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Ma W, Tang W, Kwok JS, Tong AH, Lo CW, Chu AT, Chung BH. A review on trends in development and translation of omics signatures in cancer. Comput Struct Biotechnol J 2024; 23:954-971. [PMID: 38385061 PMCID: PMC10879706 DOI: 10.1016/j.csbj.2024.01.024] [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: 10/27/2023] [Revised: 01/31/2024] [Accepted: 01/31/2024] [Indexed: 02/23/2024] Open
Abstract
The field of cancer genomics and transcriptomics has evolved from targeted profiling to swift sequencing of individual tumor genome and transcriptome. The steady growth in genome, epigenome, and transcriptome datasets on a genome-wide scale has significantly increased our capability in capturing signatures that represent both the intrinsic and extrinsic biological features of tumors. These biological differences can help in precise molecular subtyping of cancer, predicting tumor progression, metastatic potential, and resistance to therapeutic agents. In this review, we summarized the current development of genomic, methylomic, transcriptomic, proteomic and metabolic signatures in the field of cancer research and highlighted their potentials in clinical applications to improve diagnosis, prognosis, and treatment decision in cancer patients.
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Affiliation(s)
- Wei Ma
- Hong Kong Genome Institute, Hong Kong, China
| | - Wenshu Tang
- Hong Kong Genome Institute, Hong Kong, China
| | | | | | | | | | - Brian H.Y. Chung
- Hong Kong Genome Institute, Hong Kong, China
- Department of Pediatrics and Adolescent Medicine, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Hong Kong Genome Project
- Hong Kong Genome Institute, Hong Kong, China
- Department of Pediatrics and Adolescent Medicine, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
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Li Z, Chen J, Xu B, Zhao W, Zha H, Han Y, Shen W, Dong Y, Zhao N, Zhang M, He K, Li Z, Liu X. Correlation between small-cell lung cancer serum protein/peptides determined by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and chemotherapy efficacy. Clin Proteomics 2024; 21:35. [PMID: 38764042 PMCID: PMC11103996 DOI: 10.1186/s12014-024-09483-8] [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: 12/13/2023] [Accepted: 04/22/2024] [Indexed: 05/21/2024] Open
Abstract
BACKGROUND Currently, no effective measures are available to predict the curative efficacy of small-cell lung cancer (SCLC) chemotherapy. We expect to develop a method for effectively predicting the SCLC chemotherapy efficacy and prognosis in clinical practice in order to offer more pertinent therapeutic protocols for individual patients. METHODS We adopted matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and ClinPro Tools system to detect serum samples from 154 SCLC patients with different curative efficacy of standard chemotherapy and analyze the different peptides/proteins of SCLC patients to discover predictive tumor markers related to chemotherapy efficacy. Ten peptide/protein peaks were significantly different in the two groups. RESULTS A genetic algorithm model consisting of four peptides/proteins was developed from the training group to separate patients with different chemotherapy efficacies. Among them, three peptides/proteins (m/z 3323.35, 6649.03 and 6451.08) showed high expression in the disease progression group, whereas the peptide/protein at m/z 4283.18 was highly expressed in the disease response group. The classifier exhibited an accuracy of 91.4% (53/58) in the validation group. The survival analysis showed that the median progression-free survival (PFS) of 30 SCLC patients in disease response group was 9.0 months; in 28 cases in disease progression group, the median PFS was 3.0 months, a statistically significant difference (χ2 = 46.98, P < 0.001). The median overall survival (OS) of the two groups was 13.0 months and 7.0 months, a statistically significant difference (χ2 = 40.64, P < 0.001). CONCLUSIONS These peptides/proteins may be used as potential biological markers for prediction of the curative efficacy and prognosis for SCLC patients treated with standard regimen chemotherapy.
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Affiliation(s)
- Zhihua Li
- Department of Oncology, PLA Rocket Force Characteristic Medical Center, Beijing, 100088, China
| | - Junnan Chen
- Department of Oncology, PLA Rocket Force Characteristic Medical Center, Beijing, 100088, China
| | - Bin Xu
- National Center of Biomedical Analysis, Beijing, 100850, China
| | - Wei Zhao
- Department of Oncology, PLA Rocket Force Characteristic Medical Center, Beijing, 100088, China
| | - Haoran Zha
- Department of Oncology, PLA Rocket Force Characteristic Medical Center, Beijing, 100088, China
| | - Yalin Han
- Department of Oncology, PLA Rocket Force Characteristic Medical Center, Beijing, 100088, China
| | - Wennan Shen
- Department of Oncology, PLA Rocket Force Characteristic Medical Center, Beijing, 100088, China
| | - Yuemei Dong
- Department of Oncology, PLA Rocket Force Characteristic Medical Center, Beijing, 100088, China
| | - Nan Zhao
- Department of Oncology, PLA Rocket Force Characteristic Medical Center, Beijing, 100088, China
| | - Manze Zhang
- Department of Oncology, PLA Rocket Force Characteristic Medical Center, Beijing, 100088, China
| | - Kun He
- National Center of Biomedical Analysis, Beijing, 100850, China
| | - Zhaoxia Li
- Department of Oncology, PLA Rocket Force Characteristic Medical Center, Beijing, 100088, China.
| | - Xiaoqing Liu
- Department of Oncology, The Fifth Medical Center of PLA General Hospital, Beijing, 100071, China.
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Tsai YT, Schlom J, Donahue RN. Blood-based biomarkers in patients with non-small cell lung cancer treated with immune checkpoint blockade. J Exp Clin Cancer Res 2024; 43:82. [PMID: 38493133 PMCID: PMC10944611 DOI: 10.1186/s13046-024-02969-1] [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] [Received: 12/05/2023] [Accepted: 01/30/2024] [Indexed: 03/18/2024] Open
Abstract
The paradigm of non-small cell lung cancer (NSCLC) treatment has been profoundly influenced by the development of immune checkpoint inhibitors (ICI), but the range of clinical responses observed among patients poses significant challenges. To date, analyses of tumor biopsies are the only parameter used to guide prognosis to ICI therapy. Tumor biopsies, however, are often difficult to obtain and tissue-based biomarkers are limited by intratumoral heterogeneity and temporal variability. In response, there has been a growing emphasis on the development of "liquid biopsy"‒ derived biomarkers, which offer a minimally invasive means to dynamically monitor the immune status of NSCLC patients either before and/or during the course of treatment. Here we review studies in which multiple blood-based biomarkers encompassing circulating soluble analytes, immune cell subsets, circulating tumor DNA, blood-based tumor mutational burden, and circulating tumor cells have shown promising associations with the clinical response of NSCLC patients to ICI therapy. These investigations have unveiled compelling correlations between the peripheral immune status of patients both before and during ICI therapy and patient outcomes, which include response rates, progression-free survival, and overall survival. There is need for rigorous validation and standardization of these blood-based assays for broader clinical application. Integration of multiple blood-based biomarkers into comprehensive panels or algorithms also has the potential to enhance predictive accuracy. Further research aimed at longitudinal monitoring of circulating biomarkers is also crucial to comprehend immune dynamics and resistance mechanisms and should be used alongside tissue-based methods that interrogate the tumor microenvironment to guide treatment decisions and may inform on the development of novel therapeutic strategies. The data reviewed here reinforce the opportunity to refine patient stratification, optimize treatments, and improve outcomes not only in NSCLC but also in the wider spectrum of solid tumors undergoing immunotherapy.
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Affiliation(s)
- Yo-Ting Tsai
- Center for Immuno-Oncology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jeffrey Schlom
- Center for Immuno-Oncology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Renee N Donahue
- Center for Immuno-Oncology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
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Shaban N, Raevskiy M, Zakharova G, Shipunova V, Deyev S, Suntsova M, Sorokin M, Buzdin A, Kamashev D. Human Blood Serum Counteracts EGFR/HER2-Targeted Drug Lapatinib Impact on Squamous Carcinoma SK-BR-3 Cell Growth and Gene Expression. BIOCHEMISTRY. BIOKHIMIIA 2024; 89:487-506. [PMID: 38648768 DOI: 10.1134/s000629792403009x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 01/17/2024] [Accepted: 02/20/2024] [Indexed: 04/25/2024]
Abstract
Lapatinib is a targeted therapeutic inhibiting HER2 and EGFR proteins. It is used for the therapy of HER2-positive breast cancer, although not all the patients respond to it. Using human blood serum samples from 14 female donors (separately taken or combined), we found that human blood serum dramatically abolishes the lapatinib-mediated inhibition of growth of the human breast squamous carcinoma SK-BR-3 cell line. This antagonism between lapatinib and human serum was associated with cancelation of the drug induced G1/S cell cycle transition arrest. RNA sequencing revealed 308 differentially expressed genes in the presence of lapatinib. Remarkably, when combined with lapatinib, human blood serum showed the capacity of restoring both the rate of cell growth, and the expression of 96.1% of the genes expression of which were altered by the lapatinib treatment alone. Co-administration of EGF with lapatinib also restores the cell growth and cancels alteration of expression of 95.8% of the genes specific to lapatinib treatment of SK-BR-3 cells. Differential gene expression analysis also showed that in the presence of human serum or EGF, lapatinib was unable to inhibit the Toll-Like Receptor signaling pathway and alter expression of genes linked to the Gene Ontology term of Focal adhesion.
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Affiliation(s)
- Nina Shaban
- Moscow Institute of Physics and Technology, Dolgoprudny, 141701, Russia.
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia
- The National Medical Research Center for Endocrinology, Moscow, 117036, Russia
| | - Mikhail Raevskiy
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, 119991, Russia.
| | - Galina Zakharova
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, 119991, Russia.
| | - Victoria Shipunova
- Moscow Institute of Physics and Technology, Dolgoprudny, 141701, Russia.
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia
| | - Sergey Deyev
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia.
- "Biomarker" Research Laboratory, Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, 420008, Russia
| | - Maria Suntsova
- The National Medical Research Center for Endocrinology, Moscow, 117036, Russia.
- Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Maksim Sorokin
- Moscow Institute of Physics and Technology, Dolgoprudny, 141701, Russia.
- Sechenov First Moscow State Medical University, Moscow, 119991, Russia
- PathoBiology Group, European Organization for Research and Treatment of Cancer (EORTC), Brussels, 1200, Belgium
| | - Anton Buzdin
- Moscow Institute of Physics and Technology, Dolgoprudny, 141701, Russia.
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia
- The National Medical Research Center for Endocrinology, Moscow, 117036, Russia
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Dmitri Kamashev
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia.
- The National Medical Research Center for Endocrinology, Moscow, 117036, Russia
- Sechenov First Moscow State Medical University, Moscow, 119991, Russia
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Pei F, Gu B, Miao SM, Guan XD, Wu JF. Clinical practice of sepsis-induced immunosuppression: Current immunotherapy and future options. Chin J Traumatol 2024; 27:63-70. [PMID: 38040590 DOI: 10.1016/j.cjtee.2023.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 08/07/2023] [Accepted: 08/17/2023] [Indexed: 12/03/2023] Open
Abstract
Sepsis is a potentially fatal condition characterized by the failure of one or more organs due to a disordered host response to infection. The development of sepsis is closely linked to immune dysfunction. As a result, immunotherapy has gained traction as a promising approach to sepsis treatment, as it holds the potential to reverse immunosuppression and restore immune balance, thereby improving the prognosis of septic patients. However, due to the highly heterogeneous nature of sepsis, it is crucial to carefully select the appropriate patient population for immunotherapy. This review summarizes the current and evolved treatments for sepsis-induced immunosuppression to enhance clinicians' understanding and practical application of immunotherapy in the management of sepsis.
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Affiliation(s)
- Fei Pei
- Department of Critical Care Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China; Guangdong Clinical Research Center for Critical Care Medicine, Guangzhou, 510080, China
| | - Bin Gu
- Department of Critical Care Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China; Guangdong Clinical Research Center for Critical Care Medicine, Guangzhou, 510080, China
| | - Shu-Min Miao
- Department of Critical Care Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China; Guangdong Clinical Research Center for Critical Care Medicine, Guangzhou, 510080, China
| | - Xiang-Dong Guan
- Department of Critical Care Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China; Guangdong Clinical Research Center for Critical Care Medicine, Guangzhou, 510080, China
| | - Jian-Feng Wu
- Department of Critical Care Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China; Guangdong Clinical Research Center for Critical Care Medicine, Guangzhou, 510080, China.
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Koc MA, Wiles TA, Weinhold DC, Rightmyer S, Weaver AL, McDowell CT, Roder J, Asmellash S, Pestano GA, Roder H, Georgantas III RW. Molecular and translational biology of the blood-based VeriStrat® proteomic test used in cancer immunotherapy treatment guidance. J Mass Spectrom Adv Clin Lab 2023; 30:51-60. [PMID: 38074293 PMCID: PMC10709509 DOI: 10.1016/j.jmsacl.2023.11.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 10/16/2023] [Accepted: 11/08/2023] [Indexed: 03/09/2024] Open
Abstract
INTRODUCTION The VeriStrat® test (VS) is a blood-based assay that predicts a patient's response to therapy by analyzing eight features in a spectrum obtained from matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) analysis of human serum and plasma. In a recent analysis of the INSIGHT clinical trial (NCT03289780), it was found that the VS labels, VS Good and VS Poor, can effectively predict the responsiveness of non-small cell lung cancer (NSCLC) patients to immune checkpoint inhibitor (ICI) therapy. However, while VS measures the intensities of spectral features using MALDI-TOF analysis, the specific proteoforms underlying these features have not been comprehensively identified. OBJECTIVES The objective of this study was to identify the proteoforms that are measured by VS. METHODS To resolve the features obtained from the low-resolution MALDI-TOF procedure used to acquire mass spectra for VS DeepMALDI® analysis of serum was employed. This technique allowed for the identification of finer peaks within these features. Additionally, a combination of reversed-phase fractionation and liquid chromatography-tandem mass spectrometry (LC-MS/MS) was then used to identify the proteoforms associated with these peaks. RESULTS The analysis revealed that the primary constituents of the spectrum measured by VS are serum amyloid A1, serum amyloid A2, serum amyloid A4, C-reactive protein, and beta-2 microglobulin. CONCLUSION Proteoforms involved in host immunity were identified as significant components of these features. This newly acquired information improves our understanding of how VS can accurately predict patient response to therapy. It opens up additional studies that can expand our understanding even further.
