Phenotyping Tumor Heterogeneity through Proteogenomics: Study Models and Challenges
Abstract
:1. Introduction
2. Proteogenomics
2.1. From Single-Cell Analyses to Proteogenomics
Techniques | Advantages | Limitations and Troubleshooting | References |
---|---|---|---|
ScRNA-seq | Profile the transcriptomes of individual cells, multiplexed analyses, high throughput | RNA amplification bias, cell capture processes, detecting of non-coding RNA; improve the process and methods for controlling batch effects | [35,52] |
SCBC | Rapid, multiplexed analysis up to 42 proteins, detection of secreted and intracellular proteins | Limited by antibody availability, high cost; improve sample preparation techniques, regular updates and validation of encoded antibody libraries | [48,53] |
scWB | High-resolution profiling cell of surface and cytoplasmatic proteins at the single-cell level, rapid | Low throughput, detection of low abundance proteins and small molecular weight proteins, antibody specificity; use high-quality antibodies, improve the antibody incubation performance | [48,53] |
CyTOF | Measures multiple parameters with high sensitivity | High-quality antibodies, is expensive, not optimal for living cells, involves complex data analysis; validate antibody quality, optimize experimental conditions, unsupervised and bioinformatic approaches for data analysis | [48,49,53,54] |
CITE-Seq and REAP-seq | Combines proteomic and transcriptomic profiling at single-cell level, detect 3′ RNA ends | Limited by antibody availability, limited to detection of surface proteins, complexity of data analysis; employ high-quality antibodies for detection of intracellular protein, use advanced bioinformatics tools | [48,50] |
SCP-MS | Extensive proteome coverage through multiplexing | Isolation, digestion, protein transfer processes; improve sample preparation, enhance ion accumulation techniques | [48,51,53,55,56] |
2.2. Clinical and Preclinical Study Models to Study Tumor Heterogeneity
2.2.1. Tissue from Biopsies and Resection Specimens
2.2.2. Liquid Biopsies
2.2.3. Organoids
2.3. Applications
- 1.
- High-Grade Serous Ovarian Cancer (HGSOC)
- 2.
- Colorectal Cancer (CRC)
- 3.
- Pancreatic Ductal Adenocarcinoma (PDAC).
Application of Single-Cell Multiomics Approaches on Cancer Studies
3. Proteogenomics and Single-Cell Analyses: Criticisms and Challenges
4. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Techniques | Advantages | Limitations and Troubleshooting | References |
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ddPCR | High sensitivity and specificity for target mutation; low cost/reaction; no replicate reactions and standard curve | High cost for instrumental implementation; single- or low-plex analysis; need for PCR optimization strategies; need for positive/negative controls | [83,84,85] |
BEAMing | High sensitivity and specificity for target mutation; low cost/reaction | Single- or low-plex analysis; potential background noise and false positive risk; need of bead based PCR optimization strategies; need for stringent controls | [83,84,85] |
CAPP-Seq | High sensitivity; higher multiplexing analysis; detection of all the major mutation types; hybrid capture-based method not dependent on fragment size; cost-effective | Potential inefficient capture of fusions; potential false positive; need for large input | [83,86] |
WGBS-Seq | Gold standard for methylome analysis | Expensive for large sample number; reduced sensitivity due to DNA degradation; higher read depth needed to improve sensitivity | [83] |
TAm-Seq | High specificity and sensitivity; higher multiplexing analysis; reduced time and cost | Amplicon-based methods depend on fragment size; less sensitive compared to individual loci assays; higher read depth needed to improve sensitivity | [83,87] |
WES | Comprehensive analysis of coding region; low cost; high yield | Low sensitivity; need of advanced bioinformatics tools to manage and analyze output datasets; higher read depth needed to improve sensitivity | [83,84] |
WGS | Comprehensive analysis of tumor mutations types; potential for detailed mutational landscape | Time and cost consuming; variable sensitivity and specificity; need of stringent quality assurance; ethical issues; need of advanced bioinformatics tools to manage and analyze output datasets | [83,84] |
MS | Non-invasive and sensitive; complete characterization of proteins and PTMs, over a thousand proteins in blood, thousands in urine | High dynamic range of blood protein content; pre-analytical variations; require depletion protocols; using robust MS approaches (DIA) | [57,88] |
