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21 pages, 6280 KiB

Open AccessArticle

A Unified Transcriptional, Pharmacogenomic, and Gene Dependency Approach to Decipher the Biology, Diagnostic Markers, and Therapeutic Targets Associated with Prostate Cancer Metastasis

by Manny D. Bacolod and Francis Barany

Cancers 2021, 13(20), 5158; https://doi.org/10.3390/cancers13205158 - 14 Oct 2021

Cited by 4 | Viewed by 3243

Abstract

Our understanding of metastatic prostate cancer (mPrCa) has dramatically advanced during the genomics era. Nonetheless, many aspects of the disease may still be uncovered through reanalysis of public datasets. We integrated the expression datasets for 209 PrCa tissues (metastasis, primary, normal) with expression, [...] Read more.

Our understanding of metastatic prostate cancer (mPrCa) has dramatically advanced during the genomics era. Nonetheless, many aspects of the disease may still be uncovered through reanalysis of public datasets. We integrated the expression datasets for 209 PrCa tissues (metastasis, primary, normal) with expression, gene dependency (GD) (from CRISPR/cas9 screen), and drug viability data for hundreds of cancer lines (including PrCa). Comparative statistical and pathways analyses and functional annotations (available inhibitors, protein localization) revealed relevant pathways and potential (and previously reported) protein markers for minimally invasive mPrCa diagnostics. The transition from localized to mPrCa involved the upregulation of DNA replication, mitosis, and PLK1-mediated events. Genes highly upregulated in mPrCa and with very high average GD (~1) are potential therapeutic targets. We showed that fostamatinib (which can target PLK1 and other over-expressed serine/threonine kinases such as AURKA, MELK, NEK2, and TTK) is more active against cancer lines with more pronounced signatures of invasion (e.g., extracellular matrix organization/degradation). Furthermore, we identified surface-bound (e.g., ADAM15, CD276, ABCC5, CD36, NRP1, SCARB1) and likely secreted proteins (e.g., APLN, ANGPT2, CTHRC1, ADAM12) that are potential mPrCa diagnostic markers. Overall, we demonstrated that comprehensive analyses of public genomics data could reveal potentially clinically relevant information regarding mPrCa. Full article

(This article belongs to the Special Issue Genomics- and Proteomics-Driven Discoveries on Cancer Metastasis: Impacts on Therapeutics and Diagnostics)

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16 pages, 633 KiB

Open AccessArticle

Comparison of the Metastasis Predictive Potential of mRNA and Long Non-Coding RNA Profiling in Systemically Untreated Breast Cancer

by Thi T. N. Do, Ines Block, Mark Burton, Kristina P. Sørensen, Martin J. Larsen, Martin Bak, Søren Cold, Mads Thomassen, Qihua Tan and Torben A. Kruse

Cancers 2021, 13(19), 4907; https://doi.org/10.3390/cancers13194907 - 29 Sep 2021

Viewed by 1772

Abstract

Several gene expression signatures based on mRNAs and a few based on long non-coding RNAs (lncRNAs) have been developed to provide prognostic information beyond clinical evaluation in breast cancer (BC). However, the comparison of such signatures for predicting recurrence is very scarce. Therefore, [...] Read more.

Several gene expression signatures based on mRNAs and a few based on long non-coding RNAs (lncRNAs) have been developed to provide prognostic information beyond clinical evaluation in breast cancer (BC). However, the comparison of such signatures for predicting recurrence is very scarce. Therefore, we compared the prognostic utility of mRNAs and lncRNAs in low-risk BC patients using two different classification strategies. Frozen primary tumor samples from 160 lymph node negative and systemically untreated BC patients were included; 80 developed recurrence—i.e., regional or distant metastasis while 80 remained recurrence-free (mean follow-up of 20.9 years). Patients were pairwise matched for clinicopathological characteristics. Classification based on differential mRNA or lncRNA expression using seven individual machine learning methods and a voting scheme classified patients into risk-subgroups. Classification by the seven methods with a fixed sensitivity of ≥90% resulted in specificities ranging from 16–40% for mRNA and 38–58% for lncRNA, and after voting, specificities of 38% and 60% respectively. Classifier performance based on an alternative classification approach of balanced accuracy optimization also provided higher specificities for lncRNA than mRNA at comparable sensitivities. Thus, our results suggested that classification followed by voting improved prognostic power using lncRNAs compared to mRNAs regardless of classification strategy. Full article

