Longitudinal plasma proteomic profiling of EML4-ALK positive lung cancer receiving ALK-TKIs therapy
Copyright © 2024 Elsevier B.V. All rights reserved..
BACKGROUND: Anaplastic lymphoma kinase-tyrosine kinase inhibitors (ALK-TKIs) has demonstrated remarkable therapeutic effects in ALK-positive non-small cell lung cancer (NSCLC) patients. Identifying prognostic biomarkers can enhance the clinical efficacy of relapsed or refractory patients.
METHODS: We profiled 737 plasma proteins from 159 pre-treatment and on-treatment plasma samples of 63 ALK-positive NSCLC patients using data-independent acquisition-mass spectrometry (DIA-MS). The consensus clustering algorithm was used to identify subtypes with distinct biological features. A plasma-based prognostic model was constructed using the LASSO-Cox method. We performed the Mfuzz analysis to classify the patterns of longitudinal changes in plasma proteins during treatment. 52 baseline plasma samples from another independent ALK-TKI treatment cohort were collected to validate the potential prognostic markers using ELISA.
RESULTS: We identified three subtypes of ALK-positive NSCLC with distinct biological features and clinical efficacy. Patients in subgroup 1 exhibited activated humoral immunity and inflammatory responses, increased expression of positive acute-phase response proteins, and the worst prognosis. Then we constructed and verified a prognostic model that predicts the efficacy of ALK-TKI therapy using the expression levels of five plasma proteins (SERPINA4, ATRN, APOA4, TF, and MYOC) at baseline. Next, we explored the longitudinal changes in plasma protein expression during treatment and identified four distinct change patterns (Clusters 1-4). The longitudinal changes of acute-phase proteins during treatment can reflect the treatment status and tumor progression of patients. Finally, we validated the prognostic efficacy of baseline plasma CRP, SAA1, AHSG, SERPINA4, and TF in another independent NSCLC cohort undergoing ALK-TKI treatment.
CONCLUSIONS: This study contributes to the search for prognostic and drug-resistance biomarkers in plasma samples for ALK-TKI therapy and provides new insights into the mechanism of drug resistance and the selection of follow-up treatment.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:189 |
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| Enthalten in: |
Lung cancer (Amsterdam, Netherlands) - 189(2024) vom: 13. März, Seite 107503 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Shasha [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 04.03.2024 Date Revised 18.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.lungcan.2024.107503 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM36849828X |
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| 245 | 1 | 0 | |a Longitudinal plasma proteomic profiling of EML4-ALK positive lung cancer receiving ALK-TKIs therapy |
| 264 | 1 | |c 2024 | |
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| 500 | |a Date Completed 04.03.2024 | ||
| 500 | |a Date Revised 18.03.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2024 Elsevier B.V. All rights reserved. | ||
| 520 | |a BACKGROUND: Anaplastic lymphoma kinase-tyrosine kinase inhibitors (ALK-TKIs) has demonstrated remarkable therapeutic effects in ALK-positive non-small cell lung cancer (NSCLC) patients. Identifying prognostic biomarkers can enhance the clinical efficacy of relapsed or refractory patients | ||
| 520 | |a METHODS: We profiled 737 plasma proteins from 159 pre-treatment and on-treatment plasma samples of 63 ALK-positive NSCLC patients using data-independent acquisition-mass spectrometry (DIA-MS). The consensus clustering algorithm was used to identify subtypes with distinct biological features. A plasma-based prognostic model was constructed using the LASSO-Cox method. We performed the Mfuzz analysis to classify the patterns of longitudinal changes in plasma proteins during treatment. 52 baseline plasma samples from another independent ALK-TKI treatment cohort were collected to validate the potential prognostic markers using ELISA | ||
| 520 | |a RESULTS: We identified three subtypes of ALK-positive NSCLC with distinct biological features and clinical efficacy. Patients in subgroup 1 exhibited activated humoral immunity and inflammatory responses, increased expression of positive acute-phase response proteins, and the worst prognosis. Then we constructed and verified a prognostic model that predicts the efficacy of ALK-TKI therapy using the expression levels of five plasma proteins (SERPINA4, ATRN, APOA4, TF, and MYOC) at baseline. Next, we explored the longitudinal changes in plasma protein expression during treatment and identified four distinct change patterns (Clusters 1-4). The longitudinal changes of acute-phase proteins during treatment can reflect the treatment status and tumor progression of patients. Finally, we validated the prognostic efficacy of baseline plasma CRP, SAA1, AHSG, SERPINA4, and TF in another independent NSCLC cohort undergoing ALK-TKI treatment | ||
| 520 | |a CONCLUSIONS: This study contributes to the search for prognostic and drug-resistance biomarkers in plasma samples for ALK-TKI therapy and provides new insights into the mechanism of drug resistance and the selection of follow-up treatment | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a ALK-TKIs | |
| 650 | 4 | |a DIA-MS | |
| 650 | 4 | |a Liquid biopsy | |
| 650 | 4 | |a Prediction of efficacy | |
| 650 | 4 | |a Therapeutic monitoring | |
| 650 | 7 | |a Anaplastic Lymphoma Kinase |2 NLM | |
| 650 | 7 | |a EC 2.7.10.1 |2 NLM | |
| 650 | 7 | |a Blood Proteins |2 NLM | |
| 650 | 7 | |a Biomarkers |2 NLM | |
| 650 | 7 | |a EML4-ALK fusion protein, human |2 NLM | |
| 650 | 7 | |a Oncogene Proteins, Fusion |2 NLM | |
| 700 | 1 | |a Hao, Xuezhi |e verfasserin |4 aut | |
| 700 | 1 | |a Dai, Liyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Lou, Ning |e verfasserin |4 aut | |
| 700 | 1 | |a Fan, Guangyu |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Ruyun |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Mengwei |e verfasserin |4 aut | |
| 700 | 1 | |a Xing, Puyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Yutao |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Lin |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Zhishang |e verfasserin |4 aut | |
| 700 | 1 | |a Yao, Jiarui |e verfasserin |4 aut | |
| 700 | 1 | |a Tang, Le |e verfasserin |4 aut | |
| 700 | 1 | |a Shi, Yuankai |e verfasserin |4 aut | |
| 700 | 1 | |a Han, Xiaohong |e verfasserin |4 aut | |
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