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Chaubal R, Gardi N, Joshi S, Pantvaidya G, Kadam R, Vanmali V, Hawaldar R, Talker E, Chitra J, Gera P, Bhatia D, Kalkar P, Gurav M, Shetty O, Desai S, Krishnan NM, Nair N, Parmar V, Dutt A, Panda B, Gupta S, Badwe R. Surgical Tumor Resection Deregulates Hallmarks of Cancer in Resected Tissue and the Surrounding Microenvironment. Mol Cancer Res 2024; 22:572-584. [PMID: 38394149 PMCID: PMC11148542 DOI: 10.1158/1541-7786.mcr-23-0265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 08/24/2023] [Accepted: 02/20/2024] [Indexed: 02/25/2024]
Abstract
Surgery exposes tumor tissue to severe hypoxia and mechanical stress leading to rapid gene expression changes in the tumor and its microenvironment, which remain poorly characterized. We biopsied tumor and adjacent normal tissues from patients with breast (n = 81) and head/neck squamous cancers (HNSC; n = 10) at the beginning (A), during (B), and end of surgery (C). Tumor/normal RNA from 46/81 patients with breast cancer was subjected to mRNA-Seq using Illumina short-read technology, and from nine patients with HNSC to whole-transcriptome microarray with Illumina BeadArray. Pathways and genes involved in 7 of 10 known cancer hallmarks, namely, tumor-promoting inflammation (TNF-A, NFK-B, IL18 pathways), activation of invasion and migration (various extracellular matrix-related pathways, cell migration), sustained proliferative signaling (K-Ras Signaling), evasion of growth suppressors (P53 signaling, regulation of cell death), deregulating cellular energetics (response to lipid, secreted factors, and adipogenesis), inducing angiogenesis (hypoxia signaling, myogenesis), and avoiding immune destruction (CTLA4 and PDL1) were significantly deregulated during surgical resection (time points A vs. B vs. C). These findings were validated using NanoString assays in independent pre/intra/post-operative breast cancer samples from 48 patients. In a comparison of gene expression data from biopsy (analogous to time point A) with surgical resection samples (analogous to time point C) from The Cancer Genome Atlas study, the top deregulated genes were the same as identified in our analysis, in five of the seven studied cancer types. This study suggests that surgical extirpation deregulates the hallmarks of cancer in primary tumors and adjacent normal tissue across different cancers. IMPLICATIONS Surgery deregulates hallmarks of cancer in human tissue.
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Affiliation(s)
- Rohan Chaubal
- Department of Surgical Oncology, Tata Memorial Hospital, Tata Memorial Centre, Mumbai, India
- Hypoxia and Clinical Genomics Lab (Clinician Scientist Laboratory), Advanced Centre for Treatment, Research, and Education in Cancer, Tata Memorial Centre, Navi Mumbai, Maharashtra, India
- Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra, India
| | - Nilesh Gardi
- Hypoxia and Clinical Genomics Lab (Clinician Scientist Laboratory), Advanced Centre for Treatment, Research, and Education in Cancer, Tata Memorial Centre, Navi Mumbai, Maharashtra, India
- Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra, India
- Department of Medical Oncology, Tata Memorial Hospital, Tata Memorial Centre, Mumbai, India
| | - Shalaka Joshi
- Department of Surgical Oncology, Tata Memorial Hospital, Tata Memorial Centre, Mumbai, India
- Hypoxia and Clinical Genomics Lab (Clinician Scientist Laboratory), Advanced Centre for Treatment, Research, and Education in Cancer, Tata Memorial Centre, Navi Mumbai, Maharashtra, India
- Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra, India
| | - Gouri Pantvaidya
- Department of Surgical Oncology, Tata Memorial Hospital, Tata Memorial Centre, Mumbai, India
- Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra, India
| | - Rasika Kadam
- Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra, India
- Department of Medical Oncology, Tata Memorial Hospital, Tata Memorial Centre, Mumbai, India
| | - Vaibhav Vanmali
- Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra, India
- Clinical Research Secretariat, Tata Memorial Hospital, Tata Memorial Centre, Mumbai, India
| | - Rohini Hawaldar
- Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra, India
- Clinical Research Secretariat, Tata Memorial Hospital, Tata Memorial Centre, Mumbai, India
| | - Elizabeth Talker
- Hypoxia and Clinical Genomics Lab (Clinician Scientist Laboratory), Advanced Centre for Treatment, Research, and Education in Cancer, Tata Memorial Centre, Navi Mumbai, Maharashtra, India
- Department of Medical Oncology, Tata Memorial Hospital, Tata Memorial Centre, Mumbai, India
| | - Jaya Chitra
- Department of Surgical Oncology, Tata Memorial Hospital, Tata Memorial Centre, Mumbai, India
