Biology subjects

Perou, C. M.

Publications and source records attributed to Perou, C. M..

15 recordsLinked to original sources

bioRxiv2026

Vulnerability of triple negative breast cancer to daraxonrasib pan-RAS(on) inhibition

Triple-Negative Breast Cancer (TNBC) is a clinical subtype where aberrant expression of receptor tyrosine kinases (RTKs) leads to the activation of the RAS-RAF-MEK-ERK pathway contributing to tumor progression. Therapies targeting components of the RAF-MEK-ERK pathway in TNBC have been disappointing due to rapid onset of adaptive reprogramming of the RTK landscape leading to pathway reactivation. To overcome the dynamic heterogeneity of RTK expression and their convergence in activating RAS, treatment with the pan-RAS(on) inhibitor daraxonrasib (RMC-6236), which inhibits RAS-GTP regulation of RAF kinases, inhibited proliferation independent of TNBC kinotype or oncogenic RAS mutation in three of four TNBC cell lines in vitro and two patient-derived TNBC xenografts in mice. Daraxonrasib in combination with the MEK inhibitor trametinib prevented reactivation of the RAF-MEK-ERK pathway observed with each inhibitor alone and further enhanced inhibition of tumor growth in TNBC cell lines and xenografts. Inhibiting pan-RAS(on) RAS-GTP, with daraxonrasib, alone or in combination with a MEK inhibitor, provides a new strategy for the treatment of TNBC that inhibits RTK activation of the RAS-RAF-MEK-ERK pathway and tumor proliferation.

cancer biology↗

bioRxiv2026

Transcriptomic profiling of mouse mammary tumors enables prognostic and predictive biomarker discovery for human breast cancers

The development and validation of prognostic and predictive biomarkers in breast cancer is limited by the availability of well-annotated datasets linking tumor molecular features to treatment response and survival outcomes. To address this need, we generated an extensive mouse models dataset comprised of 26 immunocompetent mammary tumor models spanning diverse genetic backgrounds, epithelial-mesenchymal states, the basal-luminal axis, and distinct immune microenvironments. For each model, we measured survival under no treatment, immune checkpoint inhibition (ICI), and carboplatin/paclitaxel chemotherapy. We performed RNA-seq on baseline tumors and on 7-day on-treatment samples for both regimens. Using baseline murine tumor gene expression features, we trained a machine learning Elastic Net model that predicted survival outcomes on multiple human breast cancer datasets with performance comparable to that of existing prognostic assays. We next trained models for ICI benefit, using either the untreated or 7-day ICI treated samples; both models predicted ICI benefit on human ICI treated datasets, with the 7-day treated tumor model showing better performance. We also developed a predictor of carboplatin/paclitaxel response that performed well in mice but did not generalize to human chemotherapy cohorts. Finally, we compared multiple computational approaches, including XGBoost, random forests, and support vector regression; all methods successfully predicted survival outcomes, with Elastic Net offering the best performance and interpretability. These results indicate conserved cancer biology between mouse and human tumors for prognosis and ICI response and establish this large preclinical dataset with linked phenotypic and genomic data, as a resource for benchmarking computational methods for survival prediction. SignificanceThe development of a genomically and phenotypically diverse murine tumor dataset with linked treatment outcomes establishes a robust translational resource to develop, test, and benchmark clinically relevant prognostic and therapeutic response biomarkers.

cancer biology↗

bioRxiv2026

MERTK inhibition cooperates with immunomodulatory cyclophosphamide to induce CXCL9⁺ monocyte-macrophage programming and durable anti-tumor immunity in triple negative breast cancer

