Single-cell RNA sequencing of head and neck squamous cell carcinoma patient-derived xenografts treated with cetuximab
Data files
Sep 03, 2026 version files 216.88 GB
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README.md
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UW-SCC185_ctrl_S1.zip
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UW-SCC185_ctrl_S251.zip
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UW-SCC185_ctx_S253.zip
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UW-SCC185_ctx_S3.zip
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UW-SCC194_ctrl.zip
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UW-SCC194_ctx.zip
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Abstract
Cetuximab, an EGFR-targeting monoclonal antibody, benefits only a subset of patients with head and neck cancer (HNC), and the tumor-intrinsic programs distinguishing responders from non-responders remain incompletely defined. This dataset was generated as part of a single-site, single-arm, open-label window-of-opportunity study in which patients with HNC received two doses of preoperative cetuximab prior to surgical resection. Patient-derived xenografts (PDXs) were established from study biopsies, and single-cell RNA sequencing of cetuximab-treated xenograft tissue was used to compare tumors that were clinically sensitive versus resistant to cetuximab. Two PDX models are represented: UW-SCC-185 and UW-SCC-194.
To capture biological heterogeneity within each model, tumors from multiple biological replicates were pooled prior to dissociation (n = 10 for UW-SCC-185; n = 12 for UW-SCC-194). Solid tumors were dissociated to single-cell suspensions, yielding suspensions with average viability exceeding 75%. Libraries were constructed using the PIPseq 3' Single Cell RNA Kit v4.0Plus (Fluent BioSciences/Illumina) — T20 for UW-SCC-185 and T2 for UW-SCC-194 — following the manufacturer's protocol, and sequenced on an Illumina NovaSeq X to a depth of at least 20,000 reads per cell. Reads were processed with PIPseeker against a combined human–mouse reference (GRCh38.p13 + GRCm39) to distinguish human tumor cells from murine stromal cells; quality control, normalization, clustering, and annotation were performed in [Seurat version 5.3.0]. After filtering and downsamples to 3000 cells for both UW-SCC-185 and UW-SCC-194.
Analysis of these data indicated that cetuximab sensitivity is marked by broad transcriptional remodeling, whereas resistance is associated with stable HER-family signaling and elevated TAM receptor activity. Raw sequencing data are deposited here. This Dryad record contains the processed derivatives required to reproduce the published analyses: filtered gene–cell count matrices, per-cell metadata (model, species assignment, cluster and cell-type annotations).
These data are reusable for studies of acquired and intrinsic EGFR-inhibitor resistance, AXL/TAM receptor signaling in solid tumors, comparative analysis of HNC tumor cell states across PDX models, and benchmarking of PIPseq chemistry and human–mouse read deconvolution.
Dataset DOI: 10.5061/dryad.s7h44j1ph
Description of the data and file structure
This dataset contains single‑cell RNA sequencing (scRNA‑seq) data from two head and neck squamous cell carcinoma (HNSCC) patient‑derived xenograft (PDX) models, UW‑SCC‑185 (cetuximab‑sensitive) and UW‑SCC‑194 (cetuximab‑resistant), treated with vehicle or cetuximab. This goal of this dataset is to compare treatment-sensitive and treatment-resistant tumors and test the hypothesis that low AXL correlates with clinical response to cetuximab in patients with head and neck cancer (HNC).
Biological replicates (n = 10 for UW-SCC 185, n = 12 for UW-SCC 194) were combined prior to dissociation to encompass biologic heterogeneity. Dissociation from solid tumor to single cell suspension was performed as described previously in Berube et al., 2024, protocols.io. High quality cell suspensions were prepared with an average viability of over 75%. scRNA-seq libraries were constructed using the PIPseq 3’ Single Cell RNA Kit v4.0Plus (T20 for UW-SCC 185, T2 for UW-SCC 194), following the manufacturer’s protocol. For UW-SCC 185 one T20 vial was used for each treatment group and for UW-SCC 194 two T2 vials were used for each treatment group to increase total number of cells sequenced. Libraries were sequenced on an Illumina NovaSeq X sequencer with a sequencing depth of at least 20,000 reads per cell. Each .zip folder described below contains raw FASTQ files.
Files and variables
The FASTQ naming structure is not identical across all files. Naming conventions differ between runs because each patient‑derived xenograft (PDX) model was processed and sequenced separately.
