Exai Bio announced new data from a 3,300-subject study demonstrating that its novel RNA- and AI-based platform can detect multiple cancer types at the earliest stages using a single, standard blood sample. Notably, Exai’s platform maintained high sensitivity and specificity in both independent training and testing cohorts, highlighting the strength of its unique generative AI technology. In the independent testing cohort, overall stage I sensitivity across eight cancer types was 87% at 95% specificity. These multi-cancer early detection data were presented at a poster session at the American Society for Clinical Oncology (ASCO) 2023 annual meeting.

“These results demonstrate that Exai’s RNA- and AI-based platform can detect stage I cancer at very high sensitivity across multiple cancers while also maintaining the high specificity which is required for real-world clinical utility,” says Patrick Arensdorf, chief executive officer of Exai Bio. “Exai is developing blood tests that have both high accuracy and cancer biology insights to improve cancer care.”

“This large, rigorously designed study represents a significant achievement in the field of early cancer detection,” says Lee Schwartzberg, MD, chief, Medical Oncology and Hematology at the Renown Health-Pennington Cancer Institute. “The results demonstrate that Exai’s platform may close the performance gap of other blood-based approaches and deliver what patients and clinicians need: high cancer detection rates at early stages coupled with fewer false positives.”

Exai’s multi-cancer early detection (MCED) study analyzed eight cancer types including lung, stomach, pancreas, kidney, colorectal, breast, prostate, and bladder, thus representing the majority of the cancer burden on society. The study demonstrates successful detection of stage I breast and prostate cancers which, to date, have both been extremely challenging to detect at early stages using other blood-based approaches. When a cancer signal was detected in the testing cohort, Exai’s platform could identify the tissue of origin with 88% accuracy using the top predicted cancer type and 95% accuracy considering the top two predictions.

In addition to the multi-cancer early detection data, Exai’s abstract entitled “Consensus molecular subtypes and orphan non-coding RNAs in colorectal cancer” was published in the 2023 ASCO Annual Meeting proceedings. These data build upon Exai’s previously published subtyping data in breast cancer, demonstrating that oncRNAs are also predictive of detection of colorectal cancer subtypes. Providing subtype information from a blood test in both initial diagnosis and treatment monitoring settings has the potential to both improve clinical decision-making and expand therapeutic options for patients.

The new MCED and colorectal cancer data at ASCO further expand the growing body of evidence that Exai’s platform can be used across multiple tumor types and clinical applications, all using standard blood samples. Previously, Exai has presented non-small-cell lung cancer data at the American Association for Cancer Research (AACR) meeting 2023, breast cancer early detection and screening data at the San Antonio Breast Cancer Symposium (SABCS) 2022, monitoring and molecular residual disease detection data in breast cancer at SABCS 2021, and colorectal cancer early detection and screening data at the 2022 European Society for Medical Oncology (ESMO) meeting.

Exai’s platform uses RNA sequencing to identify a novel category of cancer-associated, small non-coding RNAs, termed orphan non-coding RNAs (oncRNAs). OncRNAs are actively secreted from living cancer cells and are stable and abundant in the blood of cancer patients. Exai has created a catalog of hundreds of thousands of oncRNAs and thousands of patient oncRNA profiles, spanning all major cancer types. When combined with proprietary artificial intelligence technology, the Exai platform has multiple technical and operational advantages over tests that focus on circulating tumor DNA, according to the company. These include superior sensitivity and specificity, as well as the ability to reveal dynamic changes in the biology of a patient’s tumor over time. Exai’s universal platform can be used across multiple cancer care settings such as screening and early detection, monitoring, molecular residual disease, and therapy selection.

Featured image: Exai Bio