September 12, 2023

Wclc 2023

AI-Based Early Detection and Subtyping of Non-Small Cell Lung Cancer from Blood Samples Using Orphan Non-Coding RNAs

Mehran Karimzadeh1

1Exai Bio Inc., Palo Alto, CA

Generative AI allows us to identify the pattern of oncRNAs in blood, enhancing the sensitivity and specificity of an early detection assay

Slide of a flowchart showing how generative AI allows us to identify the pattern of oncRNAs in blood.

X: oncRNAs

Q: annotated RNAs

Z, S; Latent variables of the underlying distribution

; re-constructed oncRNAs

NSCLC study cohort design with an emphasis on early-stage detection

  • 887 subjects including 320 NSCLC cases and 567 individuals withoutcancer diagnosis
  • NSCLC and non-cancer subjects were comparable with regards to age, sex, and BMI
  • Individuals were recruited as part of four independent studies
Graphic table of NSCLC study of demographics with emphasis on early stage cancer detection.
Graphic table of NSCLC cohort sample size.

Exai’s generative AI model allows learning a cross-validated pattern of oncRNAs among multiple studies

Generative AI model vs Linear model
for cancer detection from serum oncRNAs

Slide of a flowchart showing how generative AI allows us to identify the pattern of oncRNAs in blood.

Model was trained with 10-fold cross validation. Error bars and ROC boundaries show 95% confidence interval.

oncRNAs and AI enable novel subtyping of tumors from blood

Accuracy on par with tissue immunohistochemistry (IHC); potential to expand actionable therapeutic indications

Slide of a flowchart showing how generative AI allows us to identify the pattern of oncRNAs in blood.

X: oncRNAs

Q: annotated RNAs

Z, S; Latent variables of the underlying distribution

; re-constructed oncRNAs

Stang, Andreas, et al. "Diagnostic agreement in the histopathological evaluation of lung cancer tissue in a population-based case-control study." Lung cancer 52.1 (2006): 29-36.

Summary

Unique RNA &
AI technology

  • Stable, abundant and specific to cancer
  • Large catalogue enables detection of cancer patterns
  • Tissue-derived oncRNA signature is reliably detected in blood

Powerful results
in NSCLC

  • Accurately detects NSCLC
  • High sensitivity to detect smallest tumors and across subtypes
  • Potential for liquid histology and subtyping under therapy monitoring conditions

One universal assay with broad clinical applications across multiple cancers

  • Highly efficient technology platform using one workflow
  • Single assay supports rapid product development across tumor types and monitoring applications in oncology
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