A novel treatment strategy utilizing panobinostat for high-risk and treatment-refractory hepatoblastoma.

BACKGROUND & AIMS: Patients with metastatic, treatment-refractory, and relapsed hepatoblastoma (HB) have survival rates of less than 50% due to limited treatment options. To develop new therapeutic strategies for these patients, our laboratory has developed a preclinical testing pipeline. Given that histone deacetylase (HDAC) inhibition has been proposed for HB, we hypothesized that we could find an effective combination treatment strategy utilizing HDAC inhibition. METHODS: RNA sequencing, microarray, NanoString, and immunohistochemistry data of patient HB samples were analyzed for HDAC class expression. Patient-derived spheroids (PDSp) were used to screen combination chemotherapy with an HDAC inhibitor, panobinostat. Patient-derived xenograft (PDX) mouse models were developed and treated with the combination therapy that showed the highest efficacy in the PDSp drug screen. RESULTS: HDAC RNA and protein expression were elevated in HB tumors compared to normal livers. Panobinostat (IC50 of 0.013-0.059 µM) showed strong in vitro effects and was associated with lower cell viability than other HDAC inhibitors. PDSp demonstrated the highest level of cell death with combination treatment of vincristine/irinotecan/panobinostat (VIP). All four models responded to VIP therapy with a decrease in tumor size compared to placebo. After 6 weeks of treatment, two models demonstrated necrotic cell death, with lower Ki67 expression, decreased serum alpha fetoprotein and reduced tumor burden compared to paired VI- and placebo-treated groups. CONCLUSIONS: Utilizing a preclinical HB pipeline, we demonstrate that panobinostat in combination with VI chemotherapy can induce an effective tumor response in models developed from patients with high-risk, relapsed, and treatment-refractory HB. IMPACT AND IMPLICATIONS: Patients with treatment-refractory hepatoblastoma have limited treatment options with survival rates of less than 50%. Our manuscript demonstrates that combination therapy with vincristine, irinotecan, and panobinostat reduces the size of high-risk, relapsed, and treatment-refractory tumors. With this work we provide preclinical evidence to support utilizing this combination therapy as an arm in future clinical trials.

PRMT blockade induces defective DNA replication stress response and synergizes with PARP inhibition.

Multiple cancers exhibit aberrant protein arginine methylation by both type I arginine methyltransferases, predominately protein arginine methyltransferase 1 (PRMT1) and to a lesser extent PRMT4, and by type II PRMTs, predominately PRMT5. Here, we perform targeted proteomics following inhibition of PRMT1, PRMT4, and PRMT5 across 12 cancer cell lines. We find that inhibition of type I and II PRMTs suppresses phosphorylated and total ATR in cancer cells. Loss of ATR from PRMT inhibition results in defective DNA replication stress response activation, including from PARP inhibitors. Inhibition of type I and II PRMTs is synergistic with PARP inhibition regardless of homologous recombination function, but type I PRMT inhibition is more toxic to non-malignant cells. Finally, we demonstrate that the combination of PARP and PRMT5 inhibition improves survival in both BRCA-mutant and wild-type patient-derived xenografts without toxicity. Taken together, these results demonstrate that PRMT5 inhibition may be a well-tolerated approach to sensitize tumors to PARP inhibition.

Interferon-Induced Bone Marrow Stromal Antigen 2 (BST2) Is A Functional Tumor-Initiating Cell Marker In Triple-Negative Breast Cancer.

Tumor-initiating cells (TIC) are a tumor cell subpopulation thought to be responsible for therapeutic resistance and metastasis. Using a S ignal T ransducer and A ctivator of T ranscription (STAT) reporter, and a STAT-responsive lineage tracing system, we enriched for cells with enhanced mammosphere-forming potential in some, but not all, triple-negative breast cancer xenograft models (TNBC) indicating TIC-related and TIC-independent functions for STAT signaling. Single-cell RNA sequencing (scRNA-seq) of reporter-tagged xenografts identified a common interferon-associated transcriptional state, previously linked to inflammation and macrophage differentiation, in TIC. Similar transcriptional states exist in human breast cancer patient scRNA-seq datasets. Flow cytometric sorting using bone marrow stromal cell antigen 2 (BST2), a marker of this state, enriched for TIC, and BST2 knockdown reduced mammosphere-forming potential. These results suggest TIC may exploit the interferon response pathway to promote their activity in TNBC. Our results lay the groundwork to target interferon-associated pathways in TIC in a subset of TNBC.

Deconvolution of cancer cell states by the XDec-SM method.

Proper characterization of cancer cell states within the tumor microenvironment is a key to accurately identifying matching experimental models and the development of precision therapies. To reconstruct this information from bulk RNA-seq profiles, we developed the XDec Simplex Mapping (XDec-SM) reference-optional deconvolution method that maps tumors and the states of constituent cells onto a biologically interpretable low-dimensional space. The method identifies gene sets informative for deconvolution from relevant single-cell profiling data when such profiles are available. When applied to breast tumors in The Cancer Genome Atlas (TCGA), XDec-SM infers the identity of constituent cell types and their proportions. XDec-SM also infers cancer cells states within individual tumors that associate with DNA methylation patterns, driver somatic mutations, pathway activation and metabolic coupling between stromal and breast cancer cells. By projecting tumors, cancer cell lines, and PDX models onto the same map, we identify in vitro and in vivo models with matching cancer cell states. Map position is also predictive of therapy response, thus opening the prospects for precision therapy informed by experiments in model systems matched to tumors in vivo by cancer cell state.

