Tissue-based absolute quantification using large-scale TMT and LFQ experiments.

Relative and absolute intensity-based protein quantification across cell lines, tissue atlases and tumour datasets is increasingly available in public datasets. These atlases enable researchers to explore fundamental biological questions, such as protein existence, expression location, quantity and correlation with RNA expression. Most studies provide MS1 feature-based label-free quantitative (LFQ) datasets; however, growing numbers of isobaric tandem mass tags (TMT) datasets remain unexplored. Here, we compare traditional intensity-based absolute quantification (iBAQ) proteome abundance ranking to an analogous method using reporter ion proteome abundance ranking with data from an experiment where LFQ and TMT were measured on the same samples. This new TMT method substitutes reporter ion intensities for MS1 feature intensities in the iBAQ framework. Additionally, we compared LFQ-iBAQ values to TMT-iBAQ values from two independent large-scale tissue atlas datasets (one LFQ and one TMT) using robust bottom-up proteomic identification, normalisation and quantitation workflows.

A Comprehensive Evaluation of Consensus Spectrum Generation Methods in Proteomics.

Spectrum clustering is a powerful strategy to minimize redundant mass spectra by grouping them based on similarity, with the aim of forming groups of mass spectra from the same repeatedly measured analytes. Each such group of near-identical spectra can be represented by its so-called consensus spectrum for downstream processing. Although several algorithms for spectrum clustering have been adequately benchmarked and tested, the influence of the consensus spectrum generation step is rarely evaluated. Here, we present an implementation and benchmark of common consensus spectrum algorithms, including spectrum averaging, spectrum binning, the most similar spectrum, and the best-identified spectrum. We have analyzed diverse public data sets using two different clustering algorithms (spectra-cluster and MaRaCluster) to evaluate how the consensus spectrum generation procedure influences downstream peptide identification. The BEST and BIN methods were found the most reliable methods for consensus spectrum generation, including for data sets with post-translational modifications (PTM) such as phosphorylation. All source code and data of the present study are freely available on GitHub at https://github.com/statisticalbiotechnology/representative-spectra-benchmark.

Proteome of fluid from human ovarian small antral follicles reveals insights in folliculogenesis and oocyte maturation.

STUDY QUESTION: Is it possible to identify by mass spectrometry a wider range of proteins and key proteins involved in folliculogenesis and oocyte growth and development by studying follicular fluid (FF) from human small antral follicles (hSAF)? SUMMARY ANSWER: The largest number of proteins currently reported in human FF was identified in this study analysing hSAF where several proteins showed a strong relationship with follicular developmental processes. WHAT IS KNOWN ALREADY: Protein composition of human ovarian FF constitutes the microenvironment for oocyte development. Previous proteomics studies have analysed fluids from pre-ovulatory follicles, where large numbers of plasma constituents are transferred through the follicular basal membrane. This attenuates the detection of low abundant proteins, however, the basal membrane of small antral follicles is less permeable, making it possible to detect a large number of proteins, and thereby offering further insights in folliculogenesis. STUDY DESIGN, SIZE, DURATION: Proteins in FF from unstimulated hSAF (size 6.1 ± 0.4 mm) were characterised by mass spectrometry, supported by high-throughput and targeted proteomics and bioinformatics. The FF protein profiles from hSAF containing oocytes, capable or not of maturing to metaphase II of the second meiotic division during an IVM (n = 13, from 6 women), were also analysed. PARTICIPANTS/MATERIALS, SETTING, METHODS: We collected FF from hSAF of ovaries that had been surgically removed from 31 women (∼28.5 years old) undergoing unilateral ovariectomy for fertility preservation. MAIN RESULTS AND THE ROLE OF CHANCE: In total, 2461 proteins were identified, of which 1108 identified for the first time in FF. Of the identified proteins, 24 were related to follicular regulatory processes. A total of 35 and 65 proteins were down- and up-regulated, respectively, in fluid from hSAF surrounding oocytes capable of maturing (to MII). We found that changes at the protein level occur already in FF from small antral follicles related to subsequent oocyte maturation. LIMITATIONS, REASONS FOR CAUTION: A possible limitation of our study is the uncertainty of the proportion of the sampled follicles that are undergoing atresia. Although the FF samples were carefully aspirated and processed to remove possible contaminants, we cannot ensure the absence of some proteins derived from cellular lysis provoked by technical reasons. WIDER IMPLICATIONS OF THE FINDINGS: This study is, to our knowledge, the first proteomics characterisation of FF from hSAF obtained from women in their natural menstrual cycle. We demonstrated that the analysis by mass spectrometry of FF from hSAF allows the identification of a greater number of proteins compared to the results obtained from previous analyses of larger follicles. Significant differences found at the protein level in hSAF fluid could predict the ability of the enclosed oocyte to sustain meiotic resumption. If this can be confirmed in further studies, it demonstrates that the viability of the oocyte is determined early on in follicular development and this may open up new pathways for augmenting or attenuating subsequent oocyte viability in the pre-ovulatory follicle ready to undergo ovulation. STUDY FUNDING/COMPETING INTEREST(S): The authors thank the financial support from ReproUnion, which is funded by the Interreg V EU programme. No conflict of interest was reported by the authors. TRIAL REGISTRATION NUMBER: N/A.

