ECOG-ACRIN and Caris Life Sciences unveil first findings from a multi-year collaboration to advance AI-powered multimodal tools for breast cancer recurrence risk stratification

(Press-News.org) Today at the San Antonio Breast Cancer Symposium (SABCS), researchers presented the initial findings from a major multi-year collaboration between the ECOG-ACRIN Cancer Research Group (ECOG-ACRIN) and Caris Life Sciences® (Caris) focused on transforming recurrence risk assessment in early-stage breast cancer through artificial intelligence (AI). The public-private partnership pairs ECOG-ACRIN’s extensive clinical trial expertise and biorepository resources with Caris’ comprehensive MI Cancer Seek® whole exome and whole transcriptome profiling, whole slide imaging, and advanced machine learning platforms.

The research teams developed multimodal models to more precisely stratify recurrence risk in early-stage breast cancer. The models integrate histopathologic imaging, clinical, and molecular data generated from TAILORx, one of the world’s largest and most rigorously annotated breast cancer research repositories. This level of multimodal integration is unprecedented at this scale in early breast cancer prognostication.

Early-stage breast cancer represents a large and heterogeneous patient population in which treatment decisions frequently hinge on uncertain recurrence risk. Of the approximately 310,720 new cases diagnosed in the United States each year, an estimated 60% are early-stage (American Cancer Society), underscoring the broad application and clinical relevance of more accurate and individualized risk assessment.

“Realized through collaboration between ECOG-ACRIN, NCI, and Caris Life Sciences, this public-private partnership represents a methodological, logistical, and collaborative integration of datasets from the historically impactful TAILORx trial to further extend the benefits for breast cancer patients," said ECOG-ACRIN Group Co-Chair Peter J. O’Dwyer, MD. “The advance in personalized medicine afforded in this work, in turn, helps to advance the potential of AI to refine treatment and improve outcomes.”

Across analytic evaluations, the multimodal AI models demonstrated enhanced prognostic performance compared to existing recurrence-risk assessment methods, highlighting their potential to support more personalized treatment decision-making in early-stage breast cancer.

“By integrating imaging, clinical data, and molecular profiling, we are advancing beyond single-dimension diagnostics to deliver a more precise and comprehensive understanding of recurrence risk in breast cancer,” said Caris EVP and Chief Medical Officer George W. Sledge, Jr., MD. “The development of these models underscores the transformative power of multimodal AI and machine learning in precision oncology.”

Both AI models—including development approaches, integrated biomarker features, and demonstrated prognostic improvements—were presented in today’s SABCS sessions.

1.Multimodal Artificial Intelligence (AI) Models Integrating Image, Clinical, and Molecular Data for Predicting Early and Late Breast Cancer Recurrence in TAILORx, presented by Joseph A. Sparano, MD (Mount Sinai Tisch Cancer Center). Late-Breaking Abstract GS1-08 was presented during SABCS General Session 1.

In this project, researchers developed and prospectively validated a multimodal model integrating pathomic imaging (I), clinical (C), and expanded molecular (M+) data from 4,462 TAILORx tumor specimens. The expanded M+ gene expression panel includes 42 tumor genes associated with breast cancer recurrence derived from five commercially available gene assays, including the Oncotype DX (ODX) 21-gene recurrence score and a set of highly variable genes. Based on the results of the TAILORx trial, ODX is widely used in clinical practice for its prognostic information on recurrence and predictive information on chemotherapy benefit; however, its ability to forecast recurrence beyond the 5-year mark is limited.

The findings from this study will ultimately provide crucial support for the development of a new diagnostic test for women with HR-positive, HER2-negative, node-negative breast cancer that more accurately estimates recurrence risk, especially late recurrence 5 or more years after diagnosis.

“Although the TAILORx trial was the first randomized trial to establish the role of the 21-gene recurrence score to guide chemotherapy use in early breast cancer, our goal was to take one step further in personalizing cancer therapy by developing a new diagnostic test using tumor specimens derived from the trial,” said Dr. Sparano.

Dr. Sparano noted that the team developed an AI model that evaluates not only tumor gene expression but also uses deep learning of digitized H&E slides used for routine pathologic assessment to provide better prognostic information about cancer recurrence risk.

“We found that the expanded gene panel was a strong predictor of early recurrence within 5 years after diagnosis, the pathomic imaging was a strong predictor of late recurrence after 5 years, and when combined, a test which added both features to the prognostic information provided by clinicopathologic factors was the strongest predictor of distant recurrence out to 15 years,” he said.

2. A Multimodal-Multitask Deep Learning Model Trained in NSABP B-42 and Validated in TAILORx for Late Distant Recurrence Risk in HR+ Early Breast Cancer, presented by Eleftherios (Terry) Mamounas, MD, MPH (NSABP Foundation, Inc. and AdventHealth Cancer Institute). Abstract RF3-07 was presented during SABCS Rapid Fire Session 3.

