On October 20, 2026 One Biosciences reported data showing some ovarian cancer tumor cells are predisposed to resist chemotherapy and single-cell profiling at diagnosis can identify tumor profiles that indicate which patients are likely to experience treatment failure, ultimately enabling more personalized treatment strategies.

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Researchers from One Biosciences, French National Centre for Scientific Research (CNRS), Institut Curie, and Sorbonne Université found that the cellular changes linked to the development of resistance to cytotoxic chemotherapy cannot be explained by DNA mutations alone: the functional and epigenetic state of tumor cells play a key role and initial tumor composition predicts overall survival.

The study focused on patients with high grade serous ovarian cancer (HGSOC), the most common and aggressive type of ovarian cancer. The disease has few symptoms, is often identified late and remains difficult to treat, with a five-year survival rate below 40 percent.

"Ovarian cancer tumors are very diverse, with many different cell types. These cells can do many different things, some of which help the tumor resist chemotherapy. In this study we identified all the functional states present in a tumor both before and after treatment and found that a patient’s initial tumor profile predicted their outcome," said Céline Vallot, Ph.D., co-founder and chief scientific officer at One Biosciences. "By using single-cell profiling to identify functional states associated with chemotherapy resistance, we may be better able to predict who will respond to therapy and who won’t, giving physicians an opportunity for more personalized treatment strategies."

The study analyzed single-cell RNA profiles of tumor samples both before and after chemotherapy, allowing researchers to track cellular changes. Tumors with a higher initial concentration of chemotherapy-resistance functional states were associated with poorer overall survival. Additionally, researchers identified epigenetic markers for resistance to the chemotherapy agents utilized. Critically, the single-cell profiling technology used in this study can be applied to standard FFPE biopsy samples widely used in clinical care.

"Clinical grade single-cell profiling allows us to identify and track the cellular mechanisms of resistance not just in a research study, but in real world clinical care across a number of cancer types," said Vincent Miller, M.D., executive chairman of One Biosciences. "This study highlights the impact that knowledge could have for personalizing treatment strategies in one of the most difficult-to-treat cancers patients face."

The full study was published online in Cancer Discovery.

(Press release, One Biosciences, OCT 20, 2026, View Source [SID1234673621])

On October 20, 2026 IceCure Medical Ltd. (NASDAQ: ICCM) ("IceCure," "IceCure Medical" or the "Company"), developer of minimally-invasive cryoablation technology that destroys tumors by freezing as an option to surgical tumor removal, reported the enrollment of the first two patients at two U.S. clinical sites in its post-marketing study (the "ChoICE Study") evaluating ProSense cryoablation for the local treatment of low-risk breast cancer.

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The first patient in the study was enrolled at West Cancer Center & Research Institute in Germantown, Tennessee ("West Cancer Center") by Dr. Richard E. Fine, MD, FACS, Director of the Margaret West Comprehensive Breast Center and lead author of the ICE3 Study on cryoablation of small, low-risk breast cancer.

The second patient was enrolled at Adventist Health Glendale in Glendale, California ("Adventist Health Glendale"). The site is led by Dr. Dennis Holmes, MD, FACS, a breast surgeon and recognized authority in breast cancer cryoablation.

These initial enrollments follow the Company’s efforts to establish a network of U.S. clinical sites for the ChoICE Study. In August 2026, both West Cancer Center and Adventist Health Glendale announced their eligibility to enroll patients after signing a site agreement and receiving IRB approval. Earlier this month, IceCure announced the addition of Legacy Health’s Legacy Medical Group – Surgical Oncology at Good Samaritan Medical Center in Portland, Oregon, further expanding the ChoICE Study’s U.S. clinical network.

"Enrolling the first patients marks continued progress in the execution of our post-market strategy for ProSense," said Eyal Shamir, Chief Executive Officer of IceCure Medical. "As we continue to expand the ChoICE Study’s clinical network, we aim to generate additional real-world evidence while supporting broader clinical adoption and access to minimally invasive cryoablation for eligible patients."

The ChoICE Study is designed to enroll approximately 400 patients across up to 30 U.S. clinical sites and generate real-world data on ProSense in its U.S. Food and Drug Administration ("FDA") cleared indication. Participating sites are expected to serve both as study centers and active commercial sites, enabling them to offer ProSense cryoablation to eligible patients who are not participating in the ChoICE Study.

(Press release, IceCure Medical, OCT 20, 2026, View Source [SID1234673620])

On October 20, 2026 Lunit (KRX: 328130), a leading provider of AI for cancer diagnostics and precision oncology, reported that five studies involving its AI-powered pathology platform, Lunit SCOPE, will be presented at the European Society for Medical Oncology (ESMO) (Free ESMO Whitepaper) Congress 2026, taking place October 23–27 in Madrid, Spain.

