Serial Number Profiles In Sap

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Brunilda Chestnut

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Aug 3, 2024, 4:51:13 PM8/3/24

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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.

Human tumors engrafted into transplant-compliant recipient mice (patient-derived xenografts (PDXs)) have advantages over previous model systems of human cancer (for example, genetically engineered mouse models1,2 and cancer cell lines3) for preclinical drug efficacy studies because they allow researchers to directly study human cells and tissues in vivo4,5,6,7. Comparisons of genome characteristics and histopathology of primary tumors and xenografts of various cancer types8,9,10,11,12,13,14 have demonstrated that the biological properties of patient-derived tumors are largely preserved in xenografts. A growing body of literature supports their use in cancer drug discovery and development15,16,17.

A caveat to PDX models is that intratumoral evolution can occur during engraftment and passaging18,19,20,21,22. Such evolution could potentially modify treatment response of PDXs with respect to the patient tumors (PTs)19,23,24, particularly if the evolution were to systematically alter cancer-related genes. Recently, Ben-David et al.23 reported extensive PDX copy number divergence from the PT of origin and across passages, based mainly on large-scale assessment of copy number alteration (CNA) profiles inferred from gene expression microarray data. They raised concerns about genetic evolution in PDXs as a consequence of mouse-specific selective pressures, which could impact the capacity of PDXs to faithfully model patient treatment response. Such results contrast with reports of observations of genomic fidelity of PDX models with respect to the originating PTs and from early to late passages by direct DNA measurements in several dozen PDX models8,11,25.

Here, we resolve these contradicting observations by systematically evaluating CNA changes and the genes they affect during engraftment and passaging in a large, internationally collected set of PDX models, comparing both RNA- and DNA-based approaches. The data collected, as part of the US National Cancer Institute (NCI) PDX Development and Trial Centers Research Network (PDXNet) Consortium and EurOPDX Consortium, comprises PT and PDX samples from >500 models. Our study demonstrates that previous reports of systematic copy number divergence between PTs and PDXs are incorrect, and that there is high retention of copy number during PDX engraftment and passaging. This work also finely enumerates the copy number profiles in hundreds of publicly available models, which will enable researchers to assess the suitability of each for individualized treatment studies.

We have assembled CNA profiles of 1,451 unique samples (324 PT samples and 1,127 PDX samples), corresponding to 509 PDX models contributed by participating centers of the PDXNET, the EurOPDX Consortium and other published datasets11,26 (see Methods, Supplementary Methods, Supplementary Table 1 and Supplementary Fig. 1). We estimated the copy number from five data types (single nucleotide polymorphism (SNP) array, whole-exome sequencing (WES), low-pass whole-genome sequencing (WGS), RNA sequencing (RNA-seq) and gene expression array data), yielding 1,548 tumor datasets including samples assayed on multiple platforms (see Methods, Supplementary Methods and Supplementary Data 1). Paired normal DNA, and in some cases paired normal RNA, were also obtained to calibrate WES and RNA-seq tumor samples.

a, Numbers of PDX models for each tumor type, with models also having multiple PDX samples or having matched PT samples specified. b, Distributions of datasets by passage number and assay platform for PTs and PDX samples, separated by tumor type. Late passages include P18, P19 and P21 samples.

We further explored whether the stability of copy number during engraftment and passaging is affected by mutations in genes known to impact genome stability (see Methods). Overall, we observed that the presence of mutations in such genes does not lead to increased copy number changes during PDX engraftment and passaging (Supplementary Note 10 and Supplementary Fig. 66).

Next, we investigated whether recurrent CNA changes occur in PDXs in a tumor-type-specific fashion. To this aim, we analyzed further the WGS-based CNA profiles of large metastatic CRC and BRCA series, composed of matched trios of PT, PDX at early passage (PDX-early) and PDX at later passage (PDX-late). Genomic Identification of Significant Targets in Cancer (GISTIC)50,51 analysis was applied separately to identify recurrent CNAs in each PT, PDX-early and PDX-late cohort of CRC and BRCA (see Methods and Supplementary Table 6). As expected, CRCs and BRCAs generated different patterns of significant CNAs but, within each tumor type, GISTIC profiles of the PT, PDX-early and PDX-late cohorts were virtually indistinguishable (Fig. 5a, Extended Data Fig. 7 and Supplementary Note 13), demonstrating no gross genomic alteration systematically acquired or lost in PDXs.

A challenge for evaluating any model system is that there is no clear threshold for genomic change that determines whether the model will still reflect patient response. Genetic variation among multiregion samples within a patient can shed light on this point54,65,66,67,68 since the goal of a successful treatment would be to eradicate all of the multiple regions of the tumor. We found that the copy number differences between PT and PDX are no greater than the variations among multiregion tumor samples or intra-patient samples. Thus, concerns about the genetic stability of the PDX system are likely to be less important than the spatial heterogeneity of solid tumors themselves. This result is consistent with our results on lineage effects during passaging, which indicate that intratumoral spatial evolution is the major reason for genetic drift.

We observed no evidence for systematic mouse environment-induced selection for cancer- or treatment-related genes via copy number changes, although individual cases vary (see example in Extended Data Fig. 6c). Moreover, only a small fraction of sample pairs (2.44%; 43 out of 1,758) showed large CNA discordance (see Methods), suggesting that clonal selection out of a complex population is rare. These results indicate that the variations observed in PDXs are mainly due to spontaneous intratumoral evolution, rather than murine pressures (Supplementary Note 17).

In summary, our in-depth tracking of CNAs throughout PDX engraftment and passaging confirms that tumors engrafted and passaged in PDX models maintain a high degree of molecular fidelity to the original PTs, thus verifying their suitability for preclinical drug testing. At the same time, our study does not rule out that PDXs will evolve in individual trajectories over time; thus, for therapeutic dosing studies, the best practice is to confirm the existence of expected molecular targets and obtain sequence characterizations in the cohorts used for testing as close to the time of the treatment study as is practical.

As the terminology of tumor types/subtypes by the different contributing centers was not consistent, we used the Disease Ontology database69 ( -ontology.org/), along with cancer types listed on the NCI website ( ) and in TCGA publications70,71, to unify and group the tumor types/subtypes under broader terms, as shown in Fig. 1 and Supplementary Table 2.

The estimation of CNA profiles from SNP array was detailed previously34. In short, for Affymetrix Human SNP 6.0 arrays, PennCNV-Affy and Affymetrix Power Tools72 were used to extract the B-allele frequency and log[R ratio] from the CEL files. Due to the absence of paired normal samples, the allele-specific signal intensity for each PDX tumor was normalized relative to 300 randomly selected sex-matched Affymetrix Human SNP 6.0 array CEL files obtained from the International HapMap Project73. For Illumina Infinium Omni2.5Exome-8 SNP arrays (version 1.3 and version 1.4 kits), the Illumina GenomeStudio software was used to extract the B-allele frequency and log[R ratio] from the signal intensity of each probe. The single sample mode of the Illumina GenomeStudio was used, which normalizes the signal intensities of the probes with an Illumina in-house dataset. The single tumor version of ASCAT33 (version 2.4.3 for JAX SNP data and version 2.5.1 for SIBS SNP data) was used for GC correction, predictions of the heterozygous germline SNPs based on the SNP array platform, and estimation of ploidy, tumor content and allele-specific copy number segments. The resultant copy number segments were annotated with the log2[ratio of the total copy number relative to the predicted ploidy from ASCAT].