27714 R Biopharm 2018 06 Ad Clinical Laboratory RG PCR 60x270mm EN HiRes

RIDA®GENE multiplex real-time PCR

, /in /by
27599 Randox Evidence Full Page Advert CLI June 2018 Issue

The Evidence Series of immunoanalysers

, /in /by
27659 SSI AACC SSID ad 92x132mm CLI HR

Are you our new partner in microbiology?

, /in /by
Alison Pic 03

Biomarkers show promise for improving breast cancer treatment

, /in /by

In May it was reported that, owing to an IT error, from 2009 to 2018, approximately 450,000 women aged between 68 and 71 were not recalled for their final mammogram appointments in England. Jeremy Hunt, the health secretary has been quoted as saying that between 135 and 270 women “may have had their lives shortened as a result”.  The panic-quelling response came very quickly. The Guardian newspaper reported that Sir
Richard Peto, a professor of medical statistics at Oxford University, had written that there is still substantial uncertainty about the exact ages that mammographic screening should start and end. Additionally, a group of academics and GPs wrote a letter to The Times newspaper saying that the women should not be concerned unless they notice a lump or other symptoms and that the breast cancer screening programme mostly causes more unintended harm than good; many women and doctors avoid breast screening as it has no impact on all-cause death; and that the most dangerous and advanced cancers are not prevented by screening programmes. Breast cancer charities retorted that mammographic screening remains the best tool available for detecting breast cancer at an early and therefore more easily treatable stage and we must not forget that the programme does save lives. The UK’s NHS breast screening programme began in 1988 and national coverage was reached in the mid-1990s. However, over twenty years on, there seems to be an increasing body of data to suggest that the ‘accidental’ harm resulting from mammography because of over-detection and over-treatment of clinically unimportant lumps has been underestimated. The often-quoted figure is that for every woman whose life is extended, three receive unnecessary surgery, chemotherapy or radiotherapy. Hence our ‘best tool available’ seems to be a rather blunt tool. Biomarkers, surely, could provide the refinement needed to stratify patients according to therapy response. This will enable the delivery of individually tailored treatment plans and so will, crucially, prevent the unnecessary administration of chemotherapy and radiotherapy. Work on this is, of course, underway. The EU-funded RESPONSIFY study in Germany has already led to two parameters being included into German breast cancer guidelines for the treatment of HER2-positive breast cancer. Additionally, a gene-expression panel that predicts whether chemotherapy will be beneficial for preventing recurrence is already being used, with some success for the low and high scores. Further work is needed, but perhaps the day is in sight where women will no longer undergo unnecessary chemotherapy.

C350 Fletcher Figure 1

BRCA and beyond: the genes that influence breast cancer risk

, /in /by

Over the last twenty-five years, breast cancer genetics has moved from linkage in high-risk families to association in population-based studies. Accordingly, the genetic variants that have been identified range from rare high-penetrance mutations to common low-penetrance markers. We summarize current knowledge and consider whether understanding how these that variants influence risk could help to refine risk prediction and develop targeted therapies.

by Dr Olivia Fletcher and Dr Syed Haider

Rare high-penetrance mutations
The earliest evidence for genetic susceptibility to cancer came from epidemiological studies in the 1940s and 1950s showing increased cancer risk in the relatives of cancer patients. It was not until the 1990s that linkage analysis, i.e. the genotyping of genetic markers in large family pedigrees, led to the identification of the first breast cancer susceptibility gene, BRCA1, at 17q21 [1]. Identification of the second breast cancer susceptibility gene, BRCA2, at 13q12-13 followed relatively quickly [2]. Mutations in BRCA1 and BRCA2 are present at a frequency of approximately 1 in 800 for BRCA1 and 1 in 500 for BRCA2, they confer high relative risks of breast cancer in carriers (more than tenfold) and are associated with early onset disease [3, 4].
Moderate-risk variants
The next milestone in breast cancer genetics came in 2002 with the discovery of frameshift alteration in the checkpoint kinase 2 gene, CHEK2*1100delC. This variant was discovered using a combination of linkage and mutation screening in a large multiple-case breast cancer family from the Netherlands, followed by analysis of the CHEK2*110delC variant in high-risk breast cancer families, ‘unselected’ breast cancer cases and controls [5]. The CHEK2*1100delC variant occurs on a single haplotype indicating that all CHEK2*1100delC-carrying chromosomes arise from a single founder; this variant is confined to Northern European populations with a prevalence in controls that varies significantly between Northern European populations. Compared to truncating mutations in BRCA1 and BRCA2, the relative risk associated with CHEK2*1100delC is moderate – approximately twofold.

Subsequent to the discovery of CHEK2*1100delC, additional moderate-risk variants were identified in candidate genes including ataxia telangiectasia mutated (ATM), partner and localiser of BRCA2 (PALB2) and BRCA1 interacting protein C-terminal helicase 1 (BRIP1). These variants were discovered by sequencing of exons and exon/intron boundaries of DNA damage repair genes in breast cancer cases from high- and moderate-risk families. Variants in these genes occur in the population at combined frequencies (per gene) of around 1% and are predominantly protein-truncating mutations.

Common low-penetrance variants
It was not until 2007 that the first genome-wide association study (GWAS) of breast cancer successfully identified five common low-penetrance variants; minor allele frequencies of these variants ranged from 25 to 40% and they were associated with relative risks of 1.07 to 1.26 [6]. Detecting relative risks of this magnitude required three stages of genotyping and a total of 26 258 cases and 26 894 controls. This study was an order of magnitude larger than any previous study marking the beginning of the era of GWAS as well as large consortia. Since 2007 many more breast cancer GWASs have been published, but the major advances in identifying and cataloguing additional low-penetrance variants have come from large collaborative efforts led by the Breast Cancer Association Consortium (http://bcac.ccge.medschl.cam.ac.uk/); in particular two large analyses – the Collaborative Oncological Gene-environment study (COGS) and OncoArray [7, 8]. To date, more than 150 low-penetrance variants conferring relative risks of approximately 0.81–1.35 have been identified. Not surprisingly, the more common variants with the (relatively) more extreme breast cancer odds ratios were identified first, by the GWASs (shown in deep blue, Fig. 1); less common variants and variants with less extreme odds ratios were identified most recently, by the largest pooled analysis, OncoArray (shown in green, Fig. 1).

Contribution to the excess familial relative risk
Breast cancer, like most common cancers, shows familial aggregation; the risk of breast cancer in the first-degree relative of a breast cancer case is about twice that of the risk in the general population [3]. The proportion of this ‘familial relative risk’ that is explained by one or more variants is the metric used to quantify the relative contributions of the different classes of variants – and to estimate the number of variants that have not yet been identified. Relative proportions of all three types of variants are shown in Figure 2; mutations in BRCA1 and BRCA2 account for approximately the same proportion of the familial relative risk as the sum of the common low-penetrance variants.
Differences between coding variants and non-coding variants
One fundamental difference between the high-penetrance mutations in BRCA1 and BRCA2, the moderate-risk variants in DNA damage repair genes and the low-penetrance variants identified by GWAS is that the vast majority of low-penetrance GWAS variants map to non-coding DNA. Estimating the risk of breast cancer for individual BRCA1 and BRCA2 mutation carriers is not trivial; there is some evidence that breast cancer risks differ according to the position of the mutation within the gene [4] and for BRCA2, there is evidence of effect modification by common low-penetrance variants [9]. For the low-penetrance GWAS variants, however, the problem is rather different; while the relative risks associated with the marker single nucleotide polymorphisms (SNPs) are fairly precisely estimated, the underlying ‘causal’ variants and the genes that these variants influence remain – largely – unknown. Approaches to the functional characterisation of GWAS risk loci include fine-scale mapping of potentially large genomic regions, the analysis of SNP genotypes in relation to expression of nearby genes (eQTL) and the use of chromatin association methods [chromosome conformation capture (3C) and Chromatin Interaction Analysis by Paired-End Tag Sequencing (ChIA-PET)] of regulatory regions to determine the identities of target genes. Regulatory elements have been shown to form physical interactions with the genes that they regulate, often over long distances and frequently ‘skipping over’ proximal genes; chromatin association methods capture these interactions and use them to infer likely target genes. We have recently carried out a high-throughput, high resolution analysis of 63 breast cancer risk loci using Capture Hi-C [10]. We were able to identify 110 putative target genes mapping to 33 risk loci. Although some of these putative target genes were well-known cancer genes others were not; in depth follow-up studies will be required to determine which of these putative target genes truly influence breast cancer risk and the mechanisms by which they do so.

