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Journal ArticleDOI

A combination of platelet features allows detection of early-stage cancer

TL;DR: Multiple platelet features, including platelet count, volume and protein content, were significantly changed in lung and head of pancreas cancer patients, and a cancer type-specific combination of these platelets features can be used to discriminate between patients with early-stage cancer and healthy individuals.
About: This article is published in European Journal of Cancer.The article was published on 2017-07-01 and is currently open access. It has received 42 citations till now. The article focuses on the topics: Cancer & Beta-thromboglobulin.

Summary (3 min read)

1. Introduction

  • Detection of cancer in its early stages radically improves the effectiveness of available treatment and overall prognosis of patients [1].
  • Up to now, studies searching for biomarkers are mostly based on blood plasma or serum parameters.
  • In addition, platelets may become activated systemically or within the tumour, potentially resulting in release of platelet content into the circulation [3].
  • Altogether, data from literature suggest that several platelet characteristics are affected in cancer patients.
  • These features, either alone or in combination, may be useful tools in the detection of (early stages of) cancer.

2.1. Study design and participants

  • This study was performed in accordance with the Declaration of Helsinki and approved by the medical ethical committee of Maastricht University Medical Centerþ.
  • Informed consent was obtained from all participants.
  • Patients with clinically established and histologically proven untreated primary lung (n Z 86) or head of pancreas cancer (n Z 42, including pancreas head cancer [n Z 28], distal cholangiocarcinoma [n Z 8] and duodenumcarcinoma [n Z 6]), that were eligible for surgical resection, were included between July 2012 and October 2014.
  • Exclusion criteria were previous history of cancer, neo-adjuvant chemotherapy or radiotherapy, use of platelet-influencing drugs such as aspirin, blood or platelet transfusion during the previous 14 days, active inflammatory disease, non-healing ulcers or fractures.
  • Staging was performed in accordance with the tumour-node-metastasis (TNM) classification (version 7) of the Union for International Cancer Control [9].

2.2. Procedures

  • Blood from all patients and healthy individuals was collected.
  • In case of cancer patients, sampling occurred within 1 week before initiation of treatment.
  • To prevent platelet activation during blood collection and sample preparation, blood was collected as described before [10].
  • The procedures for quantification of platelet count, volume and activation status, as well as concentrations of various growth factors and chemokines in platelets and plasma.
  • ELISA measurements in all patient and matching control samples were performed using the same assay, simultaneously and in the same institution.

2.3. Statistics

  • Statistical analyses were performed using SPSS (version 22; SPSS Inc, Chicago, USA) and R (version 3.2.2, R core development team).
  • Data are presented as means with standard errors of the mean unless otherwise indicated.
  • Patient data were compared to data from the healthy control groups using the t-test for continuous variables and Pearson’s chi-square test for categorical variables.
  • Spearman’s rank correlation was used to test the association between variables.
  • P-values less than 0.05 were considered statistically significant.

2.4. Development of diagnostic models

  • For both cancer groups, logistic regression was used to estimate diagnostic models.
  • Model performance was assessed by Nagelkerke’s R2 statistic and by quantifying discrimination.
  • Discrimination is the model’s ability to distinguish between those that have cancer and those who do not.
  • 1000 bootstrap samples of the original data were drawn, and similar diagnostic models were estimated using these data.
  • The average difference in diagnostic model performance between the bootstrap sample and the original sample provided estimates of optimism in the performance measures.

3. Results

  • Blood was collected from 86 lung cancer patients, 42 patients diagnosed with head of pancreas cancer and a total of 92 healthy individuals.
  • Importantly, the latter group contained individuals that were matched with respect to gender and age to both groups of cancer patients.
  • All cancer patients were treatment naive at the moment of blood sampling.
  • Detailed characteristics of the study populations are presented in Table 1.
  • In the healthy individuals, both gender and age appeared to be related to some of the platelet characteristics.

3.1. Platelet characteristics in patients with untreated lung cancer

  • Platelet counts were not different as compared to controls in patients with early-stage (stage IeII) lung cancer, but significantly increased in case of advanced (stage IIIeIV) lung cancer (Fig. 1A).
  • Concentrations of VEGF, PDGF, CTAPIII, PF4 and TSP-1 sented as means with standard errors of the means.
  • Strikingly, changes in platelet content did not correspond with concentration changes of the same proteins in PFP (Fig. 1HeL).
  • No differences between patients and controls were found (Supplemental Fig. 1A and B).
  • This suggests that the increase in PDGF, CTAPIII and PF4 plasma concentrations in patients with lung cancer is mainly due to systemic or intratumoural platelet activation, leading to secretion of their content.

