Search bioRxiv⌕ Search

bioRxiv · 10.64898/2026.04.04.716464

Mature tumoroids recapitulate clinically relevant drug response through extended 3D culture in PDAC

Abstract

BackgroundDrug responses in pancreatic ductal adenocarcinoma (PDAC) vary sharply across in vitro culture formats, but most 2D-3D comparisons conflate microenvironmental cues with time-dependent cellular adaptation. As a result, conventional assays frequently overestimate drug efficacy and poorly reflect clinical pharmacology. Main findingsWe profiled MiaPaCa-2, PANC-1, and CFPAC-1 grown in an extracellular-matrix (ECM) hydrogel for 1-12 days, defining extended 3D cultures ([&ge;]10 days) as mature tumoroids, and quantified 72 h drug responses to a multi-class oncology panel using growth-rate (GR) metrics to normalize for proliferation across formats and durations. Prolonged 3D pre-culture induced broad tolerance, with typical 10-100x reductions in sensitivity to standards of care (5-fluorouracil, SN38, oxaliplatin, gemcitabine, paclitaxel), following a reproducible susceptibility hierarchy (MiaPaCa-2 > PANC-1 > CFPAC-1) after GR correction. In mature tumoroids, GR values closely approximated clinically observed plasma exposures (e.g., within <4x for 5-FU and <0.5x for gemcitabine), whereas 2D and short-term organoid assays markedly underestimated resistance, often by >100x, thereby overstating drug activity. Notably, CFPAC-1 exhibited increased sensitivity to SN38 and trametinib under mature-organoid conditions, demonstrating that microenvironmental conditioning can invert responses for selected mechanisms. Transcriptomic profiling revealed coordinated up-regulation of multiple ABC transporters with extended 3D residence, tracking resistance phenotypes across lines and implicating transporter-linked tolerance programs. SignificanceTogether, these data identify time-in-3D and the emergence of mature tumoroids as dominant, previously under-controlled determinants of PDAC pharmacology that both induce tolerance and unmask context-dependent vulnerabilities. We propose incorporating both short-term and mature-tumoroid screening arms into preclinical workflows, reporting pre-culture duration alongside GR-normalized effect sizes, and leveraging transporter-informed biomarkers to guide regimen prioritization and sequencing. This framework enhances physiological relevance, reproducibility, and translational fidelity in PDAC drug discovery.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kus, K., Earnshaw, D., Pirog, A., Siewiera, M., Kote, S., Murzyn, A. A., Swierzewski, P., Malek-Trzonkowska, N., Sandowska-Markiewicz, Z., Unrug-Bielawska, K., Statkiewicz, M., Dama, P., Krzykawski, M. P.. 2026-04-07. Mature tumoroids recapitulate clinically relevant drug response through extended 3D culture in PDAC. https://doi.org/10.64898/2026.04.04.716464

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

m6A-Driven Intratumoral Cholesterol Biosynthesis Fuels Castration-Resistant Prostate Cancer Progression

Both nuclear pore complexes (NPCs) and RNA N6-methyladenosine (m6A) machinery are indispensable for proper cellular function. Although their collaborative roles in the nuclear export of messenger RNAs (mRNAs) have been reported, it remains ambiguous whether and how this collaboration may contribute to cancer progression. Here we identify a functional cooperation between NPCs and m6A signaling that promotes the development of castration-resistant prostate cancer (CRPC). We showed that nuclear export of m6A-modified mRNAs, mediated by the interaction between RNA methyltransferase METTL3 and the nucleoporin NUP93, is functionally coupled to cholesterol biosynthesis. Given that cholesterol-fueled intratumoral androgen production is one of the mechanisms driving CRPC, we demonstrated that overexpression of the wild-type METTL3 or NUP93, but neither the enzymatically dead METTL3 nor the mutant NUP93 that loses METTL3-interacting capability, elevates intracellular levels of androgens, activates AR signaling under castrate condition, and promotes androgen-independent growth of prostate cancer cells both in vitro and in vivo. Importantly, pharmacological inhibition of METTL3 or targeted demethylation on mRNAs encoding key cholesterol biosynthesis enzymes effectively suppressed CRPC malignancy. Together, these findings uncover a therapeutically targetable m6A-METTL3-NUP93 axis that links nuclear mRNA export and metabolic reprogramming to fuel CRPC progression, providing a conceptually new strategy for the treatment of this lethal disease.

