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Label-free, real-time detection of perineural invasion and cancer margins in a murine model of head and neck cancer surgery.

ABSTRACT: Surgical management of head and neck cancer requires a careful balance between complete resection of malignancy and preservation of function. Surgeons must also determine whether to resect important cranial nerves that harbor perineural invasion (PNI), as sacrificing nerves can result in significant morbidity including facial paralysis. Our group has previously reported that Dynamic Optical Contrast Imaging (DOCI), a novel non-invasive imaging system, can determine margins between malignant and healthy tissues. Herein, we use an in vivo murine model to demonstrate that DOCI can accurately identify cancer margins and perineural invasion, concordant with companion histology. Eight C3H/HeJ male mice were injected subcutaneously into the bilateral flanks with SCCVIISF, a murine head and neck cancer cell line. DOCI imaging was performed prior to resection to determine margins. Both tumor and margins were sent for histologic sectioning. After validating that DOCI can delineate HNSCC margins, we investigated whether DOCI can identify PNI. In six C3H/HeJ male mice, the left sciatic nerve was injected with PBS and the right with SCCVIISF. After DOCI imaging, the sciatic nerves were harvested for histologic analysis. All DOCI images were acquired intraoperatively and in real-time (10 s per channel), with an operatively relevant wide field of view. DOCI values distinguishing cancer from adjacent healthy tissue types were statistically significant (P < 0.05). DOCI imaging was also able to detect perineural invasion with 100% accuracy compared to control (P < 0.05). DOCI allows for intraoperative, real-time visualization of malignant and healthy tissue margins and perineural invasion to help guide tumor resection.

SUBMITTER: Tam K

PROVIDER: S-EPMC9329308 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Introduction: Perineural invasion (PNI), a key pathological feature of head and neck squamous cell carcinoma (HNSCC), predicts poor survival. However, the associated clinical characteristics remain uncertain, and the molecular mechanisms are largely unknown. Materials and methods: HNSCC gene expression and corresponding clinical data were downloaded from The Cancer Genome Atlas (TCGA). Prognostic subgroup analysis was performed, and potential PNI risk factors were assessed with logistic regression. PNI-associated gene coexpression modules were identified with weighted gene coexpression network analysis (WGCNA), and key module gene functions and the roles of non-malignant cells in PNI were evaluated with a single-cell transcriptomic dataset (GSE103322). Results: PNI was significantly inversely associated with overall survival (HR, 2.08; 95% CI, 1.27 to 3.40; P = 0.004), especially in advanced patients (HR, 2.62; 95% CI, 1.48 to 4.64; P < 0.001). Age, gender, smoking history, and alcohol history were not risk factors. HPV-positive cases were less likely than HPV-negative cases to develop PNI (OR, 0.28; 95% CI, 0.09 to 0.76; P = 0.017). WGCNA identified a unique significantly PNI-associated coexpression module containing 357 genes, with 12 hub genes (TIMP2, MIR198, LAMA4, FAM198B, MIR4649, COL5A1, COL1A2, OLFML2B, MMP2, FBN1, ADAM12, and PDGFRB). Single-cell transcriptomic data analysis revealed that the genes in the PNI-associated module correlated with the signatures "EMT," "metastasis," and "invasion." Among non-malignant cells, fibroblasts had relatively high expression of the key genes. Conclusion: At the molecular and omic levels, we verified that PNI in HNSCC is a process of invasion rather than simple diffusion. Fibroblasts probably play an important role in PNI. Novelty & Impact Statements The study is a thorough analysis of PNI in HNSCC from the clinical level to the molecular level and presents the first description of cancer-related PNI from the omics perspective to date as far as we know. We verified that PNI in HNSCC is a process of invasion rather than simple diffusion, at the molecular and omic levels. Fibroblasts were found to probably play an important role in PNI by analyzing single-cell transcriptomic data.

