Developments for Personalized Medicine of Lung Cancer Subtypes: Mass Spectrometry-Based Clinical Proteogenomic Analysis of Oncogenic Mutations

A case of personalized and precision medicine: Pharmacometabolomic applications to rare cancer, microbiological investigation, and therapy

RATIONALE

Advances in metabolomics, together with consolidated genetic approaches, have opened the way for investigating the health of patients using a large number of molecules simultaneously, thus providing firm scientific evidence for personalized medicine and consequent interventions. Metabolomics is an ideal approach for investigating specific biochemical alterations occurring in rare clinical situations, such as those caused by rare associations between comorbidities and immunosuppression.

METHODS

Metabolomic database matching enables clear identification of molecular factors associated with a metabolic disorder and can provide a rationale for elaborating personalized therapeutic protocols. Mass spectrometry (MS) forms the basis of metabolomics and uses mass-to-charge ratios for metabolite identification. Here, we used an MS-based approach to diagnose and develop treatment options in the clinical case of a patient afflicted with a rare disease further complicated by immunosuppression. The patient's data were analyzed using proprietary databases, and a personalized and efficient therapeutic protocol was consequently elaborated.

RESULTS

The patient exhibited significant alterations in homocysteine:methionine and homocysteine:thiodiglycol acid plasma concentration ratios, and these were associated with low immune system function. This led to cysteine concentration deficiency causing extreme oxidative stress. Plasmatic thioglycolic acid concentrations were initially altered and were used for therapeutic follow-up and to evaluate cysteine levels.

CONCLUSIONS

An MS-based pharmacometabolomics approach was used to define a personalized protocol in a clinical case of rare peritoneal carcinosis with confounding immunosuppression. This personalized protocol reduced both oxidative stress and resistance to antibiotics and antiviral drugs.

Current Challenges and Implications of Proteogenomic Approaches in Prostate Cancer

In the recent past, next-generation sequencing (NGS) approaches have heralded the omics era. With NGS data burgeoning, there arose a need to disseminate the omic data better. Proteogenomics has been vividly used for characterising the functions of candidate genes and is applied in ascertaining various diseased phenotypes, including cancers. However, not much is known about the role and application of proteogenomics, especially Prostate Cancer (PCa). In this review, we outline the need for proteogenomic approaches, their applications and their role in PCa.

XMAn v2-a database of Homo sapiens mutated peptides

SUMMARY

The 'Unknown Mutation Analysis (XMAn)' database is a compilation of Homo sapiens mutated peptides in FASTA format, that was constructed for facilitating the identification of protein sequence alterations by tandem mass spectrometry detection. The database comprises 2 539 031 non-redundant mutated entries from 17 599 proteins, of which 2 377 103 are missense and 161 928 are nonsense mutations. It can be used in conjunction with search engines that seek the identification of peptide amino acid sequences by matching experimental tandem mass spectrometry data to theoretical sequences from a database.

AVAILABILITY AND IMPLEMENTATION

XMAn v2 can be accessed from github.com/lazarlab/XMAnv2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Current status of clinical proteogenomics in lung cancer

Introduction: Lung cancer is the leading cause of cancer death worldwide. Proteogenomics, a way to integrate genomics, transcriptomics, and proteomics, have emerged as a way to understand molecular causes in cancer tumorigenesis. This understanding will help identify therapeutic targets that are urgently needed to improve individual patient outcomes. Areas covered: To explore underlying molecular mechanisms of lung cancer subtypes, several efforts have used proteogenomic approaches that integrate next generation sequencing (NGS) and mass spectrometry (MS)-based technologies. Expert opinion: A large-scale, MS-based, proteomic analysis, together with both NGS-based genomic data and clinicopathological information, will facilitate establishing extensive databases for lung cancer subtypes that can be used for further proteogenomic analyzes. Proteogenomic strategies will further be understanding of how major driver mutations affect downstream molecular networks, resulting in lung cancer progression and malignancy, and how therapy-resistant cancers resistant are molecularly structured. These strategies require advanced bioinformatics based on a dynamic theory of network systems, rather than statistics, to accurately identify mutant proteins and their affected key networks.

Recent mass spectrometry-based proteomics for biomarker discovery in lung cancer, COPD, and asthma

INTRODUCTION

Lung cancer and related diseases have been one of the most common causes of deaths worldwide. Genomic-based biomarkers may hardly reflect the underlying dynamic molecular mechanism of functional protein interactions, which is the center of a disease. Recent developments in mass spectrometry (MS) have made it possible to analyze disease-relevant proteins expressed in clinical specimens by proteomic challenges. Areas covered: To understand the molecular mechanisms of lung cancer and its subtypes, chronic obstructive pulmonary disease (COPD), asthma and others, great efforts have been taken to identify numerous relevant proteins by MS-based clinical proteomic approaches. Since lung cancer is a multifactorial disease that is biologically associated with asthma and COPD among various lung diseases, this study focused on proteomic studies on biomarker discovery using various clinical specimens for lung cancer, COPD, and asthma. Expert commentary: MS-based exploratory proteomics utilizing clinical specimens, which can incorporate both experimental and bioinformatic analysis of protein-protein interaction and also can adopt proteogenomic approaches, makes it possible to reveal molecular networks that are relevant to a disease subgroup and that could differentiate between drug responders and non-responders, good and poor prognoses, drug resistance, and so on.