A renewable tissue resource of phenotypically stable, biologically and ethnically diverse, patient-derived human breast cancer xenograft models

Lineage Tracing and Single-Cell RNA Sequencing Reveal a Common Transcriptional State in Breast Cancer Tumor-Initiating Cells Characterized by IFN/STAT1 Activity

A tumor cell subpopulation of tumor-initiating cells (TIC) or "cancer stem cells" is associated with therapeutic resistance, as well as both local and distant recurrences. Signal transducer and activator of transcription (STAT) activity is elevated in TICs in claudin-low models of human triple-negative breast cancer, which enables enrichment of TICs using a STAT-responsive reporter. Lineage tracing of TICs as they undergo cell state changes could enable a better understanding of the molecular phenotypes of TIC and uncover strategies to selectively target TICs. In this study, we developed a STAT-responsive lineage-tracing system and used it in conjunction with the original reporter to enrich for cells with enhanced mammosphere-forming potential. This approach was able to detect TICs in some, but not all, basal-like triple-negative breast cancer xenograft models, indicating that STAT signaling has both TIC-related and TIC-independent functions. Single-cell RNA sequencing (RNA-seq) of reporter-tagged xenografts and clinical samples identified a common IFN/STAT1-associated transcriptional state in TICs that was previously linked to inflammation and macrophage differentiation. Surprisingly, most of the identified genes were not present in previously published TIC signatures derived using bulk RNA-seq. Finally, bone marrow stromal cell antigen-2 was identified as a cell surface marker of this state that functionally regulated TIC frequency. These results suggest that TICs may exploit the IFN/STAT1 signaling axis to promote their activity and that targeting this pathway may help eliminate TICs. Significance: Coupling single-cell transcriptomics with tumor-initiating cell enrichment identified IFN response gene expression not previously reported in bulk RNA-sequencing-derived signatures and proposed IFN/STAT1 signaling as a candidate therapeutic target in breast cancer.

Zebrafish Avatar testing preclinical study predicts chemotherapy response in breast cancer

Chemotherapy remains the mainstay in most high-risk breast cancer (BC) settings, with several equivalent options of treatment. However, the efficacy of each treatment varies between patients and there is currently no test to determine which option will be the most effective for each individual patient. Here, we developed a fast in-vivo test for BC therapy screening: the zebrafish patient-derived-xenograft model (zAvatars), where in-vivo results can be obtained in just 10 days. To determine the predictive value of the BC zAvatars we performed a preclinical study, where zAvatars were treated with the same therapy as the donor-patient and their response to therapy was compared. Our data show a 100% concordance (18 out of 18) between the zAvatar-test and the corresponding patient's clinical response to treatment. Altogether, our results suggest that the zAvatar model constitutes a promising in-vivo assay to optimize cancer treatments in a truly personalized manner.

Artificial intelligence-enabled automated analysis of transmission electron micrographs to evaluate chemotherapy impact on mitochondrial morphology in triple negative breast cancer

Advancements in transmission electron microscopy (TEM) have enabled in-depth studies of biological specimens, offering new avenues to large-scale imaging experiments with subcellular resolution. Mitochondrial structure is of growing interest in cancer biology due to its crucial role in regulating the multi-faceted functions of mitochondria. We and others have established the crucial role of mitochondria in triple-negative breast cancer (TNBC), an aggressive subtype of breast cancer with limited therapeutic options. Building upon our previous work demonstrating the regulatory role of mitochondrial structure dynamics in metabolic adaptation and survival of chemotherapy-refractory TNBC cells, we sought to extend those findings to a large-scale analysis of transmission electron micrographs. Here we present a UNet artificial intelligence (AI) model for automatic annotation and assessment of mitochondrial morphology and feature quantification. Our model is trained on 11,039 manually annotated mitochondria across 125 micrographs derived from a variety of orthotopic patient-derived xenograft (PDX) mouse model tumors and adherent cell cultures. The model achieves an F1 score of 0.85 on test micrographs at the pixel level. To validate the ability of our model to detect expected mitochondrial structural features, we utilized micrographs from mouse primary skeletal muscle cells genetically modified to lack Dynamin-related protein 1 (Drp1). The algorithm successfully detected a significant increase in mitochondrial elongation, in alignment with the well-established role of Drp1 as a driver of mitochondrial fission. Further, we subjected in vitro and in vivo TNBC models to conventional chemotherapy treatments commonly used for clinical management of TNBC, including doxorubicin, carboplatin, paclitaxel, and docetaxel (DTX). We found substantial within-sample heterogeneity of mitochondrial structure in both in vitro and in vivo TNBC models and observed a consistent reduction in mitochondrial elongation in DTX-treated specimens. We went on to compare mammary tumors and matched lung metastases in a highly metastatic PDX model of TNBC, uncovering significant reduction in mitochondrial length in metastatic lesions. Our large, curated dataset provides high statistical power to detect frequent chemotherapy-induced shifts in mitochondrial shapes and sizes in residual cells left behind after treatment. The successful application of our AI model to capture mitochondrial structure marks a step forward in high-throughput analysis of mitochondrial structures, enhancing our understanding of how morphological changes may relate to chemotherapy efficacy and mechanism of action. Finally, our large, manually curated electron micrograph dataset - now publicly available - serves as a unique gold-standard resource for developing, benchmarking, and applying computational models, while further advancing investigations into mitochondrial morphology and its impact on cancer biology.

Extracellular vesicle-mediated gene therapy targets BRAFV600E-mutant colorectal cancer by inhibiting the MEK1/2-ERK1/2 pathway

BACKGROUND

Patients with colorectal cancer (CRC) harboring BRAF mutation have a poor prognosis. The median survival time for patients with advanced BRAFV600E-mutant CRC is only approximately one year. Owing to the insensitivity to standard chemotherapy, there are still no effective and highly specific treatment strategies available in clinical practice for CRC patients with BRAF mutation. Therefore, targeting the BRAFV600E mutation site, researching and exploring novel targeted therapies are essential to improve the survival rate of patients with this CRC subtype.

AIM

This study aims to develop a precise therapeutic system for BRAFV600E CRC, based on the carrier properties of extracellular vesicles (EVs) and gene therapy targeting BRAFV600E.

METHOD

We first obtained engineered cells capable of stably producing EVs loaded with BRAFV600E nucleic acid drugs (siBRAFV600E). Next, BRAFV600E-mutant and wild-type CRC cell lines, as well as corresponding subcutaneous and metastasis models, were used to evaluate the therapeutic efficacy of EVs-siBRAFV600E and explored the mechanism. Notably, patient-derived xenograft (PDX) models, which share the same molecular characteristics, pathological features, and heterogeneity as patients do, were utilized to further explore the therapeutic efficacy and mechanisms.

RESULT

EVs-siBRAFV600E specifically inhibited BRAFV600E CRC but didn't affect BRAF wild-type CRC in vitro and vivo. EVs-siBRAFV600E exerts its therapeutic effect by regulating the MEK1/2-ERK1/2 pathway, and it has demonstrated excellent therapeutic efficacy in PDX models.

CONCLUSION

The therapeutic EVs we constructed are effective and specific for the BRAFV600E-mutant CRC. This study provides a novel strategy for the treatment of CRC patients with BRAFV600E mutation.

Nitric oxide inhibits ten-eleven translocation DNA demethylases to regulate 5mC and 5hmC across the genome

DNA methylation at cytosine bases (5-methylcytosine, 5mC) is a heritable epigenetic mark regulating gene expression. While enzymes that metabolize 5mC are well-characterized, endogenous signaling molecules that regulate DNA methylation machinery have not been described. We report that physiological nitric oxide (NO) concentrations reversibly inhibit the DNA demethylases TET and ALKBH2 by binding to the mononuclear non-heme iron atom forming a dinitrosyliron complex (DNIC) and preventing cosubstrates from binding. In cancer cells treated with exogenous NO, or endogenously synthesizing NO, 5mC and 5-hydroxymethylcytosine (5hmC) increase, with no changes in DNA methyltransferase activity. 5mC is also significantly increased in NO-producing patient-derived xenograft tumors from mice. Genome-wide methylome analysis of cells chronically treated with NO (10 days) shows enrichment of 5mC and 5hmC at gene-regulatory loci, correlating with altered expression of NO-regulated tumor-associated genes. Regulation of DNA methylation is distinctly different from canonical NO signaling and represents a unique epigenetic role for NO.

Interactions between ADGRF1 (GPR110) and extracellular matrix proteins govern its effects on tumorigenesis in HER2-positive breast cancer

BACKGROUND AND PURPOSE

We and others have previously shown that ADGRF1, an adhesion G protein-coupled receptor, is overexpressed and associated with poor survival in many cancers, including human epidermal growth factor receptor-2 (HER2) breast cancer (BC). Also, we have reported the tumour-promoting function of ADGRF1 using preclinical models of HER2+ BC. In this study, we investigated the effect of ADGRF1 overexpression in an orthotopic in vivo model as well as downstream signalling of ADGRF1 in HER2+ BC.

EXPERIMENTAL APPROACH

We utilized a doxycycline (Dox)-induced ADGRF1 overexpression system in HER2+ BC cell lines and performed various in vitro and in vivo studies. Following ADGRF1 overexpression in the presence/absence of Matrigel, laminin-111 or collagen-IV, we performed the mammosphere assay to assess the tumorigenicity of breast epithelial cells, as well as cAMP/IP1 assays and RNA-sequencing, to understand the receptor function and pharmacology. We conducted cross-linking-aided immunoprecipitation and mass spectrometry to confirm the physical interaction between ADGRF1 and the extracellular matrix proteins present in Matrigel.