Causal variants and target genes can inform risk prediction and therapy
NICE guidelines for the classification, care and management of breast cancer, based on an individual’s family history of breast and other cancers, are used to classify women into three categories: population risk (<17% lifetime risk), moderate risk (17–30% lifetime risk) and high risk (≥30% lifetime risk; https://www.nice.org.uk/guidance/CG164). The options that are available to a woman – increased surveillance, genetic testing, chemoprevention and prophylactic surgery – depend on which category she falls within. A longer-term aim of GWAS is the development of polygenic risk scores (PRS) that can be incorporated into risk prediction algorithms to refine risk estimates. A recent analysis based on 77 breast cancer-associated SNPs, estimated lifetime risks of breast cancer for women in the lowest and highest quintiles of the PRS as 5.3% (population risk) and 17.2% (moderate risk), respectively [11]. Inclusion of larger numbers of SNPs and incorporating causal variants rather than tag SNPs should improve the discriminatory power of the PRS.

In this era of stratified medicine, identifying the genes that underlie GWAS associations and hence – presumably – contribute to defining disease subgroups, also offers the potential for targeted therapies. For instance, metastatic breast cancer patients with germline BRCA1 or BRCA2 mutations who also lack HER2 expression are eligible for Olaparib [a targeted cancer drug that inhibits poly-ADP ribose polymerase (PARP)] as of January 2018. A recent study demonstrated that Olaparib-treated patients have significantly improved progression-free survival (PFS) compared to patients treated with standard-therapy (median PFS of 7 months vs 4.2 months respectively) [12]. Breast cancer patients with germline BRCA1 or BRCA2 mutations already have a defect in their DNA repair mechanisms; by blocking PARP proteins, Olaparib acts to exacerbate DNA damage and trigger cell death, specifically in cancer cells (synthetic lethality). Although defects in DNA repair can be a consequence of germline BRCA mutations, some breast cancer patients manifest defects in DNA repair in the absence of germline BRCA mutations; these patients are also regarded as BRCA deficient – a characteristic often termed as ‘BRCAness’ [13]. Scientists are actively searching for biomarkers of BRCAness in order to assess the suitability of existing PARP inhibitors for patients exhibiting BRCAness [14]. Additional clinical trials on studying efficacy of PARP inhibitors for treating other breast cancer subgroups are underway.

The associations between GWAS SNPs and disease are very modest, and this is often cited as a disadvantage when it comes to considering the genes that map to these loci as putative drug targets. However, an individual non-coding ‘causal’ SNP will usually explain only a small proportion of variation in expression of the gene(s) that it regulates; chemically targeting these genes could have a much more profound effect on disease incidence or outcome. In support of this prediction, a recent investigation by scientists from GlaxoSmithKline estimated that selecting genetically supported targets (including those identified by GWAS) could double the success rate of drugs in clinical development. Although this estimate may be less applicable to cancer drugs, where the somatic genome is as important – or more important – than the germline genome [15,] it leaves open the possibility of new therapies targeting the genes that underlie GWAS associations.

Acknowledgements
We thank Breast Cancer Now for funding this work as part of Programme Funding to the Breast Cancer Now Toby Robins Research Centre.

References
1. Miki Y, Swensen J, Shattuck-Eidens D, Futreal PA, Harshman K, Tavtigian S, Liu Q, Cochran C, Bennett LM, et al. A strong candidate for the breast and ovarian cancer susceptibility gene BRCA1. Science 1994; 266(5182): 66–71.
2. Wooster R, Bignell G, Lancaster J, Swift S, Seal S, Mangion J, Collins N, Gregory S, Gumbs C, et al. Identification of the breast cancer susceptibility gene BRCA2. Nature 1995; 378(6559): 789–792.
3. Easton DF. How many more breast cancer predisposition genes are there? Breast Cancer Res 1999; 1(1): 14–17.
4. Kuchenbaecker KB, Hopper JL, Barnes DR, Phillips KA, Mooij TM, Roos-Blom MJ, Jervis S7, van Leeuwen FE5, Milne RL, et al. Risks of breast, ovarian, and contralateral breast cancer for BRCA1 and BRCA2 mutation carriers. JAMA 2017; 317(23): 2402–2416.
5. Meijers-Heijboer H, van den Ouweland A, Klijn J, Wasielewski M, de Snoo A, Oldenburg R, Hollestelle A, Houben M, Crepin E, et al. Low-penetrance susceptibility to breast cancer due to CHEK2(*)1100delC in noncarriers of BRCA1 or BRCA2 mutations. Nat Genet 2002; 31(1): 55–59.
6. Easton DF, Pooley KA, Dunning AM, Pharoah PD, Thompson D, Ballinger DG, Struewing JP, Morrison J, Field H, et al. Genome-wide association study identifies novel breast cancer susceptibility loci. Nature 2007; 447(7148): 1087–1093.
7. Michailidou K, Hall P, Gonzalez-Neira A, Ghoussaini M, Dennis J, Milne RL, Schmidt MK, Chang-Claude J, Bojesen SE, et al. Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat Genet 2013; 45(4): 353–361e2.
8. Michailidou K, Lindstrom S, Dennis J, Beesley J, Hui S, Kar S, Lemaçon A, Soucy P, Glubb D, et al. Association analysis identifies 65 new breast cancer risk loci. Nature 2017; 551(7678): 92–94.
9. Gaudet MM, Kirchhoff T, Green T, Vijai J, Korn JM, Guiducci C, Segrè AV, McGee K, McGuffog L, et al. Common genetic variants and modification of penetrance of BRCA2-associated breast cancer. PLoS Genet 2010; 6(10): e1001183.
10. Baxter JS, Leavy OC, Dryden NH, Maguire S, Johnson N, Fedele V, Simigdala N, Martin LA, Andrews S, et al. Capture Hi-C identifies putative target genes at 33 breast cancer risk loci. Nat Commun 2018; 9(1): 1028.
11. Mavaddat N, Pharoah PD, Michailidou K, Tyrer J, Brook MN, Bolla MK, Wang Q, Dennis J, Dunning AM, et al. Prediction of breast cancer risk based on profiling with common genetic variants. J Natl Cancer Inst 2015; 107(5): pii: djv036.
12. Robson M, Im SA, Senkus E, Xu B, Domchek SM, Masuda N, Delaloge S, Li W, Tung N, et al. Olaparib for metastatic breast cancer in patients with a germline BRCA mutation. N Eng J Med 2017; 377(6): 523–533.
13. Lord CJ, Ashworth A. BRCAness revisited. Nat Rev Cancer 2016; 16(2): 110–120.
14. Davies H, Glodzik D, Morganella S, Yates LR, Staaf J, Zou X, Ramakrishna M, Martin S, Boyault S, et al. HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures. Nat Med 2017; 23(4): 517–525.
15. Nelson MR, Tipney H, Painter JL, Shen J, Nicoletti P, Shen Y, et al. The support of human genetic evidence for approved drug indications. Nat Genet 2015; 47(8): 856–860.

