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Sepsis is a medical emergency that needs rapid identification and treatment to create the best possible outcomes. However, in the early stages it can be very difficult to distinguish sepsis from uncomplicated infection. This article summarizes recent developments in sepsis nomenclature and definitions as well as providing an insight into the role that biomarkers might play in diagnosis and prognosis.
Background
Sepsis is a life-threatening condition associated with high morbidity and mortality, with the risk of death ranging from 30% to 80% depending on the severity of the disease. The World Health Organization estimates that more than 30 million people are affected by sepsis worldwide every year [1], although for reasons discussed by Candel et al., the actual epidemiology of sepsis is difficult to ascertain [2]. In the UK and USA it is thought that sepsis is the cause of around 37 000 and nearly 270 000 deaths per year, respectively [3, 4]. Outcomes of sepsis are better if it is detected and treated early, but despite the large numbers of people affected by it, public awareness of it is still low. In recent years, awareness campaigns have been launched and this year several popular TV and radio programmes in the UK have featured sepsis storylines (Call the Midwife, Coronation Street and The Archers).
Definitions
The difficulties experienced in studying the epidemiology of sepsis are likely to reflect the problems of characterization and diagnosis of the disease, which is in turn a reflection of the complex nature of the condition. Original definitions of sepsis date back to 1991, with the idea that sepsis was caused by systemic inflammatory response syndrome (SIRS) in resulting from infection. In 2001 the definitions were re-examined but left largely unchanged. In 2016, a task force re-evaluated and updated definitions of sepsis and septic shock (Box 1), taking into account improved understanding of the pathobiology of sepsis, which is now recognized to involve early activation of both pro- and anti-inflammatory responses, along with major modifications in non-immunologic pathways such as cardiovascular, neuronal, autonomic, hormonal, bioenergetic, metabolic, and coagulation [5]. A lay definition of sepsis published in 2011 [6] was also accepted by the 2016 task force (Box 1). The definitions created in 1991, 2001 and 2016 have been designated Sepsis-1, Sepsis-2 and Sepsis-3, respectively, to indicate the need for ongoing refinement.
Diagnosis of sepsis
Early diagnosis and treatment of sepsis is associated with improved outcomes, but the difficulty lies in distinguishing sepsis from uncomplicated infection. Identification of patients with sepsis is largely achieved through the use of the Sequential (or Sepsis-Related) Organ Failure Assessment (SOFA) score (Table 1) in the hospital setting or the quick SOFA (qSOFA) score (See Figure 1 “Operationalization of Clinical Criteria Identifying Patients With Sepsis and Septic Shock” in Singer et al. [5]). Commencement of treatment should occur within the first hour of admission and should not be delayed by waiting for results from the lab, as the SOFA score can be applied retrospectively. Management of sepsis also requires (amongst other things) that blood samples are taken before broad spectrum antibiotics are administered and that once the pathogen has been identified antibiotic usage can be refined to aid antimicrobial stewardship (See the Surviving Sepsis Campaign [7] and NICE guidelines [8] for full details of early sepsis management). Sepsis is most commonly caused by bacterial infection, but can also be due to fungal, viral or parasitic infection. However, identification of the pathogen and its antibiotic susceptibility and/or resistance by classic culture techniques is slow and molecular- and proteomic-based approaches, such as matrix-assisted laser desorption/ionization–time of flight (MALDI-TOF) spectroscopy, may improve turnaround times [9].
Biomarkers
The difficulty of distinguishing sepsis from uncomplicated infection has long driven the search for suitable biomarkers to aid sepsis diagnosis. An ideal biomarker would be able to distinguish sepsis from non-infectious causes of critical illness, having a fast and specific increase in sepsis and a rapid decrease after effective therapy. A number of potential biomarkers have been identified, although none are specific enough to be used alone.