3.2. A combination of platelet features discriminates lung cancer patients from controls

  • Data from 172 individuals (86 patients with lung cancer and 86 sex- and age-matched controls) were available for multivariable modelling.
  • All measured parameters were entered into the model: platelet count, MPV and concentrations of VEGF, PDGF, PF4, CTAPIII and TSP-1 in platelets and PFP.
  • Nagelkerke’s R2 of the diagnostic model was 0.572, indicating that the model fits the data well.
  • This demonstrates that the model discriminated very well between lung cancer cases and controls in their study population.

3.3. A platelet-based marker for the presence of head of pancreas cancer

  • In patients diagnosed with head of pancreas cancer, several platelet parameters were found to be substantially different from the control group as well.
  • VEGF concentration was significantly increased in platelets of patients with early- and late-stage head of pancreas cancer (Fig. 3C), while no differences in platelet PDGF, CTAPIII, PF4 and TSP-1 concentrations were observed (Supplemental Fig. 2AeD).
  • Nagelkerke’s R2 of this model was 0.418, which is indicative of good model fit.
  • This parsimonious model discriminated well between patients with head of pancreas cancer and controls, as confirmed by the boxplots of predicted probability by the diagnostic model (Fig. 4B).

4. Discussion

  • The presence of a tumour in the human body appears to influence several platelet features.
  • The elevated PF4 and CTAPIII concentrations in plasma reveal the presence of activated platelets in the circulation of patients with lung or head of pancreas cancer.
  • Altogether, their study shows that multiple platelet characteristics are changed in patients with cancer, both in early and later stages of development.
  • One important issue in this interesting study is the nature of the control group, that is clearly younger and with a different gender distribution than most of the cancer groups.
  • Funding The Netherlands Organisation for Scientific Research (project number 017.008.143, to SS).

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Citations
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Journal ArticleDOI
TL;DR: This study identified a diagnostic model based on PRR and other platelet features in whole blood and PRP samples with the potential to distinguish patients with lung cancer or colon cancer from healthy controls and could also be used to distinguish between lung cancer from the benign disease.

11 citations

Journal ArticleDOI
TL;DR: Sunitinib and aspirin are associated with increased bleeding risk, and therefore, the synergistic effects of these compounds on thrombus and fibrin formation were investigated in this article.
Abstract: Background Sunitinib is a multitarget tyrosine kinase inhibitor (TKI) used for cancer treatment. In platelets, sunitinib affects collagen-induced activation under noncoagulating conditions. We investigated (1) the effects of sunitinib on thrombus formation induced by other TK-dependent receptors, and (2) the effects under coagulating conditions. Cardiovascular disease is a comorbidity in cancer patients, resulting in possible aspirin treatment. Sunitinib and aspirin are associated with increased bleeding risk, and therefore we also investigated (3) the synergistic effects of these compounds on thrombus and fibrin formation. Methods Blood or isolated platelets from healthy volunteers or cancer patients were incubated with sunitinib and/or aspirin or vehicle. Platelet activation was determined by TK phosphorylation, flow cytometry, changes in [Ca2+]i, aggregometry, and whole blood perfusion over multiple surfaces, including collagen with(out) tissue factor (TF) was performed. Results Sunitinib reduced thrombus formation and phosphatidylserine (PS) exposure under flow on collagen type I and III. Also, sunitinib inhibited glycoprotein VI-induced TK phosphorylation and Ca2+ elevation. Upon TF-triggered coagulation, sunitinib decreased PS exposure and fibrin formation. In blood from cancer patients more pronounced effects of sunitinib were observed in lung and pancreatic as compared to neuroglioblastoma and other cancer types. Compared to sunitinib alone, sunitinib plus aspirin further reduced platelet aggregation, thrombus formation, and PS exposure on collagen under flow with(out) coagulation. Conclusion Sunitinib suppresses collagen-induced procoagulant activity and delays fibrin formation, which was aggravated by aspirin. Therefore, we urge for awareness of the combined antiplatelet effects of TKIs with aspirin, as this may result in increased risk of bleeding.

