Biology subjects

Pietenpol, J. A.

Publications and source records attributed to Pietenpol, J. A..

3 recordsLinked to original sources

bioRxiv2024

Trem2 deficiency attenuates breast cancer tumor growth in lean, but not obese or weight loss, mice and is associated with alterations of clonal T cell populations

AbstractObesity is an established risk factor for breast cancer development and worsened prognosis; however, the mechanisms for this association - and the potential benefits of weight loss - have not been fully explored. The adipose environment surrounding breast tumors, which is inflamed in obesity, has been implicated in tumor progression. An emerging therapeutic target for cancer is TREM2, a transmembrane receptor of the immunoglobulin superfamily that is expressed on macrophages in adipose tissue and tumors. We utilized genetic loss of function (Trem2+/+ and Trem2-/-) models and dietary (lean, obese, and weight loss) intervention approaches to examine impacts on postmenopausal breast cancer. Remarkably, Trem2 deficiency ameliorated tumor growth in lean, but not obese or weight loss mice. Single-cell RNA sequencing, in conjunction with VDJ sequencing of tumor and tumor-adjacent mammary adipose tissue (mATTum-adj) immune cells, revealed that tumors of lean Trem2-/- mice exhibited a shift in clonal CD8+ T cells from an exhausted to an effector memory state, accompanied with increased clonality of CD4+ Th1 cells, that was not observed in any other diet-genotype group. Notably, identical T cell clonotypes were identified in the tumor and mATTum-adj of the same mouse. Finally, an immune checkpoint study demonstrated that PD-1 therapy restricted tumor growth in lean and weight loss, but not obese mice. We conclude that weight history is relevant when considering potential efficacy of TREM2 inhibition in postmenopausal breast cancer. This work reveals immunological interactions between tumors and surrounding adipose tissue, highlighting significant differences under obese and weight loss conditions.

cancer biology↗

bioRxiv2023

Robust biomarker discovery through multiplatform multiplex image analysis of breast cancer clinical cohorts

AO_SCPLOWBSTRACTC_SCPLOWSpatial profiling of tissues promises to elucidate tumor-microenvironment interactions and enable development of spatial biomarkers to predict patient response to immunotherapy and other therapeutics. However, spatial biomarker discovery is often carried out on a single patient cohort or imaging technology, limiting statistical power and increasing the likelihood of technical artifacts. In order to analyze multiple patient cohorts profiled on different platforms, we developed methods for comparative data analysis from three disparate multiplex imaging technologies: 1) cyclic immunofluorescence data we generated from 102 breast cancer patients with clinical follow-up, in addition to publicly available 2) imaging mass cytometry and 3) multiplex ion-beam imaging data. We demonstrate similar single-cell phenotyping results across breast cancer patient cohorts imaged with these three technologies and identify cellular abundance and proximity-based biomarkers with prognostic value across platforms. In multiple platforms, we identified lymphocyte infiltration as independently associated with longer survival in triple negative and high-proliferation breast tumors. Then, a comparison of nine spatial analysis methods revealed robust spatial biomarkers. In estrogen receptor-positive disease, quiescent stromal cells close to tumor were more abundant in good prognosis tumors while tumor neighborhoods of mixed fibroblast phenotypes were enriched in poor prognosis tumors. In triple-negative breast cancer (TNBC), macrophage proximity to tumor and B cell proximity to T cells were greater in good prognosis tumors, while tumor neighborhoods of vimentin-positive fibroblasts were enriched in poor prognosis tumors. We also tested previously published spatial biomarkers in our ensemble cohort, reproducing the positive prognostic value of isolated lymphocytes and lymphocyte occupancy and failing to reproduce the prognostic value of tumor-immune mixing score in TNBC. In conclusion, we demonstrate assembly of larger clinical cohorts from diverse platforms to aid in prognostic spatial biomarker identification and validation. SO_SCPLOWTATEMENTC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWOFC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWSIGNIFICANCEC_SCPLOWOur single-cell spatial analysis of multiple clinical cohorts uncovered novel biomarkers of patient outcome in breast cancer. Additionally, our data, software, and methods will help advance spatial characterization of the tumor microenvironment.

cancer biology↗

bioRxiv2022

A Novel Mouse Model that Recapitulates the Heterogeneity of Human Triple Negative Breast Cancer

Triple-negative breast cancer (TNBC) patients have a poor prognosis and few treatment options. Mouse models of TNBC are important for development of new targeted therapies, but few TNBC mouse models exist. Here, we developed a novel TNBC murine model by mimicking two common TNBC mutations with high co-occurrence: amplification of the oncogene MYC and deletion of the tumor suppressor PTEN. This Myc;Ptenfl murine model develops TN mammary tumors that display histological and molecular features commonly found in human TNBC. We performed deep omic analyses on Myc;Ptenfl tumors including machine learning for morphologic features, bulk and single-cell RNA-sequencing, multiplex immunohistochemistry and single-cell phenotyping. Through comparison with human TNBC, we demonstrated that this new genetic mouse model develops mammary tumors with differential survival that closely resemble the inter- and intra-tumoral and microenvironmental heterogeneity of human TNBC; providing a unique pre-clinical tool for assessing the spectrum of patient TNBC biology and drug response. Statement of significanceThe development of cancer models that mimic triple-negative breast cancer (TNBC) microenvironment complexities is critical to develop effective drugs and enhance disease understanding. This study addresses a critical need in the field by identifying a murine model that faithfully mimics human TNBC heterogeneity and establishing a foundation for translating preclinical findings into effective human clinical trials.

cancer biology↗