Alterations of the microbiome are associated with colorectal cancer. Research suggests that microbiome data could improve colorectal cancer screening. Analysis of the microbiome directly from existing screening methods offers the opportunity to rapidly translate this research into practice, with the potential to develop a multifactorial colorectal cancer screening tool.

by Dr Caroline Young and Professor Philip Quirke

Current colorectal cancer screening methods
Different countries have adopted various approaches to colorectal cancer screening. They share a common goal: detection of asymptomatic adenomas or early stage carcinomas, as detection and treatment at an earlier stage is associated with improved survival [1]. Two main screening methods are in use: detection of fecal occult blood and visualization of the colon. Stool DNA testing has recently been approved but is currently prohibitively expensive.

Detection of fecal occult blood can be achieved using the guaiac fecal occult blood test (gFOBT) or an immunochemical method, fecal immunochemical test (FIT). The gFOBT method requires participants to apply stool to a gFOBT card on three occasions and return this to a screening centre through the post. Hydrogen peroxide is applied and if heme is present, blue discolouration occurs. This method has been shown to reduce mortality by 16 % [2]. The FIT method requires participants to insert a FIT probe into stool and return this to a screening centre through the post. An antibody-based assay is used to detect globin. FIT is more sensitive and specific, can be analysed quantitatively and has improved acceptability [3]. Participants in whom fecal occult blood is detected above a threshold, by either method, are referred for colonoscopy.

Alternatively, direct visualization of the colon by colonoscopy/sigmoidoscopy can be undertaken as first-line screening. Limitations include procedural risks, associated costs, workforce capacity and reduced acceptability [4].

The microbiome and colorectal cancer
The microbiome can be characterized using a number of technologies: next generation sequencing (NGS) of bacterial 16SrRNA, whole genome shotgun metagenomics of bacterial communities or the analysis of fecal metabolites (metabolomics). These techniques have enabled an appreciation of the diversity and function of the microbiome in health and disease.

Epidemiological studies demonstrate that the incidence of colorectal cancer is highest in countries with a Western culture, which encompasses Western diet, sanitation and hygiene, medication use, urbanization, etc. [5]. Migrant populations to such countries acquire the increased risk, suggesting an environmental risk factor. African Americans, who typically have a high incidence of colorectal cancer, have been shown to have different microbiomes to Native Africans, who have a low incidence of colorectal cancer [6] and the diets typical of these two groups have been shown to differentially influence the microbiome [7].

Numerous studies have found differences in the microbiome, ‘dysbiosis’, of patients with colorectal adenomas or carcinomas compared to healthy controls [8]. In general, dysbiosis is characterized by a decrease of short chain fatty acid-producing bacteria, an increase of bacteria that produce bile salts or hydrogen sulphide, an increase of pathogenic bacteria and inflammation [9]. In particular, the species Fusobacterium nucleatum, a Gram-negative oral commensal, has been associated with colorectal carcinoma in many studies.

Animal models have explored potential mechanisms [10] and interestingly show that risk is transferable with transplant of dysbiotic microbiomes. This suggests that dysbiosis may be causative or promotional of the development of colorectal cancer, rather than merely associative.

Given the association between dysbiosis and colorectal cancer, researchers have considered whether the microbiome could be used as a screening tool.

The microbiome compared to gFOBT
Several studies have compared the accuracy of the microbiome as a screening tool to gFOBT. Amiot et al. showed that a screening model combining age plus microbiome (typed by qPCR) was no better than a model combining age plus gFOBT [11]. However, metabolomic analysis [by 1(H)-NMR spectroscopy] was more accurate than gFOBT [12]. Zeller et al. created a screening model that combined metagenomic data with gFOBT results, which lead to an increase in sensitivity compared to gFOBT alone. This model was subsequently validated in a cohort of a different nationality. It showed some ability to distinguish colorectal cancer from a distinct bowel condition (inflammatory bowel disease) and could be extrapolated to NGS of 16SrRNA (a cheaper method) [13].

Zackular et al. used 16SrRNA analysis of the microbiome to create models combining microbiome data and patient metadata that were more accurate than models based on metadata alone [14]. A model comprising BMI, microbiome data and gFOBT was more accurate at distinguishing adenoma from carcinoma than gFOBT alone. Yu et al. used metagenomics to identify two discriminatory bacterial genes that they then validated as biomarkers by qPCR (a cheaper method) in a cohort of a different nationality. The area under the receiver operating characteristic (ROC) curve for discriminating carcinoma from controls was 0.84, although gFOBT or FIT screening was not performed for comparison [15].