(This article belongs to the Special Issue Genomics- and Proteomics-Driven Discoveries on Cancer Metastasis: Impacts on Therapeutics and Diagnostics)

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16 pages, 1839 KiB

Open AccessArticle

Machine Learning Approaches to Classify Primary and Metastatic Cancers Using Tissue of Origin-Based DNA Methylation Profiles

by Vijayachitra Modhukur, Shakshi Sharma, Mainak Mondal, Ankita Lawarde, Keiu Kask, Rajesh Sharma and Andres Salumets

Cancers 2021, 13(15), 3768; https://doi.org/10.3390/cancers13153768 - 27 Jul 2021

Cited by 15 | Viewed by 4568

Abstract

Metastatic cancers account for up to 90% of cancer-related deaths. The clear differentiation of metastatic cancers from primary cancers is crucial for cancer type identification and developing targeted treatment for each cancer type. DNA methylation patterns are suggested to be an intriguing target [...] Read more.

Metastatic cancers account for up to 90% of cancer-related deaths. The clear differentiation of metastatic cancers from primary cancers is crucial for cancer type identification and developing targeted treatment for each cancer type. DNA methylation patterns are suggested to be an intriguing target for cancer prediction and are also considered to be an important mediator for the transition to metastatic cancer. In the present study, we used 24 cancer types and 9303 methylome samples downloaded from publicly available data repositories, including The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). We constructed machine learning classifiers to discriminate metastatic, primary, and non-cancerous methylome samples. We applied support vector machines (SVM), Naive Bayes (NB), extreme gradient boosting (XGBoost), and random forest (RF) machine learning models to classify the cancer types based on their tissue of origin. RF outperformed the other classifiers, with an average accuracy of 99%. Moreover, we applied local interpretable model-agnostic explanations (LIME) to explain important methylation biomarkers to classify cancer types. Full article

(This article belongs to the Special Issue Genomics- and Proteomics-Driven Discoveries on Cancer Metastasis: Impacts on Therapeutics and Diagnostics)

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15 pages, 1563 KiB

Open AccessArticle

Cloud Computing Based Immunopeptidomics Utilizing Community Curated Variant Libraries Simplifies and Improves Neo-Antigen Discovery in Metastatic Melanoma

by Amol Prakash, Keira E. Mahoney and Benjamin C. Orsburn

Cancers 2021, 13(15), 3754; https://doi.org/10.3390/cancers13153754 - 26 Jul 2021

Cited by 1 | Viewed by 2434

Abstract

Unique peptide neo-antigens presented on the cell surface are attractive targets for researchers in nearly all areas of personalized medicine. Cells presenting peptides with mutated or other non-canonical sequences can be utilized for both targeted therapies and diagnostics. Today’s state-of-the-art pipelines utilize complementary [...] Read more.

Unique peptide neo-antigens presented on the cell surface are attractive targets for researchers in nearly all areas of personalized medicine. Cells presenting peptides with mutated or other non-canonical sequences can be utilized for both targeted therapies and diagnostics. Today’s state-of-the-art pipelines utilize complementary proteogenomic approaches where RNA or ribosomal sequencing data helps to create libraries from which tandem mass spectrometry data can be compared. In this study, we present an alternative approach whereby cloud computing is utilized to power neo-antigen searches against community curated databases containing more than 7 million human sequence variants. Using these expansive databases of high-quality sequences as a reference, we reanalyze the original data from two previously reported studies to identify neo-antigen targets in metastatic melanoma. Using our approach, we identify 79 percent of the non-canonical peptides reported by previous genomic analyses of these files. Furthermore, we report 18-fold more non-canonical peptides than previously reported. The novel neo-antigens we report herein can be corroborated by secondary analyses such as high predicted binding affinity, when analyzed by well-established tools such as NetMHC. Finally, we report 738 non-canonical peptides shared by at least five patient samples, and 3258 shared across the two studies. This illustrates the depth of data that is present, but typically missed by lower statistical power proteogenomic approaches. This large list of shared peptides across the two studies, their annotation, non-canonical origin, as well as MS/MS spectra from the two studies are made available on a web portal for community analysis. Full article

(This article belongs to the Special Issue Genomics- and Proteomics-Driven Discoveries on Cancer Metastasis: Impacts on Therapeutics and Diagnostics)

