BASECAMP-1: An approach to improve patient screening efficiency and to provide large datasets for precision medicine clinical trials.
e23020 Background: Precision medicine studies must overcome the challenges of identifying patients with uncommon molecular targets and small cohort sizes for correlative studies. BASECAMP-1 (NCT04981119) is a prescreening study that uses a single assay to address both problems by: 1) efficiently screening potentially eligible patients for subsequent clinical trials of Tmod logic-gated chimeric antigen receptor T-cell (CAR T) therapies; and 2) providing a large dataset available for translational studies. Methods: Initially, patients were identified for BASECAMP-1 by consenting and genotyping all potentially eligible patients for human leukocyte antigen (HLA)-A*02 heterozygosity. To improve screening efficiency, we co-developed a patient-matching program with Tempus AI, Inc (Tempus), in which Tempus informs the principal investigators of next-generation sequenced (NGS) patients with HLA-A*02 heterozygosity within their health system who may be eligible for BASECAMP-1. Patients are screened using the Tempus xT platform, which uses genomic and RNA sequencing (RNAseq) analyses to determine gene mutations and expression. Enrolled patients with colorectal (CRC), non-small cell lung, ovarian (OVCA), and pancreatic cancers (PANC) were analyzed for tumor purity, mutation distribution, HLA-A*02 loss of heterozygosity (LOH) status, and RNAseq to validate the data available for future clinical analyses. Results: As of January 3, 2025, 80 participants have been enrolled in BASECAMP-1. Through individual screening methods, 1684 patients were screened at 13 study sites, of which 30 patients had tumor-specific HLA-A*02 LOH and were enrolled (~1 eligible patient identified per 56 screened). The patient-matching program identified 282 patients with HLA-A*02 LOH at 12 sites; of these, 50 consented to the BASECAMP-1 study (~1 patient enrolled per 6 identified), demonstrating higher efficiency than the individual patient screening method. Correlative data from xT were available for 360 patients with germline HLA-A*02 and provided insights into mutational distribution and target expression. LOH status was not associated with tumor-specific differences in missense mutational distribution. RNAseq data demonstrated higher CEACAM5 expression in CRC and higher MSLN in PANC and OVCA vs other tumor types, while EGFR was consistently expressed with reduced variance across tumor types, confirming these as potential target antigens for Tmod CAR T interventional trials EVEREST-1, EVEREST-2, and DENALI-1. Conclusions: Identification and enrollment of HLA-A*02 LOH patients into BASECAMP-1 for logic-gated CAR T therapeutic studies were accelerated by the patient matching program. Concurrent xT analysis provides opportunities to augment analyses from robust propensity-matched comparisons, response correlation studies, and other translational discovery. Clinical trial information: NCT04981119 .
Authors
- Cathy Eng (ORCID: https://orcid.org/0000-0003-2335-0612)
- Matthew L. Ulrickson (ORCID: https://orcid.org/0000-0002-1458-7813)
- David G. Maloney (ORCID: https://orcid.org/0000-0002-8477-6919)
- Maria Pia Morelli (ORCID: https://orcid.org/0009-0000-9543-6584)
- Hemant S. Murthy (ORCID: https://orcid.org/0000-0003-4534-4248)
- Jennifer M. Specht (ORCID: https://orcid.org/0000-0003-1484-2113)
- Marcela V. Maus (ORCID: https://orcid.org/0000-0002-7578-0393)
- Diane M. Simeone (ORCID: https://orcid.org/0000-0001-5142-3087)
- Kedar Kirtane (ORCID: https://orcid.org/0000-0003-1302-9577)
- Julian R. Molina (ORCID: https://orcid.org/0000-0002-9962-4635)
- Jong Chul Park (ORCID: https://orcid.org/0000-0002-1052-0734)
- Patrick Grierson (ORCID: https://orcid.org/0000-0002-0624-6699)
- Sandip Pravin Patel (ORCID: https://orcid.org/0000-0002-8387-4840)
- Eric Wai-Choi Ng (ORCID: https://orcid.org/0000-0002-4258-4804)
- Kai He (ORCID: https://orcid.org/0000-0002-4378-9297)
- Jessica Tebbets (ORCID: https://orcid.org/0000-0003-2860-2006)
- Kristen Spencer (ORCID: https://orcid.org/0000-0001-5577-3439)
- Wen-Kai Weng
- J. Randolph Hecht
- Brittany Waschke
Institutions
- The University of Texas MD Anderson Cancer Center (US)
- Vanderbilt University (US)
- Washington University in St. Louis (US)
- Jacksonville College (US)
- WinnMed (US)
- Moffitt Cancer Center (US)
- University of California San Diego (US)
- NYU Langone Health (US)
- Massachusetts General Hospital (US)
- Fred Hutch Cancer Center (US)
- Integrated BioTherapeutics (United States) (US)
- Mayo Clinic in Arizona (US)
- Mayo Clinic in Florida (US)
- Banner MD Anderson Cancer Center
- UCLA Jonsson Comprehensive Cancer Center
- Vanderbilt-Ingram Cancer Center
- The Ohio State University (US)
- Stanford University (US)
Publication Details
- Journal
- Journal of Clinical Oncology
- Published
- 2025-05-28
- DOI
- https://doi.org/10.1200/jco.2025.43.16_suppl.e23020
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00