Evidence map›Paper›PMID 34162978›Full record

ArticleNPJ precision oncology2021

Criteria-based curation of a therapy-focused compendium to support treatment recommendations in precision oncology.

Frank P Lin, Subotheni Thavaneswaran, John P Grady, Mandy Ballinger, Maya Kansara, Samantha R Oakes, Jayesh Desai, Chee Khoon Lee, John Simes, David M Thomas

Open access · goldAbstract read
In one paragraph

Article in NPJ precision oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.8field-weighted citation impact, top 35% 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

8 citing papers in PubMed, 11 citations in OpenAlex.

  1. RareJTO clinical and research reports · 2026
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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

10 authors at 3 institutions in 1 country.

Frank P LinKinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia. f.lin@garvan.org.au.ORCID http://orcid.org/0000-0001-9250-6874
Subotheni ThavaneswaranNHMRC Clinical Trials Centre, University of Sydney, Sydney, NSW, Australia.
John P GradySt. Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, NSW, Australia.
Mandy BallingerSt. Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, NSW, Australia.
Maya KansaraSt. Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, NSW, Australia.
Samantha R OakesSt. Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, NSW, Australia.
Jayesh DesaiDepartment of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.ORCID http://orcid.org/0000-0003-4246-9344
Chee Khoon LeeNHMRC Clinical Trials Centre, University of Sydney, Sydney, NSW, Australia.
John SimesNHMRC Clinical Trials Centre, University of Sydney, Sydney, NSW, Australia.
David M ThomasSt. Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, NSW, Australia. d.thomas@garvan.org.au.ORCID http://orcid.org/0000-0002-2527-5428
Garvan Institute of Medical Research · AUThe University of Sydney · AUPeter MacCallum Cancer Centre · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

While several resources exist that interpret therapeutic significance of genomic alterations in cancer, many regional real-world issues limit access to drugs. There is a need for a pragmatic, evidence-based, context-adapted tool to guide clinical management based on molecular biomarkers. To this end, we have structured a compendium of approved and experimental therapies with associated biomarkers following a survey of drug regulatory databases, existing knowledge bases, and published literature. Each biomarker-disease-therapy triplet was categorised using a tiering system reflective of key therapeutic considerations: approved and reimbursed therapies with respect to a jurisdiction (Tier 1), evidence of efficacy or approval in another jurisdiction (Tier 2), evidence of antitumour activity (Tier 3), and plausible biological rationale (Tier 4). Two resistance categories were defined: lack of efficacy (Tier R1) or antitumor activity (Tier R2). Based on this framework, we curated a digital resource focused on drugs relevant in the Australian healthcare system (TOPOGRAPH: Therapy Oriented Precision Oncology Guidelines for Recommending Anticancer Pharmaceuticals). As of November 2020, TOPOGRAPH comprised 2810 biomarker-disease-therapy triplets in 989 expert-appraised entries, including 373 therapies, 199 biomarkers, and 106 cancer types. In the 345 therapies catalogued, 84 (24%) and 65 (19%) were designated Tiers 1 and 2, respectively, while 271 (79%) therapies were supported by preclinical studies, early clinical trials, retrospective studies, or case series (Tiers 3 and 4). A companion algorithm was also developed to support rational, context-appropriate treatment selection informed by molecular biomarkers. This framework can be readily adapted to build similar resources in other jurisdictions to support therapeutic decision-making.

Identifiers

PMID34162978
PMCPMC8222322
OpenAlexW3173383875

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.