








































Classification of Molecular Biomarkers
Ankeet Shah, Dominic C. Grimberg, Brant A. Inman

Duke Cancer Institute, Division of Urology, Duke University Medical Center, Durham, United States

Abstract

A “biomarker” is any measurable characteristic that indicates the presence or absence of disease or the biological 
response to a stimulus, typically an exposure or intervention. The FDA-NIH Biomarker Working Group has produced 
a document called Biomarkers, EndpointS and other Tools (BEST), which defines 7 categories of biomarkers according 
to their clinical usage: susceptibility and risk, diagnostic, monitoring, prognostic, predictive, pharmacodynamic and 
treatment response, and safety. We approach the classification of biomarkers in 2 additional ways: their bodily source 
and their measurement type. In the context of their use in genitourinary malignancy, we also consider factors that 
influence their use and reliability in clinical and research applications.

Introduction

A “biomarker” is any measurable characteristic that indicates the presence or absence of disease or the biological 
response to a stimulus, typically an exposure or intervention. The FDA-NIH Biomarker Working Group has defined 7 
categories of biomarkers according to their clinical usage: susceptibility and risk, diagnostic, monitoring, prognostic, 
predictive, pharmacodynamic and treatment response, and safety. We approach the classification of biomarkers 
in 2 additional ways: their bodily source and their measurement type. In the context of their use in genitourinary 
malignancy, we also consider factors that influence their use and reliability in clinical and research applications.

Biomarkers by Source
Blood
Blood and its various components represent a valuable source for a wide variety of molecular biomarkers. Although 
direct sampling of cells in solid tumours of urologic oncology is not accomplished with peripheral blood draws, 
circulating tumour cells, as well as cell-free circulating DNA, can be used for genomic biomarkers [1,2]. Proteomics, 
lipidomics, and metabolomics in oncology are growing fields that can also be applied to blood samples for additional 
biomarker evaluation [3].

The means used to obtain blood are less invasive than those used to obtain tissue and some biofluids, and many 
patients with urologic malignancies are likely to undergo blood draws for standard care. Blood is largely composed 
of water but also contains erythrocytes, leukocytes, platelets, fibrinogen and other clotting factors, proteins including 
albumins and globulins, glucose, and electrolytes. Importantly, these components may limit the assessment of a given 
analyte if the blood is not processed appropriately [4,5]. It is also challenging to control the variation of individual 
components that make up blood that can occur in disease states such as dehydration, infection, or malignancy [3,4,6]. 

To prevent degradation, blood and blood fractions have traditionally been cryopreserved in aliquots to limit the 
damage to target analytes caused by thawing and re-freezing within the specimen. A major critique of this approach 
is that the cost associated with cryopreservation can be significant [7,8]. Alternative methods of storage that aim to 
decrease costs tied to cryopreservation  include drying with newer methods such as lyophilization and isothermal 
vitrification; however, these methods are not yet standardized [9,10]. For low molecular-weight protein, drying on silica 

Key Words Competing Interests Article Information

Biomarker, nucleic acids, cryopreservation, 
tissue fixation, body fluids, tissues, urologic 
oncology

None declared. Received on June 30, 2020 
Accepted on August 1, 2020

Soc Int Urol J. 2020; 1(1):8–15

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chips is feasible but does not protect specimens at higher 
temperatures. Dried blood spots using a paper system 
to evaporate water and contain blood components are 
useful in settings where access to cooling is limited 
for initial specimen handling. However, DBS requires 
controlled storage conditions, and certain analytes are 
more susceptible to oxidative damage. Novel techniques 
for safeguarding blood components remain an area of 
exploration [10].

Serum and plasma
Although whole blood has many uses for biomarker 
assessment, certain measurement modalities require 
sample refinement to optimize detection of a particular 
analyte. To this end, separating the cellular fraction out 
from the liquid portion of blood facilitates spectroscopy-
based analysis with less interference from blood cells. 
The liquid fraction of blood can be isolated as either 
serum or plasma. Plasma is stored in a way that prevents 
coagulation and clot formation. Various clotting factors, 
fibrinogen, and platelets are maintained in suspension 
in plasma. Serum, on the other hand, is allowed to clot 
over 30 minutes before use and can give a cleaner sample 
when interference from platelets and other contaminants 
is undesirable. There are trade-offs of the 2 forms [4,11], 
and the liquid fraction used should be individualized to 
the analyte of interest [12].

Cellular fractions
Cellular components of blood are also used in a variety 
of biomarkers. For example, a high neutrophil to 
lymphocyte ratio has been found to be a poor prognostic 
marker of systemic inf lammation and to correspond 
to worse outcomes in a variety of malignancies [13,14], 
while anemia and thrombocy topenia are used in 
risk stratification for renal cell carcinoma [15] and 
may broadly correlate with late stage tumours [16]. 
Isolation of cellular fractions may be achieved by 
centrifugation and separation by size or using advanced 
spectroscopy [17,18]. Cellular fractions are less subject 
to coagulation when blood is stored as plasma. 
Reassessment of cellular biomarkers from blood samples 
may be facilitated with such specimens, although the 
anticoagulant or freezing technique used may affect 
the viability of cells [19,20]. Flow cytometry and other 
immunological techniques can be used to characterize 

the cellular components of blood to a high degree of 
precision using fluorescent antibody labelling [21].

