Evidence map›Paper›PMID 27895917›Full record

ReviewJournal for immunotherapy of cancer2016

Validation of biomarkers to predict response to immunotherapy in cancer: Volume I - pre-analytical and analytical validation.

Giuseppe V Masucci, Alessandra Cesano, Rachael Hawtin, Sylvia Janetzki, Jenny Zhang, Ilan Kirsch, Kevin K Dobbin, John Alvarez, Paul B Robbins, Senthamil R Selvan and 3 more

Open access · goldAbstract readReview
In one paragraph

Review in Journal for immunotherapy of cancer, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 124 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
124citing papers in PubMed, 3 pooled it
12.6field-weighted citation impact, top 1% 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

124 citing papers in PubMed, 3 syntheses or guidelines pooled it, 198 citations in OpenAlex.

  1. Guideline
  2. Pooled it
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  10. TIM-3 in AML: pathogenic roles and therapeutic targetability.Clinical and experimental medicine · 2026
    Review
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  20. Salivary biomarkers: a promising approach for predicting immunotherapy response in head and neck cancers.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025
    Review

64 more citing papers are in PubMed but not listed here.

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 11 institutions in 4 countries.

Giuseppe V MasucciDepartment of Oncology-Pathology, Karolinska Institutet, 171 76 Stockholm, Sweden.
Alessandra CesanoNanoString, Inc, 500 Fairview Avenue North, Seattle, WA 98109 USA.
Rachael HawtinNodality, Inc, 170 Harbor Way, South San Francisco, 94080 CA USA.
Sylvia JanetzkiZellNet Consulting, Inc, 555 North Avenue, Fort Lee, 07024 NJ USA.
Jenny ZhangCovaris Inc, 14 Gill St, Woburn, MA 01801 USA.
Ilan KirschAdaptive Biotechnologies, Inc, 1551 Eastlake Ave. E, Seattle, WA 98102 USA.
Kevin K DobbinDepartment of Epidemiology and Biostatistics, College of Public Health, The University of Georgia, 101 Buck Road, Athens, 30602 GA USA.
John AlvarezJanssen Research & Development, LLC, Spring House, PA 19477 USA.
Paul B RobbinsPfizer, San Diego, CA USA.
Senthamil R SelvanOmni Array Biotechnology, 15601 Crabbs Branch Way, Rockville, 20855 MD USA.
Howard Z StreicherNational Cancer Institute, National Institutes of Health, 9609 Medical Center Drive, Bethesda, 20892 MD USA.
Lisa H ButterfieldDepartment of Medicine, Surgery and Immunology, University of Pittsburgh Cancer Institute, 5117 Centre Avenue, Pittsburgh, PA 15213 USA.
Magdalena ThurinNational Cancer Institute, Cancer Diagnosis Program, DCTD, National Institutes of Health, 9609 Medical Center Drive, Bethesda, 20892 MD USA ; Adaptive Biotechnologies, Inc, 1551 Eastlake Ave. E, Seattle, WA 98102 USA.
Adaptive Biotechnologies (United States) · USArray BioPharma (United States) · USCovaris (United States) · USJanssen (Belgium) · BEKarolinska Institutet · SENanostring Technologies (United States) · USNational Institutes of Health · USNodality (United States) · USPfizer (United States) · USUniversity of Georgia · USUPMC Hillman Cancer Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Immunotherapies have emerged as one of the most promising approaches to treat patients with cancer. Recently, there have been many clinical successes using checkpoint receptor blockade, including T cell inhibitory receptors such as cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) and programmed cell death-1 (PD-1). Despite demonstrated successes in a variety of malignancies, responses only typically occur in a minority of patients in any given histology. Additionally, treatment is associated with inflammatory toxicity and high cost. Therefore, determining which patients would derive clinical benefit from immunotherapy is a compelling clinical question. Although numerous candidate biomarkers have been described, there are currently three FDA-approved assays based on PD-1 ligand expression (PD-L1) that have been clinically validated to identify patients who are more likely to benefit from a single-agent anti-PD-1/PD-L1 therapy. Because of the complexity of the immune response and tumor biology, it is unlikely that a single biomarker will be sufficient to predict clinical outcomes in response to immune-targeted therapy. Rather, the integration of multiple tumor and immune response parameters, such as protein expression, genomics, and transcriptomics, may be necessary for accurate prediction of clinical benefit. Before a candidate biomarker and/or new technology can be used in a clinical setting, several steps are necessary to demonstrate its clinical validity. Although regulatory guidelines provide general roadmaps for the validation process, their applicability to biomarkers in the cancer immunotherapy field is somewhat limited. Thus, Working Group 1 (WG1) of the Society for Immunotherapy of Cancer (SITC) Immune Biomarkers Task Force convened to address this need. In this two volume series, we discuss pre-analytical and analytical (Volume I) as well as clinical and regulatory (Volume II) aspects of the validation process as applied to predictive biomarkers for cancer immunotherapy. To illustrate the requirements for validation, we discuss examples of biomarker assays that have shown preliminary evidence of an association with clinical benefit from immunotherapeutic interventions. The scope includes only those assays and technologies that have established a certain level of validation for clinical use (fit-for-purpose). Recommendations to meet challenges and strategies to guide the choice of analytical and clinical validation design for specific assays are also provided.

Indexed as

Biological AssayBiomarkers, TumorImmunotherapyAntineoplastic Agents, ImmunologicalB7-H1 AntigenClinical Trials as TopicCTLA-4 AntigenHumansMolecular Targeted TherapyNeoplasmsPrognosisProgrammed Cell Death 1 ReceptorReproducibility of ResultsTreatment OutcomeAntineoplastic Agents, ImmunologicalB7-H1 AntigenBiomarkers, TumorCTLA-4 AntigenProgrammed Cell Death 1 ReceptorAssayBiomarkerCancerImmunotherapyStandardizationValidation

Identifiers

PMID27895917
PMCPMC5109744
OpenAlexW2551624291

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.