Cartography Biosciences Secures $67 Million Series B to Advance Precision Immunotherapy

At Cartography Biosciences, researchers are trying to give cancer immunotherapy something it’s always lacked — a map with enough detail to guide treatment safely.

The South San Francisco company, founded by scientists from Stanford, builds therapies around targets identified through its proprietary ATLAS and SUMMIT platforms. Together, they analyze how thousands of cell types behave across healthy and cancerous tissues, searching for patterns that distinguish one from the other.

It’s an ambitious vision: to find drug targets that exist only in tumors, sparing everything else. In October 2025, that vision drew $67 million in Series B funding, led by Pfizer Ventures with support from Amgen Ventures, Andreessen Horowitz, and others. The financing will move the company’s lead program into clinical trials and scale up its data infrastructure to discover the next wave of precision immunotherapies.

Cartography's Bold Goal: Map Cancer's Molecular Landscape

South San Francisco-based oncology company Cartography Biosciences is advancing a pipeline of T-cell engaging bispecific and multi-specific antibody therapeutics. The company was founded by Stanford academics who recognized that traditional methods of cancer target discovery often miss opportunities for truly tumor-specific therapies. Their approach addresses a fundamental challenge in immuno-oncology: identifying drug targets that selectively attack cancer cells while sparing healthy tissue.

Why Immunotherapy Still Needs Better Targets

Cancer immunotherapy has changed how we think about treatment, but most therapies still work for a narrow group of patients. The underlying problem isn’t the immune system — it’s the targets we give it.

Traditional discovery methods often rely on bulk sequencing and animal models that can’t fully capture how genes behave in individual human cells. A target that looks safe in a mouse can cause devastating toxicity in people if it’s also expressed, even faintly, in healthy tissue. These blind spots make it difficult to balance potency and safety.

Cartography’s founders saw that better therapies would depend on better resolution. Their lab pairs single-cell sequencing, spatial transcriptomics, and high-throughput proteomics with automated data analysis pipelines that map where each gene or protein is expressed across the body. Every dataset becomes another layer in a growing atlas of human biology.

That map is what guides their drug design — not assumptions, not models, but data built cell by cell.

ATLAS and SUMMIT: The Core Platforms

At the center of Cartography’s work are two connected systems: ATLAS and SUMMIT. Together, they form the company’s discovery engine — part dataset, part laboratory network, part software platform.

ATLAS functions as the foundation. It’s a curated collection of single-cell RNA sequencing data from thousands of healthy human cell types, integrated with tumor-specific datasets gathered from patient samples. Each dataset is generated through automated sequencing pipelines and processed through standardized bioinformatics workflows that ensure consistency across studies.

Once data are captured and structured, SUMMIT takes over. Using machine learning and multi-omic analysis, SUMMIT searches for patterns that distinguish cancer cells from their healthy counterparts. It can identify single-protein antigens, but it can also look for pairs of antigens that, when targeted together, define a tumor more precisely.

The platform’s strength lies in its integration. Automated sample prep, sequencing, and data ingestion flow directly into computational modeling, creating a loop between wet lab and software. Every experiment refines the system, making the next prediction more accurate.

The result isn’t just a list of targets; it’s a structured, searchable map of tumor biology built for drug developers.

Strategic Collaboration with Gilead Sciences

In 2024, Cartography Biosciences announced a multi-year collaboration with Gilead Sciences to identify new immunotherapy targets in triple-negative breast cancer and lung adenocarcinoma.

Under the agreement, Cartography’s platforms will handle target discovery and validation, while Gilead manages downstream development and commercialization. The deal included a $20 million upfront payment and eligibility for milestone and royalty payments tied to the success of each optioned target.

The collaboration runs on shared infrastructure — secure data environments, bioinformatics integration pipelines, and joint analytics frameworks that allow both teams to review data and model results in real time. It’s a process more akin to technology transfer than traditional pharma partnerships.

For Cartography, the Gilead partnership is a proving ground: evidence that its discovery platforms can produce targets worth advancing into the clinic. For Gilead, it’s a way to access precision oncology data without rebuilding the architecture internally.

Lead Program: CBI-1214 for Colorectal Cancer

The company’s first drug candidate, CBI-1214, takes its name from the idea that a map is only useful if it leads somewhere. The therapy is a T-cell engager designed to recognize LY6G6D, a tumor-specific antigen highly expressed in colorectal cancer but nearly absent from healthy tissues.

That distinction matters. Most colorectal cancer subtypes, especially microsatellite stable (MSS) and microsatellite instability-low (MSI-L) tumors, have limited response to current immunotherapies. CBI-1214 aims to change that by redirecting T-cells precisely toward those tumor populations while minimizing off-target activation.

The antibody’s structure was optimized using in-silico modeling, automated protein-engineering pipelines, and high-throughput binding assays that test thousands of variants before any move to animal studies. Candidate molecules then pass through a series of cell-based cytotoxicity assays and flow-cytometry screens to measure target engagement and T-cell activation.

