Altis Labs Raises $25M to Advance AI Endpoints in Oncology

Altis Labs has raised US$25 million in Series A financing to expand its AI-based oncology endpoints, which use routinely collected medical imaging to generate earlier predictions of long-term clinical outcomes.

AI Models Link Imaging with Survival Outcomes

Oncology trials commonly use measures such as objective response rate (ORR) and progression-free survival (PFS) to assess whether an investigational therapy is working. These endpoints can provide earlier signals than overall survival, but their ability to predict long-term clinical benefit varies across therapies and tumor types.

Altis built its platform around AI models trained on longitudinal datasets that connect radiology images with patient outcomes. Its flagship model, IPRO, analyzes routinely acquired radiology scans and generates survival predictions.

The company’s AI endpoint, IPRO Response Rate, provides an earlier measure of treatment effect than conventional imaging-based response assessments.

MARIPOSA Analysis Provides Phase 3 Evidence for AI Endpoint

Evidence presented at the 2026 World Conference on Lung Cancer came from an independent post-hoc analysis of Johnson & Johnson’s Phase 3 MARIPOSA trial.

In the analysis, IPRO Response Rate detected a statistically significant treatment effect 11 months before the trial’s primary PFS readout and 26 months before the final overall survival readout.

By comparison, conventional ORR did not predict the significant survival benefit observed in the trial.

The findings indicate that AI-derived imaging endpoints may provide earlier information about whether a treatment is translating into meaningful clinical benefit. However, the MARIPOSA analysis was post-hoc, making broader prospective validation across cancer types, trials and treatment settings important for wider adoption.

Investors Back Expansion of AI-Based Clinical Endpoints

Felix Baldauf-Lenschen, founder and CEO of Altis Labs, said faster and more accurate assessment of treatment benefit could accelerate clinical development and improve decision-making during trials.

Tal Zaks, partner at OrbiMed and a former oncology drug developer, highlighted the importance of early efficacy signals that reliably predict patient outcomes. He said Altis has built a longitudinal oncology dataset linking imaging with clinical outcomes and translated it into an AI-derived outcome measure supported by Phase 3 data.

Qiming Venture Partners USA managing partner Anna French said earlier efficacy measurement could address some of the costs, timelines and late-stage failure risks associated with oncology drug development.

$25 Million Round to Fund Platform Expansion

The Series A was co-led by OrbiMed and Qiming Venture Partners USA, with participation from Innovation Endeavors, Benchstrength, Fusion Fund, the Cancer Breakthrough Fund and other investors.

Altis will use the financing to expand its AI models across additional cancer types, increase commercial deployment with global biopharmaceutical partners and further develop AI-derived endpoints for oncology clinical trials.

The financing comes as drug developers continue to rely heavily on imaging-based measures for early efficacy decisions, despite limitations in predicting long-term patient outcomes.

Next Phase: Broader Oncology Validation

Altis plans to extend its AI models into additional cancer types and expand deployment through partnerships with global biopharmaceutical companies.

Further prospective validation and regulatory engagement will be important to establish how AI-derived imaging endpoints can complement or potentially supplement conventional measures in oncology trials. Their broader use could provide drug developers with earlier evidence to inform trial design, efficacy assessment and development decisions.

Reference

Altis Labs Raises US$25 Million Series A Co-Led by OrbiMed and Qiming Venture Partners USA to Accelerate Clinical Development with AI Endpoints, Altis Labs, 17 September 2026