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13 pages, 2235 KiB

Open AccessArticle

Identification of Nucleolin as a Novel AEG-1-Interacting Protein in Breast Cancer via Interactome Profiling

by Seong-Jae Lee, Kyoung-Min Choi, Geul Bang, Seo-Gyu Park, Eun-Bi Kim, Jin-Woong Choi, Young-Ho Chung, Jinyoung Kim, Seok-Geun Lee, Eunjung Kim and Jae-Young Kim

Cancers 2021, 13(11), 2842; https://doi.org/10.3390/cancers13112842 - 07 Jun 2021

Cited by 4 | Viewed by 3058

Abstract

Breast cancer is one of the most common malignant diseases worldwide. Astrocyte elevated gene-1 (AEG-1) is upregulated in breast cancer and regulates breast cancer cell proliferation and invasion. However, the molecular mechanisms by which AEG-1 promotes breast cancer have yet to be fully [...] Read more.

Breast cancer is one of the most common malignant diseases worldwide. Astrocyte elevated gene-1 (AEG-1) is upregulated in breast cancer and regulates breast cancer cell proliferation and invasion. However, the molecular mechanisms by which AEG-1 promotes breast cancer have yet to be fully elucidated. In order to delineate the function of AEG-1 in breast cancer development, we mapped the AEG-1 interactome via affinity purification followed by LC-MS/MS. We identified nucleolin (NCL) as a novel AEG-1 interacting protein, and co-immunoprecipitation experiments validated the interaction between AEG-1 and NCL in breast cancer cells. The silencing of NCL markedly reduced not only migration/invasion, but also the proliferation induced by the ectopic expression of AEG-1. Further, we found that the ectopic expression of AEG-1 induced the tyrosine phosphorylation of c-Met, and NCL knockdown markedly reduced this AEG-1 mediated phosphorylation. Taken together, our report identifies NCL as a novel mediator of the oncogenic function of AEG-1, and suggests that c-Met could be associated with the oncogenic function of the AEG-1-NCL complex in the context of breast cancer. Full article

(This article belongs to the Special Issue Genomics- and Proteomics-Driven Discoveries on Cancer Metastasis: Impacts on Therapeutics and Diagnostics)

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51 pages, 8321 KiB

Open AccessArticle

From Proteomic Mapping to Invasion-Metastasis-Cascade Systemic Biomarkering and Targeted Drugging of Mutant BRAF-Dependent Human Cutaneous Melanomagenesis

by Aikaterini F. Giannopoulou, Athanassios D. Velentzas, Athanasios K. Anagnostopoulos, Adamantia Agalou, Nikos C. Papandreou, Stamatia A. Katarachia, Dimitra G. Koumoundourou, Eumorphia G. Konstantakou, Vasiliki I. Pantazopoulou, Anastasios Delis, Maria T. Michailidi, Dimitrios Valakos, Dimitris Chatzopoulos, Popi Syntichaki, Vassiliki A. Iconomidou, Ourania E. Tsitsilonis, Issidora S. Papassideri, Gerassimos E. Voutsinas, Polydefkis Hatzopoulos, Dimitris Thanos, Dimitris Beis, Ema Anastasiadou, George Th. Tsangaris and Dimitrios J. Stravopodis Show full author list Hide full author list

Cancers 2021, 13(9), 2024; https://doi.org/10.3390/cancers13092024 - 22 Apr 2021

Cited by 6 | Viewed by 3410

Abstract

Melanoma is classified among the most notoriously aggressive human cancers. Despite the recent progress, due to its propensity for metastasis and resistance to therapy, novel biomarkers and oncogenic molecular drivers need to be promptly identified for metastatic melanoma. Hence, by employing nano liquid [...] Read more.