Triple-negative breast cancer (TNBC) has high rates of recurrence despite chemotherapy and immune checkpoint blockade (ICB). Tumor-associated macrophages (TAMs) can either suppress or support anti-tumor immunity, but the mechanisms governing these states and therapeutic targets remain unclear. Here, integrating public scRNAseq datasets with TNBC cohorts, we identify a prognostic myeloid signature defined by CXCL9hi/C1Qlow TAM programs, associated with improved survival and increased lymphocyte activation pathways. Using immunocompetent p53-null syngeneic TNBC models spanning basal-like (2153L) and claudin-low (T12) subtypes, we show that immunomodulatory cyclophosphamide (CTX) reprograms hematopoiesis toward the monocytic lineage and induces an interferon (IFN) conditioned tumor milieu that supports CXCL9 monocyte-derived macrophages (Mo.Macs) in basal-like disease. Combining CTX with the next generation MERTK-selective inhibitor UNC2371 (MRX-2843) drives complete remissions in both models, but durable long-term responses occurred selectively in the basal-like subtype model. The expansion of antigen-presenting CXCL9 Mo.Macs and reduction of C1q phagocytic TAMs are observed in responding tumors. Mechanistically, MERTK inhibition relieves MAPK/SOCS1 mediated restraint of IFN signaling driving a positive feedback loop of IRF7/STAT1/IRF1 driven CXCL9 induction. Functionally, tumor control requires CXCL9-CXCR3 dependent CD4 T cell recruitment, accumulation of stem-like memory CD4 T cells, and germinal center like immune organization in tumor-draining lymph nodes. PD-1 blockade further increases durability, preventing recurrence in most treated basal-like tumors. Together, these findings define an IFN licensed, MERTK regulated myeloid checkpoint that can be therapeutically targeted to convert suppressive TNBC microenvironments into durable adaptive immunity, supporting clinical translation of CTX + MRX-2843 based combinations in basal-like TNBC. SignificanceSuppressive myeloid programing limits effective adaptive immune engagement in TNBC usually resulting in ICB treatment resistance and tumor recurrence. This study identifies a therapeutically actionable myeloid interferon checkpoint in which MERTK inhibition stabilizes CXCL9 monocyte-macrophage programming to promote CD4 T cell dependent immune memory and durable tumor control in basal-like TNBC.

cancer biology↗

bioRxiv2024

Kinase Plasticity in Response to Vandetanib Enhances Sensitivity to Tamoxifen and Identifies Co-Treatment Strategies in Estrogen Receptor Positive Breast Cancer

Resistance to endocrine therapy (ET) is common in estrogen receptor-positive (ER+) breast cancer. Multiple studies have demonstrated that upregulation of MAPK signaling pathways contributes to ET resistance. Herein we show that vandetanib treatment suppresses MAPK signaling and enhances sensitivity to ET across ET-sensitive and ET-resistant ER+ cell lines and patient derived organoids. Vandetanib treatment reprograms transcription toward a less proliferative, more estrogen responsive, Luminal-A like state by enriching ER chromatin binding at canonical estrogen response elements. Multiplexed kinase inhibitor beads-mass spectrometry (MIB/MS) revealed kinase network reprogramming, including upregulation of PI3K and HER2 activity, as shared adaptive resistance mechanisms to vandetanib treatment. Co-treatment with the HER2 inhibitor lapatinib, further enhanced sensitivity to vandetanib. Using an operating room-to-laboratory short-term ex-vivo assay coupled to single-cell RNA sequencing, we demonstrate conserved gene expression changes in primary tumor cells, including increased HER2 activity signatures, following vandetanib treatment. Vandetanib sensitivity signatures were generated from cell line and primary human tumor cells which correlate with vandetanib sensitivity in ER+ patient-derived organoid and xenograft models. Future clinical trials of vandetanib in ER+ breast cancer should include rationally designed co-treatments based on adaptive resistance pathways, including HER2, and evaluate response signatures as biomarkers predicting patients most likely to benefit. SIGNIFICANCEVandetanib enhances sensitivity to tamoxifen in ER+ breast cancer by reprograming ER gene regulation and kinase signaling networks which define gene expression signatures associated with response and identify targetable adaptive resistance pathways.

cancer biology↗

bioRxiv2024

Quantitative proteomic mass spectrometry of protein kinases to determine dynamic heterogeneity of the human kinome