- Hypoxia and Clinical Genomics Lab (Clinician Scientist Laboratory), Advanced Centre for Treatment, Research, and Education in Cancer, Tata Memorial Centre, Navi Mumbai, Maharashtra, India
| | - Poonam Gera
- Biorepository, Advanced Centre for Treatment, Research and Education in Cancer, Tata Memorial Centre, Navi Mumbai, Maharashtra, India
| | - Dimple Bhatia
- Hypoxia and Clinical Genomics Lab (Clinician Scientist Laboratory), Advanced Centre for Treatment, Research, and Education in Cancer, Tata Memorial Centre, Navi Mumbai, Maharashtra, India
| | - Prajakta Kalkar
- Hypoxia and Clinical Genomics Lab (Clinician Scientist Laboratory), Advanced Centre for Treatment, Research, and Education in Cancer, Tata Memorial Centre, Navi Mumbai, Maharashtra, India
| | - Mamta Gurav
- Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra, India
- Department of Pathology, Tata Memorial Hospital, Tata Memorial Centre, Mumbai, India
| | - Omshree Shetty
- Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra, India
- Department of Pathology, Tata Memorial Hospital, Tata Memorial Centre, Mumbai, India
| | - Sangeeta Desai
- Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra, India
- Department of Pathology, Tata Memorial Hospital, Tata Memorial Centre, Mumbai, India
| | | | - Nita Nair
- Department of Surgical Oncology, Tata Memorial Hospital, Tata Memorial Centre, Mumbai, India
- Hypoxia and Clinical Genomics Lab (Clinician Scientist Laboratory), Advanced Centre for Treatment, Research, and Education in Cancer, Tata Memorial Centre, Navi Mumbai, Maharashtra, India
- Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra, India
| | - Vani Parmar
- Department of Surgical Oncology, Tata Memorial Hospital, Tata Memorial Centre, Mumbai, India
- Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra, India
- 3D Printing Laboratory, Advanced Centre for Treatment, Research, and Education in Cancer, Tata Memorial Centre, Navi Mumbai, Maharashtra, India
| | - Amit Dutt
- Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra, India
- Integrated Cancer Genomics Laboratory, Advanced Centre for Treatment, Research, and Education in Cancer, Tata Memorial Centre, Navi Mumbai, Maharashtra, India
| | - Binay Panda
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, India
| | - Sudeep Gupta
- Hypoxia and Clinical Genomics Lab (Clinician Scientist Laboratory), Advanced Centre for Treatment, Research, and Education in Cancer, Tata Memorial Centre, Navi Mumbai, Maharashtra, India
- Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra, India
- Department of Medical Oncology, Tata Memorial Hospital, Tata Memorial Centre, Mumbai, India
| | - Rajendra Badwe
- Department of Surgical Oncology, Tata Memorial Hospital, Tata Memorial Centre, Mumbai, India
- Hypoxia and Clinical Genomics Lab (Clinician Scientist Laboratory), Advanced Centre for Treatment, Research, and Education in Cancer, Tata Memorial Centre, Navi Mumbai, Maharashtra, India
- Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra, India
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Powell RT, Rinkenbaugh AL, Guo L, Cai S, Shao J, Zhou X, Zhang X, Jeter-Jones S, Fu C, Qi Y, Baameur Hancock F, White JB, Stephan C, Davies PJ, Moulder S, Symmans WF, Chang JT, Piwnica-Worms H. Targeting neddylation and sumoylation in chemoresistant triple negative breast cancer. NPJ Breast Cancer 2024; 10:37. [PMID: 38802426 PMCID: PMC11130334 DOI: 10.1038/s41523-024-00644-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 05/09/2024] [Indexed: 05/29/2024] Open
Abstract
Triple negative breast cancer (TNBC) accounts for 15-20% of breast cancer cases in the United States. Systemic neoadjuvant chemotherapy (NACT), with or without immunotherapy, is the current standard of care for patients with early-stage TNBC. However, up to 70% of TNBC patients have significant residual disease once NACT is completed, which is associated with a high risk of developing recurrence within two to three years of surgical resection. To identify targetable vulnerabilities in chemoresistant TNBC, we generated longitudinal patient-derived xenograft (PDX) models from TNBC tumors before and after patients received NACT. We then compiled transcriptomes and drug response profiles for all models. Transcriptomic analysis identified the enrichment of aberrant protein homeostasis pathways in models from post-NACT tumors relative to pre-NACT tumors. This observation correlated with increased sensitivity in vitro to inhibitors targeting the proteasome, heat shock proteins, and neddylation pathways. Pevonedistat, a drug annotated as a NEDD8-activating enzyme (NAE) inhibitor, was prioritized for validation in vivo and demonstrated efficacy as a single agent in multiple PDX models of TNBC. Pharmacotranscriptomic analysis identified a pathway-level correlation between pevonedistat activity and post-translational modification (PTM) machinery, particularly involving neddylation and sumoylation targets. Elevated levels of both NEDD8 and SUMO1 were observed in models exhibiting a favorable response to pevonedistat compared to those with a less favorable response in vivo. Moreover, a correlation emerged between the expression of neddylation-regulated pathways and tumor response to pevonedistat, indicating that targeting these PTM pathways may prove effective in combating chemoresistant TNBC.
