scispace - formally typeset

Journal ArticleDOI

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

01 Jul 2017-European Journal of Cancer (Pergamon)-Vol. 80, pp 5-13

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.

AbstractBackground Detection of early-stage cancer significantly improves patient survival. As platelets play an important role in cancer progression, we aimed to investigate whether platelets can be used for the discovery of early-stage cancer. Methods Patients with lung (n = 86) or head of pancreas (n = 42) cancer were included, as were healthy sex- and age-matched controls (n = 92). Blood was collected before initiation of treatment. Platelet count, volume and activation status were quantified in whole blood. Next, concentrations of vascular endothelial growth factor, platelet-derived growth factor, platelet factor 4, thrombospondin-1 and connective tissue–activating peptide III were measured in both platelets and plasma. Using the results, two multivariable diagnostic models were developed and internally validated. Findings Multiple platelet features, including platelet count, volume and protein content, were significantly changed in lung and head of pancreas cancer patients. However, the pattern of changes differed between both groups. The diagnostic model developed for lung cancer discriminated very well between patients and controls (AUC = 88.7%). Addition of smoking as a variable significantly increased the AUC of the model to 94.5%. The diagnostic model for head of pancreas cancer also performed well (AUC = 82.7%). Both models were internally validated, resulting in optimism-corrected AUC's of 86.8% and 80.8%, respectively. Interpretation In patients with lung or head of pancreas cancer, several platelet characteristics are changed compared to healthy sex- and age-matched controls. A cancer type-specific combination of these platelet features can be used to discriminate between patients with early-stage cancer and healthy individuals.

Topics: Cancer (59%), Beta-thromboglobulin (57%), Lung cancer (54%), Platelet factor 4 (54%), Head of pancreas (53%)

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).

Did you find this useful? Give us your feedback

...read more

Content maybe subject to copyright    Report

A combination of platelet features allows detection of
early-stage cancer
Citation for published version (APA):
Sabrkhany, S., Kuijpers, M. J. E., van Kuijk, S. M. J., Sanders, L., Pineda, S., Damink, S. W. M. O.,
Dingemans, A-M. C., Griffioen, A. W., & Egbrink, M. G. A. O. (2017). A combination of platelet features
allows detection of early-stage cancer. European Journal of Cancer, 80, 5-13.
https://doi.org/10.1016/j.ejca.2017.04.010
Document status and date:
Published: 01/07/2017
DOI:
10.1016/j.ejca.2017.04.010
Document Version:
Publisher's PDF, also known as Version of record
Document license:
Taverne
Please check the document version of this publication:
• A submitted manuscript is the version of the article upon submission and before peer-review. There can
be important differences between the submitted version and the official published version of record.
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.
Link to publication
General rights
Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright
owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these
rights.
• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.
• You may not further distribute the material or use it for any profit-making activity or commercial gain
• You may freely distribute the URL identifying the publication in the public portal.
If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above,
please follow below link for the End User Agreement:
www.umlib.nl/taverne-license
Take down policy
If you believe that this document breaches copyright please contact us at:
repository@maastrichtuniversity.nl
providing details and we will investigate your claim.
Download date: 10 Aug. 2022