The kinome is a dynamic system of kinases regulating signaling networks in cells and dysfunction of protein kinases contributes to many diseases. Regulation of the protein expression of kinases alters cellular responses to environmental changes and perturbations. We configured a library of 672 proteotypic peptides to quantify >300 kinases in a single LC-MS experiment using ten micrograms protein from human tissues including biopsies. This enables absolute quantitation of kinase protein abundance at attomole-femtomole expression levels, requiring no kinase enrichment and less than ten micrograms of starting protein from flash-frozen and formalin fixed paraffin embedded tissues. Breast cancer biopsies, organoids, and cell lines were analyzed using the SureQuant method, demonstrating the heterogeneity of kinase protein expression across and within breast cancer clinical subtypes. Kinome quantitation was coupled with nanoscale phosphoproteomics, providing a feasible method for novel clinical diagnosis and understanding of patient kinome responses to treatment.

biochemistry↗

bioRxiv2024

Defining the Regulatory Logic of Breast Cancer Using Single-Cell Epigenetic and Transcriptome Profiling

Annotation of the cis-regulatory elements that drive transcriptional dysregulation in cancer cells is critical to improving our understanding of tumor biology. Herein, we present a compendium of matched chromatin accessibility (scATAC-seq) and transcriptome (scRNA-seq) profiles at single-cell resolution from human breast tumors and healthy mammary tissues processed immediately following surgical resection. We identify the most likely cell-of-origin for luminal breast tumors and basal breast tumors and then introduce a novel methodology that implements linear mixed-effects models to systematically quantify associations between regions of chromatin accessibility (i.e. regulatory elements) and gene expression in malignant cells versus normal mammary epithelial cells. These data unveil regulatory elements with that switch from silencers of gene expression in normal cells to enhancers of gene expression in cancer cells, leading to the upregulation of clinically relevant oncogenes. To translate the utility of this dataset into tractable models, we generated matched scATAC-seq and scRNA-seq profiles for breast cancer cell lines, revealing, for each subtype, a conserved oncogenic gene expression program between in vitro and in vivo cells. Together, this work highlights the importance of non-coding regulatory mechanisms that underlie oncogenic processes and the ability of single-cell multi-omics to define the regulatory logic of BC cells at single-cell resolution.

genomics↗

bioRxiv2022

High-Dose Paclitaxel and its Combination with CSF1R Inhibitor in Polymeric Micelles for Chemoimmunotherapy of Triple Negative Breast Cancer

The presence of immunosuppressive immune cells in cancer is a significant barrier to the generation of therapeutic immune responses. Similarly, in vivo triple-negative breast cancer (TNBC) models often contain prevalent tumor-associated macrophages in the tumor microenvironment (TME), resulting in breast cancer initiation, invasion, and metastasis by generating immunosuppressive environment. Here, we test systemic chemoimmunotherapy using small-molecule agents, paclitaxel (PTX), and colony-stimulating factor 1 receptor (CSF1R) inhibitor, PLX3397, to enhance the adaptive T cell immunity against TNBCs in immunocompetent mouse TNBC models. PTX and PLX3397 are very poorly soluble in water and shown poor therapeutic outcomes in TNBC animal models in conventional formulation. To address the challenge for the delivery of insoluble drugs to TNBC, we use high-capacity poly(2-oxazoline) (POx)-based polymeric micelles to greatly improve the solubility and widen the therapeutic index of such drugs. The results demonstrate that high-dose PTX in POx, even as a single agent, exerts strong effects on TME and induces the long-term immune memory. In addition, we demonstrate that the PTX and PLX3397 combination provides consistent therapeutic improvement across several TNBC models, resulting from the repolarization of the immunosuppressive TME and enhanced T cell immune response that suppress both the primary tumor growth and metastasis. Overall, the work emphasizes the benefit of drug reformulation and outlines potential translational path for both PTX and PTX with PLX3397 combination therapy using POx polymeric micelles for the treatment of TNBC.

cancer biology↗

bioRxiv2022

Multimodal Prediction of Breast Cancer Recurrence Assays and Risk of Recurrence

Gene expression-based recurrence assays are strongly recommended to guide the use of chemotherapy in hormone receptor-positive, HER2-negative breast cancer, but such testing is expensive, can contribute to delays in care, and may not be available in low-resource settings. Here, we describe the training and independent validation of a deep learning model that predicts recurrence assay result and risk of recurrence using both digital histology and clinical risk factors. We demonstrate that this approach outperforms an established clinical nomogram (area under the receiver operating characteristic curve of 0.833 versus 0.765 in an external validation cohort, p = 0.003), and can identify a subset of patients with excellent prognoses who may not need further genomic testing.