Urine
Among t he least invasive liquid biomarkers to 
obtain, urine also has the advantage of a simpler 
constituent matrix than other biofluids. Urine is more 
thermodynamically stable than other biof luids and 
generally requires less processing for preservation. 
Also, in the case of urinary tract facing malignancies, 
an opportunity exists to capture tumour cells and their 
biochemical by-products. Urinary extracellular vesicles 
containing a wide variety of molecular biomarker 
classes have also been discovered. A vast majority of the 
molecular biomarker classes are identifiable in urine. 
Not all patients are able to supply urine for analysis, 
depending on their renal function or disease state. 
When urine can be provided, it is subject to variations 
in composition and pH, which can have varying effects 
on any given class of biomarker. Uniquely, urine is also 
subject to contamination by the urinary microbiome, 
which can make interpretation of the source of 
particular analytes challenging [22-25].

Ejaculate and Prostatic Secretions
Of particular relevance to prostate cancer are prostatic 
biof luids, which capture analytes more effectively 
than other sources [26]. Of course, an intact prostate 
and ejaculatory pathway is required for procuring 
these specimens. The post-prostatic massage urine is 
a proxy for capturing prostatic secretions, and so this 
particular biofluid is also subject to the constraints of 
urinary specimens noted above. There are different 
social acceptability thresholds for semen and prostatic 
secretions, compared to other biofluids, making these 
secretions more procedurally intensive to collect. 
Recent efforts have shown the ability to collect RNA, 
DNA, proteins, and other molecular biomarkers from 
these biof luids [26–30]. Few data exist on storage 
considerations of prostatic secretions, a lt hough 
cryopreservation of seminal ejaculate is a standard 
practice in fertility scenarios [2,27,30].

Tissue
Arguably, tissue is the most invasive specimen type 
to obtain, and using tissue has additional costs for 
procurement, processing, and storage. In urologic 
oncology, though, tissue samples are often already 
obtained during routine clinical practice and may 
be used to identify biomarkers that guide treatment 
or provide prognostic information [31,32]. The full 
range of molecular biomarkers can be obtained from 
tissue samples, including more direct measurement of 
immune parameters at the tumour site (eg, tumour-
infiltrating leukocytes), which influences endogenous 

Abbreviations 

DNA deoxyribonucleic acid
DNase deoxyribonuclease
FFPE formalin-fixed, paraffin-embedded
RNA ribonucleic acid
RNase ribonuclease

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immune response to tumour as well as chemotherapy 
and immunotherapy efficacy [33,34].

A major advantage of tissue specimens is the 
inherent ease with which the signal-to-noise ratio can 
be optimized in evaluating molecular biomarkers 
derived from tumours or tumour microenvironments. 
Depending on the biomarker of interest, a sample 
may be “enriched ” to exclude normal tissue and 
prioritize tumour tissue for analysis (eg, laser capture 
microdissection). Recently, efforts have been made 
to standardize the manner in which tissue samples 
for various types of tumours, are delineated from 
surrounding stroma on histopathologic analysis with 
the intent of decreasing inter-observer variability of 
certain biomarker assessments [35].

L i ke ot her s ou rc e s of bioma rkers , t i s sue -
based biomarkers are subject to degradation and 
contamination. This is particularly true in fresh 
frozen tissue samples, in which tissue will be subject 
to predictable ischemic changes in the ex vivo state, 
such as apoptosis and in situ coagulation until freezing 
occurs. The timeliness of such processing would affect 
the accuracy and quality of biomarker analysis across a 
range of analytes, including more sensitive proteins [36].

Formalin-fixed, paraffin-embedded (FFPE) samples 
increase the longevity of the specimen regardless of 
storage temperature. However, residual paraffin (even 
after appropriate treatment) can contaminate the 
analysis of such a preserved sample [36]. There are trade-
offs of additional processing considerations for FFPE 
samples obtained for clinical evaluation. These may be 
associated with different contaminants or constraints in 
methodology for evaluation, and are discussed in more 
detail below [37,38].

Biomarkers by Type
Genomic biomarkers
The European Medicines Agency, in concert with the 
International Council for Harmonisation of Technical 
Requirements for Pharmaceuticals for Human Use, 
has defined a genomic biomarker as “a measurable 
DNA and/or RNA characteristic that is an indicator of 
normal biologic processes, pathogenic processes, and/or 
response to therapeutic or other interventions”[39].

Factors affecting genomic biomarkers 
A lthough DNA and R NA are genera lly reliable 
biomarkers, there are some commonly encountered 
situations in biospecimen collection that occur in 
clinical medicine that can affect nucleic acid quantity 
and quality and impact their accuracy as biomarkers. A 
few of these conditions are described here.

