancer stem-like cells.
- Application
  - ACSCeND can enable analysis of thousands of patient samples, including settings where advanced single-cell sequencing facilities are unavailable, thereby supporting precision medicine and personalised treatment strategies.
Key Findings
- Three Cell States: The framework identified three developmental states of cancer stem-like cells—pluripotent-like, multipotent-like and unipotent-like—providing a better understanding of tumour heterogeneity.
- Large-Scale Analysis: Researchers applied the framework to over 25,000 tumour samples from international cancer databases.
- Treatment and Survival: Tumours enriched with highly potent, pluripotent-like stem cells were associated with poorer survival, greater recurrence and reduced response to immunotherapy.
- Therapeutic Potential: The study identified molecular programmes linked to tumour growth, immune evasion and drug resistance, potentially aiding new drug-target discovery and relapse prediction.