PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
April 4, 2026Cancer Research0 citations

Abstract 734: High-content image analysis of colorectal cancer patient-derived tumroids identifies high intra- and inter-patient heterogeneity associated with drug activity and mode-of-action.

View Full Paper
PEPeter W. EideNPNicolas PasquirCAChrister A. Andreassen

Key Points

  • This research aims to explore the heterogeneity in colorectal cancer tumoroids and its relationship to drug activity.
  • Cultured 4049 colorectal cancer patient-derived tumoroids for analysis.
  • Utilized ConvNeXt v2 classifiers for morphology classification.
  • Evaluated tumoroid responses to 37 drugs across multiple models.
  • Developed an algorithm (iCANdy) classifying tumoroids into 10 morphological types.
  • Achieved an F1-score of 0.80 in classifying tumoroids by predicted morphology.
  • Identified high intra- and inter-patient heterogeneity in tumoroid morphology and drug response.

Abstract

Abstract Background: Ex vivo culture of colorectal cancer (CRC) patient-derived tumoroids reveals a rich heterogenous pool of growth morphologies, within and among models. Quantitative analysis of tumoroid morphologies can uncover new biology and predictive relationships. Studies have shown that compact vs. cystic morphology reflects drug sensitivity, with cystic tumoroids being sensitive to Wnt inhibitors and resistant to MEK inhibitors as compared to compact tumoroids. Methods: Individual tumoroids (n=4049) were annotated based on visual assessment of brightfield, nuclear and nuclear dead stain multi-channel images. ConvNeXt v2 classifiers were trained on the resulting dataset, using brightfield or all three channels as inputs. Results: We developed a phenotyper algorithm (iCANdy) stratifying CRC tumoroids into 10 classes: compact, budding, acinar, cystic (thick-walled), cystic (thin-walled), invasive, fibroblastic, monolayer, single/apoptotic and mixed/other. Performance was similar for brightfield and multi-channel inputs. Classifying all tumoroids according to the nearest morphology yielded an F1-score of 0.80 and balanced accuracy of 0.75, the most ambiguous class being mixed/other as expected. iCANdy was applied on brightfield images of 10.2 million tumoroids from a total of 22k drug treatment/control wells from 57 tumoroid models, using 37 drugs or drug combinations. For untreated controls, the dominant morphology in terms of total area varied across models with compact (39%), invasive (32%), single/apoptotic (12%), budding (9%), and cystic (thick-walled) (7%) being most common. We observed high intra-patient heterogeneity, the proportion of the dominant morphology ranging from 20% to 54% with a median of 36% (excluding single/apoptotic structures). Inter-patient heterogeneity was also associated with the activity of several drugs. While atorvastatin showed a similar single-oriented shift for both compact (82%) and invasive (100%) structures, other drugs such as 5-FU exhibited strong differences (68% and 18% respectively). Other compounds with limited impact on cell viability induced morphological phenotypes indicating mode-of-actions such as cytostasis or senescence. Interestingly, methotrexate caused an invasive-oriented shift for 80% of the cystic (thick-walled) structures but only 14% of the compact clusters. Conclusions: Quantitative phenotyping of tumoroids can identify novel predictive morphological relationships, drug mode-of-action and more accurately predict drug activity. Citation Format: Peter W. Eide, Nicolas Pasquir, Christer A. Andreassen, Anne Hansen Ree, Knut M Augestad, Sebastian Meltzer, Jarle Bruun. High-content image analysis of colorectal cancer patient-derived tumroids identifies high intra- and inter-patient heterogeneity associated with drug activity and mode-of-action abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 734.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Eide et al. (2026) studied this question.

synapsesocial.com/papers/69d0b028659487ece0fa6323https://doi.org/10.1158/1538-7445.am2026-734
Ask AI
Helpful
Bookmark
Share
View Full Paper