Pre-fixation time: Pre-fixation time is the duration of 
time between obtaining the biopsy or surgical specimen 
and its preservation. As the tissue samples removed 
are ischemic during this interval, several important 
biologic processes occur in the tissue that can affect 
nucleic acids. RNA, in particular, is susceptible to the 
effects of this “cold” (ie, <37 °C) ischemia. Changes 
that are seen during cold ischemia include increased 
expression (quantity) of RNA molecules from hypoxia 
response genes (eg, hypoxia-inducible factor 1α [HIF-
1α]); digestion and loss of RNA molecules with short 
half-lives; and broad RNA degradation and reduction 
in quality, starting at about 5 to 6 hours at room 
temperature [40]. In general, the shorter the time from 
patient to preservation (preservative or freezing), the 
better. 

Formalin: Formalin fixation is a common method 
used to preserve biological tissue samples that have 
been obtained surgically or by biopsy, and subsequent 
paraffin-embedding allows for the cutting of thin 
slices for histological examination. FFPE samples 
are abundant and represent the standard method of 
clinical tissue preservation in most hospitals. Formalin 
has several effects on DNA that affect DNA quality, 
including DNA denaturation and cross-linking with 
cytosine residues [41]. As a result of these and other 
effects on DNA, formalin induces artificial mutations 
at a rate of approximately 1 mutation per 500 base 
pairs. RNA shares these formalin effects, but it is also 
affected by formalin in other ways which impede reverse 
transcription [41, 42]. Factors that increase the formalin-
induced artificial mutation rate include increasing 
formaldehyde concentration, increasing temperature, 
increasing duration of fixation, and decreasing pH [41].

Tissue nucleases: Deoxyribonucleases (DNases) and 
ribonucleases (RNases) are tissue nucleases that digest 
DNA and RNA, respectively. RNA molecules are 
particularly susceptible to degradation by RNases, and 
for this reason, RNase inhibition is part of most RNA 
extraction protocols. DNase is felt to be an important 
contributor to DNA degradation in FFPE tissue samples 
[43].

Storage conditions: The age of the FFPE sample and 
storage temperature can have an impact on nucleic acid 
quality [44]. In general, storage at −20°C is better than 
room temperature, and shorter duration of storage is 
better. 

DNA
DNA has many attributes that make it an excellent 
biomarker. First, DNA tends to be a very stable 
molecule—a biological requirement, as it directs the 
replication of all human cells—and is consequently 

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affected less by environmental conditions than many 
other molecules. Second, many characteristics are 
measurable in DNA, including single-nucleotide 
variants (formerly single-nucleotide polymorphisms), 
v a r i a bi l it y  of  s hor t  re p e at e d  s e g me nt s  (e g , 
m icros atel l ite s), epigenet ic mod i f ic at ions (eg , 
methylation), haplotypes, deletion mutations, insertion 
mutations, copy number variations, and cytogenetic 
variations (eg, translocations, duplications, deletions, or 
inversions).

One important distinction with DNA is the difference 
between germline changes and somatic changes. 
Germline DNA is the complement of genes that an 
individual is born with and can pass on to future 
progeny. Generally, blood leukocytes are used as the 
source for germline DNA, but there are scenarios (eg, 
leukemia) where this is not ideal, and buccal swabs, 
saliva, or other normal tissue are used. Most evidence 
suggests that buccal swabs and saliva yield similar DNA 
quality to blood leukocytes, although quantity is usually 
less [45,46]. Germline DNA alterations can inform the 
presence of an inherited tumour syndrome (eg, von 
Hippel-Lindau disease), a susceptibility to exposures 
(eg, glutat hione-S-transferase [GSTM1] nu ll and 
N-acetyltransferase 2 [NAT2] slow acetylator increase 
the risk for bladder cancer), an ability to metabolize 
drugs, and a susceptibility to developing certain diseases 
or adverse events associated with treatment.

Somatic DNA refers to DNA col lected from 
an affected tissue or organ, usually a tumour, and 
ref lects a change that occurred in the DNA after 
conception. Somatic alterations are not passed on to 
children. Somatic alterations are useful for predicting 
responsiveness to treatment (eg, microsatellite instability 
and programmed death 1 ligand 1 [PD-L1] response), 
determining prognosis, and diagnosing the presence or 
absence of disease.  

RNA
RNA is the transmitter of genetic information coded 
in the DNA and is therefore a significantly more 
dynamic molecule than DNA. RNA quantity and 
composition change significantly from tissue to tissue 
under normal physiologic conditions. Characteristics 
that are measured in RNA include sequences, splicing, 
expression levels, and subtype (eg, miRNA). As alluded 
to above, while RNA is a more responsive molecule and, 
perhaps, a better reflector of genetic activity within a 
particular tissue, it is also substantially less stable and is 
affected by a larger number of environmental conditions 
than DNA. 