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Affiliation(s)
| | | | - Daniel C. Weinhold
- Biodesix Inc., 2970 Wilderness Place Suite 100, Boulder, CO 80301, United States
| | - Steven Rightmyer
- Biodesix Inc., 2970 Wilderness Place Suite 100, Boulder, CO 80301, United States
| | - Amanda L. Weaver
- Biodesix Inc., 2970 Wilderness Place Suite 100, Boulder, CO 80301, United States
| | - Colin T. McDowell
- Biodesix Inc., 2970 Wilderness Place Suite 100, Boulder, CO 80301, United States
| | - Joanna Roder
- Biodesix Inc., 2970 Wilderness Place Suite 100, Boulder, CO 80301, United States
| | - Senait Asmellash
- Biodesix Inc., 2970 Wilderness Place Suite 100, Boulder, CO 80301, United States
| | - Gary A. Pestano
- Biodesix Inc., 2970 Wilderness Place Suite 100, Boulder, CO 80301, United States
| | - Heinrich Roder
- Biodesix Inc., 2970 Wilderness Place Suite 100, Boulder, CO 80301, United States
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Kamashev D, Shaban N, Lebedev T, Prassolov V, Suntsova M, Raevskiy M, Gaifullin N, Sekacheva M, Garazha A, Poddubskaya E, Sorokin M, Buzdin A. Human Blood Serum Can Diminish EGFR-Targeted Inhibition of Squamous Carcinoma Cell Growth through Reactivation of MAPK and EGFR Pathways. Cells 2023; 12:2022. [PMID: 37626832 PMCID: PMC10453612 DOI: 10.3390/cells12162022] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/03/2023] [Accepted: 08/07/2023] [Indexed: 08/27/2023] Open
Abstract
Regardless of the presence or absence of specific diagnostic mutations, many cancer patients fail to respond to EGFR-targeted therapeutics, and a personalized approach is needed to identify putative (non)responders. We found previously that human peripheral blood and EGF can modulate the activities of EGFR-specific drugs on inhibiting clonogenity in model EGFR-positive A431 squamous carcinoma cells. Here, we report that human serum can dramatically abolish the cell growth rate inhibition by EGFR-specific drugs cetuximab and erlotinib. We show that this phenomenon is linked with derepression of drug-induced G1S cell cycle transition arrest. Furthermore, A431 cell growth inhibition by cetuximab, erlotinib, and EGF correlates with a decreased activity of ERK1/2 proteins. In turn, the EGF- and human serum-mediated rescue of drug-treated A431 cells restores ERK1/2 activity in functional tests. RNA sequencing revealed 1271 and 1566 differentially expressed genes (DEGs) in the presence of cetuximab and erlotinib, respectively. Erlotinib- and cetuximab-specific DEGs significantly overlapped. Interestingly, the expression of 100% and 75% of these DEGs restores to the no-drug level when EGF or a mixed human serum sample, respectively, is added along with cetuximab. In the case of erlotinib, EGF and human serum restore the expression of 39% and 83% of DEGs, respectively. We further assessed differential molecular pathway activation levels and propose that EGF/human serum-mediated A431 resistance to EGFR drugs can be largely explained by reactivation of the MAPK signaling cascade.
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Affiliation(s)
- Dmitri Kamashev
- I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia;
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia; (N.S.); (A.B.)
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia;
| | - Nina Shaban
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia; (N.S.); (A.B.)
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia;
| | - Timofey Lebedev
- Engelhardt Institute of Molecular Biology, Moscow 119991, Russia; (T.L.); (V.P.)
| | - Vladimir Prassolov
- Engelhardt Institute of Molecular Biology, Moscow 119991, Russia; (T.L.); (V.P.)
| | - Maria Suntsova
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia;
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow 119991, Russia; (M.R.); (E.P.)
| | - Mikhail Raevskiy
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow 119991, Russia; (M.R.); (E.P.)
| | - Nurshat Gaifullin
- Department of Pathology, Faculty of Medicine, Lomonosov Moscow State University, Moscow 119992, Russia;
| | - Marina Sekacheva
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow 119991, Russia; (M.R.); (E.P.)
| | - Andrew Garazha
- Oncobox Ltd., Moscow 121205, Russia;
- Omicsway Corp., Walnut, CA 91789, USA
| | - Elena Poddubskaya
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow 119991, Russia; (M.R.); (E.P.)
| | - Maksim Sorokin
- I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia;
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia;
- PathoBiology Group, European Organization for Research and Treatment of Cancer (EORTC), 1200 Brussels, Belgium
| | - Anton Buzdin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia; (N.S.); (A.B.)
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia;
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow 119991, Russia; (M.R.); (E.P.)
- PathoBiology Group, European Organization for Research and Treatment of Cancer (EORTC), 1200 Brussels, Belgium
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Feng X, Muller DC, Zahed H, Alcala K, Guida F, Smith-Byrne K, Yuan JM, Koh WP, Wang R, Milne RL, Bassett JK, Langhammer A, Hveem K, Stevens VL, Wang Y, Johansson M, Tjønneland A, Tumino R, Sheikh M, Johansson M, Robbins HA. Evaluation of pre-diagnostic blood protein measurements for predicting survival after lung cancer diagnosis. EBioMedicine 2023; 92:104623. [PMID: 37236058 PMCID: PMC10232655 DOI: 10.1016/j.ebiom.2023.104623] [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: 12/21/2022] [Revised: 05/05/2023] [Accepted: 05/07/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND To evaluate whether circulating proteins are associated with survival after lung cancer diagnosis, and whether they can improve prediction of prognosis. METHODS We measured up to 1159 proteins in blood samples from 708 participants in 6 cohorts. Samples were collected within 3 years prior to lung cancer diagnosis. We used Cox proportional hazards models to identify proteins associated with overall mortality after lung cancer diagnosis. To evaluate model performance, we used a round-robin approach in which models were fit in 5 cohorts and evaluated in the 6th cohort. Specifically, we fit a model including 5 proteins and clinical parameters and compared its performance with clinical parameters only. FINDINGS There were 86 proteins nominally associated with mortality (p < 0.05), but only CDCP1 remained statistically significant after accounting for multiple testing (hazard ratio per standard deviation: 1.19, 95% CI: 1.10-1.30, unadjusted p = 0.00004). The external C-index for the protein-based model was 0.63 (95% CI: 0.61-0.66), compared with 0.62 (95% CI: 0.59-0.64) for the model with clinical parameters only. Inclusion of proteins did not provide a statistically significant improvement in discrimination (C-index difference: 0.015, 95% CI: -0.003 to 0.035). INTERPRETATION Blood proteins measured within 3 years prior to lung cancer diagnosis were not strongly associated with lung cancer survival, nor did they importantly improve prediction of prognosis beyond clinical information. FUNDING No explicit funding for this study. Authors and data collection supported by the US National Cancer Institute (U19CA203654), INCA (France, 2019-1-TABAC-01), Cancer Research Foundation of Northern Sweden (AMP19-962), and Swedish Department of Health Ministry.
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Affiliation(s)
- Xiaoshuang Feng
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France.
| | - David C Muller
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom; Department of Epidemiology and Biostatistics, School of Public Health, MRC-PHE, Centre for Environment and Health, Imperial College London, London, United Kingdom
| | - Hana Zahed
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Karine Alcala
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Florence Guida
- Environment and Lifestyle Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Karl Smith-Byrne
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom
| | - Jian-Min Yuan
- UPMC Hillman Cancer Centre, Pittsburgh, PA, USA; Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Woon-Puay Koh
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A∗STAR), Singapore
| | - Renwei Wang
- UPMC Hillman Cancer Centre, Pittsburgh, PA, USA
| | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Australia; School of Clinical Sciences at Monash Health, Monash University, Melbourne, Australia
| | - Julie K Bassett
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
| | - Arnulf Langhammer
- HUNT Research Center, Department of Public Health and Nursing, NTNU Norwegian University of Science and Technology, Levanger, Norway; Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Kristian Hveem
- HUNT Research Center, Department of Public Health and Nursing, NTNU Norwegian University of Science and Technology, Levanger, Norway; Department of Public Health and Nursing, K.G. Jebsen Centre for Genetic Epidemiology, Norwegian University of Science and Technology, Trondheim, Norway
| | | | - Ying Wang
- American Cancer Society, Atlanta, GA, USA
| | - Mikael Johansson
- Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden
| | - Anne Tjønneland
- Danish Cancer Society Research Center, Copenhagen, Denmark; Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Rosario Tumino
- Hyblean Association for Epidemiological Research, AIRE ONLUS Ragusa, Italy
| | - Mahdi Sheikh
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Mattias Johansson
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Hilary A Robbins
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France.
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The Role of Proteomics and Phosphoproteomics in the Discovery of Therapeutic Targets and Biomarkers in Acquired EGFR-TKI-Resistant Non-Small Cell Lung Cancer. Int J Mol Sci 2023; 24:ijms24054827. [PMID: 36902280 PMCID: PMC10003401 DOI: 10.3390/ijms24054827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 02/25/2023] [Accepted: 02/26/2023] [Indexed: 03/06/2023] Open
Abstract
The discovery of potent EGFR-tyrosine kinase inhibitors (EGFR-TKIs) has revolutionized the treatment of EGFR-mutated lung cancer. Despite the fact that EGFR-TKIs have yielded several significant benefits for lung cancer patients, the emergence of resistance to EGFR-TKIs has been a substantial impediment to improving treatment outcomes. Understanding the molecular mechanisms underlying resistance is crucial for the development of new treatments and biomarkers for disease progression. Together with the advancement in proteome and phosphoproteome analysis, a diverse set of key signaling pathways have been successfully identified that provide insight for the discovery of possible therapeutically targeted proteins. In this review, we highlight the proteome and phosphoproteomic analyses of non-small cell lung cancer (NSCLC) as well as the proteome analysis of biofluid specimens that associate with acquired resistance in response to different generations of EGFR-TKI. Furthermore, we present an overview of the targeted proteins and potential drugs that have been tested in clinical studies and discuss the challenges of implementing this discovery in future NSCLC treatment.
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Abbasian MH, Ardekani AM, Sobhani N, Roudi R. The Role of Genomics and Proteomics in Lung Cancer Early Detection and Treatment. Cancers (Basel) 2022; 14:5144. [PMID: 36291929 PMCID: PMC9600051 DOI: 10.3390/cancers14205144] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/10/2022] [Accepted: 10/18/2022] [Indexed: 08/17/2023] Open
Abstract
Lung cancer is the leading cause of cancer-related death worldwide, with non-small-cell lung cancer (NSCLC) being the primary type. Unfortunately, it is often diagnosed at advanced stages, when therapy leaves patients with a dismal prognosis. Despite the advances in genomics and proteomics in the past decade, leading to progress in developing tools for early diagnosis, targeted therapies have shown promising results; however, the 5-year survival of NSCLC patients is only about 15%. Low-dose computed tomography or chest X-ray are the main types of screening tools. Lung cancer patients without specific, actionable mutations are currently treated with conventional therapies, such as platinum-based chemotherapy; however, resistances and relapses often occur in these patients. More noninvasive, inexpensive, and safer diagnostic methods based on novel biomarkers for NSCLC are of paramount importance. In the current review, we summarize genomic and proteomic biomarkers utilized for the early detection and treatment of NSCLC. We further discuss future opportunities to improve biomarkers for early detection and the effective treatment of NSCLC.
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Affiliation(s)
- Mohammad Hadi Abbasian
- Department of Medical Genetics, National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran 1497716316, Iran
| | - Ali M. Ardekani
- Department of Medical Biotechnology, National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran 1497716316, Iran
| | - Navid Sobhani
- Department of Medicine, Section of Epidemiology and Population Sciences, Baylor College of Medicine, Houston, TX 77030, USA
| | - Raheleh Roudi
- Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, Stanford, CA 94305, USA
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11
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Poulos RC, Cai Z, Robinson PJ, Reddel RR, Zhong Q. Opportunities for pharmacoproteomics in biomarker discovery. Proteomics 2022; 23:e2200031. [PMID: 36086888 DOI: 10.1002/pmic.202200031] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/30/2022] [Accepted: 09/06/2022] [Indexed: 11/08/2022]
Abstract
Proteomic data are a uniquely valuable resource for drug response prediction and biomarker discovery because most drugs interact directly with proteins in target cells rather than with DNA or RNA. Recent advances in mass spectrometry and associated processing methods have enabled the generation of large-scale proteomic datasets. Here we review the significant opportunities that currently exist to combine large-scale proteomic data with drug-related research, a field termed pharmacoproteomics. We describe successful applications of drug response prediction using molecular data, with an emphasis on oncology. We focus on technical advances in data-independent acquisition mass spectrometry (DIA-MS) that can facilitate the discovery of protein biomarkers for drug responses, alongside the increased availability of big biomedical data. We spotlight new opportunities for machine learning in pharmacoproteomics, driven by the combination of these large datasets and improved high-performance computing. Finally, we explore the value of pre-clinical models for pharmacoproteomic studies and the accompanying challenges of clinical validation. We propose that pharmacoproteomics offers the potential for novel discovery and innovation within the cancer landscape. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Rebecca C Poulos
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Zhaoxiang Cai
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Phillip J Robinson
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Roger R Reddel
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Qing Zhong
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
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12
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Park Y, Kim MJ, Choi Y, Kim NH, Kim L, Hong SPD, Cho HG, Yu E, Chae YK. Role of mass spectrometry-based serum proteomics signatures in predicting clinical outcomes and toxicity in patients with cancer treated with immunotherapy. J Immunother Cancer 2022; 10:jitc-2021-003566. [PMID: 35347071 PMCID: PMC8961104 DOI: 10.1136/jitc-2021-003566] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/21/2022] [Indexed: 02/03/2023] Open
Abstract
Immunotherapy has fundamentally changed the landscape of cancer treatment. However, only a subset of patients respond to immunotherapy, and a significant portion experience immune-related adverse events (irAEs). In addition, the predictive ability of current biomarkers such as programmed death-ligand 1 (PD-L1) remains unreliable and establishing better potential candidate markers is of great importance in selecting patients who would benefit from immunotherapy. Here, we focus on the role of serum-based proteomic tests in predicting the response and toxicity of immunotherapy. Serum proteomic signatures refer to unique patterns of proteins which are associated with immune response in patients with cancer. These protein signatures are derived from patient serum samples based on mass spectrometry and act as biomarkers to predict response to immunotherapy. Using machine learning algorithms, serum proteomic tests were developed through training data sets from advanced non-small cell lung cancer (Host Immune Classifier, Primary Immune Response) and malignant melanoma patients (PerspectIV test). The tests effectively stratified patients into groups with good and poor treatment outcomes independent of PD-L1 expression. Here, we review current evidence in the published literature on three liquid biopsy tests that use biomarkers derived from proteomics and machine learning for use in immuno-oncology. We discuss how these tests may inform patient prognosis as well as guide treatment decisions and predict irAE of immunotherapy. Thus, mass spectrometry-based serum proteomics signatures play an important role in predicting clinical outcomes and toxicity.
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Affiliation(s)
- Yeonggyeong Park
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Min Jeong Kim
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Yoonhee Choi
- Department of Internal Medicine, NewYork-Presbyterian Queens, Flushing, New York, USA
| | - Na Hyun Kim
- Department of Internal Medicine, AMITA Health Saint Joseph Hospital Chicago, Chicago, Illinois, USA
| | - Leeseul Kim
- Department of Internal Medicine, AMITA Health Saint Francis Hospital Evanston, Evanston, Illinois, USA
| | - Seung Pyo Daniel Hong
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Hyung-Gyo Cho
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Emma Yu
- Department of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Young Kwang Chae
- Department of Hematology and Oncology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
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13
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Semi-Quantitative MALDI Measurements of Blood-Based Samples for Molecular Diagnostics. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27030997. [PMID: 35164262 PMCID: PMC8840133 DOI: 10.3390/molecules27030997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 12/21/2021] [Accepted: 12/22/2021] [Indexed: 11/23/2022]
Abstract
Accurate and precise measurement of the relative protein content of blood-based samples using mass spectrometry is challenging due to the large number of circulating proteins and the dynamic range of their abundances. Traditional spectral processing methods often struggle with accurately detecting overlapping peaks that are observed in these samples. In this work, we develop a novel spectral processing algorithm that effectively detects over 1650 peaks with over 3.5 orders of magnitude in intensity in the 3 to 30 kD m/z range. The algorithm utilizes a convolution of the peak shape to enhance peak detection, and accurate peak fitting to provide highly reproducible relative abundance estimates for both isolated peaks and overlapping peaks. We demonstrate a substantial increase in the reproducibility of the measurements of relative protein abundance when comparing this processing method to a traditional processing method for sample sets run on multiple matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) instruments. By utilizing protein set enrichment analysis, we find a sizable increase in the number of features associated with biological processes compared to previously reported results. The new processing method could be very beneficial when developing high-performance molecular diagnostic tests in disease indications.