KEY RESULTS

We found that ADGRF1 switched from a tumour-promoting to tumour-suppressive function upon interaction with laminin-111. Interaction of ADGRF1 with laminin-111 resulted in inhibition of Gαs coupling and STAT3 phosphorylation, induction of senescence, increase in HER2 expression, and improvement of sensitivity to anti-HER2 drugs in HER2+ BC.

CONCLUSIONS

ADGRF1 switches from a tumour-promoting to tumour-suppressive function upon interaction with laminin-111, leading to improvements in sensitivity to anti-HER2 drugs. Leveraging ADGRF1 interactions with laminin-111 may allow the design of novel therapies against ADGRF1 in HER2+ BC.

Modeling Drug Responses and Evolutionary Dynamics Using Patient-Derived Xenografts Reveals Precision Medicine Strategies for Triple-Negative Breast Cancer

The intertumor and intratumor heterogeneity of triple-negative breast cancers, which is reflected in diverse drug responses, interplays with tumor evolution. In this study, we developed a preclinical experimental and analytical framework using patient-derived tumor xenografts (PDTX) from patients with treatment-naïve triple-negative breast cancers to test their predictive value in personalized cancer treatment approaches. Patients and their matched PDTXs exhibited concordant drug responses to neoadjuvant therapy using two trial designs and dosing schedules. This platform enabled analysis of nongenetic mechanisms involved in relapse dynamics. Treatment resulted in permanent phenotypic changes, with functional and therapeutic consequences. High-throughput drug screening methods in ex vivo PDTX cells revealed patient-specific drug response changes dependent on first-line therapy. This was validated in vivo, as exemplified by a change in olaparib sensitivity in tumors previously treated with clinically relevant cycles of standard-of-care chemotherapy. In summary, PDTXs provide a robust tool to test patient drug responses and therapeutic regimens and to model evolutionary trajectories. However, high intermodel variability and permanent nongenomic transcriptional changes constrain their use for personalized cancer therapy. This work highlights important considerations associated with preclinical drug response modeling and potential uses of the platform to identify efficacious and preferential sequential therapeutic regimens. Significance: Patient-derived tumor xenografts from treatment-naïve breast cancer samples can predict patient drug responses and model treatment-induced phenotypic and functional evolution, making them valuable preclinical tools.

Steroid hormone receptors, exome sequencing and treatment responsiveness of breast cancer patient-derived xenografts originated in a South American country

Breast cancer (BC) patient-derived xenografts (PDX) are relevant models for precision medicine. However, there are no collections derived from South American BC patients. Since ethnicity significantly impacts clinical outcomes, it is necessary to develop PDX models from different lineages. Our goals were to a) develop BC PDX from our population; b) characterize the expression of estrogen (ER), progesterone (PR), androgen (AR) and glucocorticoid (GR) receptors, basal and luminal cytokeratins, EGFR and HER2; c) identify PDX mutations; d) evaluate the response to treatments selected based on their biological and genetic features, and e) perform BC tissue cultures (BCTC) from PDX tissues and compare in vivo and ex vivo results. Surgical fragments were maintained in a culture medium and inoculated subcutaneously into untreated NSG female mice, or treated with estradiol pellets. Other fragments were fixed in formalin for diagnosis and immunohistochemistry, and a third piece was frozen at -80°C for molecular studies or whole exome-sequencing. Tumors were serially transplanted into NSG mice. Once the PDX was established, in vivo and ex vivo drug responses were evaluated. Eight PDX were established: two ER + [BC-AR685 (PR +) and BC-AR707 (PR-)], one from a triple-negative (TN) recurrence whose primary tumor was ER + (BC-AR485), one HER2 + (BC-AR474) and four TN primary tumors (BC-AR553, BC-AR546, BC-AR631 and BC-AR687). BC-AR685 had higher levels of PR isoform A than isoform B and was sensitive to mifepristone, tamoxifen, and palbociclib. BC-AR707 was inhibited by tamoxifen and testosterone. BC-AR474 was inhibited by trastuzumab and trastuzumab emtansine. BC-AR485 was sensitive to doxorubicin and resistant to paclitaxel in vivo and ex vivo. BC-AR687 carried a PIK3CA (C420R) mutation and was sensitive to alpelisib and mTOR inhibitors. All PDX expressed AR with varying intensities. GR and AR were co-expressed in the ER + tumors and in 3 TN PDX. We report the first PDX originated from South American countries that were genetically and biologically characterized and may be used in precision medicine studies. PDX expressing AR and/or GR are powerful tools to evaluate different endocrine treatment combinations even in TN tumors.

Optimizing Xenograft Models for Breast Cancer: A Comparative Analysis of Cell-Derived and Patient-Derived Implantation Techniques in Pre-Clinical Research

Purpose

The high mortality rate of breast cancer motivates researchers to search for effective treatments. Due to their ability to simulate human conditions, xenograft models such as CDX (Cell line-Derived Xenografts) and PDX (Patient-Derived Xenografts) have gained popularity in pre-clinical research. The choice of xenograft technique is influenced by the type of tumor employed, particularly in more aggressive tumor models like TNBC with metastases. Subcutaneous or orthotopic implantation may influence tumor engraftment rates and the applicability of the models for drug testing. To optimize xenograft models and support the development of breast cancer drugs, selecting a suitable transplantation technique is essential to attaining the best results.

Methods

This scoping review used PRISMA-Scr methodology to summarize findings from eleven articles published between 2012 and 2024 on pre-clinical trials related to xenograft models for breast cancer considering PDX began traction after 2010. Using specific criteria, the review included studies from electronic platforms. The inclusion criteria ensured relevant English sources were available in full text, while the exclusion criteria eliminated certain types of articles and inadequately comprehensive studies.

Results

Subcutaneous and orthotopic implantation are critical methods for xenograft models in cancer research. Subcutaneous implantation is less invasive and more manageable but does not fully mimic the tumor's natural environment. Orthotopic implantation accurately mimic the migration, invasion, and molecular characteristics of the original tumor, although the procedure is more complex and requires specialized techniques. The specific research objectives determine their choice, the need for accurate tumor replication, and the testing convenience.

Conclusion

Orthotopic implantation is the preferable method for developing PDX and CDX models of breast cancer because it closely mimics the tumor microenvironment and metastatic behavior, yielding clinically relevant results for drug testing. Subcutaneous implantation may result in higher engraftment rates, but it cannot accurately represent the complexity of tumors.

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

Combination chemotherapy remains essential for clinical management of triple-negative breast cancer (TNBC). Consequently, responses to multiple single agents cannot be 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. Combination responses were usually no better than the best single agent, with enhanced response in only ~13% of PDX, and apparent antagonism in a comparable percentage. Single-omic comparisons showed largely non-overlapping results between genes associated with single agent and combination treatments that could be validated in independent patient cohorts. Multi-omic analyses of PDXs identified agent-specific biomarkers/biomarker combinations, nominating high Cytokeratin-5 (KRT5) as a general marker of responsiveness. Notably, integrating proteomic with transcriptomic data improved predictive modeling of pathologic complete response to combination chemotherapy. PDXs refractory to all treatments were enriched for signatures of dysregulated mitochondrial function. Targeting this process indirectly in a PDX with HDAC inhibition plus chemotherapy in vivo overcomes chemoresistance. These results suggest possible resistance mechanisms and therapeutic strategies in TNBC to overcome chemoresistance, and potentially allow optimization of chemotherapeutic regimens.

Patient-Derived Xenografts of Breast Cancer

Patient-derived xenografts (PDX) of breast cancer, obtained from the engraftment of tumour samples into immunodeficient mice, are the most effective preclinical models for studying the biology of human breast cancer and for the evaluation of new anti-cancer treatments. Notably, breast cancer PDX preserve the phenotypic and molecular characteristics of the donor tumours and reproduce the diversity of breast cancer. This preservation of breast cancer biology involves a number of different aspects, including tumour architecture and morphology, patterns of genomic alterations and gene expression, mutational status, and intra-tumour heterogeneity. For these reasons, these models have a strong predictive value in the translation of cancer therapeutics into clinical settings and can be considered as powerful and clinically relevant research tools for the identification of new treatments, mechanisms of drug resistance, and predictive biomarkers. PDX models have also been successfully used to analyse breast cancer metastasis and persister cancer cells surviving chemotherapy. Limitations of breast cancer PDX include the lack of a human immune system and the low take rate, especially for estrogen receptor (ER) and HER2-positive subtypes.