There are numerous types of RNA molecules and 
they are generally classified as the following: (a) those 

involved in protein synthesis, (b) those involved in RNA 
modification, and (c) those whose function is mainly 
regulatory [47]. A non-exhaustive summary of the main 
types of RNA is shown in Table 1.

Protein
Proteins are the workhorses of the cell and are often 
highly dysregulated in disease states. Proteins can be 
isolated from nearly all biofluids but, like all analytes, 
they are also subject to degradation and alteration. 
Human blood and urine contain proteases that cleave 
proteins into smaller peptides, which can be cleaved by 
peptidases into even smaller pieces [48]. Interestingly, 
the pattern of cleavage can be used as a signature to 
identify certain cancers [49]. Adding protease inhibitors 
to biospecimens can help reduce artifactual changes in 
proteins caused by enzymatic degradation, although 
these additions can also affect downstream applications. 

Urine can be a particularly challenging source for 
protein biomarkers because of dramatic changes in pH 
(ranges from 4 to 8), the influence of hydration status 
on protein concentration, and proteolysis that occurs 
during storage in the bladder [50]. About 30% of urinary 
proteins are derived from glomerular filtration and 70% 
from the renal tubules and urothelium, so the urine 
protein pool is a mix of systemic and local–regional 
sources [51].

Protein-based biomarkers have generally been 
focused on the quantification of a particular protein 
or isoform. However, assessment of post-translational 
modifications is also important. Post-translational 
modif ications that can important to biomarkers 
include phosphorylation, methylation, glycosylation, 
ubiquitination, acetylation, and lipidation [52].

Glycans
The attachment of carbohydrates to molecules, such as 
proteins and lipids—a process known as glycosylation—
is common, occurring in > 50% of human proteins [53]. 
Several important glycoproteins have been found to be 
good biomarkers in urology, including α-fetoprotein, 
prostate-specif ic antigen, and human chorionic 
gonadotropin. There are different forms of protein 
glycosylation, including N-linked (glycan attached to the 
nitrogen of asparagine) and O-linked (glycan attached 
to the oxygen of threonine and serine). Tumours 
may show differences in the amount, size, and type of 
glycosylation when compared with normal tissue. For 
example, N-linked glycans tend to become larger and 
more branched, whereas O-linked glycans tend to be 
truncated and expose underlying peptide epitopes. 
Other glycans can be important biomarkers, too. For 
example, glycolipids (glycans bound to lipid molecules) 
and glycosaminoglycans (mucopolysaccharides) have 
been studied as biomarkers. 

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TABLE 1.

Main Types of RNA

Protein Synthesis

Type Function

Messenger (mRNA)
 • Transcription of the information contained in DNA exons (recipe for a protein)
 • Subject to alternative splicing, which creates different protein isoforms

Ribosomal (rRNA)
 • Primary constituent of the ribosomes, where mRNA is translated into protein
 • Most abundant RNA in cells (about 80%)

Transfer (tRNA)  • Carries an amino acid matching the mRNA to the ribosome, required for translation

RNA modification

Type Function

Small nuclear (snRNA)  • Processing and splicing of mRNA in the nuclear spliceosome

Small nucleolar 
(snoRNA)

 • Involved in methylation and pseudouridylation of rRNA and tRNA

Ribonuclease P  • Riboenzyme (enzyme made of RNA) that cleaves RNA

Ribonuclease MRP  • Riboenzyme that processes rRNA in the nucleus

Regulatory

Type Function

Micro (miRNA)  • Single stranded RNA, 22 bp length, interferes with other RNAs

Small interfering 
(siRNA)

 • Double stranded RNA, 20–25 bp length, interferes with other RNAs

Long non-coding 
(lncRNA)

 • Single stranded RNA, >200 bp length, interferes with other RNAs

Short hairpin (shRNA)  • Artificial RNA molecule designed to inhibit other RNAs, has a tight hairpin turn structure

Antisense (asRNA)  • Single stranded RNA complementary to a mRNA to which it binds and inhibits

Lipids
Lipids are key molecules in cellular metabolism and 
are a critical structural component in the biological 
membranes that wrap all human cells. Lipids are 
different from other biomolecules in that they are soluble 
in organic solvents, which is an important processing 
step in lipid analysis and characterization [54]. Lipids are 
subdivided into 8 classes, each of which has had some 
biological role described in cancer biology: fatty acyls, 
glycerophospholipids, glycerolipids, sphingolipids, sterol 
lipids, prenol lipids, saccharolipids, and polyketides [55]. 
Mass spectroscopy and related techniques are the main 
tools used for profiling biological lipids.