Mass spectrometry-based analysis of formalin-fixed, paraffin-embedded distal cholangiocarcinoma identifies stromal thrombospondin-2 as a potential prognostic marker.

BACKGROUND: Distal cholangiocarcinoma is an aggressive malignancy with a dismal prognosis. Diagnostic and prognostic biomarkers for distal cholangiocarcinoma are lacking. The aim of the present study was to identify differentially expressed proteins between distal cholangiocarcinoma and normal bile duct samples. METHODS: A workflow utilizing discovery mass spectrometry and verification by parallel reaction monitoring was used to analyze surgically resected formalin-fixed, paraffin-embedded samples from distal cholangiocarcinoma patients and normal bile duct samples. Bioinformatic analysis was used for functional annotation and pathway analysis. Immunohistochemistry was performed to validate the expression of thrombospondin-2 and investigate its association with survival. RESULTS: In the discovery study, a total of 3057 proteins were identified. Eighty-seven proteins were found to be differentially expressed (q < 0.05 and fold change ≥ 2 or ≤ 0.5); 31 proteins were upregulated and 56 were downregulated in the distal cholangiocarcinoma samples compared to controls. Bioinformatic analysis revealed an abundance of differentially expressed proteins associated with the tumor reactive stroma. Parallel reaction monitoring verified 28 proteins as upregulated and 18 as downregulated in distal cholangiocarcinoma samples compared to controls. Immunohistochemical validation revealed thrombospondin-2 to be upregulated in distal cholangiocarcinoma epithelial and stromal compartments. In paired lymph node metastases samples, thrombospondin-2 expression was significantly lower; however, stromal thrombospondin-2 expression was still frequent (72%). Stromal thrombospondin-2 was an independent predictor of poor disease-free survival (HR 3.95, 95% CI 1.09-14.3; P = 0.037). CONCLUSION: Several proteins without prior association with distal cholangiocarcinoma biology were identified and verified as differentially expressed between distal cholangiocarcinoma and normal bile duct samples. These proteins can be further evaluated to elucidate their biomarker potential and role in distal cholangiocarcinoma carcinogenesis. Stromal thrombospondin-2 is a potential prognostic marker in distal cholangiocarcinoma.

Novel functional proteins coded by the human genome discovered in metastases of melanoma patients.

In the advanced stages, malignant melanoma (MM) has a very poor prognosis. Due to tremendous efforts in cancer research over the last 10 years, and the introduction of novel therapies such as targeted therapies and immunomodulators, the rather dark horizon of the median survival has dramatically changed from under 1 year to several years. With the advent of proteomics, deep-mining studies can reach low-abundant expression levels. The complexity of the proteome, however, still surpasses the dynamic range capabilities of current analytical techniques. Consequently, many predicted protein products with potential biological functions have not yet been verified in experimental proteomic data. This category of 'missing proteins' (MP) is comprised of all proteins that have been predicted but are currently unverified. As part of the initiative launched in 2016 in the USA, the European Cancer Moonshot Center has performed numerous deep proteomics analyses on samples from MM patients. In this study, nine MPs were clearly identified by mass spectrometry in MM metastases. Some MPs significantly correlated with proteins that possess identical PFAM structural domains; and other MPs were significantly associated with cancer-related proteins. This is the first study to our knowledge, where unknown and novel proteins have been annotated in metastatic melanoma tumour tissue.

