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Short summary of the research and contribution to the field

This preprint is a broad narrative review on precision oncology, with emphasis on cancer genomics, genomic alterations, signaling pathways, targeted therapy, multi-omics, artificial intelligence, deep learning, network biology, single-cell technologies, spatial omics, cancer cell mapping, cancer dependency mapping, and organoid models. The manuscript attempts to explain how molecular profiling and computational approaches can support individualized cancer diagnosis, prognosis, drug selection, biomarker discovery, and therapeutic development.

The review covers several important resources and concepts, including NGS-based cancer profiling, TCGA/Pan-Cancer Atlas, precision oncology trials, FDA-approved kinase inhibitors, cancer hallmark networks, Cancer Cell Mapping Initiative, DepMap/CCLE, single-cell multiomics, spatial biology, AI/deep learning tools such as AlphaFold, and patient-derived organoid systems. These are highly relevant topics for modern oncology and translational cancer genomics.

Overall, the topic is timely and important, but the manuscript would benefit from stronger organization, clearer review methodology, more critical interpretation, improved language editing, and better distinction between established clinical utility and emerging or speculative technologies.

Positive feedback / strengths

1. Important and timely subject. Precision oncology is a central topic in modern medical oncology, especially as NGS, multi-omics, AI, and tumor molecular profiling become increasingly integrated into clinical and translational workflows.

2. Broad coverage of relevant themes. The manuscript discusses cancer genomics, signaling pathways, targeted therapy, immunotherapy-related concepts, multiomics, deep learning, network biology, TCGA, single-cell sequencing, spatial omics, and organoid models. This broad scope can be useful for readers seeking an introductory overview.

3. Useful clinical and translational framing. The review appropriately emphasizes that patients with the same cancer type can respond differently to treatment due to tumor-specific DNA, RNA, protein, epigenetic, and microenvironmental differences.

4. Helpful inclusion of major oncology initiatives. The discussion of TCGA, Pan-Cancer Atlas, Cancer Cell Mapping Initiative, DepMap, and CCLE is valuable because these resources have shaped current approaches to biomarker discovery and cancer vulnerability mapping.

5. Good recognition of tumor heterogeneity. The manuscript correctly highlights the importance of intratumoral heterogeneity, single-cell technologies, and spatial biology for understanding treatment response and resistance.

6. Organoid section adds practical translational relevance. The discussion of patient-derived organoids and 3D cancer models is useful because these systems are increasingly important for drug testing and personalized therapy research.

Major issues

1. The manuscript needs a clearer abstract and central thesis

In the uploaded PDF, the formal Abstract section appears incomplete or absent, followed directly by keywords and a graphical abstract-style list of clinical questions. A review of this breadth needs a clear abstract summarizing the objective, scope, approach, major themes, and conclusion.

Suggested improvement: Add a structured abstract with:

• background

• purpose of the review

• major topics covered

• main conclusion

• clinical/translational relevance

The central thesis should also be sharpened. At present, the manuscript covers many important topics, but it is not always clear whether the main goal is to review precision oncology generally, AI/multiomics specifically, or network biology as the next step in oncology.

2. The review is too broad and needs stronger organization

The manuscript covers cancer biology, genomics, targeted therapy, kinase inhibitors, AI, AlphaFold, TCGA, network biology, DepMap, single-cell sequencing, spatial omics, and organoids. Each topic is relevant, but the current structure sometimes reads like a collection of broad explanatory sections rather than a focused review.

Suggested improvement: Reorganize around a clearer framework, such as:

1. Molecular basis of precision oncology

2. Genomic and multi-omic profiling technologies

3. Clinical interpretation and targeted therapy

4. AI/deep learning and network biology

5. Single-cell/spatial omics and tumor heterogeneity

6. Preclinical models and organoids

7. Current limitations and future directions

This would improve flow and reduce repetition.

3. Literature-review methodology is missing

The manuscript appears to be a narrative review, but it does not clearly describe how references were selected. For a review article, readers should understand whether the literature coverage is systematic, scoping, or narrative.

Suggested improvement: Add a short Methods / Literature Search Strategy section describing:

• databases searched

• keywords used

• date range

• inclusion/exclusion criteria

• whether preprints were included

• how clinical trials, reviews, and original studies were prioritized

• whether the review is narrative or scoping

Without this, the manuscript’s evidence selection may appear subjective.

4. Precision oncology clinical utility needs a more balanced discussion

The manuscript emphasizes the promise of precision oncology, targeted therapy, and AI-supported individualized cancer care. This is appropriate, but the review should also discuss clinical limitations more directly.

Suggested improvement: Add a section on limitations such as:

• variable benefit across tumor types

• low actionability for some patients

• resistance to targeted therapy

• tumor heterogeneity and clonal evolution

• limited access to comprehensive testing

• cost and reimbursement issues

• need for molecular tumor boards

• false positives/variants of uncertain significance

• limited clinical evidence for some biomarker-drug combinations

• tissue availability and liquid biopsy limitations

This would make the review more balanced and clinically useful.

5. AI and deep learning sections need more critical evaluation

The AI/deep learning section is relevant but often presents AI as broadly beneficial without enough discussion of practical constraints. Tools such as AlphaFold and deep learning models are important, but their clinical utility in oncology depends on validation, interpretability, generalizability, and regulatory readiness.

Suggested improvement: Discuss:

• training data quality and bias

• external validation

• data leakage

• model interpretability

• reproducibility

• clinical outcome validation

• regulatory and ethical issues

• integration into clinical workflows

• whether AI predictions directly improve treatment decisions

The manuscript should distinguish between AI tools that are research-enabling and those that are clinically validated.

