Evidence map›Paper›PMID 32859160›Full record

ArticleBMC cancer2020

A machine learning approach to optimizing cell-free DNA sequencing panels: with an application to prostate cancer.

Clinton L Cario, Emmalyn Chen, Lancelote Leong, Nima C Emami, Karen Lopez, Imelda Tenggara, Jeffry P Simko, Terence W Friedlander, Patricia S Li, Pamela L Paris and 2 more

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

Article in BMC cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.0field-weighted citation impact, top 27% 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

10 citing papers in PubMed, 20 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. 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

12 authors at 1 institution in 1 country.

Clinton L CarioProgram in Biological and Medical Informatics, University of California, San Francisco, California, 94158, USA.
Emmalyn ChenDepartment of Epidemiology and Biostatistics, University of California, San Francisco, California, 94158, USA.
Lancelote LeongDepartment of Epidemiology and Biostatistics, University of California, San Francisco, California, 94158, USA.
Nima C EmamiProgram in Biological and Medical Informatics, University of California, San Francisco, California, 94158, USA.
Karen LopezDepartment of Urology, University of California, San Francisco, California, 94158, USA.
Imelda TenggaraDepartment of Urology, University of California, San Francisco, California, 94158, USA.
Jeffry P SimkoDepartment of Urology, University of California, San Francisco, California, 94158, USA.
Terence W FriedlanderDivision of Hematology/Oncology, University of California, San Francisco, California, 94158, USA.
Patricia S LiDivision of Hematology/Oncology, University of California, San Francisco, California, 94158, USA.
Pamela L ParisDepartment of Urology, University of California, San Francisco, California, 94158, USA.
Peter R CarrollDepartment of Urology, University of California, San Francisco, California, 94158, USA.
John S WitteDepartment of Epidemiology and Biostatistics, University of California, San Francisco, California, 94158, USA. JWitte@ucsf.edu.
University of California, San Francisco · US

Funding

Genetic Epidemiology of Prostate Cancer Risk and ProgressionR01CA088164 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI VAN DEN EEDEN, STEPHEN K., WITTE, JOHN S. · 2000 to 2015
$6.1M
Genome-wide Pleiotropy Scan across Multiple CancersR01CA201358 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GRAFF, REBECCA ELIZABETH, SAKODA, LORI · 2016 to 2019
$2.5M
NCI NIH HHS R01 CA088164NCI NIH HHS R01 CA201358NIH HHS CA088164NIH HHS CA201358
6 · The paper itself

Abstract

backgroundCell-free DNA's (cfDNA) use as a biomarker in cancer is challenging due to genetic heterogeneity of malignancies and rarity of tumor-derived molecules. Here we describe and demonstrate a novel machine-learning guided panel design strategy for improving the detection of tumor variants in cfDNA. Using this approach, we first generated a model to classify and score candidate variants for inclusion on a prostate cancer targeted sequencing panel. We then used this panel to screen tumor variants from prostate cancer patients with localized disease in both in silico and hybrid capture settings.

methodsWhole Genome Sequence (WGS) data from 550 prostate tumors was analyzed to build a targeted sequencing panel of single point and small (< 200 bp) indel mutations, which was subsequently screened in silico against prostate tumor sequences from 5 patients to assess performance against commonly used alternative panel designs. The panel's ability to detect tumor-derived cfDNA variants was then assessed using prospectively collected cfDNA and tumor foci from a test set 18 prostate cancer patients with localized disease undergoing radical proctectomy.

resultsThe panel generated from this approach identified as top candidates mutations in known driver genes (e.g. HRAS) and prostate cancer related transcription factor binding sites (e.g. MYC, AR). It outperformed two commonly used designs in detecting somatic mutations found in the cfDNA of 5 prostate cancer patients when analyzed in an in silico setting. Additionally, hybrid capture and 2500X sequencing of cfDNA molecules using the panel resulted in detection of tumor variants in all 18 patients of a test set, where 15 of the 18 patients had detected variants found in multiple foci.

conclusionMachine learning-prioritized targeted sequencing panels may prove useful for broad and sensitive variant detection in the cfDNA of heterogeneous diseases. This strategy has implications for disease detection and monitoring when applied to the cfDNA isolated from prostate cancer patients.

Indexed as

Genome, HumanMachine LearningAdultAgedAged, 80 and overBase SequenceBiomarkers, TumorCirculating Tumor DNACohort StudiesHumansMaleMiddle AgedMutationProstatic NeoplasmsSequence Analysis, DNAWhole Genome SequencingBiomarkers, TumorCirculating Tumor DNACell-free DNAMachine learningPanel designProstate cancerTumor variant detection

Identifiers

PMID32859160
PMCPMC7456018
OpenAlexW3082454188

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.