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Title: How can understanding molecular cell
pathology contribute to better approaches for personalised medicine?


Count: 1930


medicine is the general term for the use of individual patient data to better
diagnose, treat and monitor diseases (Schleidgen et al., 2013). Most common personalised
medicine strategies involve genomic interventions, pharmacogenetics,
individualised therapeutic strategies, lifestyle-specific medicine and disease subtype
specific medicine, all of which are tailored to the individual characteristics
of the patient’s disease to achieve maximum therapeutic effect (Titova, Jenner
& Chaudhuri, 2017).

method of collecting personalised disease information is through the
understanding of molecular cell pathology (Drcuker & Krapfenbauer, 2013).
Alongside broader level of histopathology and imaging used traditionally within
medicine, molecular cell pathology is an investigative approach which
understands and describes the mechanisms of a disease at the macromolecular
level using patient samples (Medical Research Council, 2014). These
macromolecules include DNA, RNA and proteins, so the techniques of molecular
pathology often include those of other fields such as genetics and these
techniques can be used to distinguish between disease origins at a molecular
level (Peden & Ironside, 2012). Recent research has shown that molecular
pathology is an extremely important tool in the development and implementation
of personalised medicine especially for diseases that often present with
similar and sometimes intermittent symptomatology but have very different
molecular bases and require targeted therapy (Mills & Janitz, 2012).  This paper seeks to discuss the relevance of
molecular cell pathology using examples of neurodegenerative disorders and
cancer to show its impact on disease diagnosis, monitoring of disease
progression and individualistic treatment approach.

Molecular cell pathology in diagnosis
and prevention

diseases (NDD), one of the major killers of the modern world, are often
characterised by loss of function (ataxia) or sensory dysfunction (dementia)
resulting from the degeneration of white nerve cells in the brain and spinal
cord (Uttara et al., 2009). According to Gotovac et al. 2014, molecular cell pathology can be used
in the diagnosis, treatment and management of neurodegenerative disorders, and
the developing models of disease origin in NDDs are leading to innovative
methods of diagnosis in these diseases and allowing personalised therapy.

Parkinson’s disease (PD), one of the key concerns has been the ability to
distinguish the disease from multiple system atrophy (MSA), another disease on
the ND movement disorder spectrum (Vallelunga et al., 2014). Molecular
pathology research has identified MicroRNAs as a biomarker which can be used to
distinguish between the two diseases (Marques et al., 2017). MicroRNAs regulate
protein translation and are present in cerebrospinal fluid, and specific
microRNAs are present at differential levels in PD and MSA, allowing accurate
diagnosis using quantitative polymerase chain reaction (qPCR) techniques.

Alzheimer’s disease (AD) diagnosis using only symptomatology lacks specificity which
is only marginally raised by the use of neuroimaging as an adjunct (Wollman
& Prohovinik, 2003). The use of molecular pathology to characterise
biomarkers in the disease has therefore proven a popular approach in research.
In particular, proteomic analysis of cerebrospinal fluid has revealed the
biomarkers – amyloid-? (A?42), phosphorylated tau (p-tau) and total tau (t-tau)
which can act as diagnostic markers with relatively high levels of sensitivity
and specificity (Babic & Simic, 2012). Molecular pathology may also provide
the ability to detect AD even before its onset, as several biomarkers are
present in the preclinical phase of the disease and may provide opportunities
for preventative medicine rather than diagnosis (Baird, Westwood &
Lovestone, 2015). For example, higher levels of the inflammatory haem enzyme, myeloperoxidase,
in blood plasma correlates with the presence and development of Alzheimer’s
disease (Schreitmüller
et al., 2013).

