Specialised skin cancer spectral library for use in data-independent mass spectrometry

A crustacean neuropeptide spectral library for data-independent acquisition (DIA) mass spectrometry applications

Neuropeptides have tremendous potential for application in modern medicine, including utility as biomarkers and therapeutics. To overcome the inherent challenges associated with neuropeptide identification and characterization, data-independent acquisition (DIA) is a fitting mass spectrometry (MS) method of choice to achieve sensitive and accurate analysis. It is advantageous for preliminary neuropeptidomic studies to occur in less complex organisms, with crustacean models serving as a popular choice due to their relatively simple nervous system. With spectral libraries serving as a means to interpret DIA-MS output spectra, and Cancer borealis as a model of choice for neuropeptide analysis, we performed the first spectral library mapping of crustacean neuropeptides. Leveraging pre-existing data-dependent acquisition (DDA) spectra, a spectral library was built using PEAKS Online. The library is comprised of 333 unique neuropeptides. The identification results obtained through the use of this spectral library were compared with those achieved through library-free analysis of crustacean brain, pericardial organs (PO), and thoracic ganglia (TG) tissues. A statistically significant increase (Student's t-test, P value < 0.05) in the number of identifications achieved from the TG data was observed in the spectral library results. Furthermore, in each of the tissues, a distinctly different set of identifications was found in the library search compared to the library-free search. This work highlights the necessity for the use of spectral libraries in neuropeptide analysis, illustrating the advantage of spectral libraries for interpreting DIA spectra in a reproducible manner with greater neuropeptidomic depth.

Non-invasive diagnosis of early cutaneous squamous cell carcinoma

Early cutaneous squamous cell carcinoma (cSCC) can be challenging to diagnose using clinical criteria as it could present similar to actinic keratosis (AK) or Bowen's disease (BD), precursors of cSCC. Currently, histopathological assessment of an invasive biopsy is the gold standard for diagnosis. A non-invasive diagnostic approach would reduce patient and health system burden. Therefore, this study used non-invasive sampling by tape-stripping coupled with data-independent acquisition mass spectrometry (DIA-MS) proteomics to profile the proteome of histopathologically diagnosed AK, BD and cSCC, as well as matched normal samples. Proteomic data were analysed to identify proteins and biological functions that are significantly different between lesions. Additionally, a support vector machine (SVM) machine learning algorithm was used to assess the usefulness of proteomic data for the early diagnosis of cSCC. A total of 696 proteins were identified across the samples studied. A machine learning model constructed using the proteomic data classified premalignant (AK + BD) and malignant (cSCC) lesions at 77.5% accuracy. Differential abundance analysis identified 144 and 21 protein groups that were significantly changed in the cSCC, and BD samples compared to the normal skin, respectively (adj. p < 0.05). Changes in pivotal carcinogenic pathways such as LXR/RXR activation, production of reactive oxygen species, and Hippo signalling were observed that may explain the progression of cSCC from premalignant lesions. In summary, this study demonstrates that DIA-MS analysis of tape-stripped samples can identify non-invasive protein biomarkers with the potential to be developed into a complementary diagnostic tool for early cSCC.

Genomic and proteomic findings in early melanoma and opportunities for early diagnosis

Overdiagnosis of early melanoma is a significant problem. Due to subtle unique and overlapping clinical and histological criteria between pigmented lesions and the risk of mortality from melanoma, some benign pigmented lesions are diagnosed as melanoma. Although histopathology is the gold standard to diagnose melanoma, there is a demand to find alternatives that are more accurate and cost-effective. In the current "omics" era, there is gaining interest in biomarkers to help diagnose melanoma early and to further understand the mechanisms driving tumor progression. Genomic investigations have attempted to differentiate malignant melanoma from benign pigmented lesions. However, genetic biomarkers of early melanoma diagnosis have not yet proven their value in the clinical setting. Protein biomarkers may be more promising since they directly influence tissue phenotype, a result of by-products of genomic mutations, posttranslational modifications and environmental factors. Uncovering relevant protein biomarkers could increase confidence in their use as diagnostic signatures. Currently, proteomic investigations of melanoma progression from pigmented lesions are limited. Studies have previously characterised the melanoma proteome from cultured cell lines and clinical samples such as serum and tissue. This has been useful in understanding how melanoma progresses into metastasis and development of resistance to adjuvant therapies. Currently, most studies focus on metastatic melanoma to find potential drug therapy targets, prognostic factors and markers of resistance. This paper reviews recent advancements in the genomics and proteomic fields and reports potential avenues, which could help identify and differentiate melanoma from benign pigmented lesions and prevent the progression of melanoma.