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14
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Rich P, Mitchell RB, Schaefer E, Walker PR, Dubay JW, Boyd J, Oubre D, Page R, Khalil M, Sinha S, Boniol S, Halawani H, Santos ES, Brenner W, Orsini JM, Pauli E, Goldberg J, Veatch A, Haut M, Ghabach B, Bidyasar S, Quejada M, Khan W, Huang K, Traylor L, Akerley W. Real-world performance of blood-based proteomic profiling in first-line immunotherapy treatment in advanced stage non-small cell lung cancer. J Immunother Cancer 2021; 9:jitc-2021-002989. [PMID: 34706885 PMCID: PMC8552188 DOI: 10.1136/jitc-2021-002989] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/07/2021] [Indexed: 12/02/2022] Open
Abstract
Purpose Immune checkpoint inhibition (ICI) therapy has improved patient outcomes in advanced non-small cell lung cancer (NSCLC), but better biomarkers are needed. A clinically validated, blood-based proteomic test, or host immune classifier (HIC), was assessed for its ability to predict ICI therapy outcomes in this real-world, prospectively designed, observational study. Materials and methods The prospectively designed, observational registry study INSIGHT (Clinical Effectiveness Assessment of VeriStrat® Testing and Validation of Immunotherapy Tests in NSCLC Subjects) (NCT03289780) includes 35 US sites having enrolled over 3570 NSCLC patients at any stage and line of therapy. After enrolment and prior to therapy initiation, all patients are tested and designated HIC-Hot (HIC-H) or HIC-Cold (HIC-C). A prespecified interim analysis was performed after 1-year follow-up with the first 2000 enrolled patients. We report the overall survival (OS) of patients with advanced stage (IIIB and IV) NSCLC treated in the first-line (ICI-containing therapies n=284; all first-line therapies n=877), by treatment type and in HIC-defined subgroups. Results OS for HIC-H patients was longer than OS for HIC-C patients across treatment regimens, including ICI. For patients treated with all ICI regimens, median OS was not reached (95% CI 15.4 to undefined months) for HIC-H (n=196) vs 5.0 months (95% CI 2.9 to 6.4) for HIC-C patients (n=88); HR=0.38 (95% CI 0.27 to 0.53), p<0.0001. For ICI monotherapy, OS was 16.8 vs 2.8 months (HR=0.36 (95% CI 0.22 to 0.58), p<0.0001) and for ICI with chemotherapy OS was unreached vs 6.4 months (HR=0.41 (95% CI 0.26 to 0.67), p=0.0003). HIC results were independent of programmed death ligand 1 (PD-L1). In a subgroup with PD-L1 ≥50% and performance status 0–1, HIC stratified survival significantly for ICI monotherapy but not ICI with chemotherapy. Conclusion Blood-based HIC proteomic testing provides clinically meaningful information for immunotherapy treatment decision in NSCLC independent of PD-L1. The data suggest that HIC-C patients should not be treated with ICI alone regardless of their PD-L1 expression.
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Affiliation(s)
- Patricia Rich
- Lung Cancer, Piedmont Physicians Group, Atlanta, Georgia, USA
| | | | - Eric Schaefer
- Highlands Oncology Group, Fayetteville, Arkansas, USA
| | - Paul R Walker
- Leo W Jenkins Cancer Center, Brody School of Medicine at East Carolina University, Greenville, North Carolina, USA
| | - John W Dubay
- Lewis and Faye Manderson Cancer Center at DCH Regional Medical Center, Tuscaloosa, Alabama, USA
| | - Jason Boyd
- Southeastern Medical Oncology Center, Goldsboro, North Carolina, USA
| | - David Oubre
- Pontchartrain Cancer Center, Covington, Louisiana, USA
| | - Ray Page
- The Center for Cancer and Blood Disorders, Fort Worth, Texas, USA
| | - Mazen Khalil
- St. Bernards Hospital, Inc, Jonesboro, Arkansas, USA
| | - Suman Sinha
- Christus Saint Michael Health System, Texarkana, Texas, USA
| | - Scott Boniol
- Christus Cancer Treatment Center, Shreveport, Louisiana, USA
| | - Hafez Halawani
- St. Frances Cabrini Hospital Cancer Center, Alexandria, Louisiana, USA
| | - Edgardo S Santos
- Florida Precision Oncology, Division of Genesis Care, Aventura, Florida, USA
| | - Warren Brenner
- Lynn Clinical Research Institute, Boca Raton, Florida, USA
| | | | - Emily Pauli
- Clearview Cancer Institute, Huntsville, Alabama, USA
| | - Jonathan Goldberg
- Clinical Research Alliance, Caremount Medical, Mount Kisco, New York, USA
| | - Andrea Veatch
- Northwest Medical Specialties, Puyallup, Washington, USA
| | - Mitchell Haut
- Hematology and Oncology Associates, Inc, Canton, Ohio, USA
| | | | | | | | | | - Kan Huang
- Phelps County Regional Medical Center, Rolla, Missouri, USA
| | | | - Wallace Akerley
- Huntsman Cancer Institute Cancer Hospital, Salt Lake City, Utah, USA
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15
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Roder J, Maguire L, Georgantas R, Roder H. Explaining multivariate molecular diagnostic tests via Shapley values. BMC Med Inform Decis Mak 2021; 21:211. [PMID: 34238309 PMCID: PMC8265031 DOI: 10.1186/s12911-021-01569-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 06/29/2021] [Indexed: 11/17/2022] Open
Abstract
Background Machine learning (ML) can be an effective tool to extract information from attribute-rich molecular datasets for the generation of molecular diagnostic tests. However, the way in which the resulting scores or classifications are produced from the input data may not be transparent. Algorithmic explainability or interpretability has become a focus of ML research. Shapley values, first introduced in game theory, can provide explanations of the result generated from a specific set of input data by a complex ML algorithm. Methods For a multivariate molecular diagnostic test in clinical use (the VeriStrat® test), we calculate and discuss the interpretation of exact Shapley values. We also employ some standard approximation techniques for Shapley value computation (local interpretable model-agnostic explanation (LIME) and Shapley Additive Explanations (SHAP) based methods) and compare the results with exact Shapley values. Results Exact Shapley values calculated for data collected from a cohort of 256 patients showed that the relative importance of attributes for test classification varied by sample. While all eight features used in the VeriStrat® test contributed equally to classification for some samples, other samples showed more complex patterns of attribute importance for classification generation. Exact Shapley values and Shapley-based interaction metrics were able to provide interpretable classification explanations at the sample or patient level, while patient subgroups could be defined by comparing Shapley value profiles between patients. LIME and SHAP approximation approaches, even those seeking to include correlations between attributes, produced results that were quantitatively and, in some cases qualitatively, different from the exact Shapley values. Conclusions Shapley values can be used to determine the relative importance of input attributes to the result generated by a multivariate molecular diagnostic test for an individual sample or patient. Patient subgroups defined by Shapley value profiles may motivate translational research. However, correlations inherent in molecular data and the typically small ML training sets available for molecular diagnostic test development may cause some approximation methods to produce approximate Shapley values that differ both qualitatively and quantitatively from exact Shapley values. Hence, caution is advised when using approximate methods to evaluate Shapley explanations of the results of molecular diagnostic tests. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-021-01569-9.
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Affiliation(s)
- Joanna Roder
- Biodesix, Inc., 2970 Wilderness Place, Ste100, Boulder, CO, 80301, USA.
| | - Laura Maguire
- Biodesix, Inc., 2970 Wilderness Place, Ste100, Boulder, CO, 80301, USA
| | - Robert Georgantas
- Biodesix, Inc., 2970 Wilderness Place, Ste100, Boulder, CO, 80301, USA
| | - Heinrich Roder
- Biodesix, Inc., 2970 Wilderness Place, Ste100, Boulder, CO, 80301, USA
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16
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Detection of Hepatocellular Carcinoma in a High-Risk Population by a Mass Spectrometry-Based Test. Cancers (Basel) 2021; 13:cancers13133109. [PMID: 34206321 PMCID: PMC8268628 DOI: 10.3390/cancers13133109] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 06/12/2021] [Accepted: 06/14/2021] [Indexed: 02/07/2023] Open
Abstract
Simple Summary Liver cancer is one of the most common causes of cancer worldwide, but unfortunately, current technology has a limited ability to detect it early in high-risk patients. This study investigates a machine learning algorithm based on protein levels in the blood that can be used to help with diagnosis. The test shows promising results, especially in patients with smaller tumors and compared to current blood detection tests. This research suggests an important role in the future for machine learning algorithm-based blood detection tests. Abstract Hepatocellular carcinoma (HCC) is one of the fastest growing causes of cancer-related death. Guidelines recommend obtaining a screening ultrasound with or without alpha-fetoprotein (AFP) every 6 months in at-risk adults. AFP as a screening biomarker is plagued by low sensitivity/specificity, prompting interest in discovering alternatives. Mass spectrometry-based techniques are promising in their ability to identify potential biomarkers. This study aimed to use machine learning utilizing spectral data and AFP to create a model for early detection. Serum samples were collected from three separate cohorts, and data were compiled to make Development, Internal Validation, and Independent Validation sets. AFP levels were measured, and Deep MALDI® analysis was used to generate mass spectra. Spectral data were input into the VeriStrat® classification algorithm. Machine learning techniques then classified each sample as “Cancer” or “No Cancer”. Sensitivity and specificity of the test were >80% to detect HCC. High specificity of the test was independent of cause and severity of underlying disease. When compared to AFP, there was improved cancer detection for all tumor sizes, especially small lesions. Overall, a machine learning algorithm incorporating mass spectral data and AFP values from serum samples offers a novel approach to diagnose HCC. Given the small sample size of the Independent Validation set, a further independent, prospective study is warranted.
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Kamashev D, Sorokin M, Kochergina I, Drobyshev A, Vladimirova U, Zolotovskaia M, Vorotnikov I, Shaban N, Raevskiy M, Kuzmin D, Buzdin A. Human blood serum can donor-specifically antagonize effects of EGFR-targeted drugs on squamous carcinoma cell growth. Heliyon 2021; 7:e06394. [PMID: 33748471 PMCID: PMC7966997 DOI: 10.1016/j.heliyon.2021.e06394] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 10/29/2020] [Accepted: 02/25/2021] [Indexed: 02/09/2023] Open
Abstract
Many patients fail to respond to EGFR-targeted therapeutics, and personalized diagnostics is needed to identify putative responders. We investigated 1630 colorectal and lung squamous carcinomas and 1357 normal lung and colon samples and observed huge variation in EGFR pathway activation in both cancerous and healthy tissues, irrespectively on EGFR gene mutation status. We investigated whether human blood serum can affect squamous carcinoma cell growth and EGFR drug response. We demonstrate that human serum antagonizes the effects of EGFR-targeted drugs erlotinib and cetuximab on A431 squamous carcinoma cells by increasing IC50 by about 2- and 20-fold, respectively. The effects on clonogenicity varied significantly across the individual serum samples in every experiment, with up to 100% differences. EGF concentration could explain many effects of blood serum samples, and EGFR ligands-depleted serum showed lesser effect on drug sensitivity.
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Affiliation(s)
- Dmitry Kamashev
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 16/10, Miklukho-Maklaya St., Moscow 117997, Russia
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, 8-2, Trubetskaya St., Moscow 119992, Russia
| | - Maksim Sorokin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 16/10, Miklukho-Maklaya St., Moscow 117997, Russia
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, 8-2, Trubetskaya St., Moscow 119992, Russia
- Moscow Institute of Physics and Technology (National Research University), Moscow Region 141700, Russia
| | - Irina Kochergina
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 16/10, Miklukho-Maklaya St., Moscow 117997, Russia
| | - Aleksey Drobyshev
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 16/10, Miklukho-Maklaya St., Moscow 117997, Russia
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, 8-2, Trubetskaya St., Moscow 119992, Russia
- Moscow Institute of Physics and Technology (National Research University), Moscow Region 141700, Russia
| | - Uliana Vladimirova
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 16/10, Miklukho-Maklaya St., Moscow 117997, Russia
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, 8-2, Trubetskaya St., Moscow 119992, Russia
| | - Marianna Zolotovskaia
- Moscow Institute of Physics and Technology (National Research University), Moscow Region 141700, Russia
| | - Igor Vorotnikov
- Blokhin National Medical Research Center of Oncology of the Ministry of Health of Russia
| | - Nina Shaban
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 16/10, Miklukho-Maklaya St., Moscow 117997, Russia
- Moscow Institute of Physics and Technology (National Research University), Moscow Region 141700, Russia
| | - Mikhail Raevskiy
- Moscow Institute of Physics and Technology (National Research University), Moscow Region 141700, Russia
- OmicsWay Corp., Walnut, CA, USA
| | - Denis Kuzmin
- Moscow Institute of Physics and Technology (National Research University), Moscow Region 141700, Russia
| | - Anton Buzdin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 16/10, Miklukho-Maklaya St., Moscow 117997, Russia
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, 8-2, Trubetskaya St., Moscow 119992, Russia
- Moscow Institute of Physics and Technology (National Research University), Moscow Region 141700, Russia
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18
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Jia B, Dong Z, Wu D, Zhao J, Wu M, An T, Wang Y, Zhuo M, Li J, Wang Y, Zhang J, Zhao X, Li S, Li J, Ma M, Chen C, Yang X, Zhong J, Chen H, Wang J, Chi Y, Zhai X, Cui S, Zhang R, Ma Q, Fang J, Wang Z. Prediction of the VeriStrat test in first-line therapy of pemetrexed-based regimens for advanced lung adenocarcinoma patients. Cancer Cell Int 2020; 20:590. [PMID: 33298069 PMCID: PMC7724790 DOI: 10.1186/s12935-020-01662-5] [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: 07/04/2020] [Accepted: 11/18/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Although advanced non-squamous non-small cell lung cancer (NSCLC) patients have significantly better survival outcomes after pemetrexed based treatment, a subset of patients still show intrinsic resistance and progress rapidly. Therefore we aimed to use a blood-based protein signature (VeriStrat, VS) to analyze whether VS could identify the subset of patients who had poor efficacy on pemetrexed therapy. METHODS This study retrospectively analysed 72 advanced lung adenocarcinoma patients who received first-line pemetrexed/platinum or combined with bevacizumab treatment. RESULTS Plasma samples from these patients were analysed using VS and classified into the Good (VS-G) or Poor (VS-P) group. The relationship between efficacy and VS status was further investigated. Of the 72 patients included in this study, 35 (48.6%) were treated with pemetrexed plus platinum and 37 (51.4%) were treated with pemetrexed/platinum combined with bevacizumab. Among all patients, 60 (83.3%) and 12 (16.7%) patients were classified as VS-G and VS-P, respectively. VS-G patients had significantly better median progression-free survival (PFS) (Unreached vs. 4.2 months; P < 0.001) than VS-P patients. In addition, the partial response (PR) rate was higher in the VS-G group than that in the VS-P group (46.7% vs. 25.0%, P = 0.212). Subgroup analysis showed that PFS was also significantly longer in the VS-G group than that in the VS-P group regardless of whether patients received chemotherapy alone or chemotherapy plus bevacizumab. CONCLUSIONS Our study indicated that VS might be considered as a novel and valid method to predict the efficacy of pemetrexed-based therapy and identify a subset of advanced lung adenocarcinoma patients who had intrinsic resistance to pemetrexed based regimens. However, larger sample studies are still needed to further confirm this result.
