Lunit Announces Collaboration with 10x Genomics to Integrate AI-Enabled Pathology Analysis with Spatial Molecular Data for Oncology Clinical Research

Lunit’s AI pathology platform will be integrated into 10x Genomics’ spatial biology workflow to enhance oncology biomarker research.

Rhea-AI Summary

Lunit and 10x Genomics (TXG) are collaborating to integrate Lunit's AI-enabled H&E pathology analysis with 10x Genomics' spatial molecular technologies for oncology clinical research. Lunit SCOPE IO will be incorporated into 10x's oncology biomarker discovery workflow to analyze Hematoxylin and Eosin pathology images alongside spatial molecular data from 10x's Xenium and Atera platforms.

The combined approach is intended to help researchers relate tissue morphology to tumor biology and identify biomarkers linked to treatment response and resistance. Initial work will support oncology clinical research studies focused on antibody-drug conjugates and immunotherapy response prediction, as part of 10x's broader effort to explore potential future diagnostic applications of spatial technologies in oncology. Lunit SCOPE IO, which is for research use only, characterizes tumor and stromal regions, immune-cell distribution, tertiary lymphoid structures and other tumor microenvironment features to complement molecular profiling.

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Market reaction after oncology research partnership: TXG -3.70%

$64.35 $67.82 Day Range

$8.44B Market Cap

Following this news, TXG has declined 3.70%, reflecting a moderate negative market reaction. Our momentum scanner has triggered 3 alerts so far, indicating moderate trading interest and price volatility. The stock is currently trading at $64.80. Trading volume is above average at 1.7x the average, suggesting increased trading activity.

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Market Context

A 1.2% gain was recorded after 10x's June 17 collaboration with Cleveland Clinic, which also targete...

Analysis

A 1.2% gain was recorded after 10x's June 17 collaboration with Cleveland Clinic, which also targeted biomarkers for antibody-drug conjugate and immunotherapy response. The current Lunit collaboration applies complementary pathology analysis to that research theme; a July 29 collaboration had a 1.55% decline.

Historical Context

2 past events · Latest: Jun 17

2 events

1. Jun 17

   Diagnostic research collaboration

   24h Move

   +1.2%

   Cleveland Clinic collaboration targeted biomarkers for antibody-drug conjugate and immunotherapy response

2. Jul 29

   Diagnostic research collaboration

   24h Move

   -1.6%

   Lausanne collaboration advanced spatial technology applications for cancer diagnostic research

24h Move is the share-price change in the day after each event; other market factors may also have contributed.

Key Terms

hematoxylin and eosin, deep learning, tumor microenvironment, antibody-drug conjugates, +1 more

5 terms

hematoxylin and eosin medical

"analyze Hematoxylin and Eosin (H&E) pathology images"

Hematoxylin and eosin is the standard two-color staining method pathologists use to colorize thin slices of tissue under a microscope so cells and structures are visible, like adding highlighter and ink to a black-and-white photo. Investors should care because H&E slides are the basic evidence for diagnosing disease, assessing how a therapy affects tissues, and supporting clinical and regulatory decisions that can drive a healthcare company’s value.

deep learning technical

"uses deep learning to analyze whole-slide H&E pathology images"

Deep learning is a type of artificial intelligence that uses multiple layers of computer models to recognize patterns and make decisions from large amounts of data, similar to how someone improves at a task by practicing many examples. Investors care because companies that harness deep learning can automate work, improve products, cut costs or create new revenue streams — but adoption also involves investment, data and regulatory risks that can influence profits and valuation.

tumor microenvironment medical

"other features of the tumor microenvironment"

The tumor microenvironment is the immediate area surrounding a cancer cell, made up of nearby cells, blood vessels, and support structures that influence how the cancer grows and spreads. It functions like a bustling neighborhood that can either help or hinder the tumor’s development. For investors, understanding changes in this environment can signal the effectiveness of treatments and potential shifts in a cancer-related market.

antibody-drug conjugates medical

"studies focused on antibody-drug conjugates and immunotherapy response prediction"

A class of targeted cancer medicines that combine a lab-made antibody (which finds and sticks to specific markers on tumor cells) with a powerful cell-killing drug linked together so the toxic payload is delivered directly to the tumor. Think of it like a guided missile that reduces collateral damage compared with traditional chemotherapy; for investors, success or failure of these drugs drives clinical, regulatory and commercial value and can sharply affect a biotech company’s prospects and stock price.

spatial molecular data technical

"alongside spatial molecular data generated from 10x's oncology-focused"

Spatial molecular data are measurements of biological molecules (like RNA, proteins, or metabolites) that preserve where each measurement came from inside a tissue or cell sample, so you know not just what is present but exactly where it is located. This matters to investors because it lets researchers map disease processes, drug targets, and biomarker patterns with neighborhood-level detail—like turning a list of ingredients into a labeled map of a recipe—informing the value of companies working on diagnostics, therapeutics, and research tools.

AI-generated analysis. How Rhea-AI works. Not financial advice.