Project description:Introduction Postoperative radiotherapy (PORT) is routinely recommended for patients with head and neck squamous cell carcinoma (HNSCC) based on pathologic risk factors (pRFs) such as perineural invasion (PNI). Patients with PNI as the sole pRF after resection of HNSCC are uncommon and their prognosis is less clear. The aim of this study is to assess the role of PNI as a sole risk factor in patients with otherwise pathologically low-risk HNSCC. Methods Patients with HNSCC of the oral cavity, pharynx, or larynx treated with primary surgical resection from 2013 to 2018 were identified from an institutional cancer registry. Those with pRFs (pathologic T3-4 disease, lymphovascular space invasion [LVSI], multiple positive lymph nodes, close [within 2 mm] or positive margins, extranodal extension [ENE], or recurrent disease) were excluded, yielding an otherwise pathologically low-risk cohort with or without incidental, pathologic PNI. Locoregional control (LRC), overall survival (OS) and disease-specific survival (DSS) were estimated and compared between PNI groups and by adjuvant therapy. Results A total of 1,058 patients were identified as having undergone surgical resection. Exclusion of patients with other pRFs, those with unknown PNI, and oral cavity patients with depth of invasion > 10 mm yielded a study cohort of 85 patients. Eight patients (10% of study group, <1% of all patients) had PNI as the sole pRF, none of which had clinical signs or symptoms of perineural tumor spread. The remaining 77 were negative for PNI and thus pathologically low risk. Patients with PNI were more likely to have oral cavity cancer, to be younger, and to have received PORT than those without PNI; no patient received concurrent chemotherapy. At a median follow-up of 46.4 months, two- and five-year LRC rates were 81.4% and 78.5%, respectively. No differences were noted between PNI-positive and PNI-negative groups (p=0.73) or PORT v. no-PORT groups (p=0.39). While the utility of PORT is not possible to assess given limited sample size, four patients with PNI who did not receive PORT did not experience locoregional failure. Seventeen patients overall experienced locoregional failure and 14 were ultimately salvaged. Five-year OS and DSS were 77.4% and 90.8%, respectively. Conclusion Patients with pathologically low-risk HNSCC after surgical resection experience high rates of LRC. In this large institutional cohort, PNI as the sole pRF was exceedingly rare, and the benefit of adjuvant therapies is difficult to assess. Within this limitation, PORT remains the standard of care for patients with PNI to reduce the risk of locoregional failure. Further collaborative studies are required to adequately assess the prognostic impact of PNI alone in resected HNSCC.

Project description:Perineural invasion is a prevalent pathological finding in head and neck squamous cell carcinoma and a risk factor for unfavorable survival. An adequate diagnosis of perineural invasion by pathologic examination is limited due to the availability of tumor samples from surgical resection, which can arise in cases of definitive nonsurgical treatment. To address this medical need, we established a random forest prediction model for the risk assessment of perineural invasion, including occult perineural invasion, and characterized distinct cellular and molecular features based on our new and extended classification. RNA sequencing data of head and neck squamous cell carcinoma from The Cancer Genome Atlas were used as a training cohort to identify differentially expressed genes that are associated with perineural invasion. A random forest classification model was established based on these differentially expressed genes and was validated by inspection of H&E-stained whole image slides. Differences in epigenetic regulation and the mutational landscape were detected by an integrative analysis of multiomics data and single-cell RNA-sequencing data were analyzed. We identified a 44-gene expression signature related to perineural invasion and enriched for genes mainly expressed in cancer cells according to single-cell RNA-sequencing data. A machine learning model was trained based on the expression pattern of the 44-gene set with the unique feature to predict occult perineural invasion. This extended classification model enabled a more accurate analysis of alterations in the mutational landscape and epigenetic regulation by DNA methylation as well as quantitative and qualitative differences in the cellular composition in the tumor microenvironment between head and neck squamous cell carcinoma with or without perineural invasion. In conclusion, the newly established model could not only complement histopathologic examination as an additional diagnostic tool but also guide the identification of new drug targets for therapeutic intervention in future clinical trials with head and neck squamous cell carcinoma patients at a higher risk for treatment failure due to perineural invasion.

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Label-free, real-time detection of perineural invasion and cancer margins in a murine model of head and neck cancer surgery.

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