Findings published in Cancer Research Communications highlight potential to inform extended endocrine therapy decisions
IRVING, Texas – August 6, 2026 – Caris Life Sciences®, Inc. (NASDAQ: CAI), a leading TechBio company, today announced the publication of a study in Cancer Research Communications demonstrating the ability of a multimodal, multitask deep learning model to estimate late distant recurrence risk in hormone receptor-positive (HR+) early breast cancer.
The study, titled “Development and Validation of a Multimodal-Multitask Deep Learning Approach for Estimating Late Distant Recurrence Risk in Hormone Receptor–Positive Early Breast Cancer,” was conducted by a team of researchers from across the public and private sectors, including Caris Life Sciences and two leading cooperative research organizations, the NSABP Foundation/NRG Oncology and the ECOG-ACRIN Cancer Research Group (ECOG-ACRIN).
HR+ breast cancer represents approximately 70–80% of all breast cancer diagnoses and is associated with a prolonged risk of recurrence that can persist well beyond the initial five years of endocrine therapy, which is standard of care. Recurrence can happen at the original tumor site or further away in the body (distant recurrence). While extended endocrine therapy for an additional five years may reduce this risk, it comes with a trade-off of prolonged, challenging side effects. Identifying which patients are most likely to benefit remains a significant clinical challenge.
“This study underscores the transformative potential of artificial intelligence to extract clinically meaningful insights from routinely collected data,” said George W. Sledge, MD, Chief Medical Officer. “By integrating AI-powered analysis of standard pathology images with clinical variables, this approach offers a promising path toward more precise risk stratification and may help inform individualized decisions regarding extended endocrine therapy for patients with hormone receptor-positive breast cancer.”
The multimodal AI model integrates digitized hematoxylin and eosin (H&E) pathology images with clinicopathologic data to generate risk predictions for late distant recurrence in both node-positive and node-negative HR+ breast cancer. It was developed using banked tumor specimens contributed by 2,271 patients in the NSABP B-42 clinical trial. Through a public-private partnership with ECOG-ACRIN, it was externally validated in an independent cohort of 4,300 banked specimens from patients who participated in the landmark TAILORx study.
In the NSABP B-42 cohort, the study identified patients with substantially different outcomes, with a 10-year absolute distant recurrence risk difference of nearly 8% between high- and low-risk groups. External validation in the independent TAILORx cohort confirmed the model’s prognostic performance and demonstrated that it independently predicted late distant recurrence risk, even after accounting for established clinical risk factors and the Oncotype DX Recurrence Score.
Exploratory analyses further suggested that patients classified as high risk experienced greater absolute benefit from extended letrozole therapy compared to those classified as low risk, supporting the model’s potential utility in informing discussions regarding extended endocrine therapy.
Existing genomic assays provide valuable prognostic insights but may be limited by cost, accessibility, and turnaround time. The findings from this study suggest that AI-based analysis of routinely available pathology slides and clinical data could offer a scalable and accessible alternative or complement to existing tools.
By leveraging widely available diagnostic data, this approach may enable oncologists to better identify patients at elevated risk of late recurrence and support more personalized discussions regarding the benefits and risks of extended endocrine therapy.
In early May, Caris launched Caris MI Clarity™, the first prognostic test designed to deliver insight into both early and late distant recurrence risk (years 0 through 5 and 5 through 15) for postmenopausal patients with HR+/HER2-negative, node-negative early-stage breast cancer at the time of diagnosis.
The new version includes decision support, not just prognosis. Adding information for chemotherapy decision support, identifying which patients are likely to benefit from chemo. Extended endocrine therapy decision support, informing treatment beyond the first five years. Late-window ordering in years 3 to 5, so recurrence risk can be reassessed during treatment, not only at diagnosis. MI Clarity unifies early and late distant risk into a single test, replacing two existing expensive tests with multi-week turnaround times.
The publication link is available on the Caris website.
About Caris Life Sciences
Caris Life Sciences® (Caris) is a leading TechBio company actively developing and commercializing innovative solutions to transform healthcare. Through comprehensive molecular profiling (Whole Genome, Whole Exome and Whole Transcriptome Sequencing), advanced AI and machine learning, 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 for developing 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 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.
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