article

Review: biomarkers towards personalised therapy in cancer

Since the sequence of the human genome was published some 20 years ago, omics strategies have enabled the generation of detailed molecular signatures of cancers and their subtypes.

This new information has spurred efforts towards stratification of patients bearing those signatures, development of targeted therapies, and has opened up the paradigm of precision medicine – by using biomarkers. However, the inherent complexity and ever-shifting genetic landscape of cancer has seen many targeted therapies become provocateurs of treatment-induced phenotypes and acquired resistance. Even promising biomarkers that have been identified in attempts to bypass this harbour issues concerning translation and implication into the clinic. Yet there is hope. Evidence from the successful use of biomarkers for diagnosis, screening and management of cancers such as breast and prostate has resulted in earlier diagnoses, higher survival and lower morbidity rates, thus validating the clinical relevance of this concept. Additionally, research into new biomarkers such as PD-1 and CTLA4, and new biomarker classes such as microRNAs and exosomes, coupled with the promise of stronger connections between the realms of molecular biology and biotechnology, means that the new era of biomarker discovery for precision medicine in cancer begins now.

Cancer has existed for millennia, and efforts to fight it have been met with galling failures. We have come a long way from Sidney Farber’s attempts to cure leukaemia by administering folic acid mimics. Following the sequencing of the human genome and development of high-throughput sequencing techniques, the number of genetic signatures illustrative of cancer subtypes has soared. However, in spite of these developments mortality has not significantly improved over the past few decades.1,2 The complex nature of treating a genetically multifarious disease with the propensity to develop therapy resistance quickly is reflected in the low approval rate of new drugs. Furthermore, the ethical and human considerations of sending a drug that may at best marginally improve survival through clinical trials are substantial.

While cancer incidence across our ageing population is increasing, treatment still relies heavily on a technique that has existed as long as medicine itself – surgery – and a cache of chemotherapeutics rife with noxious side effects. The targeted therapies that do exist can push development of further genetic/epigenetic irregularities leading to resistance, metastatic propagation and recurrent disease.3,4 Efficient management of this scenario hangs on identifying suitable treatment strategies from the outset, and monitoring treatment response both throughout and after the course of a disease.

Thus, as the prospect of a cancer cure may seem increasingly distant, development of biomarkers to aid in decision making and predict treatment outcome for personalised therapy now represents the Holy Grail of cancer treatment.

What are biomarkers?

Though many definitions exist, the term biomarker can be applied to any measurable biological entity that is representative of a physiological state.5 They are classed in the following way:

  • Diagnostic
  • Prognostic – providing a forecast of a patient’s disease progression, irrespective of treatment strategy
  • Predictive – allowing insight into likely response or resistance to a targeted therapy.6,7

Omics have represented the major route for biomarker discovery. Most have been identified following delineation of genetic signatures in biopsy tissue. Other omics and deep-sequencing strategies are likely to uncover a wealth of information in relation not only to protein-coding genes, but non-coding elements such as microRNAs, as well as proteins and metabolites.8-13

Biomarkers are detectable either in tumour tissue at the point of biopsy, or circulating in blood, urine and other fluids. Tissue biomarkers are not considered minimally invasive and time to detection and analysis can be long. However, while circulating markers can be easier to monitor during disease progression, their origin and accuracy is often put into question.

Ideally, quantification of biomarkers should also generate reproducible, highly specific and sensitive information. Statistical analysis of biomarker efficacy is a notably important step in development, particularly for the translation from bench to clinic. Interested readers are directed to the reviews by Buyse et al 7 and Duffy et al.14

Despite concerted efforts in biomarker development, only a limited number have been approved for clinical use and so far, no single marker is used independently. Commonly, biomarkers are used in combination with additional features such as tumour grade and differentiation status. The following summarises some of the best studied.

Tissue biomarkers – breast cancer evidence

Between 1998 and 2003 enormous and commendable effort was invested into refining subtypes of cancer, particularly breast cancers (BC).15,16 Similar attempts were made for other cancers,17-22 but none have quite reached the level of clinical significance as in BC. Crucially, this work led to delineation of subtypes characterised exclusively by expression of targetable genes. Table 1 lists biomarkers in the form of genetic rearrangements, fusions and mutations, mostly identified in those pioneering screens. The majority of these markers, as well as having a functional role in oncogenesis, can stratify patients to be treated with therapies designed against them or the pathways through which they function.