cancer biology↗

bioRxiv2021

Patient-derived triple negative breast cancer organoids provide robust model systems that recapitulate tumor intrinsic characteristics

Triple negative breast cancer (TNBC) is an aggressive form of breast cancer with poor patient outcomes, and an unmet clinical need for targeted therapies and better model systems. Here, we developed and comprehensively characterized a diverse biobank of normal and breast cancer patient-derived organoids (PDOs) with a focus on TNBCs. PDOs recapitulated patient tumor intrinsic properties and a subset of PDOs can be propagated for long-term culture (LT-TNBCs). Single cell profiling of PDOs identified cell types and gene candidates affiliated with different aspects of cancer progression. The LT-TNBC organoids exhibit signatures of aggressive MYC-driven basal-like breast cancers and are largely comprised of luminal progenitor (LP)-like cells. The TNBC LP-like cells are distinct from normal LPs and exhibit hyperactivation of NOTCH and MYC signaling. Overall, our study validates TNBC PDOs as robust models for understanding breast cancer biology and progression, paving the way for personalized medicine and tailored treatment options. Statement of SignificanceA comprehensive analysis of TNBC patient-derived organoids is presented by genomic, transcriptomic, and in-vivo analyses, providing insights into cellular heterogeneity and mechanisms of tumorigenesis at the single cell level.

cancer biology↗

bioRxiv2021

Tumor suppressor PLK2 may serve as a biomarker in triple-negative breast cancer for improved response to PLK1 therapeutics

Polo-like kinase (PLK) family members play important roles in cell cycle regulation. The founding member PLK1 is oncogenic and preclinically validated as a cancer therapeutic target. Paradoxically, PLK2 (chromosome 5q11.2) is frequently deleted in human breast cancers, preferentially in basal-like and triple-negative breast cancer subtypes. Here, we found that PLK2 was tumor suppressive in breast cancer and knockdown of PLK1 rescued phenotypes induced by PLK2-loss both in vitro and in vivo. We also demonstrated that PLK2 directly interacted with PLK1 at prometaphase and that mutations in the kinase domain of PLK2, but not polo-box binding domains, changed their interaction pattern. Furthermore, treatment of syngeneic transplantation mouse tumor models and patient-derived xenografts using the PLK1 inhibitor volasertib alone, or in combination with carboplatin, indicated that PLK2-low breast tumors had a significantly better response to these drugs. Re-expression of PLK2 in an inducible PLK2-null mouse model reduced the therapeutic efficacy of volasertib. Taken together, our data suggest PLK2 loss may serve as a biomarker to predict response to PLK1 therapeutics, alone and in combination with chemotherapy. SignificanceThe tumor suppressive role of PLK2, and its relationship with the oncogene PLK1, provide a mechanistic rationalization to use PLK1 inhibitors in combination with chemotherapy to treat PLK2 low/deleted tumors. TNBC, and other cancers with low PLK2 expression, are such candidates to leverage precision medicine to identify patients who might benefit from treatment with these inhibitors.

cancer biology↗

bioRxiv2020

A pan-cancer analysis of the frequency of DNA alterations across cell cycle activity levels

BackgroundPan-cancer genomic analyses based on the magnitude of pathway activity are currently lacking. Focusing on the cell cycle, we examined the DNA mutations and chromosome arm-level aneuploidy within tumours with low, intermediate and high cell cycle activity. Patients and methodsMatching mRNA, gene mutational status, chromosomal arm-level aberrations and clinico-pathological data was assembled from pan-cancer studies of 9,515 patients with 32 different cancers. Cell cycle activity was estimated from mRNA data using the cell cycle score (CCS) signature. Barplots were used to visualise mutation and chromosomal aberration frequency within CCS subgroups. Kaplan-Meier and multivariable Cox-regression analyses were used to determine survival differences between CCS subgroups. ResultsCell cycle activity varied broadly across and within all cancers. TP53 and PIK3CA mutations were common in all CCS subgroups but with increasing frequency as cell cycle activity levels increased (P < 0.001). Mutations in BRAF and gains in 16p were less frequent CCS high tumours (P < 0.001). In Kaplan-Meier analysis, patients whose tumours were CCS Low had a longer PFI relative to intermediate or high (P < 0.001) and this significance remained in multivariable analysis (CCS intermediate: HR = 1.37; 95% CI 1.17 - 1.60, CCS high: 1.54; 1.29 - 1.84, CCS Low = Ref). ConclusionsCell cycle activity varies across and within cancers and whilst similar DNA alterations can be found at all activity levels, some notable exceptions exist. These data also demonstrate that independent prognostic information can be derived on a pan-cancer level from a simple measure of cell cycle activity.