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Affiliation(s)
- Bo Jia
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Thoracic Medical Oncology, Peking University Cancer Hospital & Institute, 52 Fucheng Road, Haidian District, Beijing, 100142, China
| | - Zhi Dong
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of GI Oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Di Wu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Thoracic Oncology II, Peking University Cancer Hospital & Institute, 52 Fucheng Road, Haidian District, 100142, Beijing, China
| | - Jun Zhao
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Thoracic Medical Oncology, Peking University Cancer Hospital & Institute, 52 Fucheng Road, Haidian District, Beijing, 100142, China
| | - Meina Wu
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Thoracic Medical Oncology, Peking University Cancer Hospital & Institute, 52 Fucheng Road, Haidian District, Beijing, 100142, China
| | - Tongtong An
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Thoracic Medical Oncology, Peking University Cancer Hospital & Institute, 52 Fucheng Road, Haidian District, Beijing, 100142, China
| | - Yuyan Wang
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Thoracic Medical Oncology, Peking University Cancer Hospital & Institute, 52 Fucheng Road, Haidian District, Beijing, 100142, China
| | - Minglei Zhuo
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Thoracic Medical Oncology, Peking University Cancer Hospital & Institute, 52 Fucheng Road, Haidian District, Beijing, 100142, China
| | - Jianjie Li
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Thoracic Medical Oncology, Peking University Cancer Hospital & Institute, 52 Fucheng Road, Haidian District, Beijing, 100142, China
| | - Yang Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Thoracic Oncology II, Peking University Cancer Hospital & Institute, 52 Fucheng Road, Haidian District, 100142, Beijing, China
| | - Jie Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Thoracic Oncology II, Peking University Cancer Hospital & Institute, 52 Fucheng Road, Haidian District, 100142, Beijing, China
| | - Xinghui Zhao
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Thoracic Medical Oncology, Peking University Cancer Hospital & Institute, 52 Fucheng Road, Haidian District, Beijing, 100142, China
| | - Sheng Li
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Thoracic Medical Oncology, Peking University Cancer Hospital & Institute, 52 Fucheng Road, Haidian District, Beijing, 100142, China
| | - Junfeng Li
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Thoracic Medical Oncology, Peking University Cancer Hospital & Institute, 52 Fucheng Road, Haidian District, Beijing, 100142, China
| | - Menglei Ma
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Thoracic Medical Oncology, Peking University Cancer Hospital & Institute, 52 Fucheng Road, Haidian District, Beijing, 100142, China
| | - Chen Chen
- Center for Clinical Laboratory Medicine, Chinese PLA General Hospital, The First Medical Center), Beijing, China
| | - Xue Yang
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Thoracic Medical Oncology, Peking University Cancer Hospital & Institute, 52 Fucheng Road, Haidian District, Beijing, 100142, China
| | - Jia Zhong
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Thoracic Medical Oncology, Peking University Cancer Hospital & Institute, 52 Fucheng Road, Haidian District, Beijing, 100142, China
| | - Hanxiao Chen
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Thoracic Medical Oncology, Peking University Cancer Hospital & Institute, 52 Fucheng Road, Haidian District, Beijing, 100142, China
| | - Jingjing Wang
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Thoracic Medical Oncology, Peking University Cancer Hospital & Institute, 52 Fucheng Road, Haidian District, Beijing, 100142, China
| | - Yujia Chi
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Thoracic Medical Oncology, Peking University Cancer Hospital & Institute, 52 Fucheng Road, Haidian District, Beijing, 100142, China
| | - Xiaoyu Zhai
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Thoracic Medical Oncology, Peking University Cancer Hospital & Institute, 52 Fucheng Road, Haidian District, Beijing, 100142, China
| | - Song Cui
- Bioyong Technologies Inc, Beijing, China
| | - Rong Zhang
- Bioyong Technologies Inc, Beijing, China
| | - Qingwei Ma
- Bioyong Technologies Inc, Beijing, China
| | - Jian Fang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Thoracic Oncology II, Peking University Cancer Hospital & Institute, 52 Fucheng Road, Haidian District, 100142, Beijing, China.
| | - Ziping Wang
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Thoracic Medical Oncology, Peking University Cancer Hospital & Institute, 52 Fucheng Road, Haidian District, Beijing, 100142, China.
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Kaiser NK, Steers M, Nichols CM, Mellert H, Pestano GA. Design and Characterization of a Novel Blood Collection and Transportation Device for Proteomic Applications. Diagnostics (Basel) 2020; 10:E1032. [PMID: 33276497 PMCID: PMC7761483 DOI: 10.3390/diagnostics10121032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 11/27/2020] [Accepted: 11/30/2020] [Indexed: 11/29/2022] Open
Abstract
A major hurdle for blood-based proteomic diagnostics is efficient transport of specimens from the collection site to the testing laboratory. Dried blood spots have shown utility for diagnostic applications, specifically those where red blood cell hemolysis and contamination of specimens with hemoglobin is not confounding. Conversely, applications that are sensitive to the presence of the hemoglobin subunits require blood separation, which relies on centrifugation to collect plasma/serum, and then cold-chain custody during shipping. All these factors introduce complexities and potentially increased costs. Here we report on a novel whole blood-collection device (BCD) that efficiently separates the liquid from cellular components, minimizes hemolysis in the plasma fraction, and maintains protein integrity during ambient transport. The simplicity of the design makes the device ideal for field use. Whole blood is acquired through venipuncture and applied to the device with an exact volume pipette. The BCD design was based on lateral-flow principles in which whole blood was applied to a defined area, allowing two minutes for blood absorption into the separation membrane, then closed for shipment. The diagnostic utility of the device was further demonstrated with shipments from multiple sites (n = 33) across the U.S. sent to two different centralized laboratories for analyses using liquid chromatography/mass spectrometry (LC/MS/MS) and matrix assisted laser desorption/ionization-time of flight (MALDI-ToF) commercial assays. Specimens showed high levels of result label concordance for the LC/MS/MS assay (Negative Predictive Value = 98%) and MALDI-ToF assay (100% result concordance). The overall goal of the device is to simplify specimen transport to the laboratory and produce clinical test results equivalent to established collection methods.
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Affiliation(s)
- Nathan K. Kaiser
- Biodesix Inc., 2970 Wilderness Place Suite 100, Boulder, CO 80301, USA; (M.S.); (C.M.N.); (H.M.); (G.A.P.)
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20
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Tonry C, Finn S, Armstrong J, Pennington SR. Clinical proteomics for prostate cancer: understanding prostate cancer pathology and protein biomarkers for improved disease management. Clin Proteomics 2020; 17:41. [PMID: 33292167 PMCID: PMC7678104 DOI: 10.1186/s12014-020-09305-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 11/11/2020] [Indexed: 12/12/2022] Open
Abstract
Following the introduction of routine Prostate Specific Antigen (PSA) screening in the early 1990′s, Prostate Cancer (PCa) is often detected at an early stage. There are also a growing number of treatment options available and so the associated mortality rate is generally low. However, PCa is an extremely complex and heterogenous disease and many patients suffer disease recurrence following initial therapy. Disease recurrence commonly results in metastasis and metastatic PCa has an average survival rate of just 3–5 years. A significant problem in the clinical management of PCa is being able to differentiate between patients who will respond to standard therapies and those who may benefit from more aggressive intervention at an earlier stage. It is also acknowledged that for many men the disease is not life threatenting. Hence, there is a growing desire to identify patients who can be spared the significant side effects associated with PCa treatment until such time (if ever) their disease progresses to the point where treatment is required. To these important clinical needs, current biomarkers and clinical methods for patient stratification and personlised treatment are insufficient. This review provides a comprehensive overview of the complexities of PCa pathology and disease management. In this context it is possible to review current biomarkers and proteomic technologies that will support development of biomarker-driven decision tools to meet current important clinical needs. With such an in-depth understanding of disease pathology, the development of novel clinical biomarkers can proceed in an efficient and effective manner, such that they have a better chance of improving patient outcomes.
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Affiliation(s)
- Claire Tonry
- UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Stephen Finn
- Department of Histopathology and Morbid Anatomy, Trinity Translational Medicine Institute, Trinity College Dublin, Dublin 8, Ireland
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21
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Chae YK, Kim WB, Davis AA, Park LC, Anker JF, Simon NI, Rhee K, Song J, Cho A, Chang S, Ko T, Oh M, Bhave M, Viveiros P. Mass spectrometry-based serum proteomic signature as a potential biomarker for survival in patients with non-small cell lung cancer receiving immunotherapy. Transl Lung Cancer Res 2020; 9:1015-1028. [PMID: 32953481 PMCID: PMC7481587 DOI: 10.21037/tlcr-20-148] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Background VeriStrat test is a serum assay which uses a mass spectrometry (MS)-based proteomic signature derived from machine learning. It is currently used as a prognostic marker for patients with non-small cell lung cancer (NSCLC) receiving chemotherapy. However, little is known about its role for NSCLC patients receiving immune checkpoint inhibitors (ICIs). Methods This is a retrospective study that includes 47 patients with advanced stage NSCLC without an activating EGFR mutation, who underwent the VeriStrat test from 2016 to 2018. Spectra from blood samples were evaluated to assign patients into the VeriStrat ‘Good’ (VS-G) or VeriStrat ‘Poor’ (VS-P) risk group. The clinical outcomes of 32 patients who received programmed cell death 1 (PD-1) inhibitors nivolumab or pembrolizumab were analyzed by VeriStrat status. Results The VS-G group demonstrated significantly higher progression-free survival (PFS) and overall survival (OS) compared to the VS-P group among overall NSCLC patients regardless of treatment (median PFS of 7.1 vs. 4.2 months, P=0.013, and median OS, not reached vs. 17.2 months, P=0.012). Among NSCLC patients treated with ICIs, VS-G classification was associated with significantly increased PFS in comparison to VS-P classification (median PFS of 6.2 vs. 3.0 months, P=0.012), while the differences in OS trended towards significance (median OS, not reached vs. 16.5 months P=0.076). Multivariate analysis showed that the VeriStrat status was significantly correlated with PFS and OS in NSCLC patients treated with ICIs (P=0.017, P=0.034, respectively). Conclusions MS-based serum proteomic signature has potential as a biomarker for survival outcome in NSCLC patients receiving immunotherapy.
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Affiliation(s)
- Young Kwang Chae
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Won Bin Kim
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Andrew A Davis
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Lee Chun Park
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.,Division of Hematology/Oncology, Internal Medicine, Kosin University, Busan, Republic of Korea
| | - Jonathan F Anker
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Nicholas I Simon
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Kyunghoon Rhee
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Junho Song
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Anderson Cho
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Sangmin Chang
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Taeyeong Ko
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Michael Oh
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Manali Bhave
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Pedro Viveiros
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
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22
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Definition and Independent Validation of a Proteomic-Classifier in Ovarian Cancer. Cancers (Basel) 2020; 12:cancers12092519. [PMID: 32899818 PMCID: PMC7564837 DOI: 10.3390/cancers12092519] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Revised: 08/25/2020] [Accepted: 08/27/2020] [Indexed: 12/13/2022] Open
Abstract
Simple Summary The heterogeneity of epithelial ovarian cancer and its associated molecular biological characteristics are continuously integrated in the development of therapy guidelines. In a next step, future therapy recommendations might also be able to focus on the patient’s systemic status, not only the tumor’s molecular pattern. Therefore, new methods to identify and validate host-related biomarkers need to be established. Using mass spectrometry, we developed and independently validated a blood-based proteomic classifier, stratifying epithelial ovarian cancer patients into good and poor survival groups. We also determined an age dependence of the prognostic performance of this classifier and its association with important biological processes. This work highlights that, just like molecular markers of the tumor itself, the systemic condition of a patient (partly reflected in proteomic patterns) also influences survival and therapy response and could therefore be integrated into future processes of therapy planning. Abstract Mass-spectrometry-based analyses have identified a variety of candidate protein biomarkers that might be crucial for epithelial ovarian cancer (EOC) development and therapy response. Comprehensive validation studies of the biological and clinical implications of proteomics are needed to advance them toward clinical use. Using the Deep MALDI method of mass spectrometry, we developed and independently validated (development cohort: n = 199, validation cohort: n = 135) a blood-based proteomic classifier, stratifying EOC patients into good and poor survival groups. We also determined an age dependency of the prognostic performance of this classifier, and our protein set enrichment analysis showed that the good and poor proteomic phenotypes were associated with, respectively, lower and higher levels of complement activation, inflammatory response, and acute phase reactants. This work highlights that, just like molecular markers of the tumor itself, the systemic condition of a patient (partly reflected in proteomic patterns) also influences survival and therapy response in a subset of ovarian cancer patients and could therefore be integrated into future processes of therapy planning.
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Leal TA, Argento AC, Bhadra K, Hogarth DK, Grigorieva J, Hartfield RM, McDonald RC, Bonomi PD. Prognostic performance of proteomic testing in advanced non-small cell lung cancer: a systematic literature review and meta-analysis. Curr Med Res Opin 2020; 36:1497-1505. [PMID: 32615813 DOI: 10.1080/03007995.2020.1790346] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
OBJECTIVE Timely assessment of patient-specific prognosis is critical to oncology care involving a shared decision-making approach, but clinical prognostic factors traditionally used in NSCLC have limitations. We examine a proteomic test to address these limitations. METHODS This study examines the prognostic performance of the VeriStrat blood-based proteomic test that measures the inflammatory disease state of patients with advanced NSCLC. A systematic literature review (SLR) was performed, yielding cohorts in which the hazard ratio (HR) was reported for overall survival (OS) of patients with VeriStrat Poor (VSPoor) test results versus VeriStrat Good (VSGood). A study-level meta-analysis of OS HRs was performed in subgroups defined by lines of therapy and treatment regimens. RESULTS Twenty-four cohorts met SLR criteria. Meta-analyses in five subgroups (first-line platinum-based chemotherapy, second-line single-agent chemotherapy, first-line EGFR-tyrosine kinase inhibitor (TKI) therapy, and second- and higher-line TKI therapy, and best supportive care) resulted in statistically significant (p ≤ .001) summary effect sizes for OS HRs of 0.42, 0.54, 0.41, 0.52, and 0.50, respectively, indicating increased OS by about two-fold for patients who test VSGood. No significant heterogeneity was seen in any subgroup (p > .05). CONCLUSIONS Advanced NSCLC patients classified VSGood have significantly longer OS than those classified VSPoor. The summary effect size for OS HRs around 0.4-0.5 indicates that the expected median survival of those with a VSGood classification is approximately 2-2.5 times as long as those with VSPoor. The robust prognostic performance of the VeriStrat test across various lines of therapy and treatment regimens has clinical implications for treatment shared decision-making and potential for novel treatment strategies.
