Genetically Defined, Syngeneic Organoid Platform for Developing Combination Therapies for Ovarian Cancer
Bibliographic record
Abstract
Abstract The paucity of genetically informed, immunocompetent tumor models impedes evaluation of conventional, targeted, and immune therapies. By engineering mouse fallopian tube epithelial organoids using lentiviral gene transduction and/or CRISPR/Cas9 mutagenesis, we generated multiple high-grade serous tubo-ovarian cancer (HGSC) models exhibiting mutational combinations seen in patients with HGSC. Detailed analysis of homologous recombination (HR)–proficient (Trp53−/−;Ccne1OE;Akt2OE;KrasOE), HR-deficient (Trp53−/−;Brca1−/−;MycOE), and unclassified (Trp53−/−;Pten−/−;Nf1−/−) organoids revealed differences in in vitro properties (proliferation, differentiation, and “secretome”), copy-number aberrations, and tumorigenicity. Tumorigenic organoids had variable sensitivity to HGSC chemotherapeutics, and evoked distinct immune microenvironments that could be modulated by neutralizing organoid-produced chemokines/cytokines. These findings enabled development of a chemotherapy/immunotherapy regimen that yielded durable, T cell–dependent responses in Trp53−/−;Ccne1OE;Akt2OE;Kras HGSC; in contrast, Trp53−/−;Pten−/−;Nf1−/− tumors failed to respond. Mouse and human HGSC models showed genotype-dependent similarities in chemosensitivity, secretome, and immune microenvironment. Genotype-informed, syngeneic organoid models could provide a platform for the rapid evaluation of tumor biology and therapeutics. Significance: The lack of genetically informed, diverse, immunocompetent models poses a major barrier to therapeutic development for many malignancies. Using engineered fallopian tube organoids to study the cell-autonomous and cell-nonautonomous effects of specific combinations of mutations found in HGSC, we suggest an effective combination treatment for the currently intractable CCNE1-amplified subgroup. This article is highlighted in the In This Issue feature, p. 211
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".