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The studies highlight how AI-powered quantitative pathology analysis can help researchers examine cancer biomarkers and tumor tissue characteristics across different cancer types. By generating detailed measurements from pathology images, Lunit SCOPE supports research into the biological and clinical significance of tumor features.

Among the five studies, three highlight applications of AI-powered pathology in breast, lung, and pancreatic cancer research.

The first study is part of the AIforHER project, led by France’s Institut de Cancérologie de l’Ouest (ICO). The project aims to develop and evaluate AI-powered solutions for more precise HER2 assessment in breast cancer. Accurate HER2 classification has become increasingly important as HER2-targeted therapies expand to patients with lower levels of HER2 expression, including HER2-ultralow breast cancer.

As part of the initiative, multiple AI solutions were evaluated under standardized conditions to assess their ability to classify HER2 expression levels. Lunit will present findings from Study A, the initial comparative evaluation, in a poster presentation at ESMO (Free ESMO Whitepaper) 2026. Following this evaluation, Lunit was selected as one of only two companies to advance to Study B and is currently participating in the follow-up study, which is evaluating the solution in a real-world clinical setting. The research explores the potential of AI-assisted HER2 assessment to support the identification of patients who may be eligible for HER2-targeted therapies, particularly in cases where subtle differences in HER2 expression are difficult to distinguish.

In a large-scale Japanese study involving 993 patients with surgically resected non-small cell lung cancer (NSCLC), researchers compared PD-L1 tumor proportion scores (TPS) assessed independently by a pathologist and Lunit SCOPE PD-L1. The overall concordance rate was 73% (κ=0.58), with the lowest concordance observed in tumors classified as PD-L1 negative (TPS <1%) by the pathologist. Of the 211 tumors scored as TPS <1% by the pathologist but TPS ≥1% by AI, 204 (97%) were classified as having low PD-L1 expression (TPS 1–10%) by AI. The findings highlight differences between manual and AI-based PD-L1 assessment, particularly at low expression levels, while providing insights into areas that warrant further clinical investigation.

In another study involving 181 patients with pancreatic ductal adenocarcinoma (PDAC), researchers used Lunit SCOPE IO to quantify tumor-stroma ratios and endothelial cell proportions in pathology images, examining their associations with KRAS mutation status and survival outcomes. While higher tumor-stroma ratios were associated with worse progression-free survival overall (HR=1.89, p=0.046), the opposite association was observed in patients with KRAS G12D-mutated tumors (HR=0.50, p=0.047). Higher endothelial cell proportions were also associated with favorable survival outcomes in tumors with lower stromal content, but not in those with higher stromal content. These findings highlight how the prognostic significance of tumor tissue characteristics can vary depending on molecular subtype and tissue context, suggesting the potential of AI-powered quantitative pathology to uncover complex relationships between tumor biology and patient outcomes.

Together, these studies demonstrate how AI-powered quantitative pathology can support research across different cancer types, from examining biomarker assessment differences to investigating the relationship between tumor tissue characteristics and clinical outcomes.

"AI is helping researchers uncover new insights from pathology images across different cancer types," said Brandon Suh, CEO of Lunit. "These studies reflect the diverse ways Lunit SCOPE can support cancer research. We will continue to support researchers and expand collaborations that advance our understanding of cancer biology and precision oncology."

*Lunit SCOPE products are for research use only. Not for use in diagnostic procedures or decisions.

Research Presentations at ESMO (Free ESMO Whitepaper) 2026:

1. Poster Presentation [#287P / Oct. 25, 12:00–12:45 PM]: Comparative evaluation of 7 artificial intelligence (AI) tools for HER2-low and ultralow scoring in breast cancer (BC)

2. Poster Presentation [#313P / Oct. 25, 12:00–12:45 PM]: A large-scale concordance comparing digital and manual PD-L1 scoring using surgical specimens of non-small cell lung cancer in the LC-SCRUM-Advantage/MRD study

3. Poster Presentation [#322P / Oct. 25, 12:00–12:45 PM]: AI-driven stromal profiling in PDAC: Context-dependent endothelial cell prognosis and KRAS subtype-specific paradoxes

4. Poster Presentation [#346P / Oct. 25, 12:00–12:45 PM]: Tertiary lymphoid structures (TLS) add prognostic value beyond tumor-infiltrating lymphocyte (TIL) density in patients treated with immune checkpoint inhibitor (ICI)-based therapies: A multi-institutional pan-cancer analysis

5. Poster Presentation [#3810P / Oct. 26, 12:00–12:45 PM]: Artificial intelligence-based endothelial cell profiling reveals a prognostic signature in the tumor microenvironment of small-cell lung cancer

(Press release, Lunit, OCT 20, 2026, View Source [SID1234673619])

On October 20, 2026 Insilico Medicine ("Insilico", HKEX: 3696), a clinical-stage generative artificial intelligence (AI)-driven drug discovery company, reported that the company has achieved the early drug discovery milestone in its multi-year AI-driven drug discovery collaboration with Servier, an independent international pharmaceutical group governed by a foundation.