The authors
Olivia Fletcher* PhD, Syed Haider PhD
Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London SW3 6JB, UK

*Corresponding author
E-mail: Olivia.fletcher@icr.ac.uk

C351 Wood BCBM Risk Factors

Risk factors for development of breast cancer bone metastasis

, /in /by

Breast cancer bone metastasis results in a significant reduction in patient quality of life and upon metastatic spread the disease is considered incurable. Molecules have been identified which predict the risk of developing bone metastases. This review discusses these key molecules and their potential utility within patient treatment decisions.

by Dr Steven L. Wood and Prof. Janet E. Brown

Introduction
Invasive breast cancer is diagnosed in over 55 000 women every year within the UK [1]. Despite recent advances in breast cancer treatment around 10 000 women die from breast cancer in the UK annually, almost all as a result of metastatic spread, which can occur years after apparently successful initial treatment. Over 70% of all advanced breast cancer patients develop metastatic spread to the skeleton [2, 3]. Disseminated tumour cells within bone can remain dormant for many years before finally becoming reactivated, leading primarily to bone resorption (osteolytic lesions), but also to unbalanced bone formation in response (osteoblastic lesions). Current treatments to reduce/prevent the skeletal complications in patients with established breast cancer bone metastasis (BCBM) involve the use of antiresorptive agents such as bisphosphonates [such as zoledronic acid (ZA)] [4]. An antiresorptive treatment has also been developed which utilizes antibodies directed towards key molecules within BCBM-induced bone destruction, i.e. denosumab [5]. These antiresorptive agents have been highly effective in improving quality of life for patients with BCBM, but do not improve survival once metastasis is established.

Recently, however, large studies have shown that bisphosphonates given as adjuvant treatment in early breast cancer, alongside other standard treatments, lead to a reduction in the numbers of postmenopausal patients developing bone metastasis [6]. Adjuvant treatment also leads to improved overall survival and adjuvant bisphosphonate therapy is now entering standard practice. However, these treatments are not without side effects, including osteonecrosis of the jaw [7, 8]. Since only a minority of women will develop bone metastasis, biomarkers are required to identify those patients at highest risk, enabling therapy to be targeted to those who will benefit, sparing those who will not.

Risk factors
Clinicopathological and demographic risk factors
Breast cancer is a heterogeneous disease and pathological staging and grading systems are widely used in routine practice. Although not generally specific for indicating risk of bone metastasis, these systems do categorize patients into sub-groups that determine appropriate treatment and risk of progression. The human epidermal growth factor receptor 2 (HER2) and estrogen receptor (ER) have both prognostic and predictive value and are routinely measured. ER is a hormone-regulated nuclear transcription factor that binds estrogen, with consequent expression of genes including the progesterone receptor (PR). Patients with HER2-positive breast cancer have a poorer prognosis, but targeted treatments are now available. Like ER, HER2 is also a predictive marker, identifying patients who are likely to respond to targeted treatments.

Histological subtype, tumour grade, lymph node involvement and body-mass index all impact on the general risk of metastasis and, therefore, of BCBM. It is well-recognized that bone metastases more commonly develop in ER-positive patients; they can also occur in ER-negative patients. Although these pathological categories are routinely examined, there has been a recent strong research emphasis upon the discovery of molecular risk factors for development of metastasis, including BCBM.
Molecular risk factors for bone metastasis
Genetic risk factors
There is good evidence that the risk of breast cancer spread to bone can be predicted both on the basis of the intrinsic genetic subtype of the primary tumour as well as the presence of recently identified bone metastasis genes.

Breast cancers can be classified into five intrinsic subtypes – luminal A, luminal B, HER2 enriched, basal-like and normal-like. Luminal-subtype tumours metastasize predominantly to bone [9, 10]. Basal-like tumours metastasize predominantly to the lymph-nodes, brain and lung, with bone being a relatively infrequent site of metastatic spread [9]. In this way, intrinsic tumour subtypes, which reflect the expression of multiple genes, can influence the probability of breast cancer spread to different target tissues.

Genes that predict BCBM have been discovered using de novo unbiased genetic screening approaches – including gene copy-number analysis (CNA) – to identify regions of gene amplification specific to BCBM. In one such study, bone-homing variants of breast cancer cells were isolated by repeated intracardiac injection within immunocompromised mice and isolation of metastatic cells from bone [11]. Comparison of the parental and bone-homing cells identified a genetic region, 16q23, amplified within the bone-homing cells which encoded the gene for the musculoaponeurotic fibrosarcoma oncogene (MAF) transcription factor [11]. Further studies identified the role of MAF as a transcriptional regulator of parathyroid hormone-related protein (PTHrP) – a key regulator molecule within the vicious cycle of bone destruction within BCBM [6]. The MAF-status of primary tumours has the ability to predict the benefit of ZA treatment [12]. Patients with MAF-negative tumours have increased disease-free survival upon ZA treatment compared to control patients; however, the beneficial effects of ZA treatment are not observed in patients with MAF-positive tumours [12].

Breast cancer cells which have metastasized to bone frequently remain dormant for many years as disseminated tumour cells (DTCs). Growth signals that are still not completely understood trigger eventual activation of these DTCs and the formation of macro-metastatic lesions. In a recent study using functional genetic screening a protein kinase [mitogen and stress-activated kinase-1 (MSK1)] has been identified, which in ER-positive breast cancer cells promotes breast cancer cell differentiation and inhibits migration to bone [13]. This suggests that the level of expression of MSK1 within ER-positive breast cancer cells could be used to stratify patients in terms of risk of developing BCBM.

Protein-expression risk factors within BCBM
Several studies have focused on altered protein expression within BCBM. Immunohistochemical measurement of the levels of cyclo-oxygenase-2 (COX2), cytokeratin-5/6 (CK5/6), C-X-C chemokine receptor-4 (CXCR4), parathyroid hormone receptor-1 (PTHR1), osteoprotogerin (OPN) and calcium-sensing receptor (CaSR) within primary patient tumours evaluated their potential as potential predictors of the subsequent development of BCBM [14]. The absence of cytoplasmic OPN in this study was observed to be an independent risk factor for the development of BCBM, whereas expression of PTHR1 was observed to be associated with BCBM; however, the association was not significant within multivariate analysis, thus PTHr1 levels are not an independent predictor of BCBM [14].

Quantitative proteomic analysis of parental MDA-MB-231 triple-negative breast cancer cells and comparison with a bone-homing variant of these cells isolated by repeated intracardiac injection within immunocompromised mice, identified two proteins as predictive of development of BCBM: PDZ-domain containing protein (GIPC1) and macrophage capping-protein (CAPG) [15]. In rigorous adjusted Cox regression analyses, high expression of both CAPG and GIPC1 within primary tumours was associated with a higher risk for development of BCBM within both a training set (n=427) and a subsequent validation set (n=297) of patients selected from the large randomized AZURE trial of adjuvant ZA (AZURE-ISRCTN79831382) [15]. GAPGhigh/GIPC1high status was not associated with development of bone metastasis following ZA treatment suggesting that these two markers are also predictive of treatment benefit.

Bone morphogenetic protein-7 (BMP7) is a cytokine which can elicit diverse signalling outcomes within breast cancer cells, including altering the rates of cell migration, invasion and apoptosis, as well as its role in bone formation [16]. In a study of the level of expression of BMP7 within breast cancer primary tumours, high expression of BMP7 correlated with a reduced time to development of BCBM within invasive ductal carcinomas [17]. In this study BMP7 levels did not correlate with time to BCBM within invasive lobular carcinoma [17].

