Tumour budding-based grading as independent prognostic biomarker in HPV-positive and HPV-negative head and neck cancer

Tumor-stroma contact ratio - a novel predictive factor for tumor response to chemoradiotherapy in locally advanced oropharyngeal cancer

The growth pattern of oropharyngeal squamous cell carcinomas (OPSCC) varies from compact tumor cell aggregates to diffusely infiltrating tumor cell-clusters. The influence of the growth pattern on local tumor control and survival has been studied mainly for surgically treated oral cavity carcinomas on a visual basis. In this study, we used multiplex immunofluorescence staining (mIF) to examine the antigens pan-cytokeratin, p16INK4a, Ki67, CD271, PD-L1, and CD8 in pretherapeutic biopsies from 86 OPSCC. We introduce Tumor-stroma contact ratio (TSC), a novel parameter, to quantify the relationship between tumor cells in contact with the stromal surface and the total number of epithelial tumor cells. mIF tumor cores were analyzed at the single-cell level, and tumor-stromal contact area was quantified using the R package "Spatstat". TSC was correlated with the visually assessed invasion pattern by two independent investigators. Furthermore, TSC was analyzed in relation to clinical parameters and patient survival data to evaluate its potential prognostic significance. Higher TSC correlated with poor response to (chemo-)radiotherapy (r = 0.3, p < 0.01), and shorter overall (OS) and progression-free (PFS) survival (median OS: 13 vs 136 months, p < 0.0001; median PFS: 5 vs 85 months, p < 0.0001). Visual categorization of growth pattern according to established criteria of tumor aggressiveness showed interobserver variability increasing with more nuanced categories (2 categories: k = 0.7, 95 %-CI: 0.55 - 0.85; 4 categories k = 0.48, 95 %-CI: 0.35 - 0.61). In conclusion, TSC is an objective and reproducible computer-based parameter to quantify tumor-stroma contact area. We demonstrate its relevance for the response of oropharyngeal carcinomas to primary (chemo-)radiotherapy.

Segmentation-Free Outcome Prediction from Head and Neck Cancer PET/CT Images: Deep Learning-Based Feature Extraction from Multi-Angle Maximum Intensity Projections (MA-MIPs)

We introduce an innovative, simple, effective segmentation-free approach for survival analysis of head and neck cancer (HNC) patients from PET/CT images. By harnessing deep learning-based feature extraction techniques and multi-angle maximum intensity projections (MA-MIPs) applied to Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) images, our proposed method eliminates the need for manual segmentations of regions-of-interest (ROIs) such as primary tumors and involved lymph nodes. Instead, a state-of-the-art object detection model is trained utilizing the CT images to perform automatic cropping of the head and neck anatomical area, instead of only the lesions or involved lymph nodes on the PET volumes. A pre-trained deep convolutional neural network backbone is then utilized to extract deep features from MA-MIPs obtained from 72 multi-angel axial rotations of the cropped PET volumes. These deep features extracted from multiple projection views of the PET volumes are then aggregated and fused, and employed to perform recurrence-free survival analysis on a cohort of 489 HNC patients. The proposed approach outperforms the best performing method on the target dataset for the task of recurrence-free survival analysis. By circumventing the manual delineation of the malignancies on the FDG PET-CT images, our approach eliminates the dependency on subjective interpretations and highly enhances the reproducibility of the proposed survival analysis method. The code for this work is publicly released.

Predictors of Local Invasion in Infiltrative Basal Cell Carcinoma: Tumour Budding Outperforms the WHO Subtyping

Tumour budding (TB) correlates with increased local invasion in various neoplasms. Certain basal cell carcinomas (BCCs) exhibit local aggressiveness. Detecting adverse prognostic factors in partial biopsies could aid in identifying cases with heightened local risk. The absolute number of TB (≤ 3 tumour cells) in excision specimens of 271 infiltrative BCCs (0: absent; 1: 1-2 foci; 2: ≥ 3 foci; 3: ≥ 10 foci), the histopathological subtype and depth of infiltration, perineural invasion, and other histological features were evaluated. A significant correlation was found between TB and both depth of infiltration (rho 0.445, p < 0.001) and perineural invasion (p = 0.009). In the multivariate analysis of depth and perineural invasion (multiple regression, stepwise), TB was identified as a significant covariate together with diameter, inflammation, and perineural invasion for the former, and depth for the latter. Conversely, no correlation existed between the WHO histological subtypes (infiltrating, sclerosing, and micronodular), and depth of infiltration or perineural invasion. This study demonstrates the value of TB as a biomarker for local invasiveness in BCC. In routine practice, a count of ≥ 3 TB foci in lesions incompletely excised or with narrow tumour-free surgical margins would be a straightforward and reproducible method to guide BCC treatment.

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

Clinical outcome for patients suffering from HPV-negative 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 and metastatic outgrowth. 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 clinical head and neck cancer cohorts, 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.

TGFβ-induced EN1 promotes tumor budding of adenoid cystic carcinoma in patient-derived organoid model

Adenoid cystic carcinoma (ACC) and basal cell adenoma (BCA) share many histological characteristics and often need a differential diagnosis in clinical pathology. Recently, we found homeobox protein engrailed-1 (EN1) was a potential diagnostic marker for ACC in an organoids library of salivary gland tumors (SGTs). Here we aim to confirm EN1 as a differential diagnostic marker for ACC, and further investigate the regulatory mechanism and biological function of EN1 in tumor progression. The transcriptional analysis, quantitative polymerase chain reaction, Western blot and immunohistochemistry staining were performed and revealed that EN1 was specifically and highly expressed in ACC, and accurately differentiated ACC from BCA. Furthermore, TGFβ signaling pathway was found associated with ACC, and the regulation of EN1 through TGFβ was detected in the human ACC cell lines and patient-derived organoids (PDOs). TGFβ-induced EN1 was important in promoting tumor budding in the PDOs model. Interestingly, a high level of EN1 and TGFβ1 in the budding tips was observed in ACC clinical samples, and the expression of EN1 and TGFβ1 in ACC was significantly associated with the clinical stage. In summary, our study verified EN1 is a good diagnostic marker to differentiate ACC from BCA. TGFβ-induced EN1 facilitates the tumor budding of ACC, which might be an important mechanism related to the malignant phenotype of ACC.