Genetic aberrations, whether they are
somatic or hereditary are capable of causing cancer (Erole et al., 2012).
In cancer pathology, the parading of
specific gene rearrangements and their mutations are useful to health
practitioners to confirm the diagnosis of particular
cancers, namely sarcomas and lymphomas (Szabó et al., 2011). Moreover, the testing of gene also allows
practitioners to know the risk of cancer development in individuals in their lifetime so that preventive measures can be given to the patients who pose a risk of
cancer (Schiavon et al., 2012). For example, BRCA1 and BRCA2 are
crucial genes that regulate cellular function and mutation testing for cancer
are done in patients especially patients with familial ovarian and breast cancer
background. (McCarthy and Armstrong, 2014) Dysfunctional BRCA1 and BRCA2 proteins
pose risk in genomic instability due to DNA repair, transcription regulation,
and protein ubiquitination dysfunction which ultimately leads to cancer

Monitoring disease progression

The pathophysiological
mechanisms underlying Huntingdon’s disease (HD) have proven more difficult to
uncover, and so molecular pathology approaches in research are more limited for
the diagnosis of the disease. However, some biomarkers have been identified
which allow the tracking of disease progression in HD and may be promising
targets for personalised medicine and allow the tailoring of therapies
depending on disease stage (Ryu et al., 2014). In particular, positron emission
tomography (PET) imaging allows the tracking of biomarkers such as
phosphodiesterase 10A (PDE10A), which is currently the earliest known
biological marker of HD-related change (Wilson et al., 2017). This early
detection and tracking could allow doctors to tailor dosage of therapies such
as tetrabenzanine and implement early disease counselling (Frank, 2014).
Although there are currently no clinically-useful therapies which alter the
course of disease, the use of early-detection and tracking markers such as PDE10A
could eventually allow doctors to triage patients before disease onset to
implement preventative medicine. Similar biomarkers have also been identified
for Alzheimer’s disease and Parkinson’s (Eskildsen et al., 2015), though none
of these have yet been transferred to clinical use.

The usage of molecular cell pathology to
monitor progression of diseases such as cancer are currently little although
successfully conducted studies are reported. In a longitudinal study in colon
cancer patients, a subgroup of patients developed tumour after surgery. (Papadatos-Pastos
et al., 2015) Phosphatidylinositol
3-kinase (PI3K) pathway regulates proliferation and cell metabolism and several
mutations in PI3K was evident in patients with tumour recurrence. Therefore, the
study suggested the use of PI3K as a prognostic marker for stratifying colon
cancer patients according to their risk of recurrence, thus indicating a
possibility of relapse after treatment. This phenomenon, known as “addiction of
oncogenes” uses molecular cell pathology to identify a specific biomarker which
could provide an insight on cancer progression in individual patients.


Molecular cell pathology and novel

well as allowing greater discrimination between diseases in diagnosis and personalised
tracking of disease progression, molecular pathology also has a critical role
to play in the development of novel therapeutic strategies addressed to each
patient independently. Advances in research examining the development of
Parkinson’s, Alzheimer’s and prion diseases have shown that these diseases are
characterised by three common features. First, the loss of neuronal function.
Second, the basis of the disease in protein misfolding and aggregation and
thirdly the lack of a therapy which is able to halt or reverse the progression
of symptoms (Rowinska-Zyrek, Salerno & Kozlowski, 2015). Close links have
also been shown between the molecular mechanisms in PD and HD, with processes
such as oxidative stress, mitochondrial dysfunction and protein handling
implicated in their development. As a result, agents which improve
mitochondrial function and targeted therapies which interfere with protein
misfolding have been suggested as potential therapeutic options (Schapira et
al., 2014), although such research is in the early stages.

there are no currently any therapies based on molecular cell pathology in NDDs which
can be used clinically, the field of pharmacogenomics may provide a way for
molecular pathology to be used to personalise existing therapeutic strategies
for patients with NDD on the basis of
genotypes (Cacabelos et al., 2015; Jalalian
et al., 2013). Often involving alteration of drug or its dosage, this
field attempts to reconcile the observed variations in drug response of
different patients with variations in their genetic makeup, thus, attempting to
find reliable genetic correlation for increased, reduced or
functionally-different drug action.