Original Research
A combination of platelet features allows detection of
early-stage cancer
Siamack Sabrkhany
a
, Marijke J.E. Kuijpers
b
, Sander M.J. van Kuijk
c
,
Linda Sanders
a
, Sharo Pineda
a
, Steven W.M. Olde Damink
d
,
Anne-Marie C. Dingemans
e
, Arjan W. Griffioen
f
,
Mirjam G.A. oude Egbrink
a,
*
a
Department of Physiology, Cardiovascular Research Institute Maastricht, Maastricht University Medical Centerþ,
Maastricht, The Netherlands
b
Department of Biochemistry, Maastricht University Medical Centerþ, Maastricht, The Netherlands
c
Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centerþ,
Maastricht, The Netherlands
d
Department of Surgery, Maastricht University Medical Centerþ, Maastricht, The Netherlands
e
Department of Pulmonology, Maastricht University Medical Centerþ, Maastricht, The Netherlands
f
Department of Medical Oncology, Angiogenesis Laboratory, VU Medical Center, Amsterdam, The Netherlands
Received 21 February 2017; received in revised form 2 April 2017; accepted 5 April 2017
Available online 17 May 2017
KEYWORDS
Platelets;
Biomarker;
Cancer
Abstract Background: Detection of early-stage cancer significantly improve s patient sur -
vival. As platelets play an important role in cancer progression, we aimedtoinvestigate
whether platelets can be used for the discovery of early-stage cancer.
Methods: Patients with lung (n Z 86) or head of pancreas (n Z 42) cancer were included, as
were healthy sex- and age-matched controls (n Z 92). Blood was collected before initiation of
treatment. Platelet count, volume and activation status were quantified in whole blood. Next,
concentrations of vascular endothelial growth factor, platelet-d erived growth factor, platelet
factor 4, thrombospondin-1 and connective tissueeactivating peptide III were measured in
both platelets and plasma. Using the results, two multivariable diagnostic models were devel-
oped and internally validated.
Findings: Multiple platelet features, including platelet count, volume and protein content,
were significantly changed in lung and head of pancreas cancer patients. However, the pattern
* Corresponding author: Department of Physiology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands. Fax: þ31 43
3884166.
E-mail address: m.oudeegbrink@maastrichtuniversity.nl (M.G.A. oude Egbrink).
http://dx.doi.org/10.1016/j.ejca.2017.04.010
0959-8049/ª 2017 Elsevier Ltd. All rights reserved.
Available online at www.sciencedirect.com
ScienceDirect
journal homepage: www.ejcancer.com
European Journal of Cancer 80 (2017) 5e13

of changes differed b etween both groups. The diagnostic model developed for lung cancer
discriminated very well between patients and controls (AUC Z 88.7%). Addition of smoking
as a variable significantly increased the AUC of the model to 94.5%. The diagnostic model for
head of pancreas cancer also performed well (AUC Z 82.7%). Both mod els were internally
validated, resulting in optimism-corrected AUC’s of 86.8% and 80.8%, respectively.
Interpretation: In patients with lung or head of pancreas cancer, several platelet characteristics
are changed compared to healthy sex- and age-matched controls. A cancer type-specific com-
bination of these platelet features can be used to discriminate between patients with early-stage
cancer and healthy individuals.
ª 2017 Elsevier Ltd. All rights reserved.
1. Introduction
Detection of cancer in its early stages radically improves
the effectiveness of availab le treatment and overall
prognosis of patients [1]. Up to now, studi es searching
for biomarkers are mostly based on blood plasma or
serum parameters. A limitation of this approach is that
platelets and their content are neglected [2].
Circulating platelets contain numerous proteins,
including growth factors, chemokines and proteases,
which are synthesised by megakaryocytes or absorbed
from the blood by the platelets themselves [3]. There-
fore, the presence of a growth factor-producing tumour
can influence platelet content. Concentrations of
angiogenic factors, like vascular endothelial growth
factor (VEGF) and platelet-derived growth factor
(PDGF), and the angiostatic platelet factor 4 (PF4) have
been reported to be higher in platelets of patients with
cancer than in platelets of healthy individuals [4,5].
Another recent study suggests that platelet mRNA
profiles are also altered in patients with cancer, enabling
discrimination between cancer patients and healthy in-
dividuals [6]. In addition, platelets may become acti-
vated systemically or within the tumour, potentially
resulting in release of platelet content into the circula-
tion [3]. Next to platelet content and platelet activation,
platelet count is also frequently changed in cancer pa-
tients [7]. Tumours can increase platelet production by
secretion of thrombopoietic cytokines, leading to para-
neoplastic thrombocytosis [7,8].
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. It
was the aim of the present study to investigate, in two
different groups of cancer patients, whether and how
platelet features are changed in the presence of a
tumour. In addition, we succeeded to combine these
features into two internally validated diagnostic models,
one for lung cancer and another one for head of
pancreas cancer, to discriminate between patients and
healthy individuals.
2. Methods
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 par-
ticipants. Patients with clinically established and histo-
logically 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-adjuv ant 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].A
sex- and age-matched healthy control population for
both cancer groups was included as well (Table 1).
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, are described extensively in the Appendix.
In short, platelet count and volume were determined in
whole blood with a Beckman coulter counter, and
platelet activation was quantified in whole blood using
flow cytometry. The remaining blood was centrifuged
several times to obtain platelet-free plasma (PFP) and
S. Sabrkhany et al. / European Journal of Cancer 80 (2017) 5e136