Components of the bone extracellular matrix are potential markers for BCBM risk and several proteins have been studied in this regard including bone sialoprotein (BSP), osteopontin and osteocalcin [18]. BSP is a component of the bone mineralized cell-matrix which can perform numerous functions, including integrin-binding and the regulation of angiogenesis [19]. Serum levels of BSP were observed to be higher in patients with bone-only metastasis of breast cancer compared to patients with both osseous and visceral metastases within both univariate and multivariate analysis, with a circulating BSP concentration of ≥24 ng/ml acting as a significant factor for prediction of BCBM risk [20].

Bone turnover markers to monitor development of BCBM
Bone turnover markers are products of active bone resorption and formation. Several of these markers are products of collagen metabolism including procollagen-I N-terminal extension pro-peptide (PINP) and procollagen-I C-terminal extension peptide (PICP) – markers of bone formation, as well as C-terminal type-I collagen telopeptide (CTX) and C-terminal telopeptide (ICTP) – markers of bone resorption [21]. In a study measuring the levels of P1NP, CTX and 1-CTP within 872 patient-serum samples taken at baseline in the AZURE trial of adjuvant ZA, levels of P1NP, CTX and 1-CTP were all found to be prognostic for future BCBM, but none of these markers were prognostic for non-skeletal metastasis overall survival or treatment benefit from ZA [22].

In a related study [23], Lipton et al. investigated CTX in 621 postmenopausal early breast cancer patients in a 5-year phase III trial of tamoxifen +/− octreotide. Higher pre-treatment CTX was associated with shorter bone-only recurrence-free survival. However, there was no statistically significant association with first event in the bone plus concurrent relapse elsewhere or with first recurrence at other distant sites.

In a related study serum levels of total and bone-specific alkaline phosphatase (BSAP), CTX, ICTP, osteocalcin, N-terminal telopeptide of collagen (NTX), PINP and tartrate resistant acid phosphatase (TRACP5b; a marker of bone resorption), were measured in postmenopausal women with early stage luminal-type invasive ductal carcinoma (IDC) [24]. In this study TRACP5b levels most accurately predicted the development of BCBM, with a 3-marker panel (BSAP, PINP and TRACP5b) serving as an accurate marker panel for BCBM [24].
Conclusion
The metastatic spread of breast cancer cells to bone is a multistep process in which cancer cells must enter and survive within the circulation, and then finally leave the circulation and enter (and adapt to) the bone micro-environment. Molecular profiling of breast cancer cells at both the genetic and protein level has identified a series of molecules which play pivotal roles in this complex process. As such, differential expression of these molecules within primary patient tumour samples may be used to stratify patients with early breast cancer, in terms of BCBM risk and guiding treatment decisions. To date, the intrinsic tumour subtype has proven to be the most effective observation predicting risk of BCBM development; however, recent studies have identified new molecular components within bone metastatic breast cancer cells (including key transcription factors and proteins important in cell signalling and cell migration) that may form the basis of future tests.

Once within bone, breast cancer cells trigger alterations in the bone micro-environment that favour survival of DTCs. Later when macroscopic metastases form, the altered rates of bone formation and breakdown lead to the generation of bone metabolic products that can be measured within patients. Altered levels of these bone metabolic products predict BCBM development and can also be used to monitor treatment responses. Extracellular matrix components including BSAP, PINP, TRACP5b, CTX and 1-CTP have proven particularly useful in this regard.

Studies to date have occasionally produced conflicting results. This may reflect the use of widely differing sample sources (ranging from animal model systems to patient-derived samples), as well as variations in the patient cohorts used for different clinical studies. Despite these limitations, key molecules are becoming evident that can be measured and used to predict the risk of BCBM. Future studies using these candidate molecules in larger, multicentre clinical trials will further refine a testing panel for prediction of BCBM risk.

References
1. Cancer Research UK (CRUK). Breast cancer statistics (http: //www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/breast-cancer).
2. Scheid V, Buzdar AU, Smith TL, Hortobagyi GN. Clinical course of breast cancer patients with osseous metastasis treated with combination chemotherapy. Cancer 1986; 58(12): 2589–2593.
3. Coleman RE. Metastatic bone disease: clinical features, pathophysiology and treatment strategies. Cancer Treat Rev 2001; 27(3): 165–176.
4. Wilson C, Bell R, Hinsley S, Marshall H, Brown J, Cameron D, Dodwell D, Coleman R. Adjuvant zoledronic acid reduces fractures in breast cancer patients; an AZURE (BIG 01/04) study. Eur J Cancer 2018; 94: 70–78.
5. Lipton A, Fizazi K, Stopeck AT, Henry DH, Smith MR, Shore N, Martin M, Vadhan-Raj S, Brown JE, et al. Effect of denosumab versus zoledronic acid in preventing skeletal-related events in patients with bone metastases by baseline characteristics. Eur J Cancer 2016; 53: 75–83.
6. Guise TA, Kozlow WM, Heras-Herzig A, Padalecki SS, Yin JJ, Chirgwin JM. Molecular mechanisms of breast cancer metastases to bone. Clin Breast Cancer 2005; 5 Suppl(2): S46–53.
7. Stopeck AT, Fizazi K, Body JJ, Brown JE, Carducci M, Diel I, Fujiwara Y, Martín M, Paterson A, et al. Safety of long-term denosumab therapy: results from the open label extension phase of two phase 3 studies in patients with metastatic breast and prostate cancer. Support Care Cancer 2016; 24(1): 447–455.
8. Rathbone EJ, Brown JE, Marshall HC, Collinson M, Liversedge V, Murden GA, Cameron D, Bell R, Spensley S, et al. Osteonecrosis of the jaw and oral health-related quality of life after adjuvant zoledronic acid: an adjuvant zoledronic acid to reduce recurrence trial subprotocol (BIG01/04). J Clin Oncol 2013; 31(21): 2685–2691.
9. Huber KE, Carey LA, Wazer DE. Breast cancer molecular subtypes in patients with locally advanced disease: impact on prognosis, patterns of recurrence, and response to therapy. Semin Radiat Oncol 2009; 19(4): 204–210.
10. Ignatov A, Eggemann H, Burger E, Ignatov T. Patterns of breast cancer relapse in accordance to biological subtype. J Cancer Res Clin Oncol 2018; doi: 10.1007/s00432-018-2644-2.
11. Pavlovic M, Arnal-Estape A, Rojo F, Bellmunt A, Tarragona M, Guiu M, Planet E, Garcia-Albéniz X, Morales M, et al. Enhanced MAF oncogene expression and breast cancer bone metastasis. J Natl Cancer Inst 2015; 107(12): djv256.
12. Coleman R, Hall A, Albanell J, Hanby A, Bell R, Cameron D, Dodwell D, Marshall H, Jean-Mairet J, et al. Effect of MAF amplification on treatment outcomes with adjuvant zoledronic acid in early breast cancer: a secondary analysis of the international, open-label, randomised, controlled, phase 3 AZURE (BIG 01/04) trial. Lancet Oncol 2017; 18(11): 1543–1552.
13. Gawrzak S, Rinaldi L, Gregorio S, Arenas EJ, Salvador F, Urosevic J, Figueras-Puig C, Rojo F, Del Barco Barrantes I, et al. MSK1 regulates luminal cell differentiation and metastatic dormancy in ER(+) breast cancer. Nat Cell Biol 2018; 20(2): 211–221.
14. Winczura P, Sosinska-Mielcarek K, Duchnowska R, Badzio A, Lakomy J, Majewska H, Pęksa R, Pieczyńska B, Radecka B, et al. Immunohistochemical Predictors of Bone Metastases in Breast Cancer Patients. Pathol Oncol Res 2015; 21(4): 1229–1236.
15. Westbrook JA, Cairns DA, Peng J, Speirs V, Hanby AM, Holen I, et al. CAPG and GIPC1: breast cancer biomarkers for bone metastasis development and treatment. J Natl Cancer Inst 2016; 108(4): doi: 10.1093/jnci/djv360.
16. Alarmo EL, Parssinen J, Ketolainen JM, Savinainen K, Karhu R, Kallioniemi A. BMP7 influences proliferation, migration, and invasion of breast cancer cells. Cancer Lett 2009; 275(1): 35–43.
17. Alarmo EL, Korhonen T, Kuukasjarvi T, Huhtala H, Holli K, Kallioniemi A. Bone morphogenetic protein 7 expression associates with bone metastasis in breast carcinomas. Ann Oncol 2008; 19(2): 308–314.
18. Bahrami A, Hassanian SM, Khazaei M, Hasanzadeh M, Shahidsales S, Maftouh M, Ferns GA, Avan A. The therapeutic potential of targeting tumor microenvironment in breast cancer: rational strategies and recent progress. J Cell Biochem 2018; 119(1): 111–122.
19. Bouleftour W, Granito RN, Vanden-Bossche A, Sabido O, Roche B, Thomas M, Linossier MT, Aubin JE, Lafage-Proust MH, et al. Bone shaft revascularization after marrow ablation is dramatically accelerated in BSP-/- mice, along with faster hematopoietic recolonization. J Cell Physiol 2017; 232(9): 2528–2537.
20. Bellahcene A, Kroll M, Liebens F, Castronovo V. Bone sialoprotein expression in primary human breast cancer is associated with bone metastases development. J Bone Miner Res 1996; 11(5): 665–670.
21. Glendenning P, Chubb SAP, Vasikaran S. Clinical utility of bone turnover markers in the management of common metabolic bone diseases in adults. Clin Chim Acta 2018; 481: 161–170.
22. Brown J, Rathbone E, Hinsley S, Gregory W, Gossiel F, Marshall H, et al. Associations between serum bone biomarkers in early breast cancer and development of bone metastasis: results from the AZURE (BIG01/04) trial. J Natl Cancer Inst 2018; doi: 10.1093/jnci/djx280.
23. Lipton A, Chapman JA, Demers L, Shepherd LE, Han L, Wilson CF, Pritchard KI, Leitzel KE, Ali SM, Pollak M. Elevated bone turnover predicts for bone metastasis in postmenopausal breast cancer: results of NCIC CTG MA.14. J Clin Oncol 2011; 29(27): 3605–3610.
24. Lumachi F, Basso SM, Camozzi V, Tozzoli R, Spaziante R, Ermani M. Bone turnover markers in women with early stage breast cancer who developed bone metastases. A prospective study with multivariate logistic regression analysis of accuracy. Clin Chim Acta 2016; 460: 227–230.