6. Multi-omics integration needs a clearer technical framework

The manuscript states that genomics, transcriptomics, proteomics, metabolomics, single-cell multiomics, and spatial omics can support precision oncology, but it does not clearly explain how these layers are integrated analytically or clinically.

Suggested improvement: Add a table or figure showing:

Omics layerWhat it measuresClinical relevanceExample technologyCurrent limitation

For example:

• Genomics: SNVs, indels, CNVs, fusions

• Transcriptomics: expression, immune signatures, fusion transcripts

• Epigenomics: methylation, chromatin accessibility

• Proteomics: pathway activation, phosphorylation

• Metabolomics: metabolic dependencies

• Single-cell/spatial omics: heterogeneity and tumor microenvironment

This would make the review more actionable.

7. Network biology section should include clearer examples and limitations

The manuscript discusses network biology, Cancer Cell Mapping Initiative, NeST maps, and protein interaction networks. This is a strong topic, but the section should more clearly explain how network biology improves precision oncology beyond standard gene-level interpretation.

Suggested improvement: Clarify:

• how driver mutations converge on pathways/networks

• how network biology identifies therapeutic vulnerabilities

• how network findings are validated experimentally

• how network models are used for patient stratification

• limitations of protein-protein interaction data

• uncertainty in network inference

• need for functional validation

A figure showing the path from mutation → pathway/network → therapeutic hypothesis would be helpful.

8. Tables and figures need updating, formatting, and permission clarification

The FDA-approved kinase inhibitor table is useful but long and formatting-heavy. It should be updated, cleaned, and clearly dated. Also, several figures appear adapted or reused from prior publications, including the cancer hallmark network figure and single-cell heterogeneity figure.

Suggested improvement: The authors should:

• verify all drug names, approval years, targets, and indications

• specify the date of table update

• improve formatting and readability

• cite original sources properly

• confirm figure permissions/licensing

• consider replacing reused figures with original schematic figures

This is important for publication readiness.

9. Several factual, terminology, and formatting issues need correction

The manuscript contains terminology and formatting inconsistencies, including spelling/spacing issues, repeated text, inconsistent abbreviation use, and possible reference numbering problems. For example, “TGCA” appears where “TCGA” is likely intended, “DeepMap” appears where “DepMap” may be intended, and some cancer/drug names are inconsistently spaced or formatted.

Suggested improvement: A detailed technical edit should verify:

• TCGA / Pan-Cancer Atlas terminology

• DepMap / CCLE descriptions

• kinase inhibitor names and targets

• AI/deep learning terminology

• citation numbering

• spelling and grammar

• table formatting

• duplicated statements

10. The review should end with clearer future directions

The conclusion is optimistic but broad. It would be stronger if it provided specific next steps for the field.

Suggested improvement: Add a focused future-directions section covering:

• prospective clinical validation of multi-omics-guided therapy

• molecular tumor board integration

• standardized reporting of multi-omic results

• AI model validation and transparency

• equitable access to genomic testing

• integration of ctDNA and tissue profiling

• functional validation using organoids and CRISPR screens

• resistance monitoring and longitudinal profiling

This would make the review more useful for clinicians and researchers.

Minor issues

1. Add a complete abstract. The PDF should include a formal abstract rather than only keywords and graphical abstract questions.

2. Clarify review type. State whether this is a narrative review, scoping review, or perspective article.

3. Improve section transitions. Some sections move abruptly from general cancer biology to precision oncology, AI, TCGA, single-cell sequencing, and organoids.

4. Reduce repetition. Several sections repeat the need for targeted therapy, individualized treatment, and molecular profiling.

5. Define key terms early. Define precision oncology, multiomics, network biology, deep learning, tumor heterogeneity, and organoids at first use.

6. Improve graphical abstract. The graphical abstract currently reads more like a list of clinical questions. It could be converted into a true schematic workflow.

7. Add clinical examples. Examples such as EGFR/ALK/ROS1 in NSCLC, BRAF in melanoma, HER2 in breast/gastric cancer, BRCA/PARP inhibitors, NTRK fusions, MSI-H/TMB immunotherapy, and IDH inhibitors would strengthen the review.

8. Distinguish targeted therapy from precision oncology. Not all targeted therapy is precision oncology, and not all precision oncology is genomics-only. This distinction should be clearer.

9. Discuss liquid biopsy. ctDNA and liquid biopsy are central to modern precision oncology and should receive more explicit attention.

10. Discuss clinical reporting standards. The review could mention variant tiers, actionability frameworks, and precision oncology knowledge bases such as OncoKB, CIViC, ClinVar, COSMIC, or AMP/ASCO/CAP-style interpretation frameworks.

11. Improve language editing. The manuscript needs grammar, punctuation, spelling, and sentence-structure editing throughout.

12. Fix reference gaps and duplicates. The reference list should be checked carefully for numbering continuity, duplicate references, and proper formatting.

Overall assessment

This is a timely and potentially useful narrative review on precision oncology, cancer genomics, multiomics, AI/deep learning, network biology, tumor heterogeneity, and organoid-based translational research. The manuscript’s main strength is its broad coverage of major concepts that are shaping modern oncology and individualized cancer therapy.

However, the manuscript needs substantial revision before it can serve as a strong scholarly review. The most important improvements are to add a complete abstract, define a clearer central thesis, include a literature-search strategy, reorganize the structure, strengthen critical discussion of clinical limitations, improve technical accuracy, and refine figures/tables. The AI, multiomics, and network biology sections would benefit from more specific examples, clearer clinical relevance, and stronger discussion of validation challenges.

With these changes, the manuscript could become a clearer and more balanced review for readers interested in the evolving role of genomics, multiomics, computational modeling, and translational platforms in precision oncology.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they used generative AI to come up with new ideas for their review.