Short-term effect of pharmacologically induced alterations in testosterone levels on common blood biomarkers in a controlled healthy human model.

Testosterone deficiency in males is associated with serious comorbidities such as cardiovascular disease, diabetes type two, and also an increased risk of premature death. The pathogenetic mechanism behind this association, however, has not yet been clarified and is potentially bidirectional. The aim of this clinical trial was to gain insight into the short-term effect of changes in testosterone on blood analytes in healthy young men. Thirty healthy young male volunteers were recruited and monitored in our designed human model. Blood sampling was performed prior to and 3 weeks after pharmacological castration with a gonadotropin-releasing hormone antagonist. Subsequently, testosterone replacement with 1000 mg testosterone undecanoate was given and additional blood samples were collected 2 weeks later. The alterations in the levels of 37 routine biomarkers were statistically analysed. Eight biomarkers changed significantly in a similar manner as testosterone between the time points (e.g. prostate specific antigen, creatinine and magnesium), whereas seven other markers changed in the inverse manner as testosterone, including sexual hormone-binding globulin, urea, aspartate aminotransferase and alanine aminotransferase. Most of our results were supported by data from other studies. The designed controlled human model yielded changes in known biomarkers suggesting that low testosterone has a negative effect on health in young healthy men.

Clinical protein science in translational medicine targeting malignant melanoma.

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.

Sodium dodecyl sulfate free gel electrophoresis/electroelution sorting for peptide fractionation.

Shotgun proteomics based on peptide fractionation by using liquid chromatography has become the common procedure for proteomic studies, although in the very beginning of the field, protein separation by using electrophoresis was the main tool. Nonetheless, during the last two decades, the electrophoretic techniques for peptide mixtures fractionation have evolved as a result of relevant technological improvements. We also proposed the combination of sodium dodecyl sulfate polyacrylamide gel electrophoresis for protein fractionation and sodium dodecyl sulfate free polyacrylamide gel electrophoresis for peptide separation as a novel procedure for proteomic studies. Here, we present an optimized device for sodium dodecyl sulfate free polyacrylamide gel electrophoresis improving peptide recoveries respect to the established electrophoretic technique off gel electrophoresis meanwhile conserving the excellent resolution described for the former technique in slab gel based systems. The device simultaneously allows the separation and the collection of fractionated peptides in solution.

Quantitative Assessment of Urea In-Solution Lys-C/Trypsin Digestions Reveals Superior Performance at Room Temperature over Traditional Proteolysis at 37 °C.

Urea-containing buffer solutions are generally used in proteomic studies to aid protein denaturation and solubilization during cell and tissue lysis. It is well-known, however, that urea can lead to carbamylation of peptides and proteins and, subsequently, incomplete digestion of proteins. By the use of cells and tissues that had been lysed with urea, different solution digestion strategies were quantitatively assessed. In comparison with traditional proteolysis at 37 °C, urea in-solution digestion performed at room temperature improved peptide and protein identification and quantitation and had a minimum impact on miscleavage rates. Furthermore, the signal intensities and the number of carbamylated and pyroglutamic acid-modified peptides decreased. Overall, this led to a reduction in the negative effects often observed for such modifications. Data are available via ProteomeXchange with identifier PXD009426.

Mass spectrometry evaluation of a neuroblastoma SH-SY5Y cell culture protocol.

Cell line-based proteomics studies are susceptible to intrinsic biological variation that contributes to increasing false positive claims; most of the methods that follow these changes offer a limited understanding of the biological system. We applied a quantitative proteomic strategy (iTRAQ) to detect intrinsic protein variation across SH-SY5Y cell culture replicates. More than 95% of the quantified proteins presented a coefficient of variation (CV) < 20% between biological replicates and the variable proteins, which included cytoskeleton, cytoplasmic and housekeeping proteins, are widely reported in proteomic studies. We recommend this approach as an additional quality control before starting any proteomic experiment.

Characterization of low-abundance species in the active pharmaceutical ingredient of CIGB-300: A clinical-grade anticancer synthetic peptide.

