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Franzén B, Auer G, Lewensohn R. Minimally invasive biopsy-based diagnostics in support of precision cancer medicine. Mol Oncol 2024. [PMID: 38519839 DOI: 10.1002/1878-0261.13640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 01/31/2024] [Accepted: 03/14/2024] [Indexed: 03/25/2024] Open
Abstract
Precision cancer medicine (PCM) to support the treatment of solid tumors requires minimally invasive diagnostics. Here, we describe the development of fine-needle aspiration biopsy-based (FNA) molecular cytology which will be increasingly important in diagnostics and adaptive treatment. We provide support for FNA-based molecular cytology having a significant potential to replace core needle biopsy (CNB) as a patient-friendly potent technique for tumor sampling for various tumor types. This is not only because CNB is a more traumatic procedure and may be associated with more complications compared to FNA-based sampling, but also due to the recently developed molecular methods used with FNA. Recent studies show that image-guided FNA in combination with ultrasensitive molecular methods also offers opportunities for characterization of the tumor microenvironment which can aid therapeutic decisions. Here we provide arguments for an increased implementation of molecular FNA-based sampling as a patient-friendly diagnostic method, which may, due to its repeatability, facilitate regular sampling that is needed during different treatment lines, to provide tumor information, supporting treatment decisions, shortening lead times in healthcare, and benefit healthcare economics.
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Affiliation(s)
- Bo Franzén
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Cancer Centre Karolinska (CCK) Foundation, Karolinska University Hospital, Stockholm, Sweden
| | - Gert Auer
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Rolf Lewensohn
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Theme Cancer, Medical Unit Head and Neck, Lung, and Skin Tumors, Thoracic Oncology Center, Karolinska University Hospital, Stockholm, Sweden
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Röbeck P, Franzén B, Cantera-Ahlman R, Dragomir A, Auer G, Jorulf H, Jacobsson SP, Viktorsson K, Lewensohn R, Häggman M, Ladjevardi S. Multiplex protein analysis and ensemble machine learning methods of fine needle aspirates from prostate cancer patients reveal potential diagnostic signatures associated with tumour grade. Cytopathology 2023; 34:286-294. [PMID: 36840380 DOI: 10.1111/cyt.13226] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 02/06/2023] [Accepted: 02/16/2023] [Indexed: 02/26/2023]
Abstract
BACKGROUND Improved molecular diagnosis is needed in prostate cancer (PC). Fine needle aspiration (FNA) is a minimally invasive biopsy technique, less traumatic compared to core needle biopsy, and could be useful for diagnosis of PC. Molecular biomarkers (BMs) in FNA-samples can be assessed for prediction, eg of immunotherapy efficacy before treatment as well as at treatment decision time points during disease progression. METHODS In the present pilot study, the expression levels of 151 BM proteins were analysed by proximity extension assay in FNA-samples from 16 patients, including benign prostate lesions (n = 3) and cancers (n = 13). An ensemble data analysis strategy was applied using several machine learning models. RESULTS Twelve potentially predictive BM proteins correlating with International Society of Urological Pathology grade groups were identified, among them vimentin, tissue factor pathway inhibitor 2, and integrin beta-5. The validity of the results was supported by network analysis that showed functional associations between most of the identified putative BMs. We also showed that multiple immune checkpoint targets can be assessed (eg PD-L1, CD137, and Galectin-9), which may support the selection of immunotherapy in advanced PC. Results are promising but need further validation in a larger cohort. CONCLUSIONS Our pilot study represents a "proof of concept" and shows that multiplex profiling of potential diagnostic and predictive BM proteins is feasible on tumour material obtained by FNA sampling of prostate cancer. Moreover, our results demonstrate that an ensemble data analysis strategy may facilitate the identification of BM signatures in pilot studies when the patient cohort is limited.