Co-Inhibition of tGLI1 and GP130 Using FDA-Approved Ketoconazole and Bazedoxifene Is Synergistic Against the Growth and Metastasis of HER2-Enriched and Triple-Negative Breast Cancers

Breast cancer stem cells (CSCs) are resistant to most cancer therapeutics and contribute to tumor recurrence and metastasis. Two breast CSC-promoting transcription factors, truncated glioma-associated oncogene homolog 1 (tGLI1) and signal transducer and activator of transcription 3 (STAT3), have been reported to be frequently co-expressed in HER2-enriched breast cancer and triple-negative breast cancer (TNBC), undergo protein-protein interactions for gene regulation and activation, and functionally cooperate to promote breast CSCs. STAT3 can be activated by activated interleukin-6 receptor/glycoprotein-130 (IL-6R/GP130). Co-targeting of tGLI1 and IL-6R/GP130 has not been investigated in breast cancer or any tumor type. Here, we report that tGLI1 and GP130 are co-overexpressed in the majority of HER2-enriched breast cancers and TNBCs at 53.8% and 44.4%, respectively. tGLI1+IL-6/IL-6R/GP130 signaling is frequently co-enriched and co-activated in HER2-enriched breast cancer and TNBC when compared to luminal subtypes. tGLI1+GP130 co-overexpression strongly promotes CSCs of HER2-enriched breast cancer and TNBC. FDA-approved tGLI1 inhibitor Ketoconazole and GP130 inhibitor Bazedoxifene synergize against breast cancer proliferation and CSC phenotypes in vitro and reduce TNBC tumor growth and metastatic burden in vivo. Our study demonstrates, for the first time, that co-targeting tGLI1 and IL-6R/GP130/STAT3 signaling pathways is synergistic against HER2-enriched breast cancer and TNBC, warranting future clinical investigations.

IRE1α silences dsRNA to prevent taxane-induced pyroptosis in triple-negative breast cancer

Chemotherapy is often combined with immune checkpoint inhibitor (ICIs) to enhance immunotherapy responses. Despite the approval of chemo-immunotherapy in multiple human cancers, many immunologically cold tumors remain unresponsive. The mechanisms determining the immunogenicity of chemotherapy are elusive. Here, we identify the ER stress sensor IRE1α as a critical checkpoint that restricts the immunostimulatory effects of taxane chemotherapy and prevents the innate immune recognition of immunologically cold triple-negative breast cancer (TNBC). IRE1α RNase silences taxane-induced double-stranded RNA (dsRNA) through regulated IRE1-dependent decay (RIDD) to prevent NLRP3 inflammasome-dependent pyroptosis. Inhibition of IRE1α in Trp53-/- TNBC allows taxane to induce extensive dsRNAs that are sensed by ZBP1, which in turn activates NLRP3-GSDMD-mediated pyroptosis. Consequently, IRE1α RNase inhibitor plus taxane converts PD-L1-negative, ICI-unresponsive TNBC tumors into PD-L1high immunogenic tumors that are hyper-sensitive to ICI. We reveal IRE1α as a cancer cell defense mechanism that prevents taxane-induced danger signal accumulation and pyroptotic cell death.

An annotated biobank of triple negative breast cancer patient-derived xenografts featuring treatment-naïve and longitudinal samples throughout neoadjuvant chemotherapy

Triple negative breast cancer (TNBC) that fails to respond to neoadjuvant chemotherapy (NACT) can be lethal. Developing effective strategies to eradicate chemoresistant disease requires experimental models that recapitulate the heterogeneity characteristic of TNBC. To that end, we established a biobank of 92 orthotopic patient-derived xenograft (PDX) models of TNBC from the tumors of 75 patients enrolled in the ARTEMIS clinical trial ( NCT02276443 ) at MD Anderson Cancer Center, including 12 longitudinal sets generated from serial patient biopsies collected throughout NACT and from metastatic disease. Models were established from both chemosensitive and chemoresistant tumors, and nearly 30% of PDX models were capable of lung metastasis. Comprehensive molecular profiling demonstrated conservation of genomes and transcriptomes between patient and corresponding PDX tumors, with representation of all major transcriptional subtypes. Transcriptional changes observed in the longitudinal PDX models highlight dysregulation in pathways associated with DNA integrity, extracellular matrix interactions, the ubiquitin-proteasome system, epigenetics, and inflammatory signaling. These alterations reveal a complex network of adaptations associated with chemoresistance. This PDX biobank provides a valuable resource for tackling the most pressing issues facing the clinical management of TNBC, namely chemoresistance and metastasis.

ALDH1A3 is the switch that determines the balance of ALDH+ and CD24-CD44+ cancer stem cells, EMT-MET, and glucose metabolism in breast cancer

Plasticity is an inherent feature of cancer stem cells (CSCs) and regulates the balance of key processes required at different stages of breast cancer progression, including epithelial-to-mesenchymal transition (EMT) versus mesenchymal-to-epithelial transition (MET), and glycolysis versus oxidative phosphorylation. Understanding the key factors that regulate the switch between these processes could lead to novel therapeutic strategies that limit tumor progression. We found that aldehyde dehydrogenase 1A3 (ALDH1A3) regulates these cancer-promoting processes and the abundance of the two distinct breast CSC populations defined by high ALDH activity and CD24-CD44+ cell surface expression. While ALDH1A3 increases ALDH+ breast cancer cells, it inversely suppresses the CD24-CD44+ population by retinoic acid signaling-mediated gene expression changes. This switch in CSC populations induced by ALDH1A3 was paired with decreased migration but increased invasion and an intermediate EMT phenotype. We also demonstrate that ALDH1A3 increases oxidative phosphorylation and decreases glycolysis and reactive oxygen species (ROS). The effects of ALDH1A3 reduction were countered with the glycolysis inhibitor 2-deoxy-D-glucose (2DG). In cell culture and tumor xenograft models, 2DG suppresses the increase in the CD24-CD44+ population and ROS induced by ALDH1A3 knockdown. Combined inhibition of ALDH1A3 and glycolysis best reduces breast tumor growth and tumor-initiating cells, suggesting that the combination of targeting ALDH1A3 and glycolysis has therapeutic potential for limiting CSCs and tumor progression. Together, these findings identify ALDH1A3 as a key regulator of processes required for breast cancer progression and depletion of ALDH1A3 makes breast cancer cells more susceptible to glycolysis inhibition.

Single-cell analysis of breast cancer metastasis reveals epithelial-mesenchymal plasticity signatures associated with poor outcomes

Metastasis is the leading cause of cancer-related deaths. It is unclear how intratumor heterogeneity (ITH) contributes to metastasis and how metastatic cells adapt to distant tissue environments. The study of these adaptations is challenged by the limited access to patient material and a lack of experimental models that appropriately recapitulate ITH. To investigate metastatic cell adaptations and the contribution of ITH to metastasis, we analyzed single-cell transcriptomes of matched primary tumors and metastases from patient-derived xenograft models of breast cancer. We found profound transcriptional differences between the primary tumor and metastatic cells. Primary tumors upregulated several metabolic genes, whereas motility pathway genes were upregulated in micrometastases, and stress response signaling was upregulated during progression. Additionally, we identified primary tumor gene signatures that were associated with increased metastatic potential and correlated with patient outcomes. Immune-regulatory control pathways were enriched in poorly metastatic primary tumors, whereas genes involved in epithelial-mesenchymal transition were upregulated in highly metastatic tumors. We found that ITH was dominated by epithelial-mesenchymal plasticity (EMP), which presented as a dynamic continuum with intermediate EMP cell states characterized by specific genes such as CRYAB and S100A2. Elevated expression of an intermediate EMP signature correlated with worse patient outcomes. Our findings identified inhibition of the intermediate EMP cell state as a potential therapeutic target to block metastasis.

Leveraging preclinical models of metastatic breast cancer

Women that present to the clinic with established breast cancer metastases have limited treatment options. Yet, the majority of preclinical studies are actually not directed at developing treatment regimens for established metastatic disease. In this review we will discuss the current state of preclinical macro-metastatic breast cancer models, including, but not limited to syngeneic GEMM, PDX and xenografts. Challenges within these models which are often overlooked include fluorophore-immunogenic neoantigens, differences in experimental vs spontaneous metastasis and tumor heterogeneity. Furthermore, due to cell plasticity in the tumor immune microenvironment (TIME) of the metastatic landscape, the treatment efficacy of newly approved immune checkpoint blockade (ICB) may differ in metastatic sites as compared to primary localized tumors.

Predicting Tumor Volume Doubling Time and Progression-Free Survival in Untreated Patients from Patient-Derived-Xenograft (PDX) Models: A Translational Model-Based Approach

Tumor volume doubling time (TVDT) has been shown to be a potential surrogate marker of biological tumor activity. However, its availability in clinics is strongly limited due to ethical and practical reasons, as its assessment requires at least two subsequent tumor volume measurements in untreated patients. Here, a translational modeling framework to predict TVDT distributions in untreated cancer patient populations from tumor growth data in patient-derived xenograft (PDX) mice is proposed. Eleven solid cancer types were considered. For each of them, a set of tumor growth studies in PDX mice was selected and analyzed through a mathematical model to characterize the distribution of the exponential tumor growth rate in mice. Then, assuming an exponential growth of the tumor mass in humans, the growth rates were scaled from PDX mice to humans through an allometric scaling approach and used to predict TVDTs in untreated patients. A very good agreement was found between model predicted and clinically observed TVDTs, with 91% of the predicted TVDT medians fell within 1.5-fold of observations. Further, exploiting the intrinsic relationship between tumor growth dynamics and progression free survival (PFS), the exponential growth rates in humans were used to generate the expected PFS curves in absence of anticancer treatment. Predicted curves were extremely close to published PFS data from studies involving patient cohorts treated with supportive care or low effective therapies. The proposed approach shows promise as a potential tool to increase knowledge about TVDT in humans without the need of directly measuring tumor dimensions in untreated patients, and to predict PFS curves in untreated patients, that could fill the absence of placebo-controlled arms against which to compare treaded arms during clinical trials. However, further validation and refinement are needed to fully assess its effectiveness in this regard.