CIGB-300 is a first-in-class synthetic peptide-based drug of 25 amino acids currently undergoing clinical trials in cancer patients. It contains an amidated disulfide cyclic undecapeptide fused to the TAT cell-penetrating peptide through a beta-alanine spacer. CIGB-300 inhibits the CK2-mediated phosphorylation leading to apoptosis of tumor cells in vitro, and in vivo in cancer patients. Despite the clinical development of CIGB-300, the characterization of peptide-related impurities present in the active pharmaceutical ingredient has not been reported earlier. In the decision tree of ICHQ3A(R2) guidelines, the daily doses intake, the abundance, and the identity of the peptide-related species are pivotal nodes that define actions to be taken (reporting, identification, and qualification). For this, purity was first assessed by reverse-phase chromatography (>97%) and low-abundance impurities (≤0.27%) were collected and identified by mass spectrometry. Most of the impurities were generated during peptide synthesis, the spontaneous air oxidation of the reduced peptide, and the lyophilization step. The most abundant impurity, with no biological activity, was the full-length peptide containing Met17 transformed into a sulfoxide residue. Interestingly, parallel and antiparallel dimers of CIGB-300 linked by 2 intermolecular disulfide bonds exhibited a higher antiproliferative activity than the CIGB-300 monomer. Likewise, very low abundance trimers and tetramers of CIGB-300 linked by disulfide bonds (≤0.01%) were also detected. Here we describe for the first time the presence of active dimeric species whose feasibility as novel CIGB-300 derived entities merits further investigation.

PRIDE Inspector Toolsuite: Moving Toward a Universal Visualization Tool for Proteomics Data Standard Formats and Quality Assessment of ProteomeXchange Datasets.

The original PRIDE Inspector tool was developed as an open source standalone tool to enable the visualization and validation of mass-spectrometry (MS)-based proteomics data before data submission or already publicly available in the Proteomics Identifications (PRIDE) database. The initial implementation of the tool focused on visualizing PRIDE data by supporting the PRIDE XML format and a direct access to private (password protected) and public experiments in PRIDE.The ProteomeXchange (PX) Consortium has been set up to enable a better integration of existing public proteomics repositories, maximizing its benefit to the scientific community through the implementation of standard submission and dissemination pipelines. Within the Consortium, PRIDE is focused on supporting submissions of tandem MS data. The increasing use and popularity of the new Proteomics Standards Initiative (PSI) data standards such as mzIdentML and mzTab, and the diversity of workflows supported by the PX resources, prompted us to design and implement a new suite of algorithms and libraries that would build upon the success of the original PRIDE Inspector and would enable users to visualize and validate PX "complete" submissions. The PRIDE Inspector Toolsuite supports the handling and visualization of different experimental output files, ranging from spectra (mzML, mzXML, and the most popular peak lists formats) and peptide and protein identification results (mzIdentML, PRIDE XML, mzTab) to quantification data (mzTab, PRIDE XML), using a modular and extensible set of open-source, cross-platform libraries. We believe that the PRIDE Inspector Toolsuite represents a milestone in the visualization and quality assessment of proteomics data. It is freely available at http://github.com/PRIDE-Toolsuite/.

ms-data-core-api: an open-source, metadata-oriented library for computational proteomics.

UNLABELLED: The ms-data-core-api is a free, open-source library for developing computational proteomics tools and pipelines. The Application Programming Interface, written in Java, enables rapid tool creation by providing a robust, pluggable programming interface and common data model. The data model is based on controlled vocabularies/ontologies and captures the whole range of data types included in common proteomics experimental workflows, going from spectra to peptide/protein identifications to quantitative results. The library contains readers for three of the most used Proteomics Standards Initiative standard file formats: mzML, mzIdentML, and mzTab. In addition to mzML, it also supports other common mass spectra data formats: dta, ms2, mgf, pkl, apl (text-based), mzXML and mzData (XML-based). Also, it can be used to read PRIDE XML, the original format used by the PRIDE database, one of the world-leading proteomics resources. Finally, we present a set of algorithms and tools whose implementation illustrates the simplicity of developing applications using the library. AVAILABILITY AND IMPLEMENTATION: The software is freely available at https://github.com/PRIDE-Utilities/ms-data-core-api. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online CONTACT: juan@ebi.ac.uk.