platelet pellet. In both platelets and PFP, the concen-
trations of the angiogenic proteins VEGF and PDGF,
as well as the angiostatic factors PF4, thrombospondin-
1 (TSP-1) and connective tissue-activating peptide III
(CTAPIII) were measured using specific human DuoSet
ELISA assays (R&D Systems, Abingdon, United
Kingdom). ELISA measur ements in all patient and
matching control samples were performed using the
same assay, simultaneously and in the same institution.
2.3. Statistics
Statistical analyses wer e 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. All platelet parameters were
added, after which stepwise backward elimination was
used to arrive at a more parsimonious model. The
derivation of the diagnostic model for lung cancer was
repeated to also include smoking status (current, former
and never) to assess its performance when combined with
arguably the strongest known predictor for lung cancer.
Model performance was assessed by Nagelkerke’s R
2
statistic and by quantifying discrimination. Nagel-
kerke’s R
2
can be us ed to quantify the predict ive
strength of a diagnostic model. Discrimination is the
model’s ability to distinguish between those that have
cancer and those who do not. Discrimination was
quantified by the area under the receiver operating
characteristic curve (AUC). In addition, boxplots were
constructed to visually assess how the diagnostic model
distinguishes cases from controls.
Since the number of pred ictors that was entered in the
models was higher than recommended using the 10
events-per-variable rule of thumb, the risk of overfitting
the diagnostic models was relatively high. Therefore, the
models were internally validated using standard boot-
strapping techniques [11]. In this step, 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 pe rformance
measures. These measures of optimism were subse-
quently subtracted from the performance measures we
computed, to reflect the likely performance of the
models in future patients.
Table 1
Patient demographics and clinical profiles.
Lung cancer Head of pancreas cancer
Patients (n Z 86) Controls (n Z 86) P-value Patients (n Z 42) Controls (n Z 42) P-value
Age
Years (SD) 65.8 (8.6) 64.9 (9.8) 0.52 67.6 (10.5) 67.7 (10.7) 0.96
Gender 0.64
1
1.00
1
Male 53 (61.6%) 50 (58.1%) 21 (50.0%) 21 (50.0%)
Female 33 (38.4%) 36 (41.9%) 21 (50.0%) 21 (50.0%)
Smoking <0.001
1
0.68
1
Current 30 (34.9%) 13 (15.1%) 9 (21.4%) 6 (14.3%)
Former 48 (55.8%) 18 (20.9%) 10 (23.8%) 10 (23.8%)
Never 8 (9.3%) 55 (64.0%) 23 (54.8%) 26 (61.9%)
TNM stage
IeII 39 (45.3%) e 29 (69.0%) e
IIIeIV 47 (54.7%) e 13 (31.0%) e
Primary tumour location
Lung 86 eee
Pancreas head ee 28 e
Duodenum ee 6 e
Distal biliary duct ee 8 e
Histology
Adenocarcinoma 43 e 38 e
Squamous cell carcinoma 21 eee
Large-cell carcinoma 2 eee
Small-cell carcinoma 15 eee
Other 5 e 4 e
Patient baseline characteristics were compared to data from the healthy control groups using the t-test for continuous variables and
1
Pearson’s chi-
square test for categorical variables. P-values less than 0.05 were considered statistically significant.
SD Z standard deviation; TNM, tumour-node-metastasis.
S. Sabrkhany et al. / European Journal of Cancer 80 (2017) 5e13 7