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Affiliation(s)
- Reid T Powell
- Center for Translational Cancer Research, Institute of Bioscience and Technology Texas A&M Health Science Center, Houston, TX, USA
| | - Amanda L Rinkenbaugh
- Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Lei Guo
- Center for Translational Cancer Research, Institute of Bioscience and Technology Texas A&M Health Science Center, Houston, TX, USA
| | - Shirong Cai
- Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jiansu Shao
- Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xinhui Zhou
- Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xiaomei Zhang
- Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sabrina Jeter-Jones
- Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Chunxiao Fu
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yuan Qi
- Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Faiza Baameur Hancock
- Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jason B White
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Clifford Stephan
- Center for Translational Cancer Research, Institute of Bioscience and Technology Texas A&M Health Science Center, Houston, TX, USA
| | - Peter J Davies
- Center for Translational Cancer Research, Institute of Bioscience and Technology Texas A&M Health Science Center, Houston, TX, USA
| | - Stacy Moulder
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Eli Lilly and Company, Indianapolis, IN, USA
| | - W Fraser Symmans
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jeffrey T Chang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Integrative Biology and Pharmacology, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Helen Piwnica-Worms
- Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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Li X, Hao J, Li J, Zhao Z, Shang X, Li M. Pathway Activation Analysis for Pan-Cancer Personalized Characterization Based on Riemannian Manifold. Int J Mol Sci 2024; 25:4411. [PMID: 38673997 PMCID: PMC11050713 DOI: 10.3390/ijms25084411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 04/08/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
The pathogenesis of carcinoma is believed to come from the combined effect of polygenic variation, and the initiation and progression of malignant tumors are closely related to the dysregulation of biological pathways. Quantifying the alteration in pathway activation and identifying coordinated patterns of pathway dysfunction are the imperative part of understanding the malignancy process and distinguishing different tumor stages or clinical outcomes of individual patients. In this study, we have conducted in silico pathway activation analysis using Riemannian manifold (RiePath) toward pan-cancer personalized characterization, which is the first attempt to apply the Riemannian manifold theory to measure the extent of pathway dysregulation in individual patient on the tangent space of the Riemannian manifold. RiePath effectively integrates pathway and gene expression information, not only generating a relatively low-dimensional and biologically relevant representation, but also identifying a robust panel of biologically meaningful pathway signatures as biomarkers. The pan-cancer analysis across 16 cancer types reveals the capability of RiePath to evaluate pathway activation accurately and identify clinical outcome-related pathways. We believe that RiePath has the potential to provide new prospects in understanding the molecular mechanisms of complex diseases and may find broader applications in predicting biomarkers for other intricate diseases.
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Affiliation(s)
- Xingyi Li
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China; (X.L.); (J.H.); (X.S.)
| | - Jun Hao
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China; (X.L.); (J.H.); (X.S.)
| | - Junming Li
- School of Software, Northwestern Polytechnical University, Xi’an 710072, China; (J.L.); (Z.Z.)
| | - Zhelin Zhao
- School of Software, Northwestern Polytechnical University, Xi’an 710072, China; (J.L.); (Z.Z.)
