Pharmacologic profiling of patient-derived xenograft models of primary treatment-naïve triple-negative breast cancer

Triple-negative breast cancer (TNBC) accounts for 15–20% of breast cancer cases in the United States, lacks targeted therapeutic options, and is associated with a 40–80% risk of recurrence. Thus, identifying actionable targets in treatment-naïve and chemoresistant TNBC is a critical unmet medical need. To address this need, we performed high-throughput drug viability screens on human tumor cells isolated from 16 patient-derived xenograft models of treatment-naïve primary TNBC. The models span a range of TNBC subtypes and exhibit a diverse set of putative driver mutations, thus providing a unique patient-derived, molecularly annotated pharmacologic resource that is reflective of TNBC. We identified therapeutically actionable targets including kinesin spindle protein (KSP). The KSP inhibitor targets the mitotic spindle through mechanisms independent of microtubule stability and showed efficacy in models that were resistant to microtubule inhibitors used as part of the current standard of care for TNBC. We also observed subtype selectivity of Prima-1Met, which showed higher levels of efficacy in the mesenchymal subtype. Coupling pharmacologic data with genomic and transcriptomic information, we showed that Prima-1Met activity was independent of its canonical target, mutant p53, and was better associated with glutathione metabolism, providing an alternate molecularly defined biomarker for this drug.

Molecular attributes of the PDX cohort. We compared the genomes and transcriptomes of PDTCs from each PDX model to their respective patient tumor cells and to TNBC as a whole to establish the translational relevance of our models. In TNBC, TP53 was the most frequently mutated gene. TP53 mutation occurs in approximately 75% of cases, with more infrequent mutations occurring in other cancer-associated genes 20,21 . TP53 mutation occurred in 47% of our PDX models (42% of samples when including patient data) with less frequent mutations found in other genes, consistent with what has been previously described in TNBC 22 (Fig. 2A). We next applied the molecular sub-classification of TNBC 23 to our PDX collection, which revealed that our cohort consisted of two basal-like 1 (BL1), four basal-like 2 (BL2), one immune-modulatory (IM), four luminal androgen receptor (LAR), four mesenchymal (M), and one unstable (UNS) subtypes (Fig. 2B), thus providing a diverse sampling of molecular subtypes that captured all but mesenchymal stem-like. To determine whether the gene expression profiles of the patient tumor cells were retained in their corresponding PDX tumor, we correlated the profiles of each PDX tumor sample with each patient tumor sample. The analysis showed a strong positive Pearson correlation coefficient between each model and its respective patient tumor (Fig. 2C). Taken together, these data demonstrate that our collection of PDX models maintain characteristics of their respective patient tumors and are representative of the majority of TNBC subtypes from both mutational and transcriptomic perspectives. Finally, we looked at the sub-clonal architecture of the nine matched patient and PDX derived models with high quality samples. While we cannot rule out undetected large-scale clonal expansion events, the exome sequencing revealed smaller shifts in the prevalence of subclones of approximately 30% (Supplemental Fig. 1). Similarly, we observed that certain mutations were lost when comparing the patient and PDX samples ( Fig. 2A). While this is a potential limitation in the translational relevance of these models, others have shown that the retention of the exact sub-clonal architecture is not necessary to recapitulate pharmacologic susceptibilities in vivo 24,25 . Generation of chemical profiles. Next, we generated chemical profiles for PDTCs from each PDX model using the method schematized in Fig. 3A. In brief, PDTCs were seeded into 384-well plates and grown as non-  Fig. 2A) and allowed to recover overnight. Next, the PDTCs were screened against a mechanistically annotated chemical library composed from the Broad Informer Library (CTRP_vr2), the NCI-approved clinical oncology library (version 5), and an in-house library of clinical oncology drugs totaling 634 chemical entities with a drug exposure window of 72 h, which was previously optimized using growth data (Supplemental Fig. 2B). We demonstrated a highly consistent Z′, a common measure of assay robustness, with an average performance greater than 0.75 across all PDX models. Further, we showed a high degree of technical reproducibility with an average minimum significance ratio below two, indicative of a highly robust and technically reproducible assay (Supplemental Fig. 3A,B). Primary screening assays were done with 3-4 technical replicates at three dose concentrations (10.0, 1.0, 0.1 uM), depending on the number of viable PDTCs isolated, which varied between PDX models. Drug effects were normalized to the vehicle control (DMSO 0.1% v/v) using the following formula FA = 1-(X c /X DMSO ), where X c is the luminescence signal for each data point and X DMSO is the median luminescence from on-plate DMSO control wells. The normalized data were then fit across the three-point dose-response curve and summarized by calculating the area under the fitted curve. One potential limitation of this method is that it fails to account for alterations in tumor   www.nature.com/scientificreports/ cell growth rate that may confound chemical susceptibilities 26 . Another limitation of this data set is the limited resolution provided by a fixed-concentration three-point dose-response curve, which may fail to recapitulate chemical selectivity for very potent drugs. However, quantification of sub-maximal efficacy is still possible in those contexts. Despite these limitations, we were still able to draw a series of relevant conclusions from the drug screening results obtained using the methods outlined above. Hierarchical clustering of the pharmacologic profiles across PDX models demonstrated that LAR and M subtypes tended to co-cluster (Chi-squared p = 0.37, p = 0.19 when cluster_1 was excluded as an orphan), while the basal-like subtypes were intermixed (Fig. 3B,C). Next, we used chemical target and pathway annotations to visualize the response of PDTCs to families of drugs used in the screen. Analysis of AUC data for compounds targeting the same pathway or with similar mechanism of action showed highly correlated response profiles ( Supplementary Fig. 3C). These results are another metric attesting to the reproducibility and validity of the drug screeing results. Independent clustering of chemical entities revealed three sub-clusters, which we subsequently refer to as "inactive", "selective", and "pan-active" (Fig. 3B). Inactive describes those drugs that showed no or minimal activity in the majority of PDTCs, whereas pan-active describes drugs that exhibited broad activity across many models. Members of the anthracycline family (e.g., doxorubicin, epirubicin, and duanorubucin) were pan-active in these screens, further validating the use of these drugs as a front-line therapy in TNBC. Selective describes drugs showing large differences in susceptibility across models, and these can provide insight into therapeutically actionable targets that are relevant to specific molecular subtypes or can describe heterogeneity in molecular signaling cascades; thus, selective drugs are interesting from a personalized medicine standpoint.

Inhibition of mitotic spindle proteins.
We first looked at molecularly defined pathway activity from single sample gene set enrichment analysis (ssGSEA) to provide a starting point for rationalizing the observed drug activities. From this analysis, we selected the Hallmark pathway representations, which are a well-recognized and curated set of 50 pathways 27 . The mitotic spindle pathway was ranked as one of the most consistently active pathways across all PDTCs (Fig. 4A). Indeed, indirect targeting of the mitotic spindle using microtubule poisons such as paclitaxel is considered the mainstay neoadjuvant chemotherapy for TNBC 28 . Yet multiple PDTCs tested here exhibited resistance to taxane-based therapies, which was a feature that was recapitulated in matched PDX models treated in vivo with paclitaxel (Supplemental Fig. 4). Given the clinical importance of this drug and the observation of resistance in treatment-naïve TNBC, we sought to identify alternate compounds that could inhibit the mitotic spindle with improved activity.
The panel of drugs that target the mitotic spindle in this library include agents that target microtubule stabilization (e.g., taxanes), microtubule destabilization (e.g., vinca alkaloids), microtubule associated kinesin proteins (e.g., kinesin spindle protein [KSP] inhibitors), and mitotic kinases (e.g., AURKA inhibitors) (Fig. 4B). From the HTS data, we observed that taxanes had a broad spectrum of activity, including activity against PDTCs that were partially or fully resistant to paclitaxel in vitro (Fig. 4C). Vincristine sulfate and related molecules showed greater potency relative to taxanes; however, complete resistance was still observed in some cases (Fig. 4D). Thus, despite the potency of taxanes and vinca alkaloids, we observed sub-maximal efficacy in certain PDTCs, which reflect the presence of resistant clonal populations within these tumors. Further studies are warranted to identify mechanisms of resistance. However, the KSP inhibitor SB-743921, which was present in two of the sub-libraries, was pan-active and fully efficacious in all PDTCs tested and reduced the viability of PDTCs that were resistant to both taxane and vinca in vitro (Fig. 4D,E).