3. Results
Blood was co llected 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 indivi duals that were matched with
respect to gender and age to both groups of cancer pa-
tients. All cancer patients were treatment naive at the
moment of blood sampling. Detailed characteristics of
the study populations are presented in Table 1. The lung
cancer group comprised significantly more (current and
former) smokers than the control group. In the healthy
individuals, both gender and age appeared to be related
to some of the platelet characteristics. Mean plate let
count appeared to be higher in females than males (235
versus 201 10
9
/L; p < 0.01), while ag e showed to be
negatively correlated with platelet concentrations of
PDGF (Spearman’s r: e0.43; p < 0.0001), PF4 (r:
e0.31; p < 0.01) and CTAPIII (r: e0.21; p < 0.05).
3.1. Platelet characteristics in patients with untreated lung
cancer
Platelet counts were not different as compared to con-
trols in patients with early-stage (stage IeII) lung can-
cer, but significantly increased in case of advanced (stage
IIIeIV) lung cancer (Fig. 1A). Mean platelet volume
(MPV) was higher in early-stage (stage IeII) lung cancer
only (Fig. 1B).
Concentrations of VEGF, PDGF, CTAPIII, PF4 and
TSP-1 in platelets appeared to differ significantly be-
tween lung cancer patients and controls. VEGF con-
centration was significantly elevated in platelets of both
stage IeII and stage IIIeIV patients (Fig. 1C), while
platelet PDGF concentration was elevated in stage IeII
only (Fig. 1D). In contrast, concentrations of CTAPIII,
PF4 and TSP-1 were similar to control in early-stage
cancer but significantly reduced in platelets of patients
with advanced lung cancer (Fig. 1EeG).
C I-II III-IV
200
250
300
350
***
Platelet count (x10^9/L
Platelet count (x10
9
/L)
C I-II III-IV
15
16
17
18
19
20
***
CTAPIII (ng/10^6 platelets)
CI-IIIII-IV
0
200
400
600
800
TSP-1 (ng/ml PFP)
C I-II III-IV
40
50
60
70
80
TSP-1 (ng/10^6 platelets)
**
C I-II III-IV
100
150
200
250
300
350
***
*
PF4 (ng/ml PFP)
C I-II III-IV
250
300
350
400
450
500
*
PDGF (pg/ml PFP)
CI-IIIII-IV
20
40
60
80
*
VEGF (pg/ml PFP)
TNM-stageTNM-stage
TNM-stage
TNM-stageTNM-stage
C I-II III-IV
25
30
35
40
45
***
PF4 (ng/10^6 platelets)
C I-II III-IV
0.4
0.6
0.8
1.0
1.2
***
***
VEGF (pg/10^6 platelets)
C I-II III-IV
30
40
50
60
**
PDGF (ng/10^6 platelets)
C I-II III-IV
6.0
6.5
7.0
7.5
8.0
**
Mean platelet volume (fL)
Healthy controls, C
Lung cancer, stage I-II
Lung cancer, stage III-IV
A
MPV (fL)
B
VEGF (pg/10
6
platelets)
C
PF4 (ng/10
6
platelets)
TSP-1 (ng/10
6
platelets)
VEGF (pg/ml PFP)
PDGF (pg/ml PFP)
PF4 (ng/ml PFP)
TSP-1 (ng/ml PFP)
PDGF (ng/10
6
platelets)
GFD
I
H
J
K
L
C I-II III-IV
200
250
300
350
400
450
**
***
CTAPIII (ng/ml PFP)
CTAPIII (ng/ml PFP) CTAPIII (ng/10
6
platelets)
J
E
Fig. 1. Lung cancer is associated with changes in platelet count, mean platelet volume (MPV), growth factor content and activation.
Platelet characteristics were measured in healthy controls (C, n Z 86) and in patients with lung cancer, that were subdivided according to
TNM-stage (IeII, n Z 39; IIIeIV, n Z 47). (AeB) Whole blood platelet count and MPV were measured after blood collection. (CeL)
Platelets and platelet-free plasma (PFP) were isolated from whole blood. Concentrations of VEGF, PDGF, CTAPIII, PF4 and TSP-1
were determined in platelets (CeG) and PFP (HeL). Data are presented as means with standard errors of the means. *p < 0.05;
**p < 0.01; ***p < 0.001. TNM, tumour-node-metastasis; PDGF, platelet-derived growth factor; PF4, platelet factor 4; CTAPIII,
connective tissueeactivating peptide III; TSP-1, thrombospondin-1; VEGF, vascular endothelial growth factor.
S. Sabrkhany et al. / European Journal of Cancer 80 (2017) 5e138

