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Differential transcriptional invasion signatures from patient derived organoid models define a functional prognostic tool for head and neck cancer

ABSTRACT: Clinical outcome for patients suffering from head and neck squamous cell carcinoma (HNSCC) remains poor. This is mostly due to highly invasive tumors that cause loco-regional relapses after initial therapeutic intervention. The molecular pathways governing the detrimental invasive growth modes in HNSCC remain however understudied. Here, we have established HNSCC patient derived organoid (PDO) models that recapitulate 3-dimensional invasion in vitro. Single cell mRNA sequencing was applied to study the differences between non-invasive and invasive conditions, and in a collective versus single cell invading PDO model. Differential expression analysis under invasive conditions in Collagen gels reveals an overall upregulation of a YAP-centered transcriptional program, irrespective of the invasion mode. However, we find that collectively invading HNSCC PDO cells show elevated levels of YAP transcription targets when compared to single cell invasion. Also, collectively invading cells are characterized by increased nuclear translocation of YAP within the invasive strands, which coincides with Collagen-I matrix alignment at the invasive front. Using gene set enrichment analysis, we identify immune cell-like migratory pathways in the single cell invading HNSCC PDO, while collective invasion is characterized by overt upregulation of adhesion and migratory pathways. Lastly, based on a clinical head and neck cancer cohort, we demonstrate that the identified collective invasion signature provides a candidate prognostic platform for survival in HNSCC. By uncoupling collective and single cell invasive programs, we have established invasion signatures that may guide new therapeutic options.

ORGANISM(S): Homo sapiens

PROVIDER: GSE244956 | GEO | 2024/07/03

REPOSITORIES: GEO

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Project description:Cell motility and invasion initiate metastasis. However, only a subpopulation of cancer cells within a tumor will ultimately become invasive. Due to this stochastic and transient nature, in an experimental setting, migrating and invading cells need to be isolated from the general population in order to study the gene expression profiles linked to these processes. This report describes microarray analysis on RNA derived from migrated or invaded subpopulations of triple negative breast cancer cells in a Transwell set-up, at two different time points during motility and invasion, pre-determined as M-bM-^@M-^\earlyM-bM-^@M-^] and M-bM-^@M-^\lateM-bM-^@M-^] in real-time kinetic assessments. Invasion- and migration-related gene expression signatures were generated through comparison with non-invasive cells, remaining at the upper side of the Transwell membranes. Late-phase signatures of both invasion and migration indicated poor prognosis in a series of breast cancer data sets. Furthermore, evaluation of the genes constituting the prognostic invasion-related gene signature revealed KruM-LM-^Hppel-like factor 9 (KLF9) as a putative suppressor of invasive growth in breast cancer. Next to loss in invasive vs non-invasive cell lines, KLF9 also showed significantly lower expression levels in the M-bM-^@M-^\earlyM-bM-^@M-^] invasive cell population, in several public expression data sets and in clinical breast cancer samples when compared to normal tissue. Overexpression of EGFP-KLF9 fusion protein significantly altered morphology and blocked invasion and growth of MDA-MB-231 cells in vitro. In addition, KLF9 expression correlated inversely with mitotic activity in clinical samples, indicating anti-proliferative effects. After selection of two time points (M-bM-^@M-^\earlyM-bM-^@M-^] and M-bM-^@M-^\lateM-bM-^@M-^]), RNA from invasive and migratory MDA-MB-231 cells was isolated from Transwell membranes and hybridized onto an Illumina HumanHT-12 v4 Expression beadchip. Gene expressions of migrated/invaded subpopulations vs non-motile cells were compared.

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Differential transcriptional invasion signatures from patient derived organoid models define a functional prognostic tool for head and neck cancer

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