Sclerosis (MS) is an example of a common NDD where genomics plays an important
role in disease response (Evande et al., 2017). More than 100 genetic risk
factors have been identified with the development of MS, and there are a
variety of disease-modifying therapies available including interferon beta,
tumour necrosis factor inhibitors and glatiramer acetate. Variants in the class
II region of the human leukocyte antigen (HLA) genes have been associated with
the efficacy of glatiramer acetate, but not interferon beta (Tsareva et al.,
2016). This makes these variant forms of the HLA class II genes an excellent
candidate for determining through molecular pathology whether a patient is
likely to be susceptible to glatiramer acetate therapy before beginning a
potentially expensive, time-consuming course of treatment which may have little

Unlike neurodegenerative disorders, molecular
cell pathology is currently used for treatment in various types of cancer. Majority
of breast cancer tend to be epithelial where the intrinsic subtypes encompass HER2+, basal-like cancer, and luminal A & B (Yersal and Barutca, 2014). Previously, tumor types were defined
through histological classification. Nowadays, depending on the identification
of specific molecular subtypes in breast cancer diagnosis, a more personalized
approach in the field of medicine are used for targeted therapies. For example,
Herceptin is only administered to patients with overexpression of the human
epidermal growth factor receptor type 2 (HER2) receptor and patients with
ER+/PR+ (oestrogen and progesterone hormone receptors) are treated with
Tamoxifen. (Dowsett et al., 2008) The clinical benefit obtained from
this is approach over “one size fits all” is that it takes into account
various epigenetic and genetic alterations, which are critical drivers for breast tumour biology and hence affect the way
in which the tumour responds to certain treatment schemes.


Effective treatment of lung cancer is
currently dependent on the understanding of oncological targets in lung cancer
patients that needs the use of molecular
cell pathology. The approach identifies driver mutations like gene
rearrangements for the development of drugs and pathway interventions. The new
approach has changed the consideration of lung cancer as a homogenous disease, which can only be treated through surgical pneumonectomy (Dorman et al., 2015). Conversely,
understanding molecular cell pathology helps practitioners to use immuno-histochemical
marker cells to define distinct histopathological cell subgroups which has
helped in successful chemotherapies often involving a combination of chemotherapeutic agents
(Grizzle, Srivastava, and Manne, 2011; Laughney et al., 2012). The practice has been adopted in the medical field as the standard care in the
treatment of advanced small cell lung
cancer, thus making sure that advanced cancers are treated with effective
chemotherapeutic agents as well. (Morabito et al., 2014)



cell pathology is an emerging powerful tool to understand diseases and to
characterise them in an individualistic way that has not been previously
possible. Traditionally, patients have been placed into a particular ‘disease
group’ such as lymphoma, Parkinson’s disease or type 2 diabetes and treated
according to a common guideline but molecular pathology allows a much more
specific categorisation of an individual’s disease in terms of their cellular
molecular expression and genomics.

diseases, which have resisted attempts to develop drugs which halt or reverse
the disease, this could provide a powerful clinical method for tailoring
therapies to the precise molecular defects in these individuals, a personalised
medicine. Molecular pathology may also allow diagnosis to occur much earlier
than disease onset and allow tailoring of therapy to disease progression. Pharmacogenomic
approaches may allow ineffective therapies to be excluded before beginning
expensive therapy with potential side effects. However, the use of ‘may’ and
‘could’ in almost all research in this field indicates what an early stage this
research is at, and with time, promising research is to be transferred to a
clinical setting for useful personalised medicine.



M. and Šimi?, G., 2012. Proteomic analysis of cerebrospinal fluid in
Alzheimer’s disease. MEDIX, 18, pp.101-102.

A.L., Westwood, S. and Lovestone, S., 2015. Blood-based proteomic biomarkers of
Alzheimer’s disease pathology. Frontiers in neurology, 6, pp.215-219.

R., Torrellas, C., Cacabelos, P., Villanueva, M.J., Piñeiro, S. and Solveira,
C., 2015. Pharmacogenetics of neurodegenerative disorders. In Preventive and
Predictive Genetics: Towards Personalised Medicine (pp. 173-240). Springer
International Publishing.