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.

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.

RANK is a poor prognosis marker and a therapeutic target in ER-negative postmenopausal breast cancer.

Despite strong preclinical data, the therapeutic benefit of the RANKL inhibitor, denosumab, in breast cancer patients, beyond the bone, is unclear. Aiming to select patients who may benefit from denosumab, we hereby analyzed RANK and RANKL protein expression in more than 2,000 breast tumors (777 estrogen receptor-negative, ER- ) from four independent cohorts. RANK protein expression was more frequent in ER- tumors, where it associated with poor outcome and poor response to chemotherapy. In ER- breast cancer patient-derived orthoxenografts (PDXs), RANKL inhibition reduced tumor cell proliferation and stemness, regulated tumor immunity and metabolism, and improved response to chemotherapy. Intriguingly, tumor RANK protein expression associated with poor prognosis in postmenopausal breast cancer patients, activation of NFKB signaling, and modulation of immune and metabolic pathways, suggesting that RANK signaling increases after menopause. Our results demonstrate that RANK protein expression is an independent biomarker of poor prognosis in postmenopausal and ER- breast cancer patients and support the therapeutic benefit of RANK pathway inhibitors, such as denosumab, in breast cancer patients with RANK+ ER- tumors after menopause.

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.

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.

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.

Proteogenomic Markers of Chemotherapy Resistance and Response in Triple-Negative Breast Cancer.

Microscaled proteogenomics was deployed to probe the molecular basis for differential response to neoadjuvant carboplatin and docetaxel combination chemotherapy for triple-negative breast cancer (TNBC). Proteomic analyses of pretreatment patient biopsies uniquely revealed metabolic pathways, including oxidative phosphorylation, adipogenesis, and fatty acid metabolism, that were associated with resistance. Both proteomics and transcriptomics revealed that sensitivity was marked by elevation of DNA repair, E2F targets, G2-M checkpoint, interferon-gamma signaling, and immune-checkpoint components. Proteogenomic analyses of somatic copy-number aberrations identified a resistance-associated 19q13.31-33 deletion where LIG1, POLD1, and XRCC1 are located. In orthogonal datasets, LIG1 (DNA ligase I) gene deletion and/or low mRNA expression levels were associated with lack of pathologic complete response, higher chromosomal instability index (CIN), and poor prognosis in TNBC, as well as carboplatin-selective resistance in TNBC preclinical models. Hemizygous loss of LIG1 was also associated with higher CIN and poor prognosis in other cancer types, demonstrating broader clinical implications. SIGNIFICANCE: Proteogenomic analysis of triple-negative breast tumors revealed a complex landscape of chemotherapy response associations, including a 19q13.31-33 somatic deletion encoding genes serving lagging-strand DNA synthesis (LIG1, POLD1, and XRCC1), that correlate with lack of pathologic response, carboplatin-selective resistance, and, in pan-cancer studies, poor prognosis and CIN. This article is highlighted in the In This Issue feature, p. 2483.

Poziotinib Inhibits HER2-Mutant-Driven Therapeutic Resistance and Multiorgan Metastasis in Breast Cancer.

The pan-HER tyrosine kinase inhibitor (TKI) neratinib is therapeutically active against metastatic breast cancers harboring activating HER2 mutations, but responses are variable and often not durable. Here we demonstrate that recurrent HER2 mutations have differential effects on endocrine therapy responsiveness, metastasis, and pan-HER TKI therapeutic sensitivity. The prevalence and prognostic significance may also depend on whether the HER2 mutant has arisen in the context of lobular versus ductal histology. The most highly recurrent HER2 mutant, L755S, was particularly resistant to neratinib but sensitive to the pan-HER TKI poziotinib, alone or in combination with fulvestrant. Poziotinib reduced tumor growth, diminished multiorgan metastasis, and inhibited mTOR activation more effectively than neratinib. Similar therapeutic effects of poziotinib were observed in both an engineered HER2L755S MCF7 model and a patient-derived xenograft harboring a HER2G778_P780dup mutation. Overall, these findings support the need for clinical evaluation of poziotinib for the treatment of HER2-mutant metastatic breast cancer. SIGNIFICANCE: Evaluation of the functional impact of HER2 mutations on therapy-induced resistance and metastasis identifies robust antitumor activity of poziotinib and supports the clinical evaluation of poziotinib in ER+ HER2 mutant breast cancer.

In Vivo Modeling of Human Breast Cancer Using Cell Line and Patient-Derived Xenografts.