Subtype selective drug activity. Having observed the trend that LAR and M subtypes were separable using pharmacologic profiles (Fig. 3B,C), we next investigated the selectivity of targeted agents across molecular subtypes. To address this, drug activity was compared across molecular subtypes, excluding the IM and UNS classifications due to limited observations, using analysis of variance (ANOVA). From this analysis, we identified 16 drugs that had at least one significant interaction (p < 0.05) between molecular classes (Fig. 5). Protein synthesis inhibitors (homoharringtonine and omacetaxine mepesuccinate) and inhibitors of Kit/FLT3 (Chir 258) and VEGFR/MET (tivozanib) showed moderately higher levels of sensitivity in basal-like subtypes. LAR was characterized with significant loss of sensitivity to multiple drugs targeting NAE1 (pevonedistat and MLN4924), DNA-damaging agents (gemcitabine hydrochloride and cytarabine), and PAK4 inhibitors (PF-3758309) in addition to the targets mentioned above. Interestingly, the ROCK inhibitor Y-27632 displayed selectivity in LAR PDX models. PDX models from the M subtype had higher activity with Prima-1 and Prima-1 Met .
Exploration of Prima-1 Met selectivity. Prima-1 and its analogue Prima-1 Met (also known as APR-246) showed higher efficacy in the M subtype, with appreciable but lower efficacy in BL2 backgrounds (Fig. 6A). The primary mechanism of action for these drugs is to covalently modify mutant p53, resulting in a conformational change that restores activity 29 . Because TP53 is the most frequently mutated gene in this cohort and in TNBC as a whole, we investigated whether there was a pharmacogenomic association between Prima-1 Met activity and TP53 mutation status. ANOVA and post-hoc Sidak multiple testing correction revealed no significant interaction between Prima-1 Met sensitivity and TP53 mutation status (p > 0.99). However, this analysis did identify a significant pharmacogenomic interaction between Chk1 inhibitor AZD7762 and TP53 mutation (p < 0.01), which confirms the findings of other research 30 and provides additional validation of this method (Fig. 6B). Overexpression of MDM2 can also phenocopy TP53 loss of function mutations, which prompted us to explore whether there was an association between the AUC of Prima-1 Met activity and the gene expression of MDM2, which showed no significant correlation (Pearson p = 0.88, r = 0.05). Collectively, these data led us to explore potential alternate mechanisms to rationalize Prima-1 Met activity. www.nature.com/scientificreports/ Prima-1 Met has been shown to induce apoptosis by increasing the expression of TP73 and NOXA and depleting glutathione pools, resulting in increases in reactive oxygen species, independent of TP53 mutation status 31 . In an unbiased correlative analysis between drug activity and ssGSEA pathways, we found significant correlations with glutathione metabolism and conjugation (Fig. 6C,D). These data are consistent with established alternate mechanisms of action for Prima-1 Met and implicate Prima-1 Met as working through a glutathione-dependent mechanism in this context.