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China; (X.L.); (J.H.); (X.S.)
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
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Chan AP, Choi Y, Rangan A, Zhang G, Podder A, Berens M, Sharma S, Pirrotte P, Byron S, Duggan D, Schork NJ. Interrogating the Human Diplome: Computational Methods, Emerging Applications, and Challenges. Methods Mol Biol 2023; 2590:1-30. [PMID: 36335489 DOI: 10.1007/978-1-0716-2819-5_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Human DNA sequencing protocols have revolutionized human biology, biomedical science, and clinical practice, but still have very important limitations. One limitation is that most protocols do not separate or assemble (i.e., "phase") the nucleotide content of each of the maternally and paternally derived chromosomal homologs making up the 22 autosomal pairs and the chromosomal pair making up the pseudo-autosomal region of the sex chromosomes. This has led to a dearth of studies and a consequent underappreciation of many phenomena of fundamental importance to basic and clinical genomic science. We discuss a few protocols for obtaining phase information as well as their limitations, including those that could be used in tumor phasing settings. We then describe a number of biological and clinical phenomena that require phase information. These include phenomena that require precise knowledge of the nucleotide sequence in a chromosomal segment from germline or somatic cells, such as DNA binding events, and insight into unique cis vs. trans-acting functionally impactful variant combinations-for example, variants implicated in a phenotype governed by compound heterozygosity. In addition, we also comment on the need for reliable and consensus-based diploid-context computational workflows for variant identification as well as the need for laboratory-based functional verification strategies for validating cis vs. trans effects of variant combinations. We also briefly describe available resources, example studies, as well as areas of further research, and ultimately argue that the science behind the study of human diploidy, referred to as "diplomics," which will be enabled by nucleotide-level resolution of phased genomes, is a logical next step in the analysis of human genome biology.
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Affiliation(s)
- Agnes P Chan
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
| | - Yongwook Choi
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
| | - Aditya Rangan
- Courant Institute of Mathematical Sciences at New York University, New York, NY, USA
| | - Guangfa Zhang
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
| | - Avijit Podder
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
| | - Michael Berens
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
- The City of Hope National Medical Center, Duarte, CA, USA
| | - Sunil Sharma
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
- The City of Hope National Medical Center, Duarte, CA, USA
| | - Patrick Pirrotte
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
- The City of Hope National Medical Center, Duarte, CA, USA
| | - Sara Byron
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
- The City of Hope National Medical Center, Duarte, CA, USA
| | - Dave Duggan
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
- The City of Hope National Medical Center, Duarte, CA, USA
| | - Nicholas J Schork
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA.
- The City of Hope National Medical Center, Duarte, CA, USA.
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Wieder C, Lai RPJ, Ebbels TMD. Single sample pathway analysis in metabolomics: performance evaluation and application. BMC Bioinformatics 2022; 23:481. [PMID: 36376837 PMCID: PMC9664704 DOI: 10.1186/s12859-022-05005-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 10/25/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Single sample pathway analysis (ssPA) transforms molecular level omics data to the pathway level, enabling the discovery of patient-specific pathway signatures. Compared to conventional pathway analysis, ssPA overcomes the limitations by enabling multi-group comparisons, alongside facilitating numerous downstream analyses such as pathway-based machine learning. While in transcriptomics ssPA is a widely used technique, there is little literature evaluating its suitability for metabolomics. Here we provide a benchmark of established ssPA methods (ssGSEA, GSVA, SVD (PLAGE), and z-score) alongside the evaluation of two novel methods we propose: ssClustPA and kPCA, using semi-synthetic metabolomics data. We then demonstrate how ssPA can facilitate pathway-based interpretation of metabolomics data by performing a case-study on inflammatory bowel disease mass spectrometry data, using clustering to determine subtype-specific pathway signatures. RESULTS While GSEA-based and z-score methods outperformed the others in terms of recall, clustering/dimensionality reduction-based methods provided higher precision at moderate-to-high effect sizes. A case study applying ssPA to inflammatory bowel disease data demonstrates how these methods yield a much richer depth of interpretation than conventional approaches, for example by clustering pathway scores to visualise a pathway-based patient subtype-specific correlation network. We also developed the sspa python package (freely available at https://pypi.org/project/sspa/ ), providing implementations of all the methods benchmarked in this study. CONCLUSION This work underscores the value ssPA methods can add to metabolomic studies and provides a useful reference for those wishing to apply ssPA methods to metabolomics data.
