Source-linked AI summary
Predictive genomics: A cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data
Edwin Wang, Naif Zaman, Shauna Mcgee, Jean-Sébastien Milanese, Ali Masoudi-Nejad, Maureen O'Connor
TL;DR
Cancer genome sequencing produces extensive genomic information, but translating it into mechanistic and clinical predictions remains challenging. This review proposes cancer hallmark network modeling to connect mutant genotypes with cellular and clinical phenotypes, reporting 80% accuracy for breast-cancer-subtype-specific drug-target prediction and outlining applications in treatment, recurrence, metastasis, and risk assessment. The framework still requires more comprehensive mutational-pattern catalogs and optimized network scoring and operational signatures.
Problem
The biological complexity and volume of tumor genomic alterations make it challenging to extract mechanistic information and predict cancer phenotypes for clinical management.
Method
The review models cancer systems with hallmark networks and links mutant genotypes to regulatory, cellular, and clinical phenotypes.
Results
80% accuracy was reported for predicting breast-cancer-subtype-specific drug targets using the core cell-survival and proliferation network.
Takeaways & Limitations
The framework is intended to support prediction of drug targets, recurrence, metastasis, treatment consequences, and cancer risks from genome-sequencing profiles.
Takeaways & Limitations
The approach requires comprehensive catalogs of mutational patterns and optimized network scoring functions and operational signatures.
Abstract
from arXiv · showhide
We discuss a cancer hallmark network framework for modelling genome-sequencing data to predict cancer clonal evolution and associated clinical phenotypes. Strategies of using this framework in conjunction with genome sequencing data in an attempt to predict personalized drug targets, drug resistance, and metastasis for a cancer patient, as well as cancer risks for a healthy individual are discussed. Accurate prediction of cancer clonal evolution and clinical phenotypes will have substantial impact on timely diagnosis, personalized management and prevention of cancer.
2. Center for Bioinformatics, McGill University, Montreal, QC H3G 0B1, Canada
This section identifies the Center for Bioinformatics at McGill University in Montreal, Canada.
- The Center for Bioinformatics is affiliated with McGill University.The listed location is Montreal, Quebec.
- The affiliation specifies Montreal, Quebec, Canada.The postal code given is H3G 0B1.
5. Department of Medicine, Laval University, Quebec, QC G1V 0A6, Canada
This section identifies the Laboratory of Systems Biology and Bioinformatics at the University of Tehran, Iran.
- The Laboratory of Systems Biology and Bioinformatics is affiliated with the Institute of Biochemistry and Biophysics.
- The laboratory is located at the University of Tehran in Tehran, Iran.
- The listed section contains an institutional affiliation rather than scientific results.
1 Introduction
The introduction frames genome sequencing as a growing source of tumor data while emphasizing the challenge of translating genomic complexity into clinical predictions.
- More than 10,000 tumor genomes have been sequenced, and sequencing costs are expected to continue falling.The HiSeq X Ten was described as sequencing a whole human genome for $1,000.
- Tumor genomes contain extensive mutations, deletions, amplifications, and chromosome gains and losses that may form informative patterns.
- The review proposes linking mutant genotypes to cellular and clinical genotypes through cancer hallmark network modeling.
- The framework is presented for predicting drug targets, tumor recurrence, and cancer risks from individual genome-sequencing profiles.
2 Cancer hallmarks and their networks
The paper organizes cancer biology into interacting hallmark networks, quantifies their regulatory and cellular states, and uses genomic alterations to model cancer phenotypes. It highlights mutation, survival, and EMT networks alongside genome duplication as an emerging hallmark.
- Cancer hallmarks and their networks: Cancer hallmarks organize diverse tumor traits, including proliferation, resistance to cell death, angiogenesis, immortality, invasion, abnormal metabolism, immune evasion, and genome instability.
- Cancer hallmarks and their networks: Genome duplication is proposed as an emerging hallmark because it generates aneuploidy and may be rate-limiting during tumor development.Approximately 50% of solid tumors were reported to have undergone genome duplication.
- Cancer hallmarks and their networks: Intertwined hallmark signaling pathways are grouped into networks, including the survival and proliferation network.
- Cancer hallmarks and their networks: The mutation network is described as a master driver, while survival and EMT networks support proliferation and initial metastatic dissemination.
