COAGULATION BIOMARKER


Table 2. Thrombin generation parameters obtained in the absence and presence of thrombomodulin in patients compared with controls.


TG–TM Controls (n=52) Patients (n=522)


Lag time Min


Ratio


Time to peak Min


Ratio Peak


nM (%) ETP nM.min (%)


1273 (995–1591) 81.6 (62.4–103)


1354 (977–1824) 91.1 (67.5–122)


0.010 <0.001


1.97 (1.59–3.04) 1.17 (0.92–1.81)


4.07 (3.44–5.83) 1.19 (0.97–1.71)


260 (160–337) 80.0 (47.8–105)


1.97 (1.53–2.81) 1.17 (0.91–1.67)


3.85 (3.02–5.20) 1.11 (0.87–1.51)


304 (202–404) 97.6 (61.6–128)


0.473 0.481


0.001 0.001


<0.001 <0.001


P value TG+TM


Controls (n=52) Patients (n=522) Lag time


2.29 (1.82–3.56)*


Time to peak 4.01 (3.40–5.57) –


Peak


146 (53.8–241)† –


ETP


581 (219–910)† –


ETP: endogenous thrombin potential; TG: thrombin generation; TM: thrombomodulin *


P<0.05. † P<0.001 versus respective TM values.


precision for both scores was similar, with both showing an area under the curve (AUC) of approximately 0.76. According to selected cut- off values,


both scores were then able to stratify the patients into three risk categories. The E-DR rates were set at 0, 4.7 and 13.5% in the low, intermediate and high-risk categories (hazard ratio [HR]: 8.7; P<0.05, low vs. high risk). Even better discrimination was shown between low- and high-risk categories reached by the nETP-based model (0 vs. 13.5%, HR: 8.7). Without the inclusion of ETP, the only clinicopathological variable associated with E-DR was the TN molecular subtype. A risk stratification using this single variable could not match the ETP-based model. This was able to improve risk assessment and identify a group at low risk, with a cumulative incidence of 0.8% for absolute ETP and 0% for nETP, while increasing the number of patients in the high-risk category.


Improving high-risk patient assessment


The findings anticipate that the wider use of fully automated thrombin generation by means of the ST Genesia is likely to improve the early diagnosis of breast cancer in high-risk patients. Furthermore, with standardised results and greater reproducibility, the ST Genesia opens up the possibility that thrombin generation testing can be fully integrated into the clinical laboratory’s workflow processes, delivering greater productivity and overall efficiencies.


While it is felt that the predictive score 25


should be validated further in a separate cohort, the study is confident that measuring ETP through a standardised and automated system could soon become the basis for a widely-used test. This would help clinicians identify breast cancer patients at highest risk, enabling them to be prioritised for targeted surveillance and specific treatment strategies.


thromboembolic disease is associated with a poorer prognosis from subsequent malignancy. Br J Cancer 2009; 101 (5): 840–2. doi: 10.1038/sj.bjc.6605210.


PPi


References 1 Rasak NB, Jones G, Bhandari M, Berndt MC,


Metharom P. Cancer-associated thrombosis: an overview of mechanisms, risk factors, and treatment. Cancers (Basel) 2018; 10 (10): 380. doi: 10.3390/cancers10100380.


2 Eichinger S. Cancer associated thrombosis: risk factors and outcomes. Thromb Res 2016; 140 (Suppl 1): S12–S17. doi: 10.1016/S0049-3848(16)30092-5.


3 Mahajan A, Brunson A, White R, Wun T. The epidemiology of cancer-associated venous thromboembolism: an update. Semin Thromb Hemost 2019; 45 (4): 321–5. doi: 10.1055/s-0039-1688494.


4 Walker AJ, West J, Card TR, Crooks C, Kirwan CC, Grainge MJ. When are breast cancer patients at highest risk of venous thromboembolism? A cohort study using English health care data. Blood 2016; 127 (7): 849–57. doi: 10.1182/blood-2015-01-625582.


5 von Tempelhoff GF, Schönmann N, Heilmann L. Thrombosis – a clue of poor prognosis in primary non-metastatic breast cancer? Breast Cancer Res Treat 2002; 73 (3): 275–7. doi: 10.1023/a:1015864322007.


6 Jones A, Stockton DL, Simpson AJ, Murchison JT. Idiopathic venous


7 Marchetti M, Giaccherini C, Masci G et al.; HYPERCAN Investigators. Thrombin generation predicts early recurrence in breast cancer patients. J Thromb Haemost 2020; 18 (9): 2220–31. doi: 10.1111/jth.14891.


8 Gomez-Rosas P, Pesenty M, Verzeroli C et al.; HYPERCAN Investigators. Validation of the role of thrombin generation potential by a fully automated system in the identification of breast cancer patients at high risk of disease recurrence. TH Open 2021; 5 (1): e56–e65. doi: 10.1055/s-0040- 1722609. eCollection 2021 Jan.


9 Falanga A, Santoro A, Labianca R et al.; HYPERCAN Study Group. Hypercoagulation screening as an innovative tool for risk assessment, early diagnosis and prognosis in cancer: the HYPERCAN study. Thromb Res 2016; 140 (Suppl 1): S55–9. doi: 10.1016/S0049-3848(16)30099-8.


10 Gamba S, Raffaerli, Marchettti M et al. Hypercoagulable State as innovative tool for risk assessment and early cancer diagnosis: Data from HYPERCAN Prospective Study. Thromb Res 2018; 164 (Suppl 1): S241. doi: 10.1016/ j.thromres.2018.02.131.


Further information is available from: Diagnostica Stago UK Theale Lakes Business Park 12 Moulden Way, Sulhamstead Reading RG7 4GB Tel: +44 (0)845 054 0614 Web: www.stago-uk.com


APRIL 2021 WWW.PATHOLOGYINPRACTICE.COM


817 (393–1345)† –


<0.001


Data are reported as median and range (5th–95th percentiles). In the absence of thrombomodulin (–TM), non-normalised and normalised values are provided for each TG parameter. In the presence of thrombomodulin (+TM), TG parameters are provided only as absolute values.


205 (91.9–339)† –


0.039 2.23 (1.71–3.20)* 0.064 P value


3.88 (3.21–5.14) –


<0.001


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