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lncRNA - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...

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Updated: Jun 17, 2025

Modeling Breast Cancer via an Intraductal Injection of Cre-expressing Adenovirus into the Mouse Mammary Gland

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A Genomics-Driven Artificial Intelligence-Based Model Classifies Breast Invasive Lobular Carcinoma and Discovers CDH1

Fresia Pareja¹, Higinio Dopeso¹, Yi Kan Wang²

¹Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.

Cancer Research

|August 6, 2024

Summary

This summary is machine-generated.

This study developed an artificial intelligence (AI) model for breast cancer diagnosis using genetic mutations as ground truth. The AI model accurately identified invasive lobular carcinoma (ILC) and uncovered new genetic mechanisms driving the cancer.

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Area of Science:

Pathology
Genomics
Artificial Intelligence

Background:

Artificial intelligence (AI) in cancer diagnosis often relies on subjective histologic features for training.
Developing objective ground truths is crucial for robust AI model development in pathology.

Purpose of the Study:

To develop an AI model for diagnosing invasive lobular carcinoma (ILC) using CDH1 biallelic mutations as a genetic ground truth.
To explore alternative CDH1 inactivating mechanisms in breast neoplasms.
To establish a framework for utilizing orthogonal ground truths in AI development for whole-slide imaging.

Main Methods:

Developed an AI model trained on whole-slide images of breast neoplasms.
Utilized CDH1 biallelic mutations, pathognomonic for ILC, as the genetic ground truth.
Validated the AI model's performance on internal and external cohorts.

Main Results:

The AI model achieved high accuracy in predicting CDH1 biallelic mutations (0.95) and diagnosing ILC (0.96).
Identified alternative CDH1 inactivating mechanisms in 74% of samples lacking expected mutations.
Demonstrated strong diagnostic accuracy (0.95 and 0.89) in validation cohorts.
Correlated AI model's latent features with explainable histopathologic features.

Conclusions:

AI models trained with genetic ground truths can robustly classify ILC and uncover novel biologic insights.
This approach provides a basis for orthogonal ground truth utilization in diagnostic AI for pathology.
Genetic alterations with strong genotypic-phenotypic correlations can enhance AI development for cancer diagnosis and discovery.