Melanoma is classified among the most notoriously aggressive human cancers. Despite the recent progress, due to its propensity for metastasis and resistance to therapy, novel biomarkers and oncogenic molecular drivers need to be promptly identified for metastatic melanoma. Hence, by employing nano liquid chromatography-tandem mass spectrometry deep proteomics technology, advanced bioinformatics algorithms, immunofluorescence, western blotting, wound healing protocols, molecular modeling programs, and MTT assays, we comparatively examined the respective proteomic contents of WM115 primary (n = 3955 proteins) and WM266-4 metastatic (n = 6681 proteins) melanoma cells. It proved that WM115 and WM266-4 cells have engaged hybrid epithelial-to-mesenchymal transition/mesenchymal-to-epithelial transition states, with TGF-β controlling their motility in vitro. They are characterized by different signatures of SOX-dependent neural crest-like stemness and distinct architectures of the cytoskeleton network. Multiple signaling pathways have already been activated from the primary melanoma stage, whereas HIF1α, the major hypoxia-inducible factor, can be exclusively observed in metastatic melanoma cells. Invasion-metastasis cascade-specific sub-routines of activated Caspase-3-triggered apoptosis and LC3B-II-dependent constitutive autophagy were also unveiled. Importantly, WM115 and WM266-4 cells exhibited diverse drug response profiles, with epirubicin holding considerable promise as a beneficial drug for metastatic melanoma clinical management. It is the proteome navigation that enables systemic biomarkering and targeted drugging to open new therapeutic windows for advanced disease. Full article

(This article belongs to the Special Issue Genomics- and Proteomics-Driven Discoveries on Cancer Metastasis: Impacts on Therapeutics and Diagnostics)

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Review

Jump to: Research

20 pages, 2118 KiB

Open AccessReview

TYK2 in Cancer Metastases: Genomic and Proteomic Discovery

by Dana C. Borcherding, Kevin He, Neha V. Amin and Angela C. Hirbe

Cancers 2021, 13(16), 4171; https://doi.org/10.3390/cancers13164171 - 19 Aug 2021

Cited by 17 | Viewed by 6875

Abstract

Advances in genomic analysis and proteomic tools have rapidly expanded identification of biomarkers and molecular targets important to cancer development and metastasis. On an individual basis, personalized medicine approaches allow better characterization of tumors and patient prognosis, leading to more targeted treatments by [...] Read more.

Advances in genomic analysis and proteomic tools have rapidly expanded identification of biomarkers and molecular targets important to cancer development and metastasis. On an individual basis, personalized medicine approaches allow better characterization of tumors and patient prognosis, leading to more targeted treatments by detection of specific gene mutations, overexpression, or activity. Genomic and proteomic screens by our lab and others have revealed tyrosine kinase 2 (TYK2) as an oncogene promoting progression and metastases of many types of carcinomas, sarcomas, and hematologic cancers. TYK2 is a Janus kinase (JAK) that acts as an intermediary between cytokine receptors and STAT transcription factors. TYK2 signals to stimulate proliferation and metastasis while inhibiting apoptosis of cancer cells. This review focuses on the growing evidence from genomic and proteomic screens, as well as molecular studies that link TYK2 to cancer prevalence, prognosis, and metastasis. In addition, pharmacological inhibition of TYK2 is currently used clinically for autoimmune diseases, and now provides promising treatment modalities as effective therapeutic agents against multiple types of cancer. Full article

(This article belongs to the Special Issue Genomics- and Proteomics-Driven Discoveries on Cancer Metastasis: Impacts on Therapeutics and Diagnostics)

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19 pages, 903 KiB

Open AccessFeature PaperReview

Functional Genomic Analysis of Breast Cancer Metastasis: Implications for Diagnosis and Therapy

by Ziqi Yu, Mei Song, Lotfi Chouchane and Xiaojing Ma

Cancers 2021, 13(13), 3276; https://doi.org/10.3390/cancers13133276 - 30 Jun 2021

Cited by 6 | Viewed by 2551

Abstract

Breast cancer (BC) is one of the most diagnosed cancers worldwide and is the second cause of cancer related death in women. The most frequent cause of BC-related deaths, like many cancers, is metastasis. However, metastasis is a complicated and poorly understood process [...] Read more.

Breast cancer (BC) is one of the most diagnosed cancers worldwide and is the second cause of cancer related death in women. The most frequent cause of BC-related deaths, like many cancers, is metastasis. However, metastasis is a complicated and poorly understood process for which there is a shortage of accurate prognostic indicators and effective treatments. With the rapid and ever-evolving development and application of genomic sequencing technologies, many novel molecules were identified that play previously unappreciated and important roles in the various stages of metastasis. In this review, we summarize current advancements in the functional genomic analysis of BC metastasis and discuss about the potential prognostic and therapeutic implications from the recent genomic findings. Full article

(This article belongs to the Special Issue Genomics- and Proteomics-Driven Discoveries on Cancer Metastasis: Impacts on Therapeutics and Diagnostics)

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