# Omics-based molecular classifications empowering in precision oncology
Researcher: Ge Zhang
Chinese name: 张格
ORCID identity anchor: https://orcid.org/0000-0002-3116-3246
Canonical researcher: https://drgezhang.com/#person
Journal: Cellular Oncology
Year: 2024
Type: Article
DOI: 10.1007/s13402-023-00912-8
Canonical page: https://drgezhang.com/papers/doi-10-1007-s13402-023-00912-8.html
Markdown record: https://drgezhang.com/papers/doi-10-1007-s13402-023-00912-8.md
## Full Authors
1. Zhaokai Zhou
2. Ting Lin
3. Shuang Chen
4. Ge Zhang
5. Yudi Xu
6. Haijiao Zou
7. Aoyang Zhou
8. Yuyuan Zhang
9. Siyuan Weng
10. Xinwei Han
11. Zaoqu Liu
## Research & review context
### Research fields
- Cancer molecular classification
- Translational oncology
### Review topics
- Biomarker-directed research designs
- Integrative omics interpretation
### Scientific role
Narrative framework connecting molecular diagnosis, treatment stratification and prognosis, with explicit data and model limitations.
### Evidence position
Secondary literature synthesis; primary clinical and experimental evidence remains in the studies it cites.
### Related concepts
- Tumor heterogeneity
- Algorithm interpretability
- Preclinical model fidelity
### Possible citation contexts
- Background for comparing single-omics, integrated and spatial approaches to cancer classification.
- Methods discussion of biomarker-oriented trial designs and translation requirements; cite primary studies for efficacy or accuracy.
Possible literature-review contexts, not evidence of existing citations. Read the findings and Evidence Scope before citing the original article.
## Evidence Base
**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.
| Evidence | Value |
| --- | --- |
| Main biomarker layers in Fig. 1 | 4: genomic, epigenomic, transcriptomic and proteomic |
| Review illustrations | 4 figures and 1 comparison table |
| Primary patient cohort | Not applicable: narrative review |
### Counting note
Counts describe the article's structure. They are not patient denominators, independently validated models or pooled treatment effects.
## Research Question
How does omics-based molecular classification connect cancer diagnosis, treatment stratification and prognosis, and what limits its clinical translation?
## Author Evidence Summary
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.
## Key Findings
### KF1: The review organizes molecular diagnosis across genomic and epigenomic, transcriptomic and proteomic biomarkers.
Context: Section 2 synthesizes earlier biomarker research; the article does not perform a new diagnostic-accuracy comparison.
| Evidence | Value |
| --- | --- |
| Illustrated biomarker layers | 4 in Fig. 1: genomic, epigenomic, transcriptomic and proteomic |
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).
### KF2: Integrated and spatial omics are presented as complementary approaches to characterize heterogeneity beyond a single molecular layer.
Context: The review summarizes published subtyping and spatial-profiling examples; it does not train or externally validate a new integrated classifier.
| Evidence | 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.
### KF3: The review compares basket, umbrella, platform and N-of-1 designs as frameworks for molecularly informed oncology research.
Context: This is a narrative trial-design comparison, not a trial performed by the review authors or a pooled estimate of efficacy.
| Evidence | Value |
| --- | --- |
| Design categories compared | 4: basket, umbrella, platform and N-of-1 |
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.
### KF4: Data integration, clinical interpretability and the fidelity of preclinical tumor models remain translation challenges in the review's research agenda.
Context: AI, organoids and three-dimensional tumor models are discussed as possible tools, not as proof that a multi-omics treatment strategy improves patient outcomes.
| Evidence | 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.
## Review Design & Evidence Synthesis
### Review profile
- Study Design: Narrative review of omics-based molecular classification in precision oncology
- Evidence Type: Secondary synthesis of published molecular, preclinical and clinical literature
- Diseases Covered: Solid tumors, including lung, breast, colorectal, endometrial, pancreatic and liver cancers
- Translation Scope: Conceptual and methodological context; therapy selection and clinical benefit require the relevant primary validation studies.