Breast cancer consists of numerous subtypes, bearing differences in molecular and phenotypic characteristics.23 The following four major subtypes classify patients:

  • Overexpressing or with amplified HER2 = HER2 overexpression
  • Negative for HER2 with overexpression of hormone receptors = Luminal A
  • Positive for HER2 and hormone receptors = Luminal B
  • No overexpression of these markers = Triple negative

A number of other classifiers exist to further group these patients, including the Ki-67 index.24,25 

Crucially, alongside the characterisation of BC subtypes has been the development of assays to further predict outcomes including metastasis and local recurrence, and response to therapy. These include MammaPrint, a 70-gene microarray to prevent low-risk patients undergoing unnecessary treatment,26 and Oncotype DX, a 21-gene assay for evaluating recurrence.27,28 These assays act as independent, minimally invasive and accurate biomarkers, and have begun to prove the value of incorporating biotechnological methods in biomarker application.

Circulating tumour markers – prostate cancer evidence

Circulating tumour-associated markers have enhanced diagnostic specificity and prognostication used in conjunction with conventional methods.^29 They are often overexpressed as a by-product of upregulated or amplified oncogenes within the tumour, or released at higher concentrations from an enlarged tissue.30

The introduction of the prostate specific antigen (PSA) as a measure of prostate cancer (PC) has significantly improved PC management, with earlier diagnoses and fewer deaths.31 Use of PSA has also increased the number of cases diagnosed at the localised level leading to fewer deaths from metastases.30,32 This marker demonstrates efficacy as a prognostic indicator in patients, as high levels correlate to clinical stage and Gleason scores.33

Though useful, PSA lacks accuracy as an ideal biomarker. A number of patients are initially diagnosed with low-grade PC. Since quality of life is significantly impaired following treatment, these patients are monitored by continual examinations and PSA measurements in a protocol known as active surveillance.34,35 However, PSA can also be increased by other conditions, inflammation, exercise and metabolism.36,37 Also, paradoxically, very low levels of PSA are occasionally observed in a highly aggressive form of PC.4,38

In an attempt to overcome accuracy issues, the use of derivatives and isoforms of PSA has been suggested. However, there have been conflicting results in their ability to improve specificity whilst maintaining the economic benefits of the PSA test.39 Additional markers for PC include use of the 4K score, the prostate health index, and urine biomarkers such as the TMPRSS2:ERG gene fusion.32,39,40

Breast

HER2 positive expression, Hormone receptor negative (HER2 overexpression subtype)

IHC (of full protein or extracellular domain), CISH (for amplification), FISH

Yes

Yes

Yes

trastuzamab, ado-trastuzumab emtansine, lapatinib, pertuzumab

Hormone receptor positive, HER2 negative, (Luminal A subtype)

IHC

Yes

Yes

Yes

palbociclib, hormonal therapy, eg, tamoxifen

Hormone receptor positive, HER2 positive (Luminal B)

IHC

Yes

Yes

Yes

Hormonal therapy, eg, tamoxifen

Hormone receptor and HER2 negative (triple negative)

IHC

Yes

Yes

 

olaparib*

Breast, prostate

Ki-67

IHC

Yes

Yes

 

Ovarian

BRCA1/2 mutation/loss

IHC

 

Yes

 

olaparib

CML, ALL

Philadelphia chromosome-positive BCR-ABL fusion

Karyotyping, FISH, or qPCR

Yes

Yes

 

bosutinib and nilotinib (CML), ponatinib, dasatinib, imatinib

CML

BCR-ABL T315I mutation**

qPCR

Yes

Yes

Yes

ponatinib

GIST

KIT mutation

qPCR

 

Yes

 

imatinib

CML

GIST

PDGFR

qPCR, FISH

 

Yes

 

CRC, NSCLC

KRAS mutations

IHC, qPCR

 

 

Yes***

Metastatic melanoma, NSCLC, RCC

PD-1 overexpression

 

IHC

 

Yes

Yes

nivolumab

Hodgkin lymphoma

pembrolizumab

Urothelial carcinoma, NSCLC

PD-L1

IHC

 

Yes

Yes

atezolizumab

Advanced melanoma

CTLA4

IHC

 

Yes

Yes

ipilimumab

NSCLC,  HNSCC, (KRAS-wild-type) CRC

EGFR mutations

IHC, qPCR

 

 

 

Yes

Yes

gefitinib, erlotinib (NSCLC), cetuximab (HNSCC), panitumumab (CRC)

