ArticleSystematic reviews2020
Prognosis of PD-L1 in human breast cancer: protocol for a systematic review and meta-analysis.
Article in Systematic reviews, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
Who cites it
8 citing papers in PubMed, 21 citations in OpenAlex.
- Clinical Outcomes and Prognostic Factors in Metastatic Triple-Negative Breast Cancer: A Real-World Data Analysis.World journal of oncology · 2026Article
- Article
- Nanotechnology Applications in Breast Cancer Immunotherapy.Small (Weinheim an der Bergstrasse, Germany) · 2024Review
- Study of PD-L1 Expression with Association of Pathological Factors and Molecular Subtypes in Breast Carcinoma.Journal of laboratory physicians · 2022Article
- Expression of immune checkpoints (PD-L1 and IDO) and tumour-infiltrating lymphocytes in breast cancer.Heliyon · 2022Article
- Article
- Review
- Advances in CAR-T Cell Genetic Engineering Strategies to Overcome Hurdles in Solid Tumors Treatment.Frontiers in immunology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBreast cancer is one of the most common malignancies in women worldwide, and one of the leading causes of cancer-related death. Programmed cell death 1 (PD-1) and its ligand (PD-L1) are key physiologic suppressors of the cytotoxic immune reaction. Some authors advocate that PD-L1 expression may help in breast cancer prognosis.
methodsWe will conduct a systematic review of observational or interventional studies evaluating the prognostic ability of PD-L1 expression levels in predicting positive clinical outcomes in Human Breast Cancer. A sensitive search strategy will be employed in MEDLINE, EMBASE, LILACS, The Grey Literature Report, OpenGrey, OAIster, and Cochrane CENTRAL. Two reviewers will independently screen all identified references for eligibility and extract data. The outcomes evaluated will be Overall Survival, Breast Cancer-specific Survival, Disease-free Survival, Recurrence-free Survival, Positive Lymph Node, and Distant Metastasis. The outcomes will be extracted directly from the studies, if available. Methodological quality and bias of included studies will be assessed using a standardized checklist and overall quality of evidence will be assessed through the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. If meta-analysis is possible, the measures of association will be calculated using bivariate random-effects models. Statistical heterogeneity will be evaluated with I DISCUSSION: Immunomodulation seems to be a promising strategy in solid tumors. Breast cancer is the most common malignancies in women worldwide, and one of the leading causes of cancer death. PD-1 and PD-L1 are key physiologic suppressors of the cytotoxic immune reaction.
trial registrationSystematic review registration: CRD42019121118 (PROSPERO).
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What OpenQuestion holds
Registered trials
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.