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. Better assays for recurrence risk are needed to tailor aggressive therapy for patients who need it and avoid overtreatment and unnecessary toxicity for those at low risk. The purpose of this study was to determine if patient-derived xenograft (PDX) engraftment of newly diagnosed breast tumors can serve as an accurate predictor of recurrence and death from breast cancer. METHODS: This study was a blinded noninterventional trial comprising 80 patients with newly diagnosed, nonmetastatic, estrogen receptor (ER)-negative or ER-low breast cancer. RESULTS: PDX engraftment was strongly associated with relapse in 1 year: 8 of 18 (44.4%) patients whose tumors engrafted relapsed versus 1 of 62 (1.6%) patients whose tumors did not engraft (P < .0001). Patients whose tumors engrafted had a hazard ratio (HR) for relapse of 17.5. HRs for OS and breast cancer-specific survival in PDX+ patients were 21.1 and 39.5, respectively. CONCLUSION: We report that the ability of a tumor to engraft as a PDX predicts early recurrence by serving as a functional readout of aggressiveness and prospectively identifies the most devastating tumors. This provides new opportunity to develop surrogate assays, such as biomarkers of engraftment, which will extend the clinical feasibility of this finding.

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.

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

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. The multitargeted tyrosine kinase inhibitor (TKI) cabozantinib and mTORC1/2 inhibitor sapanisertib emerged as the most effective drugs, particularly when combined. The combination demonstrated favorable tolerability and inhibited tumor growth or induced tumor regression in all models, including two from patients who experienced treatment failure with FDA-approved TKI and immunotherapy combinations. In cabozantinib-treated samples, imaging analysis revealed a significant reduction in vascular density, and single-nucleus RNA sequencing (snRNA-seq) analysis indicated a decreased proportion of endothelial cells in the tumors. SnRNA-seq data further identified a tumor subpopulation enriched with cell-cycle activity that exhibited heightened sensitivity to the cabozantinib and sapanisertib combination. Conversely, activation of the epithelial-mesenchymal transition pathway, detected at the protein level, was associated with drug resistance in residual tumors following combination treatment. The combination effectively restrained ERK phosphorylation and reduced expression of ERK downstream transcription factors and their target genes implicated in cell-cycle control and apoptosis. This study highlights the potential of the cabozantinib plus sapanisertib combination as a promising treatment approach for patients with RCC, particularly those whose tumors progressed on immune checkpoint inhibitors and other TKIs. SIGNIFICANCE: The molecular-guided therapeutic strategy of combining cabozantinib and sapanisertib restrains ERK activity to effectively suppress growth of renal cell carcinomas, including those unresponsive to immune checkpoint inhibitors.

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. We then detail PDxO validation and characterization and drug response assay. Our PDxO drug screening platform can predict therapy response in vivo and inform functional precision oncology for patients. For complete details on the use and execution of this protocol, please refer to Guillen et al.1.

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. To demonstrate their ability to function as a preclinical cohort, PDXs were characterized using DNA sequencing, transcriptomics, and proteomics to show consistency with clinical samples. We then developed a network-based approach (CTD/WGCNA) to identify biomarkers of response to carboplatin (MSI1, TMSB15A, ARHGDIB, GGT1, SV2A, SEC14L2, SERPINI1, ADAMTS20, DGKQ) and docetaxel (c, MAGED4, CERS1, ST8SIA2, KIF24, PARPBP). CTD/WGCNA multigene biomarkers are predictive in PDX datasets (RNAseq and Affymetrix) for both taxane- (docetaxel or paclitaxel) and platinum-based (carboplatin or cisplatin) response, thereby demonstrating cross-expression platform and cross-drug class robustness. These biomarkers were also predictive in clinical datasets, thus demonstrating translational potential.

A path to translation: How 3D patient tumor avatars enable next generation precision oncology.

3D patient tumor avatars (3D-PTAs) hold promise for next-generation precision medicine. Here, we describe the benefits and challenges of 3D-PTA technologies and necessary future steps to realize their potential for clinical decision making. 3D-PTAs require standardization criteria and prospective trials to establish clinical benefits. Innovative trial designs that combine omics and 3D-PTA readouts may lead to more accurate clinical predictors, and an integrated platform that combines diagnostic and therapeutic development will accelerate new treatments for patients with refractory disease.