Patient-Derived Organoids as Therapy Screening Platforms in Cancer Patients

Patient-derived organoids (PDOs) developed ex vivo and in vitro are increasingly used for therapeutic screening. They provide a more physiologically relevant model for drug discovery and development compared to traditional cell lines. However, several challenges remain to be addressed to fully realize the potential of PDOs in therapeutic screening. This paper summarizes recent advancements in PDO development and the enhancement of PDO culture models. This is achieved by leveraging materials engineering and microfabrication technologies, including organs-on-a-chip and droplet microfluidics. Additionally, this work discusses the application of PDOs in therapy screening to meet diverse requirements and overcome bottlenecks in cancer treatment. Furthermore, this work introduces tools for data processing and analysis of organoids, along with their microenvironment. These tools aim to achieve enhanced readouts. Finally, this work explores the challenges and future perspectives of using PDOs in drug development and personalized screening for cancer patients.

Anti-EGFR Antibody-Drug Conjugate Carrying an Inhibitor Targeting CDK Restricts Triple-Negative Breast Cancer Growth

PURPOSE

Anti-EGFR antibodies show limited response in breast cancer, partly due to activation of compensatory pathways. Furthermore, despite the clinical success of cyclin-dependent kinase (CDK) 4/6 inhibitors in hormone receptor-positive tumors, aggressive triple-negative breast cancers (TNBC) are largely resistant due to CDK2/cyclin E expression, whereas free CDK2 inhibitors display normal tissue toxicity, limiting their therapeutic application. A cetuximab-based antibody drug conjugate (ADC) carrying a CDK inhibitor selected based on oncogene dysregulation, alongside patient subgroup stratification, may provide EGFR-targeted delivery.

EXPERIMENTAL DESIGN

Expressions of G1/S-phase cell cycle regulators were evaluated alongside EGFR in breast cancer. We conjugated cetuximab with CDK inhibitor SNS-032, for specific delivery to EGFR-expressing cells. We assessed ADC internalization and its antitumor functions in vitro and in orthotopically grown basal-like/TNBC xenografts.

RESULTS

Transcriptomic (6,173 primary, 27 baseline, and matched post-chemotherapy residual tumors), single-cell RNA sequencing (150,290 cells, 27 treatment-naïve tumors), and spatial transcriptomic (43 tumor sections, 22 TNBCs) analyses confirmed expression of CDK2 and its cyclin partners in basal-like/TNBCs, associated with EGFR. Spatiotemporal live-cell imaging and super-resolution confocal microscopy demonstrated ADC colocalization with late lysosomal clusters. The ADC inhibited cell cycle progression, induced cytotoxicity against high EGFR-expressing tumor cells, and bystander killing of neighboring EGFR-low tumor cells, but minimal effects on immune cells. Despite carrying a small molar fraction (1.65%) of the SNS-032 inhibitor, the ADC restricted EGFR-expressing spheroid and cell line/patient-derived xenograft tumor growth.

CONCLUSIONS

Exploiting EGFR overexpression, and dysregulated cell cycle in aggressive and treatment-refractory tumors, a cetuximab-CDK inhibitor ADC may provide selective and efficacious delivery of cell cycle-targeted agents to basal-like/TNBCs, including chemotherapy-resistant residual disease.

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. In this study, we developed an extensive, pan-cancer repository of >1,000 PDX and paired parental tumor H&E images. These images, curated from the PDX Development and Trial Centers Research Network Consortium, had a range of associated genomic and transcriptomic data, clinical metadata, pathologic assessments of cell composition, and, in several cases, detailed pathologic annotations of neoplastic, stromal, and necrotic regions. The amenability of these images to deep learning was highlighted through three applications: (i) development of a classifier for neoplastic, stromal, and necrotic regions; (ii) development of a predictor of xenograft-transplant lymphoproliferative disorder; and (iii) application of a published predictor of microsatellite instability. Together, this PDX Development and Trial Centers Research Network image repository provides a valuable resource for controlled digital pathology analysis, both for the evaluation of technical issues and for the development of computational image-based methods that make clinical predictions based on PDX treatment studies. Significance: A pan-cancer repository of >1,000 patient-derived xenograft hematoxylin and eosin-stained images will facilitate cancer biology investigations through histopathologic analysis and contributes important model system data that expand existing human histology repositories.

Patient-Derived-Xenografts in Mice: A Preclinical Platform for Cancer Research

The use of patient-derived xenografts (PDXs) has dramatically improved drug development programs. PDXs (1) reproduce the pathological features and the genomic profile of the parental tumors more precisely than other preclinical models, and (2) more faithfully predict therapy response. However, PDXs have limitations. These include the inability to completely capture tumor heterogeneity and the role of the immune system, the low engraftment efficiency of certain tumor types, and the consequences of the human-host interactions. Recently, the use of novel mouse strains and specialized engraftment techniques has enabled the generation of "humanized" PDXs, partially overcoming such limitations. Importantly, establishing, characterizing, and maintaining PDXs is costly and requires a significant regulatory, administrative, clinical, and laboratory infrastructure. In this review, we will retrace the historical milestones that led to the implementation of PDXs for cancer research, review the most recent innovations in the field, and discuss future avenues to tackle deficiencies that still exist.

Mutant RAS-driven Secretome Causes Skeletal Muscle Defects in Breast Cancer

Cancer-induced skeletal muscle defects differ in severity between individuals with the same cancer type. Cancer subtype-specific genomic aberrations are suggested to mediate these differences, but experimental validation studies are very limited. We utilized three different breast cancer patient-derived xenograft (PDX) models to correlate cancer subtype with skeletal muscle defects. PDXs were derived from brain metastasis of triple-negative breast cancer (TNBC), estrogen receptor-positive/progesterone receptor-positive (ER+/PR+) primary breast cancer from a BRCA2-mutation carrier, and pleural effusion from an ER+/PR- breast cancer. While impaired skeletal muscle function as measured through rotarod performance and reduced levels of circulating and/or skeletal muscle miR-486 were common across all three PDXs, only TNBC-derived PDX activated phospho-p38 in skeletal muscle. To further extend these results, we generated transformed variants of human primary breast epithelial cells from healthy donors using HRASG12V or PIK3CAH1047R mutant oncogenes. Mutations in RAS oncogene or its modulators are found in approximately 37% of metastatic breast cancers, which is often associated with skeletal muscle defects. Although cells transformed with both oncogenes generated adenocarcinomas in NSG mice, only HRASG12V-derived tumors caused skeletal muscle defects affecting rotarod performance, skeletal muscle contraction force, and miR-486, Pax7, pAKT, and p53 levels in skeletal muscle. Circulating levels of the chemokine CXCL1 were elevated only in animals with tumors containing HRASG12V mutation. Because RAS pathway aberrations are found in 19% of cancers, evaluating skeletal muscle defects in the context of genomic aberrations in cancers, particularly RAS pathway mutations, may accelerate development of therapeutic modalities to overcome cancer-induced systemic effects.

SIGNIFICANCE

Mutant RAS- and PIK3CA-driven breast cancers distinctly affect the function of skeletal muscle. Therefore, research and therapeutic targeting of cancer-induced systemic effects need to take aberrant cancer genome into consideration.

The Power and Promise of Patient-Derived Xenografts of Human Breast Cancer

In 2016, a group of researchers engaged in the development of patient-derived xenografts (PDXs) of human breast cancer provided a comprehensive review of the state of the field. In that review, they summarized the clinical problem that PDXs might address, the technical approaches to their generation (including a discussion of host animals and transplant conditions tested), and presented transplantation success (take) rates across groups and across transplantation conditions. At the time, there were just over 500 unique PDX models created by these investigators representing all three clinically defined subtypes (ER+, HER2+, and TNBC). Today, many of these PDX resources have at least doubled in size, and several more PDX development groups now exist, such that there may be well upward of 1000 PDX models of human breast cancer in existence worldwide. They also presented a series of open questions for the field. Many of these questions have been addressed. However, several remain open, or only partially addressed. Herein, we revisit these questions, and recount the progress that has been made in a number of areas with respect to generation, characterization, and use of PDXs in translational research, and re-present questions that remain open. These open questions, and others, are now being addressed not only by individual investigators, but also large, well-funded consortia including the PDXNet program of the National Cancer Institute in the United States, and the EuroPDX Consortium, an organization of PDX developers across Europe. Finally, we discuss the new opportunities in PDX-based research.

Factors associated with engraftment success of patient-derived xenografts of breast cancer

BACKGROUND

Patient-derived xenograft (PDX) models serve as a valuable tool for the preclinical evaluation of novel therapies. They closely replicate the genetic, phenotypic, and histopathological characteristics of primary breast tumors. Despite their promise, the rate of successful PDX engraftment is various in the literature. This study aimed to identify the key factors associated with successful PDX engraftment of primary breast cancer.

METHODS

We integrated clinicopathological data with morphological attributes quantified using a trained artificial intelligence (AI) model to identify the principal factors affecting PDX engraftment.