The microbiome compared to FIT
As FIT is replacing gFOBT in many screening programmes and has a higher sensitivity, comparing the accuracy of the microbiome as a screening tool with FIT is more appropriate.

Baxter et al. used 16SrRNA to create a screening model that combined microbiome data and FIT to discriminate healthy controls from cases with either adenoma or carcinoma [16]. This model was more sensitive but less specific than FIT alone; it detected 70% of cancers and 37% of adenomas which were missed by FIT. Liang et al. [17] identified four bacterial species (one being F. nucleatum) by qPCR that could distinguish colorectal carcinoma from healthy controls with greater accuracy than FIT. Combining microbiome and FIT data afforded greater accuracy still.

Goedert et al. [18] analysed the microbiome by 16SrRNA in patients with a positive FIT result at baseline. The microbiome data gave an area under the ROC curve for discriminating between healthy controls and colorectal adenoma of 0.767.

Limitations of current research
The studies mentioned above show promise for the microbiome as a potential colorectal cancer screening tool. However, they should be interpreted with a degree of caution, owing to a number of limitations which mean that aspects of the studies do not realistically reflect screening conditions. Several of the studies assessed participants at increased risk of colorectal cancer or who were symptomatic. Some collected stool samples following bowel preparation and colonoscopy; one study found that this did not affect the significance of results [16], whereas another found that it did [15]. Several studies included adenomas <10 mm within their control groups. Many of the studies created models that distinguished adenomas from carcinomas or carcinomas from healthy controls; few designed models to discriminate between healthy controls and participants with any colorectal lesion (i.e. either adenoma or carcinoma).

All of the studies used whole stool samples that were refrigerated or frozen by participants at home or delivered within a limited time window to research centres. This method of sample collection would not translate to national screening programmes, which already struggle with poor participant uptake. In light of this, researchers have, therefore, investigated whether the microbiome can be analysed directly from the existing screening tools, gFOBT or FIT.

Analysing the microbiome directly from existing screening tools
Sinha et al. emphasize the need to assess reproducibility, stability over time and how accurately results reflect the gold standard (fresh or immediately frozen stool) when analysing different methods of microbiome sample collection [19]. They found that 16SrRNA microbiome results were similar when analysed from unprocessed or processed gFOBT cards and, in addition to Dominianni et al. [20], showed stability after storage at room temperature for several days. This work was extended by Taylor et al. [21] who demonstrated that the microbiome is stable when analysed by 16SrRNA from processed gFOBT cards stored at room temperature for up to 3 years.

Lotfield et al. showed that metabolomic assessment of the microbiome by ultra-performance liquid chromatography and high resolution/tandem mass spectrometry was stable and accurate (albeit with a degree of bias affecting certain metabolite groups) when analysed directly from gFOBT samples but not from FIT samples [22]. This suggests that different methods of sample collection may be more or less appropriate dependent upon the method of microbiome analysis.

These studies have assessed methods of microbiome sample collection from healthy volunteers. Baxter et al. [23] have analysed the microbiome directly from processed FIT from subjects with normal bowels, colorectal adenomas or carcinomas. Their study comes with the caveat that some of the stool samples were collected after bowel preparation and colonoscopy; samples were stored at −80 °C before being thawed and transferred to FIT; FIT was refrigerated for up to 2 days, processed, then stored at −20 °C before being thawed for microbiome analysis. The study demonstrated that a screening model to discriminate between healthy controls and subjects with any colonic lesion had a similar area under the ROC curve whether microbiome analysis was performed directly from FIT samples or whole stool samples.

As an alternative to stool, Westenbrink et al. analysed microbiome-related volatile organic compounds from urine [24] and described a similar sensitivity for the detection of colorectal cancer as gFOBT or FIT.

Conclusion
Research suggests that there is potential for microbiome analysis to both augment and to be integrated with existing screening methods. The landscape of colorectal cancer screening is changing [25]; it seems likely that a more sophisticated, multifactorial screening tool will be adopted. Microbiome analysis is likely to contribute and may even offer information beyond that of screening, e.g. prevention or treatment targets [26]. Furthermore, collection of longitudinal, population-based microbiome data via national screening programmes will transform the field of microbiome research.