The authors
Steven L. Wood MA, PhD; Prof. Janet E. Brown* BMedSci, MB BS, MSc, MD, FRCP
Academic Unit of Clinical Oncology, Department of Oncology and Metabolism,
University of Sheffield, UK

*Corresponding author
E-mail: j.e.brown@sheffield.ac.uk

C348 Kroupis fig1

Biomarkers in age-related macular degeneration

, /in /by

Age-related macular degeneration is a late-onset disease of the eye macula that can result in blindness and in a significant deterioration of quality of life. Genetics and oxidative stress from light exposure and smoking are major risk factors. In this brief report, we discuss genetic and plasma epigenetic biomarkers that are examined for their association with the disease.

by Prof. Christos Kroupis, Prof. George Kitsos, Prof. Marilita M. Moschos and Prof. Michael B. Petersen

Introduction
Age-related macular degeneration (AMD) is a slow and progressive disease of the macula, i.e. the central part of the retina, and the leading cause of irreversible visual loss in the Western world. Globally, AMD accounts for 8.7% of all blindness and is predicted to affect 196 million people by 2020; it is more prevalent in populations of European descent than those of Asian and African descent [1]. With the loss of central vision frequently involving both eyes, AMD is a debilitating condition affecting daily tasks such as reading and driving, and ultimately having severe consequences on independence and quality of life. AMD is a late-onset disease with a complex etiology. Major risk factors contributing to susceptibility include age, family history (genetics), light exposure and smoking [2–4].

AMD can be considered a multifactorial dysfunction of the retinal photoreceptor cells and their support system, which includes the retinal pigment epithelium (RPE), Bruch’s membrane (BrM), and the choroidal vasculature. The fundamental cause of vision loss in AMD is the progressive damage to photoreceptors, which can be triggered by RPE dysfunction and atrophy, impaired transport of oxygen, nutrients and metabolites between vessels and outer retinal cells and leakage from choroidal capillaries that invade the retina through the RPE [5].

Light entering the eye is focused on the retina, where delicately specialized rod and cone photoreceptors allow its transduction into chemical signals to visual centers in the brain. Photoreceptors are metabolically active neurons with oxygen requirements that are among the highest in the human body. In humans, rods and cones exhibit a distinct topography; the macula (6-mm diameter) contains a cone-dominated fovea (0.8-mm diameter) that is associated with high-acuity vision [5] (Fig. 1a). Just posterior to the photoreceptors, the RPE consists of polarized epithelial cells located at the base of the retina as a single layer of hexagonal cells that are densely packed with pigment granules (melanosomes). The RPE is firmly attached to the underlying basement membrane (BrM). The RPE provides the nutrients needed to maintain visual function by light-sensitive outer segments of the photoreceptors. RPE melanosomes absorb excess incoming light, which protects the retina from light damage. Other critical roles for the RPE involve phagocytosing shed outer retinal segments and scavenging photoreceptor debris, thus, serving as part of the waste-disposal system for the retina. The RPE is known to produce and to secrete a variety of growth factors to help build and sustain the choroid and photoreceptors [6]. The choroid is an extensive vascular meshwork of capillaries lining the posterior part of the eye that supplies nutrients utilized by the retina and acts as a conduit for the by-products of photoreceptor and RPE metabolism [5]. The inner aspect of the choroid, next to the RPE, is the BrM, a laminar extracellular matrix composed mainly of collagen and elastin. Accumulating evidence suggests that the molecular, structural and functional properties of the BrM are dependent on age, genetics, environmental factors, retinal location and disease state. As a result, some properties of the BrM are unique to each human individual at a given age and, therefore, affect uniquely the progression of AMD [6].

AMD pathology
There are two AMD forms: dry (in 90% of patients) and wet (in 10%). In the dry form of AMD, apoptosis of the RPE, neuroretina and choriocapillaris progresses slowly and causes permanent central vision loss. Initially, the BrM exhibits increased deposition of cholesterol and calcium with age. Drusen genesis is a sign of AMD progression (Fig. 1b). Drusen are amorphic extracellular deposits of lipids, proteins, inflammatory molecules in the space between RPE and BrM. The alternative complement path is activated by lipofuscin constituents (which are mostly by-products of the retinal vision cycle) as a response to the inflammatory process connected with drusen genesis. Unfortunately, as we age, mitochondrial function decreases (and mtDNA mutations accumulate) and, therefore, oxidative damage increases. In parallel, antioxidant capacity decreases and the efficiency of repair systems and cytoprotective ubiquitin proteolytic system become impaired [4]. Environmental factors associated with increased production of reactive oxygen species (ROS), such as increased light exposure and cigarette smoking, are additive and have been linked with AMD risk. Collectively, these factors create an environment in which proteins, DNA and lipids become oxidatively damaged. The combination of inadequately neutralized oxidized proteins in the drusen and inflammation associated with OSEs (oxidative specific epitopes) induce focal loss of RPE cells, degeneration of the overlying photoreceptors and vision loss as described in Figure 2 [4].

