Evidence map›Paper›PMID 32914018›Full record

ArticleJCO precision oncology2019

Real-World Outcomes of an Automated Physician Support System for Genome-Driven Oncology.

Jessica J Tao, Michael H Eubank, Alison M Schram, Nicholas Cangemi, Erika Pamer, Ezra Y Rosen, Nikolaus Schultz, Debyani Chakravarty, John Philip, Jaclyn F Hechtman and 11 more

Open access · hybridAbstract read
In one paragraph

Article in JCO precision oncology, 2019. 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
0.7field-weighted citation impact, top 36% 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

5 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Basket Trials: Past, Present, and Future.Annual review of cancer biology · 2024
    Article
  3. Article
  4. Review
  5. 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

21 authors at 2 institutions in 1 country.

Jessica J TaoMemorial Sloan Kettering Cancer Center, New York, NY.
Michael H EubankMemorial Sloan Kettering Cancer Center, New York, NY.
Alison M SchramMemorial Sloan Kettering Cancer Center, New York, NY.
Nicholas CangemiMemorial Sloan Kettering Cancer Center, New York, NY.
Erika PamerMemorial Sloan Kettering Cancer Center, New York, NY.
Ezra Y RosenMemorial Sloan Kettering Cancer Center, New York, NY.
Nikolaus SchultzMemorial Sloan Kettering Cancer Center, New York, NY.
Debyani ChakravartyMemorial Sloan Kettering Cancer Center, New York, NY.
John PhilipMemorial Sloan Kettering Cancer Center, New York, NY.
Jaclyn F HechtmanMemorial Sloan Kettering Cancer Center, New York, NY.
James J HardingMemorial Sloan Kettering Cancer Center, New York, NY.
Lillian M SmythMemorial Sloan Kettering Cancer Center, New York, NY.
Komal L JhaveriMemorial Sloan Kettering Cancer Center, New York, NY.
Alexander DrilonMemorial Sloan Kettering Cancer Center, New York, NY.
Marc LadanyiMemorial Sloan Kettering Cancer Center, New York, NY.
David B SolitMemorial Sloan Kettering Cancer Center, New York, NY.
Ahmet ZehirMemorial Sloan Kettering Cancer Center, New York, NY.
Michael F BergerMemorial Sloan Kettering Cancer Center, New York, NY.
Peter D StetsonMemorial Sloan Kettering Cancer Center, New York, NY.
Stuart M GardosMemorial Sloan Kettering Cancer Center, New York, NY.
David M HymanMemorial Sloan Kettering Cancer Center, New York, NY.
Memorial Sloan Kettering Cancer Center · USCornell University · US

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Nicholas D Socci · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

purposeMatching patients to investigational therapies requires new tools to support physician decision making. We designed and implemented Precision Insight Support Engine (PRECISE), an automated, just-in-time, clinical-grade informatics platform to identify and dynamically track patients on the basis of molecular and clinical criteria. Real-world use of this tool was analyzed to determine whether PRECISE facilitated enrollment to early-phase, genome-driven trials. MATERIALS AND

methodsWe analyzed patients who were enrolled in genome-driven, early-phase trials using PRECISE at Memorial Sloan Kettering Cancer Center between April 2014 and January 2018. Primary end point was the proportion of enrolled patients who were successfully identified using PRECISE before enrollment. Secondary end points included time from sequencing and PRECISE identification to enrollment. Reasons for a failure to identify genomically matched patients were also explored.

resultsData were analyzed from 41 therapeutic trials led by 19 principal investigators. In total, 755 patients were accrued to these studies during the period that PRECISE was used. PRECISE successfully identified 327 patients (43%) before enrollment. Patients were diagnosed with 29 tumor types and harbored alterations in 43 oncogenes, most commonly

conclusionPRECISE identified 43% of all patients accrued to a diverse cohort of early-phase, genome-matched studies. Purpose-built informatics platforms represent a novel and potentially effective method for matching patients to molecularly selected studies.

Identifiers

PMID32914018
PMCPMC7446398
OpenAlexW2963286802

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.