S.N., Baranova, K., Knoll, J.H., Urquhart, B.L., Mariani, G., Carcangiu, M.L.
and Rogan, P.K., 2015. Genomic signatures for paclitaxel and gemcitabine
resistance in breast cancer-derived by machine learning. Mol Oncol, pp.89-146.

M., Allred, C., Knox, J., Quinn, E., Salter, J., Wale, C., Cuzick, J.,
Houghton, J., Williams, N., Mallon, E. and Bishop, H., 2008. The relationship
between quantitative estrogen and progesterone receptor expression and human
epidermal growth factor receptor 2 (HER-2) status with recurrence in the
Arimidex, Tamoxifen, Alone or in Combination trial. Journal of clinical
oncology, 26(7), pp.1059-1065.

E. and Krapfenbauer, K., 2013. Pitfalls and limitations in translation from
biomarker discovery to clinical utility in predictive and personalised
medicine. EPMA journal, 4(1), p.7.

P., Bosch, A., Pérez-Fidalgo, J.A. and Lluch, A., 2012. Molecular biology in
breast cancer: intrinsic subtypes and signaling pathways. Cancer treatment
reviews, 38(6), pp.698-707.

S.F., Coupé, P., Fonov, V.S., Pruessner, J.C., Collins, D.L. and Alzheimer’s
Disease Neuroimaging Initiative, 2015. Structural imaging biomarkers of
Alzheimer’s disease: predicting disease progression. Neurobiology of aging, 36,

R.E., Dutta, R., Tan, C., Grem, J.L. and Mahato, R.I., 2017. Pharmacogenomics
of Neurodegenerative Diseases: Roles in Personalized Medicines. In Neuroimmune
Pharmacology (pp. 959-970). Springer International Publishing.

S., 2014. Treatment of Huntington’s disease. Neurotherapeutics, 11(1),

K., Hajnšek, S., Paši?, M.B., Pivac, N. and Borove?ki, F., 2014. Personalized
medicine in neurodegenerative diseases: how far away?. Molecular diagnosis
& therapy, 18(1), pp.17-24.

W.E., Srivastava, S. and Manne, U., 2011. Translational pathology of neoplasia.
Cancer Biomarkers, 9(1-6), pp.7-20.

A., Mashohor, S.B., Mahmud, H.R., Saripan, M.I.B., Ramli, A.R.B. and Karasfi,
B., 2013. Computer-aided detection/diagnosis of breast cancer in mammography
and ultrasound: a review. Clinical Imaging, 37(3), pp.420-426.

A.M., Krishnaswamy, V., Rizzo, E.J., Schwab, M.C., Barth, R.J., Pogue, B.W.,
Paulsen, K.D. and Wells, W.A., 2012. Scatter spectroscopic imaging
distinguishes between breast pathologies in tissues relevant to surgical margin
assessment. Clinical Cancer Research, 18(22), pp.6315-6325.

T.M., Kuiperij, H.B., Bruinsma, I.B., van Rumund, A., Aerts, M.B., Esselink,
R.A., Bloem, B.R. and Verbeek, M.M., 2017. MicroRNAs in cerebrospinal fluid as
potential biomarkers for Parkinson’s disease and multiple system atrophy.
Molecular neurobiology, 54(10), pp.7736-7745.

A. and Armstrong, K. (2014). The Role of Testing forBRCA1andBRCA2Mutations in
Cancer Prevention. JAMA Internal Medicine, 174(7), p.1023.

Research Council, 2014. Molecular Pathology Review. London: MRC

J.D. and Janitz, M., 2012. Alternative splicing of mRNA in the molecular
pathology of neurodegenerative diseases. Neurobiology of aging, 33(5),

A., Carillio, G., Daniele, G., Piccirillo, M., Montanino, A., Costanzo, R.,
Sandomenico, C., Giordano, P., Normanno, N., Perrone, F., Rocco, G. and Di
Maio, M. (2014). Treatment of small cell lung cancer. Critical Reviews in
Oncology/Hematology, 91(3), pp.257-270.