- Evidence domains: Molecular biomarkers and liquid biopsy, Single-omics, integrated and spatial classification, Biomarker-oriented trial designs, Computational integration and preclinical tumor models
## What This Review Adds
- An organized route from omics biomarker classes to molecular stratification and clinical-study design.
- A secondary-literature framework that separates the promise of integration from the need for interpretable algorithms, suitable models and clinical validation.
## Evidence Scope
### Supports
- A conceptual overview of omics biomarker layers and molecular classification in oncology.
- Context for discussing biomarker-oriented trial designs and translation challenges.
### Does Not Establish
- A new patient cohort, newly validated classifier or new therapeutic experiment.
- A pooled treatment effect, diagnostic accuracy or causal benefit of molecular classification.
- Clinical superiority of multi-omics for every cancer or independent confirmation of all cited primary-study results.
- Systematic completeness of the literature search or a meta-analysis.
### Limitations
- This is a narrative synthesis without a reported systematic search or pooled effect estimation.
- Results from cited cohorts, trials and experiments remain secondary evidence here; no new primary cohort or validation is reported.
- The public ResearchGate text reproduces a typeset copy uploaded by Zhaokai Zhou on 2 February 2024. The publisher record confirms article identity, but publisher-PDF byte equivalence was not verified.
- Only structural and qualitative review claims are summarized. Numerical results from individual cited studies and the table's assignment of particular trials to design categories are not independently validated.
## When This Study Is Useful to Cite
### CU1: How can omics layers be organized when reviewing molecular cancer classification?
Use this review as a secondary overview of biomarker layers and integrated or spatial classification approaches, without attributing a new classifier to it.
Evidence: [KF1](#kf1), [KF2](#kf2)
### CU2: Which trial-design frameworks are discussed for molecularly informed oncology?
The review compares four design frameworks; cite original trial protocols and results for the design and efficacy of any individual trial.
Evidence: [KF3](#kf3)
### CU3: What translation challenges accompany multi-omics classification?
The article discusses heterogeneous data integration, algorithm interpretability and preclinical-model fidelity as research challenges.
Evidence: [KF4](#kf4)
## What This Study Should Not Be Cited to Claim
- A new patient cohort, newly validated classifier or new therapeutic experiment.
- A pooled treatment effect, diagnostic accuracy or causal benefit of molecular classification.
- Clinical superiority of multi-omics for every cancer or independent confirmation of all cited primary-study results.
- Systematic completeness of the literature search or a meta-analysis.
## Evidence Matrix
### The review organizes molecular diagnosis across genomic and epigenomic, transcriptomic and proteomic biomarkers.
- Context: Section 2 synthesizes earlier biomarker research; the article does not perform a new diagnostic-accuracy comparison.
- Finding: Illustrated biomarker layers: 4 in Fig. 1: genomic, epigenomic, transcriptomic and proteomic
- Evidence Level: Narrative secondary synthesis
- Scope: Qualitative review context; primary-study findings require the original sources.
- 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).
- Evidence refs: [KF1](#kf1)
### Integrated and spatial omics are presented as complementary approaches to characterize heterogeneity beyond a single molecular layer.
- Context: The review summarizes published subtyping and spatial-profiling examples; it does not train or externally validate a new integrated classifier.
- Finding: 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.
- Evidence Level: Narrative secondary synthesis
- Scope: Qualitative review context; primary-study findings require the original sources.
- Source Locator: Section 3.1.4, Multi-omics integrated analysis-based molecular typing; Section 3.1.5, Spatial omics; Fig. 3 and its caption.
- Evidence refs: [KF2](#kf2)
### The review compares basket, umbrella, platform and N-of-1 designs as frameworks for molecularly informed oncology research.
- Context: This is a narrative trial-design comparison, not a trial performed by the review authors or a pooled estimate of efficacy.
