A three-gene expression signature associated with positive surgical margins in tongue squamous cell carcinomas: Predicting surgical resectability from tumour biology?

Enhancing oral squamous cell carcinoma prediction: the prognostic power of the worst pattern of invasion and the limited impact of molecular resection margins

Objective

Oral squamous cell carcinoma (OSCC) originates from the mucosal lining of the oral cavity. Almost half of newly diagnosed cases are classified as advanced stage IV disease, which makes resection difficult. In this study, we investigated the pathological features and mutation profiles of tumor margins in OSCC.

Methods

We performed hierarchical clustering of principal components to identify distinct patterns of tumor growth and their association with patient prognosis. We also used next-generation sequencing to analyze somatic mutations in tumor and marginal tissue samples.

Results

Our analyses uncovered that the grade of worst pattern of invasion (WPOI) is strongly associated with depth of invasion and patient survival in multivariable analysis. Mutations were primarily detected in the DNA isolated from tumors, but several mutations were also identified in marginal tissue. In total, we uncovered 29 mutated genes, mainly tumor suppressor genes involved in DNA repair including BRCA genes; however none of these mutations significantly correlated with a higher chance of relapse in our medium-size cohort. Some resection margins that appeared histologically normal harbored tumorigenic mutations in TP53 and CDKN2A genes.

Conclusion

Even histologically normal margins may contain molecular alterations that are not detectable by conventional histopathological methods, but NCCN classification system still outperforms other methods in the prediction of the probability of disease relapse.

Evaluating the relevance of surgical margins. Part one: The problems with current methodology

The goal of cancer surgery is to achieve a "clean" microscopic resection, with no residual tumour remaining in the wound. To achieve that goal, the surgeon typically incorporates a measured buffer of grossly normal tissue about the entire circumference of the tumour. Microscopic analysis of the resection boundaries is then performed to determine if all traces of the tumour have been completely removed. This analysis is thought to provide a surrogate indication as to the likelihood for that tumour to recur after surgery. However, it is recognised that tumour recurrence may not occur even when microscopic evidence of tumour has been identified at the resection margins, and recurrence can also occur when conventional histology has considered the tumour to have been completely removed. The explanations for this dichotomy are numerous and include technical and practical limitations of the processing methodology, and also several surgeon-related and tumour-related reasons. Ultimately, the inability to confidently determine when a tumour has been removed sufficiently to prevent recurrence can impact on the ability to provide owners with confident treatment advice. In this article, the authors describe the challenges with defining the true extent of the tumour margin from the perspective of the surgeon, the pathologist and the tumour. The authors also provide an analysis of why our current efforts to ensure that all traces of the local tumour have been successfully removed may provide an imperfect assessment of the risk of recurrence.

Defining the relevance of surgical margins. Part two: Strategies to improve prediction of recurrence risk

Due to the complex nature of tumour biology and the integration between host tissues and molecular processes of the tumour cells, a continued reliance on the status of the microscopic cellular margin should not remain our only determinant of the success of a curative-intent surgery for patients with cancer. Based on current evidence, relying on a purely cellular focus to provide a binary indication of treatment success can provide an incomplete interpretation of potential outcome. A more holistic analysis of the cancer margin may be required. If we are to move ahead from our current situation - and allow treatment plans to be more intelligently tailored to meet the requirements of each individual tumour - we need to improve our utilisation of techniques that either improve recognition of residual tumour cells within the surgical field or enable a more comprehensive interrogation of tumour biology that identifies a risk of recurrence. In the second article in this series on defining the relevance of surgical margins, the authors discuss possible alternative strategies for margin assessment and evaluation in the canine and feline cancer patient. These strategies include considering adoption of the residual tumour classification scheme; intra-operative imaging systems including fluorescence-guided surgery, optical coherence tomography and Raman spectroscopy; molecular analysis and whole transcriptome analysis of tissues; and the development of a biologic index (nomogram). These techniques may allow evaluation of individual tumour biology and the status of the resection margin in ways that are different to our current techniques. Ultimately, these techniques seek to better define the risk of tumour recurrence following surgery and provide the surgeon and patient with more confidence in margin assessment.