In the advanced dry form of AMD, geographic atrophy (GA) develops from large, confluent drusen proceeds to hyperpigmentation and then, to cell apoptosis. At present, there is no effective treatment of the dry form. In the wet form, the cause of potential central vision loss is choroidal neovascularization (CNV). An inflammatory reaction initiates pathological angiogenesis that penetrates through defects in the BrM and the RPE layers to the subretinal space, where exudation and bleeding destroy photoreceptors. Commonly used anti-VEGF factors given in repeated intravitreal injections inhibit neovascularization and can stabilize vision acuity in most wet AMD patients.

Genetic biomarkers in AMD
Identification of associated genetic variants can help uncover disease mechanisms and provide entry points for therapy. Linkage of AMD families to 1q32 and the complement factor H (CFH) gene by many groups in 2005, led to the identification of the first common genome-wide significant risk variant, Y402H (rs1061170, g.43097C>T) with variable frequencies across various populations. This SNP (single nucleotide polymorphism) results in an impaired alternative complement pathway inactivation. This discovery propagated numerous genetic and genomic studies that have contributed to our understanding of the pathological mechanisms contributing to AMD. Notably, the subsequent association of common and rare alleles at or near several additional complement genes (CFH, C2/CFB, C3, CFI and C9) has led to the ‘inflammation hypotheses’, with cumulative evidence from genetics and histopathological studies [3]. Another major non-complement pathway AMD-associated locus lies on chromosome 10q26 (LOC387715) and many studies have demonstrated a strong association between AMD and the ARMS2 gene that encodes for a small 107-amino acid protein. ARMS2 A69S SNP (rs10490924, g.5270G>T) is a mutation associated with subsequent mitochondrial dysfunction, ROS generation and accumulation of somatic mitochondrial DNA mutations. These initial promising findings prompted world-wide efforts and culminated in the AMD Gene consortium 2013 study where 19 common variants were associated with the disease in a large number of patients with the use of SNP microarrays; still the two aforementioned SNPs possessed the highest odds ratios (OR) for AMD development (between 2.4 and 2.7) with some differences in their effect according to their different allele frequencies in various populations. It was estimated that these 19 variants can explain ~45% of the genetic heterogeneity in AMD patients above 85 years old; the two main AMD associations with CFH and ARMS2 genes account for a significant 25% of the total cases [5]. Therefore, we and other groups have developed fast, high-throughput robust and accurate genotyping assays for their accurate detection (Fig. 3) [7–9]. Early identification of individuals at risk provides an opportunity to prevent or attenuate the AMD disease. Homozygosity for both CFH and ARMS2 risk alleles increases the progression to advanced AMD stages (GA or CNV) to 48% compared to 5% for those carrying wild-type alleles in both genes [10]. Models incorporating these alleles and/or an expanded variant panel along with smoking and body mass index have been the basis for various commercial tests estimating AMD risk, such as RetnaGene (Nicox), Macula/Vita Risk (ArcticDx), Asper Ophthalmics, etc. Potential nutrigenetic antioxidant interventions have been proposed based on CFH and ARMS2 genotypes [11, 12]. In dry AMD where no therapy exists, anti-complement antibodies are in clinical trials right now (eculizumab, lampalizumab) and genetic tests providing information for complement polymorphisms could select appropriate patients that could benefit from such therapy.

The largest and latest 2016 AMD Gene Consortium study identified additional loci by using an Illumina human core exome array for >12 million variants in 16,144 advanced AMD patients versus 17,832 controls; 52 independently associated common and rare variants were distributed across 34 loci [13]. Now that technological advances permit – with the advent of next-generation sequencing platforms – it would be extremely useful to validate AMD-specific gene-panels for these patients.
Plasma epigenetic biomarkers in AMD
A small, non-coding micro(mi)RNA (18–24 nt) binds to specific mRNAs – depending on its sequence – and results in their degradation by cleavage, translational repression and/or polyA-deadenylation. One miRNA can target many mRNAs but also one mRNA can be targeted by many miRNAs. Emerging evidence arising from tissue studies suggest that beside environmental and genetic factors, epigenetic mechanisms (such as miRNA regulation of gene expression) are relevant to AMD and are providing an exciting new avenue for research and therapy. Sera and plasma (which are easily collected non-invasively) contain cell free DNA, RNA and circulating nucleic acids that can serve as potential biomarkers. The miRNAs identified in human plasma are known to be relatively stable, as they have been found to be resistant to RNase degradation. A recent study has identified a plasma miRNA expression profile specific for AMD patients [14]. Plasma miRNA expression was first screened for multiple miRNAs and then, those showing differences between patients and healthy controls were further explored with individual, specific RT-qPCR assays in a larger number of samples. In another study exploring wet and dry AMD differences in plasma, the miRNA expression analysis revealed increased expression of miR661 and miR3121 in dry AMD patients and miR4258, miR889 and let7 in wet AMD patients compared to controls [15].

References
1. Wong WL, Su X, Li X, Cheung CM, Klein R, Cheng CY, Wong TY. Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis. Lancet Glob Health 2014; 2: e106–e116.
2. Kokotas H, Grigoriadou M, Petersen MB. Age-related macular degeneration: genetic and clinical findings. Clin Chem Lab Med 2011; 49: 601–616.
3. Tan PL, Bowes RC, Katsanis N. AMD and the alternative complement pathway: genetics and functional implications. Hum Genomics 2016; 10: 23.
4. Chiras D, Kitsos G, Petersen MB, Skalidakis I, Kroupis C. Oxidative stress in dry age-related macular degeneration and exfoliation syndrome. Crit Rev Clin Lab Sci 2015; 52: 12–27.
5. Fritsche LG, Fariss RN, Stambolian D, Abecasis GR, Curcio CA, Swaroop A. Age-related macular degeneration: genetics and biology coming together. Annu Rev Genomics Hum Genet 2014; 15: 151–171.
6. Bhutto I, Lutty G. Understanding age-related macular degeneration (AMD): relationships between the photoreceptor/retinal pigment epithelium/Bruch’s membrane/choriocapillaris complex. Mol Aspects Med 2012; 33: 295–317.
7. Velissari A, Skalidakis I, Oliveira SC, Koutsandrea C, Kitsos G, Petersen MB, Kroupis C. Novel association of FCGR2A polymorphism with age-related macular degeneration (AMD) and development of a novel CFH real-time genotyping method. Clin Chem Lab Med 2015; 53: 1521–15219.
8. Sarli A, Skalidakis I, Velissari A, Koutsandrea C, Stefaniotou M, Petersen MB, Kroupis C, Kitsos G, Moschos MM. Investigation of associations of ARMS2, CD14, and TLR4 gene polymorphisms with wet age-related macular degeneration in a Greek population. Clin Ophthalmol 2017; 11: 1347–1358.
9. Xu Y, Guan N, Xu J, Yang X, Ma K, Zhou H, Zhang F, Snellingen T, Jiao Y, et al. Association of CFH, LOC387715, and HTRA1 polymorphisms with exudative age-related macular degeneration in a northern Chinese population. Mol Vis 2008; 14: 1373–1381.
10. Seddon JM, Francis PJ, George S, Schultz DW, Rosner B, Klein ML. Association of CFH Y402H and LOC387715 A69S with progression of age-related macular degeneration. JAMA 2007; 297: 1793–1800.
11. Awh CC, Hawken S, Zanke BW. Treatment response to antioxidants and zinc based on CFH and ARMS2 genetic risk allele number in the Age-Related Eye Disease Study. Ophthalmology 2015; 122: 162–169.
12. Vavvas DG, Small KW, Awh CC, Zanke BW, Tibshirani RJ, Kustra R. CFH and ARMS2 genetic risk determines progression to neovascular age-related macular degeneration after antioxidant and zinc supplementation. Proc Natl Acad Sci U S A 2018; 115: E696–E704.
13. Fritsche LG, Igl W, Bailey JN, Grassmann F, Sengupta S, Bragg-Gresham JL, Burdon KP, Hebbring SJ, Wen C, et al. A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants. Nat Genet 2016; 48: 134–143.
14. Ertekin S, Yıldırım O, Dinç E, Ayaz L, Fidancı SB, Tamer L. Evaluation of circulating miRNAs in wet age-related macular degeneration. Mol Vis 2014; 20: 1057–1066.
15. Szemraj M, Bielecka-Kowalska A, Oszajca K, Krajewska M, Goś R, Jurowski P, Kowalski M, Szemraj J. Serum microRNAs as potential biomarkers of AMD. Med Sci Monit 2015; 21: 2734–2742.