Historically, human breast cancer has been modeled largely in vitro using long-established cell lines primarily in two-dimensional culture, but also in three-dimensional cultures of varying cellular and molecular complexities. A subset of cell line models has also been used in vivo as cell line-derived xenografts (CDX). While outstanding for conducting detailed molecular analysis of regulatory mechanisms that may function in vivo, results of drug response studies using long-established cell lines have largely failed to translate clinically. In an attempt to address this shortcoming, many laboratories have succeeded in developing clinically annotated patient-derived xenograft (PDX) models of human cancers, including breast, in a variety of host systems. While immunocompromised mice are the predominant host, the immunocompromised rat and pig, zebrafish, as well as the chicken egg chorioallantoic membrane (CAM) have also emerged as potential host platforms to help address perceived shortcomings of immunocompromised mice. With any modeling platform, the two main issues to be resolved are criteria for "credentialing" the models as valid models to represent human cancer, and utility with respect to the ability to generate clinically relevant translational research data. Such data are beginning to emerge, particularly with the activities of PDX consortia such as the NCI PDXNet Program, EuroPDX, and the International Breast Cancer Consortium, as well as a host of pharmaceutical companies and contract research organizations (CRO). This review focuses primarily on these important aspects of PDX-related research, with a focus on breast cancer.

PHGDH heterogeneity potentiates cancer cell dissemination and metastasis.

Cancer metastasis requires the transient activation of cellular programs enabling dissemination and seeding in distant organs1. Genetic, transcriptional and translational heterogeneity contributes to this dynamic process2,3. Metabolic heterogeneity has also been observed4, yet its role in cancer progression is less explored. Here we find that the loss of phosphoglycerate dehydrogenase (PHGDH) potentiates metastatic dissemination. Specifically, we find that heterogeneous or low PHGDH expression in primary tumours of patients with breast cancer is associated with decreased metastasis-free survival time. In mice, circulating tumour cells and early metastatic lesions are enriched with Phgdhlow cancer cells, and silencing Phgdh in primary tumours increases metastasis formation. Mechanistically, Phgdh interacts with the glycolytic enzyme phosphofructokinase, and the loss of this interaction activates the hexosamine-sialic acid pathway, which provides precursors for protein glycosylation. As a consequence, aberrant protein glycosylation occurs, including increased sialylation of integrin αvß3, which potentiates cell migration and invasion. Inhibition of sialylation counteracts the metastatic ability of Phgdhlow cancer cells. In conclusion, although the catalytic activity of PHGDH supports cancer cell proliferation, low PHGDH protein expression non-catalytically potentiates cancer dissemination and metastasis formation. Thus, the presence of PHDGH heterogeneity in primary tumours could be considered a sign of tumour aggressiveness.

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.

Surgical Procedure for Implantation of Human Tumor Tissue into the Epithelium-Free Mammary Fat Pad of Immunocompromised Mice to Generate Patient-Derived Xenografts (PDX).

Implantation of cancerous tissue obtained from patient biopsies or surgical procedures to create patient-derived xenograft models (PDX) has greatly enhanced the ability of scientists and physicians to perform translational research involving pre-clinical testing. PDX models are more representative of patient cancer tumors than traditional cell lines as they retain the three-dimensional architecture of the tumor, and are genomically, transcriptomically, and proteomically analogous. These models can be used for a variety of experiments to evaluate response of tumors to experimental therapeutic agents and to aid in the elucidation of response and resistance mechanisms of existing therapies. Orthotopic transplantation of breast cancer tissue into the mammary fat pad has been proven to be the most robust method of establishing breast cancer PDX models. Here we describe the procedure for implanting patient tumor tissue, or existing PDX tissue, into the mammary fat pad of immunocompromised mice, with notes, where necessary, for transplantation of normal or preneoplastic tissue.

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.

Transcriptional Reprogramming Differentiates Active from Inactive ESR1 Fusions in Endocrine Therapy-Refractory Metastatic Breast Cancer.

Genomic analysis has recently identified multiple ESR1 gene translocations in estrogen receptor alpha-positive (ERα+) metastatic breast cancer (MBC) that encode chimeric proteins whereby the ESR1 ligand binding domain (LBD) is replaced by C-terminal sequences from many different gene partners. Here we functionally screened 15 ESR1 fusions and identified 10 that promoted estradiol-independent cell growth, motility, invasion, epithelial-to-mesenchymal transition, and resistance to fulvestrant. RNA sequencing identified a gene expression pattern specific to functionally active ESR1 gene fusions that was subsequently reduced to a diagnostic 24-gene signature. This signature was further examined in 20 ERα+ patient-derived xenografts and in 55 ERα+ MBC samples. The 24-gene signature successfully identified cases harboring ESR1 gene fusions and also accurately diagnosed the presence of activating ESR1 LBD point mutations. Therefore, the 24-gene signature represents an efficient approach to screening samples for the presence of diverse somatic ESR1 mutations and translocations that drive endocrine treatment failure in MBC. SIGNIFICANCE: This study identifies a gene signature diagnostic for functional ESR1 fusions that drive poor outcome in advanced breast cancer, which could also help guide precision medicine approaches in patients harboring ESR1 mutations.