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Affiliation(s)
- Ticiana A Leal
- Carbone Cancer Center, University of Wisconsin, Madison, WI, USA
| | - Angela C Argento
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Krish Bhadra
- Rees Skillern Cancer Institute, CHI Memorial, Chattanooga, TN, USA
| | - D Kyle Hogarth
- Department of Medicine, University of Chicago, Chicago, IL, USA
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24
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Mass Spectrometry-Based Multivariate Proteomic Tests for Prediction of Outcomes on Immune Checkpoint Blockade Therapy: The Modern Analytical Approach. Int J Mol Sci 2020; 21:ijms21030838. [PMID: 32012941 PMCID: PMC7036840 DOI: 10.3390/ijms21030838] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 01/24/2020] [Accepted: 01/26/2020] [Indexed: 02/06/2023] Open
Abstract
The remarkable success of immune checkpoint inhibitors (ICIs) has given hope of cure for some patients with advanced cancer; however, the fraction of responding patients is 15-35%, depending on tumor type, and the proportion of durable responses is even smaller. Identification of biomarkers with strong predictive potential remains a priority. Until now most of the efforts were focused on biomarkers associated with the assumed mechanism of action of ICIs, such as levels of expression of programmed death-ligand 1 (PD-L1) and mutation load in tumor tissue, as a proxy of immunogenicity; however, their performance is unsatisfactory. Several assays designed to capture the complexity of the disease by measuring the immune response in tumor microenvironment show promise but still need validation in independent studies. The circulating proteome contains an additional layer of information characterizing tumor-host interactions that can be integrated into multivariate tests using modern machine learning techniques. Here we describe several validated serum-based proteomic tests and their utility in the context of ICIs. We discuss test performances, demonstrate their independence from currently used biomarkers, and discuss various aspects of associated biological mechanisms. We propose that serum-based multivariate proteomic tests add a missing piece to the puzzle of predicting benefit from ICIs.
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25
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Morris CB, Poland JC, May JC, McLean JA. Fundamentals of Ion Mobility-Mass Spectrometry for the Analysis of Biomolecules. Methods Mol Biol 2020; 2084:1-31. [PMID: 31729651 DOI: 10.1007/978-1-0716-0030-6_1] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Ion mobility-mass spectrometry (IM-MS) combines complementary size- and mass-selective separations into a single analytical platform. This chapter provides context for both the instrumental arrangements and key application areas that are commonly encountered in bioanalytical settings. New advances in these high-throughput strategies are described with description of complementary informatics tools to effectively utilize these data-intensive measurements. Rapid separations such as these are especially important in systems, synthetic, and chemical biology in which many small molecules are transient and correspond to various biological classes for integrated omics measurements. This chapter highlights the fundamentals of IM-MS and its applications toward biomolecular separations and discusses methods currently being used in the fields of proteomics, lipidomics, and metabolomics.
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Affiliation(s)
- Caleb B Morris
- Department of Chemistry, Center for Innovative Technology, Institute of Chemical Biology, Vanderbilt University, Nashville, TN, USA.,Vanderbilt-Ingram Cancer Center, Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, USA
| | - James C Poland
- Department of Chemistry, Center for Innovative Technology, Institute of Chemical Biology, Vanderbilt University, Nashville, TN, USA.,Vanderbilt-Ingram Cancer Center, Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, USA
| | - Jody C May
- Department of Chemistry, Center for Innovative Technology, Institute of Chemical Biology, Vanderbilt University, Nashville, TN, USA.,Vanderbilt-Ingram Cancer Center, Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, USA
| | - John A McLean
- Department of Chemistry, Center for Innovative Technology, Institute of Chemical Biology, Vanderbilt University, Nashville, TN, USA. .,Vanderbilt-Ingram Cancer Center, Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, USA.
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26
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Grigorieva J, Asmellash S, Oliveira C, Roder H, Net L, Roder J. Application of protein set enrichment analysis to correlation of protein functional sets with mass spectral features and multivariate proteomic tests. CLINICAL MASS SPECTROMETRY 2020. [DOI: 10.1016/j.clinms.2019.09.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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27
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He H, Xu C, Cheng Z, Qian X, Zheng L. Drug Combinatorial Therapies for the Treatment of KRAS Mutated Lung Cancers. Curr Top Med Chem 2019; 19:2128-2142. [PMID: 31475900 DOI: 10.2174/1568026619666190902150555] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 06/23/2019] [Accepted: 07/04/2019] [Indexed: 02/08/2023]
Abstract
KRAS is the most common oncogene to be mutated in lung cancer, and therapeutics directly targeting KRAS have proven to be challenging. The mutations of KRAS are associated with poor prognosis, and resistance to both adjuvant therapy and targeted EGFR TKI. EGFR TKIs provide significant clinical benefit for patients whose tumors bear EGFR mutations. However, tumors with KRAS mutations rarely respond to the EGFR TKI therapy. Thus, combination therapy is essential for the treatment of lung cancers with KRAS mutations. EGFR TKI combined with inhibitors of MAPKs, PI3K/mTOR, HDAC, Wee1, PARP, CDK and Hsp90, even miRNAs and immunotherapy, were reviewed. Although the effects of the combination vary, the combined therapeutics are one of the best options at present to treat KRAS mutant lung cancer.
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Affiliation(s)
- Hao He
- School of Pharmacy, Xi'an Medical University, Xi'an, Shaanxi, China
| | - Chang Xu
- National Vaccine & Serum Institute, Beijing, China
| | - Zhao Cheng
- School of Pharmacy, Xi'an Medical University, Xi'an, Shaanxi, China
| | - Xiaoying Qian
- School of Pharmacy, Xi'an Medical University, Xi'an, Shaanxi, China
| | - Lei Zheng
- School of Pharmacy, Xi'an Medical University, Xi'an, Shaanxi, China
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Abstract
Abstract
Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. This can be achieved by leveraging omics information for accurate molecular characterization of tumors. Tumor tissue biopsies are currently the main source of information for molecular profiling. However, biopsies are invasive and limited in resolving spatiotemporal heterogeneity in tumor tissues. Alternative non-invasive liquid biopsies can exploit patient’s body fluids to access multiple layers of tumor-specific biological information (genomes, epigenomes, transcriptomes, proteomes, metabolomes, circulating tumor cells, and exosomes). Analysis and integration of these large and diverse datasets using statistical and machine learning approaches can yield important insights into tumor biology and lead to discovery of new diagnostic, predictive, and prognostic biomarkers. Translation of these new diagnostic tools into standard clinical practice could transform oncology, as demonstrated by a number of liquid biopsy assays already entering clinical use. In this review, we highlight successes and challenges facing the rapidly evolving field of cancer biomarker research.
Lay Summary
Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. The discovery of biomarkers for precision oncology has been accelerated by high-throughput experimental and computational methods, which can inform fine-grained characterization of tumors for clinical decision-making. Moreover, advances in the liquid biopsy field allow non-invasive sampling of patient’s body fluids with the aim of analyzing circulating biomarkers, obviating the need for invasive tumor tissue biopsies. In this review, we highlight successes and challenges facing the rapidly evolving field of liquid biopsy cancer biomarker research.
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29
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Lazzari C, Gregorc V, Santarpia M. Impact of clinical features of epidermal growth factor receptor (EGFR)-mutated non-small cell lung cancer (NSCLC) patients on osimertinib efficacy. J Thorac Dis 2019; 11:4400-4403. [PMID: 31903227 PMCID: PMC6940227 DOI: 10.21037/jtd.2019.10.67] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Accepted: 10/16/2019] [Indexed: 11/06/2022]
Affiliation(s)
- Chiara Lazzari
- Division of Experimental Medicine, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Vanesa Gregorc
- Division of Experimental Medicine, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Mariacarmela Santarpia
- Medical Oncology Unit, Department of Human Pathology “G. Barresi”, University of Messina, Messina, Italy
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30
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Wu Z, Dai Y, Chen LA. The Prediction Of Epidermal Growth Factor Receptor Mutation And Prognosis Of EGFR Tyrosine Kinase Inhibitor By Serum Ferritin In Advanced NSCLC. Cancer Manag Res 2019; 11:8835-8843. [PMID: 31632143 PMCID: PMC6789963 DOI: 10.2147/cmar.s216037] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Accepted: 08/24/2019] [Indexed: 11/23/2022] Open
Abstract
Purpose To investigate the association between level of serum ferritin (SF) and epidermal growth factor receptor (EGFR) mutations and to analyse the impact of SF level on survival times in advanced non-small-cell lung cancer (NSCLC) patients taking EGFR tyrosine kinase inhibitors (EGFR-TKIs). Methods A total of 301 patients who were admitted to the Chinese PLA general hospital from August 2015 to August 2017 were enrolled. The association between tumour markers, including SF, CEA, and EGFR mutation, and their impact on the prognosis of patients taking EGFR-TKIs was investigated. Results In all patients, the percentage of patients with EGFR mutations was 52.5% (158/301). EGFR mutations were more likely to be detected in younger (<60 years old), adenocarcinoma patients, non-smokers, women, CEA≥5 µg/L and serum ferritin ≥129 µg/L for females or ≥329 µg/L for males (p<0.05). Increased serum ferritin was an independent factor for predicting EGFR mutations (odds ratio (OR)=4.593, 95% CI (2.673–7.890); P <0.001), and an area under curve (AUC) of 0.711 was shown to predict EGFR mutations with a sensitivity of 81.7% and a specificity of 65.2% in women. Sensitivity increased to 91.1% when combining SF and CEA in all patients. SF was also an independent factor (HR=3.531, 95% CI (2.288–5.448); P<0.001) for predicting progression-free survival (PFS) of patients on EGFR-TKIs, analysed by a Cox proportional hazard model, as PFS was shorter in patients with higher SF (15.0 mo. (SF < 129 µg/L for females or <329 for males) vs 10.0 mo. (129–258 µg/L for females or 329–658 µg/L for males) vs 7.3 mo. (>258 µg/L (>258 µg/L for females or >658 µg/L for males) p<0.001). Conclusion SF was a significant predictor of EGFR mutation with moderate diagnostic accuracy, and combining SF and CEA increased the diagnostic sensitivity and specificity for EGFR mutations. SF was also useful for predicting survival in EGFR-TKIs.
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Affiliation(s)
- Zhen Wu
- Respiratory Department, Chinese PLA General Hospital, Beijing, People's Republic of China
| | - Yu Dai
- Respiratory Department, Chinese PLA General Hospital, Beijing, People's Republic of China
| | - Liang-An Chen
- Respiratory Department, Chinese PLA General Hospital, Beijing, People's Republic of China
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31
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The clinical role of VeriStrat testing in patients with advanced non-small cell lung cancer considered unfit for first-line platinum-based chemotherapy. Eur J Cancer 2019; 120:86-96. [PMID: 31499384 PMCID: PMC6859789 DOI: 10.1016/j.ejca.2019.07.025] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Accepted: 07/29/2019] [Indexed: 01/16/2023]
Abstract
Purpose We previously demonstrated that the median survival of patients with poor prognosis non–small cell lung cancer (NSCLC) considered unfit for first-line platinum chemotherapy was <4 months. We evaluated whether VeriStrat could be used as a prognostic or predictive biomarker in this population. Experimental design We conducted a randomised double-blind trial among patients with untreated advanced NSCLC considered unfit for platinum chemotherapy because of poor performance status (PS) or multiple comorbidities. All patients received active supportive care (ASC) and were treated with either oral erlotinib or placebo daily. Five hundred twenty-seven patients had plasma samples for VeriStrat classification: good (VeriStrat Good [VSG]) or poor (VeriStrat Poor [VSP]). Main end-point was overall survival. Results Fifty-five percent patients had VSG, and 83% had Eastern Cooperative Oncology Group (ECOG) 2–3 at baseline. VeriStrat was strongly associated with survival. Among patients managed with ASC only, the adjusted hazard ratio (HR) was 0.54 (p < 0.001) for VSG versus VSP. The association was consistent across patient factors: HR = 0.25 (p = 0.004) and HR = 0.56 (p < 0.001) for ECOG 0–1 and 2–3, respectively, HR = 0.49 (0070 < 0.001) for age≥75 years and HR = 0.59 (p = 0.007) for stage IV. Several ECOG 2–3 patients had long survival: 2-year survival was 8% for VSG patients who had ASC, compared with 0% for VSP. VeriStrat status did not predict benefit from erlotinib treatment because the HRs for erlotinib versus placebo were similar between VSG and VSP patients. Conclusions VeriStrat was not a predictive marker for survival when considering first-line erlotinib for patients with NSCLC who had poor PS and were not recommended for platinum doublet therapies. However, VeriStrat was an independent prognostic marker of survival. It represents an objective measurement that could be considered alongside other patient factors to provide a more refined assessment of prognosis for this particular patient group. VSG patients could be selected for treatment trials because of better survival, while VSP patients can continue to be treated conservatively or offered trials of less toxic agents. Trial registration ISRCTN Number ISRCTN02370070. 83% advanced NSCLC patients unfit for chemotherapy have poor performance status. VeriStrat (proteomic blood test) is an independent prognostic marker for survival. Patients classified as VeriStrat Good were less likely to die than those classified as VeriStrat Poor. VeriStrat can refine patient prognosis in order to alter treatment management.
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32
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Roder H, Oliveira C, Net L, Linstid B, Tsypin M, Roder J. Robust identification of molecular phenotypes using semi-supervised learning. BMC Bioinformatics 2019; 20:273. [PMID: 31138112 PMCID: PMC6540576 DOI: 10.1186/s12859-019-2885-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2018] [Accepted: 05/08/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Modern molecular profiling techniques are yielding vast amounts of data from patient samples that could be utilized with machine learning methods to provide important biological insights and improvements in patient outcomes. Unsupervised methods have been successfully used to identify molecularly-defined disease subtypes. However, these approaches do not take advantage of potential additional clinical outcome information. Supervised methods can be implemented when training classes are apparent (e.g., responders or non-responders to treatment). However, training classes can be difficult to define when assessing relative benefit of one therapy over another using gold standard clinical endpoints, since it is often not clear how much benefit each individual patient receives. RESULTS We introduce an iterative approach to binary classification tasks based on the simultaneous refinement of training class labels and classifiers towards self-consistency. As training labels are refined during the process, the method is well suited to cases where training class definitions are not obvious or noisy. Clinical data, including time-to-event endpoints, can be incorporated into the approach to enable the iterative refinement to identify molecular phenotypes associated with a particular clinical variable. Using synthetic data, we show how this approach can be used to increase the accuracy of identification of outcome-related phenotypes and their associated molecular attributes. Further, we demonstrate that the advantages of the method persist in real world genomic datasets, allowing the reliable identification of molecular phenotypes and estimation of their association with outcome that generalizes to validation datasets. We show that at convergence of the iterative refinement, there is a consistent incorporation of the molecular data into the classifier yielding the molecular phenotype and that this allows a robust identification of associated attributes and the underlying biological processes. CONCLUSIONS The consistent incorporation of the structure of the molecular data into the classifier helps to minimize overfitting and facilitates not only good generalization of classification and molecular phenotypes, but also reliable identification of biologically relevant features and elucidation of underlying biological processes.