Receptor-activated human α2-macroglobulin interacts with the envelope protein of dengue virus and protects virions from temperature-induced inactivation through multivalent binding.

Based on the hypothesis that interactions between virions and serum components may influence the outcome of dengue virus (DENV) infections, we decided to use affinity chromatography with domain III from the envelope (E) protein of DENV2 (DIIIE2) as a ligand to isolate virus-binding proteins from human plasma. This approach yielded serum amyloid P (SAP) and α2-macroglobulin (α2M) as novel viral interactors. After confirming the specific binding of both SAP and α2M to DIIIE2 by ELISA, the latter interaction was examined in greater detail. We obtain evidence suggesting that the binding species was actually the receptor-activated form of α2M (α2M*), that α2M* could bind monovalently to recombinant domain III from all four DENV serotypes with affinities in the micromolar range ranking as DENV4>DENV1~DENV2>DENV3 and that this interaction exhibited a strong avidity effect when multivalent binding was favoured (KD 8 × 10(-8) M for DIIIE2). We also showed that α2M* bound to DENV virions of the four serotypes, protecting the virus from temperature-induced inactivation in the absence of serum and enhancing infectivity. The latter effect exhibited an ED50 of 2.9 × 10(-8) M, also suggesting an avidity effect due to multivalent binding. These results will further contribute to the characterization of the virus-host factor interaction network during human DENV infection.

Effectively addressing complex proteomic search spaces with peptide spectrum matching.

SUMMARY: Protein identification by mass spectrometry is commonly accomplished using a peptide sequence matching search algorithm, whose sensitivity varies inversely with the size of the sequence database and the number of post-translational modifications considered. We present the Spectrum Identification Machine, a peptide sequence matching tool that capitalizes on the high-intensity b1-fragment ion of tandem mass spectra of peptides coupled in solution with phenylisotiocyanate to confidently sequence the first amino acid and ultimately reduce the search space. We demonstrate that in complex search spaces, a gain of some 120% in sensitivity can be achieved. AVAILABILITY: All data generated and the software are freely available for academic use at http://proteomics.fiocruz.br/software/sim. CONTACT: paulo@pcarvalho.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Deep Profiling of Plasma Proteoforms with Engineered Nanoparticles for Top-down Proteomics.

The dynamic range challenge for detection of proteins and their proteoforms in human plasma has been well documented. Here, we use the nanoparticle protein corona approach to enrich low-abundant proteins selectively and reproducibly from human plasma and use top-down proteomics to quantify differential enrichment for the 2841 detected proteoforms from 114 proteins. Furthermore, nanoparticle enrichment allowed top-down detection of proteoforms between ∼1 µg/mL and ∼10 pg/mL in absolute abundance, providing up to 10 5 -fold increase in proteome depth over neat plasma in which only proteoforms from abundant proteins (>1 µg/mL) were detected. The ability to monitor medium and some low abundant proteoforms through reproducible enrichment significantly extends the applicability of proteoform research by adding depth beyond albumin, immunoglobins and apolipoproteins to uncover many involved in immunity and cell signaling. As proteoforms carry unique information content relative to peptides, this report opens the door to deeper proteoform sequencing in clinical proteomics of disease or aging cohorts.

Top-Down Proteomics Identifies Plasma Proteoform Signatures of Liver Cirrhosis Progression.

Cirrhosis, advanced liver disease, affects 2-5 million Americans. While most patients have compensated cirrhosis and may be fairly asymptomatic, many decompensate and experience life-threatening complications such as gastrointestinal bleeding, confusion (hepatic encephalopathy), and ascites, reducing life expectancy from 12 to less than 2 years. Among patients with compensated cirrhosis, identifying patients at high risk of decompensation is critical to optimize care and reduce morbidity and mortality. Therefore, it is important to preferentially direct them towards specialty care which cannot be provided to all patients with cirrhosis. We used discovery Top-down Proteomics (TDP) to identify differentially expressed proteoforms (DEPs) in the plasma of patients with progressive stages of liver cirrhosis with the ultimate goal to identify candidate biomarkers of disease progression. In this pilot study, we identified 209 DEPs across three stages of cirrhosis (compensated, compensated with portal hypertension, and decompensated), of which 115 derived from proteins enriched in the liver at a transcriptional level and discriminated the three stages of cirrhosis. Enrichment analyses demonstrated DEPs are involved in several metabolic and immunological processes known to be impacted by cirrhosis progression. We have preliminarily defined the plasma proteoform signatures of cirrhosis patients, setting the stage for ongoing discovery and validation of biomarkers for early diagnosis, risk stratification, and disease monitoring.

