Evidence map›Paper›PMID 37490642›Full record

ArticleJCO clinical cancer informatics2023

Multi-Omic Integration of Blood-Based Tumor-Associated Genomic and Lipidomic Profiles Using Machine Learning Models in Metastatic Prostate Cancer.

Shikai Fang, Shandian Zhe, Hui-Ming Lin, Arun A Azad, Heidi Fettke, Edmond M Kwan, Lisa Horvath, Blossom Mak, Tiantian Zheng, Pan Du and 3 more

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JCO clinical cancer informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.6field-weighted citation impact, top 16% of its field
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

6 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
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

13 authors at 5 institutions in 3 countries.

Shikai FangUniversity of Utah, The School of Computing, Scientific Computing and Imaging Institute, Salt Lake City, UT.
Shandian ZheThe School of Computing, University of Utah, Salt Lake City, UT.
Hui-Ming LinGarvan Institute for Medical Research, Darlinghurst, Sydney, New South Wales, Australia.ORCID 0000-0003-4892-6008
Arun A AzadSir Peter MacCallum Department of Oncology, Department of Medical Oncology, University of Melbourne, Melbourne, Australia.ORCID 0000-0001-7350-5622
Heidi FettkeSir Peter MacCallum Department of Oncology, Department of Medical Oncology, University of Melbourne, Melbourne, Australia.ORCID 0000-0002-5784-3338
Edmond M KwanVancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, Canada.ORCID 0000-0002-7053-680X
Lisa HorvathGarvan Institute for Medical Research, Darlinghurst, Sydney, New South Wales, Australia.
Blossom MakGarvan Institute for Medical Research, Darlinghurst, Sydney, New South Wales, Australia.ORCID 0000-0003-3068-2498
Tiantian ZhengPredicine Inc, Hayward, CA.
Pan DuPredicine Inc, Hayward, CA.
Shidong JiaPredicine Inc, Hayward, CA.ORCID 0000-0001-5854-819X
Robert M KirbyThe School of Computing, Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT.ORCID 0000-0001-5712-4141
Manish KohliDivision of Oncology, Department of Internal Medicine, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT.ORCID 0000-0002-9735-6614
University of Utah · USGarvan Institute of Medical Research · AUThe University of Melbourne · AUThe University of Sydney · AUUniversity of British Columbia · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo determine prognostic and predictive clinical outcomes in metastatic hormone-sensitive prostate cancer (mHSPC) and metastatic castrate-resistant prostate cancer (mCRPC) on the basis of a combination of plasma-derived genomic alterations and lipid features in a longitudinal cohort of patients with advanced prostate cancer.

methodsA multifeature classifier was constructed to predict clinical outcomes using plasma-based genomic alterations detected in 120 genes and 772 lipidomic species as informative features in a cohort of 71 patients with mHSPC and 144 patients with mCRPC. Outcomes of interest were collected over 11 years of follow-up. These included in mHSPC state early failure of androgen-deprivation therapy (ADT) and exceptional responders to ADT; early death (poor prognosis) and long-term survivors in mCRPC state. The approach was to build binary classification models that identified discriminative candidates with optimal weights to predict outcomes. To achieve this, we built multi-omic feature-based classifiers using traditional machine learning (ML) methods, including logistic regression with sparse regularization, multi-kernel Gaussian process regression, and support vector machines.

resultsThe levels of specific ceramides (d18:1/14:0 and d18:1/17:0), and the presence of

conclusionUsing a ML approach that incorporates multiple omic features improves the prediction accuracy for metastatic prostate cancer outcomes significantly. Validation of these models will be needed in independent data sets in future.

Indexed as

Prostatic Neoplasms, Castration-ResistantAndrogen AntagonistsGenomicsHumansLipidomicsMaleMultiomicsRetrospective StudiesAndrogen Antagonists

Identifiers

PMID37490642
PMCPMC10569777
OpenAlexW4385235870

What OpenQuestion 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 OpenQuestion graph.