Clinical protein science in translational medicine targeting malignant melanoma

Abstract

Melanoma of the skin is the sixth most common type of cancer in Europe and accounts for 3.4% of all diagnosed cancers. More alarming is the degree of recurrence that occurs with approximately 20% of patients lethally relapsing following treatment. Malignant melanoma is a highly aggressive skin cancer and metastases rapidly extend to the regional lymph nodes (stage 3) and to distal organs (stage 4). Targeted oncotherapy is one of the standard treatment for progressive stage 4 melanoma, and BRAF inhibitors (e.g. vemurafenib, dabrafenib) combined with MEK inhibitor (e.g. trametinib) can effectively counter BRAFV600E-mutated melanomas. Compared to conventional chemotherapy, targeted BRAFV600E inhibition achieves a significantly higher response rate. After a period of cancer control, however, most responsive patients develop resistance to the therapy and lethal progression. The many underlying factors potentially causing resistance to BRAF inhibitors have been extensively studied. Nevertheless, the remaining unsolved clinical questions necessitate alternative research approaches to address the molecular mechanisms underlying metastatic and treatment-resistant melanoma. In broader terms, proteomics can address clinical questions far beyond the reach of genomics, by measuring, i.e. the relative abundance of protein products, post-translational modifications (PTMs), protein localisation, turnover, protein interactions and protein function. More specifically, proteomic analysis of body fluids and tissues in a given medical and clinical setting can aid in the identification of cancer biomarkers and novel therapeutic targets. Achieving this goal requires the development of a robust and reproducible clinical proteomic platform that encompasses automated biobanking of patient samples, tissue sectioning and histological examination, efficient protein extraction, enzymatic digestion, mass spectrometry-based quantitative protein analysis by label-free or labelling technologies and/or enrichment of peptides with specific PTMs. By combining data from, e.g. phosphoproteomics and acetylomics, the protein expression profiles of different melanoma stages can provide a solid framework for understanding the biology and progression of the disease. When complemented by proteogenomics, customised protein sequence databases generated from patient-specific genomic and transcriptomic data aid in interpreting clinical proteomic biomarker data to provide a deeper and more comprehensive molecular characterisation of cellular functions underlying disease progression. In parallel to a streamlined, patient-centric, clinical proteomic pipeline, mass spectrometry-based imaging can aid in interrogating the spatial distribution of drugs and drug metabolites within tissues at single-cell resolution. These developments are an important advancement in studying drug action and efficacy in vivo and will aid in the development of more effective and safer strategies for the treatment of melanoma. A collaborative effort of gargantuan proportions between academia and healthcare professionals has led to the initiation, establishment and development of a cutting-edge cancer research centre with a specialisation in melanoma and lung cancer. The primary research focus of the European Cancer Moonshot Lund Center is to understand the impact that drugs have on cancer at an individualised and personalised level. Simultaneously, the centre increases awareness of the relentless battle against cancer and attracts global interest in the exceptional research performed at the centre.

Keywords: Cancer moonshot; Clinical proteomics; Malignant melanoma; Post-translational modifications; Translational medicine.

Fig. 1

Progression of melanoma with prognostic factors at each stage. Tumour thickness is a key determinant in predicting prognostic outcome. With time, metastases develop and infiltrate multiple organs

Fig. 2

(A) Representative clinical pictures of superficial spreading melanoma (a), nodular melanoma (b), acral melanoma (c) and lentigo maligna melanoma (d). Regression (e) exhibits a whitish, non-specific macule without a palpable tumour, whereas the bulky tumour phase (f) shows a large, ulcerated, rapidly growing nodule. (B) Clinical images of minimal residual disease. Case study of a 75-year-old woman who had had a tumour removed from the primary site over a decade earlier. After 10 years of dormancy, the tumour recurred locally. Upon removal of the recurrent tumour, several weeks later satellite tumours developed. (C) Clinical images of a young female with a completely excised scalp lesion. After 5 years, local relapse followed by lymph node metastases and satellite tumour formation occurred. During targeted BRAFV600E therapy, the patient developed resistance and new terminal melanomas developed (all from the database of the Onco-dermatological Unit, Department of Dermatology and Allergology, University of Szeged)

Fig. 3

Schematic illustration of proteoforms formed by gene coded regions, undergoing post-translational modification

Fig. 4

Clinical proteomic workflow. Tissue sections are processed to extract the proteins. These are digested and analysed by LC-MS/MS. Peptides are identified and quantitated via labelling approaches or by label-free methods. Peptides with specific PTMs can be enriched and also analysed by LC-MS/MS.

