ReviewMolecular cancer2026
Patient-derived organoids for precision oncology in colorectal cancer: from tumor modeling to clinical translation.
Review in Molecular cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Precision oncology in colorectal cancer (CRC) has been profoundly informed by genomic and transcriptomic profiling, yet molecular stratification remains only partially predictive of therapeutic benefit. This reflects a broader challenge: moving from static molecular information to dynamic, adaptive, and time-resolved predictions of drug response for individual patients along their clinical history. In CRC, this challenge is compounded by extensive inter- and intratumoral heterogeneity, clonal evolution, chromosomal instability, phenotypic plasticity and tumor-microenvironment (TME) interactions, all of which reshape therapeutic vulnerabilities over time.Patient-derived organoids (PDOs) have emerged as a functional platform to address this gap, with CRC providing one of the most fertile contexts for their development and application. By preserving key histopathological, molecular, and phenotypic features of parental tumors while remaining experimentally tractable, expandable, and amenable to pharmacological and genetic perturbation, PDOs add a functional layer to precision oncology in CRC. Living organoid biobanks and ex vivo screening studies have shown that PDOs can capture interpatient heterogeneity, uncover mechanisms of sensitivity and resistance, and, in selected contexts, mirror clinical responses to chemotherapy and targeted agents. The same experimental tractability has made these systems powerful tools for investigating tumor initiation and clonal evolution, while enabling exploration of cellular plasticity through the lens of dynamic cellular hierarchies. In parallel, next-generation CRC organoid systems incorporating stromal and immune components may extend functional testing to TME-dependent processes, including response to immunotherapy.In this review, we discuss the conceptual foundations of organoid technology and its emergence as a functional precision oncology platform in CRC. We examine how CRC organoids have advanced understanding of tumor initiation, evolution, chromosomal instability, and phenotypic plasticity; summarize their applications in drug discovery, therapy-response prediction, and resistance modeling; and highlight the development of next-generation multicellular organoid systems incorporating immune and stromal components. Finally, we address the major technical, biological, and clinical challenges that must be overcome to translate PDO-based approaches into robust and clinically actionable tools for precision medicine.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.