File: UW-SCC194_ctrl.zip
Description: Contains 4 individual FASTQ files
- 30067_1_R1_001.fastq.gz
- 30067_1_R2_001.fastq.gz
- 30067_2_R1_001.fastq.gz
- 30067_2_R2_001.fastq.gz
Using the naming strategy a_b_c_d.fastq.gz
a) sample number used internally for sample identification; this sample is from the control group
b) index of PIPseq vials: 1 for the 1st vial and 2 for the second vial
c) read number (either R1 or R2 for paired-end reads)
d) final segment, which is always 001
File: UW-SCC194_ctx.zip
Description: contains 4 individual fastq files
- 30068_1_R1_001.fastq.gz
- 30068_1_R2_001.fastq.gz
- 30068_2_R1_001.fastq.gz
- 30068_2_R2_001.fastq.gz
Using the naming strategy a_b_c_d.fastq.gz
a) sample number used internally for sample identification; this sample is from the cetuximab-treated group
b) index of PIPseq vials, 1 for the 1st vial and 2 for the second vial
c) read number (either R1 or R2 for paired-end reads)
d) final segment, which is always 001
File: UW-SCC185_ctrl_S1.zip
Description: Contains 6 individual fastq files
- 2023-12-11-pip-ctrl_S1_L001_R1_001.fastq.gz
- 2023-12-11-pip-ctrl_S1_L001_R2_001.fastq.gz
- 2023-12-11-pip-ctrl_S1_L002_R1_001.fastq.gz
- 2023-12-11-pip-ctrl_S1_L002_R2_001.fastq.gz
- 2023-12-11-pip-ctrl_S1_L003_R1_001.fastq.gz
- 2023-12-11-pip-ctrl_S1_L003_R2_001.fastq.gz
Using the naming strategy a-b-c_d_e_f_g.fastq.gz
a) sample submission date
b) library preparation kit (PIPseq)
c) treatment group (this sample in from the control group)
d) sample number (S1 is for the first sequencing run)
e) lane number (multiple lanes (L001, L002, L003) represent technical splits of the same library)
f) read number (either R1 or R2 for paired-end reads)
g) final segment, which is always 001
File: UW-SCC185_ctrl_S251.zip
Description: Contains 4 individual fastq files
- 2023-12-11-pip-ctrl_S251_L001_R1_001.fastq.gz
- 2023-12-11-pip-ctrl_S251_L001_R2_001.fastq.gz
- 2023-12-11-pip-ctrl_S251_L002_R1_001.fastq.gz
- 2023-12-11-pip-ctrl_S251_L002_R2_001.fastq.gz
Using the naming strategy a-b-c_d_e_f_g.fastq.gz
a) sample submission date
b) library preparation kit (PIPseq)
c) treatment group (this sample in from the control group)
d) sample number (S251 is for the second sequencing run to obtain amount of reads requested)
e) lane number (multiple lanes (L001, L002, L003) represent technical splits of the same library)
f) read number (either R1 or R2 for paired-end reads)
g) final segment, which is always 001
File: UW-SCC185_ctx_S3.zip
Description: Contains 6 individual fastq files
- 2023-12-11-pip-ctx_S3_L001_R1_001.fastq.gz
- 2023-12-11-pip-ctx_S3_L001_R2_001.fastq.gz
- 2023-12-11-pip-ctx_S3_L002_R1_001.fastq.gz
- 2023-12-11-pip-ctx_S3_L002_R2_001.fastq.gz
- 2023-12-11-pip-ctx_S3_L003_R1_001.fastq.gz
- 2023-12-11-pip-ctx_S3_L003_R2_001.fastq.gz
Using the naming strategy a-b-c_d_e_f_g.fastq.gz
a) sample submission date
b) library preparation kit (PIPseq)
c) treatment group (this sample in from the cetuximab-treated group)
d) sample number (S3 is for the first sequencing run)
e) lane number (multiple lanes (L001, L002, L003) represent technical splits of the same library)
f) read number (either R1 or R2 for paired-end reads)
g) final segment, which is always 001
File: UW-SCC185_ctx_S253.zip
Description: Contains 4 individual fastq files
- 2023-12-11-pip-ctx_S253_L001_R1_001.fastq.gz
- 2023-12-11-pip-ctx_S253_L001_R2_001.fastq.gz
- 2023-12-11-pip-ctx_S253_L002_R1_001.fastq.gz
- 2023-12-11-pip-ctx_S253_L002_R2_001.fastq.gz
Using the naming strategy a-b-c_d_e_f_g.fastq.gz
a) sample submission date
b) library preparation kit (PIPseq)
c) treatment group (this sample in from the cetuximab-treated group)
d) sample number (S253 is for the second sequencing run to obtain amount of reads requested)
e) lane number (multiple lanes (L001, L002, L003) represent technical splits of the same library)
f) read number (either R1 or R2 for paired-end reads)
g) final segment, which is always 001
Code/software
These FASTQ files can be processed using PIPseekerV3 or any standard scRNA‑seq pipeline (Cell Ranger, STARsolo, Alevin‑fry, etc.) after custom reference creation. A combined human and mouse reference is recommended because these are PDX models.
Human subjects data
The data in this record were generated from patient-derived xenograft (PDX) tissue, not from patient specimens directly. The PDX models represented here (UW-SCC-185 and UW-SCC-194) were established from tumor tissue obtained under a protocol approved by the University of Wisconsin–Madison Health Sciences Institutional Review Board, protocol #2018-1232 and registered at ClinicalTrials.gov (NCT03769311). All donors provided written informed consent for their tissue to be used to generate these models and for research use of data derived from them.
No patient-level data are included in this deposit. The record contains sequencing data and derived expression matrices from xenograft tumor tissue, annotated only by internal model identifiers (UW-SCC-185, UW-SCC-194). No names, medical record numbers, dates, geographic identifiers, or any other identifiers enumerated under the HIPAA Privacy Rule are present, and no clinical or demographic information about the tissue donors is included. The internal model identifiers carry no embedded donor information; any link between model identifier and donor is held separately by the study team under IRB-approved procedures and is neither included in nor derivable from this deposit.
All data in this data set is appropriate for unrestricted public sharing in Dryad, in accordance with the applicable consent, ethical approvals, and institutional requirements. No individual-level human genomic data are included in the submission.
Biological replicates (n = 10 for UW-SCC 185, n = 12 for UW-SCC 194) were combined prior to dissociation to encompass biologic heterogeneity. Dissociation from solid tumor to single-cell suspension was performed as described previously (55). High quality cell suspensions were prepared with an average viability of over 75%. scRNA-seq libraries were constructed using the PIPseq 3’ Single Cell RNA Kit v4.0Plus (T20 for UW-SCC 185, T2 for UW-SCC 194), following the manufacturer’s protocol. Libraries were sequenced on an Illumina NovaSeq X sequencer with a sequencing depth of at least 20,000 reads per cell.