References
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2. Hewitson P, Glasziou PP, Irwig L, Towler B, Watson E. Screening for colorectal cancer using the faecal occult blood test, Hemoccult. Cochrane Database Syst Rev. 2007; DOI: 10.1002/14651858.CD001216.pub2
3. Schreuders EH, Grobbee EJ, Spaander MC, Kuipers EJ. Advances in fecal tests for colorectal cancer screening. Curr Treat Options Gastroenterol. 2016; 14(1): 152–162.
4. US Preventive Services Task Force, Bibbins-Domingo K, Grossman DC, Curry SJ, Davidson KW, Epling JW Jr, García FA, Gillman MW, Harper DM, et al. Screening for colorectal cancer: US preventive services task force recommendation statement. JAMA 2016; 315(23): 2564–2575.
5. Haggar FA, Boushey RP. colorectal cancer epidemiology: incidence, mortality, survival, and risk factors. Clin Colon Rectal Surg. 2009; 22(4): 191–197.
6. Ou J, Carbonero F, Zoetendal EG, DeLany JP, Wang M, Newton K, Gaskins HR, O’Keefe SJ. Diet, microbiota, and microbial metabolites in colon cancer risk in rural Africans and African Americans. Am J Clin Nutr. 2013; 98(1): 111–120.
7. David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, Ling AV, Devlin AS, Varma Y, et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 2014; 505(7484): 559–563.
8. Borges-Canha M, Portela-Cidade JP, Dinis-Ribeiro M, Leite-Moreira AF, Pimentel- Nunes P. Role of colonic microbiota in colorectal carcinogenesis: a systematic review. Rev Esp Enferm Dig. 2015; 107(11): 659–671.
9. Sun J, Kato I. Gut microbiota, inflammation and colorectal cancer. Genes Dis. 2016; 3(2): 130–143.
10. Keku TO, Dulal S, Deveaux A, Jovov B, Han X. The gastrointestinal microbiota and colorectal cancer. Am J Physiol Gastrointest Liver Physiol. 2015; 308(5): G351–363.
11. Amiot A, Mansour H, Baumgaertner I, Delchier JC, Tournigand C, Furet JP, Carrau JP, Canoui-Poitrine F, Sobhani I; CRC group of Val De Marne. The detection of the methylated Wif-1 gene is more accurate than a fecal occult blood test for colorectal cancer screening. PLoS One 2014; 9(7): e99233.
12. Amiot A, Dona AC, Wijeyesekera A, Tournigand C, Baumgaertner I, Lebaleur Y, Sobhani I, Holmes E. (1)H NMR spectroscopy of fecal extracts enables detection of advanced colorectal neoplasia. J Prot Res. 2015; 14(9): 3871–3881.
13. Zeller G, Tap J, Voigt AY, Sunagawa S, Kultima JR, Costea PI, Amiot A, Böhm J, Brunetti F, et al. Potential of fecal microbiota for early-stage detection of colorectal cancer. Mol Syst Biol. 2014; 10: 766.
14. Zackular JP, Rogers MA, Ruffin MT 4th, Schloss PD. The human gut microbiome as a screening tool for colorectal cancer. Cancer Prev Res (Phila). 2014; 7(11): 1112–1121.
15. Yu J, Feng Q, Wong SH, Zhang D, yi Liang Q, Qin Y, et al. Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer. Gut 2015; DOI: 10.1136/gutjnl-2015-309800.
16. Baxter NT, Ruffin MT 4th, Rogers MA, Schloss PD. Microbiota-based model improves the sensitivity of fecal immunochemical test for detecting colonic lesions. Genome Med. 2016; 8(1): 37.
17. Liang JQ, Chiu J, Chen Y, Huang Y, Higashimori A, Fang JY, Brim H, Ashktorab H, Ng SC, et al. Fecal bacteria act as novel biomarkers for non-invasive diagnosis of colorectal cancer. Clin Cancer Res. 2016; DOI: 10.1158/1078-0432.CCR-16-1599.
18. Goedert JJ, Gong Y, Hua X, Zhong H, He Y, Peng P, Yu G, Wang W, Ravel J, et al. Fecal microbiota characteristics of patients with colorectal adenoma detected by screening: a population-based study. EBioMedicine 2015; 2(6): 597–603.
19. Sinha R, Chen J, Amir A, Vogtmann E, Shi J, Inman KS, Flores R, Sampson J, Knight R, Chia N. Collecting fecal samples for microbiome analyses in epidemiology studies. Cancer Epidemiol Biomarkers Prev. 2016; 25(2): 407–416.
20. Dominianni C, Wu J, Hayes RB, Ahn J. Comparison of methods for fecal microbiome biospecimen collection. BMC Microbiol. 2014; 14: 103.
21. Taylor M, Wood H, Halloran S, Quirke P. Examining the potential use and long term stability of guaiac faecal occult blood test cards for microbial DNA 16srRNA sequencing. J Clin Pathol. Accepted for publication.
22. Loftfield E, Vogtmann E, Sampson JN, Moore SC, Nelson H, Knight R, Chia N, Sinha R. Comparison of collection methods for fecal samples for discovery metabolomics in epidemiologic studies. Cancer Epidemiol Biomarkers Prev. 2016; 25(11): 1483–1490.
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24. Westenbrink E, Arasaradnam RP, O’Connell N, Bailey C, Nwokolo C, Bardhan KD, Covington JA. Development and application of a new electronic nose instrument for the detection of colorectal cancer. Biosens Bioelectron. 2015; 67: 733–738.
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The authors
Caroline Young* MA, BMBCh; Philip Quirke BM, PhD, FRCPath, FMedSci
Wellcome Trust Brenner Building, St James University Hospital, Leeds LS9 7TF, UK