RESULTS

Multivariate logistic regression analyses demonstrated that several factors, including a high Ki-67 labeling index (Ki-67LI) (p < 0.001), younger age at diagnosis (p = 0.032), post neoadjuvant chemotherapy (NAC) (p = 0.006), higher histologic grade (p = 0.039), larger tumor size (p = 0.029), and AI-assessed higher intratumoral necrosis (p = 0.027) and intratumoral invasive carcinoma (p = 0.040) proportions, were significant factors for successful PDX engraftment (area under the curve [AUC] 0.905). In the NAC group, a higher Ki-67LI (p < 0.001), lower Miller-Payne grade (p < 0.001), and reduced proportion of intratumoral normal breast glands as assessed by AI (p = 0.06) collectively provided excellent prediction accuracy for successful PDX engraftment (AUC 0.89).

CONCLUSIONS

We found that high Ki-67LI, younger age, post-NAC status, higher histologic grade, larger tumor size, and specific morphological attributes were significant factors for predicting successful PDX engraftment of primary breast cancer.

The impact of erythropoiesis-stimulating agents administration concomitantly with adjuvant anti-HER2 treatments on the outcomes of patients with early breast cancer: a sub-analysis of the ALTTO study

PURPOSE

To assess whether erythropoiesis-stimulating agents (ESA) administration impacts the outcomes of patients with HER2-positive early breast cancer (EBC).

METHODS

ALTTO (NCT00490139) patients were categorized by ESA use during adjuvant anti-HER2 treatment. Disease-free-survival (DFS), overall survival (OS), and time-to-distant recurrence (TTDR) were analyzed by ESA administration, with subgroup analyses according to prognostic factors. Log-rank tests and Cox modeling were performed. Adverse events (AEs) of ESA-interest were compared.

RESULTS

Among 8381 patients recruited in ALTTO, 123 (1.5%) received ESA concomitantly with study treatment. The median age of patients receiving ESA was 54 years, 39.0% premenopausal, most had tumor size > 2 cm (56.9%), node-positive (58.5%), and positive estrogen receptor expression (61.8%). Median follow-up was shorter in the ESA group [6.1 years (IQR 5.3-7.0) vs. 6.9 years (6.0-7.1); p < 0.001]. There was no DFS difference by ESA administration (log-rank p = 0.70), with 3- and 7-year DFS of 89.2% (95% CI 81.8-93.8%) and 81.6% (71.4-88.5%) in ESA group vs. 88.3% (87.6-89.0%) and 80.0% (79.1-80.9%) in No-ESA group. In subgroup analyses, the interaction of ESA administration with menopausal status was statistically significant (unadjusted p = 0.024; stratified p = 0.033), favoring premenopausal women receiving ESA. We observed no significant association of ESA administration with OS (log-rank p = 0.57; 7-year OS in ESA 88.6% vs. 90.0% in non-ESA) or TTDR. ESA-interest AEs were experienced by eight (6.5%) patients receiving ESA and 417 (5.1%) in the No-ESA group (p = 0.41).

CONCLUSION

ESA administration to patients receiving adjuvant anti-HER2 treatment for HER2-positive EBC was safe and not associated with a negative impact on survival outcomes.

Development of Orthotopic Patient-Derived Xenograft Models of Pediatric Intracranial Tumors

The development of clinically relevant and reliable models of central nervous system tumors has been instrumental in advancing the field of Neuro-Oncology. The orthotopic intracranial injection is widely used to study the growth, invasion, and spread of tumors in a controlled environment. Orthotopic models are performed to examine tumor cells isolated from a specific region in a patient in the same site or location in an animal model. Orthotopic brain tumor models are also utilized for preclinical testing of therapeutics as they closely recapitulate the behavior of such cancer and the brain environment of patients. Below, we describe our experiences in the development of murine models of pediatric brain tumors including diffuse midline glioma (DMG), glioblastoma (GBM), and medulloblastoma. The method provides an overview of intracranial stereotactic injections in mice.

Generation of Orthotopic and Subcutaneous Patient-Derived Xenograft Models from Diverse Clinical Tissue Samples of Pediatric Extracranial Solid Tumors

Realistic and renewable laboratory models that accurately reflect the distinct clinical features of childhood cancers have enormous potential to speed research progress. These models help us to understand disease biology, develop new research methods, advance new therapies to clinical trial, and implement personalized medicine. This chapter describes methods to generate patient-derived xenograft models of neuroblastoma and rhabdomyosarcoma, two tumor types for which children with high-risk disease have abysmal survival outcomes and survivors have lifelong-debilitating effects from treatment. Further, this protocol addresses model development from diverse clinical tumor tissue samples, subcutaneous and orthotopic engraftment, and approaches to avoid model loss.

Engineering CD276/B7-H3-targeted antibody-drug conjugates with enhanced cancer-eradicating capability

CD276/B7-H3 represents a promising target for cancer therapy based on widespread overexpression in both cancer cells and tumor-associated stroma. In previous preclinical studies, CD276 antibody-drug conjugates (ADCs) exploiting a talirine-type pyrrolobenzodiazepine (PBD) payload showed potent activity against various solid tumors but with a narrow therapeutic index and dosing regimen higher than that tolerated in clinical trials using other antibody-talirine conjugates. Here, we describe the development of a modified talirine PBD-based fully human CD276 ADC, called m276-SL-PBD, that is cross-species (human/mouse) reactive and can eradicate large 500-1,000-mm3 triple-negative breast cancer xenografts at doses 10- to 40-fold lower than the maximum tolerated dose. By combining CD276 targeting with judicious genetic and chemical ADC engineering, improved ADC purification, and payload sensitivity screening, these studies demonstrate that the therapeutic index of ADCs can be substantially increased, providing an advanced ADC development platform for potent and selective targeting of multiple solid tumor types.

SIRT6 promotes metastasis and relapse in HER2-positive breast cancer

The histone deacetylase sirtuin 6 (SIRT6) has been endowed with anti-cancer capabilities in many tumor types. Here, we investigate the impact of SIRT6-overexpression (SIRT6-OE) in Delta16HER2 mice, which are a bona fide model of HER2-positive breast cancer. After an initial delay in the tumor onset, SIRT6-OE induces a more aggressive phenotype of Delta16HER2 tumors promoting the formation of higher number of tumor foci and metastases than controls. This phenotype of SIRT6-OE tumors is associated with cancer stem cell (CSC)-like features and tumor dormancy, and low senescence and oxidative DNA damage. Accordingly, a sub-set of HER2-positive breast cancer patients with concurrent SIRT6-OE has a significant poorer relapse-free survival (RFS) probability than patients with low expression of SIRT6. ChIP-seq, RNA-seq and RT-PCR experiments indicate that SIRT6-OE represses the expression of the T-box transcription factor 3 (Tbx3) by deacetylation of H3K9ac. Accordingly, loss-of-function mutations of TBX3 or low TBX3 expression levels are predictive of poor prognosis in HER2-positive breast cancer patients. Our work indicates that high levels of SIRT6 are indicative of poor prognosis and high risk of metastasis in HER2-positive breast cancer and suggests further investigation of TBX3 as a downstream target of SIRT6 and co-marker of poor-prognosis. Our results point to a breast cancer subtype-specific effect of SIRT6 and warrant future studies dissecting the mechanisms of SIRT6 regulation in different breast cancer subtypes.

Patient Characteristics Associated with Growth of Patient-Derived Tumor Implants in Mice (Patient-Derived Xenografts)

Background: patient-derived xenografts (PDXs) have defined the field of translational cancer research in recent years, becoming one of the most-used tools in early drug development. The process of establishing cancer models in mice has turned out to be challenging, since little research focuses on evaluating which factors impact engraftment success. We sought to determine the clinical, pathological, or molecular factors which may predict better engraftment rates in PDXs. Methods: between March 2017 and January 2021, tumor samples obtained from patients with primary or metastatic cancer were implanted into athymic nude mice. A full comprehensive evaluation of baseline factors associated with the patients and patients' tumors was performed, with the goal of potentially identifying predictive markers of engraftment. We focused on clinical (patient factors) pathological (patients' tumor samples) and molecular (patients' tumor samples) characteristics, analyzed either by immunohistochemistry (IHC) or next-generation sequencing (NGS), which were associated with the likelihood of final engraftment, as well as with tumor growth rates in xenografts. Results: a total of 585 tumor samples were collected and implanted. Twenty-one failed to engraft, due to lack of malignant cells. Of 564 tumor-positive samples, 187 (33.2%) grew at time of analysis. The study was able to find correlation and predictive value for engraftment for the following: the use of systemic antibiotics by the patient within 2 weeks of sampling (38.1% (72/189) antibiotics- group vs. 30.7% (115/375) no-antibiotics) (p = 0.048), and the administration of systemic steroids to the patients within 2 weeks of sampling (41.5% (34/48) steroids vs. 31.7% (153/329), no-steroids) (p = 0.049). Regarding patient's baseline tests, we found certain markers could help predict final engraftment success: for lactate dehydrogenase (LDH) levels, 34.1% (140/411) of tumors derived from patients with baseline blood LDH levels above the upper limit of normality (ULN) achieved growth, against 30.7% (47/153) with normal LDH (p = 0.047). Histological tumor characteristics, such as grade of differentiation, were also correlated. Grade 1: 25.4% (47/187), grade 2: 34.8% (65/187) and grade 3: 40.1% (75/187) tumors achieved successful growth (p = 0.043), suggesting the higher the grade, the higher the likelihood of success. Similarly, higher ki67 levels were also correlated with better engraftment rates: low (Ki67 < 15%): 8.9% (9/45) achieved growth vs. high (Ki67 ≥ 15%): 31% (35/113) (p: 0.002). Other markers of aggressiveness such as the presence of lymphovascular invasion in tumor sample of origin was also predictive: 42.2% (97/230) with lymphovascular vs. 26.9% (90/334) of samples with no invasion (p = 0.0001). From the molecular standpoint, mismatch-repair-deficient (MMRd) tumors showed better engraftment rates: 62.1% (18/29) achieved growth vs. 40.8% (75/184) of proficient tumors (p = 0.026). A total of 84 PDX were breast models, among which 57.9% (11/19) ER-negative models grew, vs. 15.4% (10/65) of ER-positive models (p = 0.0001), also consonant with ER-negative tumors being more aggressive. BRAFmut cancers are more likely to achieve engraftment during the development of PDX models. Lastly, tumor growth rates during first passages can help establish a cutoff point for the decision-making process during PDX development, since the higher the tumor grades, the higher the likelihood of success. Conclusions: tumors with higher grade and Ki67 protein expression, lymphovascular and/or perineural invasion, with dMMR and are negative for ER expression have a higher probability of achieving growth in the process of PDX development. The use of steroids and/or antibiotics in the patient prior to sampling can also impact the likelihood of success in PDX development. Lastly, establishing a cutoff point for tumor growth rates could guide the decision-making process during PDX development.