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Affiliation(s)
- Heinrich Roder
- Biodesix Inc, 2970 Wilderness Pl, Ste100, Boulder, CO, 80301, USA
| | - Carlos Oliveira
- Biodesix Inc, 2970 Wilderness Pl, Ste100, Boulder, CO, 80301, USA
| | - Lelia Net
- Biodesix Inc, 2970 Wilderness Pl, Ste100, Boulder, CO, 80301, USA
| | - Benjamin Linstid
- Biodesix Inc, 2970 Wilderness Pl, Ste100, Boulder, CO, 80301, USA
| | - Maxim Tsypin
- Biodesix Inc, 2970 Wilderness Pl, Ste100, Boulder, CO, 80301, USA
| | - Joanna Roder
- Biodesix Inc, 2970 Wilderness Pl, Ste100, Boulder, CO, 80301, USA.
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Nagamine A, Araki T, Nagano D, Miyazaki M, Yamamoto K. L-Lactate dehydrogenase B may be a predictive marker for sensitivity to anti-EGFR monoclonal antibodies in colorectal cancer cell lines. Oncol Lett 2019; 17:4710-4716. [PMID: 30944657 DOI: 10.3892/ol.2019.10075] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Accepted: 01/31/2019] [Indexed: 01/19/2023] Open
Abstract
Recently, proteins derived from cancer cells have been widely investigated as biomarkers for predicting the efficacy of chemotherapy. In this study, to identify a sensitive biomarker for the efficacy of anti-epidermal growth factor receptor monoclonal antibodies (anti-EGFR mAbs), proteins derived from 6 colorectal cancer (CRC) cell lines with different sensitivities to cetuximab, an anti-EGFR mAb, were analyzed. Cytoplasmic and membrane proteins extracted from each CRC cell line were digested using trypsin and analyzed comprehensively using mass spectrometry. As a result, 148 and 146 peaks from cytoplasmic proteins and 363 and 267 peaks from membrane proteins were extracted as specific peaks for cetuximab-resistant and -sensitive CRC cell lines, respectively. By analyzing the proteins identified from the peptide peaks, cytoplasmic L-lactate dehydrogenase B (LDHB) was detected as a marker of cetuximab sensitivity, and it was confirmed that LDHB expression was increased in cetuximab-resistant CRC cell lines. Furthermore, LDHB expression levels were significantly upregulated with the acquisition of resistance to cetuximab in cetuximab-sensitive CRC cell lines. In conclusion, LDHB was identified as an important factor affecting cetuximab sensitivity using comprehensive proteome analysis for the first time.
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Affiliation(s)
- Ayumu Nagamine
- Department of Clinical Pharmacology and Therapeutics, Gunma University Graduate School of Medicine, Maebashi, Gunma 371-8511, Japan.,Department of Pharmacy, Gunma University Hospital, Maebashi, Gunma 371-8511, Japan
| | - Takuya Araki
- Department of Clinical Pharmacology and Therapeutics, Gunma University Graduate School of Medicine, Maebashi, Gunma 371-8511, Japan.,Department of Pharmacy, Gunma University Hospital, Maebashi, Gunma 371-8511, Japan
| | - Daisuke Nagano
- Department of Clinical Pharmacology and Therapeutics, Gunma University Graduate School of Medicine, Maebashi, Gunma 371-8511, Japan
| | - Mitsue Miyazaki
- Division of Endocrinology Metabolism and Signal Research, Gunma University Initiative for Advanced Research and Institute for Molecular and Cellular Regulation, Maebashi, Gunma 371-8511, Japan
| | - Koujirou Yamamoto
- Department of Clinical Pharmacology and Therapeutics, Gunma University Graduate School of Medicine, Maebashi, Gunma 371-8511, Japan.,Department of Pharmacy, Gunma University Hospital, Maebashi, Gunma 371-8511, Japan
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Lu S. Development of treatment options for Chinese patients with advanced squamous cell lung cancer: focus on afatinib. Onco Targets Ther 2019; 12:1521-1538. [PMID: 30863118 PMCID: PMC6390854 DOI: 10.2147/ott.s188296] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Lung cancer is the leading cause of cancer death in China, and approximately one third of these cancers are squamous cell carcinoma (SqCC) of the lung. Ethnic diversity and country-specific environmental factors can account for interindividual variations in response to and tolerability of anticancer therapies. Although several targeted therapies have recently been approved for patients with relapsed/refractory SqCC of the lung, only afatinib, an irreversible ErbB family blocker, has data of Chinese patients. In the Phase III LUX-Lung 8 trial, afatinib demonstrated a significant clinical benefit vs the reversible first-generation EGFR tyrosine kinase inhibitor erlotinib in both the overall population and the Chinese subset, with a manageable safety profile. Emerging biomarker data from LUX-Lung 8 suggest that patients with ErbB mutations, especially ErbB2, and those classified as “good” in the VeriStrat® proteomic test, may benefit from afatinib treatment in particular, regardless of ethnicity, and may get a long-term response. In conclusion, afatinib is a valid second-line option for Chinese patients with SqCC of the lung, and specific biomarkers may help guide in treatment decision-making. Ongoing studies will provide further guidance on afatinib’s place in the treatment algorithm, alongside the other novel targeted therapies.
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Affiliation(s)
- Shun Lu
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai 200030, China,
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35
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Cao Y, Li Z, Mao L, Cao H, Kong J, Yu B, Yu C, Liao W. The use of proteomic technologies to study molecular mechanisms of multidrug resistance in cancer. Eur J Med Chem 2019; 162:423-434. [DOI: 10.1016/j.ejmech.2018.10.001] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Revised: 09/27/2018] [Accepted: 10/01/2018] [Indexed: 01/18/2023]
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36
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Belczacka I, Latosinska A, Metzger J, Marx D, Vlahou A, Mischak H, Frantzi M. Proteomics biomarkers for solid tumors: Current status and future prospects. MASS SPECTROMETRY REVIEWS 2019; 38:49-78. [PMID: 29889308 DOI: 10.1002/mas.21572] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Accepted: 05/08/2018] [Indexed: 06/08/2023]
Abstract
Cancer is a heterogeneous multifactorial disease, which continues to be one of the main causes of death worldwide. Despite the extensive efforts for establishing accurate diagnostic assays and efficient therapeutic schemes, disease prevalence is on the rise, in part, however, also due to improved early detection. For years, studies were focused on genomics and transcriptomics, aiming at the discovery of new tests with diagnostic or prognostic potential. However, cancer phenotypic characteristics seem most likely to be a direct reflection of changes in protein metabolism and function, which are also the targets of most drugs. Investigations at the protein level are therefore advantageous particularly in the case of in-depth characterization of tumor progression and invasiveness. Innovative high-throughput proteomic technologies are available to accurately evaluate cancer formation and progression and to investigate the functional role of key proteins in cancer. Employing these new highly sensitive proteomic technologies, cancer biomarkers may be detectable that contribute to diagnosis and guide curative treatment when still possible. In this review, the recent advances in proteomic biomarker research in cancer are outlined, with special emphasis placed on the identification of diagnostic and prognostic biomarkers for solid tumors. In view of the increasing number of screening programs and clinical trials investigating new treatment options, we discuss the molecular connections of the biomarkers as well as their potential as clinically useful tools for diagnosis, risk stratification and therapy monitoring of solid tumors.
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Affiliation(s)
- Iwona Belczacka
- Mosaiques-Diagnostics GmbH, Hannover, Germany
- University Hospital RWTH Aachen, Institute for Molecular Cardiovascular Research (IMCAR), Aachen, Germany
| | | | | | - David Marx
- Hôpitaux Universitaires de Strasbourg, Service de Transplantation Rénale, Strasbourg, France
- Laboratoire de Spectrométrie de Masse BioOrganique (LSMBO), University of Strasbourg, National Center for Scientific Research (CNRS), Institut Pluridisciplinaire Hubert Curien (IPHC) UMR 7178, Strasbourg, France
| | - Antonia Vlahou
- Biotechnology Division, Biomedical Research Foundation, Academy of Athens (BRFAA), Athens, Greece
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37
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Wang Z, Zhang X. Single Cell Proteomics for Molecular Targets in Lung Cancer: High-Dimensional Data Acquisition and Analysis. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2018; 1068:73-87. [PMID: 29943297 DOI: 10.1007/978-981-13-0502-3_7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
In the proteomic and genomic era, lung cancer researchers are increasingly under challenge with traditional protein analyzing tools. High output, multiplexed analytical procedures are in demand for disclosing the post-translational modification, molecular interactions and signaling pathways of proteins precisely, specifically, dynamically and systematically, as well as for identifying novel proteins and their functions. This could be better realized by single-cell proteomic methods than conventional proteomic methods. Using single-cell proteomic tools including flow cytometry, mass cytometry, microfluidics and chip technologies, chemical cytometry, single-cell western blotting, the quantity and functions of proteins are analyzed simultaneously. Aside from deciphering disease mechanisms, single-cell proteomic techniques facilitate the identification and screening of biomarkers, molecular targets and promising compounds as well. This review summarized single-cell proteomic tools and their use in lung cancer.
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Affiliation(s)
- Zheng Wang
- Department of Respiratory and Critical Care Medicine, Zhengzhou University People's Hospital, Zhengzhou, China
| | - Xiaoju Zhang
- Department of Respiratory and Critical Care Medicine, Zhengzhou University People's Hospital, Zhengzhou, China. .,Biomedical Research Center, Zhengzhou University People's Hospital, Zhengzhou, China.
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38
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Hughes NP, Xu L, Nielsen CH, Chang E, Hori SS, Natarajan A, Lee S, Kjær A, Kani K, Wang SX, Mallick P, Gambhir SS. A blood biomarker for monitoring response to anti-EGFR therapy. Cancer Biomark 2018; 22:333-344. [PMID: 29689709 DOI: 10.3233/cbm-171149] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
BACKGROUND AND OBJECTIVE To monitor therapies targeted to epidermal growth factor receptors (EGFR) in non-small cell lung cancer (NSCLC), we investigated Peroxiredoxin 6 (PRDX6) as a biomarker of response to anti-EGFR agents. METHODS We studied cells that are sensitive (H3255, HCC827) or resistant (H1975, H460) to gefitinib. PRDX6 was examined with either gefitinib or vehicle treatment using enzyme-linked immunosorbent assays. We created xenograft models from one sensitive (HCC827) and one resistant cell line (H1975) and monitored serum PRDX6 levels during treatment. RESULTS PRDX6 levels in cell media from sensitive cell lines increased significantly after gefitinib treatment vs. vehicle, whereas there was no significant difference for resistant lines. PRDX6 accumulation over time correlated positively with gefitinib sensitivity. Serum PRDX6 levels in gefitinib-sensitive xenograft models increased markedly during the first 24 hours of treatment and then decreased dramatically during the following 48 hours. Differences in serum PRDX6 levels between vehicle and gefitinib-treated animals could not be explained by differences in tumor burden. CONCLUSIONS Our results show that changes in serum PRDX6 during the course of gefitinib treatment of xenograft models provide insight into tumor response and such an approach offers several advantages over imaging-based strategies for monitoring response to anti-EGFR agents.
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Affiliation(s)
- Nicholas P Hughes
- Molecular Imaging Program at Stanford, Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.,Molecular Imaging Program at Stanford, Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Lingyun Xu
- Molecular Imaging Program at Stanford, Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.,Canary Center at Stanford for Cancer Early Detection, Palo Alto, CA, USA.,Molecular Imaging Program at Stanford, Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Carsten H Nielsen
- Molecular Imaging Program at Stanford, Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.,Department of Clinical Physiology, Nuclear Medicine and PET, Center for Diagnostic Investigations, Rigshospitalet, Copenhagen, Denmark.,Cluster for Molecular Imaging, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark.,Molecular Imaging Program at Stanford, Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Edwin Chang
- Molecular Imaging Program at Stanford, Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.,Canary Center at Stanford for Cancer Early Detection, Palo Alto, CA, USA.,Molecular Imaging Program at Stanford, Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Sharon S Hori
- Molecular Imaging Program at Stanford, Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.,Canary Center at Stanford for Cancer Early Detection, Palo Alto, CA, USA
| | - Arutselvan Natarajan
- Molecular Imaging Program at Stanford, Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.,Canary Center at Stanford for Cancer Early Detection, Palo Alto, CA, USA
| | - Samantha Lee
- Canary Center at Stanford for Cancer Early Detection, Palo Alto, CA, USA
| | - Andreas Kjær
- Department of Clinical Physiology, Nuclear Medicine and PET, Center for Diagnostic Investigations, Rigshospitalet, Copenhagen, Denmark.,Cluster for Molecular Imaging, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kian Kani
- Lawrence J. Ellison Institute of Transformative Medicine, University of Southern California, Los Angeles, CA, USA
| | - Shan X Wang
- Department of Bioengineering, Stanford University, Stanford, CA, USA.,Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA
| | - Parag Mallick
- Molecular Imaging Program at Stanford, Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.,Canary Center at Stanford for Cancer Early Detection, Palo Alto, CA, USA
| | - Sanjiv Sam Gambhir
- Molecular Imaging Program at Stanford, Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.,Canary Center at Stanford for Cancer Early Detection, Palo Alto, CA, USA.,Department of Bioengineering, Stanford University, Stanford, CA, USA.,Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA
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39
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Buttigliero C, Shepherd FA, Barlesi F, Schwartz B, Orlov S, Favaretto AG, Santoro A, Hirsh V, Ramlau R, Blackler AR, Roder J, Spigel D, Novello S, Akerley W, Scagliotti GV. Retrospective Assessment of a Serum Proteomic Test in a Phase III Study Comparing Erlotinib plus Placebo with Erlotinib plus Tivantinib (MARQUEE) in Previously Treated Patients with Advanced Non-Small Cell Lung Cancer. Oncologist 2018; 24:e251-e259. [PMID: 30139835 DOI: 10.1634/theoncologist.2018-0089] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Accepted: 07/05/2018] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND The VeriStrat test provides accurate predictions of outcomes in all lines of therapy for patients with non-small cell lung cancer (NSCLC). We investigated the predictive and prognostic role of VeriStrat in patients enrolled on the MARQUEE phase III trial of tivantinib plus erlotinib (T+E) versus placebo plus erlotinib (P+E) in previously treated patients with advanced NSCLC. METHODS Pretreatment plasma samples were available for 996 patients and were analyzed by matrix-assisted laser desorption/ionization-time of flight mass spectrometry to generate VeriStrat labels (good, VS-G, or poor, VS-P). RESULTS Overall, no significant benefit in overall survival (OS) and progression-free survival (PFS) were observed for the addition of tivantinib to erlotinib. Regardless of treatment arm, patients who were classified as VS-G had significantly longer PFS (3.8 mo for T+E arm, 2.0 mo for P+E arm) and OS (11.6 mo for T+E, 10.2 mo for P+E arm) than patients classified as VS-P (PFS: 1.9 mo for both arms, hazard ratio [HR], 0.584; 95% confidence interval [CI], 0.468-0.733; p < .0001 for T+E, HR, 0.686; 95% CI, 0.546-0.870; p = .0015 for P+E; OS: 4.0 mo for both arms, HR, 0.333; 95% CI, 0.264-0.422; p < .0001 for T+E; HR, 0.449; 95% CI, 0.353-0.576; p < .0001 for P+E). The VS-G population had higher OS than the VS-P population within Eastern Cooperative Oncology Group (ECOG) performance score (PS) categories. VS-G patients on the T+E arm had longer PFS, but not OS, than VS-G patients on the P+E arm (p = .0108). Among EGFR mutation-positive patients, those with VS-G status had a median OS more than twice that of any other group (OS: 31.6 mo for T+E and 22.8 mo for P+E), whereas VS-P patients had similar survival rates as VS-G, EGFR-wild type patients (OS: 13.7 mo for T+E and 6.5 mo for P+E). CONCLUSION In these analyses, VeriStrat showed a prognostic role within EGOC PS categories and regardless of treatment arm and EGFR status, suggesting that VeriStrat could be used to identify EGFR mutation-positive patients who will have a poor response to EGFR tyrosine kinase inhibitors. IMPLICATIONS FOR PRACTICE This study suggests that VeriStrat testing could enhance the prognostic role of performance status and smoking status and replicates findings from other trials that showed that the VeriStrat test identifies EGFR mutation-positive patients likely to have a poor response to EGFR tyrosine kinase inhibitors (TKIs). Although these findings should be confirmed in other populations, VeriStrat use could be considered in EGFR mutation-positive patients as an additional prognostic tool, and these results suggest that EGFR mutation-positive patients with VeriStrat "poor" classification could benefit from other therapeutic agents given in conjunction with TKI monotherapy.