Imaging
Although it may not seem intuitive, imaging can 
also serve as a biomarker [56,57]. Examples of widely 

available imaging-based biomarkers include basic 
radiological lesion characteristics (eg, size, shape, 
location), lesion density (computed tomography), 
lesion echogenicit y (u lt rasou nd), lesion sig na l 
intensity (magnetic resonance imaging), and contrast 
enhancement. The Response Evaluation Criteria In 
Solid Tumors (RECIST) criteria for evaluating tumour 
response to therapy is a radiological biomarker that is 
commonly used in clinical trials [58,59]. Functional 
molecular imaging has been further developed, whereby 
specific molecular features are studied using novel 
radiological ligands. For example, in positron emission 
tomography (PET) imaging, functional biomarkers 
are being explored to improve the detection of cancer, 
including, 18F-fluorodeoxyglucose (18F-FDG), carbon 
11 choline (11C-choline), 68Gallium prostate-specific 
membrane antigen (68Ga-PSMA), and numerous others. 

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In other cases, theranostic imaging is being pursued 
whereby a molecular target is imaged in a patient in vivo 
before the administration of a targeted agent against that 
molecular target [60].

Pathology
The histological evaluation of tissue samples (or blood 
smears) is not only a routine clinical component of 
cancer care but also an important source of clinical 
biomarkers. Many standard descriptors of tissue 
morphology can be quantified and used as biomarkers. 
Common examples in genitourinary oncology include 
tumour grade, presence of lymphovascular invasion, 
presence of mitoses, and histological tumour type and 
subtype. More recently, digital imaging has allowed 
for a new era of digital pathology, in which pattern 
recognition and artificial intelligence software tools can 

be used to characterize tissue sections with increasingly 
precise and reproducible methods [61,62]. It is highly 
likely that in the future digital pathology tools will form 
the backbone of the analysis of most tissue sections.

Conclusions
Biomarkers can be obtained and characterized from a 
highly diverse set of biological sources of measurement. 
There is no clear optimal biomarker, and each has 
inherent strengths and f laws. The future will likely 
consist of a collation of large networks of biomarkers 
that are merged computationally to provide a consensus 
picture of the pathological process that is occurring 
in the patient. This will undoubtedly require new 
informatic and artificial intelligence tools but will also 
lead to a new era of precision medicine.

References

1. Vandekerkhove G, Struss WJ, Annala M, et al. Circulating Tumor 
DNA Abundance and Potential Utilit y in De Novo Metastatic 
Prostate Cancer. Eur Urol. 2019;75 (4):667-75. DOI: 10.1016/j.
eururo.2018.12.042

2. How Kit A , Nielsen HM, Tos t J. DN A met hylation b ased 
biomarkers: practical considerations and applications. Biochimie. 
2012;94(11):2314-37. DOI: 10.1016/j.biochi.2012.07.014

3. Loke SY, Lee ASG. The future of blood-based biomarkers for the 
early detection of breast cancer. Eur J Cancer. 2018;92:54-68. DOI: 
10.1016/j.ejca.2017.12.025

4. Pietrowska M, Wlosowicz A, Gawin M, Widlak P. MS-Based 
Proteomic Analysis of serum and plasma: problem of high abundant 
components and lights and shadows of albumin removal. Adv Exp 
Med Biol. 2019;1073:57-76. DOI: 10.1007/978-3-030-12298-0_3

5. O’Connell GC, Treadway MB, Petrone AB, et al. Leukocyte dynamics 
influence reference gene stabilit y in whole blood: data-driven 
qRT-PCR normalization is a robust alternative for measurement 
of transcriptional biomarkers. Lab Med. 2017;48(4):346-56. DOI: 
10.1093/labmed/lmx035

6. Matomaki P, Kainulainen H, Kyrolainen H. Corrected whole blood 
biomarkers - the equation of Dill and Costill revisited. Physiol Rep. 
2018;6(12):e13749. DOI: 10.14814/phy2.13749

7. Mitchell BL, Yasui Y, Li CI, Fitzpatrick AL, Lampe PD. Impact of 
freeze-thaw cycles and storage time on plasma samples used in 
mass spectrometr y based biomarker discover y projects. Cancer 
Inform. 2005;1:98-104.

8. Scaramuzzino DA, Schulte K, Mack BN, Soriano TF, Fritsche HA. 
Five-year stability study of free and total prostate-specific antigen 
concentrations in serum specimens collected and stored at -70 
degrees C or less. Int J Biol Markers. 2007;22(3):206-13.

9. Elliot G. Preser vation of biologics in a dr y state: advances in 
isothermal vitrification technology. Cryobiology. 2013;67(3):428. 
DOI: https://doi.org/10.1016/j.cryobiol.2013.09.115

10. Kluge JA, Li AB, Kahn BT, Michaud DS, Omenetto FG, Kaplan DL. 
Silk-based blood stabilization for diagnostics. Proc Natl Acad Sci U 
S A. 2016;113(21):5892-7. DOI: 10.1073/pnas.1602493113

11. Jackson DH, Banks RE. Banking of clinical samples for proteomic 
biomarker studies: a consideration of logistical issues with a focus 
on pre-analytical variation. Proteomics Clin Appl. 2010;4(3):250-70. 
DOI: 10.1002/prca.200900220