Figures (5)
Citations
More filters

Journal ArticleDOI
19 Dec 2019-Cancers
TL;DR: In this regard, liquid biopsy represents a minimally invasive and more comprehensive option for early detection and investigation of this tumor.
Abstract: Non-small cell lung cancer is one leading cause of death worldwide, and patients would greatly benefit from an early diagnosis. Since targeted and immunotherapies have emerged as novel approaches for more tailored treatments, repeated assessments of the tumor biology have become pivotal to drive clinical decisions. Currently, tumor tissue biopsy is the gold standard to investigate potentially actionable biomarkers, but this procedure is invasive and may prove inadequate to represent the whole malignancy. In this regard, liquid biopsy represents a minimally invasive and more comprehensive option for early detection and investigation of this tumor. Today, cell-free DNA is the only approved circulating marker to select patients for a targeted therapy. Conversely, the other tumor-derived markers (i.e., circulating tumor cells, miRNAs, exosomes, and tumor educated platelets) are still at a pre-clinical phase, although they show promising results for their application in screening programs or as prognostic/predictive biomarkers. The main challenges for their clinical translation are the lack of reliable cutoffs and, especially for miRNAs, the great variability among the studies. Moreover, no established tool has been approved for circulating tumor cells and exosome isolation. Finally, large prospective clinical trials are mandatory to provide evidence of their clinical utility.

47 citations


Cites background from "A combination of platelet features ..."

  • ...Platelet derived growth factor, VEGF, and other growth factors produced by tumor cells are known to change the expression of mRNA present in platelets, leading to a specific spliced mRNA signature [91,92]....

    [...]


Journal ArticleDOI
30 May 2019-Blood
TL;DR: This work provides further insight into TEPs, and focuses on the evaluation of biomarker types, including circulating tumor DNA, circulating tumor cells, extracellular vesicles, and tumor-educated platelets.
Abstract: Liquid biopsies have been considered the holy grail in achieving effective cancer management, with blood tests offering a minimally invasive, safe, and sensitive alternative or complementary approach for tissue biopsies. Currently, blood-based liquid biopsy measurements focus on the evaluation of biomarker types, including circulating tumor DNA, circulating tumor cells, extracellular vesicles (exosomes and oncosomes), and tumor-educated platelets (TEPs). Despite the potential of individual techniques, each has its own advantages and disadvantages. Here, we provide further insight into TEPs.