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Affiliation(s)
- Consuelo Buttigliero
- Division of Medical Oncology, Department of Oncology, University of Torino at San Luigi Gonzaga Hospital, Turin, Italy
| | | | | | | | - Sergey Orlov
- St. Petersburg State Medical University, St. Petersburg, Russian Federation
| | | | | | - Vera Hirsh
- McGill University Health Centre, Montreal, Canada
| | - Rodryg Ramlau
- Oncology Department, Poznan University of Medical Sciences, Poznan, Poland
| | | | | | - David Spigel
- Tennessee Oncology Associates, Nashville, Tennessee, USA
| | - Silvia Novello
- Division of Medical Oncology, Department of Oncology, University of Torino at San Luigi Gonzaga Hospital, Turin, Italy
| | | | - Giorgio V Scagliotti
- Division of Medical Oncology, Department of Oncology, University of Torino at San Luigi Gonzaga Hospital, Turin, Italy
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40
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An J, Tang CH, Wang N, Liu Y, Lv J, Xu B, Li XY, Guo WF, Gao HJ, He K, Liu XQ. Serum peptide expression and treatment responses in patients with advanced non-small-cell lung cancer. Oncol Lett 2018; 15:9307-9316. [PMID: 29844828 DOI: 10.3892/ol.2018.8460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Accepted: 01/19/2018] [Indexed: 11/05/2022] Open
Abstract
Epidermal growth factor receptor (EGFR) mutation is an important predictor for response to personalized treatments of patients with advanced non-small-cell lung cancer (NSCLC). However its usage is limited due to the difficult of obtaining tissue specimens. A novel prediction system using matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has been reported to be a perspective tool in European countries to identify patients who are likely to benefit from EGFR tyrosine kinase inhibitor (TKI) treatment. In the present study, MALDI-TOF MS was used on pretreatment serum samples of patients with advanced non-small-cell lung cancer to discriminate the spectra between disease control and disease progression groups in one cohort of Chinese patients. The candidate features for classification were subsequently validated in a blinded fashion in another set of patients. The correlation between plasma EGFR mutation status and the intensities of representative spectra for classification was evaluated. A total of 103 patients that were treated with EGFR-TKIs were included. It was determined that 8 polypeptides peaks were significant different between the disease control and disease progression group. A total of 6 polypeptides were established in the classification algorithm. The sensitivity of the algorithm to predict treatment responses was 76.2% (16/21) and the specificity was 81.8% (18/22). The accuracy rate of the algorithm was 79.1% (34/43). A total of 3 polypeptides were significantly correlated with EGFR mutations (P=0.04, P=0.03 and P=0.04, respectively). The present study confirmed that MALDI-TOF MS analysis can be used to predict responses to EGFR-TKI treatment of the Asian population where the EGFR mutation status differs from the European population. Furthermore, the expression intensities of the three polypeptides in the classification model were associated with EGFR mutation.
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Affiliation(s)
- Juan An
- Department of Lung Cancer, Affiliated Hospital of Academy of Military Medical Sciences, Beijing 100071, P.R. China.,Department of Oncology, The General Hospital of PLA Rocket Force, Beijing 100088, P.R. China
| | - Chuan-Hao Tang
- Department of Lung Cancer, Affiliated Hospital of Academy of Military Medical Sciences, Beijing 100071, P.R. China
| | - Na Wang
- National Center of Biomedical Analysis, Academy of Military Medical Sciences, Beijing 100850, P.R. China
| | - Yi Liu
- Department of Oncology, Affiliated Hospital of Academy of Military Medical Sciences, Beijing 100071, P.R. China
| | - Jin Lv
- Department of Oncology, The General Hospital of PLA Rocket Force, Beijing 100088, P.R. China
| | - Bin Xu
- National Center of Biomedical Analysis, Academy of Military Medical Sciences, Beijing 100850, P.R. China
| | - Xiao-Yan Li
- Department of Lung Cancer, Affiliated Hospital of Academy of Military Medical Sciences, Beijing 100071, P.R. China
| | - Wan-Feng Guo
- Department of Lung Cancer, Affiliated Hospital of Academy of Military Medical Sciences, Beijing 100071, P.R. China
| | - Hong-Jun Gao
- Department of Lung Cancer, Affiliated Hospital of Academy of Military Medical Sciences, Beijing 100071, P.R. China
| | - Kun He
- National Center of Biomedical Analysis, Academy of Military Medical Sciences, Beijing 100850, P.R. China
| | - Xiao-Qing Liu
- Department of Lung Cancer, Affiliated Hospital of Academy of Military Medical Sciences, Beijing 100071, P.R. China
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Spigel DR, Burris HA, Greco FA, Shih KC, Gian VG, Lipman AJ, Daniel DB, Waterhouse DM, Finney L, Heymach JV, Hainsworth JD. Erlotinib plus either pazopanib or placebo in patients with previously treated advanced non-small cell lung cancer: A randomized, placebo-controlled phase 2 trial with correlated serum proteomic signatures. Cancer 2018; 124:2355-2364. [PMID: 29645086 DOI: 10.1002/cncr.31290] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Revised: 11/06/2017] [Accepted: 11/14/2017] [Indexed: 11/07/2022]
Abstract
BACKGROUND This study compared the efficacy and safety of treatment with erlotinib plus pazopanib versus erlotinib plus placebo in patients with previously treated advanced non-small cell lung cancer (NSCLC). METHODS Patients with progressive-stage IV NSCLC after either 1 or 2 previous chemotherapy regimens were randomized to receive erlotinib (150 mg by mouth daily) with either pazopanib (600 mg by mouth daily) or placebo. During treatment, patients were evaluated every 8 weeks until disease progression or unacceptable toxicity. After a study amendment, pretreatment serum specimens for the VeriStrat assay were collected. The predictive value of the VeriStrat score (good vs poor) for progression-free survival (PFS) and overall survival (OS) was assessed in the overall population and in each treatment group. RESULTS One hundred ninety-two eligible patients were randomized between February 2010 and February 2011. PFS was prolonged with erlotinib plus pazopanib versus erlotinib plus placebo (median, 2.6 vs 1.8 months; hazard ratio, 0.58; P = .001). There was no difference in the OS of the 2 groups. A good VeriStrat score predicted longer PFS and OS in the entire group and predicted longer PFS in the subgroup receiving erlotinib plus pazopanib. The addition of pazopanib increased toxicity, and this was consistent with the known toxicity profile. CONCLUSIONS The addition of pazopanib to erlotinib in an unselected group of patients with previously treated NSCLC improved PFS and increased treatment-related toxicity, but it had no influence on OS. The efficacy of both regimens was modest. Patients receiving erlotinib plus pazopanib had longer PFS if they had a good VeriStrat score versus a poor one. Cancer 2018;124:2355-64. © 2018 American Cancer Society.
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Affiliation(s)
- David R Spigel
- Sarah Cannon Research Institute, Nashville, Tennessee.,Tennessee Oncology, Nashville, Tennessee
| | - Howard A Burris
- Sarah Cannon Research Institute, Nashville, Tennessee.,Tennessee Oncology, Nashville, Tennessee
| | - F Anthony Greco
- Sarah Cannon Research Institute, Nashville, Tennessee.,Tennessee Oncology, Nashville, Tennessee
| | - Kent C Shih
- Sarah Cannon Research Institute, Nashville, Tennessee.,Tennessee Oncology, Nashville, Tennessee
| | - Victor G Gian
- Sarah Cannon Research Institute, Nashville, Tennessee.,Tennessee Oncology, Nashville, Tennessee
| | - Andrew J Lipman
- Sarah Cannon Research Institute, Nashville, Tennessee.,Florida Cancer Specialists, Naples, Florida
| | - Davey B Daniel
- Sarah Cannon Research Institute, Nashville, Tennessee.,Tennessee Oncology, Chattanooga, Tennessee
| | | | | | - John V Heymach
- The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - John D Hainsworth
- Sarah Cannon Research Institute, Nashville, Tennessee.,Tennessee Oncology, Nashville, Tennessee
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Song J, Shi J, Dong D, Fang M, Zhong W, Wang K, Wu N, Huang Y, Liu Z, Cheng Y, Gan Y, Zhou Y, Zhou P, Chen B, Liang C, Liu Z, Li W, Tian J. A New Approach to Predict Progression-free Survival in Stage IV EGFR-mutant NSCLC Patients with EGFR-TKI Therapy. Clin Cancer Res 2018; 24:3583-3592. [PMID: 29563137 DOI: 10.1158/1078-0432.ccr-17-2507] [Citation(s) in RCA: 121] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Revised: 12/16/2017] [Accepted: 03/16/2018] [Indexed: 02/05/2023]
Abstract
Purpose: We established a CT-derived approach to achieve accurate progression-free survival (PFS) prediction to EGFR tyrosine kinase inhibitors (TKI) therapy in multicenter, stage IV EGFR-mutated non-small cell lung cancer (NSCLC) patients.Experimental Design: A total of 1,032 CT-based phenotypic characteristics were extracted according to the intensity, shape, and texture of NSCLC pretherapy images. On the basis of these CT features extracted from 117 stage IV EGFR-mutant NSCLC patients, a CT-based phenotypic signature was proposed using a Cox regression model with LASSO penalty for the survival risk stratification of EGFR-TKI therapy. The signature was validated using two independent cohorts (101 and 96 patients, respectively). The benefit of EGFR-TKIs in stratified patients was then compared with another stage-IV EGFR-mutant NSCLC cohort only treated with standard chemotherapy (56 patients). Furthermore, an individualized prediction model incorporating the phenotypic signature and clinicopathologic risk characteristics was proposed for PFS prediction, and also validated by multicenter cohorts.Results: The signature consisted of 12 CT features demonstrated good accuracy for discriminating patients with rapid and slow progression to EGFR-TKI therapy in three cohorts (HR: 3.61, 3.77, and 3.67, respectively). Rapid progression patients received EGFR TKIs did not show significant difference with patients underwent chemotherapy for progression-free survival benefit (P = 0.682). Decision curve analysis revealed that the proposed model significantly improved the clinical benefit compared with the clinicopathologic-based characteristics model (P < 0.0001).Conclusions: The proposed CT-based predictive strategy can achieve individualized prediction of PFS probability to EGFR-TKI therapy in NSCLCs, which holds promise of improving the pretherapy personalized management of TKIs. Clin Cancer Res; 24(15); 3583-92. ©2018 AACR.
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Affiliation(s)
- Jiangdian Song
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Medical Informatics, China Medical University, Shenyang, Liaoning, China.,Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, Liaoning, China
| | - Jingyun Shi
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Mengjie Fang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Wenzhao Zhong
- Guangdong Lung Cancer Institute, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Kun Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Ning Wu
- PET-CT center, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yanqi Huang
- Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Yue Cheng
- Department of Respiratory and Critical Care Medicine, West China Hospital, Chengdu, China
| | - Yuncui Gan
- Department of Respiratory and Critical Care Medicine, West China Hospital, Chengdu, China
| | - Yongzhao Zhou
- Department of Respiratory and Critical Care Medicine, West China Hospital, Chengdu, China
| | - Ping Zhou
- Department of Respiratory and Critical Care Medicine, West China Hospital, Chengdu, China
| | - Bojiang Chen
- Department of Respiratory and Critical Care Medicine, West China Hospital, Chengdu, China
| | - Changhong Liang
- Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zaiyi Liu
- Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, West China Hospital, Chengdu, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China. .,University of Chinese Academy of Sciences, Beijing, China.,Beijing Key Laboratory of Molecular Imaging, Beijing, China
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43
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Fidler MJ, Fhied CL, Roder J, Basu S, Sayidine S, Fughhi I, Pool M, Batus M, Bonomi P, Borgia JA. The serum-based VeriStrat® test is associated with proinflammatory reactants and clinical outcome in non-small cell lung cancer patients. BMC Cancer 2018; 18:310. [PMID: 29558888 PMCID: PMC5861613 DOI: 10.1186/s12885-018-4193-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2017] [Accepted: 03/06/2018] [Indexed: 12/15/2022] Open
Abstract
Background The VeriStrat test is a serum proteomic signature originally discovered in non-responders to second line gefitinib treatment and subsequently used to predict differential benefit from erlotinib versus chemotherapy in previously treated advanced non-small cell lung cancer (NSCLC). Multiple studies highlight the clinical utility of the VeriStrat test, however, the mechanistic connection between VeriStrat-poor classification and poor prognosis in untreated and previously treated patients is still an active area of research. The aim of this study was to correlate VeriStrat status with other circulating biomarkers in advanced NSCLC patients – each with respect to clinical outcomes. Methods Serum samples were prospectively collected from 57 patients receiving salvage chemotherapy and 70 non-EGFR mutated patients receiving erlotinib. Patients were classified as either VeriStrat good or poor based on the VeriStrat test. Luminex immunoassays were used to measure circulating levels of 102 distinct biomarkers implicated in tumor aggressiveness and treatment resistance. A Cox PH model was used to evaluate associations between biomarker levels and clinical outcome, whereas the association of VeriStrat classifications with biomarker levels was assessed via the Mann-Whitney Rank Sum test. Results VeriStrat was prognostic for outcome within the erlotinib treated patients (HR = 0.29, p < 0.0001) and predictive of differential treatment benefit between erlotinib and chemotherapy ((interaction HR = 0.25; interaction p = 0.0035). A total of 27 biomarkers out of 102 unique analytes were found to be significantly associated with OS (Cox PH p ≤ 0.05), whereas 16 biomarkers were found to be associated with PFS. Thrombospondin-2, C-reactive protein, TNF-receptor I, and placental growth factor were the analytes most highly associated with OS, all with Cox PH p-values ≤0.0001. VeriStrat status was found to be significantly associated with 23 circulating biomarkers (Mann-Whitney Rank Sum p ≤ 0.05), 6 of which had p < 0.001, including C-reactive protein, IL-6, serum amyloid A, CYFRA 21.1, IGF-II, osteopontin, and ferritin. Conclusions Strong associations were observed between survival and VeriStrat classifications as well as select circulating biomarkers associated with fibrosis, inflammation, and acute phase reactants as part of this study. The associations between these biomarkers and VeriStrat classification might have therapeutic implications for poor prognosis NSCLC patients, particularly with new immunotherapeutic treatment options. Electronic supplementary material The online version of this article (10.1186/s12885-018-4193-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Mary Jo Fidler
- Sections of Medical Oncology at Rush University Medical Center, Chicago, USA
| | | | | | - Sanjib Basu
- Preventative Medicine, Rush University Medical Center, Chicago, USA
| | | | - Ibtihaj Fughhi
- Sections of Medical Oncology at Rush University Medical Center, Chicago, USA
| | - Mark Pool
- Pathology, Rush University Medical Center, Chicago, USA
| | - Marta Batus
- Sections of Medical Oncology at Rush University Medical Center, Chicago, USA
| | - Philip Bonomi
- Sections of Medical Oncology at Rush University Medical Center, Chicago, USA
| | - Jeffrey A Borgia
- Pathology, Rush University Medical Center, Chicago, USA. .,Cell and Molecular Medicine at Rush University Medical Center, Il, Chicago, 60612, USA. .,Departments of Pathology and Cell & Molecular Medicine, Rush University Medical Center, 570 Jelke Southcenter Bldg.,1750 W. Harrison St, Chicago, IL, 60612, USA.