Predicting immune checkpoint therapy response in three independent metastatic melanoma cohorts.

Introduction: While Immune checkpoint inhibition (ICI) therapy shows significant efficacy in metastatic melanoma, only about 50% respond, lacking reliable predictive methods. We introduce a panel of six proteins aimed at predicting response to ICI therapy. Methods: Evaluating previously reported proteins in two untreated melanoma cohorts, we used a published predictive model (EaSIeR score) to identify potential proteins distinguishing responders and non-responders. Results: Six proteins initially identified in the ICI cohort correlated with predicted response in the untreated cohort. Additionally, three proteins correlated with patient survival, both at the protein, and at the transcript levels, in an independent immunotherapy treated cohort. Discussion: Our study identifies predictive biomarkers across three melanoma cohorts, suggesting their use in therapeutic decision-making.

Predicting immune checkpoint therapy response in three independent metastatic melanoma cohorts.

While Immune checkpoint inhibition (ICI) therapy shows significant efficacy in metastatic melanoma, only about 50% respond, lacking reliable predictive methods. We introduce a panel of six proteins aimed at predicting response to ICI therapy. Evaluating previously reported proteins in two untreated melanoma cohorts, we used a published predictive model (EaSIeR score) to identify potential proteins distinguishing responders and non-responders. Six proteins initially identified in the ICI cohort correlated with predicted response in the untreated cohort. Additionally, three proteins correlated with patient survival, both at the protein, and at the transcript levels, in an independent immunotherapy treated cohort. Our study identifies predictive biomarkers across three melanoma cohorts, suggesting their use in therapeutic decision-making.

Identifying Ferroptosis Inducers, HDAC, and RTK Inhibitor Sensitivity in Melanoma Subtypes through Unbiased Drug Target Prediction.

The utilization of PD1 and CTLA4 inhibitors has revolutionized the treatment of malignant melanoma (MM). However, resistance to targeted and immune-checkpoint-based therapies still poses a significant problem. Here we mine large scale MM proteogenomic data integrating it with MM cell line dependency screen, and drug sensitivity data to identify druggable targets and forecast treatment efficacy and resistance. Leveraging protein profiles from established MM subtypes and molecular structures of 82 cancer treatment drugs, we identified nine candidate hub proteins, mTOR, FYN, PIK3CB, EGFR, MAPK3, MAP4K1, MAP2K1, SRC and AKT1, across five distinct MM subtypes. These proteins serve as potential drug targets applicable to one or multiple MM subtypes. By analyzing transcriptomic data from 48 publicly accessible melanoma cell lines sourced from Achilles and CRISPR dependency screens, we forecasted 162 potentially targetable genes. We also identified genetic resistance in 260 genes across at least one melanoma subtype. In addition, we employed publicly available compound sensitivity data (Cancer Therapeutics Response Portal, CTRPv2) on the cell lines to assess the correlation of compound effectiveness within each subtype. We have identified 20 compounds exhibiting potential drug impact in at least one melanoma subtype. Remarkably, employing this unbiased approach, we have uncovered compounds targeting ferroptosis, that demonstrate a striking 30x fold difference in sensitivity among different subtypes. This implies that the proteogenomic classification of melanoma has the potential to predict sensitivity to ferroptosis compounds. Our results suggest innovative and novel therapeutic strategies by stratifying melanoma samples through proteomic profiling, offering a spectrum of novel therapeutic interventions and prospects for combination therapy. Highlights: (1) Proteogenomic subtype classification can define the landscape of genetic dependencies in melanoma (2) Nine proteins from molecular subtypes were identified as potential drug targets for specified MM patients (3) 20 compounds identified that show potential effectiveness in at least one melanoma subtype (4) Proteogenomics can predict specific ferroptosis inducers, HDAC, and RTK Inhibitor sensitivity in melanoma subtypes.