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Affiliation(s)
- Cecilia Wieder
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Rachel P J Lai
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, UK
| | - Timothy M D Ebbels
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, UK.
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Lai J, Chen W, Zhao A, Huang J. Determination of a DNA repair-related gene signature with potential implications for prognosis and therapeutic response in pancreatic adenocarcinoma. Front Oncol 2022; 12:939891. [PMID: 36353555 PMCID: PMC9638008 DOI: 10.3389/fonc.2022.939891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 10/06/2022] [Indexed: 11/30/2022] Open
Abstract
Background Pancreatic adenocarcinoma (PAAD) is one of the leading causes of cancer death worldwide. Alterations in DNA repair-related genes (DRGs) are observed in a variety of cancers and have been shown to affect the development and treatment of cancers. The aim of this study was to develop a DRG-related signature for predicting prognosis and therapeutic response in PAAD. Methods We constructed a DRG signature using least absolute shrinkage and selection operator (LASSO) Cox regression analysis in the TCGA training set. GEO datasets were used as the validation set. A predictive nomogram was constructed based on multivariate Cox regression. Calibration curve and decision curve analysis (DCA) were applied to validate the performance of the nomogram. The CIBERSORT and ssGSEA algorithms were utilized to explore the relationship between the prognostic signature and immune cell infiltration. The pRRophetic algorithm was used to estimate sensitivity to chemotherapeutic agents. The CellMiner database and PAAD cell lines were used to investigate the relationship between DRG expression and therapeutic response. Results We developed a DRG signature consisting of three DRGs (RECQL, POLQ, and RAD17) that can predict prognosis in PAAD patients. A prognostic nomogram combining the risk score and clinical factors was developed for prognostic prediction. The DCA curve and the calibration curve demonstrated that the nomogram has a higher net benefit than the risk score and TNM staging system. Immune infiltration analysis demonstrated that the risk score was positively correlated with the proportions of activated NK cells and monocytes. Drug sensitivity analysis indicated that the signature has potential predictive value for chemotherapy. Analyses utilizing the CellMiner database showed that RAD17 expression is correlated with oxaliplatin. The dynamic changes in three DRGs in response to oxaliplatin were examined by RT-qPCR, and the results show that RAD17 is upregulated in response to oxaliplatin in PAAD cell lines. Conclusion We constructed and validated a novel DRG signature for prediction of the prognosis and drug sensitivity of patients with PAAD. Our study provides a theoretical basis for further unraveling the molecular pathogenesis of PAAD and helps clinicians tailor systemic therapies within the framework of individualized treatment.
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Affiliation(s)
- Jinzhi Lai
- Department of Oncology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Weijie Chen
- Department of Surgical Oncology, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
| | - Aiyue Zhao
- Department of Oncology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
- *Correspondence: Aiyue Zhao, ; Jingshan Huang,
| | - Jingshan Huang
- Department of General Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
- *Correspondence: Aiyue Zhao, ; Jingshan Huang,
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7
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Sarhadi VK, Armengol G. Molecular Biomarkers in Cancer. Biomolecules 2022; 12:1021. [PMID: 35892331 PMCID: PMC9331210 DOI: 10.3390/biom12081021] [Citation(s) in RCA: 74] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 07/18/2022] [Accepted: 07/20/2022] [Indexed: 11/17/2022] Open
Abstract
Molecular cancer biomarkers are any measurable molecular indicator of risk of cancer, occurrence of cancer, or patient outcome. They may include germline or somatic genetic variants, epigenetic signatures, transcriptional changes, and proteomic signatures. These indicators are based on biomolecules, such as nucleic acids and proteins, that can be detected in samples obtained from tissues through tumor biopsy or, more easily and non-invasively, from blood (or serum or plasma), saliva, buccal swabs, stool, urine, etc. Detection technologies have advanced tremendously over the last decades, including techniques such as next-generation sequencing, nanotechnology, or methods to study circulating tumor DNA/RNA or exosomes. Clinical applications of biomarkers are extensive. They can be used as tools for cancer risk assessment, screening and early detection of cancer, accurate diagnosis, patient prognosis, prediction of response to therapy, and cancer surveillance and monitoring response. Therefore, they can help to optimize making decisions in clinical practice. Moreover, precision oncology is needed for newly developed targeted therapies, as they are functional only in patients with specific cancer genetic mutations, and biomarkers are the tools used for the identification of these subsets of patients. Improvement in the field of cancer biomarkers is, however, needed to overcome the scientific challenge of developing new biomarkers with greater sensitivity, specificity, and positive predictive value.