Fig. 5

a, b Sample processing strategy for deep analysis of the melanoma proteome by mass spectrometry. Three different types of solid samples are stored in the biobank from melanoma patients: primary tumours, lymph nodes and distant organ metastases. Samples selected for analysis were cryo-sectioned. Fifteen slices are used for MS analysis and one section is prepared for histology to determine the tumour cell content and the percentage of other tissues that are present. The MM slices are prepared for quantitative proteomics

Fig. 6

The principles of TMT multiplex labelling. Tissue samples from ten patients are processed and enzymatically digested. The resultant peptides are individually labelled with the TMT 10-plex reagents and all 10 samples are mixed together with a ‘standard’ comprised of a pool of peptides from all patients. The combined 11 labelled samples are analysed by LC-MS/MS to identify and quantitate the peptides/proteins

Fig. 7

a Identified proteins from melanoma cell lines (3), lymph node metastases (9) and primary tumours (11) from an unfractionated total protein digest. Each sample was analysed as a single LC-MS/MS run. The MS data was obtained using a data-dependent acquisition method. b Principal component analysis performed with the normalised and standardised abundance intensities of the proteins identified in all samples

Fig. 8

Heat map of the relative abundance of proteins found dysregulated between melanoma primary tumours and lymph node metastases (left). Biological pathways over-represented in proteins dysregulated between the different sample types

Fig. 9

Ingenuity Pathway Analysis (IPA) for the proteins identified by the PLS-Cox analysis as significantly related to survival in a cohort of 111 lymph node melanoma metastases. Two of the top protein–protein relationship subnetworks that are enriched in the query proteins were merged. Blue, proteins with expression negatively correlated with survival. Red, proteins positively correlated with survival. Solid lines, direct relationships. Dashed lines, indirect relationships. Subcellular localisation is indicated

Fig. 10

Illustration of pathway signalling where phosphorylation signalling PTMs have been sequenced and annotated in melanoma tumours from patients

Fig. 11

Strategy to identify and determine the stoichiometry of acetylation sites

Fig. 12

Data integration and most common outcomes. Omic and clinical information combined with histopathological data are integrated through bioinformatic tools based on machine learning and statistical approaches. As a result, it is possible to discover new biomarkers and to obtain a better understanding of the disease

Fig. 13

MS/MS spectra of peptides from subunits 4 and 9 of poly ADP-ribose polymerase (PARP) confirming the occurrence of mutations and single amino acid substitutions in the sequences. a Substitution of the alanine residue at position 899 for a threonine in PARP-4, b substitution of a proline residue at position 1328 for a threonine in PARP-4, c substitution of a glycine residue at position 1265 for an alanine in PARP-4, and d substitution of a tyrosine residue at position 493 for a cysteine in PARP-9. The designation for the fragment ion signals in the MS/MS spectra is according to the Roepstorff–Fohlmann–Biemann nomenclature

Fig. 14

First reported study where MALDI-MSI was used to demonstrate localisation of a drug administered at therapeutic levels in humans. The study demonstrated that the ipratropium precursor ion (m/z 332.332) is rapidly absorbed into the airway wall partitioned within submucosal spaces containing the targeted airway smooth muscle

Fig. 15

(1) Immunohistochemistry image of a frozen melanoma tissue from a lymph node. BRAF V600E specific antibody was used for immunohistochemistry with DAB stating. BRAF V600E was expressed in the cytoplasm in the melanoma cells. The lymphocytes were used as a negative control. (2) Vemurafenib distribution in melanoma tissue. Adjacent tissue sections were used for immunohistochemistry. (a) High-resolution MSI spectrum of vemurafenib (m/z 490.079); (b and c) low-resolution ion trap MS/MS data for two fragment ions of vemurafenib (m/z 383.1 and 262.1); (d) haematoxylin and eosin (H&E) staining demonstrated the distribution of the cancer cells and lymphocytes; (e) overlaid image of the MSI vemurafenib distribution and histology showed the vemurafenib signal originated from the melanoma cells and not the lymphocytes; (f) chemical structure of vemurafenib

Fig. 16

a H&E-stained primary tumour from a malignant melanoma patient. Four different regions highlighting the composition of the tissue have been marked by a pathologist. b Principal component analysis based on MALDI-MS spectra from each of the four ROIs of the tissue slice. The results were projected on the three first principal components

Fig. 17

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