Patient-Derived Xenografts of Triple-Negative Breast Cancer Enable Deconvolution and Prediction of Chemotherapy Responses.

Unlabelled: Combination chemotherapy remains essential for clinical management of triple-negative breast cancer (TNBC). Consequently, responses to individual agents cannot be easily delineated at the single patient level, even though some patients might not require all drugs in the combination. Herein, we conduct multi-omic analyses of orthotopic TNBC patient-derived xenografts (PDXs) treated with single agent carboplatin, docetaxel, or the combination.

A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis.

Patient-derived xenografts (PDX) model human intra- and intertumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histologic imaging via hematoxylin and eosin (H&E) staining is routinely performed on PDX samples, which could be harnessed for computational analysis. Prior studies of large clinical H&E image repositories have shown that deep learning analysis can identify intercellular and morphologic signals correlated with disease phenotype and therapeutic response.

TOWARDS Study: Patient-Derived Xenograft Engraftment Predicts Poor Survival in Patients With Newly Diagnosed Triple-Negative Breast Cancer.

Purpose: Assessing risk of recurrence for nonmetastatic triple-negative breast cancer (TNBC) is a key determinant of therapeutic strategy. The best predictor of recurrence risk is failure to achieve a pathologic complete response after preoperative chemotherapy, but it imperfectly correlates with the definitive end points of relapse-free and overall survival (OS). The inability to accurately predict recurrence has led to increasingly toxic treatment regimens for patients with early-stage TNBC.

Combining the Tyrosine Kinase Inhibitor Cabozantinib and the mTORC1/2 Inhibitor Sapanisertib Blocks ERK Pathway Activity and Suppresses Tumor Growth in Renal Cell Carcinoma.

Unlabelled: Current treatment approaches for renal cell carcinoma (RCC) face challenges in achieving durable tumor responses due to tumor heterogeneity and drug resistance. Combination therapies that leverage tumor molecular profiles could offer an avenue for enhancing treatment efficacy and addressing the limitations of current therapies. To identify effective strategies for treating RCC, we selected ten drugs guided by tumor biology to test in six RCC patient-derived xenograft (PDX) models.

Breast cancer PDxO cultures for drug discovery and functional precision oncology.

Patient-derived xenografts (PDXs) have clinical value but are time-, cost-, and labor-intensive and thus ill-suited for large-scale experiments. Here, we present a protocol to convert PDX tumors into PDxOs for long-term cultures amenable to moderate-throughput drug screens, including in-depth PDxO validation. We describe steps for PDxO preparation and mouse cell removal.

Identifying biomarkers of differential chemotherapy response in TNBC patient-derived xenografts with a CTD/WGCNA approach.

Although systemic chemotherapy remains the standard of care for TNBC, even combination chemotherapy is often ineffective. The identification of biomarkers for differential chemotherapy response would allow for the selection of responsive patients, thus maximizing efficacy and minimizing toxicities. Here, we leverage TNBC PDXs to identify biomarkers of response.

Immunologically "cold" triple negative breast cancers engraft at a higher rate in patient derived xenografts.

TNBC is a heterogeneous subtype of breast cancer, and only a subset of TNBC can be established as PDXs. Here, we show that there is an engraftment bias toward TNBC with low levels of immune cell infiltration. Additionally, TNBC that failed to engraft show gene expression consistent with a cancer-promoting immunological state, leading us to hypothesize that the immunological state of the tumor and possibly the state of the immune system of the host may be essential for engraftment.

Multiparametric quantitative phase imaging for real-time, single cell, drug screening in breast cancer.

Quantitative phase imaging (QPI) measures the growth rate of individual cells by quantifying changes in mass versus time. Here, we use the breast cancer cell lines MCF-7, BT-474, and MDA-MB-231 to validate QPI as a multiparametric approach for determining response to single-agent therapies. Our method allows for rapid determination of drug sensitivity, cytotoxicity, heterogeneity, and time of response for up to 100,000 individual cells or small clusters in a single experiment.

A human breast cancer-derived xenograft and organoid platform for drug discovery and precision oncology.

Models that recapitulate the complexity of human tumors are urgently needed to develop more effective cancer therapies. We report a bank of human patient-derived xenografts (PDXs) and matched organoid cultures from tumors that represent the greatest unmet need: endocrine-resistant, treatment-refractory and metastatic breast cancers. We leverage matched PDXs and PDX-derived organoids (PDxO) for drug screening that is feasible and cost-effective with in vivo validation.

Comprehensive characterization of 536 patient-derived xenograft models prioritizes candidatesfor targeted treatment.