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44
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Grossi F, Genova C, Rijavec E, Barletta G, Biello F, Dal Bello MG, Meyer K, Roder J, Roder H, Grigorieva J. Prognostic role of the VeriStrat test in first line patients with non-small cell lung cancer treated with platinum-based chemotherapy. Lung Cancer 2018; 117:64-69. [DOI: 10.1016/j.lungcan.2017.12.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Revised: 09/23/2017] [Accepted: 12/12/2017] [Indexed: 01/29/2023]
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45
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Wu DC, Wang KY, Wang SSW, Huang CM, Lee YW, Chen MI, Chuang SA, Chen SH, Lu YW, Lin CC, Lee KW, Hsu WH, Wu KP, Chen YJ. Exploring the expression bar code of SAA variants for gastric cancer detection. Proteomics 2018; 17. [PMID: 28493537 DOI: 10.1002/pmic.201600356] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2016] [Revised: 03/18/2017] [Accepted: 04/26/2017] [Indexed: 12/30/2022]
Abstract
We reported an integrated platform to explore serum protein variant pattern in cancer and its utility as a new class of biomarker panel for diagnosis. On the model study of serum amyloid A (SAA), we employed nanoprobe-based affinity mass spectrometry for enrichment, identification and quantitation of SAA variants from serum of 105 gastric cancer patients in comparison with 54 gastritis patients, 54 controls, and 120 patients from other cancer. The result revealed surprisingly heterogeneous and most comprehensive SAA bar code to date, which comprises 24 SAA variants including SAA1- and SAA2-encoded products, polymorphic isoforms, N-terminal-truncated forms, and three novel SAA oxidized isotypes, in which the variant-specific peptide sequence were also confirmed by LC-MS/MS. A diagnostic model was developed for dimension reduction and computational classification of the 24 SAA-variant bar code, providing good discrimination (AUC = 0.85 ± 3.2E-3) for differentiating gastric cancer group from gastritis and normal groups (sensitivity, 0.76; specificity, 0.81) and was validated with external validation cohort (sensitivity, 0.71; specificity, 0.74). Our platform not only shed light on the occurrence and modification extent of under-represented serum protein variants in cancer, but also suggested a new concept of diagnostic platform by serum protein variant profile.
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Affiliation(s)
- Deng-Chyang Wu
- Division of Gastroenterology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.,Cancer Center, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
| | - Kai-Yi Wang
- Department of Chemistry, National Taiwan University, Taipei, Taiwan.,Nano Science and Technology Program, Taiwan International Graduate Program, Academia Sinica and National Taiwan University, Taipei, Taiwan
| | - Sophie S W Wang
- Division of Gastroenterology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.,Cancer Center, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
| | - Ching-Min Huang
- Institute of Biomedical Informatics, National Yang Ming University, Taipei, Taiwan
| | - Yi-Wei Lee
- Institute of Biomedical Informatics, National Yang Ming University, Taipei, Taiwan
| | | | - Szu-An Chuang
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
| | - Shu-Hua Chen
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
| | - Ying-Wei Lu
- Department of Chemistry, National Tsing Hua University, Hsinchu, Taiwan
| | - Chun-Cheng Lin
- Department of Chemistry, National Tsing Hua University, Hsinchu, Taiwan
| | - Ka-Wo Lee
- Department of Otolaryngology, Kaohsiung Medical University Hospital and Department of Otolaryngology, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Wen-Hung Hsu
- Division of Gastroenterology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Kun-Pin Wu
- Institute of Biomedical Informatics, National Yang Ming University, Taipei, Taiwan
| | - Yu-Ju Chen
- Department of Chemistry, National Taiwan University, Taipei, Taiwan.,Nano Science and Technology Program, Taiwan International Graduate Program, Academia Sinica and National Taiwan University, Taipei, Taiwan.,Institute of Chemistry, Academia Sinica, Taipei, Taiwan
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46
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Bronte G, Franchina T, Alù M, Sortino G, Celesia C, Passiglia F, Savio G, Laudani A, Russo A, Picone A, Rizzo S, De Tursi M, Gambale E, Bazan V, Natoli C, Blasi L, Adamo V, Russo A. The comparison of outcomes from tyrosine kinase inhibitor monotherapy in second- or third-line for advanced non-small-cell lung cancer patients with wild-type or unknown EGFR status. Oncotarget 2017; 7:35803-35812. [PMID: 26993607 PMCID: PMC5094963 DOI: 10.18632/oncotarget.8130] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2016] [Accepted: 02/28/2016] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND Second-line treatment for advanced non-small-cell lung cancer (NSCLC) patients includes monotherapy with a third-generation cytotoxic drug (CT) or a tyrosine kinase inhibitor (TKI). These options are the actual standard for EGFR wild-type (WT) status, as patients with EGFR mutations achieve greater benefit by the use of TKI in first-line treatment. Some clinical trials and meta-analyses investigated the comparison between CT and TKI in second-line, but data are conflicting. METHODS We designed a retrospective trial to gather information about TKI sensitivity in comparison with CT. We selected from clinical records patients treated with at least 1 line of CT and at least 1 line of TKI. We collected data about age, sex, performance status, comorbidity, smoking status, histotype, metastatic sites, EGFR status, treatment schedule, better response and time-to-progression (TTP) for each line of treatment and overall survival (OS). RESULTS 93 patients met selection criteria. Mean age 66,7 (range: 46-84). M/F ratio is 3:1. 39 EGFR-WT and 54 EGFR-UK. All patients received erlotinib or gefitinib as second-line treatment or erlotinib as third-line treatment. No TTP differences were observed for both second-line (HR:0,91; p = 0,6333) and third-line (HR:1.1; p = 0,6951) treatment (TKI vs CT). A trend of a benefit in OS in favor of 3rd-line TKI (HR:0,68; p = 0,11). CONCLUSIONS This study explores the role of TKIs in EGFR non-mutated NSCLC patients. OS analysis highlights a trend to a benefit in patients who received TKI in third-line, even if this result is statistically non-significant. Further analysis are needed to find an explanation for this observation.
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Affiliation(s)
- Giuseppe Bronte
- Department of Surgical, Oncological and Oral Sciences, University of Palermo, Palermo, Italy
| | - Tindara Franchina
- Medical Oncology Unit-AOOR Papardo-Piemonte, Messina and Department of Human Pathology, University of Messina, Messina, Italy
| | | | - Giovanni Sortino
- Department of Surgical, Oncological and Oral Sciences, University of Palermo, Palermo, Italy
| | - Claudia Celesia
- Department of Surgical, Oncological and Oral Sciences, University of Palermo, Palermo, Italy
| | - Francesco Passiglia
- Department of Surgical, Oncological and Oral Sciences, University of Palermo, Palermo, Italy
| | | | - Agata Laudani
- Medical Oncology Unit, A.R.N.A.S. Civico, Palermo, Italy
| | - Alessandro Russo
- Medical Oncology Unit-AOOR Papardo-Piemonte, Messina and Department of Human Pathology, University of Messina, Messina, Italy
| | - Antonio Picone
- Medical Oncology Unit-AOOR Papardo-Piemonte, Messina and Department of Human Pathology, University of Messina, Messina, Italy
| | - Sergio Rizzo
- Department of Surgical, Oncological and Oral Sciences, University of Palermo, Palermo, Italy
| | - Michele De Tursi
- Department of Medical, Oral and Biotechnological Sciences, University "G. D'Annunzio", Chieti, Italy
| | - Elisabetta Gambale
- Department of Medical, Oral and Biotechnological Sciences, University "G. D'Annunzio", Chieti, Italy
| | - Viviana Bazan
- Department of Surgical, Oncological and Oral Sciences, University of Palermo, Palermo, Italy
| | - Clara Natoli
- Department of Medical, Oral and Biotechnological Sciences, University "G. D'Annunzio", Chieti, Italy
| | - Livio Blasi
- Medical Oncology Unit, A.R.N.A.S. Civico, Palermo, Italy
| | - Vincenzo Adamo
- Medical Oncology Unit-AOOR Papardo-Piemonte, Messina and Department of Human Pathology, University of Messina, Messina, Italy
| | - Antonio Russo
- Department of Surgical, Oncological and Oral Sciences, University of Palermo, Palermo, Italy
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Abstract
Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.
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48
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Su DW, Nieva J. Biophysical technologies for understanding circulating tumor cell biology and metastasis. Transl Lung Cancer Res 2017; 6:473-485. [PMID: 28904890 DOI: 10.21037/tlcr.2017.05.08] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
An understanding of cancer evolution in lung cancer with its associated resistance to therapy can only be achieved with repeated sampling and analysis of the cancer. Given the high risks and costs associated with repeat physical biopsy, alternative technologies must be applied. Several modalities exist for analysis and re-analysis of cancer biology. Among them are the CellSearch platform, the CTC chip, and the high-definition CTC platform. While the former is primarily able to provide prognosticating information in the form of CTC enumeration, the latter two have the advantage of serving as a platform to study tumor biology. Techniques for analysis of single cell genomics, as well as protein expression on a single cell basis provide scientists with the capacity to understand cancer cell populations as a collection of individual cells, rather than as an average of all cells. A multimodal combination of circulating tumor DNAs (ctDNAs), CTCs, proteomics, and CTC-derived xenografts (CDXs) to create computational models useful in diagnosis, prognostication, and predictiveness to treatment is likely the future of tailoring individualized cancer care.
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Affiliation(s)
- Derrick W Su
- Norris Cancer Center, University of Southern California, Los Angeles, USA
| | - Jorge Nieva
- Norris Cancer Center, University of Southern California, Los Angeles, USA
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49
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Abstract
Lung cancer is the most common cause of cancer-related death worldwide, less than 7% of patients survive 10 years following diagnosis across all stages of lung cancer. Late stage of diagnosis and lack of effective and personalized medicine reflect the need for a better understanding of the mechanisms that underlie lung cancer progression. Quantitative proteomics provides the relative different protein abundance in normal and cancer patients which offers the information for molecular interactions, signaling pathways, and biomarker identification. Here we introduce both theoretical and practical applications in the use of quantitative proteomics approaches, with principles of current technologies and methodologies including gel-based, label free, stable isotope labeling as well as targeted proteomics. Predictive markers of drug resistance, candidate biomarkers for diagnosis, and prognostic markers in lung cancer have also been discovered and analyzed by quantitative proteomic analysis. Moreover, construction of protein networks enables to provide an opportunity to interpret disease pathway and improve our understanding in cancer therapeutic strategies, allowing the discovery of molecular markers and new therapeutic targets for lung cancer.
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Affiliation(s)
| | - Hsueh-Fen Juan
- Institute of Molecular and Cellular Biology, National Taiwan University, Taipei, Taiwan. .,Department of Life Science, National Taiwan University, Taipei, Taiwan. .,Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan.
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50
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Wang L, Tang C, Xu B, Yang L, Qu L, Li L, Li X, Wang W, Qin H, Gao H, He K, Liu X. Mass spectrometry-based serum peptidome profiling accurately and reliably predicts outcomes of pemetrexed plus platinum chemotherapy in patients with advanced lung adenocarcinoma. PLoS One 2017; 12:e0179000. [PMID: 28594947 PMCID: PMC5464620 DOI: 10.1371/journal.pone.0179000] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2017] [Accepted: 05/22/2017] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Although pemetrexed plus cis/carboplatin has become the most effective chemotherapy regimen for patients with advanced lung adenocarcinoma, predictive biomarkers are not yet available, and new tools to identify chemosensitive patients who would likely benefit from this treatment are desperately needed. In this study, we constructed and validated predictive peptide models using the serum peptidome profiles of two datasets. METHODS One hundred eighty-three patients treated with first-line platinum-based pemetrexed treatment for advanced lung adenocarcinoma were retrospectively enrolled and randomized into the training (n = 92) or validation (n = 91) set, and pre-treatment serum samples were analyzed using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and ClinProTools software. Serum peptidome profiles from the training set were used to identify potential predictive peptide biomarkers and construct a predictive peptide model for accurate group discrimination; which was then used to classify validation samples into "good" and "poor" outcome groups. The clinical outcomes of objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), and overall survival (OS) were analyzed based on the classification result. RESULTS Eight potential peptide biomarkers were identified. A predictive peptide model based on four distinct m/z features (2,142.12, 3,316.19, 4,281.94, and 6,624.02 Da) was developed based on the clinical outcomes of training set patients after first-line pemetrexed plus platinum treatment. In the validation set, the good group had significantly higher ORR (49.1% vs. 8.3%, P <0.001) and DCR (96.4% vs. 47.2%, P <0.001), and longer PFS (7.3 months vs. 2.7 months, P <0.001) vs. the poor group. However, the model did not predict OS (13.6 months vs. 12.7 months, P = 0.0675). CONCLUSION Our predictive peptide model could predict pemetrexed plus platinum treatment outcomes in patients with advanced lung adenocarcinoma and might thus facilitate appropriate patient selection. Further studies are needed to confirm these findings.
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Affiliation(s)
- Lin Wang
- Department of Lung Cancer, Affiliated Hospital of Academy of Military Medical Sciences, Beijing, China
| | - Chuanhao Tang
- Department of Lung Cancer, Affiliated Hospital of Academy of Military Medical Sciences, Beijing, China
| | - Bin Xu
- National Center of Biomedical Analysis, Beijing, China
| | - Lin Yang
- Department of Lung Cancer, Affiliated Hospital of Academy of Military Medical Sciences, Beijing, China
| | - Lili Qu
- Department of Lung Cancer, Affiliated Hospital of Academy of Military Medical Sciences, Beijing, China
| | - Liangliang Li
- Department of Lung Cancer, Affiliated Hospital of Academy of Military Medical Sciences, Beijing, China
| | - Xiaoyan Li
- Department of Lung Cancer, Affiliated Hospital of Academy of Military Medical Sciences, Beijing, China
| | - Weixia Wang
- Department of Lung Cancer, Affiliated Hospital of Academy of Military Medical Sciences, Beijing, China
| | - Haifeng Qin
- Department of Lung Cancer, Affiliated Hospital of Academy of Military Medical Sciences, Beijing, China
| | - Hongjun Gao
- Department of Lung Cancer, Affiliated Hospital of Academy of Military Medical Sciences, Beijing, China
| | - Kun He
- National Center of Biomedical Analysis, Beijing, China
| | - Xiaoqing Liu
- Department of Lung Cancer, Affiliated Hospital of Academy of Military Medical Sciences, Beijing, China
- * E-mail:
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