Conclusions and clinical implications
In this study, we established and validated a preclinical pharmaco-genomic resource generated from PDX models from representative treatment-naïve tumors of patients with primary TNBC. We performed multiplexed highdimensional analysis including bulk whole exome and RNA sequencing as well as high-throughput chemical screening of human tumor cells to generate a resource for pharmaco-genomic and transcriptomic studies aimed at understanding and targeting TNBC. This resource expands upon other breast cancer-focused PDX biobanks by providing a molecularly diverse, TNBC-specific collection of models with matched pharmacologic profiles 12,16 . In addition, the PDX models in our collection were derived from the primary breast tumors of untreated TNBC patients. Most published PDX collections to date have been generated from primary or metastatic tumors from pretreated patients 16 . Thus, our collection provides a unique resource to study tumors that have not undergone   www.nature.com/scientificreports/ therapy-induced selection and to identify potential therapies for targeting resistance in the setting of primary, treatment-naïve breast cancer. Other high-throughput chemical screens with PDTCs have identified a wide range of actionable susceptibilities, which were largely recapitulated in our dataset. For example, Matossian et al. identified DNA synthesis, microtubule, and topoisomerase inhibitors as the top classes of drugs exhibiting activity against PDX-derived mesenchymal tumor cells 32 . In a study by Bruna et al., anthracyclines, NAMPT and BCL-2 inhibitors were found to have the highest levels of activity against a subset of 10 basal-like TNBCs, while responses to taxanes and mTOR inhibitors were more variable 16 . We observed varying degrees of activity to DNA synthesis, microtubule, and mTOR inhibitors in our study, while anthracyclines, BCL-2 and NAMPT inhibitors tended to be pan-active. In a study by Turner et al., a panel of 10 short-term cultures of PDTCs were screened against ~ 1300 drugs at a single concentration of 10 µM using bioluminescence as the primary read-out of viability 33 . A panel of four basal-like TNBC PDX models were then prioritized and tested in combination with the proteasome inhibitor carfilzomib or the EGFR inhibitor afatinib against a library of 176 pan-active drugs identified in the primary screen. From this, the survivin inhibitor YM-155 demonstrated synergy when combined with afatinib. In our study, YM-155 was strongly active in all PDTCs while afatinib was moderately active, and carfilzomib was inactive in the majority of basal-like PDTCs. Another study testing a panel of 12 chemotherapeutic agents on basal-like PDTCs, demonstrated susceptibility towards bortezomib, dacarbazine, and cyclophosphamide 34 . In our study bortezomib was pan-active while dacarbazine and cyclophosphamide were inactive, albeit we tested those drugs at greater than 100-fold lower concentrations. Cross comparisons of all of these datasets should take into account drug concentrations, conditions used for culturing PDTCs, and therapies that the tumors had been exposed to prior to the PDX model generation.
Exploratory analysis of the pharmacologic profiles generated from our PDX cohort identified multiple targeted therapies with appreciable preclinical activity. Importantly, many of the drugs identified in our study have direct translational applicability, with pre-established pharmacokinetic and toxicity profiles already performed in other disease implications. These include targeted therapies to overcome intrinsic taxane resistance, which is considered a mainstay therapy in TNBC, using an alternate mechanism to target the mitotic spindle. Targeting mitotic proteins such as KSP has previously been proposed as an efficacious mechanism to target the mitotic spindle. Other research groups have observed potent inhibition of cell growth in immortalized cell lines and antineoplastic potential in xenograft models with KSP inhibitors 35 . KSP inhibitors were also identified as a   www.nature.com/scientificreports/ potentially synergistic target when combined with vinblastine in TNBC using a computational approach that combined contrasting phenotypes 36 . A first-in-human phase I/II clinical trial using SB-743921 in advanced solid tumors showed efficacy and a more favorable toxicity profile compared to taxanes 37 . Our data, in combination with published literature, support revitalized interest in using KSP inhibitors, and further mechanistic studies in the context of TNBC are warranted. Prima-1 Met demonstrated selectivity towards the M subtype, which was independent of TP53 mutation status. A recently completed phase I clinical trial conducted in Sweden using single-agent Prima-1 Met in hematologic and prostate malignancies showed measurable target engagement (i.e., induction of genes regulated by p53) in vivo. As of 2019, phase II/III clinical trials have begun in the United States using Prima-1 Met in combination with azacitidine for myelodysplastic syndrome and acute myeloid leukemia 38 . Others have proposed using Prima-1 Met in TNBC due to the high rate of TP53 mutations 39 . In those studies, Prima-1 Met was tested against a panel of TNBC cell lines with and without TP53 mutations to demonstrate p53-dependent activity 39,40 . However, in our study we did not observe TP53 mutation-dependent activity but rather found glutathione pathways, an established alternate target of Prima-1 Met , as a potential alternate biomarker predictive of activity. Thus, use of Prima-1 Met has the potential to impact a large spectrum of TNBC patients who either harbor TP53 mutations or have alterations in glutathione metabolism. Further validation of these findings will be required to translate them into clinical settings.

