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Circulating platelets: a novel liquid biopsy source for cancer diagnostics and therapy stratification
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

As conventional tissue biopsies have several drawbacks, much effort has been directed toward the development of minimal-invasive liquid biopsy platforms for detecting and profiling cancer.

Platelets are the second most abundant cells in blood and have very versatile functions both in physiological and pathophysiological conditions. When exposed to tumors and their environment, platelets exchange biomolecules with tumor cells changing the platelets’ RNA profile, resulting in tumor-mediated education of the platelets. Our research group and collaborators have previously shown that platelets sequester material while in circulation and with that ability accumulate cancer specific information. Platelet RNA profiles or detection of tumor-derived biomarkers within them may provide insight into ongoing cancer-related processes in a patient, allowing for implementation of personalized therapy strategies.

This thesis evaluates whether circulating platelets could have a potential role (as a liquid biopsy source) in cancer diagnostics, therapy stratification, and monitoring of the disease. Gene expression analysis using digital droplet PCR and RNA-sequencing were the main methods used to address this. Prostate Cancer is the main model used in this thesis but this platform is applicable to other tumor types such as colorectal-, breast-, and lung cancer.

We found platelets of cancer patients to contain tumor-derived information enabling selection of biomarker panels discriminating early stage cancer patients from healthy individuals as well as therapy responders from non-responders with high accuracy. The RNA transcript within the platelets was more informative in regards to therapy stratification compared to circulating free DNA of matched patient samples, in which genomic changes were analyzed. Combining both increased the accuracy in predicting therapy outcome.

Platelets show usefulness as a novel liquid biopsy source for early detection and individualizing patient therapy decisions (for personalized medicine). The techniques used are promising but large-scale validation is necessary.

Place, publisher, year, edition, pages
Umeå: Umeå University , 2018. , p. 46
Series
Umeå University medical dissertations, ISSN 0346-6612 ; 1952
Keywords [en]
platelets, biomarkers, liquid biopsy, therapy stratification, personalised medicine, Prostate cancer
National Category
Cancer and Oncology
Identifiers
URN: urn:nbn:se:umu:diva-146032ISBN: 978-91-7601-838-5 (print)OAI: oai:DiVA.org:umu-146032DiVA, id: diva2:1193387
Public defence
2018-04-20, Sal 933, 9 trp, byggnad 3A, Umeå, 09:00 (English)
Opponent
Supervisors
Available from: 2018-03-28 Created: 2018-03-26 Last updated: 2018-06-09Bibliographically approved
List of papers
1. Platelets harbor prostate cancer biomarkers and the ability to predict therapeutic response to abiraterone in castration resistant patients
Open this publication in new window or tab >>Platelets harbor prostate cancer biomarkers and the ability to predict therapeutic response to abiraterone in castration resistant patients
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2018 (English)In: The Prostate, ISSN 0270-4137, E-ISSN 1097-0045, Vol. 78, no 1, p. 48-53Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: Novel therapies for castration resistant prostate cancer (CRPC) have been introduced in the clinic with possibilities for individualized treatment plans. Best practice of those expensive drugs requires predictive biomarker monitoring. This study used circulating biomarker analysis to follow cancer-derived transcripts implicated in therapy resistance.

METHOD: The isolated platelet population of blood samples and digital-PCR were used to identify selected biomarker transcripts in patients with CRPC prior chemo- or androgen synthesis inhibiting therapy.

