Cellular Oncology
How does omics-based molecular classification connect cancer diagnosis, treatment stratification and prognosis, and what limits its clinical translation?
This narrative review organizes published work around molecular diagnosis, treatment stratification and prognosis. It covers genomic and epigenomic, transcriptomic and proteomic biomarkers, then discusses integrated and spatial omics as ways to characterize tumor heterogeneity. A separate section compares biomarker-oriented clinical-trial designs. The final sections address data integration, algorithm interpretability and the limitations of preclinical tumor models. These are a synthesis and research agenda: clinical effects and model performance described in the review belong to the cited studies, not to a new cohort or validation experiment conducted for this article.
Zhaokai Zhou, Ting Lin, Shuang Chen, Ge Zhang, Yudi Xu, Haijiao Zou, Aoyang Zhou, Yuyuan Zhang, Siyuan Weng, Xinwei Han, Zaoqu Liu. Omics-based molecular classifications empowering in precision oncology. Cellular Oncology. 2024. https://doi.org/10.1007/s13402-023-00912-8
Omics-based molecular classifications empowering in precision oncology
Narrative review connecting molecular biomarkers, single- and multi-omics cancer classification, biomarker-oriented trial designs and translational constraints; it provides no new patient cohort or pooled treatment effect.
Counts describe the article's structure. They are not patient denominators, independently validated models or pooled treatment effects.
Section 2 synthesizes earlier biomarker research; the article does not perform a new diagnostic-accuracy comparison.
Source locator: Section 2.1, Molecular understanding of tumor biomarkers; subsections 2.1.1-2.1.3; Fig. 1A-D and its caption (author-uploaded full text).
The review summarizes published subtyping and spatial-profiling examples; it does not train or externally validate a new integrated classifier.
| Measure | Value |
|---|---|
| Integration discussion | Section 3.1.4 contrasts single-layer views with multi-omics integration and proteogenomics. |
| Spatial extension | Section 3.1.5 discusses spatial organization within the tumor microenvironment. |
Source locator: Section 3.1.4, Multi-omics integrated analysis-based molecular typing; Section 3.1.5, Spatial omics; Fig. 3 and its caption.
This is a narrative trial-design comparison, not a trial performed by the review authors or a pooled estimate of efficacy.
Source locator: Section 3.2, Innovative clinical trial-layouts for precision medicine; Table 1, Four major clinical trials' pros, cons, and representative trials. Only the design framework is summarized; classifications of individual trial examples are not independently endorsed.
AI, organoids and three-dimensional tumor models are discussed as possible tools, not as proof that a multi-omics treatment strategy improves patient outcomes.
| Measure | Value |
|---|---|
| Computational challenge | Sections 5.1 and 6 discuss heterogeneous data integration and clinical interpretability. |
| Preclinical-model challenge | Section 5.2 discusses missing human tumor-microenvironment features and changes during prolonged culture; Fig. 4 illustrates model construction. |
Source locator: Section 5.1, Intricate data network and artificial intelligence; Section 5.2, Preclinical tumor models drawbacks and progression; Fig. 4 caption; Section 6, Conclusion.
Use this review as a secondary overview of biomarker layers and integrated or spatial classification approaches, without attributing a new classifier to it.
The review compares four design frameworks; cite original trial protocols and results for the design and efficacy of any individual trial.
Evidence: KF3
The article discusses heterogeneous data integration, algorithm interpretability and preclinical-model fidelity as research challenges.
Evidence: KF4
| Row ID | Component | Context | Finding | Evidence Level | Scope | Source Locator | Evidence refs |
|---|---|---|---|---|---|---|---|
| evidence-kf1 | The review organizes molecular diagnosis across genomic and epigenomic, transcriptomic and proteomic biomarkers. | Section 2 synthesizes earlier biomarker research; the article does not perform a new diagnostic-accuracy comparison. | Illustrated biomarker layers: 4 in Fig. 1: genomic, epigenomic, transcriptomic and proteomic | Narrative secondary synthesis | Qualitative review context; primary-study findings require the original sources. | Section 2.1, Molecular understanding of tumor biomarkers; subsections 2.1.1-2.1.3; Fig. 1A-D and its caption (author-uploaded full text). | KF1 |
| evidence-kf2 | Integrated and spatial omics are presented as complementary approaches to characterize heterogeneity beyond a single molecular layer. | The review summarizes published subtyping and spatial-profiling examples; it does not train or externally validate a new integrated classifier. | Integration discussion: Section 3.1.4 contrasts single-layer views with multi-omics integration and proteogenomics.; Spatial extension: Section 3.1.5 discusses spatial organization within the tumor microenvironment. | Narrative secondary synthesis | Qualitative review context; primary-study findings require the original sources. | Section 3.1.4, Multi-omics integrated analysis-based molecular typing; Section 3.1.5, Spatial omics; Fig. 3 and its caption. | KF2 |
| evidence-kf3 | The review compares basket, umbrella, platform and N-of-1 designs as frameworks for molecularly informed oncology research. | This is a narrative trial-design comparison, not a trial performed by the review authors or a pooled estimate of efficacy. | Design categories compared: 4: basket, umbrella, platform and N-of-1 | Narrative secondary synthesis | Trial-design context, without endorsement of individual trial taxonomy or efficacy. | Section 3.2, Innovative clinical trial-layouts for precision medicine; Table 1, Four major clinical trials' pros, cons, and representative trials. Only the design framework is summarized; classifications of individual trial examples are not independently endorsed. | KF3 |
| evidence-kf4 | Data integration, clinical interpretability and the fidelity of preclinical tumor models remain translation challenges in the review's research agenda. | AI, organoids and three-dimensional tumor models are discussed as possible tools, not as proof that a multi-omics treatment strategy improves patient outcomes. | Computational challenge: Sections 5.1 and 6 discuss heterogeneous data integration and clinical interpretability.; Preclinical-model challenge: Section 5.2 discusses missing human tumor-microenvironment features and changes during prolonged culture; Fig. 4 illustrates model construction. | Narrative secondary synthesis | Qualitative review context; primary-study findings require the original sources. | Section 5.1, Intricate data network and artificial intelligence; Section 5.2, Preclinical tumor models drawbacks and progression; Fig. 4 caption; Section 6, Conclusion. | KF4 |
Its biomarker overview distinguishes genomic, epigenomic, transcriptomic and proteomic information. These are categories of prior molecular research, not four new validation cohorts.
Evidence: KF1
No. It discusses published integration and spatial-profiling approaches. Performance or treatment-response claims for any particular classifier must be checked in the cited primary study.
Evidence: KF2
The review compares basket, umbrella, platform and N-of-1 designs. This comparison provides context for molecularly informed trial design; it does not supply a pooled efficacy estimate.
Evidence: KF3
The review identifies complex data integration, the need for clinical interpretability and limitations of preclinical models. Its discussion of AI, organoids and three-dimensional models is a research agenda, not clinical proof of benefit.
Evidence: KF4
Study-level author-maintained evidence summary. Not participant-level data and not a ready-to-pool meta-analysis dataset. For formal citation, use the original article DOI.
Download study-level evidence CSV (UTF-8)Formula safety: cells starting with =, +, -, @ (after leading whitespace), or tab/CR/LF are prefixed with an apostrophe. Treat all cells as text; remove only that safety prefix when recovering the original string.
These records identify the same original work; database inclusion is not evidence of scientific validity or clinical benefit.