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Affiliation(s)
- Pontus Röbeck
- Department of Urology, Uppsala University, Uppsala, Sweden
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Bo Franzén
- Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Rafaele Cantera-Ahlman
- Department of Urology, Uppsala University, Uppsala, Sweden
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Anca Dragomir
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Gert Auer
- Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Håkan Jorulf
- Department of Urology, Uppsala University, Uppsala, Sweden
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Sven P Jacobsson
- Department of Analytical Chemistry, Stockholm University, Stockholm, Sweden
| | - Kristina Viktorsson
- Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Rolf Lewensohn
- Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden
- Theme Cancer, Medical Unit Head and Neck, Lung, and Skin Tumors, Thoracic Oncology Center, Karolinska University Hospital, Solna, Sweden
| | - Michael Häggman
- Department of Urology, Uppsala University, Uppsala, Sweden
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Sam Ladjevardi
- Department of Urology, Uppsala University, Uppsala, Sweden
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
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Pasic I, Ren AH, Nampoothiri RV, Prassas I, Lipton JH, Mattsson J, Diamandis EP, Michelis FV. Multiplex proteomics using proximity extension assay for the identification of protein biomarkers predictive of acute graft-vs.-host disease in allogeneic hematopoietic cell transplantation. Clin Chem Lab Med 2023; 61:1005-1014. [PMID: 36655501 DOI: 10.1515/cclm-2022-0916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 01/04/2023] [Indexed: 01/20/2023]
Abstract
OBJECTIVES Allogeneic hematopoietic cell transplantation (HCT) is associated with acute graft-vs.-host disease (aGVHD). The presented study applied a novel multiplex antibody-based proximity extension assay (PEA) proteomic platform that can detect thousands of serum proteins simultaneously for the identification of potential biomarkers of aGVHD. METHODS Serum samples from 28 patients who underwent allogeneic HCT for acute myeloid leukemia (AML) were analyzed; 17 were diagnosed with grade II-IV aGVHD while 11 patients were not. Samples collected on day -6, day 0, +14, +30, +60 and +90 post-HCT were analyzed for the relative concentrations of 552 proteins. The concentration of each protein from baseline to the closest time point before onset of aGVHD, or to the latest time point in control patients, was documented. RESULTS Individualized analysis identified 26 proteins demonstrating ≥3-fold increase at aGVHD onset compared to baseline, eliminating proteins with a similar increase in controls. Another approach used paired t-testing and logistic regression that identified a four-marker panel, including SLAMF7, IL-1ra, BTN3A2 and DAB2, where individual log-likelihood ratios ranged from 3.99 to 8.15 (logistic regression, p=0.004-0.046). When combined, the four-marker panel demonstrated an area under the curve (AUC) of 0.90 (95% CI: 0.78-1.00; p=0.0006) with high negative predictive value of 81.8% and positive predictive value of 86.7%. All four markers play a physiological role in immune regulation. Among these, three were also present in the individualized analysis (SLAMF7, IL-1ra and BTN3A2). CONCLUSIONS We conclude that serum proteins identified using multiplex proteomics, particularly SLAMF7, IL-1ra, BTN3A2 and DAB2, may potentially predict aGVHD.
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Affiliation(s)
- Ivan Pasic
- Hans Messner Allogeneic Transplant Program, Princess Margaret Hospital Cancer Centre, University Health Network, Toronto, ON, Canada.,Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Annie H Ren
- Department of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Ram Vasudevan Nampoothiri
- Hans Messner Allogeneic Transplant Program, Princess Margaret Hospital Cancer Centre, University Health Network, Toronto, ON, Canada.,Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Ioannis Prassas
- Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Toronto, ON, Canada
| | - Jeffrey H Lipton
- Hans Messner Allogeneic Transplant Program, Princess Margaret Hospital Cancer Centre, University Health Network, Toronto, ON, Canada.,Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Jonas Mattsson
- Hans Messner Allogeneic Transplant Program, Princess Margaret Hospital Cancer Centre, University Health Network, Toronto, ON, Canada.,Department of Medicine, University of Toronto, Toronto, ON, Canada.,Gloria and Seymour Epstein Chair in Cell Therapy and Transplantation, Princess Margaret Hospital Cancer Centre, Toronto, ON, Canada
| | - Eleftherios P Diamandis
- Department of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, ON, Canada.,Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Toronto, ON, Canada.,Department of Clinical Biochemistry, University Health Network, Toronto, ON, Canada
| | - Fotios V Michelis
- Hans Messner Allogeneic Transplant Program, Princess Margaret Hospital Cancer Centre, University Health Network, Toronto, ON, Canada.,Department of Medicine, University of Toronto, Toronto, ON, Canada
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Bodén E, Andreasson J, Hirdman G, Malmsjö M, Lindstedt S. Quantitative Proteomics Indicate Radical Removal of Non-Small Cell Lung Cancer and Predict Outcome. Biomedicines 2022; 10:2738. [PMID: 36359256 PMCID: PMC9687227 DOI: 10.3390/biomedicines10112738] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 10/21/2022] [Accepted: 10/25/2022] [Indexed: 12/03/2022] Open
Abstract
Non-small cell lung cancer (NSCLC) is associated with low survival rates, often due to late diagnosis and lack of personalized medicine. Diagnosing and monitoring NSCLC using blood samples has lately gained interest due to its less invasive nature. In the present study, plasma was collected at three timepoints and analyzed using proximity extension assay technology and quantitative real-time polymerase chain reaction in patients with primary NSCLC stages IA-IIIA undergoing surgery. Results were adjusted for patient demographics, tumor, node, metastasis (TNM) stage, and multiple testing. Major histocompatibility (MHC) class 1 polypeptide-related sequence A/B (MIC-A/B) and tumor necrosis factor ligand superfamily member 6 (FASLG) were significantly increased post-surgery, suggesting radical removal of cancerous cells. Levels of hepatocyte growth factor (HGF) initially increased postoperatively but were later lowered, potentially indicating radical removal of malignant cells. The levels of FASLG in patients who later died or had a relapse of NSCLC were lower at all three timepoints compared to surviving patients without relapse, indicating that FASLG may be used as a prognostic biomarker. The biomarkers were confirmed using microarray data. In conclusion, quantitative proteomics could be used for NSCLC identification but may also provide information on radical surgical removal of NSCLC and post-surgical prognosis.
