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Wild Bioscience (Wild Bio) the Irish co-founded Oxford spinout that aims to improve crop varieties sustainably has raised $60m in an EIT-led round.

Co-founded by Irish man Prof Steve Kelly, the Wild Bio $60m Series A round was led by the Ellison Institute of Technology (EIT), to help advance the spin-out’s mission to develop improved crop varieties using AI and precision breeding. Other participants included existing investors Oxford Science Enterprises (OSE), Braavos Capital, and the University of Oxford.

Wild Bio specialises in crop genetics, using a data-driven approach to “improve crop productivity, climate resilience, and agricultural sustainability”.

“The Wild Bio platform deciphers hundreds of millions of years of plant evolution to identify promising genetic improvements from wild species,” according to the company. “These evolutionary innovations are then used to guide precision breeding strategies for modern elite crop varieties.”

Wild Bio has its origins in the University of Oxford, from where founders Dr Ross Hendron and Irishman Prof Steve Kelly spun out the business in 2021, in order to move their scientific research out of the lab and onto the farm. Today the company has a team of 30 in their Oxford headquarters, and has field trials in four countries.

“Advancing agriculture has limitless potential to help people and the planet,” said Dr Ross Hendron, Co-founder and CEO of Wild Bio. “So to achieve meaningful, scalable impact, we need the right investors who are truly aligned with that big vision. I’m deeply grateful to EIT and to our current investors for sharing our excitement about what we’ve accomplished so far, and for their united support as we embark on this ambitious growth journey together.”

His co-founder and Wild Bio chief science officer, Prof Steve Kelly, who is also head of the Plant Biology Institute at EIT says that combining the research at EIT and Wild will “create a powerful synergy that could reshape sustainable agriculture on a global scale”.

“Together, we will accelerate our ability to bring new technologies to market and deliver innovative solutions that enhance crop resilience, boost yields, and promote environmental sustainability,” he said.

Founder of EIT Larry Ellison – better known to most as the chair of Oracle – welcomed the investment: “The ultimate goal is to grow these new crop varieties on a commercial scale and help provide food security around the world. EIT is committed to working with Wild Bio to reach this goal.”

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At the ESMO Congress 2025 in Berlin, Dr. Martina Carullo (Pisa, Italy) presented groundbreaking findings from the translational program of AtezoTRIBE and AVETRIC, two clinical trials exploring immunotherapy in proficient mismatch repair (pMMR) metastatic colorectal cancer (mCRC). Using the Lunit SCOPE IO artificial intelligence (AI) platform, the investigators developed and validated a digital biomarker capable of predicting which pMMR tumors benefit from immune checkpoint inhibition—an area long considered resistant to immunotherapy.

Background

Immune checkpoint inhibitors (ICIs) have transformed treatment outcomes for patients with deficient mismatch repair (dMMR/MSI-H) mCRC, but have shown minimal activity in pMMR tumors, which constitute the vast majority of metastatic cases. Identifying predictive markers within this refractory group remains one of the central challenges in gastrointestinal oncology.

Recent advances in AI-driven pathology have enabled quantitative analysis of the tumor microenvironment on digitized hematoxylin and eosin (H&E) slides. By mapping immune, stromal, and tumor cell populations, AI models can capture subtle biological patterns invisible to traditional pathology. The present study used this approach to generate an AI-derived biomarker that could distinguish responders from non-responders to ICI-based regimens in pMMR mCRC.

Methods

The analysis incorporated pre-treatment tumor slides from patients enrolled in AtezoTRIBE (NCT03721653) and AVETRIC (NCT04513951). In AtezoTRIBE, patients received FOLFOXIRI/bevacizumab with or without atezolizumab, while in AVETRIC, the regimen was FOLFOXIRI/cetuximab/avelumab.

Using the Lunit SCOPE IO platform, the research team quantified the density of lymphocytes, fibroblasts, macrophages, endothelial, mitotic, and tumor cells within both cancer areas and surrounding stroma. A multivariate Cox regression model was trained on the atezolizumab-treated arm of AtezoTRIBE to identify cellular features most predictive of progression-free survival (PFS). A cut-off optimized for PFS was then applied to classify tumors as biomarker-high or biomarker-low, with AVETRIC serving as an external validation set.

Results from AtezoTRIBE

The AI-based analysis was conducted on whole-slide images from 161 patients. The resulting biomarker integrated densities of tumor and mitotic cells in the cancer area, lymphocytes in the tumor core, and fibroblasts, macrophages, and endothelial cells in the stroma. Of the evaluated patients, 113 (70%) were classified as biomarker-high, a group characterized by older age (p = 0.030) and a higher frequency of liver metastases (p = 0.023).

In the atezolizumab arm, biomarker-high patients achieved significantly superior outcomes compared with biomarker-low ones, with PFS p = 0.036 and overall survival (OS) p = 0.024. No such association was observed in the control arm (PFS p = 0.564; OS p = 0.186).

A formal treatment–biomarker interaction analysis showed a stronger benefit from atezolizumab among biomarker-high patients (HR for PFS 0.69; 95% CI 0.45–1.04 and HR for OS 0.54; 95% CI 0.33–0.88), while biomarker-low tumors did not derive advantage (HR for PFS 1.34; 95% CI 0.66–2.72 and HR for OS 1.70; 95% CI 0.69–4.20).

Validation in AVETRIC

The model was independently tested on 48 patients from the AVETRIC trial. Thirty-six (75%) were classified as biomarker-high and displayed numerically improved outcomes compared with biomarker-low cases, with PFS p = 0.043and OS p = 0.053. The validation confirmed the reproducibility of the AI-generated signature across different ICI-based regimens and patient populations.

Interpretation

This dual-trial analysis demonstrates that an AI-defined histologic signature reflecting immune–stromal interactions can predict the benefit of immunotherapy even in microsatellite-stable disease. The biomarker captures a complex interplay between immune infiltration and stromal architecture—features often underestimated by genomic profiling alone.

By transforming standard H&E slides into predictive, quantifiable datasets, this work illustrates how digital pathology and AI can refine patient selection for immunotherapy and accelerate the transition toward precision oncology in pMMR mCRC.

You can read the full abstract here.

Conclusion

The AtezoTRIBE and AVETRIC translational analyses show that AI-derived tumor microenvironment biomarkerscan identify subsets of pMMR colorectal cancer patients who benefit from ICI-based therapy. In AtezoTRIBE, biomarker-high status correlated with significantly longer PFS (p = 0.036) and OS (p = 0.024) under atezolizumab, and these findings were validated in AVETRIC (PFS p = 0.043; OS p = 0.053).

These results mark an important advance in AI-assisted immuno-oncology, suggesting that digital pathology could soon complement molecular testing in guiding treatment for colorectal cancer beyond MSI status.