Procalcitonin and C-reactive protein
The most-studied biomarkers are procalcitonin and C-reactive protein (CRP). CRP is an acute-phase protein that is secreted from the liver in the response to inflammatory processes and is therefore sensitive but not specific for sepsis. Procalcitonin, again is produced in response to inflammation and infection, and is so far the only biomarker to be used clinically, as it differentiates better than CRP between infectious and non-infectious causes of critical illness. A meta-analysis found that procalcitonin had a mean sensitivity and specificity of around 70% and an area under receiver operator characteristic curve of less than 0.80 [10]. However as levels of procalcitonin are known to be raised after surgery, trauma and viral infection, the Surviving Sepsis Campaign concluded that procalcitonin levels are not adequate to distinguish sepsis from other causes of inflammation [11], although it may be useful for indicating when treatment with antibiotics can end [12].
Interleukin 6 (IL-6)
IL-6 was initially a biomarker of interest for rapid sepsis diagnosis as it has a fast kinetic profile – the concentration increases within 2 hours of onset of sepsis and decreases within 6 hours. However, the results from studies have been mixed, with some suggesting that it was able to discriminate between sepsis and non-infectious illness, whereas others found that procalcitonin was better, hence it has not been added to current guidelines [11].
Promising biomarkers
A number of other biomarkers have been identified that show promise include soluble urokinase-type plasminogen activator receptor, presepsin and proadrenomedullin [2, 13]. Additionally, recently, reduced serum levels of fetuin-A (a major hepatokine) were found to be independently associated with predicting progression to septic shock and higher rates of mortality [14].
Biomarker panels
Even today, no single biomarker has the diagnostic strength to identify patients suffering from sepsis and it is likely that assessing panels of biomarkers will increase the sensitivity and accuracy of diagnosis of sepsis, compared to any individual biomarker (for example, see the study by Kofoed et al. [15]). More recently, the power of mass spectrometry and “-omics studies” is being investigated with some promise, although still suffering from limitations [13].
References
1. Sepsis. World Health Organization 2018; http://www.who.int/news-room/fact-sheets/detail/sepsis.
2. Candel FJ, et al. Current aspects in sepsis approach. Turning things around. Rev Esp Quimioter 2018; 31(4): 298–315.
3. Improving outcomes for patients with sepsis: a cross-system action plan. NHS England 2015; https://www.england.nhs.uk/wp-content/uploads/2015/08/Sepsis-Action-Plan-23.12.15-v1.pdf.
4. Sepsis. Centers for Disease Control and Prevention 2018; https://www.cdc.gov/sepsis/datareports/index.html.
5. Singer M, et al. The Third International Consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 2016; 315(8): 801–810.
6. Czura CJ. Merinoff symposium 2010: Sepsis – speaking with one voice. Mol Med 2011; 17(1-2): 2–3.
7. Surviving Sepsis Campaign: International guidelines for management of sepsis and septic shock: 2016. Surviving Sepsis Campaign 2016; http://www.survivingsepsis.org/Guidelines/Pages/default.aspx.
8. Sepsis: recognition, diagnosis and early management; NICE guideline [NG51]. National Institutes for Health and Care Excellence 2017; https://www.nice.org.uk/guidance/NG51/chapter/Recommendations#identifying-people-with-suspected-sepsis.
9. Ward KM, Harris R. Sepsis: earlier organism identification using MALDI-TOF. Clin Lab Int 2015; Nov: 14–18.
10. Wacker C, et al. Procalcitonin as a diagnostic marker for sepsis: a systematic review and meta-analysis. Lancet Infect Dis 2013; 13: 426–435.
11. Dellinger RP, et al. Surviving sepsis campaign: international guidelines for management of severe sepsis and septic shock: 2012. Crit Care Med 2013; 41(2): 580–637.
12. Sager R, et al. Procalcitonin-guided diagnosis and antibiotic stewardship revisited. BMC Med 2017; 15: 15.
13. Ludwig KR, Hummon AB. Mass spectrometry for the discovery of biomarkers of sepsis. Mol Biosyst 2017; 13(4): 648–664.
14. Karampela. Karampela I, Kandri E, Antonakos G, Vogiatzakis E, Christodoulatos GS, Nikolaidou A, Dimopoulos G, Armaganidis A, Dalamaga M. Kinetics of circulating fetuin-A may predict mortality independently from adiponectin, high molecular weight adiponectin and prognostic factors in critically ill patients with sepsis: A prospective study. J Crit Care 2017; 41: 78–85.