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. We find that QPI EC50 values are concordant with CellTiter-Glo (CTG), a gold standard metabolic endpoint assay. In addition, we apply multiparametric QPI to characterize cytostatic/cytotoxic and rapid/slow responses and track the emergence of resistant subpopulations. Thus, QPI reveals dynamic changes in response heterogeneity in addition to average population responses, a key advantage over endpoint viability or metabolic assays. Overall, multiparametric QPI reveals a rich picture of cell growth by capturing the dynamics of single-cell responses to candidate therapies.

PDXNet portal: patient-derived Xenograft model, data, workflow and tool discovery.

We created the PDX Network (PDXNet) portal (https://portal.pdxnetwork.org/) to centralize access to the National Cancer Institute-funded PDXNet consortium resources, to facilitate collaboration among researchers and to make these data easily available for research. The portal includes sections for resources, analysis results, metrics for PDXNet activities, data processing protocols and training materials for processing PDX data. Currently, the portal contains PDXNet model information and data resources from 334 new models across 33 cancer types. Tissue samples of these models were deposited in the NCI's Patient-Derived Model Repository (PDMR) for public access. These models have 2134 associated sequencing files from 873 samples across 308 patients, which are hosted on the Cancer Genomics Cloud powered by Seven Bridges and the NCI Cancer Data Service for long-term storage and access with dbGaP permissions. The portal includes results from freely available, robust, validated and standardized analysis workflows on PDXNet sequencing files and PDMR data (3857 samples from 629 patients across 85 disease types). The PDXNet portal is continuously updated with new data and is of significant utility to the cancer research community as it provides a centralized location for PDXNet resources, which support multi-agent treatment studies, determination of sensitivity and resistance mechanisms, and preclinical trials.

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. Moreover, we demonstrate the feasibility of using these models for precision oncology in real time with clinical care in a case of triple-negative breast cancer (TNBC) with early metastatic recurrence. Our results uncovered a Food and Drug Administration (FDA)-approved drug with high efficacy against the models. Treatment with this therapy resulted in a complete response for the individual and a progression-free survival (PFS) period more than three times longer than their previous therapies. This work provides valuable methods and resources for functional precision medicine and drug development for human breast cancer.

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. Here, we report on dynamic genomic landscapes and pharmacogenomic associations, including associations between activating oncogenic events and drugs, correlations between whole-genome duplications and subclone events, and the potential PDX models for NCI-MATCH trials. Lastly, we provide a web portal having comprehensive pan-cancer PDX genomic profiles and source code to facilitate identification of more druggable events and further insights into PDXs' recapitulation of human tumors.

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). Breast CSCs frequently exist as a minority population in therapy resistant tumors. In this study, we show that cytoplasmic complexes composed of steroid receptor (SR) co-activators, PELP1 and SRC-3, modulate breast CSC expansion through upregulation of the HIF-activated metabolic target genes PFKFB3 and PFKFB4. Seahorse metabolic assays demonstrated that cytoplasmic PELP1 influences cellular metabolism by increasing both glycolysis and mitochondrial respiration. PELP1 interacts with PFKFB3 and PFKFB4 proteins, and inhibition of PFKFB3 and PFKFB4 kinase activity blocks PELP1-induced tumorspheres and protein-protein interactions with SRC-3. PFKFB4 knockdown inhibited in vivo emergence of circulating tumor cell (CTC) populations in mammary intraductal (MIND) models. Application of PFKFB inhibitors in combination with ER targeted therapies blocked tumorsphere formation in multiple models of advanced breast cancer including tamoxifen (TamR) and paclitaxel (TaxR) resistant models, murine tumor cells, and ER+ patient-derived organoids (PDxO). Together, our data suggest that PELP1, SRC-3, and PFKFBs cooperate to drive ER+ tumor cell populations that include CSCs and CTCs. Identifying non-ER pharmacological targets offers a useful approach to blocking metastatic escape from standard of care ER/estrogen (E2)-targeted strategies to overcome endocrine and chemotherapy resistance.

Erratum: "Toward improved models of human cancer" [APL Bioeng. 5, 010901 (2021)].

[This corrects the article DOI: 10.1063/5.0030534.].

Large-Scale Identification of Clonal Hematopoiesis and Mutations Recurrent in Blood Cancers.