Methods
Collection of patient-derived materials. All research conducted in human patients followed national guidelines including the Common Rule (https ://www.hhs.gov/ohrp/human subje cts/commo nrule /) and the Health Insurance Portability and Accountability Act (HIPPA) privacy and security rules 41 . All patients from

Development and optimization of a high-throughput chemical screen.
To optimize assay conditions for establishing short-term cultures of PDTCs (Supplemental Fig. 2A), we generated a single 384-well plate assay to simultaneously optimize cell seeding densities, test different culture media, and determine the growth properties of PDTCs under each condition. Different culture media were tested in the presence and absence of serum over three different cell seeding densities, and cell growth was monitored using an ATP bioluminescent reporter (CellTiter-Glo) over 7 days (Supplemental Fig. 2B). From these experiments, we determined that Mam-moCult medium maximally supported the viability and growth of PDTCs in vitro over 7 days in roughly half of the 24 models tested. In this medium, cells grew as either single-cell or self-associating non-adherent suspensions (Supplemental Fig. 4A). PDTCs that maintained viability or proliferative capacity were prioritized for full screening. Medium supplemented with serum improved viability of some PDTCs; however, serum-containing medium was not considered for screening assays due to the potential of depleting stem-like populations and altering tumor cell behavior. It was also determined that seeding 2000 cells/well was optimal for the screens. Once culture conditions were optimized, a focused screen was performed to identify a library of compounds that could be used as controls for tumor cell killing. This library consisted of eight non-redundant toxic oncology drugs (top four shown in Supplemental Fig. 2C). Anisomycin was found to robustly decrease cell viability across all PDX models and was used as an on-plate control to monitor assay performance during screening.
Screening assays. Before plating, cell number and viability were determined by mixing 10 uL of culture media containing tumor cells with 10 uL trypan blue solution in a disposable counting slide, which was then read using a TC10 automated cell counter (Bio-Rab). Next, 2000 viable cells/well were transferred into barcoded 384-µ well clear plates (Griener, Cat No. 781091) using a MultiDrop Combi Reagent dispenser (Thermo). All drug libraries were diluted in DMSO and arrayed on Echo certified low dead volume plates (LDV, Labcyte). Drugs were subsequently transferred from the LDV source plate into assay plates using an Echo liquid handling machine (Labcyte). Wells were treated such that the final concentration of DMSO in media did not exceed 1% (v/v).

Statistics and software.
We processed the next-generation sequencing data using the BETSY system 42 .
We called variants and estimated gene expression values as previously described 19 , except that we identified contaminating mouse host reads for subtraction using Xenome 43 . PyClone 44 was used to identify sub-clonal populations in the patient and PDX data. Using this software, sub-clones were identified using a robustness score of greater than 0.5 and had to contain at least 2 mutations. Data organization and statistical analysis was performed using an automated screening tracking workflow developed in Pipeline Pilot (2018 Server, BIOVIA) and R statistics. The log concentration was fitted to the normalized response using a cascade model which leverages www.nature.com/scientificreports/ iteratively reweighted least squared to fit the response surface to a four-parameter logistic or linear model. AUC and IC 50 values were generated from the fitted dose-response curve. Dose-response figures used for this publication were rendered in Graphpad Prism. Identification of subtype selective molecules was performed using the AOV function in R. Pharmacogenomic associations with mutant TP53 and time series analysis of in vivo taxane data where performed and graphed using Graphpad Prism.

Data availability
Normalized data frames (PDTC by Drug AUC, Gene expression (TPM), and ssGSEA pathway scores) and open-source code that are critical to the analysis and generation of figures are available at https ://githu b.com/ ReidT Powel l/TNBC_profi les_1. Analytical workflows generated in proprietary environments are available upon reasonable request.