Evidence map›Paper›PMID 39689262›Full record

ReviewAnnual review of biomedical engineering2025

Systems Biology of the Cancer Cell.

Kevin A Janes, Matthew J Lazzara

Abstract readReview
In one paragraph

Review in Annual review of biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Kevin A JanesDepartment of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, USA.
Matthew J LazzaraDepartment of Chemical Engineering, University of Virginia, Charlottesville, Virginia, USA.

Funding

Tissue Repository and Animal Models CoreP01CA171983 · NCI · UNIVERSITY OF VIRGINIA · PI CHALFANT, CHARLES E. · 2013 to 2024
$19.9M
Systems Analysis of Stress-adapted Cancer Organelles (SASCO) CenterU54CA274499 · NCI · UNIVERSITY OF VIRGINIA · PI Kevin A Janes, Matthew J Lazzara · 2022 to 2026
$13.4M
A premalignant chronology of cell-state variability in basal-like breast cancerR01CA256199 · NCI · UNIVERSITY OF VIRGINIA · PI Andrew Carl Dudley, Kevin A Janes · 2022 to 2026
$3.4M
Optimal control models of epithelial-mesenchymal transition for the design of pancreas cancer combination therapyU01CA243007 · NCI · UNIVERSITY OF VIRGINIA · PI LAZZARA, MATTHEW J · 2019 to 2023
$2.5M
Promoting Receptor Protein Tyrosine Phosphatase Activity by TargetingTransmembrane Domain InteractionsR01GM139998 · NIGMS · LEHIGH UNIVERSITY · PI LAZZARA, MATTHEW J, THEVENIN, DAMIEN · 2020 to 2023
$1.7M
NCI NIH HHS P01 CA171983NCI NIH HHS R01 CA256199NCI NIH HHS U01 CA243007NCI NIH HHS U54 CA274499NIGMS NIH HHS R01 GM139998
6 · The paper itself

Abstract

Questions in cancer have engaged systems biologists for decades. During that time, the quantity of molecular data has exploded, but the need for abstractions, formal models, and simplifying insights has remained the same. This review brings together classic breakthroughs and recent findings in the field of cancer systems biology, focusing on cancer cell pathways for tumorigenesis and therapeutic response. Cancer cells mutate and transduce information from their environment to alter gene expression, metabolism, and phenotypic states. Understanding the molecular architectures that make each of these steps possible is a long-term goal of cancer systems biology pursued by iterating between quantitative models and experiments. We argue that such iteration is the best path to deploying targeted therapies intelligently so that each patient receives the maximum benefit for their cancer.

Indexed as

Models, BiologicalNeoplasmsSystems BiologyAnimalsGene Expression Regulation, NeoplasticHumansSignal TransductionBoolean networksdifferential equationmathematical modelpartial least squaresregressionsignal transduction

Identifiers

PMID39689262
PMCPMC13574367

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.