ABCB1 Regulates Immune Genes in Breast Cancer

Background

Resistance to standard chemotherapy is a critical problem for breast cancer patients. The ATP-binding cassette (ABC) superfamily transporters actively pump out drugs and play an important role in chemoresistance. ABCB1 (ABC subfamily B, member 1, also named as multidrug resistance protein 1, MDR1) and suppressive myeloid-derived suppressor cells (MDSCs) potentially involve in chemoresistance of breast cancer. The relationship between ABCB1 and immune genes in breast cancer has not been widely studied.

Methods

Microarray and RNA sequencing data were obtained from The Cancer Genome Atlas Breast Invasive Carcinoma in Genomic Data Commons Data Portal and Gene Expression Omnibus database. A patient-derived xenograft (PDX) model of HER2+ breast cancer was established to investigate the association between ABCB1 and immune genes in breast cancer.

Results

Expression of ABCB1 increased in doxorubicin-selected MCF-7/ADR cells. High expression of ABCB1 mRNA is correlated with lymph-node metastasis and worse overall survival in patients with breast cancer. ABCB1 is positively correlated with IL6, CSF1, CSF3, and PTGS2. In the HER2+ stage IIA breast cancer PDX model, both doxorubicin and paclitaxel suppressed growth of P2 tumors. IL6, CSF1, CSF3, and PTGS2 expression were suppressed by paclitaxel but not doxorubicin. Intrasplenic MDSCs, including CD11b+Ly6G+ and CD11b+Ly6C+ cells, were more abundant than intratumor MDSCs in PDX-carrying nude mice. Clinically, the patient developed cancer recurrence after adjuvant chemotherapy with doxorubicin-based regimen and was well controlled after paclitaxel-trastuzumab combined therapy.

Conclusion

ABCB1 was a poor predictor of HER2+ LN- breast cancer. Regulation of immune genes by ABCB1 contributed to cancer recurrence and treatment effect. The PDX model was suitable for investigation the expression of target genes and expansion of immune cells.

A small-molecule inhibitor of TopBP1 exerts anti-MYC activity and synergy with PARP inhibitors

We have previously identified TopBP1 (topoisomerase IIβ-binding protein 1) as a promising target for cancer therapy, given its role in the convergence of Rb, PI(3)K/Akt, and p53 pathways. Based on this, we conducted a large-scale molecular docking screening to identify a small-molecule inhibitor that specifically targets the BRCT7/8 domains of TopBP1, which we have named 5D4. Our studies show that 5D4 inhibits TopBP1 interactions with E2F1, mutant p53, and Cancerous Inhibitor of Protein Phosphatase 2A. This leads to the activation of E2F1-mediated apoptosis and the inhibition of mutant p53 gain of function. In addition, 5D4 disrupts the interaction of TopBP1 with MIZ1, which in turn allows MIZ1 to bind to its target gene promoters and repress MYC activity. Moreover, 5D4 inhibits the association of the TopBP1-PLK1 complex and prevents the formation of Rad51 foci. When combined with inhibitors of PARP1/2 or PARP14, 5D4 synergizes to effectively block cancer cell proliferation. Our animal studies have demonstrated the antitumor activity of 5D4 in breast and ovarian cancer xenograft models. Moreover, the effectiveness of 5D4 is further enhanced when combined with a PARP1/2 inhibitor talazoparib. Taken together, our findings strongly support the potential use of TopBP1-BRCT7/8 inhibitors as a targeted cancer therapy.

Kinome Reprogramming Is a Targetable Vulnerability in ESR1 Fusion-Driven Breast Cancer

Transcriptionally active ESR1 fusions (ESR1-TAF) are a potent cause of breast cancer endocrine therapy (ET) resistance. ESR1-TAFs are not directly druggable because the C-terminal estrogen/anti-estrogen-binding domain is replaced with translocated in-frame partner gene sequences that confer constitutive transactivation. To discover alternative treatments, a mass spectrometry (MS)-based kinase inhibitor pulldown assay (KIPA) was deployed to identify druggable kinases that are upregulated by diverse ESR1-TAFs. Subsequent explorations of drug sensitivity validated RET kinase as a common therapeutic vulnerability despite remarkable ESR1-TAF C-terminal sequence and structural diversity. Organoids and xenografts from a pan-ET-resistant patient-derived xenograft model that harbors the ESR1-e6>YAP1 TAF were concordantly inhibited by the selective RET inhibitor pralsetinib to a similar extent as the CDK4/6 inhibitor palbociclib. Together, these findings provide preclinical rationale for clinical evaluation of RET inhibition for the treatment of ESR1-TAF-driven ET-resistant breast cancer.

SIGNIFICANCE

Kinome analysis of ESR1 translocated and mutated breast tumors using drug bead-based mass spectrometry followed by drug-sensitivity studies nominates RET as a therapeutic target. See related commentary by Wu and Subbiah, p. 3159.

Unlocking Translational Potential: Conditionally Reprogrammed Cells in Advancing Breast Cancer Research

Preclinical in vitro models play an important role in studying cancer cell biology and facilitating translational research, especially in the identification of drug targets and drug discovery studies. This is particularly relevant in breast cancer, where the global burden of disease is quite high based on prevalence and a relatively high rate of lethality. Predictive tools to select patients who will be responsive to invasive or morbid therapies (radiotherapy, chemotherapy, immunotherapy, and/or surgery) are relatively lacking. To be clinically relevant, a model must accurately replicate the biology and cellular heterogeneity of the primary tumor. Addressing these requirements and overcoming the limitations of most existing cancer cell lines, which are typically derived from a single clone, we have recently developed conditional reprogramming (CR) technology. The CR technology refers to a co-culture system of primary human normal or tumor cells with irradiated murine fibroblasts in the presence of a Rho-associated kinase inhibitor to allow the primary cells to acquire stem cell properties and the ability to proliferate indefinitely in vitro without any exogenous gene or viral transfection. This innovative approach fulfills many of these needs and offers an alternative that surpasses the deficiencies associated with traditional cancer cell lines. These CR cells (CRCs) can be reprogrammed to maintain a highly proliferative state and reproduce the genomic and histological characteristics of the parental tissue. Therefore, CR technology may be a clinically relevant model to test and predict drug sensitivity, conduct gene profile analysis and xenograft research, and undertake personalized medicine. This review discusses studies that have applied CR technology to conduct breast cancer research.

Establishment of a high-fidelity patient-derived xenograft model for cervical cancer enables the evaluation of patient's response to conventional and novel therapies

BACKGROUND

Recurrent or metastatic cervical cancer (r/m CC) often has poor prognosis owing to its limited treatment options. The development of novel therapeutic strategies has been hindered by the lack of preclinical models that accurately reflect the biological and genomic heterogeneity of cervical cancer (CC). Herein, we aimed to establish a large patient-derived xenograft (PDX) biobank for CC, evaluate the consistency of the biologic indicators between PDX and primary tumor tissues of patients, and explore its utility for assessing patient's response to conventional and novel therapies.

METHODS

Sixty-nine fresh CC tumor tissues were implanted directly into immunodeficient mice to establish PDX models. The concordance of the PDX models with their corresponding primary tumors (PTs) was compared based on the clinical pathological features, protein biomarker levels, and genomic features through hematoxylin & eosin staining, immunohistochemistry, and whole exome sequencing, respectively. Moreover, the clinical information of CC patients, RNA transcriptome and immune phenotyping of primary tumors were integrated to identify the potential parameters that could affect the success of xenograft engraftment. Subsequently, PDX model was evaluated for its capacity to mirror patient's response to chemotherapy. Finally, PDX model and PDX-derived organoid (PDXO) were utilized to evaluate the therapeutic efficacy of neratinib and adoptive cell therapy (ACT) combination strategy for CC patients with human epidermal growth factor receptor 2 (HER2) mutation.