- Finding: Design categories compared: 4: basket, umbrella, platform and N-of-1
- Evidence Level: Narrative secondary synthesis
- Scope: Trial-design context, without endorsement of individual trial taxonomy or 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.
- Evidence refs: [KF3](#kf3)
### Data integration, clinical interpretability and the fidelity of preclinical tumor models remain translation challenges in the review's research agenda.
- Context: AI, organoids and three-dimensional tumor models are discussed as possible tools, not as proof that a multi-omics treatment strategy improves patient outcomes.
- Finding: 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.
- Evidence Level: Narrative secondary synthesis
- Scope: Qualitative review context; primary-study findings require the original sources.
- 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.
- Evidence refs: [KF4](#kf4)
## Q&A
### Which biomarker layers are distinguished in the review?
Its biomarker overview distinguishes genomic, epigenomic, transcriptomic and proteomic information. These are categories of prior molecular research, not four new validation cohorts.
Evidence: KF1
### Does this review validate a new multi-omics classifier?
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
### Which clinical-trial design frameworks are compared?
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
### What prevents a molecular classification from automatically becoming a treatment recommendation?
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
## Concepts & Entities
### Fields
Precision oncology, Multi-omics
### Methods
Molecular classification, Proteogenomics, Spatial omics, Liquid biopsy, Biomarker-oriented trial design
### Models
Organoids, Three-dimensional tumor models
## Related Research
- [Proteomics appending a complementary dimension to precision oncotherapy](https://drgezhang.com/papers/doi-10-1016-j-csbj-2024-04-044.html) — Complementary narrative review focused on proteomic measurement and integration; related conceptual context, not independent clinical validation. (DOI: 10.1016/j.csbj.2024.04.044)
## Publication & Provenance
- DOI: https://doi.org/10.1007/s13402-023-00912-8
- Publisher: https://link.springer.com/article/10.1007/s13402-023-00912-8
- PubMed: https://pubmed.ncbi.nlm.nih.gov/38294647/
- Pmc Url: https://pmc.ncbi.nlm.nih.gov/articles/PMC12974067/
- Fulltext Source Url: https://www.researchgate.net/publication/377849648_Omics-based_molecular_classifications_empowering_in_precision_oncology
- Evidence Basis: Complete publicly readable author-uploaded typeset text on ResearchGate: Sections 1-6, Table 1, captions to Figs. 1-4, references and declarations. Subsequent ResearchGate recommendations and citation excerpts were excluded.
- Source Version: ResearchGate typeset copy uploaded by Zhaokai Zhou on 2 February 2024; accepted 23 December 2023 and copyright Springer Nature Switzerland AG 2024. Official record: Version of Record 31 January 2024, Cellular Oncology 47, 759-777, June 2024. Identity matches; binary equality with the publisher PDF is not established.
- Access Date: 2026-10-26
- Visual Verification Scope: Official public thumbnail images of Figs. 1, 3 and 4 were inspected for schematic subject matter and agree with the captions. No effect size or performance value was read from an image. All selected claims are supported by the readable body/table text.
- Reuse Boundary: Publisher states exclusive rights; no open reuse license was verified. This page contains original, bounded paraphrases only and does not redistribute the PDF or figures.
- Correction Check: Official publisher article and same-day Crossref metadata were checked. No correction relation was returned or displayed in the inspected records; this is not an exhaustive guarantee that no later update exists.
## Evidence-page notice
Evidence synthesis of a published narrative review. The reviewed full text is the publicly readable text of an author-uploaded typeset copy on ResearchGate, matched to the publisher's article identity. It is not a new clinical study, and the reviewed copy has not been verified byte-for-byte against the publisher PDF.
## Links
- DOI: https://doi.org/10.1007/s13402-023-00912-8
- HTML page: https://drgezhang.com/papers/doi-10-1007-s13402-023-00912-8.html
- Google Scholar query: https://scholar.google.com/scholar?q=%22Omics-based%20molecular%20classifications%20empowering%20in%20precision%20oncology%22
- ORCID: https://orcid.org/0000-0002-3116-3246