9 citations

Journal ArticleDOI
TL;DR: This study constructed a tumor tissue-based model to better understand how the angiogenic network is regulated by opposing mediators at the extracellular level and identified a counterintuitive result that the secretion of the anti-angiogenic factor PF4 can enhance pro-angIogenic signaling by elevating the levels of the interstitial and surface-level pro-Angiogenic species.
Abstract: Tumor angiogenesis is regulated by pro- and anti-angiogenic factors. Anti-angiogenic agents target the interconnected network of angiogenic factors to inhibit neovascularization, which subsequently impedes tumor growth. Due to the complexity of this network, optimizing anti-angiogenic cancer treatments requires detailed knowledge at a systems level. In this study, we constructed a tumor tissue-based model to better understand how the angiogenic network is regulated by opposing mediators at the extracellular level. We consider the network comprised of two pro-angiogenic factors: vascular endothelial growth factor (VEGF) and basic fibroblast growth factor (FGF2), and two anti-angiogenic factors: thrombospondin-1 (TSP1) and platelet factor 4 (PF4). The model's prediction of angiogenic factors' distribution in tumor tissue reveals the localization of different factors and indicates the angiogenic state of the tumor. We explored how the distributions are affected by the secretion of the pro- and anti-angiogenic factors, illustrating how the angiogenic network is regulated in the extracellular space. Interestingly, we identified a counterintuitive result that the secretion of the anti-angiogenic factor PF4 can enhance pro-angiogenic signaling by elevating the levels of the interstitial and surface-level pro-angiogenic species. This counterintuitive situation is pertinent to the clinical setting, such as the release of anti-angiogenic factors in platelet activation or the administration of exogenous PF4 for anti-angiogenic therapy. Our study provides mechanistic insights into this counterintuitive result and highlights the role of heparan sulfate proteoglycans in regulating the interactions between angiogenic factors. This work complements previous studies aimed at understanding the formation of angiogenic complexes in tumor tissue and helps in the development of anti-cancer strategies targeting angiogenesis.

9 citations

Journal ArticleDOI
TL;DR: The complexity of these concepts is presented, considering platelets as biomarkers for diagnosis, prognosis and potentially as therapeutic targets in cancer.
Abstract: There is increasing awareness that platelets play a significant role in creating a hypercoagulable environment that mediates tumor progression, beyond their classical hemostatic function. Platelets have heterogenic responses to agonists, and differential release and uptake of bioactive molecules may be manipulated via reciprocal cross-talk with cells of the tumor microenvironment. Platelets thus promote tumor progression by enhancing tumor growth, promoting the development of tumor-associated vasculature and encouraging invasion. In the metastatic process, platelets form the shield that protects tumor cells from high-velocity forces and immunosurveillance, while ensuring the establishment of the pre-metastatic niche. This review presents the complexity of these concepts, considering platelets as biomarkers for diagnosis, prognosis and potentially as therapeutic targets in cancer.

8 citations

Journal ArticleDOI
TL;DR: In this paper, a review summarises recent researches in connections between platelets and pancreatic cancer and the existing data showed different underlying mechanisms were involved in their complex crosstalk.
Abstract: Platelets have been recognized as key players in hemostasis, thrombosis, and cancer. Preclinical and clinical researches evidenced that tumorigenesis and metastasis can be promoted by platelets through a wide variety of crosstalk between cancer cells and platelets. Pancreatic cancer is a devastating disease with high morbidity and mortality worldwide. Although the relationship between pancreatic cancer and platelets in clinical diagnosis is described, the interplay between pancreatic cancer and platelets, the underlying pathological mechanism and pathways remain a matter of intensive study. This review summaries recent researches in connections between platelets and pancreatic cancer. The existing data showed different underlying mechanisms were involved in their complex crosstalk. Typically, pancreatic tumor accelerates platelet aggregation which forms thrombosis. Furthermore, extracellular vesicles released by platelets promote communication in a neoplastic microenvironment and illustrate how these interactions drive disease progression. We also discuss the advantages of novel model organoids in pancreatic cancer research. A more in-depth understanding of tumor and platelets crosstalk which is based on organoids and translational therapies may provide potential diagnostic and therapeutic strategies for pancreatic cancer progression.

