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. 2023 Mar 1;18(3):e0282177.
doi: 10.1371/journal.pone.0282177. eCollection 2023.

Therapeutic implications of transcriptomics in head and neck cancer patient-derived xenografts

Rex H Lee  1 , Ritu Roy  2 , Hua Li  1 , Aaron Hechmer  2 , Tian Ran Zhu  1 , Adila Izgutdina  1 , Adam B Olshen  2   3 , Daniel E Johnson  1 , Jennifer R Grandis  1
Affiliations

Therapeutic implications of transcriptomics in head and neck cancer patient-derived xenografts

Rex H Lee et al. PLoS One. .

Abstract

There are currently no clinical strategies utilizing tumor gene expression to inform therapeutic selection for patients with head and neck squamous cell carcinoma (HNSCC). One of the challenges in developing predictive biomarkers is the limited characterization of preclinical HNSCC models. Patient-derived xenografts (PDXs) are increasingly recognized as translationally relevant preclinical avatars for human tumors; however, the overall transcriptomic concordance of HNSCC PDXs with primary human HNSCC is understudied, especially in human papillomavirus-associated (HPV+) disease. Here, we characterized 64 HNSCC PDXs (16 HPV+ and 48 HPV-) at the transcriptomic level using RNA-sequencing. The range of human-specific reads per PDX varied from 64.6%-96.5%, with a comparison of the most differentially expressed genes before and after removal of mouse transcripts revealing no significant benefit to filtering out mouse mRNA reads in this cohort. We demonstrate that four previously established HNSCC molecular subtypes found in The Cancer Genome Atlas (TCGA) are also clearly recapitulated in HNSCC PDXs. Unsupervised hierarchical clustering yielded a striking natural division of HNSCC PDXs by HPV status, with C19orf57 (BRME1), a gene previously correlated with positive response to cisplatin in cervical cancer, among the most significantly differentially expressed genes between HPV+ and HPV- PDXs. In vivo experiments demonstrated a possible relationship between increased C19orf57 expression and superior anti-tumor responses of PDXs to cisplatin, which should be investigated further. These findings highlight the value of PDXs as models for HPV+ and HPV- HNSCC, providing a resource for future discovery of predictive biomarkers to guide treatment selection in HNSCC.

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Conflict of interest statement

I have read the journal’s policy and the authors of this manuscript have the following competing interests: D.E.J. and J.R.G. are co-inventors of cyclic STAT3 decoy and have financial interests in Bluedot Bio. Bluedot Bio has licensed patents related to cyclic STAT3 decoy.

Figures

Fig 1
Fig 1. Mapping and filtering species-specific reads in the PDX transcriptome.
(A) Bar Chart depicting the percentage of mRNA reads [y-axis] uniquely mapped to human (red) and mouse (blue) genomes, or ambiguous (green), neither (yellow), or both genomes (cyan) for each of the 64 PDX models in this study [x-axis]. (B) Hierarchical clustering based on the top 2,000 most variable genes between PDXs, using an “unfiltered” set with all reads (left) and “filtered” set with mouse reads removed (right). (C) Schematic of the distinction between “sample-level” (comparing clustergram columns [blue]) and “gene-level” (comparing clustergram rows [purple]) correlation comparisons. (D) Representative sample-level correlation plots from PDXs with the highest, intermediate, and lowest percentage of human reads (PDX 52, 96.52% human; PDX 1, 87.11% human; PDX 17, 64.64% human).
Fig 2
Fig 2. PDXs recapitulate molecular HNSCC subtypes identified in TCGA.
Comparison of gene expression patterns between HNSCC samples in TCGA (left, vertical columns) and the HNSCC PDXs of this cohort (right, vertical columns), with rows representing TCGA classifier genes. The four well-characterized HNSCC molecular TCGA subtypes are conserved in the PDX collection (black, atypical; red, classical; green, mesenchymal; blue, basal).
Fig 3
Fig 3. Unsupervised hierarchical clustering separates PDXs by HPV status and primary tumor site.
Hierarchical clustering of RNA sequencing data via Ward’s linkage method yields two major groups of PDXs (A and B), each of which is further divided into two subgroups (A1, A2, B1, B2). PDXs (columns) align by common expression of individual genes (rows). All HPV+ tumors clustered together in a single subgroup, A1, which contained no HPV- PDXs. Demographic and clinical characteristics are shown above the clustergram and include age, sex, stage, site, and HPV status.
Fig 4
Fig 4. C19orf57 expression and cisplatin response in PDXs.
(A) Therapeutic response of PDXs to cisplatin, arranged in order of increasing C19orf57 expression. (PDXs 36 and 30 are HPV+; PDXs 13, 15, 12, and 18 are HPV-). (B) C19orf57 expression, in units of log2(counts/million), for each of the six PDXs tested for cisplatin response.
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