- Quantifying cancer hallmark traits and networks: Most hallmark traits lack established quantitative measures, motivating quantitative modeling of both traits and networks.
- Quantifying cancer hallmark traits and networks: Network operational signatures encode regulatory relationships and strengths, connecting genomic alterations with cellular or clinical phenotypes.
- Quantifying cancer hallmark traits and networks: Hallmark traits can be quantified with measures such as proliferation rate, Ki-67, mutation density, copy-number alteration density, and tumor or circulating-cell volume.
3 Principles of the cancer hallmark network framework
The framework models cancer as interacting hallmark networks shaped by evolutionary dynamics, with mutation and survival networks forming a self-promoting loop. It links genomic alterations to network states, clonal evolution, tumor progression, and potentially tissue-specific clinical phenotypes.
- Hallmark networks provide an organizing framework for modeling cancer phenotypes and predicting clonal evolutionary paths several steps ahead.The framework uses higher-order rules between networks to prioritize those relevant to distinct tumorigenesis stages.
- Self-promoting positive feedback loop: The mutation network acts as a master regulator that generates genomic instability, while the survival network selects fitter, fast-growing cancer cells through positive feedback.This interaction is proposed as the main evolutionary driving loop within cancer cells.
- Self-promoting positive feedback loop: Genomic alterations can affect components of other hallmark networks, enabling acquisition of additional cancer traits and reinforcing selection of efficient survival networks.The loop is coupled to intercellular influences from stromal and host immune systems that can shape cancer evolution.
- Self-promoting positive feedback loop: The proposed evolutionary endpoint is hyperproliferation, accompanied by more tumor-suppressor mutations or deletions and more oncogene mutations or amplifications.This reasoning is supported by a reported correlation between oncogene and tumor-suppressor distributions and chromosome-arm copy-number alterations.
- Genome duplication and feedforward evolution: Genome duplication is proposed as a rate-limiting transition that can trigger a feedforward loop involving dedifferentiation, angiogenesis, immune escape, and survival networks.Interactions between mutation and stromal networks may activate genome duplication, producing simultaneous amplifications and deletions.
- Metastasis and mutational specificity: Higher mutation-network activity can generate alterations affecting the EMT network, enabling motility followed by circulation and colonization of distant organs.The framework also considers mutational signatures as tissue- and tumor-type-dependent patterns, including distinct APOBEC profiles.
4 Strategies for constructing predictive models using a hallmark network framework
The framework combines hallmark networks with tumor and germline genomic profiles to model clonal evolution and predict patient outcomes, cancer risk, and intervention effects.
- Tumor clonal expansions add mutations to pre-existing clone profiles, allowing hallmark networks to be updated for state transitions and trait changes.
- Sequenced tumor genomes provide network operational signatures for models predicting tumor evolution and cancer risk from germline mutations.
- For patients with primary tumors, models target recurrence, metastasis, drug selection, and consequences of specific treatments.
- Different metastatic stages may involve distinct survival-network rewiring, implying that drug targets can differ between primary tumors, circulating cells, and colonized metastases.
- Drug-resistance prediction can use treatment-induced mutational patterns identified by sequencing cancer cell lines exposed to specific drugs.
- Metastasis models can simulate EMT, mutation, and immune-escaping network mutations using tumor-derived, aging-associated, and host immune-repertoire sequencing data.
- These predictions could clarify recurrent-tumor evolution and adjuvant-therapy effects, supporting strategies to delay or prevent recurrence and malignant progression.
- For healthy individuals, germline, aging, and environmental mutational profiles can be mapped onto hallmark networks to estimate whether and when cancer may develop.
5 Challenges
The framework is constrained by incomplete hallmark-network information and insufficient catalogs of relevant mutational patterns, alongside a need for improved scoring functions and operational signatures.
- Only a few hallmark networks, including cell survival, mutation, and EMT, are currently information-rich.
- A reasonably complete stroma-network is unavailable because most signaling molecules and pathways remain unidentified.
- Incomplete stroma-network information complicates modeling interactions between stromal and cancer cells during malignant progression.
- Mutational patterns from aging, chemotherapy, and common germline mutations have not been extensively catalogued.
- The framework requires comprehensive mutational-pattern compendia and optimized network scoring functions and operational signatures.