Mapping tumor heterogeneity: ex-vivo drug responses in matched primary and metastatic tumors using a 3D functional precision platform
Nitiyanandan, R.; Trus, I.; Maestri, C.; Parker, R. J.; Cance, W.; Apfel, B.; Apfel, C. C.
Show abstract
Introduction: Tumors are known to develop genomic heterogeneity over time, particularly in metastatic disease, raising concerns that a single-site biopsy may be insufficient to accurately predict response to medications. Methods: We used our 3D microtumor functional profiling platform that preserves key features of the native tumor microenvironment. We tested (i) six split samples from primary tumors and (ii) seven samples from primary tumors (ovarian, colon, and breast cancer) matched with their locoregional or distant metastases. Fresh tissue specimens were processed within 24 hours after surgery and treated with NCCN guideline-recommended chemo or targeted therapies at concentrations over four orders of magnitude. Cytotoxicity was evaluated after three days to generate dose response curves. The response correlations between the split sample or between the two matched sites were quantified using the concordance correlation coefficient ({rho}c) for the drug efficacy metrics. Results: We compared a total of 275 drug responses. The six split samples resulted in a strong correlation between the two tumor areas ({rho}c=0.95, p<0.001), supporting high assay reproducibility across distinct regions of the same tumor. Locoregional metastases were highly correlated with their primaries (ovarian to omentum {rho}c=0.97, p<0.001; ovarian to liver capsule {rho}c=0.93, p<0.001; colon to peritoneum {rho}c=0.91, p<0.001). A lesser correlation was observed with lymphogenic metastases (breast to axilla {rho}c=0.87, p<0.001), and the correlations of hematogenic metastases were even lower (colon to lung {rho}c=0.69 p<0.001; ovarian to soft tissue/colon {rho}c=0.58, p<0.001). No correlation was observed among specimens that were resistant to all tested medications. Conclusion: Tumor drug responses were highly preserved in locoregional metastases, whereas somewhat greater divergence was observed in lymphogenic or hematogenic metastases. Single-site functional profiling may remain informative for identifying treatment resistance and might reduce the risk of ineffective therapy selection in metastatic disease.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Multiplexed drug testing of tumor slices using a microfluidic platform 92%
- Multi-omics analysis of serial samples from metastatic TNBC patients on PARP inhibitor monotherapy provide insight into rational PARP inhibitor therapy combinations 91%
- Real-World Benchmarking and Validation of Foundation Model Transformers for Endometrial Cancer Subtyping from Histopathology 91%
Similar papers in this journal
- Novel kinome profiling technology reveals drug treatment is patient and 2D/3D model dependent in GBM 92%
- Extracellular vesicle molecular signatures characterize metastatic dynamicity in ovarian cancer 91%
- Diagnostic accuracy and safety of coaxial core-needle biopsy (CNB) system in Oncology patients treated in a specialist cancer centre with prospective validation within clinical trial data 91%
Similar papers in this journal
Similar papers in this journal
- A suspension technique for efficient large-scale cancer organoid culturing and perturbation screens 92%
- Intracellular Optical Doppler Phenotypes of Chemosensitivity in Human Epithelial Ovarian Cancer 92%
- Long-term maintenance of patient-specific characteristics in tumoroids from six cancer indications in a common base culture media system 92%
Similar papers in this journal
- Frequent post-treatment monitoring of colorectal cancer using individualized ctDNA validated by multi-regional molecular profiling 93%
- Spatial, Temporal, and Molecular Heterogeneity of ADC targets in High-Grade Serous Ovarian Carcinoma 91%
- Bypassing cisplatin resistance in Nrf2 hyperactivated head and neck cancer through effective PI3Kinase targeting 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.