*Corresponding author
E-mail: caroline.young4@nhs.net

Ovarian cancer is difficult to diagnose early, with consequent poor survival. Evidence suggests many cases may originate in precursor lesions in the fallopian tubes. Differential expression of specific proteins in the fallopian tubes of women with high-grade serous ovarian cancer, detected by immunohistochemistry, shows promise as a potential novel diagnostic marker.

by Dr Kezia Gaitskell and Prof. Ahmed Ashour Ahmed

Background
Ovarian cancer is the seventh greatest cause of cancer mortality amongst women worldwide, and the fifth greatest cause amongst women in more developed regions [1]. In the USA, 60% of women with ovarian cancer already have distant metastases at diagnosis, for which the 5-year survival is less than 30% [2]. Early clinical diagnosis of ovarian cancer is difficult, as symptoms are often non-specific, such as abdominal distention, urinary frequency, or abdominal pain [3].

Current evidence on ovarian cancer diagnosis and screening
Diagnostic investigations include imaging (e.g. ultrasound, CT or MRI of the pelvis and abdomen), together with blood tests for tumour markers – particularly cancer antigen 125 (CA-125) [4]. However, although CA-125 is the main biomarker used in the diagnosis of ovarian cancer, it is far from perfect in sensitivity and specificity: although approximately 80% of women with epithelial ovarian cancer will have a CA-125 concentration above the cut-off value of 35 IU/mL, CA-125 may also be elevated with other cancers (including liver, pancreatic, lung, and endometrial cancers), and physiological or benign conditions (including menses, pregnancy, cirrhosis, salpingitis, pancreatitis and endometriosis) [5].

A variety of additional putative tumour markers have been suggested for use in combination with CA-125, but current guidelines from the National Comprehensive Cancer Network in the USA are that there is insufficient evidence for their usefulness in detecting early-stage ovarian cancer [4], although the European Group on Tumor Markers suggests that human epididymis protein 4 (HE4) may be helpful for the differential diagnosis of pelvic masses, particularly in premenopausal women [5].

There has been considerable interest in finding markers of early disease, that could enable earlier diagnosis or screening for ovarian cancer. Two large randomized controlled trials have been performed of screening for ovarian cancer: the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) in the USA [6], and the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) in the UK [7]. Both trials randomized women to either no screening, or screening with the CA-125 blood test with or without trans-vaginal ultrasound. Unfortunately, neither trial could demonstrate a clear mortality benefit with screening, although there was a suggestion of benefit in some secondary analyses in the UKCTOCS trial [7].