The authors
Christos Kroupis*1 MSc, PhD; George Kitsos2 MD, PhD; Marilita M. Moschos3 MD, PhD; Michael B. Petersen4 MD, PhD

1Department of Clinical Biochemistry and Molecular Diagnostics, Attikon University General Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
2Department of Ophthalmology, University General Hospital of Ioannina, Ioannina, Greece
31st Department of Ophthalmology, “G. Gennimatas” General Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
4Department of Clinical Genetics, Aalborg University Hospital, Aalborg, Denmark

*Corresponding author
E-mail: ckroupis@med.uoa.gr

C349 Pagani Figure 1 crop

New NGS sequencing approach of paired diagnostic and remission samples to detect somatic mitochondrial DNA mutations in leukemia

, /in /by

Mitochondrial DNA mutations (mtDNA) have been described that are associated with leukemia. To identify somatic mutations it is necessary to have a control tissue from the same individual for comparison. In this review we describe a new next-generation sequencing approach to identify leukemia-associated mtDNA mutations by using remission samples as control.

by Dr Ilaria Stefania Pagani

Introduction
The identification of acquired somatic mutations in leukemic samples is of considerable importance for diagnosis and prognostication. In order to identify somatic mutations it is necessary to have a control tissue from the same individual for comparison. Non-hematopoietic tissues, such as mesenchymal stromal cells (MSCs) or hair follicles are preferred, but not always available. When patients with leukemia achieve remission, the remission peripheral blood (PB) may be a suitable and easily available control tissue. This article will provide recommendations for the identification of tumour-associated mtDNA somatic mutations, highlighting advantages and disadvantages of the method.

mtDNA characteristics
Human mitochondrial (mt) DNA is a 16 569 bp double-stranded, circular DNA molecule that encodes 13 polypeptides of the oxidative phosphorylation system (OXPHOS), 22 transfer RNAs and 2 ribosomal RNAs. Several important differences between the mt genome and the nuclear genome complicate the study of mtDNA mutations. Ninety-three percent of the sequence consists of coding DNA, introns are absent, the only non-coding region is at the level of the D-loop containing the promoters of the genes and it is maternally inherited. Each cell has a variable number of mitochondria (typically several hundred) and each mitochondrion contains a variable number of genomes (typically 2–10). Consequently, mtDNA mutations do not follow the pattern of a diploid genome: rather, a cell may have a single mt genotype (homoplasmy) or multiple mt genotypes (heteroplasmy). Heteroplasmy may be at any frequency, could vary between cells and many variants will be below the limit of detection of Sanger sequencing, and therefore technically difficult to validate [1]. To date, more than 400 mtDNA mutations have been associated with human diseases, most of them being heteroplasmic. Therefore, an accurate determination of the level of heteroplasmy is important for disease association studies [2].

mtDNA mutations and cancer
MtDNA mutations may potentially contribute to a cell to becoming cancerous, leading to invasion and metastasis [3]. Heteroplasmic somatic mtDNA mutations have been reported in hematological neoplasms, including myelodysplastic syndromes, chronic lymphocytic leukemia, chronic myeloid leukemia (CML), acute myeloid leukemia, and acute lymphoblastic leukemia (ALL) [1]. Many cancer types, including leukemia, have a tendency to be highly glycolytic, increasing the production of the reactive oxygen species (ROS), that lead to genomic instability. The mtDNA genome is susceptible to ROS-induced mutations owing to the high oxidative stress in the mitochondrion and limited DNA-repair mechanisms [3]. The identification of acquired somatic mutations in leukemic samples is of considerable importance for diagnosis and prognostication. In a study in acute myeloid leukemia, for example, patients with mutated NADH dehydrogenase subunit 4 (ND4) showed greater overall survival than patients with wild-type ND4 [4].

mtDNA somatic mutations: the problem of control tissue
MtDNA acquires somatic mutations at a rate 10-fold higher than nuclear DNA, so mtDNA single nucleotide variants (SNVs) accumulate with age, and may be tissue-specific [5]. This means that there is no absolutely reliable source of ‘germline’ mtDNA, especially in older individuals [1]. Somatic mutations must be distinguished from non-pathogenic germline variants by comparison with a control tissue sample. Non-hematopoietic tissues, such as buccal cells, hair follicles or MSCs  are  preferred, but not always available. PB cells from a post-treatment remission sample may be used as alternative. This method is widely used for nuclear mutations, but less commonly for mt mutations [1]. Blood samples are readily accessible from leukemia patients who achieve morphological remission after treatment. Therefore, a method for the detection of leukemia-associated mtDNA mutations based on comparison with a remission sample may be useful.
A new approach to identify mtDNA somatic mutations at diagnosis by using remission samples as control tissue
Pagani IS and colleagues developed a next-generation sequencing (NGS) approach for the identification of leukemia-associated mtDNA mutations using samples from CML patients at diagnosis and in remission following treatment with tyrosine kinase inhibitors (TKIs) [1]. This approach could also be applied to both hematopoietic and  non-hematopoietic cancers, such as epithelial tumours, in which a tumour biopsy specimen can be compared with the normal mucosa.

Twenty-six chronic phase CML patients enrolled in the Australasian Leukaemia and Lymphoma Group CML9 trial (TIDEL-II; ID: ACTRN12607000325404) [6] took part in the study [6]. PB samples from leucocytes at diagnosis before commencing TKI treatment, and remission after 12 months of therapy were compared. Hair follicles (n=4), bone marrow MSCs (n=18), or both (n=4) were used as non-hematopoietic control samples. The comparison of a diagnostic sample with a non-hematopoietic control tissue is the standard method to identify somatic mutations in leukemia [1]. The concordance between this classic method and the diagnosis versus remission approach has been investigated.