Melanoma

BRAF V600 mutation

qPCR

 

Yes

Yes

vemurafenib

NSCLC

ALK rearrangement (EML4-, KIF5B- and TFG-ALK fusions)

FISH, IHC

 

Yes

Yes

ceritinib, crizotinib

Table 1: Current molecular biomarkers used for diagnosis or treatment monitoring in cancer

Immunogenic biomarkers

The diverse genetic reconfigurations that occur during oncogenesis provides the immune system with tumour-associated antigens,41 which are recognised and elicit an immune response. They include the carcinoembryonic antigen (CEA), carbohydrate antigens 15-3, 19-9, 125 and 145, and cancer testis antigens (Table 2). These are upregulated specifically in cancer, or in limited circumstances in healthy people.42

Similar to PSA, these antigens are often overexpressed as a result of aberrant expression or post-translational modifications of genes.43-45 Though they have demonstrated high sensitivities and specificities for diagnosis,45,46 their biology is still uncertain.42,47 As well as utility in diagnosis, they also have potential as prognostic/predictive biomarkers, with evidence of upregulation sometime before clinical presentation and function in promoting oncogenesis and metastasis.48-50 Additionally, their properties as immune-detectable antigens make them potential candidates for immunotherapy or cancer vaccines in the future.

A key recent discovery that certain proteins act as ‘immune checkpoints’ by regulating the T-cell response has led to a deeper understanding of how tumours modulate immunity. These proteins usually function to prevent autoimmunity51 but are also overexpressed in tumours that co-opt these functions as a means of generating an oncogenic immune-privileged environment. They include programmed death protein PD-1, its ligand PD-L1, and cytotoxic T-lymphocyte associated protein CTLA-4.

Immune checkpoint blockade immunotherapies have recently been developed, and their clinical success has been by no means insignificant. Indeed, anti-CTLA-4 monoclonal antibody ipilimumab was the first drug to improve survival for metastatic melanoma patients.51,52 Currently, clinical approval is limited to a few cancers, but considering widespread aberrant expression of PD-L1, PD-1 and CTLA-4, it is likely these drugs will soon be approved for others.53-57

Not all patients who respond to these drugs, however, show a higher expression of immune checkpoints, and vice versa.58 The drugs have also been shown to induce quite severe autoimmune effects.59-61 Thus, further biomarkers are needed to stratify patients, in order to fully harness the potential of this exciting new development.

Oncogenic infectious agents as biomarkers

Risk factors for head and neck squamous cell carcinomas (HNSCC) include high alcohol and tobacco consumption, and infection with human papilloma virus (HPV). HPV acts as a risk factor, oncogenic driver and, due to markedly improved responses to therapy observed HPV-positive patients, it is also an important prognostic biomarker.62

HNSCCs are treated with surgery followed by radiotherapy, which carries significant morbidity. Use of HPV as a biomarker could allow for dose de-escalation during radiotherapy treatment to minimise adverse effects for low-risk patients. Evidence has demonstrated success in this concept,63 but since not all HPV-positive patients will belong solely to the HPV-positive class, there are risks associated with undertreating patients.

In cervical cancers, HPV infection is not as clear a prognostic biomarker, with conflicting results dependent on serotype,64,65 and in contrast to HNSCC, patients generally have a bad prognosis.

Other cancers associated with infection include gastric cancer (GC) patients harbouring Helicobacter pylori infection, and nasopharyngeal carcinoma (NPC) patients with Epstein Barr virus (EBV) infection. H pylori infection is considered necessary for progression to cancer in the majority of GC cases.66 Likewise, EBV infections are found in almost all NPCs. EBV DNA can also be detected in blood, with high levels correlating to advanced disease and poor prognosis.67,68

For each of these cases, not all infected individuals go on to develop cancer. Thus, work is needed to understand why cancer develops in some but not all cases of infection, and to further investigate the biomarker potential.