15. Kofoed K, et al. Use of plasma C-reactive protein, procalcitonin, neutrophils, macrophage migration inhibitory factor, soluble urokinase-type plasminogen activator receptor, and soluble triggering receptor expressed on myeloid cells-1 in combination to diagnose infections: a prospective study. Crit Care 2007; 11(2): R38.
Prostate cancer is the most common cancer in men and diagnosis involves a combination of assessments. Levels of the biomarker prostate-specific antigen (PSA) are commonly measured, but do not always equate to cancer status. Testing of PSA in combination with other biomarkers may help to improve diagnostic and prognostic accuracy, as well as minimizing overdiagnosis as well as unnecessary intervention. This article provides a summary of the information currently known about biomarkers showing promise for use in prostate cancer screening.
by Dr Alexandra Tabakin, Dr Sung Un Bang and Dr Isaac Y. Kim
Introduction
Prostate cancer is the most commonly diagnosed cancer type in the USA and second leading cause of cancer deaths in men. In 2018, there will be an estimated 164 690 new cases with an estimated 29 430 deaths [1]. Although many prostate cancers are indolent in nature and can be safely monitored with active surveillance, a significant proportion of patients will require intervention with surgery, radiation, or other therapies. One of the major challenges in treating prostate cancer is risk stratification and differentiating clinically significant prostate cancers in order to avoid overdiagnosis and overtreatment [2]. To do so, patients undergo risk stratification, which conventionally includes a combination of prostate-specific antigen (PSA) screening, digital rectal exam (DRE), trans-rectal ultrasonography (TRUS)-guided biopsy. Currently clinically insignificant prostate cancer, according to Epstein criteria, is defined as cancer with preoperative PSA <10ng/ml, clinical stage <T1c, Gleason score <6, PSA density <0.15, <2 positive biopsy cores, and <50% cancer involvement in any core [3–5]. However, as we learn more about the heterogeneity of prostate cancer tumours, various serum biomarkers have been investigated to improve diagnostic and prognostic accuracy and minimize treatment. In this review, we discuss various biomarkers and their utilization for the prediction of clinically significant prostate cancer.
Serum biomarkers
PSA
PSA, or prostate-specific antigen, is a serine protease released by the prostate. PSA gained notoriety in the 1980s when it was reported to have various uses including screening, monitoring disease progression, and detecting recurrence of the cancer. As PSA screening for prostate cancer increased, the incidence of prostate cancer also rose in the 1990s. However, PSA only has a 25–40% specificity rate for prostate cancer and can be elevated with infection, trauma, and benign prostatic hyperplasia (BPH) [6]. In fact, about 15% of men with a low level of PSA (<4.0 ng/ml) have prostate cancer [7].
Because the harms of biopsy and prostate cancer treatment may outweigh the benefits in some patients with clinically insignificant prostate cancer, efforts have been made to improve the validity of PSA in differentiating benign conditions and prostate cancer. One such effort is the use of free PSA; those with an elevated PSA and a lower serum percentage of free PSA (%f-PSA) are more likely to have BPH rather than prostate cancer [8]. The Prostate Health Index (PHI) formula incorporates serum total PSA, free PSA, and the [−2]proPSA to discriminate Gleason 3+4 and greater cancers with 90% sensitivity and 17% specificity [9]. 4K score is another novel test which is comprised of total PSA, free PSA, intact PSA, and human kallikrein-related peptidase 2 to detect clinically significant prostate cancer and discern those who would benefit from a prostate biopsy while preventing 30–58% of biopsies [10].
PAP
Prostatic acid phosphatase (PAP) was the first popularized prostate cancer serum biomarker, but was eventually replaced by PSA, as it is less sensitive in diagnosing prostate cancer and detecting recurrence. Elevated PAP level has been associated with a high risk of bone metastases [11], significantly shortened overall survival [12] as well as disease-free survival [13], and increased risk of biochemical recurrence [14]. Interestingly, recent studies have shown that PAP may be useful in detecting high-risk clinically significant prostate cancer patients; it is speculated that PAP may be associated with micro-metastatic disease prior to treatment, and therefore, predict response to treatment [15].