genomics↗

bioRxiv2019

A framework for transcriptome-wide association studies in breast cancer in diverse study populations

BackgroundThe relationship between germline genetic variation and breast cancer survival is largely unknown, especially in understudied minority populations who often have poorer survival. Genome-wide association studies (GWAS) have interrogated breast cancer survival but often are underpowered due to subtype heterogeneity and many clinical covariates and detect loci in non-coding regions that are difficult to interpret. Transcriptome-wide association studies (TWAS) show increased power in detecting functionally-relevant loci by leveraging expression quantitative trait loci (eQTLs) from external reference panels in relevant tissues. However, ancestry- or race-specific reference panels may be needed to draw correct inference in ancestrally-diverse cohorts. Such panels for breast cancer are lacking.\n\nResultsWe provide a framework for TWAS for breast cancer in diverse populations, using data from the Carolina Breast Cancer Study (CBCS), a North Carolina population-based cohort that oversampled black women. We perform eQTL analysis for 406 breast cancer-related genes to train race-stratified predictive models of tumor expression from germline genotypes. Using these models, we impute expression in independent data from CBCS and TCGA, accounting for sampling variability in assessing performance. These models are not applicable across race, and their predictive performance varies across tumor subtype. Within CBCS (N = 3,828), at a false discovery-adjusted significance of 0.10 and stratifying for race, we identify associations in black women near AURKA, CAPN13, PIK3CA, and SERPINB5 via TWAS that are underpowered in GWAS.\n\nConclusionsWe show that carefully implemented and thoroughly validated TWAS is an efficient approach for understanding the genetics underpinning breast cancer outcomes in diverse populations.

cancer biology↗

bioRxiv2019

SCDC: Bulk Gene Expression Deconvolution by Multiple Single-Cell RNA Sequencing References

Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.

bioinformatics↗

bioRxiv2018

An Integrative Boosting Approach for Predicting Survival Time With Multiple Genomics Platforms

Recent technological advances have made it possible to collect multiple types of genomics data on the same set of patients. It is of great interest to integrate multiple genomics data types together for predicting disease outcomes. We propose a variable selection method, termed Integrative Boosting (I-Boost), that makes proper use of all available clinical and genomics data in predicting individual patient survival time. Through simulation studies and applications to data sets from The Cancer Genome Atlas, we demonstrate that I-Boost provides substantially higher prediction accuracy than existing variable selection methods. Using I-Boost, we show that (1) the integration of multiple genomics platforms with clinical variables significantly improves the prediction accuracy for survival time over the use of clinical variables alone; (2) gene expression values are typically more prognostic of survival time than other genomics data types; and (3) gene modules/signatures are at least as prognostic as the collection of individual gene expression data.

bioinformatics↗

bioRxiv2017

A class of GATA3 mutation reprograms the breast cancertranscriptional network through gain and loss of function

GATA3 is frequently mutated in breast cancer; these mutations are widely presumed to be loss of function. Here, we address molecular alterations downstream of a novel class of GATA3 mutations, revealing both gain and loss of function. Mutation of one allele of GATA3 led to loss of binding and decreased expression at a subset of genes, including Progesterone Receptor. At other loci, associated with epithelial to mesenchymal transition, gain of binding at a novel sequence motif correlated with increased gene expression. These results demonstrate that not all GATA3 mutations are equivalent and that these mutations impact breast cancer through gain and loss of function.

cell biology↗