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Under the collaboration, the two companies initiated two distinct R&D programs for oncology on challenging targets leveraging Insilico’s proprietary end-to-end AI platform, Pharma.AI. Target nominations have been completed for both programs. Following the successful fulfillment of predefined program criteria, Insilico has reached the early drug discovery milestone for the first program. The milestone triggers an undisclosed milestone payment from Servier to Insilico.

First announced in early 2026, the multi-year R&D collaboration combines Insilico’s generative AI capabilities with Servier’s clinical expertise to discover and develop novel small-molecule therapeutics for challenging targets in oncology. Under the agreement, Insilico leads the AI-driven discovery and early development phases, while Servier shares R&D expenses and will assume responsibility for subsequent clinical validation and commercialization. The collaboration carries a total value of up to $888 million with $32 million in upfront and near-term R&D payments.

The rapid progress of the collaboration highlights how generative AI, combined with extensive drug discovery and development expertise, can transform biopharmaceutical partnerships. By integrating Insilico’s computational biology capabilities with Servier’s development expertise, the collaboration has helped shorten early-stage research timelines and establish a scalable, data-driven framework for future joint R&D programs in the industry.

"I am glad to see that within 10 months of launching our collaboration, we identified and initiated 2 programs under the collaboration and achieved our first R&D milestone, highlighting the efficiency and quality that AI can bring to drug discovery." said Alex Zhavoronkov, PhD, founder, CEO and CBO of Insilico Medicine. "The milestone demonstrates the capabilities of Insilico’s specific AI driven R&D platform, Pharma.AI, to decipher complex biological mechanisms and rapidly generate high-quality compounds. In addition, during the first ten months of this year, we identified 11 developmental candidates for our internal pipelines, further validating the consistent and scalable R&D productivity enabled by generative AI. Servier’s unwavering commitment to innovation makes it an ideal partner as we explore new frontiers in AI-driven oncology drug discovery. We look forward to accelerating these innovative programs and advancing them toward transformative clinical candidates."

Driven by broad global recognition of its AI platform and pipeline assets, Insilico achieved total revenue of approximately $106 million for the first half of 2026, representing a year-over-year increase of 287%, alongside an adjusted net profit exceeding $51 million, marking its first profitable half-year since listing.

This financial milestone was primarily propelled by a series of out-licensing, co-development, and R&D collaborations with leading global partners, including Eli Lilly, Servier, Takeda, SK Biopharmaceuticals, Qilu Pharmaceutical, Hygtia Therapeutics, China Medical System (CMS), and Tenacia Biotechnology. According to the 2026 interim report, as of the middle of August, Insilico’s disclosed deal value for signed collaborations in 2026 alone has reached approximately $7.3 billion, bringing the company’s cumulative total signed deal value since 2021 to nearly $11 billion.

To date, Insilico announced the nomination of 11 preclinical candidate compounds (PCCs) in the first 10 months of 2026, breaking the company’s historical record for annual pipeline output pushing the total number of PCCs nominated since 2021 to 35, validating the R&D efficiency of generative AI through reproducible, real-world examples. According to the 2026 interim report, as of the middle of August, Insilico has achieved 8 clinical milestones across its internal and partnered pipelines. Leading these R&D advances is Rentosertib (ISM001-055), the world’s first potential first-in-class drug candidate enabled by generative AI. Rentosertib has officially entered Phase III clinical trials for the treatment of idiopathic pulmonary fibrosis (IPF), taking a crucial step toward clinical validation and commercialization.

(Press release, Insilico Medicine, OCT 20, 2026, View Source [SID1234673618])

On October 20, 2026 AVEO Oncology, an LG Chem company, (AVEO) reported that an abstract was accepted for presentation at the upcoming 2026 European Society for Medical Oncology (ESMO) (Free ESMO Whitepaper) Congress in Madrid, Spain this October 23-27.

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The poster will summarize results from a Phase 1b study designed to test the safety and tolerability of rilogrotug, a humanized IgG1 anti-GDF-15 monoclonal antibody (NCT05865535).

Mike Ferraresso, President and Chief Executive Officer, stated, "We are excited to present this Phase 1b data showing the safety and tolerability of rilogrotug. Improvements were also observed in weight gain, skeleton muscle area, and appetite. We are encouraged by these results as we advance toward Phase 2 development of rilogrotug in cancer cachexia to further explore the potential benefit in these patients."

Presentation Details

Title:

A Phase 1b dose escalation study of an anti-GDF-15 monoclonal antibody, rilogrotug (rilo, AV-380), for cancer cachexia

First Author:

Eric Roeland, MD, Oregon Health and Science University

Abstract #:

4191

Presentation #:

3917P

Poster Session:

Supportive Care

Date and Time:

Saturday, October 24, 2026, 12:00pm – 12:45 PM CEST

(Press release, AVEO, OCT 20, 2026, View Source [SID1234673617])