Clonal hematopoiesis of indeterminate potential (CHIP) is characterized by detectable hematopoietic-associated gene mutations in a person without evidence of hematologic malignancy. We sought to identify additional cancer-presenting mutations useable for CHIP detection by performing a data mining analysis of 48 somatic mutation studies reporting mutations at diagnoses of 7,430 adult and pediatric patients with hematologic malignancies. Following extraction of 20,141 protein-altering mutations, we identified 434 significantly recurrent mutation hotspots, 364 of which occurred at loci confidently assessable for CHIP. We then performed an additional large-scale analysis of whole exome sequencing data from 4,538 persons belonging to three non-cancer cohorts for clonal mutations. We found the combined cohort prevalence of CHIP with mutations identical to those reported at blood cancer mutation hotspots to be 1.8%, and that some of these CHIP mutations occurred in children. Our findings may help to improve CHIP detection and pre-cancer surveillance for both children and adults.

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. CNA inferences based on DNA sequencing and microarray data displayed substantially higher resolution and dynamic range than gene expression-based inferences, and they also showed strong CNA conservation from PTs through late-passage PDXs. CNA recurrence analysis of 130 colorectal and breast PT/PDX-early/PDX-late trios confirmed high-resolution CNA retention. We observed no significant enrichment of cancer-related genes in PDX-specific CNAs across models. Moreover, CNA differences between patient and PDX tumors were comparable to variations in multiregion samples within patients. Our study demonstrates the lack of systematic copy number evolution driven by the PDX mouse host.

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. Still, no model is perfect. This Perspective attempts to address ways that we can improve the clinical translatability of models used for cancer research, from the point of view of researchers who mainly conduct cancer studies in vivo.

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). TXNIP is a negative regulator of glucose uptake; thus, its induction by RocA links translation to the availability of glucose. MondoA is the principal regulator of glucose-induced transcription, and its activity is triggered by the glycolytic intermediate, glucose 6-phosphate (G6P). MondoA responds to G6P generated by cytoplasmic glucose and mitochondrial ATP (mtATP), suggesting a critical role in the cellular response to these energy sources. TXNIP expression is entirely dependent on MondoA; therefore, we investigated how protein synthesis inhibitors impact its transcriptional activity. METHODS: We investigated how translation regulates MondoA activity using cell line models and loss-of-function approaches. We examined how protein synthesis inhibitors effect gene expression and metabolism using RNA-sequencing and metabolomics, respectively. The biological impact of RocA was evaluated using cell lines and patient-derived xenograft organoid (PDxO) models. RESULTS: We discovered that multiple protein synthesis inhibitors, including RocA, increase TXNIP expression in a manner that depends on MondoA, a functional electron transport chain and mtATP synthesis. Furthermore, RocA and cycloheximide increase mtATP and G6P levels, respectively, and TXNIP induction depends on interactions between the voltage-dependent anion channel (VDAC) and hexokinase (HK), which generates G6P. RocA treatment impacts the regulation of ~ 1200 genes, and ~ 250 of those genes are MondoA-dependent. RocA treatment is cytotoxic to triple negative breast cancer (TNBC) cell lines and shows preferential cytotoxicity against estrogen receptor negative (ER-) PDxO breast cancer models. Finally, RocA-driven cytotoxicity is partially dependent on MondoA or TXNIP. CONCLUSIONS: Our data suggest that protein synthesis inhibitors rewire metabolism, resulting in an increase in mtATP and G6P, the latter driving MondoA-dependent transcriptional activity. Further, MondoA is a critical component of the cellular transcriptional response to RocA. Our functional assays suggest that RocA or similar translation inhibitors may show efficacy against ER- breast tumors and that the levels of MondoA and TXNIP should be considered when exploring these potential treatment options.

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. In this study, we utilized integrated genomic analysis of DNA methylation, chromatin accessibility, transcription factor binding, and gene expression in large collections of breast cancer cell lines and patient tumors to identify transcription factors responsible for the basal-like gene expression program. Glucocorticoid receptor (GR) and STAT3 bind to the same genomic regulatory regions, which were specifically open and unmethylated in basal-like breast cancer. These transcription factors cooperated to regulate expression of hundreds of genes in the basal-like gene expression signature, which were associated with poor prognosis. Combination treatment with small-molecule inhibitors of both transcription factors resulted in synergistic decreases in cell growth in cell lines and patient-derived organoid models. This study demonstrates that GR and STAT3 cooperate to regulate the basal-like breast cancer gene expression program and provides the basis for improved therapy for basal-like triple-negative breast cancer through rational combination of STAT3 and GR inhibitors. SIGNIFICANCE: This study demonstrates that GR and STAT3 cooperate to activate the canonical gene expression signature of basal-like triple-negative breast cancer and that combination treatment with STAT3 and GR inhibitors could provide synergistic therapeutic efficacy.