Evidence map›Paper›PMID 30848103›Full record

ArticleCancer medicine2019

Clinical validation of an immunohistochemistry-based CanAssist-Breast test for distant recurrence prediction in hormone receptor-positive breast cancer patients.

Manjiri M Bakre, Charusheila Ramkumar, Arun Kumar Attuluri, Chetana Basavaraj, Chandra Prakash, Ljubomir Buturovic, Lekshmi Madhav, Nirupama Naidu, Prathima R, S P Somashekhar and 3 more

Open access · goldAbstract readValidation Study
In one paragraph

Article in Cancer medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
1.8field-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

21 citing papers in PubMed, 47 citations in OpenAlex.

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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

13 authors at 6 institutions in 4 countries.

Manjiri M BakreOncoStem Diagnostics Private Limited, Bangalore, India.ORCID 0000-0003-1160-8102
Charusheila RamkumarOncoStem Diagnostics Private Limited, Bangalore, India.
Arun Kumar AttuluriOncoStem Diagnostics Private Limited, Bangalore, India.
Chetana BasavarajOncoStem Diagnostics Private Limited, Bangalore, India.
Chandra PrakashOncoStem Diagnostics Private Limited, Bangalore, India.
Ljubomir ButurovicClinical Persona Inc, East Palo Alto, California.
Lekshmi MadhavOncoStem Diagnostics Private Limited, Bangalore, India.
Nirupama NaiduOncoStem Diagnostics Private Limited, Bangalore, India.
Prathima ROncoStem Diagnostics Private Limited, Bangalore, India.
S P SomashekharManipal Hospital and Comprehensive Cancer Centre, Bangalore, India.
Sudeep GuptaTata Memorial Hospital, Mumbai, India.
Dinesh Chandra DovalRajiv Gandhi Cancer Institute, New Delhi, India.
Mark D PegramStanford University Medical Center, Palo Alto, California.
CS Diagnostics · DEManipal Hospital · INPro Persona · NLRajiv Gandhi Cancer Institute and Research Centre · INStanford Medicine · USTata Memorial Hospital · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

CanAssist-Breast (CAB) is an immunohistochemistry (IHC)-based prognostic test for early-stage Hormone Receptor (HR+)-positive breast cancer patients. CAB uses a Support Vector Machine (SVM) trained algorithm which utilizes expression levels of five biomarkers (CD44, ABCC4, ABCC11, N-Cadherin, and Pan-Cadherin) and three clinical parameters such as tumor size, grade, and node status as inputs to generate a risk score and categorizes patients as low- or high-risk for distant recurrence within 5 years of diagnosis. In this study, we present clinical validation of CAB. CAB was validated using a retrospective cohort of 857 patients. All patients were treated either with endocrine therapy or chemoendocrine therapy. Risk categorization by CAB was analyzed by calculating Distant Metastasis-Free Survival (DMFS) and recurrence rates using Kaplan-Meier survival curves. Multivariate analysis was performed to calculate Hazard ratios (HR) for CAB high-risk vs low-risk patients. The results showed that Distant Metastasis-Free Survival (DMFS) was significantly different (P-0.002) between low- (DMFS: 95%) and high-risk (DMFS: 80%) categories in the endocrine therapy treated alone subgroup (n = 195) as well as in the total cohort (n = 857, low-risk DMFS: 95%, high-risk DMFS: 84%, P < 0.0001). In addition, the segregation of the risk categories was significant (P = 0.0005) in node-positive patients, with a difference in DMFS of 12%. In multivariate analysis, CAB risk score was the most significant predictor of distant recurrence with hazard ratio of 3.2048 (P < 0.0001). CAB stratified patients into discrete risk categories with high statistical significance compared to Ki-67 and IHC4 score-based stratification. CAB stratified a higher percentage of the cohort (82%) as low-risk than IHC4 score (41.6%) and could re-stratify >74% of high Ki-67 and IHC4 score intermediate-risk zone patients into low-risk category. Overall the data suggest that CAB can effectively predict risk of distant recurrence with clear dichotomous high- or low-risk categorization.

Indexed as

AdultAgedAlgorithmsBiomarkers, TumorBreast NeoplasmsFemaleHumansKaplan-Meier EstimateLymphatic MetastasisMiddle AgedNeoplasm GradingNeoplasm MetastasisNeoplasm StagingPrognosisReceptors, EstrogenReceptors, ProgesteroneBiomarkers, TumorReceptors, EstrogenReceptors, ProgesteroneCanAssist-Breastdistant recurrenceearly-stage breast cancerimmunohistochemistryprognosticationsupport vector machine

Identifiers

PMID30848103
PMCPMC6488210
OpenAlexW2921336367

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.