RPPA | Robust in parallel large sample profiling, sensitive, cost effective | Requires high-quality antibodies, complex experimental workflow, prolonged process; validation of antibodies, optimize signals and workflow | [27,88,89] |
SOMAscan | High-affinity protein-binding reagents, expands targeted proteomics toolkit | Limited aptamers compared to antibodies, preliminary exploration of PTM biomarkers; developing more high-quality aptamers, advancing PTM-oriented aptamer development | [28,88] |
Model Study | Proteogenomic Techniques | Applications | Aims | References |
---|---|---|---|---|
tissue biopsies (OCT) | (WGS), (RNA-Seq) and global proteomic and phosphoproteomic analyses (TMT LC-MS/MS) and RRPA. | LUAD | (-) to identify protein and RNA signatures predicting survival of patients. (-) to identify potential therapeutic vulnerabilities (proteogenomic signatures) among subtypes by proteomics and phosphoproteomics networks. | [146] |
tissue biopsies (FFT) | Panel sequencing, DNA methylation analysis, proteomic analyses (TMT LC-MS/MS and DDA/DIA LC-MS/MS) and synthetic peptide analysis. | NSCLC | (-) to detect molecular phenotypes and cancer-related proteins for the identification of specific cancer dependencies and immune-evasion mechanisms. | [147] |
tissue biopsies (FFT) | (WES), (RNA-Seq), single nucleotide polymorphism array and proteomic analyses (LC-MS/MS). | CRC | (-) to characterize molecular heterogeneity of colorectal cancer and liver metastasis. (-) to predict functional correlation with genomic abnormalities for potential prognostic value. | [148] |
tissue biopsies (FFT and FFPE) and PDX | (WGS), (RNA-Seq), MSK-IMPACT data, proteomic, phosphoproteomic and targeted proteomic analyses (TMT LC-MS/MS and MRM-MS). | HGSOC | (-) to identify distinct proteogenomic signatures that predicts chemotherapy-refractory cancers and implicates potential therapeutic vulnerabilities. | [149] |
tissue biopsies (FFT and FFPE) | (WGS), (RNA-Seq), MSK-IMPACT targeted cancer gene sequencing and proteomic analysis (LC-MS/MS). | CRC | (-) to identify distinct proteogenomic subtypes of colorectal cancer characterize primaries and liver metastases. (-) to study tumor progression and its heterogeneity. | [150] |
three colorectal cancer databases, cell lines databases and PDO | Single-cell data, DNA methylation, RNA, and copy number alteration data along with global TMT LC-MS/MS proteomic data. | CRC | (-) to identify significant prognostic biomarkers and potential therapeutic targets. (-) to characterize different CRC subtypes associated with R-loop binding proteins. | [151] |
tissue biopsies (FFT) | (WES), (WGS), (RNA-Seq), DNA methylation analyses, miRNA sequencing, proteomic, phosphoproteomic and glycoproteomic analyses (TMT LC-MS/MS and DIA LC-MS/MS). | PDAC | (-) to delineate phenotypic effects related to genomics and epigenomics aberrations in PDAC for identification of potential novel therapeutic targets. (-) to detect proteogenomic features, clinical biomarker of PDAC subtypes and specific to neoplastic ductal epithelial cells. | [152] |
tissue biopsies (FFPE), 2D in vitro model | (WES), (RNA-Seq), proteomic and phosphoproteomic analyses (LC-MS/MS). | PDAC | (-) to decipher the impact of genomic alterations in gene expression, protein abundance, and phosphorylation modification for prognostic value. (-) to monitor PDAC cancer development and progression by in vivo functional experiments. | [153] |
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Piana, D.; Iavarone, F.; De Paolis, E.; Daniele, G.; Parisella, F.; Minucci, A.; Greco, V.; Urbani, A. Phenotyping Tumor Heterogeneity through Proteogenomics: Study Models and Challenges. Int. J. Mol. Sci. 2024, 25, 8830. https://doi.org/10.3390/ijms25168830
Piana D, Iavarone F, De Paolis E, Daniele G, Parisella F, Minucci A, Greco V, Urbani A. Phenotyping Tumor Heterogeneity through Proteogenomics: Study Models and Challenges. International Journal of Molecular Sciences. 2024; 25(16):8830. https://doi.org/10.3390/ijms25168830
Chicago/Turabian StylePiana, Diletta, Federica Iavarone, Elisa De Paolis, Gennaro Daniele, Federico Parisella, Angelo Minucci, Viviana Greco, and Andrea Urbani. 2024. "Phenotyping Tumor Heterogeneity through Proteogenomics: Study Models and Challenges" International Journal of Molecular Sciences 25, no. 16: 8830. https://doi.org/10.3390/ijms25168830