NLR, ANC, ALC
Inflammation and tissue microenvironment are important factors in cancer development. Although the temporal relationship is not well established, a lymphocyte-mediated immune response is thought to occur early in the development of prostate cancer. Neutrophil-to-lymphocyte ratio (NLR) compares the activity of both neutrophils and lymphocytes in the inflammatory response. NLR is a useful prognostic biomarker in mCRPC, where a higher score correlates less relative lymphocyte activity and a worse prognosis. When looking at localized low-risk prostate cancer, studies have shown that NLR is not associated with upstaging, upgrading, or biochemical recurrence. However, increased absolute lymphocyte count (ALC) and absolute neutrophil count (ANC) were associated with upstaging and lower 5-year biochemical recurrence-free survival. More development on these markers may guide clinicians in re-stratifying patients who meet conventional criteria for low-risk prostate cancer [16].
Urine biomarkers
PCA3
Urinary prostate cancer antigen 3 (PCA3) is among the most promising non-invasive biomarker among non-PSA based tests, as it is overexpressed in over 95% of prostate cancers [6]. PCA3 is a long noncoding RNA collected from shed prostate cells during urination. PCA3 is a more specific biomarker for prostate cancer because, unlike PSA, PCA3 levels are not influenced by prostate size, BPH, prostatitis, or the use of 5α-reductase inhibitors [17]. At the genetic level, PCA3 may help distinguish between prostate cancer and high-grade prostatic intraepithelial neoplasia (HG-PIN), as PCA3 is seldom expressed in HG-PIN [18]. Several studies have demonstrated that a higher PCA3 score correlates with clinically significant prostate cancer and larger tumour volume, therefore potentially aiding in selecting patients for active surveillance [19]. In 2012, the FDA approved the PROGENSA PCA3 assay predict men who would benefit from a repeat biopsy in those with a previous negative prostate biopsy [6]. In a recent meta-analysis of 46 clinical trials, the sensitivity and specificity of PROGENSA PCA3 was 65% and 73%, respectively [20].
Genetic mutations
TMPRSS2::ERG
TMPRSS2::ERG, or T2:ERG, gene fusions constitute 90% of gene fusions implicated in prostate cancer, as well as half of all prostate cancers [21, 22]. When the androgen responsive regulatory element TMPRSS2 and the gene for the transcription factor ERG are fused together, androgen-driven genes are overexpressed and tumorigenesis occurs [21]. When used alone, urinary T2:ERG RNA has a reported 86% specificity and 45% sensitivity in prostate cancer detection. However, when used in conjunction with PCA3, specificity and sensitivity improve to 90% and 80%, respectively [6, 23]. Moreover, this test may prevent up to 42% of unnecessary biopsies, limiting healthcare costs [24].
PTEN
Loss of the tumour-suppressor gene, PTEN, or phosphatase and tensin homologue, leading to activation of the PI3K/AKT/mTOR signalling has been found in both early stage and castrate-resistant prostate cancers. Preclinical data show that PI3K pathway activation is related to resistance to androgen deprivation, leading to disease progression and poor response to treatment. In mouse models, conditional deletion of PTEN initially led to the development of prostate hyperplasia and later on invasive and metastatic prostate cancers likely by modulating the p110β catalytic subunit of PI3K. In addition, ablation of p110β hindered AKT signalling and reduced tumorigenesis [25]. In a cohort of 77 men, loss of PTEN at initial prostate biopsy was predictive of the development of castrate-resistant prostate cancer, response to androgen deprivation therapy, and prostate-cancer-specific mortality [26]. Additionally, decreased PTEN expression has been associated with increased risk of biochemical and clinical recurrence after prostatectomy [27]. Therefore, the association between castrate-resistant prostate cancer and PTEN loss as well as PI3K/AKT/mTOR pathway activation suggests that PTEN may have prognostic value in prostate cancer risk stratification.
CHD1
The CHD1 gene, encoding chromodomain helicase DNA-binding protein 1, is commonly deleted in 10–26% of all prostate cancers. The loss of CHD1 affects the ability to repair DNA double-strand breaks via homologous recombination. CHD1 deletion has generally been associated with a poor prognosis. However, studies have demonstrated tumours with CHD1 deletions to be sensitive to both PARP inhibitors and carboplatin, both in vitro and in vivo, suggesting that future research may be able to identify to response to treatment based off of tumour genotypes [28].
