Evidence map›Paper›PMID 37196763›Full record

ArticleMolecular & cellular proteomics : MCP2023

An Inflection Point in Cancer Protein Biomarkers: What was and What's Next.

Anna D Barker, Mario M Alba, Parag Mallick, David B Agus, Jerry S H Lee

Abstract read
In one paragraph

Article in Molecular & cellular proteomics : MCP, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Trial
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  4. Article
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  10. Review
  11. Clinical Proteomics: A Promise Becoming Reality.Molecular & cellular proteomics : MCP · 2024
    Article
  12. Liquid biopsy for early detection of lung cancer.Chinese medical journal pulmonary and critical care medicine · 2023
    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

5 authors.

Anna D BarkerLawrence J. Ellison Institute for Transformative Medicine, Los Angeles, California, USA; Complex Adaptive Systems Initiative and School of Life Sciences, Arizona State University, Tempe, Arizona, USA. Electronic address: ABarker@eitm.org.
Mario M AlbaDepartment of Pharmacology and Pharmaceutical Sciences, USC Alfred E. Mann School of Pharmacy, University of Southern California, Los Angeles, California, USA.
Parag MallickCanary Center at Stanford for Cancer Early Detection, Stanford University, Stanford, California, USA; Department of Radiology, Stanford University, Stanford, California, USA.
David B AgusLawrence J. Ellison Institute for Transformative Medicine, Los Angeles, California, USA; Keck School of Medicine, University of Southern California, Los Angeles, California, USA; Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA.
Jerry S H LeeLawrence J. Ellison Institute for Transformative Medicine, Los Angeles, California, USA; Keck School of Medicine, University of Southern California, Los Angeles, California, USA; Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biomarkers remain the highest value proposition in cancer medicine today-especially protein biomarkers. Despite decades of evolving regulatory frameworks to facilitate the review of emerging technologies, biomarkers have been mostly about promise with very little to show for improvements in human health. Cancer is an emergent property of a complex system, and deconvoluting the integrative and dynamic nature of the overall system through biomarkers is a daunting proposition. The last 2 decades have seen an explosion of multiomics profiling and a range of advanced technologies for precision medicine, including the emergence of liquid biopsy, exciting advances in single-cell analysis, artificial intelligence (machine and deep learning) for data analysis, and many other advanced technologies that promise to transform biomarker discovery. Combining multiple omics modalities to acquire a more comprehensive landscape of the disease state, we are increasingly developing biomarkers to support therapy selection and patient monitoring. Furthering precision medicine, especially in oncology, necessitates moving away from the lens of reductionist thinking toward viewing and understanding that complex diseases are, in fact, complex adaptive systems. As such, we believe it is necessary to redefine biomarkers as representations of biological system states at different hierarchical levels of biological order. This definition could include traditional molecular, histologic, radiographic, or physiological characteristics, as well as emerging classes of digital markers and complex algorithms. To succeed in the future, we must move past purely observational individual studies and instead start building a mechanistic framework to enable integrative analysis of new studies within the context of prior studies. Identifying information in complex systems and applying theoretical constructs, such as information theory, to study cancer as a disease of dysregulated communication could prove to be "game changing" for the clinical outcome of cancer patients.

Indexed as

Biomarkers, TumorNeoplasmsArtificial IntelligenceBiomarkersHumansBiomarkersBiomarkers, Tumorcancer biomarkersclinical proteomicscomplex adaptive systemsprotein biomarkersproteomics

Identifiers

PMID37196763
PMCPMC10388583

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.