Evidence map›Paper›PMID 32511030›Full record

ArticleAmerican Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting2020

Leveraging Patient-Derived Models for Immunotherapy Research.

Katerina Politi

Abstract read
In one paragraph

Article in American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

1 author.

Katerina PolitiDepartments of Pathology and Internal Medicine (Section of Medical Oncology), Yale Cancer Center, Yale School of Medicine, New Haven, CT.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Understanding and Overcoming Resistance to Cancer Immunotherapy Due to Defective Antigen PresentationR01CA230275 · NCI · YALE UNIVERSITY · PI KAECH, SUSAN M, POLITI, KATERINA ABIGAIL · 2019 to 2023
$3.7M
NCATS NIH HHS UL1 TR001863NCI NIH HHS R01 CA230275
6 · The paper itself

Abstract

As cancer immunotherapies become mainstream for the treatment of many different cancer types and the numbers of new agents continue to increase, the need for experimental models is also rising. An approach to develop and study models for immune-oncology that has garnered intense interest in recent years is that of using patient-derived models. Patient-derived models can recapitulate many of the features and heterogeneity of the corresponding human tumors. Historically these models have been used to study cancer cell-intrinsic properties of tumors and drugs that target tumor cells directly. In recent years, increasing recognition of the role immune cells play in cancer and how these represent good therapeutic targets has led to efforts to optimize and use patient-derived models for cancer immunotherapy studies. Patient-derived models are now being used to study the properties of cancer cells that modulate their ability to respond to immune stimulation. Further efforts are underway to use and develop patient-derived models that incorporate human immune cells in vitro and in vivo (humanized mice) so that cancer cell-immune cell interactions can be studied in the context of cancer immunotherapies. As these models are further refined, leveraging patient-derived models for cancer immunotherapy research can provide insight into mechanisms of sensitivity and resistance to cancer immunotherapies, uncover new targets, reveal how specific agents work, and be used to evaluate the antitumor efficacy of therapeutic regimens.

Indexed as

AnimalsDisease Models, AnimalHumansImmunotherapyMiceNeoplasmsResearch Design

Identifiers

PMID32511030
PMCPMC8136600

What OpenQuestion holds

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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.