**7. Conclusion**

**Biomarker Cancer type Clinical use Specimen**

benign disease

benign disease

progression of disease

development of disease

progression, response to

Detection of tumours, aid in selection of patients

progression and survival

disease (professional and home

monitoring

Colorectal Monitoring progression of disease

therapy

therapy

therapy

prognosis

Serum

Serum, plasma

Whole blood

Urine

Faeces

Pro2PSA Prostate Discriminating cancer from

Free PSA Prostate Discriminating cancer from

Total PSA Prostate Prostate cancer diagnosis and

ROMA (HE4 + CA-125) Ovarian Prediction of malignancy OVA1 (multiple proteins) Ovarian Prediction of malignancy HE4 Ovarian Monitoring recurrence or

AFP-L3% Hepatocellular Risk assessment for

CA27.29 Breast Monitoring disease response to

CA15–3 Breast Monitoring disease response to

p63 protein Prostate Aid in differential diagnosis FFPE tissue

Breast Prediction of cancer

Alpha-fetoprotein (AFP) Testicular Management of cancer Amniotic fluid

**Table 2.** List of FDA-approved tumour markers commonly used in clinical practice which mainly consist of serum and

Bladder Diagnosis and monitoring of

use)

Colorectal Detection of faecal occult blood (home use)

CA19–9 Pancreatic Monitoring disease status CA-125 Ovarian Monitoring disease

Thyroglobulin Thyroid Aid in monitoring Alpha-fetoprotein (AFP) Testicular Management of cancer Carcinoembryonic antigen Not specified Aid in management and

tumours

Oestrogen receptor (ER) Breast Prognosis, response to therapy Progesterone receptor (PR) Breast Prognosis, response to therapy Her-2/neu Breast Assessment for therapy

c-Kit Gastrointestinal stromal

Circulating tumour cells (EpCAM, CD45, cytokeratins 8, 18+, 19+)

Nuclear Mitotic Apparatus protein

Human haemoglobin (faecal occult

(NuMA, NMP22)

plasma biomarkers.

blood)

Fibrin/fibrinogen degradation

180 Autoantibodies and Cytokines

product (DR-70)

Autoantibodies have gained considerable attention in the medical diagnostic field as candidate diagnostic and prognostic biomarkers in many different disease areas, since they are in theory detectable many years before clinical symptoms appear. This particular property of autoantibodies makes them attractive tools for early diagnosis of disease. However, identification and validation of autoantibody biomarkers has historically been constrained by the available technological approaches and the high attrition rates during studies on larger cohorts.

To increase the success rate in biomarker discovery and validation, the correct technique as well as the right number of samples and analytes to be used for each phase should be carefully planned and designed as depicted in **Figure 6**. The current gold standard for biomarker validation remains ELISA, which is regularly utilised for confirmatory studies as it allows a relatively high-throughput of samples and is a versatile and robust tool. Thus, protein microarray analysis is often compared against the quantitative data of ELISA assays [79]. However, ELISAs routinely permit only single antigen detection per well and often require relatively large volumes of samples compared to other more miniaturised, high-throughput methods. This leaves substantial scope for protein microarrays to be used in both the discovery and validation of panels of autoantibody biomarkers, since they represent a sensitive, highly reproducible, multiplexed and high throughput experimental platform for autoantibody quantitation; this will undoubtedly be aided by the underlying protein microarray platforms themselves gaining regulatory approval for use as clinical diagnostics.

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**Figure 6.** Phases of biomarker discovery pipeline. Each phase requires different technologies and study design.