Circulating biologics
Circulating tumour cells
Circulating tumour cells (CTCs) are defined as cells that leave the site of a primary cancer, travel through the bloodstream, and settle at other sites in the body where they grow into new tumours, or metastases [29]. In 2004, CellSearch Circulating Tumor Cell System was approved by the FDA as an assay for enumerating CTCs. In recent studies, CTC count, deemed the ‘liquid biopsy’, has been shown to have a role in predicting overall survival and response to treatment, risk stratification, and detecting metastases. In addition, using CTCs to detect biomarkers, such as ERG, PTEN, and AR may allow for personalized treatments based on tumour genomes [9, 30].
Circulating exosomes
Circulating prostate cancer-related exosomes are double-membrane vesicles carrying RNA and pro-oncogenic molecules, which induce malignant transformation in normal cells. ExoDx prostate Intelliscore urine exosome assay (developed by Exosome Diagnostics Inc.) detects exosomal RNA expression of ERG, PCA3 and SPDEF (SAM pointed domain containing ETS transcription factor) in voided urine samples. A score is generated that is able to predict high-grade prostate cancer (Gleason >7) with a negative predictive value of 91%. This assay may be useful in discriminating clinically significant prostate cancer and reducing the number of unnecessary biopsies [31].
Conclusion
The emergence and widespread usage of PSA as a routine screening test for prostate cancer allows for early detection and risk stratification. Testing for PSA in combination with novel biomarkers such as PCA3, TMPRSS2::ERG, PTEN, and others may improve our diagnostic abilities, given the heterogeneity of prostate cancers and their treatments. There are many challenges in establishing useful biomarkers including creating affordable and accessible testing, determining cut-offs, and determining accuracy. As the clinical utility of these biomarkers is further defined, we hope to better risk stratify patients and select appropriate treatments early on in diagnosis. Further research efforts should focus on the synergism between the Epstein criteria, biomarker utility, and how to best bring these tools from bench to bedside.
References
1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA Cancer J Clin 2018; 68(1): 7–30.
2. Lees K, Durve M, Parker C. Active surveillance in prostate cancer: patient selection and triggers for intervention. Curr Opin Urol 2012; 22(3): 210–215.
3. D’Amico AV, Whittington R, Malkowicz SB, Weinstein M, Tomaszewski JE, Schultz D, et al. Predicting prostate specific antigen outcome preoperatively in the prostate specific antigen era. J Urol 2001; 166(6): 2185–2188.
4. Epstein JI, Chan DW, Sokoll LJ, Walsh PC, Cox JL, Rittenhouse H, et al. Nonpalpable stage T1c prostate cancer: prediction of insignificant disease using free/total prostate specific antigen levels and needle biopsy findings. J Urol 1998; 160(6): 2407–2411.
5. Klotz L, Zhang L, Lam A, Nam R, Mamedov A, Loblaw A. Clinical results of long-term follow-up of a large, active surveillance cohort with localized prostate cancer. J Clin Oncol 2010; 28(1): 126–131.
6. Sanguedolce F, Cormio A, Brunelli M, D’Amuri A, Carrieri G, Bufo P, et al. Urine TMPRSS2: ERG fusion transcript as a biomarker for prostate cancer: literature review. Clin Genitourin Cancer 2016; 14(2): 117–121.
7. Prensner JR, Rubin MA, Wei JT, Chinnaiyan AM. Beyond PSA: the next generation of prostate cancer biomarkers. Sci Transl Med 2012; 4(127): 127rv3.
8. Grossklaus DJ, Smith JA, Shappell SB, Coffey CS, Chang SS, Cookson MS. The free/total prostate-specific antigen ratio (%fPSA) is the best predictor of tumor involvement in the radical prostatectomy specimen among men with an elevated PSA. Urol Oncol 2002; 7(5): 195–198.
9. Chistiakov DA, Myasoedova VA, Grechko AV, Melnichenko AA, Orekhov AN. New biomarkers for diagnosis and prognosis of localized prostate cancer. Semin Cancer Biol 2018; doi: 10.1016/j.semcancer.2018.01.012.
