A proteogenomic approach to target neoantigens in solid tumors

Verma, Ayushi; Halder, Ankit; Marathe, Soumitra; Purwar, Rahul; Srivastava, Sanjeeva

doi:10.1080/14789450.2020.1881889

Cited by 5 publications

(2 citation statements)

References 64 publications

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“…The advent of various consortia like CPTAC (Clinical Proteomics Tumor Analysis Consortium) and HUPO (Human Proteome Organization) has further unleashed the power of proteomics in clinical applications [ 2 – 4 ]. At the data analysis front, the recent advancements in several stand-alone tools and databases have facilitated the development of integrated omics pipelines and meta-analysis workflow that expedites the understanding the disease pathophysiology, identification of newer biomarkers, and predicting novel therapeutic modalities [ 5 , 6 ]. A perfect cancer biomarker is defined as one that is highly specific, selective, easily detectable at an early stage of the disease, and measurable at a low cost, however, identifying a biomarker with all these desired features is a highly arduous task [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

A large-scale targeted proteomics of serum and tissue shows the utility of classifying high grade and low grade meningioma tumors

Halder,

Biswas,

Chauhan

et al. 2023

Clin Proteom

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Background Meningiomas are the most prevalent primary brain tumors. Due to their increasing burden on healthcare, meningiomas have become a pivot of translational research globally. Despite many studies in the field of discovery proteomics, the identification of grade-specific markers for meningioma is still a paradox and requires thorough investigation. The potential of the reported markers in different studies needs further verification in large and independent sample cohorts to identify the best set of markers with a better clinical perspective. Methods A total of 53 fresh frozen tumor tissue and 51 serum samples were acquired from meningioma patients respectively along with healthy controls, to validate the prospect of reported differentially expressed proteins and claimed markers of Meningioma mined from numerous manuscripts and knowledgebases. A small subset of Glioma/Glioblastoma samples were also included to investigate inter-tumor segregation. Furthermore, a simple Machine Learning (ML) based analysis was performed to evaluate the classification accuracy of the list of proteins. Results A list of 15 proteins from tissue and 12 proteins from serum were found to be the best segregator using a feature selection-based machine learning strategy with an accuracy of around 80% in predicting low grade (WHO grade I) and high grade (WHO grade II and WHO grade III) meningiomas. In addition, the discriminant analysis could also unveil the complexity of meningioma grading from a segregation pattern, which leads to the understanding of transition phases between the grades. Conclusions The identified list of validated markers could play an instrumental role in the classification of meningioma as well as provide novel clinical perspectives in regard to prognosis and therapeutic targets.

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Section: Introductionmentioning

confidence: 99%

A large-scale targeted proteomics of serum and tissue shows the utility of classifying high grade and low grade meningioma tumors

Halder,

Biswas,

Chauhan

et al. 2023

Clin Proteom

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“…However, these prediction methods also had high false positive rates [ 6 ]. More recently, improved mass spectrometry technologies associated with comprehensive genomic analyses facilitated the generation of large datasets of naturally processed antigens presented on a broad range of MHC alleles, also called human leukocyte antigen (HLA) in humans [ 7 , 8 , 9 ]. These new datasets allowed for incorporation of peptide processing and presentation and led to the significant improvement of prediction algorithms [ 10 , 11 , 12 , 13 , 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Sources of Cancer Neoantigens beyond Single-Nucleotide Variants

Capietto¹,

Hoshyar²,

Delamarre³

2022

IJMS

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The success of checkpoint blockade therapy against cancer has unequivocally shown that cancer cells can be effectively recognized by the immune system and eliminated. However, the identity of the cancer antigens that elicit protective immunity remains to be fully explored. Over the last decade, most of the focus has been on somatic mutations derived from non-synonymous single-nucleotide variants (SNVs) and small insertion/deletion mutations (indels) that accumulate during cancer progression. Mutated peptides can be presented on MHC molecules and give rise to novel antigens or neoantigens, which have been shown to induce potent anti-tumor immune responses. A limitation with SNV-neoantigens is that they are patient-specific and their accurate prediction is critical for the development of effective immunotherapies. In addition, cancer types with low mutation burden may not display sufficient high-quality [SNV/small indels] neoantigens to alone stimulate effective T cell responses. Accumulating evidence suggests the existence of alternative sources of cancer neoantigens, such as gene fusions, alternative splicing variants, post-translational modifications, and transposable elements, which may be attractive novel targets for immunotherapy. In this review, we describe the recent technological advances in the identification of these novel sources of neoantigens, the experimental evidence for their presentation on MHC molecules and their immunogenicity, as well as the current clinical development stage of immunotherapy targeting these neoantigens.

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