D., Rabbie, R., Ross, P. and Sarker, D. (2015). The role of the PI3K pathway in
colorectal cancer. Critical Reviews in Oncology/Hematology, 94(1), pp.18-30.


A. and Ironside, J., 2012. Molecular pathology in neurodegenerative diseases. Current
drug targets, 13(12), pp.1548-1559.

M., Salerno, M. and Kozlowski, H., 2015. Neurodegenerative
diseases–understanding their molecular bases and progress in the development of
potential treatments. Coordination Chemistry Reviews, 284, pp.298-312.

J.H., Lee, S., Son, S., Kim, S.H., Leary, J.F., Choi, K. and Kwon, I.C., 2014.
Theranostic nanoparticles for future personalized medicine. Journal of
Controlled Release, 190, pp.477-484.

A.H., Olanow, C.W., Greenamyre, J.T. and Bezard, E., 2014. Slowing of
neurodegeneration in Parkinson’s disease and Huntington’s disease: future
therapeutic perspectives. The Lancet, 384(9942), pp.545-555.

G., Smid, M., Gupta, G.P., Redana, S., Santini, D. and Martens, J.W., 2012.
Heterogeneity of breast cancer: gene signatures and beyond. In Diagnostic,
Prognostic and Therapeutic Value of Gene Signatures (pp. 13-25). Humana Press.

S., Klingler, C., Bertram, T., Rogowski, W.H. and Marckmann, G., 2013. What is
personalized medicine: sharpening a vague term based on a systematic literature
review. BMC medical ethics, 14(1), p.55.

Schreitmüller, B., Laske, C., Stransky, E. and Stellos,
K. (2013). Increased myeloperoxidase (MPO) plasma levels in patients with
Alzheimer’s disease. Alzheimer’s & Dementia, 9(4), p.P235.


B., Nelh?bel, G.A., Kárpáti, A., Kenessey, I., Jóri, B., Székely, C., Peták,
I., Lotz, G., Heged?s, Z., Heged?s, B. and Füle, T., 2011. Clinical
significance of genetic alterations and expression of epidermal growth factor
receptor (EGFR) in head and neck squamous cell carcinomas. Oral Oncology,
47(6), pp.487-496.

N., Jenner, P. and Chaudhuri, K.R., 2017. The future of Parkinson’s
treatment–personalised and precision medicine. Eur Neurol Rev, 12, pp.15-6.

E., Kulakova, O., Boyko, A. and Favorova, O., 2016. Pharmacogenetics of
multiple sclerosis: personalized therapy with immunomodulatory drugs.
Pharmacogenetics and genomics, 26(3), pp.103-115.

A., Ragusa, M., Di Mauro, S., Iannitti, T., Pilleri, M., Biundo, R., Weis, L.,
Di Pietro, C., De Iuliis, A., Nicoletti, A. and Zappia, M., 2014.
Identification of circulating microRNAs for the differential diagnosis of
Parkinson’s disease and Multiple System Atrophy. Frontiers in cellular neuroscience,

L.C. and Jucker, M., 2015. Neurodegenerative diseases: expanding the prion
concept. Annual review of neuroscience, 38, pp.87-103.

H., De Micco, R., Niccolini, F. and Politis, M., 2017. Molecular imaging
markers to track huntington’s disease pathology. Frontiers in neurology, 8.

D.E. and Prohovnik, I., 2003. Sensitivity and specificity of neuroimaging for
the diagnosis of Alzheimer’s disease. Dialogues in clinical neuroscience, 5(1),

Health Organization, 2006. Neurological disorders: public health challenges.
World Health Organization.

O. and Barutca, S., 2014. Biological subtypes of breast cancer: Prognostic and
therapeutic implications. World journal of clinical oncology, 5(3), p.412.






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