10. Parekh DJ, Punnen S, Sjoberg DD, Asroff SW, Bailen JL, Cochran JS, et al. A Multi-institutional prospective trial in the USA confirms that the 4Kscore accurately identifies men with high-grade prostate cancer. Eur Urol 2015; 68(3): 464–470.
11. Whitesel JA, Donohue RE, Mani JH, Mohr S, Scanavino DJ, Augspurger RR, et al. Acid phosphatase: its influence on the management of carcinoma of the prostate. J Urol 1984; 131(1): 70–71.
12. Johnson DE, Prout GR, Scott WW, Schmidt JD, Gibbons RP, et al. Clinical significance of serum acid phosphatase levels in advanced prostatic carcinoma. Urology 1976; 8(2): 123–126.
13. Moul JW, Connelly RR, Perahia B, McLeod DG. The contemporary value of pretreatment prostatic acid phosphatase to predict pathological stage and recurrence in radical prostatectomy cases. J Urol 1998; 159(3): 935–940.
14. Han M, Piantadosi S, Zahurak ML, Sokoll LJ, Chan DW, Epstein JI, et al. Serum acid phosphatase level and biochemical recurrence following radical prostatectomy for men with clinically localized prostate cancer. Urology 2001; 57(4): 707–711.
15. Faiena I, Kim S, Farber N, Kwon YS, Shinder B, Patel N, et al. Predicting clinically significant prostate cancer based on pre-operative patient profile and serum biomarkers. Oncotarget 2017; 8(65): 109783–109790.
16. Kwon YS, Han CS, Yu JW, Kim S, Modi P, Davis R, et al. Neutrophil and lymphocyte counts as clinical markers for stratifying low-risk prostate cancer. Clin Genitourin Cancer 2016; 14(1): e1–8.
17. Roobol MJ, Schröder FH, van Leeuwen P, Wolters T, van den Bergh RCN, van Leenders GJLH, et al. Performance of the prostate cancer antigen 3 (PCA3) gene and prostate-specific antigen in prescreened men: exploring the value of PCA3 for a first-line diagnostic test. Eur Urol 2010; 58(4): 475–481.
18. Wei W, Leng J, Shao H, Wang W. High PCA3 scores in urine correlate with poor-prognosis factors in prostate cancer patients. Int J Clin Exp Med 2015; 8(9): 16606–16612.
19. Ploussard G, Epstein JI, Montironi R, Carroll PR, Wirth M, Grimm M-O, et al. The contemporary concept of significant versus insignificant prostate cancer. Eur Urol 2011; 60(2): 291–303.
20. Cui Y, Cao W, Li Q, Shen H, Liu C, Deng J, et al. Evaluation of prostate cancer antigen 3 for detecting prostate cancer: a systematic review and meta-analysis. Sci Rep 2016; 6: 25776.
21. Burkhardt L, Fuchs S, Krohn A, Masser S, Mader M, Kluth M, et al. CHD1 is a 5q21 tumor suppressor required for ERG rearrangement in prostate cancer. Cancer Res 2013; 73(9): 2795–2805.
22. Tomlins SA, Laxman B, Varambally S, Cao X, Yu J, Helgeson BE, et al. Role of the TMPRSS2-ERG gene fusion in prostate cancer. Neoplasia 2008; 10(2): 177–188.
23. Salami SS, Schmidt F, Laxman B, Regan MM, Rickman DS, Scherr D, et al. Combining urinary detection of TMPRSS2: ERG and PCA3 with serum PSA to predict diagnosis of prostate cancer. Urol Oncol 31(5): 566–571.
24. Sanda MG, Feng Z, Howard DH, Tomlins SA, Sokoll LJ, Chan DW, et al. Association between combined TMPRSS2:ERG and PCA3 RNA urinary testing and detection of aggressive prostate cancer. JAMA Oncol 2017; 3(8): 1085–1093.
25. Crumbaker M, Khoja L, Joshua AM. AR Signaling and the PI3K pathway in prostate cancer. Cancers 2017; 9(4): doi: 10.3390/cancers9040034.
26. Mithal P, Allott E, Gerber L, Reid J, Welbourn W, Tikishvili E, et al. PTEN loss in biopsy tissue predicts poor clinical outcomes in prostate cancer. Int J Urol 2014; 21(12): 1209–1214.