Patients with early-stage, hormone receptor–positive (HR+) breast cancer are at risk for distant recurrence several years after diagnosis and initial treatment, making long-term risk assessment critical. Assessment of clinical factors alone (tumor size, grade, node status) is insufficient for precise risk stratification. Furthermore, there is a lack of personalized tools to guide decisions about the use of extended endocrine therapy (EET) beyond the standard 5 years.

Dr. Mamounas presented a multimodal–multitask deep learning algorithm designed to estimate late distant recurrence (DR) risk and help identify patients most likely to benefit from EET. Originally developed and validated in the NSABP B-42 randomized phase 3 trial, the model demonstrated strong prognostic performance, identifying those with minimal recurrence risk after a standard 5-year course of adjuvant endocrine therapy who could be spared additional treatment.

The ECOG-ACRIN/Caris research team conducted a new external validation study of the model in 4,300 patients from the TAILORx trial. In TAILORx, the model demonstrated robust late distant recurrence prognostication independent of other known prognostic factors, supporting its potential clinical utility as a scalable, cost-effective alternative to genomic assays using routine H&E and clinical data.

“These findings expand the available tools for late recurrence assessment and can potentially further refine the selection of candidates for extended endocrine therapy,” said Dr. Mamounas.

About ECOG-ACRIN

The ECOG-ACRIN Cancer Research Group (ECOG-ACRIN) is an expansive membership-based scientific organization known for advancing precision medicine and biomarker research through its leadership of major national clinical trials, including TAILORx, NCI-MATCH, ComboMATCH, and many others, that integrate cutting-edge genomic approaches. With nearly 21,000 research professionals and advocates from over 1400 hospitals and cancer centers across the United States and worldwide, the organization fosters collaboration through more than 40 scientific committees to design studies spanning the spectrum of cancer care, from early detection to management of advanced disease. It is funded primarily by the National Cancer Institute, one of the U.S. National Institutes of Health. To learn more, visit www.ecog-acrin.org and follow us on X@EAonc, Facebook, LinkedIn, YouTube, and Instagram.

About Caris Life Sciences

Caris Life Sciences® (Caris) is a leading, patient-centric, next-generation AI TechBio company and precision medicine pioneer that is actively developing and commercializing innovative solutions to transform healthcare. Through comprehensive molecular profiling (Whole Exome and Whole Transcriptome Sequencing) and the application of advanced AI and machine learning algorithms at scale, Caris has created the large-scale, multimodal clinico-genomic database and computing capability needed to analyze and further unravel the molecular complexity of disease. This convergence of next-generation sequencing, AI and machine learning technologies, and high-performance computing provides a differentiated platform to develop the latest generation of advanced precision medicine diagnostic solutions for early detection, diagnosis, monitoring, therapy selection and drug development.

Caris was founded with a vision to realize the potential of precision medicine in order to improve the human condition. Headquartered in Irving, Texas, Caris has offices in Phoenix, New York, Cambridge (MA), Tokyo, Japan and Basel, Switzerland. Caris or its distributor partners provide services in the U.S. and other international markets. To learn more, visit CarisLifeSciences.com.

Forward-Looking Statements

This press release contains forward-looking statements regarding the development and potential availability of new diagnostic tests, AI-powered tools for breast cancer recurrence risk assessment, and the expected benefits and applications of the described research collaboration. You should not rely upon forward-looking statements as predictions of future events. Caris Life Sciences cannot guarantee that the future results, discoveries, or performance reflected in forward-looking statements will be achieved or occur. Forward-looking statements involve known and unknown risks and uncertainties, including: the ability to successfully execute the research plan and achieve target discovery milestones; technical challenges in model validation and regulatory requirements relating to diagnostic test development; the uncertainty in translating research discoveries into commercially available diagnostic tests; developments in the precision medicine and AI diagnostics industry; regulatory requirements and approvals related to new diagnostic solutions; and the ability to protect any intellectual property developed through this collaboration. Caris Life Sciences undertakes no obligation to update any forward-looking statements to reflect changes in events, circumstances or our beliefs after the date of this press release, except as required by law.

About TAILORx

The Trial Assigning Individualized Options for Treatment (Rx), called TAILORx, provided an evidence-based answer to the question of which patients with estrogen receptor-positive (ER+), human epidermal growth factor receptor 2-negative (HER2-) early-stage breast cancer (no spread to the surrounding lymph nodes) may safely forego chemotherapy following surgery. The trial showed that chemotherapy may be avoided in patients with a score of 0-25 on the Oncotype DX Breast Recurrence Score™ test who are postmenopausal or older than 50 at diagnosis, and also in most patients who are younger than 50 or premenopausal (Sparano JA et al. N Engl J Med. 2018). With longer follow-up (12 years of survival and recurrence outcomes), the main study findings remain unchanged.

One critically important aspect of TAILORx was the development of the biorepository for future research. TAILORx was supported by the National Cancer Institute (NCI), part of the National Institutes of Health, along with the Breast Cancer Research Foundation, Susan G. Komen, and the Breast Cancer Research Stamp. The study was conducted by ECOG-ACRIN. Other NCI funded network groups participated in the study.

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