Principe et applicabilité de la chirurgie de précision aux cancers de la tête et du cou

La chirurgie est la modalité de traitement curatif la plus fréquemment utilisée dans les cancers de la tête et du cou. Elle est intégrée dans des schémas de stratification thérapeutique précis, mais la conduite de l’acte chirurgical et son évaluation ne tiennent, la plupart du temps, pas compte de la biologie tumorale. Nous présentons dans cette revue plusieurs études qui montrent comment les analyses de la biologie tumorale pourraient préciser les indications et le contour d’une résection chirurgicale, personnaliser la prise en charge péri-opératoire du patient, et faciliter la détection des récurrences tumorales. Ces études apportent ainsi une preuve de principe qu’une chirurgie de précision, c’est-à-dire adossée à la biologie tumorale, à la façon de la médecine de précision pour d’autres cancers, est applicable aux cancers de la tête et du cou.

A Transcriptomic Analysis of Head and Neck Squamous Cell Carcinomas for Prognostic Indications

Survival analysis of the Cancer Genome Atlas (TCGA) dataset is a well-known method for discovering gene expression-based prognostic biomarkers of head and neck squamous cell carcinoma (HNSCC). A cutoff point is usually used in survival analysis for patient dichotomization when using continuous gene expression values. There is some optimization software for cutoff determination. However, the software's predetermined cutoffs are usually set at the medians or quantiles of gene expression values. There are also few clinicopathological features available in pre-processed datasets. We applied an in-house workflow, including data retrieving and pre-processing, feature selection, sliding-window cutoff selection, Kaplan-Meier survival analysis, and Cox proportional hazard modeling for biomarker discovery. In our approach for the TCGA HNSCC cohort, we scanned human protein-coding genes to find optimal cutoff values. After adjustments with confounders, clinical tumor stage and surgical margin involvement were found to be independent risk factors for prognosis. According to the results tables that show hazard ratios with Bonferroni-adjusted p values under the optimal cutoff, three biomarker candidates, CAMK2N1, CALML5, and FCGBP, are significantly associated with overall survival. We validated this discovery by using the another independent HNSCC dataset (GSE65858). Thus, we suggest that transcriptomic analysis could help with biomarker discovery. Moreover, the robustness of the biomarkers we identified should be ensured through several additional tests with independent datasets.

Contribution of Genomics to the Surgical Management and Study of Oral Cancer

Background

Oral squamous cell carcinoma (OSCC) is the most frequent type of tumor arising from the oral cavity. Surgery is the cornerstone of the treatment of these cancers. Tumor biology has long been overlooked as an important contributor to the outcome of surgical procedures, but recent studies are challenging this concept. Molecular analyses of tumor DNA or RNA provide a rich source of information about the biology of OSCC.

Methods

We searched for relevant articles using PubMed. We examined in particular the prospect of applying molecular methods for minimally invasive exploration of OSCC biology.

Results

We examined five potential applications of genomics to the surgical management and study of OSCC: i) assessing oral potentially malignant lesions; ii) tumor staging prior to surgery; iii) predicting postoperative risk in locally advanced tumors; iv) measuring minimal residual disease and optimizing the longitudinal monitoring of OSCC; and v) predicting the efficacy of medical treatment.

Conclusions

Genomic information can be harnessed in order to identify new biomarkers that could improve the staging, choice of therapy and management of OSCC. The identification of new biomarkers is awaited for better personalization of the surgical treatment of OSCC.

Identification of a Transcriptomic Prognostic Signature by Machine Learning Using a Combination of Small Cohorts of Prostate Cancer