Development of candidate cancer treatments is a resource-intensive process, with the research community continuing to investigate options beyond static genomic characterization. Toward this goal, we have established the genomic landscapes of 536 patient-derived xenograft (PDX) models across 25 cancer types, together with mutation, copy number, fusion, transcriptomic profiles, and NCI-MATCH arms. Compared with human tumors, PDXs typically have higher purity and fit to investigate dynamic driver events and molecular properties via multiple time points from same case PDXs.

Erratum: "Toward improved models of human cancer" [APL Bioeng. , 010901 (2021)].

[This corrects the article DOI: 10.1063/5.0030534.

PELP1/SRC-3-dependent regulation of metabolic PFKFB kinases drives therapy resistant ER breast cancer.

Recurrence of metastatic breast cancer stemming from acquired endocrine and chemotherapy resistance remains a health burden for women with luminal (ER) breast cancer. Disseminated ER tumor cells can remain viable but quiescent for years to decades. Contributing factors to metastatic spread include the maintenance and expansion of breast cancer stem cells (CSCs).

Toward improved models of human cancer.

Human cancer is a complex and heterogeneous collection of diseases that kills more than 18 million people every year worldwide. Despite advances in detection, diagnosis, and treatments for cancers, new strategies are needed to combat deadly cancers. Models of human cancer continue to evolve for preclinical research and have culminated in patient-derived systems that better represent the diversity and complexity of cancer.

Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts.

Patient-derived xenografts (PDXs) are resected human tumors engrafted into mice for preclinical studies and therapeutic testing. It has been proposed that the mouse host affects tumor evolution during PDX engraftment and propagation, affecting the accuracy of PDX modeling of human cancer. Here, we exhaustively analyze copy number alterations (CNAs) in 1,451 PDX and matched patient tumor (PT) samples from 509 PDX models.

Protein synthesis inhibitors stimulate MondoA transcriptional activity by driving an accumulation of glucose 6-phosphate.

Background: Protein synthesis is regulated by the availability of amino acids, the engagement of growth factor signaling pathways, and adenosine triphosphate (ATP) levels sufficient to support translation. Crosstalk between these inputs is extensive, yet other regulatory mechanisms remain to be characterized. For example, the translation initiation inhibitor rocaglamide A (RocA) induces thioredoxin-interacting protein (TXNIP).

STAT3 and GR Cooperate to Drive Gene Expression and Growth of Basal-Like Triple-Negative Breast Cancer.

Breast cancers are divided into subtypes with different prognoses and treatment responses based on global differences in gene expression. Luminal breast cancer gene expression and proliferation are driven by estrogen receptor alpha, and targeting this transcription factor is the most effective therapy for this subtype. By contrast, it remains unclear which transcription factors drive the gene expression signature that defines basal-like triple-negative breast cancer, and there are no targeted therapies approved to treat this aggressive subtype.

ETV4 Is Necessary for Estrogen Signaling and Growth in Endometrial Cancer Cells.

Estrogen signaling through estrogen receptor alpha (ER) plays a major role in endometrial cancer risk and progression, however, the molecular mechanisms underlying ER's regulatory role in endometrial cancer are poorly understood. In breast cancer cells, ER genomic binding is enabled by FOXA1 and GATA3, but the transcription factors that control ER genomic binding in endometrial cancer cells remain unknown. We previously identified ETV4 as a candidate factor controlling ER genomic binding in endometrial cancer cells, and here we explore the functional importance of ETV4.

Publisher Correction: Protective autophagy elicited by RAF→MEK→ERK inhibition suggests a treatment strategy for RAS-driven cancers.

In the version of this article initially published, the label over the bottom schematic in Fig. 1a was "pH > 5.0"; it should have been "pH < 5.

Protective autophagy elicited by RAF→MEK→ERK inhibition suggests a treatment strategy for RAS-driven cancers.

Pancreatic ductal adenocarcinoma (PDA) was responsible for ~ 44,000 deaths in the United States in 2018 and is the epitome of a recalcitrant cancer driven by a pharmacologically intractable oncoprotein, KRAS. Downstream of KRAS, the RAF→MEK→ERK signaling pathway plays a central role in pancreatic carcinogenesis. However, paradoxically, inhibition of this pathway has provided no clinical benefit to patients with PDA.

A Cancer-Selective Zinc Ionophore Inspired by the Natural Product Naamidine A.

We present data demonstrating the natural product mimic, zinaamidole A (ZNA), is a modulator of metal ion homeostasis causing cancer-selective cell death by specifically inducing cellular Zn-uptake in transformed cells. ZNA's cancer selectivity was evaluated using metastatic, patient-derived breast cancer cells, established human breast cancer cell lines, and three-dimensional organoid models derived from normal and transformed mouse mammary glands. Structural analysis of ZNA demonstrated that the compound interacts with zinc through the N-acyl-2-aminoimidazole core.