6 citations

References
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TL;DR: The number of cancer survivors continues to increase because of both advances in early detection and treatment and the aging and growth of the population and for the public health community to better serve these survivors, the American Cancer Society and the National Cancer Institute collaborate to estimate the number of current and future cancer survivors.
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TL;DR: This paper presents a case study on survival analysis: Prediction of secondary cardiovascular events and lessons from case studies on overfitting and optimism in prediction models.
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TL;DR: The number of cancer survivors continues to increase due to the aging and growth of the population and improvements in early detection and treatment, and current treatment patterns for the most common cancer types are described based on information in the National Cancer Data Base and the SEER and SEER‐Medicare linked databases.
Abstract: The number of cancer survivors continues to increase due to the aging and growth of the population and improvements in early detection and treatment. In order for the public health community to better serve these survivors, the American Cancer Society and the National Cancer Institute collaborated to estimate the number of current and future cancer survivors using data from the Surveillance, Epidemiology, and End Results (SEER) program registries. In addition, current treatment patterns for the most common cancer types are described based on information in the National Cancer Data Base and the SEER and SEER-Medicare linked databases; treatment-related side effects are also briefly described. Nearly 14.5 million Americans with a history of cancer were alive on January 1, 2014; by January 1, 2024, that number will increase to nearly 19 million. The 3 most common prevalent cancers among males are prostate cancer (43%), colorectal cancer (9%), and melanoma (8%), and those among females are cancers of the breast (41%), uterine corpus (8%), and colon and rectum (8%). The age distribution of survivors varies substantially by cancer type. For example, the majority of prostate cancer survivors (62%) are aged 70 years or older, whereas less than one-third (32%) of melanoma survivors are in this older age group. It is important for clinicians to understand the unique medical and psychosocial needs of cancer survivors and to proactively assess and manage these issues. There are a growing number of resources that can assist patients, caregivers, and health care providers in navigating the various phases of cancer survivorship.

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TL;DR: Suggested that could be demonstrated a live birth and data and demonstrated that were excluded, and developed and could be appropriate aac evidence.
Abstract: Suggested that could be demonstrated a live birth and data. Supplementary file appendix statistics table, demonstrated that were excluded. Accessed for the therapy process light microscopic evaluation and others. Most relevant and templeton et al quiz ref idbecause. High blood cell count admission to, patients at a thorough review. Were excluded although over either a strong test sample. We developed and could be appropriate aac evidence. Setting number of a language activity they. Training programs still do not however it may differ from keynote papers on epidemiology. We excluded all studies but predictive performance three oocytes. High blood cell count less than that were drawn from to reduce the growing database containing. Consequently the basis of patients making clinical signs and other sbis. Implementation elsewhere enhances the performance measurement, methods of female age were responsible for my patients. In models are limited generalizability for aac institute public reporting results were. We did find that can be used a model. Informed consent was defined according to, permit meta analysis process starts. In predicted risks was assessed by phone at increased. In socioeconomically disadvantaged populations we used, and evidence mckibbon wilczynski hayward. That diagnoses we abstracted the performance of or patient data and increasing odds ratios. Practical aspects of how we excluded university this for antibiotic prescription. Other sbis in the primary or inhibin levels of observed clinical experience.

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Additional excerpts

  • ...Therefore, the models were internally validated using standard bootstrapping techniques [11]....

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Abstract: From the Departments of Gynecologic Oncology and Reproductive Medicine (R.L.S., A.M.N., H.D.H., J.B.-M., W.H., H.G., K.M., M.M.K.S., E.R.K., A.K.S.), Cancer Biology (R.R., G.L.-B., A.K.S.), Experimental Therapeutics (G.N.A.-P., I.T., B.O., G.L.-B.), Hematology and Oncology (C.V.P.), Pathology (M.T.D.), Benign Hematology (H.G.V., V.A.-K.), Biostatistics (D.U.), and Leukemia (F.G.), and the Center for RNA Interference and Non-Coding RNA (H.D.H., G.L.-B.,

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Frequently Asked Questions (2)
Q1. What are the contributions in "A combination of platelet features allows detection of early-stage cancer" ?

• A submitted manuscript is the version of the article upon submission and before peer-review. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher 's website. The final author version and the galley proof are versions of the publication after peer review. The final published version features the final layout of the paper including the volume, issue and page numbers. 

Future research is needed to further investigate the clinical relevance of their findings. Platelets are a new and uncharted source of information, which need to be further explored in blood-based biomarker research.