41 citations


Journal ArticleDOI
TL;DR: It is demonstrated that the platelet proteome of patients with early-stage lung or head of pancreas cancer differs considerably compared to that of healthy individuals of matched sex and age and after surgical resection of the tumor.
Abstract: Platelets play an important role in tumor growth and, at the same time, platelet characteristics are affected by cancer presence. Therefore, we investigated whether the platelet proteome harbors differentially expressed proteins associated with early-stage cancer. For this proof-of-concept study, patients with early-stage lung (n = 8) or head of pancreas cancer (n = 4) were included, as were healthy sex- and age-matched controls for both subgroups. Blood samples were collected from controls and from patients before surgery. Furthermore, from six of the patients, a second sample was collected two months after surgery. NanoLC-MS/MS-based proteomics of gel-fractionated platelet proteins was used for comparative spectral count analyses of patients to controls and before to after surgery samples. The total platelet proteome dataset included 4384 unique proteins of which 85 were significantly (criteria Fc > 1.5 and p Biological significance Currently, most blood-based diagnostics/biomarker research is performed in serum or plasma, while the content of blood cells is usually neglected. It is known that especially blood platelets, which are the main circulating pool of many bioactive proteins, such as growth factors, chemokines, and cytokines, are a potentially rich source of biomarkers. The current study is the first to measure the effect of early-stage cancer on the platelet proteome of patients. Our study demonstrates that the platelet proteome of patients with early-stage lung or head of pancreas cancer differs considerably compared to that of healthy individuals of matched sex and age. In addition, the platelet proteome of cancer patients normalized after surgical resection of the tumor. Exploiting platelet proteome differences linked to both tumor presence and disease status, we were able to demonstrate that the platelet proteome can be mined for potential biomarkers of cancer.

41 citations


Journal ArticleDOI
TL;DR: This review covers some of the current issues related to the loop between platelets and tumor aggression, including the manners of tumor cells in "educating" Platelets and biofunctional alterations of platelets upon tumor "education".
Abstract: While platelets are traditionally recognized to play a predominant role in hemostasis and thrombosis, increasing evidence verifies its involvement in malignancies. As a component of the tumor microenvironment, platelets influence carcinogenesis, tumor metastasis and chemotherapy efficiency. Platelets status is thus predictable as a hematological biomarker of cancer prognosis and a hot target for therapeutic intervention. On the other hand, the role of circulating tumor cells (CTCs) as an inducer of platelet activation and aggregation has been well acknowledged. The cross-talk between platelets and CTCs is reciprocal on that the CTCs activate platelets while platelets contribute to CTCs' survival and dissemination. This review covers some of the current issues related to the loop between platelets and tumor aggression, including the manners of tumor cells in "educating" platelets and biofunctional alterations of platelets upon tumor "education". We also highlight the potential clinical applications on the interplay between tumors and platelets. Further studies with well-designed prospective multicenter trials may contribute to clinical "liquid biopsy" diagnosis by evaluating the global changes of platelets.

21 citations


Journal ArticleDOI
TL;DR: Several inflammation markers were found to have a prognostic value in cancer and the significance of preoperative white cell ratios in determining gastrointestinal stromal tumors outcome was investigated.
Abstract: BACKGROUND AND OBJECTIVES Several inflammation markers were found to have a prognostic value in cancer. We investigated the significance of preoperative white cell ratios in determining gastrointestinal stromal tumors (GISTs) outcome. METHODS Clinicopathological features of patients who underwent surgery for GIST were reviewed. The following peripheral blood inflammation markers were calculated: neutrophil-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), platelet-lymphocyte ratio (PLR), neutrophil-white blood cell ratio (NWR), lymphocyte-white cell ratio (LWR), monocyte-white cell ratio (MWR), and platelet-white cell ratio (PWR). RESULTS We analyzed 127 patients. Three- and five-year disease-free survival (DFS) were 89.7% and 86.9%, respectively. The univariate analysis selected tumor diameter (P = 0.003), gastric location ( P = 0.024), cell type ( P = 0.024), mitosis ( P < 0.001), MLR ( P = 0.014), NLR ( P = 0.016), and PLR ( P = 0.001) as the factors associated to DFS. The independent prognostic factors for DFS were mitosis ( P = 0.001), NLR ( P = 0.015), MLR ( P = 0.015), and PLR ( P = 0.031), with MLR showing the highest statistical significance and hazard ratio (HR) value. MLR, NLR, and PLR were the only prognostic factors in the subgroup of patients with moderate to high Miettinen's risk class. A high value of MLR was associated with reduced DFS. CONCLUSION MLR, NLR, and PLR are independent prognostic factors for DFS in GISTs. We first demonstrated the role of MLR as a predictor of recurrence in GIST. Its inclusion into clinical management may improve the recurrence estimation.