RESULTS: Fifty patients received either docetaxel (n = 24) or abiraterone (n = 26) therapy, with therapy response rates of 54% and 48%, respectively. Transcripts for the PC-associated biomarkers kallikrein-related peptidase-2 and -3 (KLK2, KLK3), folate hydrolase 1 (FOLH1), and neuropeptide-Y (NPY) were uniquely present within the platelet fraction of cancer patients and not detected in healthy controls (n = 15). In the abiraterone treated cohort, the biomarkers provided information on therapy outcome, demonstrating an association between detectable biomarkers and short progression free survival (PFS) (FOLH1, P < 0.01; KLK3, P < 0.05; and NPY, P < 0.05). Patients with biomarker-negative platelets had the best outcome, while FOLH1 (P < 0.05) and NPY (P = 0.05) biomarkers provided independent predictive information in a multivariate analysis regarding PFS. KLK2 (P < 0.01), KLK3 (P < 0.001), and FOLH1 (P < 0.05) biomarkers were associated with short overall survival (OS). Combining three biomarkers in a panel (KLK3, FOLH1, and NPY) made it possible to separate long-term responders from short-term responders with 87% sensitivity and 82% specificity.

CONCLUSION: Analyzing tumor-derived biomarkers in platelets of CRPC patients enabled prediction of the outcome after abiraterone therapy with higher accuracy than baseline serum PSA or PSA response.

Place, publisher, year, edition, pages
Wiley-Blackwell, 2018
Keywords
biomarkers, liquid biopsy, personalized medicine, platelet, prostate cancer, therapy stratification
National Category
Cancer and Oncology Urology and Nephrology
Identifiers
urn:nbn:se:umu:diva-142384 (URN)10.1002/pros.23443 (DOI)000417131400007 ()29094381 (PubMedID)
Available from: 2017-11-29 Created: 2017-11-29 Last updated: 2018-06-09Bibliographically approved
2. Combining Circulating Biomarkers to Enhance Predictability of Therapy Response
Open this publication in new window or tab >>Combining Circulating Biomarkers to Enhance Predictability of Therapy Response
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(English)Manuscript (preprint) (Other academic)
National Category
Cancer and Oncology
Identifiers
urn:nbn:se:umu:diva-146051 (URN)
Available from: 2018-03-27 Created: 2018-03-27 Last updated: 2018-06-09
3. Detection of Early Stage Prostate Cancer Using Tumor Educated Platelet Profile Support Vector Machine (SVM) - Classification Algorithms
Open this publication in new window or tab >>Detection of Early Stage Prostate Cancer Using Tumor Educated Platelet Profile Support Vector Machine (SVM) - Classification Algorithms
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(English)Manuscript (preprint) (Other academic)
National Category
Cancer and Oncology
Identifiers
urn:nbn:se:umu:diva-146052 (URN)
Available from: 2018-03-27 Created: 2018-03-27 Last updated: 2018-06-09
4. Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets
Open this publication in new window or tab >>Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets
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2017 (English)In: Cancer Cell, ISSN 1535-6108, E-ISSN 1878-3686, Vol. 32, no 2, p. 238-252Article in journal (Refereed) Published
Abstract [en]

Blood-based liquid biopsies, including tumor-educated blood platelets (TEPs), have emerged as promising biomarker sources for non-invasive detection of cancer. Here we demonstrate that particle-swarm optimization (PSO)-enhanced algorithms enable efficient selection of RNA biomarker panels from platelet RNA sequencing libraries (n = 779). This resulted in accurate TEP-based detection of early- and late-stage non-small-cell lung cancer (n = 518 late-stage validation cohort, accuracy, 88%; AUC, 0.94; 95% CI, 0.92-0.96; p < 0.001; n = 106 early-stage validation cohort, accuracy, 81%; AUC, 0.89; 95% CI, 0.83-0.95; p < 0.001), independent of age of the individuals, smoking habits, whole-blood storage time, and various inflammatory conditions. PSO enabled selection of gene panels to diagnose cancer from TEPs, suggesting that swarm intelligence may also benefit the optimization of diagnostics readout of other liquid biopsy biosources.

Place, publisher, year, edition, pages
Elsevier, 2017
National Category
Cancer and Oncology
Identifiers
urn:nbn:se:umu:diva-142881 (URN)10.1016/j.ccell.2017.07.004 (DOI)000407932500010 ()28810146 (PubMedID)
Available from: 2017-12-13 Created: 2017-12-13 Last updated: 2018-06-09Bibliographically approved

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Tjon-Kon-Fat, Lee-Ann

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