12. Dittadi R, Fabricio ASC, Rainato G, et al. Preanalytical stability 
of [-2]proPSA in whole blood stored at room temperature before 
separation of serum and plasma: implications to Phi determination. 
Clin Chem Lab Med. 2019;57(4):521-31. DOI: 10.1515/cclm-2018-0596

13. Cantiello F, Russo GI, Vartolomei MD, et al. Systemic inflammatory 
markers and oncologic outcomes in patients with high-risk 
non-muscle-invasive urothelial bladder cancer. Eur Urol Oncol. 
2018;1(5):403-10. DOI: 10.1016/j.euo.2018.06.006

14. Bar tlet t EK, Flynn JR, Panageas KS, et al. High neutrophil-to-
lymphocyte ratio (NLR) is associated with treatment failure and 
death in patients who have melanoma treated with PD-1 inhibitor 
monotherapy. Cancer. 2020;126(1):76-85.  DOI: 10.1002/cncr.32506

15. Okita K, Hatakeyama S, Tanaka T, et al. Impact of disagreement 
bet ween t wo risk group models on prognosis in p atient s 
with metastatic renal-cell carcinoma. Clin Genitourin Cancer. 
2019;17(3):e440-e6. DOI: 10.1016/j.clgc.2019.01.006

16. Zhao L, He R, Long H, et al. Late-stage tumors induce anemia and 
immunosuppressive extramedullary erythroid progenitor cells. Nat 
Med. 2018;24(10):1536-44. DOI: 10.1038/s41591-018-0205-5

17. Atkins CG, Buckley K, Blades MW, Turner RFB. Raman Spectroscopy 
of blood and blood components. Appl Spectrosc. 2017;71(5):767-93. 
DOI: 10.1177/0003702816686593

18. Riedhammer C, Halbrit ter D, Weisser t R. Peripheral blood 
mononuclear cells: isolation, freezing, thawing, and culture. 
Methods Mol Biol. 2016;1304:53-61. DOI: 10.1007/7651_2014_99

13SIUJ.ORG SIUJ  •  Volume 1, Number 1  •  October 2020

Classification of Molecular Biomarkers

https://doi.org/10.1016/j.cryobiol.2013.09.115
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19. Klein A, Ramcharitar S, Christef f N, Nisbet t-Brown E, Nunez 
E, Malkin A. Effect of anticoagulants in vitro on the viability of 
lymphocytes and content of free fatty acids in plasma. In Vitro Cell 
Dev Biol. 1991;27a(4):307-11. DOI: 10.1007/bf02630908

20. Buhl T, Legler TJ, Rosenberger A, Schardt A, Schon MP, Haenssle 
HA. Controlled-rate freezer cryopreservation of highly concentrated 
peripheral blood mononuclear cells results in higher cell yields and 
superior autologous T-cell stimulation for dendritic cell-based 
immunotherapy. Cancer Immunol Immunother. 2012;61(11):2021-31. 
DOI: 10.1007/s00262-012-1262-0

21. McKinnon KM. Flow cytometry: an overview. Curr Protoc Immunol. 
2018;120:5.1.-5.1.11. DOI: 10.1002/cpim.40

22. Rodrigues D, Jeronimo C, Henrique R, et al. Biomarkers in bladder 
cancer: a metabolomic approach using in vitro and ex vivo model 
systems. Int J Cancer. 2016;139(2):256-68. DOI: 10.1002/ijc.30016

23. Wang X, Gu H, Palma-Duran SA, et al. Influence of storage 
conditions and preservatives on metabolite fingerprints in urine. 
Metabolites. 2019;9(10):203.DOI: 10.3390/metabo9100203

24. Merchant ML, Rood IM, Deegens JKJ, Klein JB. Isolation and 
characterization of urinary extracellular vesicles: implications for 
biomarker discover y. Nat Rev Nephrol. 2017;13(12):731-49. DOI: 
10.1038/nrneph.2017.148

25. De Palma G, Di Lorenzo V F, K rol S, Paradiso AV. Urinar y 
exosomal shuttle RNA: promising cancer diagnosis biomarkers 
of lower urinary tract. Int J Biol Markers. 2019;34(2):101-7. DOI: 
10.1177/1724600819827023

26. Roberts MJ, Richards RS, Gardiner RA, Selth L A. Seminal fluid: 
a useful source of prostate cancer biomarkers? Biomark Med. 
2015;9(2):77-80. DOI: 10.2217/bmm.14.110

27. Etheridge T, Straus J, Ritter MA, Jarrard DF, Huang W. Semen 
A M ACR protein as a novel method for detecting prost ate 
cancer. Urol Oncol. 2 018;3 6 (12):5 3 2.e1-.e7. DOI: 10.1016/ j.
urolonc.2018.09.010

28. Ponti G, Maccaferri M, Mandrioli M, et al. Seminal cell-free DNA 
assessment as a novel prostate cancer biomarker. Pathol Oncol Res. 
2018;24(4):941-5. DOI: 10.1007/s12253-018-0416-6

