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Deep learning-based classifier for carcinoma of unknown primary using methylation quantitative trait loci.

Adam Walker1, Camila S Fang1,2, Chanel Schroff1

  • 1Department of Pathology, NYU Langone Health and NYU Grossman School of Medicine, New York, NY, United States.

Journal of Neuropathology and Experimental Neurology
|November 28, 2024
PubMed
Summary

Identifying the origin of cancer of unknown primary (CUP) is challenging. This study developed a DNA methylation classifier using organ-specific mQTLs, achieving high accuracy in pinpointing tumor origin for better cancer treatment.

Keywords:
DNA methylationEPIC arraycancer of unknown primaryclassifierdeep learningmetastasismolecular pathology

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Area of Science:

  • Genomics
  • Oncology
  • Bioinformatics

Background:

  • Cancer of unknown primary (CUP) accounts for 2-5% of malignancies and is a common cause of cancer death.
  • Brain metastases frequently present as the initial symptom of CUP, with 20-45% of cases never receiving a definitive primary site diagnosis.
  • DNA methylation array profiling is effective for tumor classification, but developing specific classifiers requires extensive reference samples, which are scarce for CUP.

Purpose of the Study:

  • To develop a novel DNA methylation classifier for identifying the primary site of cancer of unknown primary (CUP).
  • To leverage organ-specific methylation quantitative trait loci (mQTLs) to enhance classifier accuracy and reduce sample requirements.
  • To aid pathologists and oncologists in diagnosing CUP and guiding appropriate patient therapy.

Main Methods:

  • Genome-wide DNA methylation analysis was performed on 759 carcinoma samples using Illumina EPIC arrays.
  • A deep learning-based classifier was developed utilizing organ-specific mQTLs for breast, lung, ovarian/gynecologic, colon, kidney, or testis (BLOCKT) tumors.
  • The classifier's performance was evaluated using 10-fold cross-validation.

Main Results:

  • The developed BLOCKT methylation classifier achieved an average accuracy of 93.12% and an average F1-score of 93.04% across 10-fold validation.
  • The use of organ-specific mQTLs improved classifier performance, potentially reducing the need for large reference sample sets.
  • The classifier demonstrated significant potential in identifying the tissue of origin for CUP cases.

Conclusions:

  • An organ-based DNA methylation classifier utilizing mQTLs can effectively assist in determining the primary site of CUP.
  • This tool can provide crucial diagnostic insights for oncologists, enabling more targeted and effective patient treatment strategies.
  • Improved diagnosis of CUP through methylation profiling has the potential to enhance patient outcomes.