New hypotheses of the origins of ovarian cancer
The search for potential early markers of ovarian cancer is also affected by the increasing evidence of heterogeneity between the tumour subtypes. Ovarian cancer has traditionally been divided into subtypes on the basis of microscopic morphology, the most common types being serous, endometrioid, clear cell, and mucinous tumours. There is growing evidence that these different histological tumour subtypes have different characteristic genetic mutations, and may have distinct origins [8, 9]. In particular, there is evidence that many cases of high-grade serous ovarian cancer (the most common subtype) may arise from precursor lesions in the fallopian tube epithelium, known as serous tubal intraepithelial carcinoma (STIC) (reviewed by Nik et al. [10]). These STIC lesions show dysplastic morphological changes, and also tend to show the mutations in the tumour-suppressor gene TP53 that are characteristic of high-grade serous ovarian carcinoma, and increased expression of the proliferation marker Ki-67. There is also evidence that some cases of endometrioid and clear cell ovarian cancer (less common subtypes) may arise from endometriosis (reviewed by Munksgaard & Blaakaer [11]). The origins of low-grade serous tumours and mucinous carcinomas are less certain, although several hypotheses exist.

The hypothesis that many, if not most, high-grade serous ‘ovarian’ cancers may in fact arise from the fallopian tubes has led to increasing interest in exploring changes in the fallopian tubes as potential early markers. The discovery of STIC lesions is interesting in terms of improving our understanding of pathogenesis, but is not currently useful for identifying changes early in malignancy, or pre-malignancy, in clinical practice. One limitation is that STIC lesions tend to be very focal, and are most common at the fimbrial end of the fallopian tubes, adjacent to the ovary, which is difficult to access without surgical removal of the fallopian tubes.

New findings regarding the role of SOX2
We investigated increased expression of SOX2, a key stem cell differentiation gene, as a possible marker of high-grade serous carcinogenesis within the fallopian tubes. We chose SOX2 because work from our group had shown that mutations at several sites near the SOX2 gene were ubiquitous in samples of ovarian cancer taken from multiple locations and time points in a single patient, indicating that they acted as early ‘driver’ mutations [12]. We showed that SOX2 expression (detected using immunohistochemistry) was significantly increased in the fallopian tube epithelial cells of women with high-grade serous ovarian cancer, compared to women with endometrial cancer or benign disease (e.g. uterine fibroids) [12], as illustrated in Figure 1.

We also found that SOX2 expression in the fallopian tubes was significantly increased in women with germline mutations in the tumour suppressor genes BRCA1 and BRCA2, who are known to be at higher risk of breast and ovarian cancer [12]. These women with BRCA1/2 mutations had their ovaries and fallopian tubes removed to reduce their subsequent risk of cancer, but did not have evident cancer at the time of surgery. Thus, the finding that elevated SOX2 expression was detectable in their fallopian tubes suggests that increased SOX2 expression may be an early sign of precancerous changes within the fallopian tubes.

Potential future implications
Our observation that SOX2 expression is increased in the fallopian tube epithelial cells of women with high-grade serous ovarian cancer, and women with BRCA1/2 mutations, compared to women with other cancers or benign disease, suggests that SOX2 might have a potential role as a biomarker in the early diagnosis of ovarian cancer. However, several challenges remain before testing for SOX2 expression could be considered in clinical practice – particularly the anatomical difficulty of sampling the fallopian tube epithelium without invasive surgery, and the fact that SOX2 is a nuclear marker. Our research group is currently exploring other potential cell-surface markers that correlate with SOX2 expression, which might be easier to detect.

Summary
There are many challenges in the early diagnosis of ovarian cancer. New evidence of the possible tubal origins of high-grade serous ovarian cancer is changing the approach to identifying potential new biomarkers of early disease. SOX2 has emerged as a promising marker, but further work is needed before it would be suitable for routine clinical practice.

