Evidence map›Paper›PMID 41943626›Full record

ArticleDisease models & mechanisms2026

Identification and credentialing of patient-derived xenograft models of invasive lobular carcinoma.

Jagmohan Hooda, Jennifer M Atkinson, Osama Shiraz Shah, Megan Yates, Daniel D Brown, Morgan DeBerry, Stefano Cairo, Paolo Schiavini, Hsiu-Wen Tsai, Marianna Zipeto and 3 more

Abstract read
In one paragraph

Article in Disease models & mechanisms, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Jagmohan HoodaWomen's Cancer Research Center, University of Pittsburgh Medical Center (UPMC) Hillman Cancer Center (HCC), Magee-Womens Research Institute, Pittsburgh, PA 15213, USA.ORCID 0000-0001-5001-6092
Jennifer M AtkinsonWomen's Cancer Research Center, University of Pittsburgh Medical Center (UPMC) Hillman Cancer Center (HCC), Magee-Womens Research Institute, Pittsburgh, PA 15213, USA.ORCID 0000-0001-5164-5114
Osama Shiraz ShahWomen's Cancer Research Center, University of Pittsburgh Medical Center (UPMC) Hillman Cancer Center (HCC), Magee-Womens Research Institute, Pittsburgh, PA 15213, USA.ORCID 0000-0003-4756-4535
Megan YatesWomen's Cancer Research Center, University of Pittsburgh Medical Center (UPMC) Hillman Cancer Center (HCC), Magee-Womens Research Institute, Pittsburgh, PA 15213, USA.ORCID 0000-0002-5932-1592
Daniel D BrownInstitute for Precision Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA.
Morgan DeBerryDepartment of Pathology, University of Pittsburgh, Pittsburgh, PA 15213, USA.
Stefano CairoChampions Oncology, Hackensack, NJ 20850, USA.ORCID 0000-0002-4725-5970
Paolo SchiaviniChampions Oncology, Hackensack, NJ 20850, USA.ORCID 0000-0002-4168-0917
Hsiu-Wen TsaiChampions Oncology, Hackensack, NJ 20850, USA.
Marianna ZipetoChampions Oncology, Hackensack, NJ 20850, USA.
Rohit BhargavaDepartment of Pathology, University of Pittsburgh, Pittsburgh, PA 15213, USA.ORCID 0000-0002-1225-8418
Steffi OesterreichWomen's Cancer Research Center, University of Pittsburgh Medical Center (UPMC) Hillman Cancer Center (HCC), Magee-Womens Research Institute, Pittsburgh, PA 15213, USA.ORCID 0000-0002-2537-6923
Adrian V LeeWomen's Cancer Research Center, University of Pittsburgh Medical Center (UPMC) Hillman Cancer Center (HCC), Magee-Womens Research Institute, Pittsburgh, PA 15213, USA.ORCID 0000-0001-9917-514X

Funding

VECTOR CORE FACILITYP30CA047904 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Jessie Nedrow · 1988 to 2026
$158.0M
Developing and credentialing murine models of ILCR01CA252378 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Adrian V Lee, Steffi Oesterreich · 2021 to 2026
$3.0M
High-Throughput Computing for Genomics and Bioinformatics ResearchS10OD028483 · OD · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI LEE, ADRIAN V · 2021 to 2021
$574k
Breast Cancer Research FoundationNCI NIH HHS P30 CA047904NIH HHS R01CA252378NIH HHS S10OD028483Oncology Models ForumShear Family Foundation P30CA047904University of Pittsburgh
6 · The paper itself

Abstract

Invasive lobular cancer (ILC) is the most common special breast cancer subtype, accounting for 10-15% of all cases. The pathognomonic feature of ILC is loss of E-cadherin (encoded by CDH1), leading to discohesive single-file growth. Although ILCs show better prognostic factors than 'no special type' (NST) breast cancer, patients with ILC have worse long-term outcomes. We identified and validated patient-derived xenograft (PDX) models of ILC from 122 breast cancer PDX models based on truncating CDH1 mutations and/or low CDH1 mRNA expression. Eight PDX models were selected for validation using immunohistochemistry for E-cadherin, p120, estrogen receptor, progesterone receptor and HER2. Seven models were confirmed as ILC, and one showed mixed NST-ILC features. Confirmed ILC PDX models showed enrichment of truncating CDH1 mutations, significantly lower CDH1 mRNA expression and predominantly luminal subtypes compared to NST models, consistent with human ILC characteristics. Commonly altered genes included PIK3CA (57%), CDH1 (57%) and TP53 (57%). These validated ILC PDX models provide valuable tools to advance understanding of ILC biology and support development of targeted therapies.

Indexed as

Breast NeoplasmsCarcinoma, LobularXenograft Model Antitumor AssaysAnimalsAntigens, CDCadherinsFemaleGene Expression Regulation, NeoplasticHumansMutationNeoplasm InvasivenessRNA, MessengerAntigens, CDCadherinsCDH1 protein, humanRNA, MessengerCDH1Genomic profilingInvasive lobular breast cancerPatient-derived xenograftTargeted therapy

Identifiers

PMID41943626
PMCPMC13225211

What OpenQuestion holds

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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.