RESULTS

We established a PDX biobank for CC with a success rate of 63.8% (44/69). The primary features of established PDX tumors, including clinicopathological features, the expression levels of protein biomarkers including Ki67, α-smooth muscle actin, and p16, and genomics, were highly consistent with their PTs. Furthermore, xenograft engraftment was likely influenced by the primary tumor size, the presence of follicular helper T cells and the expression of cell adhesion-related genes in primary tumor tissue. The CC derived PDX models were capable of recapitulating the patient's response to chemotherapy. In a PDX model, a novel therapeutic strategy, the combination of ACT and neratinib, was shown to effectively inhibit the growth of PDX tumors derived from CC patients with HER2-mutation.

CONCLUSIONS

We established by far the largest PDX biobank with a high engraftment rate for CC that preserves the histopathological and genetic characteristics of patient's biopsy samples, recapitulates patient's response to conventional therapy, and is capable of evaluating the efficacy of novel therapeutic modalities for CC.

PROBABILISTIC LEARNING OF TREATMENT TREES IN CANCER

Accurate identification of synergistic treatment combinations and their underlying biological mechanisms is critical across many disease domains, especially cancer. In translational oncology research, preclinical systems such as patient-derived xenografts (PDX) have emerged as a unique study design evaluating multiple treatments administered to samples from the same human tumor implanted into genetically identical mice. In this paper, we propose a novel Bayesian probabilistic tree-based framework for PDX data to investigate the hierarchical relationships between treatments by inferring treatment cluster trees, referred to as treatment trees (Rx-tree). The framework motivates a new metric of mechanistic similarity between two or more treatments accounting for inherent uncertainty in tree estimation; treatments with a high estimated similarity have potentially high mechanistic synergy. Building upon Dirichlet Diffusion Trees, we derive a closed-form marginal likelihood encoding the tree structure, which facilitates computationally efficient posterior inference via a new two-stage algorithm. Simulation studies demonstrate superior performance of the proposed method in recovering the tree structure and treatment similarities. Our analyses of a recently collated PDX dataset produce treatment similarity estimates that show a high degree of concordance with known biological mechanisms across treatments in five different cancers. More importantly, we uncover new and potentially effective combination therapies that confer synergistic regulation of specific downstream biological pathways for future clinical investigations. Our accompanying code, data, and shiny application for visualization of results are available at: https://github.com/bayesrx/RxTree.

Generation, evolution, interfering factors, applications, and challenges of patient-derived xenograft models in immunodeficient mice

Establishing appropriate preclinical models is essential for cancer research. Evidence suggests that cancer is a highly heterogeneous disease. This follows the growing use of cancer models in cancer research to avoid these differences between xenograft tumor models and patient tumors. In recent years, a patient-derived xenograft (PDX) tumor model has been actively generated and applied, which preserves both cell-cell interactions and the microenvironment of tumors by directly transplanting cancer tissue from tumors into immunodeficient mice. In addition to this, the advent of alternative hosts, such as zebrafish hosts, or in vitro models (organoids and microfluidics), has also facilitated the advancement of cancer research. However, they still have a long way to go before they become reliable models. The development of immunodeficient mice has enabled PDX to become more mature and radiate new vitality. As one of the most reliable and standard preclinical models, the PDX model in immunodeficient mice (PDX-IM) exerts important effects in drug screening, biomarker development, personalized medicine, co-clinical trials, and immunotherapy. Here, we focus on the development procedures and application of PDX-IM in detail, summarize the implications that the evolution of immunodeficient mice has brought to PDX-IM, and cover the key issues in developing PDX-IM in preclinical studies.

A convolutional neural network STIFMap reveals associations between stromal stiffness and EMT in breast cancer

Intratumor heterogeneity associates with poor patient outcome. Stromal stiffening also accompanies cancer. Whether cancers demonstrate stiffness heterogeneity, and if this is linked to tumor cell heterogeneity remains unclear. We developed a method to measure the stiffness heterogeneity in human breast tumors that quantifies the stromal stiffness each cell experiences and permits visual registration with biomarkers of tumor progression. We present Spatially Transformed Inferential Force Map (STIFMap) which exploits computer vision to precisely automate atomic force microscopy (AFM) indentation combined with a trained convolutional neural network to predict stromal elasticity with micron-resolution using collagen morphological features and ground truth AFM data. We registered high-elasticity regions within human breast tumors colocalizing with markers of mechanical activation and an epithelial-to-mesenchymal transition (EMT). The findings highlight the utility of STIFMap to assess mechanical heterogeneity of human tumors across length scales from single cells to whole tissues and implicates stromal stiffness in tumor cell heterogeneity.

A Transcriptome-Based Precision Oncology Platform for Patient-Therapy Alignment in a Diverse Set of Treatment-Resistant Malignancies

Predicting in vivo response to antineoplastics remains an elusive challenge. We performed a first-of-kind evaluation of two transcriptome-based precision cancer medicine methodologies to predict tumor sensitivity to a comprehensive repertoire of clinically relevant oncology drugs, whose mechanism of action we experimentally assessed in cognate cell lines. We enrolled patients with histologically distinct, poor-prognosis malignancies who had progressed on multiple therapies, and developed low-passage, patient-derived xenograft models that were used to validate 35 patient-specific drug predictions. Both OncoTarget, which identifies high-affinity inhibitors of individual master regulator (MR) proteins, and OncoTreat, which identifies drugs that invert the transcriptional activity of hyperconnected MR modules, produced highly significant 30-day disease control rates (68% and 91%, respectively). Moreover, of 18 OncoTreat-predicted drugs, 15 induced the predicted MR-module activity inversion in vivo. Predicted drugs significantly outperformed antineoplastic drugs selected as unpredicted controls, suggesting these methods may substantively complement existing precision cancer medicine approaches, as also illustrated by a case study.

SIGNIFICANCE

Complementary precision cancer medicine paradigms are needed to broaden the clinical benefit realized through genetic profiling and immunotherapy. In this first-in-class application, we introduce two transcriptome-based tumor-agnostic systems biology tools to predict drug response in vivo. OncoTarget and OncoTreat are scalable for the design of basket and umbrella clinical trials. This article is highlighted in the In This Issue feature, p. 1275.

Modeling the Tumor Microenvironment and Cancer Immunotherapy in Next-Generation Humanized Mice

Cancer immunotherapy has brought significant clinical benefits to numerous patients with malignant disease. However, only a fraction of patients experiences complete and durable responses to currently available immunotherapies. This highlights the need for more effective immunotherapies, combination treatments and predictive biomarkers. The molecular properties of a tumor, intratumor heterogeneity and the tumor immune microenvironment decisively shape tumor evolution, metastasis and therapy resistance and are therefore key targets for precision cancer medicine. Humanized mice that support the engraftment of patient-derived tumors and recapitulate the human tumor immune microenvironment of patients represent a promising preclinical model to address fundamental questions in precision immuno-oncology and cancer immunotherapy. In this review, we provide an overview of next-generation humanized mouse models suitable for the establishment and study of patient-derived tumors. Furthermore, we discuss the opportunities and challenges of modeling the tumor immune microenvironment and testing a variety of immunotherapeutic approaches using human immune system mouse models.

The role of tumor microenvironment in drug resistance: emerging technologies to unravel breast cancer heterogeneity

Breast cancer is a highly heterogeneous disease, at both inter- and intra-tumor levels, and this heterogeneity is a crucial determinant of malignant progression and response to treatments. In addition to genetic diversity and plasticity of cancer cells, the tumor microenvironment contributes to tumor heterogeneity shaping the physical and biological surroundings of the tumor. The activity of certain types of immune, endothelial or mesenchymal cells in the microenvironment can change the effectiveness of cancer therapies via a plethora of different mechanisms. Therefore, deciphering the interactions between the distinct cell types, their spatial organization and their specific contribution to tumor growth and drug sensitivity is still a major challenge. Dissecting intra-tumor heterogeneity is currently an urgent need to better define breast cancer biology and to develop therapeutic strategies targeting the microenvironment as helpful tools for combined and personalized treatment. In this review, we analyze the mechanisms by which the tumor microenvironment affects the characteristics of tumor heterogeneity that ultimately result in drug resistance, and we outline state of the art preclinical models and emerging technologies that will be instrumental in unraveling the impact of the tumor microenvironment on resistance to therapies.

The Potential Effect of General Anesthetics in Cancer Surgery: Meta-Analysis of Postoperative Metastasis and Inflammatory Cytokines

Metastasis or recurrence following curative surgery is the main indicator of tumor progress and is the main cause of patient death. For more than three decades, the potential for general anesthesia to affect cancer outcomes has been a subject of concern with considerable research interest. Here, we conducted this systematic review and meta-analysis to summarize the effect of inhalational anesthesia (IHNA) vs. propofol-based total intravenous anesthesia (TIVA) on metastasis and recurrence after cancer surgery from clinical and pre-clinical studies. The relative risk for metastasis/recurrence in TIVA is 0.61 (95% confidence interval (95% CI) 0.46 to 0.82, p = 0.0009) compared to IHNA. Inflammatory cytokines have been implicated in cancer metastasis following cancer surgery, thus we analyzed inflammatory cytokines levels after surgery under IHNA or TIVA. Based on pooled analysis, a lower IL-6 level was noticed in TIVA in comparison to IHNA (standardized mean difference (SMD) = 0.77, 95% CI = 0.097 to 1.44, I2 = 92%, p = 0.02) but not TNF-α or IL-10. Preclinical animal model studies show that inhalational anesthetics increase the risk of breast cancer metastasis compared to propofol. In conclusion, the current evidence suggests intravenous anesthetic propofol is associated with less metastasis/recurrence and lower postoperative IL-6 level over inhaled anesthetics in the oncological surgery. We urge more well-designed clinical and preclinical studies in this field.