The modified Polsby-Popper score, a novel quantitative histomorphological biomarker and its potential to predict lymph node positivity and cancer-specific survival in oral tongue squamous cell carcinoma

BACKGROUND

The significance of different histological spreading patterns of tumor tissue in oral tongue squamous cell carcinoma (TSCC) is well known. Our aim was to construct a numeric parameter on a continuous scale, that is, the modified Polsby-Popper (MPP) score, to describe the aggressiveness of tumor growth and infiltration, with the potential to analyze hematoxylin and eosin-stained whole slide images (WSIs) in an automated manner. We investigated the application of the MPP score in predicting survival and cervical lymph node metastases as well as in determining patients at risk in the context of different surgical margin scenarios.

METHODS

We developed a semiautomated image analysis pipeline to detect areas belonging to the tumor tissue compartment. Perimeter and area measurements of all detected tissue regions were derived, and a specific mathematical formula was applied to reflect the perimeter/area ratio in a comparable, observer-independent manner across digitized WSIs. We demonstrated the plausibility of the MPP score by correlating it with well-established clinicopathologic parameters. We then performed survival analysis to assess the relevance of the MPP score, with an emphasis on different surgical margin scenarios. Machine learning models were developed to assess the relevance of the MPP score in predicting survival and occult cervical nodal metastases.

RESULTS

The MPP score was associated with unfavorable tumor growth and infiltration patterns, the presence of lymph node metastases, the extracapsular spread of tumor cells, and higher tumor thickness. Higher MPP scores were associated with worse overall survival (OS) and tongue carcinoma-specific survival (TCSS), both when assessing all pT-categories and pT1-pT2 categories only; moreover, higher MPP scores were associated with a significantly worse TCSS in cases where a cancer-free surgical margin of <5 mm could be achieved on the main surgical specimen. This discriminatory capacity remained constant when examining pT1-pT2 categories only. Importantly, the MPP score could successfully define cases at risk in terms of metastatic disease in pT1-pT2 cancer where tumor thickness failed to exhibit a significant predictive value. Machine learning (ML) models incorporating the MPP score could predict the 5-year TCSS efficiently. Furthermore, we demonstrated that machine learning models that predict occult cervical lymph node involvement can benefit from including the MPP score.

CONCLUSIONS

We introduced an objective, quantifiable, and observer-independent parameter, the MPP score, representing the aggressiveness of tumor growth and infiltration in TSCC. We showed its prognostic relevance especially in pT1-pT2 category TSCC, and its possible use in ML models predicting TCSS and occult lymph node metastases.

Prognostic Value of Tumor Budding for Early Breast Cancer

BACKGROUND

Tumor budding (TB) is a dynamic process associated with the epithelial-mesenchymal transition and a well-established prognostic biomarker for colorectal cancer. As part of the tumor microenvironment, tumor buds demonstrate increased cell motility and invasiveness. Current evidence demonstrates that high levels of TB correlate with disease progression and worst outcomes across different solid tumors. Our work aims to demonstrate the clinical applicability of TB analysis and its utility as a prognostic factor for patients with early breast cancer (EBC).

METHODS

Retrospective, single-center, observational study, enrolling patients with EBC diagnosed in a Portuguese hospital between 2014 and 2015. TB classification was performed according to the International Tumor Budding Conference 2016 guidelines.

RESULTS

A statistically significant relation was found between higher TB score and aggressive clinicopathological features (angiolymphatic/perineural invasion-p < 0.001; tumor size-p = 0.012; nuclear grading-p < 0.001; and Ki-67 index-p = 0.011), higher number of relapses (p < 0.001), and short disease-free survival (DFS) (p < 0.001).

CONCLUSION

We demonstrate that high TB correlates with shorter DFS and aggressive clinicopathological features used in daily practice to decide on the benefit of chemotherapy for EBC. TB represents a needed prognostic biomarker for EBC, comprising a new factor to be considered in the adjuvant decision-making process by identifying patients at a high risk of relapse and with higher benefit on treatment intensification. Clinical trials incorporating TB are needed to validate its prognostic impact.

Predictive value of tumor budding in head and neck squamous cell carcinoma: an update

Head and neck squamous cell carcinoma forms an anatomically and functionally complex group of malignancies. The significant local aggressiveness and frequent regional relapses motivate ongoing research to identify more reliable and sensitive prognostic and predictive biomarkers. One emerging area of cancer biology is the evaluation of tumor budding at the advancing invasive front of various types of epithelial cancers. Recent studies suggest that tumor budding is a relatively common phenomenon in cancer progression and that it may have important prognostic implications for patients due to its potential to provide valuable insights into the biology and clinical behavior of head and neck cancer. In this review, we aim to provide information about tumor budding in head and neck squamous cell carcinoma. Thus, we hope to shed light on the complex biology of these malignancies, as well as aiding diagnostic, classification, and better characterization and thereby, looking for new avenues for improving patient outcomes.