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Affiliation(s)
- Embla Bodén
- Department of Clinical Sciences, Lund University, 22362 Lund, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, 22363 Lund, Sweden
- Lund Stem Cell Center, Lund University, 22362 Lund, Sweden
| | - Jesper Andreasson
- Department of Clinical Sciences, Lund University, 22362 Lund, Sweden
- Department of Cardiothoracic Surgery and Transplantation, Skåne University Hospital, 22242 Lund, Sweden
| | - Gabriel Hirdman
- Department of Clinical Sciences, Lund University, 22362 Lund, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, 22363 Lund, Sweden
- Lund Stem Cell Center, Lund University, 22362 Lund, Sweden
| | - Malin Malmsjö
- Department of Clinical Sciences, Lund University, 22362 Lund, Sweden
| | - Sandra Lindstedt
- Department of Clinical Sciences, Lund University, 22362 Lund, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, 22363 Lund, Sweden
- Lund Stem Cell Center, Lund University, 22362 Lund, Sweden
- Department of Cardiothoracic Surgery and Transplantation, Skåne University Hospital, 22242 Lund, Sweden
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Lin J, Zhao A, Fu D. Evaluating the tumor immune profile based on a three-gene prognostic risk model in HER2 positive breast cancer. Sci Rep 2022; 12:9311. [PMID: 35665772 DOI: 10.1038/s41598-022-13499-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 05/25/2022] [Indexed: 12/24/2022] Open
Abstract
To date, there have not been great breakthroughs in immunotherapy for HER2 positive breast cancer (HPBC). This study aimed to build a risk model that might contribute to predicting prognosis and discriminating the immune landscape in patients with HPBC. We analyzed the tumor immune profile of HPBC patients from the TCGA using the ESTIMATE algorithm. Thirty survival-related differentially expressed genes were selected according to the ImmuneScore and StromalScore. A prognostic risk model consisting of PTGDR, PNOC and CCL23 was established by LASSO analysis, and all patients were classified into the high- and low-risk score groups according to the risk scores. Subsequently, the risk model was proven to be efficient and reliable. Immune related pathways were the dominantly enriched category. ssGSEA showed stronger immune infiltration in the low-risk score group, including the infiltration of TILs, CD8 T cells, NK cells, DCs, and so on. Moreover, we found that the expression of immune checkpoint genes, including PD-L1, CTLA-4, TIGIT, TIM-3 and LAG-3, was significantly upregulated in the low-risk score group. All the results were validated with corresponding data from the GEO database. In summary, our investigation indicated that the risk model composed of PTGDR, PNOC and CCL23 has potential to predict prognosis and evaluate the tumor immune microenvironment in HPBC patients. More importantly, HPBC patients with a low-risk scores are likely to benefit from immune treatment.
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Correa Rojo A, Heylen D, Aerts J, Thas O, Hooyberghs J, Ertaylan G, Valkenborg D. Towards Building a Quantitative Proteomics Toolbox in Precision Medicine: A Mini-Review. Front Physiol 2021; 12:723510. [PMID: 34512391 PMCID: PMC8427610 DOI: 10.3389/fphys.2021.723510] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 08/05/2021] [Indexed: 12/26/2022] Open
Abstract
Precision medicine as a framework for disease diagnosis, treatment, and prevention at the molecular level has entered clinical practice. From the start, genetics has been an indispensable tool to understand and stratify the biology of chronic and complex diseases in precision medicine. However, with the advances in biomedical and omics technologies, quantitative proteomics is emerging as a powerful technology complementing genetics. Quantitative proteomics provide insight about the dynamic behaviour of proteins as they represent intermediate phenotypes. They provide direct biological insights into physiological patterns, while genetics accounting for baseline characteristics. Additionally, it opens a wide range of applications in clinical diagnostics, treatment stratification, and drug discovery. In this mini-review, we discuss the current status of quantitative proteomics in precision medicine including the available technologies and common methods to analyze quantitative proteomics data. Furthermore, we highlight the current challenges to put quantitative proteomics into clinical settings and provide a perspective to integrate proteomics data with genomics data for future applications in precision medicine.
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Affiliation(s)
- Alejandro Correa Rojo
- Data Science Institute, Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Diepenbeek, Belgium.,Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Dries Heylen
- Data Science Institute, Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Diepenbeek, Belgium.,Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Jan Aerts
- Data Science Institute, Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Diepenbeek, Belgium
| | - Olivier Thas
- Data Science Institute, Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Diepenbeek, Belgium.,Department of Applied Mathematics, Computer Science and Statistics, Faculty of Sciences, Ghent University, Ghent, Belgium.,National Institute for Applied Statistics Research Australia (NIASRA), Wollongong, NSW, Australia
| | - Jef Hooyberghs
- Flemish Institute for Technological Research (VITO), Mol, Belgium.,Theoretical Physics, Data Science Institute, Hasselt University, Diepenbeek, Belgium
| | - Gökhan Ertaylan
- Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Dirk Valkenborg
- Data Science Institute, Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Diepenbeek, Belgium
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