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10x Genomics, a leader in single-cell and spatial biology, to combine its spatial biology technologies with Lunit's AI-powered H&E pathology analysis to advance oncology biomarker discovery

SEOUL, South Korea, Sept. 10, 2026 /PRNewswire/ -- Lunit (KRX: 328130), a leading provider of AI for cancer diagnostics and precision oncology, today announced that 10x Genomics, Inc. (Nasdaq: TXG) will incorporate Lunit's AI-powered pathology platform, Lunit SCOPE IO, into its oncology biomarker discovery workflow. Lunit SCOPE IO will be used to analyze Hematoxylin and Eosin (H&E) pathology images alongside spatial molecular data generated from 10x's oncology-focused clinical research studies.

10x Genomics is a leader in single cell and spatial biology, with its technologies cited in more than 10,000 research publications and enabling discoveries across oncology, immunology, neuroscience and other fields.

The work brings together molecular insights generated using 10x's Xenium and Atera spatial platforms with tissue characterization from Lunit SCOPE IO. By analyzing pathology images alongside spatial molecular data, 10x aims to better understand how tissue morphology relates to the underlying biology of tumors and identify biomarkers associated with treatment response and resistance.

"10x Genomics has built powerful tools for understanding the molecular organization of tissue," said Brandon Suh, CEO of Lunit. "Lunit SCOPE IO adds the ability to analyze the full H&E landscape at scale. Together, we believe these technologies can help researchers connect deep molecular insight with the tissue patterns that pathologists recognize every day."

"Spatial technologies provide a fundamentally richer view of the biology within tumors and their microenvironment," said Roman Yelensky, Vice President, Clinical Applications of 10x Genomics. "By combining that molecular depth with Lunit's AI-based analysis of the same H&E images pathologists work with every day, we can give researchers a more complete view of the tumor to accelerate the discovery of biomarkers that could inform treatment strategies and enable potential future diagnostic development."

The work will initially support oncology clinical research studies focused on antibody-drug conjugates and immunotherapy response prediction. These studies are part of 10x's broader effort to explore potential future diagnostic applications of spatial technologies in oncology.

Lunit SCOPE IO uses deep learning to analyze whole-slide H&E pathology images and characterize tissue features including tumor and stromal regions, immune-cell distribution, tertiary lymphoid structures and other features of the tumor microenvironment. Used alongside 10x's spatial technologies, these analyses provide additional tissue context that complements molecular profiling in oncology clinical research.

* Lunit SCOPE IO is for research use only. Not for use in diagnostic procedures.

About Lunit

Founded in 2013, Lunit (KRX: 328130) is a global leader on a mission to conquer cancer through AI. Our clinically validated solutions span medical imaging, breast health, and biomarker analysis, empowering earlier detection, smarter treatment decisions, and more precise outcomes across the cancer care continuum.

Lunit offers a comprehensive suite spanning risk prediction and early detection to precision oncology. Our FDA-cleared Lunit INSIGHT Breast Suite and breast health solutions support cancer screening in thousands of medical institutions worldwide, while the Lunit SCOPE platform is used in research partnership with global pharma and laboratory leaders for biomarker research, and companion diagnostic development.

Trusted by over 10,000 sites in more than 65 countries, Lunit combines deep medical expertise with continuously evolving datasets to deliver measurable impact for patients, clinicians, and researchers alike. Headquartered in Seoul with global offices, Lunit is driving the worldwide fight against cancer. Learn more at lunit.io.

About 10x Genomics
10x Genomics is a life science technology company building products to accelerate the mastery of biology and advance human health. Our integrated research solutions include instruments, consumables and software for single cell and spatial biology, which help academic and translational researchers and biopharmaceutical companies understand biological systems at a resolution and scale that matches the complexity of biology. Our products are behind breakthroughs in oncology, immunology, neuroscience and more, fueling powerful discoveries that are transforming the world's understanding of health and disease. To learn more, visit 10xgenomics.com or connect with us on LinkedIn, X, Facebook, Bluesky or YouTube.

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SOURCE Lunit

FAQ

What is Lunit SCOPE IO and how is it used in this collaboration?

Lunit SCOPE IO is an AI-powered pathology platform that uses deep learning to analyze whole-slide Hematoxylin and Eosin (H&E) pathology images. It characterizes tissue features such as tumor and stromal regions, immune-cell distribution, tertiary lymphoid structures and other aspects of the tumor microenvironment. In this collaboration, it is incorporated into 10x Genomics' oncology biomarker discovery workflow and used alongside spatial molecular data from 10x's platforms to provide additional tissue context for oncology clinical research.

Which 10x Genomics technologies will be combined with Lunit’s AI pathology analysis?

The collaboration combines molecular insights from 10x Genomics' Xenium and Atera spatial platforms with tissue characterization from Lunit SCOPE IO. Pathology images analyzed by Lunit's AI are evaluated together with spatial molecular data generated by these 10x technologies in oncology-focused clinical research studies.

What types of oncology studies will be initially supported by this work?

The initial work will support oncology clinical research studies focused on antibody-drug conjugates and immunotherapy response prediction. These studies are described as part of 10x Genomics' broader effort to explore potential future diagnostic applications of spatial technologies in oncology.

Is Lunit SCOPE IO intended for clinical diagnostic use in this collaboration?

No. Lunit SCOPE IO is stated to be for research use only and not for use in diagnostic procedures. Its role in this collaboration is to support oncology clinical research by providing AI-based analysis of H&E pathology images that complements spatial molecular profiling.