Determining which treatment to provide to men with prostate cancer (PCa) is a major challenge for clinicians. Currently, the clinical risk-stratification for PCa is based on clinico-pathological variables such as Gleason grade, stage and prostate specific antigen (PSA) levels. But transcriptomic data have the potential to enable the development of more precise approaches to predict evolution of the disease. However, high quality RNA sequencing (RNA-seq) datasets along with clinical data with long follow-up allowing discovery of biochemical recurrence (BCR) biomarkers are small and rare. In this study, we propose a machine learning approach that is robust to batch effect and enables the discovery of highly predictive signatures despite using small datasets. Gene expression data were extracted from three RNA-Seq datasets cumulating a total of 171 PCa patients. Data were re-analyzed using a unique pipeline to ensure uniformity. Using a machine learning approach, a total of 14 classifiers were tested with various parameters to identify the best model and gene signature to predict BCR. Using a random forest model, we have identified a signature composed of only three genes (JUN, HES4, PPDPF) predicting BCR with better accuracy [74.2%, balanced error rate (BER) = 27%] than the clinico-pathological variables (69.2%, BER = 32%) currently in use to predict PCa evolution. This score is in the range of the studies that predicted BCR in single-cohort with a higher number of patients. We showed that it is possible to merge and analyze different small and heterogeneous datasets altogether to obtain a better signature than if they were analyzed individually, thus reducing the need for very large cohorts. This study demonstrates the feasibility to regroup different small datasets in one larger to identify a predictive genomic signature that would benefit PCa patients.

Arsenite Exposure Displaces Zinc from ZRANB2 Leading to Altered Splicing

Exposure to arsenic, a class I carcinogen, affects 200 million people globally. Skin is the major target organ, but the molecular etiology of arsenic-induced skin carcinogenesis remains unclear. Arsenite (As³⁺)-induced disruption of alternative splicing could be involved, but the mechanism is unknown. Zinc finger proteins play key roles in alternative splicing. As³⁺ can displace zinc (Zn²⁺) from C3H1 and C4 zinc finger motifs (zfm's), affecting protein function. ZRANB2, an alternative splicing regulator with two C4 zfm's integral to its structure and splicing function, was chosen as a candidate for this study. We hypothesized that As³⁺ could displace Zn²⁺ from ZRANB2, altering its structure, expression, and splicing function. As³⁺/Zn²⁺ binding and mutual displacement experiments were performed with synthetic apo-peptides corresponding to each ZRANB2 zfm, employing a combination of intrinsic fluorescence, ultraviolet spectrophotometry, zinc colorimetric assay, and liquid chromatography-tandem mass spectrometry. ZRANB2 expression in HaCaT cells acutely exposed to As³⁺ (0 or 5 μM, 0-72 h; or 0-5 μM, 6 h) was examined by RT-qPCR and immunoblotting. ZRANB2-dependent splicing of TRA2B mRNA, a known ZRANB2 target, was monitored by reverse transcription-polymerase chain reaction. As³⁺ bound to, as well as displaced Zn²⁺ from, each zfm. Also, Zn²⁺ displaced As³⁺ from As³⁺-bound zfm's acutely, albeit transiently. As³⁺ exposure induced ZRANB2 protein expression between 3 and 24 h and at all exposures tested but not ZRANB2 mRNA expression. ZRANB2-directed TRA2B splicing was impaired between 3 and 24 h post-exposure. Furthermore, ZRANB2 splicing function was also compromised at all As³⁺ exposures, starting at 100 nm. We conclude that As³⁺ exposure displaces Zn²⁺ from ZRANB2 zfm's, changing its structure and compromising splicing of its targets, and increases ZRANB2 protein expression as a homeostatic response both at environmental/toxicological exposures and therapeutically relevant doses.

Genomics and precision surgery for head and neck squamous cell carcinoma

The identification of the biological determinants that shape the response of tumors to medical therapies offers perspectives for better patient stratification and therapeutic targeting. Here, we discuss how genomics could help to improve the surgical treatment of head and neck squamous cell carcinoma (HNSCC). We examine the potential use of genomic analyses for: i) refining and standardizing the indications for surgery, ii) the choice of surgical procedure, and iii) the follow-up of patients with resected tumors. We highlight the studies that used genomics to explore the contribution of tumor biology to the outcome of surgery. We discuss the important developments that are challenging current surgical practice in HNSCC, such as neoadjuvant immunotherapy and the analysis of circulating DNA. Genomic analyses provide practical tools that could help improve the pathological diagnosis and staging of HNSCC, and increase the appreciation of the importance of tumor biology in the outcome of surgery. Identification of biomarkers will likely contribute to a move toward precision surgery of HNSCC, i.e. the personalization of surgical practice based on tumor biology.