27. Chaux A, Peskoe SB, Gonzalez-Roibon N, Schultz L, Albadine R, Hicks J, et al. Loss of PTEN expression is associated with increased risk of recurrence after prostatectomy for clinically localized prostate cancer. Mod Pathol 2012; 25(11): 1543–1549.
28. Kari V, Mansour WY, Raul SK, Baumgart SJ, Mund A, Grade M, et al. Loss of CHD1 causes DNA repair defects and enhances prostate cancer therapeutic responsiveness. EMBO Rep 2016; 17(11): 1609–1623.
29. West H, Jin JO. Circulating tumor cells. JAMA Oncol 2015; 1(3): 394.
30. Galletti G, Portella L, Tagawa ST, Kirby BJ, Giannakakou P, Nanus DM. Circulating tumor cells in prostate cancer diagnosis and monitoring: an appraisal of clinical potential. Mol Diagn Ther 2014; 18(4): 389–402.
31. McKiernan J, Donovan MJ, O’Neill V, Bentink S, Noerholm M, Belzer S, et al. A novel urine exosome gene expression assay to predict high-grade prostate cancer at initial biopsy. JAMA Oncol 2016; 2(7): 882–889.
The authors
Alexandra Tabakin MD,
Sung Un Bang MD, Isaac Yi Kim MD PhD
Section of Urologic Oncology, Rutgers Cancer Institute of New Jersey and Division of Urology, Rutgers Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
*Corresponding author
E-mail: kimiy@cinj.rutgers.edu
Lyme disease is caused by Borrelia spirochaetes: predominantly Borrelia burgdorferi in North America (but also present in Europe), and predominantly B. afzelii and B. garinii in Europe and Asia and is spread to people via infected deer ticks. Infection occurs after only a minority of tick bites, but is typified by three stages. Stage 1, early localized lyme disease is characterized by the bull’s eye rash (erythema migrans (EM)). Stage 2, early disseminated infection occurs within days to weeks after the local infection as the bacteria begin to spread through the bloodstream. Stage 3, late disseminated infection, where the infection has spread throughout the body, can occur several months later in untreated or inadequately treated patients involving chronic symptoms that can be severe and disabling. Treatment by antibiotics is effective in the early localized stage of the disease but this is often hampered by late diagnosis. Diagnosis can be delayed for a number of reasons: there is a lack of awareness in the general public (as well as GPs outside of what are thought to be the high-risk areas); approximately 25% of people do not get the typical bull’s eye rash; and symptoms can be so varied and vague that, when occurring weeks or months later, are difficult to relate back to the time of the tick bite. Knowledge of a tick bite and an associated EM rash is sufficient for diagnosis. However, in cases where there is a clinical suspicion of Lyme disease but no EM rash, laboratory testing is advised. Testing for antibodies is done via a two-tiered approach, starting with a sensitive ELISA, which, if positive or equivocal, is followed by a more specific immunoblot. However, the overall sensitivity of the two-tiered tests is only 64% when done in the early stages of infection, which is when accurate diagnosis is most needed. Because of these diagnostic limitations, the prevalence of Lyme disease is likely to be far higher than is currently thought. With increasing incidence and geographic spread of the disease, better testing for diagnosis, particularly in the early stages of infection, is perhaps required. Research is ongoing into PCR methods as well as and for the detection of OspA antigens that are shed into urine. An LLT-MELISA (lymphocyte transformation test-memory lymphocyte immunostimulation assay) has been developed and is suggested to be a useful supportive diagnostic tool, particularly in infections acquired in Europe. In the USA, next-generation sequencing (NGS) has been used for specific pathogen identification and to guide treatment decisions. With technological advances making NGS quicker and cheaper, could this eventually become the next gold standard test for Lyme disease?
The second edition of the Greiner Bio-One customer magazine bioLOGICAL is now available on their website. Interesting articles about capillary blood sampling are included in this issue. In the article by Jasna Lenicek Krleza, PhD, the reader will learn about which factors to pay special attention to in capillary blood collection to get high-quality samples. The neonatal station and small patients are also featured, together with the company’s MiniCollect® capillary blood collection system. In addition, it includes tips to help users find the most suitable vein for venipuncture.
https://tinyurl.com/ycu2f3ne
March 2024
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