19 citations


Cites background from "A combination of platelet features ..."

  • ...Both platelets and megakaryocytes are able to sequester tumor‐derived proteins by endocytosis and protect cancer cells from immune clearance, which favors their adhesion to the endothelium, thus facilitating spreading.(33) Moreover, platelets may induce a more malignant phenotype in cancer cells characterized by enhanced migratory properties....

    [...]

  • ...Moreover, platelets may induce a more malignant phenotype in cancer cells characterized by enhanced migratory properties.(33,34) There is accumulating evidence of the prognostic value of several blood cell combinations in oncologic patients....

    [...]


References
More filters

Journal ArticleDOI
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.
Abstract: 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. 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 using data from the Surveillance, Epidemiology, and End Results cancer registries. In addition, current treatment patterns for the most prevalent cancer types are presented based on information in the National Cancer Data Base and treatment-related side effects are briefly described. More than 15.5 million Americans with a history of cancer were alive on January 1, 2016, and this number is projected to reach more than 20 million by January 1, 2026. The 3 most prevalent cancers are prostate (3,306,760), colon and rectum (724,690), and melanoma (614,460) among males and breast (3,560,570), uterine corpus (757,190), and colon and rectum (727,350) among females. More than one-half (56%) of survivors were diagnosed within the past 10 years, and almost one-half (47%) are aged 70 years or older. People with a history of cancer have unique medical and psychosocial needs that require proactive assessment and management by primary care providers. Although there are a growing number of tools that can assist patients, caregivers, and clinicians in navigating the various phases of cancer survivorship, further evidence-based resources are needed to optimize care. CA Cancer J Clin 2016;66:271-289. © 2016 American Cancer Society.

4,942 citations


Book
16 Mar 2009
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.
Abstract: Introduction.- Applications of prediction models.- Study design for prediction models.- Statistical models for prediction.- Overfitting and optimism in prediction models.- Choosing between alternative statistical models.- Dealing with missing values.- Case study on dealing with missing values.- Coding of categorical and continuous predictors.- Restrictions on candidate predictors.- Selection of main effects.- Assumptions in regression models: Additivity and linearity.- Modern estimation methods.- Estimation with external methods.- Evaluation of performance.- Clinical usefulness.- Validation of prediction models.- Presentation formats.- Patterns of external validity.- Updating for a new setting.- Updating for a multiple settings.- Prediction of a binary outcome: 30-day mortality after acute myocardial infarction.- Case study on survival analysis: Prediction of secondary cardiovascular events.- Lessons from case studies.

2,442 citations


Journal ArticleDOI
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.

2,177 citations


Journal ArticleDOI
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.

960 citations


Additional excerpts

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

    [...]


Journal ArticleDOI
TL;DR: Findings support the existence of a paracrine circuit wherein increased production of thrombopoietic cytokines in tumor and host tissue leads to paraneoplasticThrombocytosis, which fuels tumor growth.
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.,

527 citations


Additional excerpts

  • ...Malignant cells produce thrombopoietic cytokines, leading to paraneoplastic thrombocytosis [8]....

    [...]

  • ...Tumours can increase platelet production by secretion of thrombopoietic cytokines, leading to paraneoplastic thrombocytosis [7,8]....

    [...]


Related Papers (5)
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.