cancer biology↗

ST6Gal2 promotes α2,6-sialylation and aggressive phenotypes in neuroblastoma cells

Neuroblastoma is the most common extracranial solid tumor of childhood. Its clinical behavior ranges from spontaneous regression to lethal, treatment-refractory disease. Aberrant 2,6-sialylation contributes to aggressive phenotypes in many cancers, but the role of ST6Gal2, a neural-enriched 2,6-sialyltransferase, in neuroblastoma is largely unexplored. Here, we examine the clinical and functional significance of ST6Gal2 in neuroblastoma. In two independent public cohorts (SEQC, n=498; Kocak, n=649), high ST6GAL2 expression was associated with significantly worse overall and event-free survival. In the SEQC cohort, ST6GAL2 expression was higher in high-risk and MYCN-amplified tumors, varied across International Neuroblastoma Staging System stages, and correlated positively with a mesenchymal transcriptional signature (Spearman {rho}=0.181). The mesenchymal correlation was reproduced in the Kocak cohort ({rho}=0.204). Stable shRNA-mediated knockdown of ST6GAL2 in SK-N-AS and SK-N-BE(2) cells reduced proliferation and viability, impaired wound closure, and decreased migration and invasion. In preliminary experiments in SK-N-AS cells, ST6GAL2 knockdown reduced binding of Sambucus nigra agglutinin, consistent with a role for ST6Gal2 in 2,6-sialylation. Together, these findings link ST6Gal2 expression to aggressive clinical and transcriptional features and pro-tumorigenic phenotypes in neuroblastoma and nominate ST6Gal2-mediated sialylation as a candidate pathway for mechanistic study.

cancer biology↗

Unsupervised transcriptomic analysis of paired pre- and post-treatment specimens reveals divergent chemoimmunomodulatory induction trajectories in breast cancer

The immunomodulatory effects of chemotherapy (chemoimmunomodulation; CIM) are clinically consequential and heterogeneous, yet no systematic framework exists for classifying the immunomodulatory trajectory a tumor follows in response to treatment (CIM trajectory). Here, we present the CIM Induction Classifier (CIMIC), an unsupervised clustering pipeline leveraging delta gene expression across 3,189 CIM-related genes to classify specimens chemoimmunomodulatory trajectory. Applied to two pre- and post-chemotherapy breast cancer (BC) datasets (NKI/SMC, N = 36; NEO, N = 19) and nine epirubicin-perturbed triple-negative BC (TNBC) cell lines, CIMIC identified two divergent CIM trajectories: a functional CIM (Fun-CIM) trajectory, broadly conserved across tumors and cell lines and characterized by induction of inflammatory cell death, antigen presentation, viral mimicry, and adaptive immune activation programs, and a dysfunctional CIM (Dys-CIM) trajectory, characterized by induction of proteostatic and metabolic stress-adaptation programs, reduced immune cell abundances and cytotoxic activity, and enrichment of aggressive BC subtypes. Using survival and longitudinal transcriptomic data in NKI/SMC (N = 20), treatment-induced increases in Fun-CIM-associated genes and ssGSEA scores were associated with reduced recurrence, whereas Dys-CIM-associated genes and scores were associated with increased recurrence. In multivariable analyses within independent chemotherapy-treated BC cohorts (METABRIC, N = 412; SCAN-B, N = 2,462), higher baseline Fun-CIM ssGSEA scores were associated with better outcomes, whereas higher baseline Dys-CIM ssGSEA scores were associated with worse outcomes. These findings establish CIM as a dynamic, trajectory-level process and position CIMIC as a framework for defining CIM trajectories and supporting future efforts to identify predictors, mechanisms, and therapeutic strategies that maximize beneficial CIM.

cancer biology↗