29. Ploussard G, de la Taille A. The role of prostate cancer antigen 3 
(PCA3) in prostate cancer detection. Expert Rev Anticancer Ther. 
2018;18(10):1013-20. DOI: 10.1080/14737140.2018.1502086

30. Goessl C, Muller M, Heicappell R, Krause H, Miller K. DNA-based 
detection of prostate cancer in blood, urine, and ejaculates. Ann N 
Y Acad Sci. 2001;945:51-8. DOI: 10.1111/j.1749-6632.2001.tb03863.x

31. Moschini M, Spahn M, Mattei A, Cheville J, Karnes RJ. Incorporation 
of tissue-based genomic biomarkers into localized prostate cancer 
clinics. BMC Med. 2016;14:67. DOI: 10.1186/s12916-016-0613-7

32. Tao DL, Bailey S, Beer TM, et al. Molecular Testing in Patients 
With Castration-Resistant Prostate Cancer and Its Impact on 
Clinical Decision Making. JCO Precis Oncol. 2017;1. DOI: 10.1200/
po.16.00067

33. Wahlin S, Nodin B, Leandersson K, Boman K, Jirstrom K. Clinical 
impact of T cells, B cells and the PD-1/PD-L1 pathway in muscle 
invasive bladder cancer: a comparative study of transurethral 
r e s e c t io n a n d c y s t e c t o m y s p e cim e n s . O n c oi m m u n olo gy. 
2019;8(11):e1644108. DOI: 10.1080/2162402x.2019.1644108

34. Xiong Y, Liu L, Xia Y, et al. Tumor infiltrating mast cells determine 
oncogenic HIF-2alpha-conferred immune evasion in clear cell renal 
cell carcinoma. Cancer Immunol Immunother. 2019;68(5):731-41. DOI: 
10.1007/s00262-019-02314-y

35. Hendry S, Salgado R, Gevaert T, et al. Assessing tumor-infiltrating 
lymphocytes in solid tumors: a practical review for pathologists and 
proposal for a standardized method from the International Immuno-
Oncology Biomarkers Working Group: Part 2: TILs in melanoma, 
gastrointestinal tract carcinomas, non-small cell lung carcinoma 
and mesothelioma, endometrial and ovarian carcinomas, Squamous 
Cell Carcinoma of the Head and Neck, Genitourinary Carcinomas, 
and Primary Brain Tumors. Adv Anat Pathol. 2017;24(6):311-35. DOI: 
10.1097/pap.0000000000000161

36. Cole LM, Clench MR, Francese S. Sample treatment for tissue 
proteomics in cancer, toxicology, and forensics. Adv Exp Med Biol. 
2019;1073:77-123. DOI: 10.1007/978-3-030-12298-0_4

37. Jacobs S. Sample processing considerations for detecting copy 
number changes in formalin-fixed, paraffin-embedded tissues. 
Cold Spring Harb Protoc. 2012;2012(11):1195-202. DOI: 10.1101/
pdb.ip071753

38. Jacobs S. Data analysis considerations for detecting copy number 
changes in formalin-fixed, paraffin-embedded tissues. Cold Spring 
Harb Protoc. 2012;2012(11):1203-9. DOI: 10.1101/pdb.ip071761

39. European Medicines Agency. ICH Topic E15. Definitions for genomic 
biomarkers, pharmacogenomics, pharmacogenetics, genomic 
data and sample coding categories2007. Available from: https:// 
.ema.europ a.eu/en/document s/scientific - guideline/ ich - e -15 -
establish-definitions-genomic-biomarkers-pharmacogenomics-
pharmacogenetics-genomic-data_en.pdf.Accessed: August 10, 
2020.

40. Grizzle WE, Otali D, Sex ton KC, Ather ton DS. Ef fects of cold 
ischemia on gene expression: a review and commentary. Biopreserv 
Biobank. 2016;14(6):548-58. DOI: 10.1089/bio.2016.0013

41. Srinivasan M, Sedmak D, Jewell S. Effect of fixatives and tissue 
processing on the content and integrity of nucleic acids. Am J 
Pathol. 2002;161(6):1961-71. DOI: 10.1016/s0002-9440(10)64472-0.

42. Evers DL, Fowler CB, Cunningham BR, Mason JT, O’Lear y TJ. 
The ef fect of formaldehyde fixation on RNA: optimization of 
formaldehyde adduct removal. J Mol Diagn. 2011;13(3):282-8. DOI: 
10.1016/j.jmoldx.2011.01.010

43. Tokuda Y, Nakamura T, Satonaka K, et al. Fundamental study on the 
mechanism of DNA degradation in tissues fixed in formaldehyde. 
J Clin Pathol. 1990;43(9):748-51. DOI: 10.1136/jcp.43.9.748

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https://www.ema.europa.eu/en/documents/scientific-guideline/ich-e-15-establish-definitions-genomic-biomarkers-pharmacogenomics-pharmacogenetics-genomic-data_en.pdf
https://www.ema.europa.eu/en/documents/scientific-guideline/ich-e-15-establish-definitions-genomic-biomarkers-pharmacogenomics-pharmacogenetics-genomic-data_en.pdf
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44. Groelz D, Viertler C, Pabst D, Dettmann N, Zatloukal K. Impact 
of storage conditions on the quality of nucleic acids in paraffin 
embedded tissues. PLoS One. 2018;13(9):e0203608. DOI: 10.1371/
journal.pone.0203608

45. King IB, Satia-Abouta J, Thornquist MD, et al. Buccal cell DNA yield, 
quality, and collection costs: comparison of methods for large-scale 
studies. Cancer Epidemiol Biomarkers Prev. 2002;11(10 Pt 1):1130-3.

