In lung cancer and other cancer types, the use of targeted therapies that inhibit important and common oncogenic driver alterations such as mutant EGFR and KRAS (G12C) and block immunosuppressive checkpoints such as PD1/PDL1 is improving patient outcomes. A major challenge to transforming cancers into chronic or curable diseases is acquired resistance, which enables lethal cancer progression in patients. Understanding the mechanisms underlying acquired resistance is essential to develop counteracting strategies that improve patient survival.
The goal of the BAATAAR-UP within the NCI ARTNet Program is to characterize the mechanisms of, and therapeutically counteract acquired resistance to molecular therapies in non-small cell lung cancer (NSCLC) by delineating the tumor-tumor microenvironment (TME) ecosystem and its on-treatment plasticity.
The overarching hypothesis is that acquired resistance to molecular therapies can be thwarted by defining and exploiting vulnerabilities in the cellular, signaling, and geographic tumor ecosystem networks that allow tumors to survive and grow during therapy.
Aim 1:
To define TME cell states, plasticity, and interactions that mechanistically drive acquired resistance to molecularly targeted therapy against oncogenic EGFR and KRAS (G12C) in NSCLC. Hypothesis: Signaling interactions between cancer and TME tumor macrophages and fibroblasts engage cancer cell survival programs allowing acquired resistance to therapy against oncogenic EGFR and KRAS.
Aim 2:
To develop mechanism-based therapeutic strategies to reverse, prevent, or delay acquired resistance to molecularly targeted therapy against oncogenic EGFR and KRAS (G12C) in NSCLC. Hypothesis: Therapeutic interception of core signaling processes arising from cancer and TME cell (tumor macrophages and fibroblasts) interactions promoting acquired resistance can thwart acquired resistance.
BAATAAR-UP investigates these mechanisms, and identify others, synergistically and iteratively via 3 Research Projects and optimal interactions with 2 Cores.
Project 1:
Clinical tumor-TME acquired resistance (Translational)
Project 2:
Patient-derived xenograft (PDX) tumor-TME acquired resistance (Translational)
Project 3:
Patient-derived organoid (PDO) tumor-TME acquired resistance (Basic)
A Data Science Core will analyze, harmonize, centralize, and share data obtained across the basic and translational continuum using innovative methods. An Administrative Core will ensure optimal project integration and internal and external interactions with the ARTNet Consortium, and scientific and lay communities.
The Principal Investigators of BAATAAR-UP Center are Drs. Trever Bivona and Jack Roth.
(For additional information, please visit NIH RePORTER)
