From the Clinic to the Bench and Back Again in One Dog Year: How a Cross-Species Pipeline to Identify New Treatments for Sarcoma Illuminates the Path Forward in Precision Medicine

Abstract

Cancer drug discovery is an inefficient process, with more than 90% of newly-discovered therapies failing to gain regulatory approval. Patient-derived models of cancer offer a promising new approach to identify new treatments; however, for rare cancers, such as sarcomas, access to patient samples is limited, which precludes development of patient-derived models. To address the limited access to patient samples, we have turned to pet dogs with naturally-occurring sarcomas. Although sarcomas make up <1% of all human cancers, sarcomas represent 15% of cancers in dogs. Because dogs have similar immune systems, an accelerated pace of cancer progression, and a shared environment with humans, studying pet dogs with cancer is ideal for bridging gaps between mouse models and human cancers. Here, we present our cross-species personalized medicine pipeline to identify new therapies for sarcomas. We explore this process through the focused study of a pet dog, Teddy, who presented with six synchronous leiomyosarcomas. Using our pipeline we identified proteasome inhibitors as a potential therapy for Teddy. Teddy was treated with bortezomib and showed a varied response across tumors. Whole exome sequencing revealed substantial genetic heterogeneity across Teddy's recurrent tumors and metastases, suggesting that intra-patient heterogeneity and tumoral adaptation were responsible for the heterogeneous clinical response. Ubiquitin proteomics coupled with exome sequencing revealed multiple candidate driver mutations in proteins related to the proteasome pathway. Together, our results demonstrate how the comparative study of canine sarcomas offers important insights into the development of personalized medicine approaches that can lead to new treatments for sarcomas in both humans and canines.

Keywords: cancer therapy; comparative oncology; precision medicine; tumor evolution; tumor heterogeneity.

Figure 1

An integrated preclinical drug discovery and validation pipeline. A 3 years old canine patient with synchronous leiomyosarcomas (LMS) was identified and recruited based on high risk of disease recurrence. Using both in vitro and in vivo patient-derived models, we identified proteasome inhibitors as candidates for validation in clinic. Clinicians applied the information from this preclinical pipeline for the treatment of the patient's recurrent and metastatic disease.

Figure 2

Patient-derived models of cancer enable seamless integration of high throughput drug screening with in vivo validations. (A) Schematic of the personalized medicine pipeline integrating in vitro and in vivo drug discovery and validation. (B) Hematoxylin and eosin stain of the patient derived xenograft model of the canine patient (LMS-D48) showing highly proliferative spindle-like cells. (C) The patient-derived cell line also displays a high proliferation rate, with an estimated doubling time of 26–36 h, and spindle-like mesenchymal morphology. (D) A species-specific PCR using mouse- and canine-specific primers confirms that the patient-derived cell line is of canine origin. (E) A preliminary drug screen of 119 FDA-approved compounds in the LMS-D48 cell line identified single standard-of-care agents and novel drug candidates. (F) Analysis of drug screen data at the pathway level showed sensitivity to protein synthesis, DNA/RNA synthesis, autophagy, and HDAC inhibitors. Novel agents, including HDAC inhibitors and proteasome inhibitors were identified as top candidates for validation.

Figure 3

High-throughput drug screens identify HSP inhibitors and proteasome inhibitors as promising therapies for personalized treatment. (A) LMS-D48 cells were plated at a density of 2,000 cells/well on plates prestamped with 2,100 drug compounds and DMSO. Cell titer glow assays were performed 72 h after cell plating to determine cell percent killing based on luminosity values. (B) Analysis of drug targets from the 2,100 screen with multiple drugs shows HSP inhibitors and proteasome inhibitors among the top pathways for which this cell line displays significant sensitivity. (C) Analysis of cellular pathways targeted by all drugs in the 2,100 drug screen shows that the cytoskeletal signaling pathway has the highest cell percent killing. (D) LMS-D48 cells were sensitive to 15 out of 19 HSP inhibitors. Among these, alvespimycin was the top candidate, with an estimated IC₅₀ of 345 nM. (E) Bortezomib was among the top drugs in the proteasome inhibitor class that killed LMS-D48 cells, with an estimated IC₅₀ value of 6 nM.

Figure 4

In vivo validation of top drug candidates reveals sensitivity of the LMS-D48 PDX to proteasome inhibition. (A) Alvespimycin (25 mg/kg) was administered intraperitoneally (i.p.) in vivo to SCID beige mice harboring LMS-D48 PDX tumors (n = 5 mice per treatment group) each in control and treatment groups. There was no statistical difference between control and treatment groups as measured by analysis of variance. (B) Bortezomib (1 mg/kg) was administered i.p. as described for alvespimycin above. Bortezomib significantly inhibited tumor growth of the PDX (p < 0.0001). (C) Representative images of resected tumors at treatment endpoint from the treatment and control groups show that control tumors are approximately twice the size of bortezomib-treated tumors (scale bar = 0.5 cm). (D) Animal weights were not significantly changed during treatment with either alvespimycin or bortezomib during the treatment course.

Figure 5

Translation of bortezomib into clinic. (A) At the time of metastatic spread of disease, the patient had lesions in the mediastinal and right iliac lymph nodes, the nasal mucosa, and local recurrence at the right pelvic limb. The patient was started on systemic bortezomib therapy at a dose of 1.3 mg/m² twice weekly and palliative radiation therapy of 8 Gy by four fractions, once weekly, for pain from the right pelvic limb lesion. Measurement of the pelvic limb lesion during therapy showed decrease in maximal tumor dimension throughout 3 weeks of radiation therapy and systemic bortezomib; though, there was an increase in size 2 weeks after both therapies were stopped. (B) CT staging studies and physical exam demonstrated an interval increase in tumor size at all sites of disease and after discontinuation of bortezomib therapy. The canine patient was then transitioned to palliative care.

Figure 6

Whole exome sequencing reveals inter-tumoral heterogeneity across the patient's tumors. (A) Phylogenetic reconstruction using the DNA compatibility algorithm supports a clade that includes the PDX, cell line, and the recurrent tumor with bootstrap support >900/1,000. (B) With the exception of the distance tree (Fitch), trees based on maximum parsimony and maximum likelihood also grouped the cancer models with the recurrent tumor. (C) A similarity matrix comparing all somatic variants from each sample shows the percentage of shared mutations across all samples. (D) Individual samples had higher numbers of mutated genes that were unique to each sample. Common shared mutations were relatively rare, reflecting the heterogeneity of the samples.

Figure 7

Ubiquitin proteomics of PDX tumors treated with bortezomib show differential ubiquitination in key pathways related to cytoskeletal dynamics and the proteasome. (A) Mass spectroscopy proteomics of ubiquitin tagged proteins identified increased ubiquitination of 160 proteins and decreased ubiquitination of 130 proteins. Multiple myosins displayed increased ubiquitination in bortezomib-treated tumors. (B) Pathway analysis of proteins with increased ubiquitination showed enrichment in pathways related to actin and proteasome subunits. (C) Pathway analysis of proteins with decreased ubiquitination showed enrichment in pathways related to adherens junctions, focal adhesions, and extracellular vesicles. (D) Genes affected by deleterious mutations in each sample were determined by analyzing the whole exome sequencing data with the Ensembl Variant Effect Predictor. Affected genes in the PDX were filtered by those in the cell line to eliminate potential contamination by mouse tissue (left). Comparison of this subset of genes with the proteins identified by proteomics analysis with increased or decreased ubiquitination in the PDXs treated with bortezomib identified an overlap of only 10 affected proteins. (E) The 10 proteins identified in (D) are shown and were affected in the tumors with high variability.