NGS assay for the mt genome
The workflow chart is represented in Figure 1. Briefly the genomic DNA (comprising a mixture of nuclear and mtDNA) was extracted by a phenol/chloroform method from PB leukocytes and non-hematopoietic tissues. The mtDNA was amplified by long-range PCR, generating two or three overlapping fragments covering the entire mt genome. The PCR amplicons were then pooled at equimolar concentrations and sequencing libraries were prepared using the Nextera XT kit (Illumina). Indexed libraries were multiplexed and run on an Illumina MiSeq instrument using the 600 cycle MiSeq Reagent kit (v3) generating 300-bp paired-end reads [1].
Somatic mutation calling from high-throughput sequencing datasets and validation
The majority of the variant-calling methods in use are based on low-coverage human re-sequencing data and diploid calls with discrete frequencies of interest (0%, 50% or 100%) [7, 8]; however, these assumptions do not apply to mtDNA. The LoFreq software (loFreq-star version 2.11, genome Institute of Singapore; http://csb5.github.io/lofreq/) was chosen because it was developed for viral and bacterial genomes as well as diploid data, and because of its ability to automate comparison with a matched control tissue for the detection of somatic mutations [8]. The revised Cambridge Reference Sequence (rCRS) for the human mt genome (NC_012920) was used as reference sequence to identify SNVs. Tumour tissue (test) and control were then compared to identify somatic mutations specific only for the tumour tissue.  Variants in common between the test and the control sample were considered to represent germline polymorphisms or mutations and were filtered out by the software. A binomial test was applied to the remaining variants to determine whether an apparent difference between samples could be due to inadequate read coverage in the control. Variants passing the binomial test were retained in the final list of putative somatic mutations (Fig. 2a) [8]. The identified mutations should be considered putative and, in common with most other NGS strategies for the discovery of novel mutations, any specific mutation of clinical interest would need to be confirmed using an independent method, as Sanger sequencing (limit of detection 20%), Sequenom MassArray, digital array (Fluidigm) or another NGS platform.

NGS: error rate, false positives and threshold
Before the application of NGS technologies, no evidence of heteroplasmy was detected, probably because of the lower sensitivity of earlier techniques [9]. NGS technologies enable the inquiry of mt heteroplasmy at the genome-wide scale with much higher resolution because many independent reads are generated for each position [2]. However, the higher error rate associated with the more sensitive NGS methodology must be taken into consideration to avoid false detection of heteroplasmy. Short-read sequencing technologies (like in Illumina systems) have a high intrinsic error rate (approximately 1 in 102–103 bases) when applied at the very high depth required to detect and measure low-level heteroplasmy. Thus, appropriate criteria for avoiding false positives due to sequencing errors are required. The most obvious way to distinguish between sequencing errors and heteroplasmy is to invoke a threshold. Two duplicate sequencing run, of which one was ultra-deep (validation run), were compared to determine sensitivity (proportion of true positives that are correctly identified as such) and specificity (proportion of true negatives that are correctly identified as such). An empirical threshold of 2% was therefore applied to distinguish true variants from sequencing errors. Variants with a variant allele fraction (VAF, the variant allele’s read depth divided by total read depth at each nucleotide position) between 2 and 98% where then considered as heteroplasmic, and variants with a VAF >2% were called homoplasmic [1]. This threshold could be refined by an iterative process in which a different threshold is identified for each nucleotide position [10], as some variation in error rate was observed. The incorporation of molecular barcodes in the initial long-range PCR would also  reduce the risk of false-positive mutations due to PCR artefact [1].

Remission samples as control tissue in the identification of the mtDNA somatic mutations at diagnosis
In the four patients who had both MSC and hair follicle DNA available as control tissue, the same mutations at diagnosis have been identified, therefore the results using the non-hematopoietic tissues as control were combined. Remission samples were then used as control tissue to determine mtDNA somatic mutations at diagnosis, and the concordance between this method and the conventional diagnosis versus the MSC/hair follicle approach was examined. Seventy-three somatic mutations (81%) were identified in common, 11 mutations (12%) were identified only in comparison with the non-hematopoietic control, and six (6.7%) only by comparison with remission samples (Fig. 2b) [1]. Divergent results occurred as the result of differences in read quality or depth at a specific nucleotide not reaching statistical significance in the algorithm. False-negative results could be encountered using remission samples as the control tissue, because of low-level heteroplasmic mutations in the control sample that would lead to the same mutation at diagnosis being removed through filtering.

Concluding remarks
Remission samples can be used as control tissues to detect candidate mtDNA somatic mutations in leukemic samples when non-hematopoietic tissues are not available. The presence of mutations at low VAF in the remission samples in common with the diagnosis tissue, could be filtered out by the LoFreq software leading to false-negative results. Therefore visual inspection of the unfiltered variants is recommended.

References
1. Pagani IS, Kok CH, Saunders VA, van der Hoek MB,  Heatley SL, Schwarer AP, Hahn CN, Hughes TP, White DL, Ross DM. A method for next-generation sequencing of paired diagnostic and remission samples to detect mitochondrial DNA mutations associated with leukemia. J Mol Diagn 2017; 19(5): 711–721.
2. Li M, Schonberg A, Schaefer M, Schroeder R, Nasidze I, Stoneking M. Detecting heteroplasmy from high-throughput sequencing of complete human mitochondrial DNA genomes. Am J Hum Genet 2010; 87(2): 237–249.
3. van Gisbergen MW, Voets AM, Starmans MH, de Coo IF, Yadak R, Hoffmann RF, Boutros PC, Smeets HJ, Dubois L, Lambin P. How do changes in the mtDNA and mitochondrial dysfunction influence cancer and cancer therapy? Challenges, opportunities and models. Mutat Res Rev Mutat Res 2015; 764: 16–30.
4. Damm F, Bunke T, Thol F, Markus B, Wagner K, Gohring G, Schlegelberger B, Heil G, Reuter CW, et al. Prognostic implications and molecular associations of NADH dehydrogenase subunit 4 (ND4) mutations in acute myeloid leukemia. Leukemia 2012; 26(2): 289–295.
5. Gattermann N. Mitochondrial DNA mutations in the hematopoietic system. Leukemia 2004; 18(1): 18–22.
6. Yeung DT, Osborn MP, White DL, Branford S, Braley J, Herschtal A, Kornhauser M, Issa S, Hiwase DK, et al. TIDEL-II: first-line use of imatinib in CML with early switch to nilotinib for failure to achieve time-dependent molecular targets. Blood 2015; 125(6): 915–923.
7. Meldrum C, Doyle MA, Tothill RW. Next-generation sequencing for cancer diagnostics: a practical perspective. Clin Biochem Rev 2011; 32(4): 177–195.
8.  Wilm A, Aw PP, Bertrand D, Yeo GH, Ong SH, Wong CH, Chiea CK, Rosemary P, Martin LH, Niranjan N. LoFreq: a sequence-quality aware, ultra-sensitive variant caller for uncovering cell-population heterogeneity from high-throughput sequencing datasets. Nucleic Acids Res 2012; 40(22): 11189–11201.
9. Chatterjee A, Dasgupta S, Sidransky D. Mitochondrial subversion in cancer. Cancer Prev Res 2011; 4(5): 638–654.
10. Kerpedjiev P, Frellsen J, Lindgreen S, Krogh A. Adaptable probabilistic mapping of short reads using position specific scoring matrices. BMC Bioinformatics 2014; 15: 100.

The author
Ilaria Stefania Pagani1,2 PhD
1Cancer Theme, South Australian Health & Medical Research Institute, Adelaide, Australia
2School of Medicine, Faculty of Health Sciences, University of Adelaide, Adelaide, Australia

*Corresponding author
E-mail: Ilaria.pagani@sahmri.com

Latest issue of Clinical laboratory

November 2025

CLi Cover nov 2025
Digital edition
All articles Archived issues

Free subscription

View more product news

Get our e-alert

The leading international magazine for Clinical laboratory Equipment for everyone in the Vitro diagnostics

Sign up today
clinlab logo blackbg 1

Prins Hendrikstraat 1
5611HH Eindhoven
The Netherlands
info@clinlabint.com

PanGlobal Media is not responsible for any error or omission that might occur in the electronic display of product or company data.