PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
May 29, 2026Journal of Clinical Oncology0 citations

Development and testing of an ex vivo, live tumor fragment platform for the prediction of immune checkpoint inhibitor response and relationship to approved biomarkers.

View Full Paper
DBDavid A. BraunHGHinco J. GiermanCSChetan Sood

Key Points

  • This research aims to evaluate a novel ex vivo platform using live tumor fragments to predict responses to immune checkpoint inhibitors in cancer patients.
  • Enrollment of patients with solid tumors eligible for immune checkpoint inhibitors from multiple clinical sites.
  • Biopsies were processed into live tumor fragments and treated with antibodies and immune checkpoint inhibitors to assess cytokine response.
  • Receiver operating characteristic and precision recall analyses identified predictive cytokines used to develop the Elephas score.
  • 85% of patient specimens passed quality control metrics, and 82% of patients with positive Elephas scores had an objective response to therapy.
  • 27% of the specimens exhibited a cytosine response to immune checkpoint inhibitors, and the score correctly identified 100% of patients with clinical progressive disease.
  • Further validation will assess these findings in additional sets.

Abstract

2646 Background: Immune checkpoint inhibitors (ICIs) have transformed the management of many advanced cancers but identifying patients who are likely or unlikely to benefit from ICI treatment remains a critical challenge. Biomarkers, such as PD-L1, mismatch repair deficiency, and tumor mutational burden, capture only static, unidimensional features of the complex tumor microenvironment, and therefore inadequately predict response to immunotherapy in patients. In contrast, live tumor fragments (LTFs) preserve the full complexity of the tumor microenvironment and enable functional ex vivo assessments of ICI activity. Previous efforts have had limited clinical applicability because they required larger resection specimens often collected at a single academic center. Here, the predictive capability of an ex vivo platform using core needle and forceps biopsies from multiple clinical sites was assessed and compared to approved biomarkers. Methods: Patients with eligible solid tumors (where ICI has an indication, including renal, non-small cell lung, bladder, colorectal, and triple-negative breast cancer, and others) were enrolled in ongoing, prospective observational trials (NCT05478538, NCT05520099, NCT06349642). Biopsies were cut into LTFs, encapsulated in hydrogel, and treated sequentially with IgG antibody followed by ICI (αPD-L1 with or without αCTLA-4). Cytokine production was assessed by a multiplexed bead-based immunoassay at multiple time points during ex vivo treatment. Using receiver operating characteristic and precision recall analyses, we identified 9 predictive cytokines (including IFN-γ, granzyme B, and CXCL9/10), which were used to develop the Elephas score (ELP-score) to assess cytokine response to ex vivo ICI treatment. Results: Of 167 eligible patient specimens, 85% (n = 142) passed quality control metrics and 130 were used for model development. Hierarchical clustering of cytokine production revealed that 27% (n = 35) of patient specimens exhibited a cytokine response to ICI. In a validation set of 20 tumors, collected from patients who were subsequently treated with ICI, ELP-score positivity correctly identified 9 of 11 patients (82%) who had an objective response (PR/CR) to ICI, including 2 patients who were negative for FDA-approved standard-of-care ICI biomarkers. Furthermore, 5 of 5 patients (100%) with clinical progressive disease were correctly characterized as ELP-score negative. Conclusions: Using routine biopsy specimens, the ex vivo live tumor platform accurately identifies patients with clinical response to ICI therapy, including those incorrectly identified by conventional biomarkers. Ongoing efforts will test these findings in an additional validation set. If confirmed, the ex vivo LTF platform could enable the identification of patients likely to benefit from ICI therapy. Clinical trial information: NCT05478538 , NCT05520099 , NCT06349642 .

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Braun et al. (2026) studied this question.

synapsesocial.com/papers/6a192e4efab5b468c44175f7https://doi.org/10.1200/jco.2026.44.16_suppl.2646
Ask AI
Helpful
Bookmark
Share
View Full Paper