# Data‐Driven Molecular Typing: A New Frontier in Esophageal Cancer Management

## TL;DR

This paper explores how big data and multi-omics approaches can identify molecular subtypes of esophageal cancer, enabling personalized treatment strategies.

## Contribution

The paper systematically reviews molecular subtyping of ESCC using big data, revealing subtype-specific biomarkers and treatment implications.

## Key findings

- Molecular subtypes of ESCC exhibit distinct genetic mutations and metabolomic profiles.
- Subtype-specific characteristics correlate with treatment responses and immunotherapy efficacy.
- Omics-based typing offers potential for personalized ESCC management.

## Abstract

Esophageal squamous cell carcinoma (ESCC) is a predominant and highly lethal form of esophageal cancer, with a five‐year survival rate below 20%. Despite advancements, most patients are diagnosed at advanced stages, limiting effective treatment options. Multi‐omics integration, encompassing somatic genomic alterations, inherited genetic mutations, transcriptomics, proteomics, metabolomics, and single‐cell sequencing, has enabled the identification of distinct molecular subtypes of ESCC.

This article systematically reviewed the current status of molecular subtyping of ESCC based on big data, summarized unique subtypes with differing treatment responses and prognostic outcomes.

Key findings included subtype‐specific genetic mutations, signaling pathway alterations, and metabolomic profiles, which offer novel biomarkers and therapeutic targets. Furthermore, this review discusses the link between molecular subtypes and immunotherapy efficacy, chemotherapy response, and drug development.

These insights highlight the potential of omics‐based molecular typing to transform ESCC management and facilitate personalized treatment strategies.