From genes to radioresistance in head and neck squamous cell carcinoma

Head and Neck Cancers (HNC) are a group of tumours located in the upper aero-digestive tract. Head and Neck Squamous Cell Carcinoma (HNSCC) represent about 90% of all HNC cases. It has been considered the sixth most malignant tumour worldwide and, despite clinical and technological advances, the five-year survival rate has not improved much in the last years. Nowadays, HNSCC is well established as a heterogeneous disease and that its development is due to accumulation of genetic events. Apart from the majority of the patients being diagnosed in an advanced stage, HNSCC is also a disease with poor therapeutic outcome. One of the therapeutic approaches is radiotherapy. However, this approach has different drawbacks like the radioresistance acquired by some tumour cells, leading to a worse prognosis. A major knowledge in radiation biology is imperative to improve this type of treatment and avoid late toxicities, maintaining patient quality of life in the subsequent years after treatment. Then, identification of genetic markers associated to radiotherapy response in patients and possible alterations in cells after radiotherapy are essential steps towards an improved diagnosis, higher survival rate and a better life quality. Not much is known about the radiation effects on cells, so, the principal aim of this study was to contribute to a more extensive knowledge about radiation treatment in HNSCC. For this, two commercial cell lines, HSC-3 and BICR-10, were used and characterized resorting to karyotyping, aCGH and MS-MLPA. These cell lines were submitted to different doses of irradiation and the resulting genetic and methylation alterations were evaluated. Our results showed a great difference in radiation response between the two cell lines, allowing the conclusion that HSC-3 was much more radiosensitive than BICR-10. Bearing this in mind, analysis of cell death, cell cycle and DNA damages was performed to try to elucidate the motifs behind this difference. The characterization of both cell lines allowed the confirmation that HSC-3 was derived from a metastatic tumour and the hypothesis that BICR-10 was derived from a dysplasia. Furthermore, this pilot study enabled the suggestion of some genetic and epigenetic alterations that cells suffer after radiation treatment. Additionally, it also allowed the association of some genetic characteristics that could be related to the differences in radiation response observable in this two cell lines. Taken together all of our results contribute to a better understanding of radiation effects on HNSCC allowing one further step towards the prediction of patients’ outcome, better choice of treatment approaches and ultimately a better quality of life.

Metabolic signature of lung cancer: a metabolomic study of human tissues and biofluids

This thesis reports the application of metabolomics to human tissues and biofluids (blood plasma and urine) to unveil the metabolic signature of primary lung cancer. In Chapter 1, a brief introduction on lung cancer epidemiology and pathogenesis, together with a review of the main metabolic dysregulations known to be associated with cancer, is presented. The metabolomics approach is also described, addressing the analytical and statistical methods employed, as well as the current state of the art on its application to clinical lung cancer studies. Chapter 2 provides the experimental details of this work, in regard to the subjects enrolled, sample collection and analysis, and data processing. In Chapter 3, the metabolic characterization of intact lung tissues (from 56 patients) by proton High Resolution Magic Angle Spinning (HRMAS) Nuclear Magnetic Resonance (NMR) spectroscopy is described. After careful assessment of acquisition conditions and thorough spectral assignment (over 50 metabolites identified), the metabolic profiles of tumour and adjacent control tissues were compared through multivariate analysis. The two tissue classes could be discriminated with 97% accuracy, with 13 metabolites significantly accounting for this discrimination: glucose and acetate (depleted in tumours), together with lactate, alanine, glutamate, GSH, taurine, creatine, phosphocholine, glycerophosphocholine, phosphoethanolamine, uracil nucleotides and peptides (increased in tumours). Some of these variations corroborated typical features of cancer metabolism (e.g., upregulated glycolysis and glutaminolysis), while others suggested less known pathways (e.g., antioxidant protection, protein degradation) to play important roles. Another major and novel finding described in this chapter was the dependence of this metabolic signature on tumour histological subtype. While main alterations in adenocarcinomas (AdC) related to phospholipid and protein metabolisms, squamous cell carcinomas (SqCC) were found to have stronger glycolytic and glutaminolytic profiles, making it possible to build a valid classification model to discriminate these two subtypes. Chapter 4 reports the NMR metabolomic study of blood plasma from over 100 patients and near 100 healthy controls, the multivariate model built having afforded a classification rate of 87%. The two groups were found to differ significantly in the levels of lactate, pyruvate, acetoacetate, LDL+VLDL lipoproteins and glycoproteins (increased in patients), together with glutamine, histidine, valine, methanol, HDL lipoproteins and two unassigned compounds (decreased in patients). Interestingly, these variations were detected from initial disease stages and the magnitude of some of them depended on the histological type, although not allowing AdC vs. SqCC discrimination. Moreover, it is shown in this chapter that age mismatch between control and cancer groups could not be ruled out as a possible confounding factor, and exploratory external validation afforded a classification rate of 85%. The NMR profiling of urine from lung cancer patients and healthy controls is presented in Chapter 5. Compared to plasma, the classification model built with urinary profiles resulted in a superior classification rate (97%). After careful assessment of possible bias from gender, age and smoking habits, a set of 19 metabolites was proposed to be cancer-related (out of which 3 were unknowns and 6 were partially identified as N-acetylated metabolites). As for plasma, these variations were detected regardless of disease stage and showed some dependency on histological subtype, the AdC vs. SqCC model built showing modest predictive power. In addition, preliminary external validation of the urine-based classification model afforded 100% sensitivity and 90% specificity, which are exciting results in terms of potential for future clinical application. Chapter 6 describes the analysis of urine from a subset of patients by a different profiling technique, namely, Ultra-Performance Liquid Chromatography coupled to Mass Spectrometry (UPLC-MS). Although the identification of discriminant metabolites was very limited, multivariate models showed high classification rate and predictive power, thus reinforcing the value of urine in the context of lung cancer diagnosis. Finally, the main conclusions of this thesis are presented in Chapter 7, highlighting the potential of integrated metabolomics of tissues and biofluids to improve current understanding of lung cancer altered metabolism and to reveal new marker profiles with diagnostic value.