References
1. Ferlay J, Soerjomataram I, et al. GLOBOCAN 2012 v1.0: Estimated cancer incidence and mortality worldwide in 2012. International Agency for Research on Cancer/World Health Organization 2013 (http://globocan.iarc.fr/Default.aspx).
2. Howlader N, Noone AM, et al. Cronin KA (eds). SEER Cancer Statistics Review, 1975-2013. National Cancer Institute, Bethesda, MD, USA 2016 (http://seer.cancer.gov/csr/1975_2013/).
3. Hamilton W, Peters TJ, et al. Risk of ovarian cancer in women with symptoms in primary care: population based case-control study. BMJ 2009; 339: b2998.
4. National Comprehensive Cancer Network. NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines): Ovarian cancer including fallopian tube cancer and primary peritoneal cancer, Version 1.2016. Ft. Washington, PA, USA. National Comprehensive Cancer Network 2016 (https://www.nccn.org/professionals/physician_gls/pdf/ovarian.pdf).
5. Soletormos G, Duffy MJ, et al. Clinical use of cancer biomarkers in epithelial ovarian cancer: updated guidelines from the European group on tumor markers. Int J Gynecol Cancer 2016; 26(1): 43–51.
6. Buys SS, Partridge E, et al. Effect of screening on ovarian cancer mortality: the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Randomized Controlled Trial. JAMA 2011; 305(22): 2295–2303.
7. Jacobs IJ, Menon U, et al. Ovarian cancer screening and mortality in the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS): a randomised controlled trial. Lancet 2016; 387(10022): 945–956.
8. Kurman RJ, Shih IeM. Molecular pathogenesis and extraovarian origin of epithelial ovarian cancer–shifting the paradigm. Hum Pathol. 2011; 42(7): 918–931.
9. Prat J. Ovarian carcinomas: five distinct diseases with different origins, genetic alterations, and clinicopathological features. Virchows Arch. 2012; 460(3): 237–249.
10. Nik NN, Vang R, et al. Origin and pathogenesis of pelvic (ovarian, tubal, and primary peritoneal) serous carcinoma. Annu Rev Pathol. 2014; 9: 27–45.
11. Munksgaard PS, Blaakaer J. The association between endometriosis and ovarian cancer: a review of histological, genetic and molecular alterations. Gynecol Oncol. 2012; 124(1): 164–169.
12. Hellner K, Miranda F, et al. Premalignant SOX2 overexpression in the fallopian tubes of ovarian cancer patients: discovery and validation studies. EBioMedicine 2016; 10: 137–149.

The authors
Kezia Gaitskell*^1,2 BM BCh; Ahmed Ashour Ahmed^1,2 MBBCh, MRCOG, PhD
¹Ovarian Cancer Cell Laboratory, Weatherall Institute of Molecular Medicine, University of Oxford, Headington, Oxford OX3 9DS, UK
²Nuffield Department of Obstetrics and Gynaecology, University of Oxford, Women’s Centre, John Radcliffe Hospital, Oxford OX3 9DU, UK

*Corresponding author
E-mail: Kezia.gaitskell@ceu.ox.ac.uk

Charlie Gard, who recently died just before his first birthday, was admitted to Great Ormond Street Hospital (GOSH) when he was two months old. Founded 265 years ago, and classed as one of the top four pediatric hospitals globally, GOSH has a dedicated international workforce, including some of the world’s leading doctors, surgeons and pediatric nurses. As well as offering tireless and expert care for its young patients, GOSH is involved in over 800 research projects.
Charlie was provisionally diagnosed with mitochondrial DNA depletion syndrome (MDDS) soon after admission, and genetic tests confirmed the diagnosis. The baby had two mutations affecting his RRM2B gene, preventing the synthesis of ribonucleotide reductase, an enzyme that plays a key role in maintaining the mitochondrial deoxinucleotide triphosphate pool needed for mitochondrial DNA synthesis. Only 15 other patients have been described with RRM2B mutations in the medical literature: in all cases rapid encephalopathy and muscle breakdown caused death within a few months of onset. A team of GOSH experts did consider experimental nucleoside bypass therapy (NBT) when the baby was five months old (ethical approval was first necessary), but they concluded that his rapidly deteriorating condition did not warrant such an intervention.
One can sympathize with the child’s parents as they desperately searched for a possible cure for their terminally ill infant, waging a protracted litigation to have him treated with experimental NBT in the US by Columbia University’s Dr Michio Hirano. One cannot, however, condone the belligerent demands of around 200, 000 members of the medically ignorant public that Charlie be released from experts at GOSH, or the interventions of ill-informed public figures such as Donald Trump and even the Pope.
Dr Hirano claimed that NBT – he ‘retains a financial interest’ in the compounds he prescribes – could benefit the patient. His relevant work concerns the TK2 mutation, a more frequent cause of MDDS with a more varied prognosis. His team has recently published a paper showing that NBT is beneficial in TK2 deficient mice, and 18 patients, mostly in Italy and Spain, are undergoing experimental treatment. No peer-reviewed papers on patient prognosis have yet been published, and there are no ongoing clinical trials with NBT. Furthermore without seeing Charlie, the GOSH team or medical notes until the child was already 11 months old, Dr Hirano (and the popular press) blamed the legal procedure, declaring that it was now too late for treatment!
An evidence-based cure for MDDS is surely needed: hopefully that will be Charlie’s legacy.