Endolysosomal trafficking defects as predictive features of antibody-drug conjugate resistance in metastatic breast cancer

Active Project Grant Awarded Last action

Award ID
F30CA310064
Recipient
Awarding agency
Obligated
$48,413

Award facts

Assistance listing
93.398 Cancer Research Manpower
Completion date 0% complete
Place of performance
Houston, TX 77030, USA

Award details come from USAspending.gov.

Description

As the agency reported it.

Abstract
Antibody-drug conjugates (adcs) are targeted cancer therapies that deliver cytotoxic drugs to tumor cells by linking them to antibodies specific to cancer antigens. Newer adcs, such as trastuzumab deruxtecan (t- DXD) and sacituzumab govitecan (SG), use cleavable peptide linkers that must be processed in lysosomes to release the drug. Efficient transport from endosomes to lysosomes is thus a unique requirement for therapeutic efficacy. While these adcs have greatly improved response rates versus traditional chemotherapy, approximately 50% of t-DXD and 75% of SG patients show no clinical benefit. There is an urgent need for tools to predict which patients will respond, to avoid unnecessary toxicities and improve outcomes. Although endolysosomal transport deficits have been implicated in ADC resistance, no clinical tool currently utilizes this information for guiding treatment. This project will test the central hypothesis that computational analysis of tumor slides stained for endolysosomal markers can accurately predict ADC response. Aim 1 will mechanistically validate markers from literature and preliminary experiments by modeling trafficking dynamics in genetically modified breast cancer cell lines, using fluorescence lifetime imaging microscopy - förster resonance energy transfer (flim-fret) pulse-chase imaging to quantify transport and cleavage rates. Aim 2 will identify trafficking biomarkers of resistance in archived breast tumor samples, using immunofluorescent staining and single-cell spatial transcriptomics. Aim 3 will bridge patient and in vitro findings by integrating marker data, in vitro transport imaging studies, and spatial transcriptomics into a multimodal machine learning framework. Embedding alignment will leverage validated in vitro features to improve patient-specific response prediction, while systematic feature reduction will define a minimal marker panel for clinical application. The resulting mechanism-driven, spatially resolved predictive model will fill a critical GAP in understanding why target antigen expression alone does not correlate with response. Designed to fit seamlessly within existing pathology workflows, this work will yield a mechanistic pilot for incorporating endolysosomal dynamics into ADC response prediction.

Quick answers

Who received award F30CA310064?

F30CA310064 was awarded to Texas A & M University System Health Science Center by National Cancer Institute (National Institutes of Health) on January 31, 2027.

How much is award F30CA310064 worth?

$48,413 has been obligated on F30CA310064.

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