MicroRNAs as biomarkers

With the view that an ideal biomarker is minimally invasive, robust and accurate, microRNAs (miRNAs) have emerged as ideal candidates. miRNAs have been implicated in almost every cancer and in mechanisms including metastasis,69,70 hypoxia71,72 and resistance to therapy.73-77 miRNAs have the potential to be prognostic/predictive biomarkers, given that miRNA signatures have been shown to change throughout the course of disease and be indicative of crucial cellular processes involved in therapy response.78 A recent study by Tavassoli et al identified miR-196a among miRNAs upregulated in patients who failed radiotherapy, and miR-9 in patients with good response.79 Other studies have identified miR-218 as a tumour-suppressive miRNA modulating chemosensitivity and inhibiting metastasis in a number of different cancers.75,77,80,81 Additionally, they have gained renewed attention as biomarkers given that they are easily detectable and stable in circulation, with levels shown to accurately reflect miRNA levels in the tumour.82,83

Translation of these markers into the clinic has been particularly challenging. One reason being that they have been found to be affected by factors such as age and gender,83 circadian rhythms84 and infections with HPV or H pylori.85,86 Full realisation of their potential must follow from investigations as to the origins of circulating miRNAs and their functions.

miRNAs are not, however, the only species of nucleic acids detectable in blood. Genomic and epigenomic alterations in cell-free circulating DNA from the tumour could represent exciting potential as biomarkers in liquid biopsies,87-90 as well as exosomes91-93 and circulating tumour cells.94 

Antigen

Cancer

Alpha-fetoprotein

Liver

CEA

Colorectal, breast, lung, liver, pancreatic, stomach and ovarian

CA 15-3

Breast, colorectal

CA 125

Ovarian

CA 19-9

Pancreatic, colorectal, gastrointestinal

CA 242

Pancreatic

CA 50

Gastrointestinal, pancreatic

CYFRA 21-1

Lung

Lactate dehydrogenase

Prostate

PSA

Cancer testis antigens

Ovarian

Table 2: Current cancer-associated antigens used in cancer diagnosis

Imaging biomarkers

Imaging, the main method being PET/CT (positron emission tomography computed tomography), is commonly used in the diagnosis and staging of cancer. In PET/CT, the amount of radiotracer taken up by the tissue increases in tumours and concurrently, higher uptake values correspond to poorer prognoses.95

Several recent studies have investigated the potential of linking imaging and expression of existing molecular biomarkers to enhance their prognostic and predictive values, such as in the recent study by He et al, which connected uptake data to response to endocrine therapy and oestrogen receptor expression in a mouse model of BC.96 Or in other studies by radiolabelling targeted therapies, as described in the review by Van Dijk et al with respect to EGFR-targeted imaging by labelling of cetuximab in HNSCC.97

Hypoxia is an established indicator of poor prognosis, due to its association with resistance to chemo- and radiotherapy.98 Hypoxia-specific radiotracers such as Cu-ATSM or F-FMISO could be used to identify more hypoxic tumours,99 which could help to optimise treatment regimens and dosage plans, or to monitor treatment response. It is likely, though, that the combination use of targeted imaging and genetic signatures will be more sensitive in cancer management, such as has recently been suggested by Suh et al and others for assessment of hypoxia.100,101 

What next?

It is likely that, whatever their application, new technologies will be necessary to measure biomarkers at the point of diagnosis. While their discovery has been achieved through high-throughput omics methods primarily on cancer tissue, sensitive, cost-effective and quick methods are now needed for efficient biomarker-driven management of cancer in the clinic. Given this situation, an additional challenge for molecular and cell biologists, clinical oncologists and geneticists alike, lies in bridging the gap between their world and that of biotechnology. Some cross-talk has already helped spur successes in the last 20 years. However, better communication is essential in order to make the best use what has already been, and will be, uncovered.

 

 

BIOGRAPHIES

Mahvash Tavassoli is Professor of Molecular Oncology, Head of the Department of Molecular Oncology and the Head and Neck Cancer Lead, King’s College London/King’s Health Partners. She obtained her PhD from the University of Sussex, UK. Following several postdoctoral training positions at Fred Hutchinson Cancer Research Centre, USA, Max-Planck Institute, Munich and the Institute of Cancer Research, London, she joined KCL in 1995. She has led a successful research team in the basic and clinical translation aspects of head and neck cancers. Her lab currently focusses on biomarker studies with the aim to develop molecular signatures that enable the prediction of patient response to radio-chemotherapy and targeted therapies.

After obtaining a BSc in Biological Sciences at the University Leeds, during which time she undertook a research project to develop a ‘biosensor’ for detection of drugs in food sources, Katheryn Begg then obtained an MSc in Biomedical Sciences at the University of Bristol, studying Wnt signalling in Zebrafish. Katheryn is now working towards her PhD at King’s College London using omics strategies and bioinformatics to identify biomarkers of treatment response in Head and neck cancer.

 

 

 

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