46. Hansen T V, Simonsen MK, Nielsen FC, Hundrup YA. Collection 
of blood, saliva, and buccal cell samples in a pilot study on the 
Danish nurse cohort: comparison of the response rate and quality of 
genomic DNA. Cancer Epidemiol Biomarkers Prev. 2007;16(10):2072-
6. DOI: 10.1158/1055-9965.Epi-07-0611

47. Xi X, Li T, Huang Y, et al. RNA biomarkers: frontier of precision 
medicine for cancer. Noncoding RNA . 2017;3(1):9. DOI: 10.3390/
ncrna3010009

48. Kushnir MM. Are samples in your freezer still good for biomarker 
discover y? Am J Clin Pathol. 2013;140(3):287-8. DOI: 10.1309/
ajcpfzyy7bbkk9je

49. Villanueva J, Shaffer DR, Philip J, Chaparro CA, Erdjument-Bromage 
H, Olshen AB, et al. Differential exoprotease activities confer tumor-
specific serum peptidome patterns. J Clin Invest. 2006;116(1):271-
84. DOI: 10.1172/jci26022

50. Thomas CE, Sexton W, Benson K, Sutphen R, Koomen J. Urine 
collection and processing for protein biomarker discover y and 
quantification. Cancer Epidemiol Biomarkers Prev. 2010;19(4):953-
9. DOI: 10.1158/1055-9965.Epi-10-0069

51. Harpole M, Davis J, Espina V. Current state of the art for enhancing 
urine biomarker discovery. Expert Rev Proteomics. 2016;13(6):609-26. 
DOI: 10.1080/14789450.2016.1190651

52. K h o u r y  G A ,  B a li b a n  R C ,  F l o u d a s  C A .  P r o t e o m e - w i d e  
post-translational modification statistics: frequency analysis and 
curation of the swiss-prot database. Sci Rep. 2011;1. DOI: 10.1038/
srep00090.

53. Kailemia MJ, Park D, Lebrilla CB. Glycans and glycoproteins  
as s p e ci f ic bio m ar ke r s f o r c an c e r. A n al Bio a n al Ch e m . 
2017;409(2):395-410. DOI: 10.1007/s00216-016-9880-6

54. Zhao Y Y, Cheng XL, Lin RC. Lipidomics applications for discovering 
biomarkers of diseases in clinical chemistry. Int Rev Cell Mol Biol. 
2014;313:1-26. DOI: 10.1016/b978-0-12-800177-6.00001-3

55. S t e p h e n s o n D J, H o e f e r lin L A , C h al f a n t C E . L ip id o mic s 
in t r anslational r ese ar ch and t he clinic al signific ance of  
lipid-based biomarkers. Transl Res. 2017;189:13-29. DOI: 10.1016/j.
trsl.2017.06.006

56. White paper on imaging biomarkers. Insights Imaging. 2010;1(2): 
42-5. DOI: 10.1007/s13244-010-0025-8

57. Medical imaging in personalised medicine: a white paper of the 
research committee of the European Society of Radiology (ESR). 
Insights Imaging. 2011;2(6):621-30. DOI: 10.1007/s13244-011-0125-0

58. Schwar t z LH, Litiere S, de Vries E, et al. RECIST 1.1-Update 
and clarification: From the RECIST commit tee. Eur J Cancer. 
2016;62:132-7 DOI: 10.1016/j.ejca.2016.03.081.

59. Seymour L, Bogaerts J, Perrone A, et al. iRECIST: guidelines for 
response criteria for use in trials testing immunotherapeutics. Lancet 
Oncol. 2017;18(3):e143-e52. DOI: 10.1016/s1470-2045(17)30074-8

60. Turner JH. An introduction to the clinical practice of theranostics 
in oncology. Br J Radiol. 2018;91(1091):20180440. DOI: 10.1259/
bjr.20180440

61. Janowczyk A, Madabhushi A. Deep learning for digital pathology 
image analysis: A comprehensive tutorial with selected use cases. 
J Pathol Inform. 2016;7:29. DOI: 10.4103/2153-3539.186902

62. Janowcz yk A, Zuo R, Gilmore H, Feldman M, Madabhushi A. 
HistoQC: an open-source quality control tool for digital pathology 
slides. JCO Cli n Cancer Inform. 2019;3:1-7. DOI: 10.1200/cci.18.00157

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Classification of Molecular Biomarkers

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