By the time a construct reaches preclinical testing, much of its performance profile has already been shaped by automation — not trial-and-error design, but reproducible engineering. Cartography expects to file an investigational new drug (IND) application by the end of the year, with Phase 1 enrollment planned for early 2026.

Leadership and Expertise

Behind Cartography’s technical vision is a leadership team fluent in both biology and computation.

Kevin Parker, Ph.D., the company’s co-founder and CEO, trained at Stanford under Howard Chang and Ansu Satpathy, two pioneers in single-cell and epigenomic research. His academic work explored how gene expression patterns evolve at the cellular level — the same foundation that underpins Cartography’s approach today.

He’s joined by co-founders Caleb Lareau, who leads platform technologies, Maxwell Mumbach, head of discovery, and Jeffrey Verboon, who oversees computational biology. Their Scientific Advisory Board includes Carl June, whose CAR-T work defined an era, alongside immunology and bioinformatics experts such as Joseph Fraietta, Emma Lundberg, and Angela Shen.

The company’s lab network reflects that dual DNA. Engineers and biologists work side by side in a hybrid wet-lab–computational environment, where automated cell-culture systems, microfluidic sequencing prep, and GPU-accelerated compute clusters operate under a single workflow architecture. Each experiment generates terabytes of data that feed directly into discovery models, shortening cycles between concept and validation.

It’s a modern kind of biotech lab — half engineering workshop, half data center — built to turn maps into medicines.

Funding and Investor Backing

Cartography’s growth has been fueled by a mix of biotech and tech-minded investors who see data as the new frontier in oncology. The company’s $67 million Series B, led by Pfizer Ventures, brought in new participation from Amgen Ventures, LG Corp, Lotte Holdings CVC, and Global BioAccess Fund, alongside returning backers Andreessen Horowitz, 8VC, and Catalio Capital Management.

The raise brings total funding to over $124 million, giving Cartography the resources to move its first therapy into the clinic while scaling its computational infrastructure. The company’s data operations now run on GPU-powered clusters capable of processing petabyte-scale datasets, while its lab network expands capacity through automated sequencing platforms and robotic sample handling systems.

With each round of financing, Cartography’s model becomes more integrated. Investors aren’t just funding drug development — they’re funding a discovery architecture designed to iterate faster, validate more rigorously, and scale across disease areas.

It’s the kind of infrastructure that makes it possible to imagine precision immunotherapy as a platform, not a pipeline.

How the Work Gets Done

Behind Cartography’s platform is a lab infrastructure built more like a data center than a traditional research space. Every experiment feeds a continuous pipeline that blends automation, cloud computing, and machine learning.

Sample preparation begins with liquid-handling robots that isolate single cells from patient tissue and feed them into microfluidic sequencing systems. Each cell’s RNA is barcoded, sequenced, and logged automatically through LIMS-integrated tracking systems. From there, data stream directly into GPU-accelerated compute clusters, where algorithms perform alignment, quantification, and feature extraction without human intervention.

In parallel, spatial transcriptomics and multiplex proteomics workflows capture information about how tumor and immune cells organize within tissue. These experiments run on automated imaging platforms linked to AI-driven analysis pipelines, turning terabytes of microscopy data into usable biological features.

All of this activity is synchronized through a custom orchestration layer that connects the wet lab to the computational backend. It monitors experiment progress, resource allocation, and data quality in real time. When the system detects outliers — a batch drift, a sequencing anomaly — it flags the event automatically, prompting validation or rerun.

The infrastructure allows a single scientist to manage workflows that once required a team of technicians. More importantly, it ensures reproducibility — every dataset, every image, every antibody screen is traceable from sample to result.

It’s not just a lab; it’s a coordinated environment built for scale, where discovery feels less like trial and error and more like engineering.

Pipeline Expansion and Future Milestones

The new funding will push CBI-1214 into Phase 1 clinical trials, but the company’s ambitions go well beyond a single program. Using its ATLAS and SUMMIT platforms, Cartography is already advancing additional bispecific and multi-specific antibody programs aimed at other solid tumors.

Each new program starts the same way: with data. The company continuously integrates fresh single-cell sequencing and tumor proteomic datasets, refining its models to uncover new antigen pairs and validate them experimentally. Automated flow-cytometry, mass spectrometry, and AI-driven image analysis provide cross-checks before any target moves forward.

This constant data integration gives Cartography a self-reinforcing discovery loop — one that grows stronger with every sample analyzed. The structure is deliberate: a scalable R&D engine designed to handle the kind of complexity that has held cancer immunotherapy back for decades.

The next few years will test whether this model can deliver not just better maps, but better outcomes. If it does, Cartography could mark the beginning of a new phase in oncology — one where precision doesn’t depend on chance, but on design.