[89Zr]-Atezolizumab-PET Imaging Reveals Longitudinal Alterations in PDL1 during Therapy in TNBC Preclinical Models

Triple-negative breast cancers (TNBCs) currently have limited treatment options; however, PD-L1 is an indicator of susceptibility to immunotherapy. Currently, assessment of PD-L1 is limited to biopsy samples. These limitations may be overcome with molecular imaging. In this work, we describe chemistry development and optimization, in vitro, in vivo, and dosimetry of [89Zr]-Atezolizumab for PD-L1 imaging. Atezolizumab was conjugated to DFO and radiolabeled with 89Zr. Tumor uptake and heterogeneity in TNBC xenograft and patient-derived xenograft (PDX) mouse models were quantified following [89Zr]-Atezolizumab-PET imaging. PD-L1 expression in TNBC PDX models undergoing therapy and immunohistochemistry (IHC) was used to validate imaging. SUV from PET imaging was quantified and used to identify heterogeneity. PET/CT imaging using [89Zr]-Atezolizumab identified a significant increase in tumor:muscle SUVmean 1 and 4 days after niraparib therapy and revealed an increased trend in PD-L1 expression following other cytotoxic therapies. A preliminary dosimetry study indicated the organs that will receive a higher dose are the spleen, adrenals, kidneys, and liver. [89Zr]-Atezolizumab PET/CT imaging reveals potential for the noninvasive detection of PD-L1-positive TNBC tumors and allows for quantitative and longitudinal assessment. This has potential significance for understanding tumor heterogeneity and monitoring early expression changes in PD-L1 induced by therapy.

3D cancer models: One step closer to in vitro human studies

Cancer immunotherapy is the great breakthrough in cancer treatment as it displayed prolonged progression-free survival over conventional therapies, yet, to date, in only a minority of patients. In order to broad cancer immunotherapy clinical applicability some roadblocks need to be overcome, first among all the lack of preclinical models that faithfully depict the local tumor microenvironment (TME), which is known to dramatically affect disease onset, progression and response to therapy. In this review, we provide the reader with a detailed overview of current 3D models developed to mimick the complexity and the dynamics of the TME, with a focus on understanding why the TME is a major target in anticancer therapy. We highlight the advantages and translational potentials of tumor spheroids, organoids and immune Tumor-on-a-Chip models in disease modeling and therapeutic response, while outlining pending challenges and limitations. Thinking forward, we focus on the possibility to integrate the know-hows of micro-engineers, cancer immunologists, pharmaceutical researchers and bioinformaticians to meet the needs of cancer researchers and clinicians interested in using these platforms with high fidelity for patient-tailored disease modeling and drug discovery.

Toward Practical Integration of Omic and Imaging Data in Co-Clinical Trials

Co-clinical trials are the concurrent or sequential evaluation of therapeutics in both patients clinically and patient-derived xenografts (PDX) pre-clinically, in a manner designed to match the pharmacokinetics and pharmacodynamics of the agent(s) used. The primary goal is to determine the degree to which PDX cohort responses recapitulate patient cohort responses at the phenotypic and molecular levels, such that pre-clinical and clinical trials can inform one another. A major issue is how to manage, integrate, and analyze the abundance of data generated across both spatial and temporal scales, as well as across species. To address this issue, we are developing MIRACCL (molecular and imaging response analysis of co-clinical trials), a web-based analytical tool. For prototyping, we simulated data for a co-clinical trial in "triple-negative" breast cancer (TNBC) by pairing pre- (T0) and on-treatment (T1) magnetic resonance imaging (MRI) from the I-SPY2 trial, as well as PDX-based T0 and T1 MRI. Baseline (T0) and on-treatment (T1) RNA expression data were also simulated for TNBC and PDX. Image features derived from both datasets were cross-referenced to omic data to evaluate MIRACCL functionality for correlating and displaying MRI-based changes in tumor size, vascularity, and cellularity with changes in mRNA expression as a function of treatment.

Stabilization of E-cadherin adhesions by COX-2/GSK3β signaling is a targetable pathway in metastatic breast cancer

Metastatic progression of epithelial cancers can be associated with epithelial-mesenchymal transition (EMT) including transcriptional inhibition of E-cadherin (CDH1) expression. Recently, EM plasticity (EMP) and E-cadherin-mediated, cluster-based metastasis and treatment resistance have become more appreciated. However, the mechanisms that maintain E-cadherin expression in this context are less understood. Through studies of inflammatory breast cancer (IBC) and a 3D tumor cell "emboli" culture paradigm, we discovered that cyclooxygenase 2 (COX-2; PTGS2), a target gene of C/EBPδ (CEBPD), or its metabolite prostaglandin E2 (PGE2) promotes protein stability of E-cadherin, β-catenin, and p120 catenin through inhibition of GSK3β. The COX-2 inhibitor celecoxib downregulated E-cadherin complex proteins and caused cell death. Coexpression of E-cadherin and COX-2 was seen in breast cancer tissues from patients with poor outcome and, along with inhibitory GSK3β phosphorylation, in patient-derived xenografts (PDX) including triple negative breast cancer (TNBC).Celecoxib alone decreased E-cadherin protein expression within xenograft tumors, though CDH1 mRNA levels increased, and reduced circulating tumor cell (CTC) clusters. In combination with paclitaxel, celecoxib attenuated or regressed lung metastases. This study has uncovered a mechanism by which metastatic breast cancer cells can maintain E-cadherin-mediated cell-to-cell adhesions and cell survival, suggesting that some patients with COX-2+/E-cadherin+ breast cancer may benefit from targeting of the PGE2 signaling pathway.

Animal Models and Their Role in Imaging-Assisted Co-Clinical Trials

The availability of high-fidelity animal models for oncology research has grown enormously in recent years, enabling preclinical studies relevant to prevention, diagnosis, and treatment of cancer to be undertaken. This has led to increased opportunities to conduct co-clinical trials, which are studies on patients that are carried out parallel to or sequentially with animal models of cancer that mirror the biology of the patients' tumors. Patient-derived xenografts (PDX) and genetically engineered mouse models (GEMM) are considered to be the models that best represent human disease and have high translational value. Notably, one element of co-clinical trials that still needs significant optimization is quantitative imaging. The National Cancer Institute has organized a Co-Clinical Imaging Resource Program (CIRP) network to establish best practices for co-clinical imaging and to optimize translational quantitative imaging methodologies. This overview describes the ten co-clinical trials of investigators from eleven institutions who are currently supported by the CIRP initiative and are members of the Animal Models and Co-clinical Trials (AMCT) Working Group. Each team describes their corresponding clinical trial, type of cancer targeted, rationale for choice of animal models, therapy, and imaging modalities. The strengths and weaknesses of the co-clinical trial design and the challenges encountered are considered. The rich research resources generated by the members of the AMCT Working Group will benefit the broad research community and improve the quality and translational impact of imaging in co-clinical trials.

Discovering Synergistic Compounds with BYL-719 in PI3K Overactivated Basal-like PDXs

Basal-like triple-negative breast cancer (TNBC) tumor cells are difficult to eliminate due to resistance mechanisms that promote survival. While this breast cancer subtype has low PIK3CA mutation rates when compared to estrogen receptor-positive (ER+) breast cancers, most basal-like TNBCs have an overactive PI3K pathway due to gene amplification or high gene expression. BYL-719 is a PIK3CA inhibitor that has been found to have low drug-drug interactions, which increases the likelihood that it could be useful for combinatorial therapy. Alpelisib (BYL-719) with fulvestrant was recently approved for treating ER+ breast cancer patients whose cancer had developed resistance to ER-targeting therapy. In these studies, a set of basal-like patient-derived xenograft (PDX) models was transcriptionally defined with bulk and single-cell RNA-sequencing and clinically actionable mutation profiles defined with Oncomine mutational profiling. This information was overlaid onto therapeutic drug screening results. BYL-719-based, synergistic two-drug combinations were identified with 20 different compounds, including everolimus, afatinib, and dronedarone, which were also found to be effective at minimizing tumor growth. These data support the use of these drug combinations towards cancers with activating PIK3CA mutations/gene amplifications or PTEN deficient/PI3K overactive pathways.

Cancer-associated fibroblasts are the main contributors to epithelial-to-mesenchymal signatures in the tumor microenvironment

Epithelial-to-mesenchymal transition (EMT) is associated with tumor initiation, metastasis, and drug resistance. However, the mechanisms underlying these associations are largely unknown. We studied several tumor types to identify the source of EMT gene expression signals and a potential mechanism of resistance to immuno-oncology treatment. Across tumor types, EMT-related gene expression was strongly associated with expression of stroma-related genes. Based on RNA sequencing of multiple patient-derived xenograft models, EMT-related gene expression was enriched in the stroma versus parenchyma. EMT-related markers were predominantly expressed by cancer-associated fibroblasts (CAFs), cells of mesenchymal origin which produce a variety of matrix proteins and growth factors. Scores derived from a 3-gene CAF transcriptional signature (COL1A1, COL1A2, COL3A1) were sufficient to reproduce association between EMT-related markers and disease prognosis. Our results suggest that CAFs are the primary source of EMT signaling and have potential roles as biomarkers and targets for immuno-oncology therapies.