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High-Dimensional Precision Medicine From Patient-Derived Xenografts.

ABSTRACT: The complexity of human cancer often results in significant heterogeneity in response to treatment. Precision medicine offers the potential to improve patient outcomes by leveraging this heterogeneity. Individualized treatment rules (ITRs) formalize precision medicine as maps from the patient covariate space into the space of allowable treatments. The optimal ITR is that which maximizes the mean of a clinical outcome in a population of interest. Patient-derived xenograft (PDX) studies permit the evaluation of multiple treatments within a single tumor, and thus are ideally suited for estimating optimal ITRs. PDX data are characterized by correlated outcomes, a high-dimensional feature space, and a large number of treatments. Here we explore machine learning methods for estimating optimal ITRs from PDX data. We analyze data from a large PDX study to identify biomarkers that are informative for developing personalized treatment recommendations in multiple cancers. We estimate optimal ITRs using regression-based (Q-learning) and direct-search methods (outcome weighted learning). Finally, we implement a superlearner approach to combine multiple estimated ITRs and show that the resulting ITR performs better than any of the input ITRs, mitigating uncertainty regarding user choice. Our results indicate that PDX data are a valuable resource for developing individualized treatment strategies in oncology. Supplementary materials for this article are available online.

SUBMITTER: Rashid NU

PROVIDER: S-EPMC8451968 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background & aimsBiliary tract tumors are uncommon but highly aggressive malignancies with poor survival outcomes. Due to their low incidence, research into effective therapeutics has been limited. Novel research platforms for pre-clinical studies are desperately needed. We sought to develop a patient-derived biliary tract cancer xenograft catalog.MethodsWith appropriate consent and approval, surplus malignant tissues were obtained from surgical resection or radiographic biopsy and implanted into immunocompromised mice. Mice were monitored for xenograft growth. Established xenografts were verified by a hepatobiliary pathologist. Xenograft characteristics were correlated with original patient/tumor characteristics and oncologic outcomes. A subset of xenografts were then genomically characterized using Mate Pair sequencing (MPseq).ResultsBetween October 2013 and January 2018, 87 patients with histologically confirmed biliary tract carcinomas were enrolled. Of the 87 patients, 47 validated PDX models were successfully generated. The majority of the PDX models were created from surgical resection specimens (n = 44, 94%), which were more likely to successfully engraft when compared to radiologic biopsies (p = 0.03). Histologic recapitulation of original patient tumor morphology was observed in all xenografts. Successful engraftment was an independent predictor for worse recurrence-free survival. MPseq showed genetically diverse tumors with frequent alterations of CDKN2A, SMAD4, NRG1, TP53. Sequencing also identified worse survival in patients with tumors containing tetraploid genomes.ConclusionsThis is the largest series of biliary tract cancer xenografts reported to date. Histologic and genomic analysis of patient-derived xenografts demonstrates accurate recapitulation of original tumor morphology with direct correlations to patient outcomes. Successful development of biliary cancer tumografts is feasible and may be used to direct subsequent therapy in high recurrence risk patients.Lay summaryPatient biliary tract tumors grown in immunocompromised mice are an invaluable resource in the treatment of biliary tract cancers. They can be used to guide individualized cancer treatment in high-risk patients.

Project description:Malignant peripheral nerve sheath tumors (MPNSTs) are aggressive soft-tissue sarcomas with a poor survival rate, presenting either sporadically or in the context of neurofibromatosis type 1 (NF1). The histological diagnosis of MPNSTs can be challenging, with different tumors exhibiting great histological and marker expression overlap. This heterogeneity could be partly responsible for the observed disparity in treatment response due to the inherent diversity of the preclinical models used. For several years, our group has been generating a large patient-derived orthotopic xenograft (PDOX) MPNST platform for identifying new precision medicine treatments. Herein, we describe the expansion of this platform using six primary tumors clinically diagnosed as MPNSTs, from which we obtained six additional PDOX mouse models and three cell lines, thus generating three pairs of in vitro-in vivo models. We extensively characterized these tumors and derived preclinical models, including genomic, epigenomic, and histological analyses. Tumors were reclassified after these analyses: three remained as MPNSTs (two being classic MPNSTs), one was a melanoma, another was a neurotrophic tyrosine receptor kinase (NTRK)-rearranged spindle cell neoplasm, and, finally, the last was an unclassifiable tumor bearing neurofibromin-2 (NF2) inactivation, a neuroblastoma RAS viral oncogene homolog (NRAS) oncogenic mutation, and a SWI/SNF-related matrix-associated actin-dependent regulator of chromatin (SMARCA4) heterozygous truncated variant. New cell lines and PDOXs faithfully recapitulated histology, marker expression, and genomic characteristics of the primary tumors. The diversity in tumor identity and their specific associated genomic alterations impacted treatment responses obtained when we used the new cell lines for testing compounds against known altered pathways in MPNSTs. In summary, we present here an extension of our MPNST precision medicine platform, with new PDOXs and cell lines, including tumor entities confounded as MPNSTs in a real clinical scenario. This platform may constitute a useful tool for obtaining correct preclinical information to guide MPNST clinical trials.

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High-Dimensional Precision Medicine From Patient-Derived Xenografts.

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