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Affiliation(s)
- Virinder Kaur Sarhadi
- Department of Oral and Maxillofacial Diseases, Helsinki University Hospital and University of Helsinki, 00290 Helsinki, Finland;
| | - Gemma Armengol
- Department of Animal Biology, Plant Biology, and Ecology, Faculty of Biosciences, Universitat Autònoma de Barcelona, 08193 Barcelona, Catalonia, Spain
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8
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Bowers RR, Jones CM, Paz EA, Barrows JK, Armeson K, Long D, Delaney J. SWAN pathway-network identification of common aneuploidy-based oncogenic drivers. Nucleic Acids Res 2022; 50:3673-3692. [PMID: 35380699 PMCID: PMC9023287 DOI: 10.1093/nar/gkac200] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 02/26/2022] [Accepted: 03/14/2022] [Indexed: 02/07/2023] Open
Abstract
Haploinsufficiency drives Darwinian evolution. Siblings, while alike in many aspects, differ due to monoallelic differences inherited from each parent. In cancer, solid tumors exhibit aneuploid genetics resulting in hundreds to thousands of monoallelic gene-level copy-number alterations (CNAs) in each tumor. Aneuploidy patterns are heterogeneous, posing a challenge to identify drivers in this high-noise genetic environment. Here, we developed Shifted Weighted Annotation Network (SWAN) analysis to assess biology impacted by cumulative monoallelic changes. SWAN enables an integrated pathway-network analysis of CNAs, RNA expression, and mutations via a simple web platform. SWAN is optimized to best prioritize known and novel tumor suppressors and oncogenes, thereby identifying drivers and potential druggable vulnerabilities within cancer CNAs. Protein homeostasis, phospholipid dephosphorylation, and ion transport pathways are commonly suppressed. An atlas of CNA pathways altered in each cancer type is released. These CNA network shifts highlight new, attractive targets to exploit in solid tumors.
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Affiliation(s)
- Robert R Bowers
- Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC, USA
| | - Christian M Jones
- Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC, USA
| | - Edwin A Paz
- Departments of Neurology, Neurobiology, and Cell Biology, and the Duke Center for Neurodegeneration & Neurotherapeutics, Duke University School of Medicine, Durham, NC, USA
| | - John K Barrows
- Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC, USA
| | - Kent E Armeson
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - David T Long
- Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC, USA
| | - Joe R Delaney
- Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC, USA
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Liu X, Su L, Li J, Ou G. Molecular Subclassification Based on Crosstalk Analysis Improves Prediction of Prognosis in Colorectal Cancer. Front Genet 2021; 12:689676. [PMID: 34804112 PMCID: PMC8600263 DOI: 10.3389/fgene.2021.689676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 09/23/2021] [Indexed: 12/09/2022] Open
Abstract
The poor performance of single-gene lists for prognostic predictions in independent cohorts has limited their clinical use. Here, we employed a pathway-based approach using embedded biological features to identify reproducible prognostic markers as an alternative. We used pathway activity score, sure independence screening, and K-means clustering analyses to identify and cluster colorectal cancer patients into two distinct subgroups, G2 (aggressive) and G1 (moderate). The differences between these two groups with respect to survival, somatic mutation, pathway activity, and tumor-infiltration by immunocytes were compared. These comparisons revealed that the survival rates in the G2 subgroup were significantly reduced compared to that in the G1 subgroup; further, the mutational burden rates in several oncogenes, including KRAS, DCLK1, and EPHA5, were significantly higher in the G2 subgroup than in the G1 subgroup. The enhanced activity of the critical pathways such as MYC and epithelial-mesenchymal transition may also lead to the progression of colorectal cancer. Taken together, we established a novel prognostic classification system that offers meritorious insights into the hallmarks of colorectal cancer.
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Affiliation(s)
- Xiaohua Liu
- State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Lili Su
- School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Jingcong Li
- State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Guoping Ou
- State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China
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