New Cancer Models Aim to Outsmart Drug-Resistant Tumors

MANASSAS, Va., and CAMBRIDGE, Mass. – April 20, 2026 – In a significant step forward for precision oncology, two leading scientific organizations, ATCC and the Broad Institute of MIT and Harvard, have announced a collaboration to engineer a new class of cancer models designed to systematically decode why life-saving drugs eventually fail. The initial focus is on non-small cell lung cancer (NSCLC), a leading cause of cancer death worldwide, and the formidable challenge of resistance to targeted therapies.

The partnership has produced a panel of 13 new NSCLC cell lines, each meticulously engineered to harbor a specific, clinically observed mechanism of resistance to osimertinib, a potent third-generation drug. By making these models and their associated data openly available, the project aims to arm a global community of researchers with the tools needed to develop more durable treatments and outmaneuver cancer's adaptive capabilities.

The Unrelenting Challenge of Resistance

For patients with NSCLC driven by mutations in the epidermal growth factor receptor (EGFR), the development of targeted therapies like osimertinib has been transformative. Unlike traditional chemotherapy, these drugs precisely target the molecular drivers of the cancer, leading to dramatic tumor shrinkage and improved survival. Osimertinib, the current standard of care, is highly effective, but its success is almost always temporary.

Inevitably, the cancer evolves. Over time, a small population of tumor cells acquires new mutations or activates alternative signaling pathways, rendering the drug ineffective and leading to disease progression. This acquired resistance is the central challenge in targeted cancer therapy. Mechanisms are diverse and complex, ranging from new mutations in the EGFR gene itself, such as the C797S mutation found in up to 15% of patients, to the activation of entirely different “bypass” pathways involving genes like MET, KRAS, and BRAF. In some cases, the cancer cells even undergo a complete identity shift, transforming into a different and more aggressive cancer type, such as small cell lung cancer.

Studying these escape routes has traditionally been a slow and arduous process. Researchers might spend months or years growing patient tumor samples in the lab—a method hampered by the scarcity of tissue—or bathing standard cell lines in a drug until resistant clones emerge, a process that can yield complex, hard-to-interpret results. This new initiative aims to shatter that bottleneck.

Engineering a Blueprint for Resistance

By leveraging the power of CRISPR gene editing and gene overexpression techniques, scientists at ATCC and the Broad Institute have created a set of tools that offer unprecedented clarity. Instead of waiting for resistance to happen, they are building it from the ground up in a controlled, systematic way.

Starting with well-characterized, osimertinib-sensitive lung cancer cell lines, the team engineered a panel of isogenic models. These models are pairs of cell lines that are genetically identical, with one crucial exception: the engineered resistance mechanism. This allows researchers to conduct side-by-side experiments, isolating the specific effects of a single resistance mutation or fusion gene—such as the PIK3CA E545K mutation or the CCDC6-RET fusion—with a precision that was previously impossible.

“With this powerful new set of tools, drug-sensitive and drug-resistant cancer cells can be studied side by side to understand therapeutic resistance and the underlying drivers,” said Ruth Cheng, PhD, CEO of ATCC. “By creating and providing these cancer models along with a rich data-set to the global research community, our hope is to reveal hidden targets and combination strategies that turn today’s treatment failures into tomorrow’s breakthrough.”

This approach accelerates the research timeline from years to months, providing a scalable platform to model the diverse ways tumors escape treatment.

An Open-Source Map to Guide Discovery

The impact of these models is amplified by the commitment to open science. The new cell lines and their vast associated genomic data will be integrated into the Cancer Dependency Map (DepMap), a landmark project led by the Broad Institute that seeks to identify every genetic vulnerability in hundreds of cancer cell models.

The collaboration will help build an emerging framework within DepMap called the Response and Resistance Map, or ResMap. This specialized map is designed to systematically chart how different cancers respond to therapies and how resistance evolves, creating a public resource for the entire scientific community.

“Drug resistance remains one of the most significant barriers to durable cancer treatment,” said Kirsty Wienand, PhD, a Senior Research Scientist in the DepMap project at the Broad Institute. “Systematically engineering resistance mechanisms in well-characterized cell models allows us to study how tumors adapt to targeted therapy. Integrating these models into DepMap will help researchers worldwide identify new vulnerabilities and potential therapeutic combinations.”

By linking the physical cell lines distributed by ATCC with the deep datasets in the DepMap portal, the initiative creates a seamless ecosystem for discovery, enabling any researcher with a compelling hypothesis to access the tools and data needed to test it.

Fueling the AI Revolution in Medicine

Beyond accelerating traditional lab work, this project is creating a data goldmine for the next frontier of medical research: artificial intelligence. AI and machine learning algorithms thrive on large, high-quality, well-structured datasets. The noise and complexity of patient data can make it difficult for algorithms to learn the precise molecular signatures of drug resistance.

These engineered models provide the clean, high-fidelity data that AI needs. Because each model has a defined genetic alteration, machine learning algorithms can be trained to recognize the specific cellular changes that confer resistance. This can dramatically accelerate the identification of novel drug targets, predict which patients will respond to which combination therapies, and help design new drugs that anticipate and block resistance before it even starts.

By combining advanced cell engineering with functional genomics and computational biology, the collaboration provides a powerful, scalable framework for turning the tide against drug resistance. The research, which will be presented at the upcoming American Association for Cancer Research (AACR) Annual Meeting, establishes a blueprint that can be extended from lung cancer to many other cancer types, promising a future where treatments are not only targeted but also durable.