Gut Health in Children With Cancer

Acute myeloid leukaemia (AML) is an aggressive cancer that occurs due to the clonal expansion of immature white blood cells, known as blasts (Chaudhury et al., 2018) with generally poor outcomes compared to childhood lymphoid leukaemia (Arad-Cohen et al., 2022). While complete remission rates are high in paediatric AML at approximately 90%, event-free survival and overall survival remain suboptimal at 45% and 65%, respectively, at 3 years, and nearly half of children will relapse (Rasche et al., 2018). Recently there has been a shift towards antigen-targeting immunotherapy in the treatment of cancers, including AML, to trigger anti-leukaemic responses (Greiner, 2019).

Intestinal mucositis is one of the most common side effects of chemotherapy, resulting from disruption of the immunological balance of the intestinal mucosal barrier (De Pietri et al., 2020). This results in alterations of absorptive and secretory functions of the intestinal mucosa, haemorrhages, intestinal dysmotility or intestinal failure (McGrath, 2019; Ohta et al., 2003). Chemotherapy-induced intestinal mucositis is thought to play a central role in the development of systemic inflammation and infections (van der Velden et al., 2014). The clinical assessment of the severity of intestinal mucositis is challenged by the lack of validated scoring scales, and objective biomarkers related to mucositis pathogenesis for assessment of its severity are therefore in demand. Decreasing plasma citrulline levels have been associated with the severity of intestinal mucositis and systemic inflammation after haematopoietic stem-cell transplantation in both adults and children (Gosselin et al., 2014; van Vliet et al., 2009).

Chemotherapy-induced intestinal mucositis causes translocation of intestinal bacteria and allows bacteria to cross the damaged mucosal barrier leading to a systemic inflammation and potentially to systemic infections, especially in immunocompromised patients (Villa and Sonis, 2015). Diseases affecting the immune system, such as inflammatory bowel disease (IBD), juvenile idiopathic arthritis (JIA), and acute leukaemia, are pathological conditions affecting the paediatric population and are often associated with alterations in the intestinal microbiota, such as a decrease in bacterial diversity (Lucafò et al., 2020). Growing evidence suggests that gut microbiota can interfere with chemotherapeutic and immunosuppressant drugs, used in the treatment of these diseases, reducing or facilitating drug efficacy (Peppas et al., 2023). Human intestinal microbiota consists of several hundred bacterial species \[(Marchesi et al., 2016)\]. The microbial community of the gut conveys significant benefits to human physiology at an intestinal epithelial and systematic inflammatory level \[(Kindon et al., 2007; McDonnell et al., 2021) (Feng et al., 2022)\].

Feeding strategies in clinical practice in paediatric cancer patients with chemotherapy-induced mucositis varies considerable but invariably requires gut rest and may require parenteral (intravenous) nutrition (Kuiken et al., 2017b)(Kuiken et al., 2017b). Parenteral nutrition is associated with significant adverse effects, namely liver injury, risk of infections, metabolic derangements, gut atrophy, dysbiosis of the intestinal microbiome (Tume et al., 2020) The gut microbiota is the most abundant type of antigen-presenting cells. Therefore, it is conceivable that parenteral nutrition may profoundly alter the gut microbiome composition and function, which could lead to detrimental effects on the intestine (Pierre, 2017).

The catastrophic impact of a fibre-devoid diet on the gut microbial recolonisation post critical illness has been described by Tanes et al,2021, the team outline the importance of introducing the right nutrition after a bout of illness (Tanes et al., 2021). The diversity and relative abundance of microbial metabolites are heavily dependent on specific dietary components \[(Morrison and Preston, 2016)\]. Non-digestible dietary fiber such as oligosaccharides and inulin demonstrate resistance to digestion in the human small intestine \[(Lattimer and Haub, 2010)\]. In the large bowel dietary fiber undergoes fermentation by colonic microbiota to produce short chain fatty acids (SCFA); acetate, butyrate and propionate, which act as the primary carbon energy source for colonocyte \[5\]. The synergistic relationship between the host and intestinal SCFA concentrations include the concomitant reduction of the luminal pH, which by itself inhibits pathogenic microorganisms and increases the absorption of some nutrients \[(Macfarlane and Macfarlane, 2012)\]. Furthermore, intestinal SCFA control the production of T-helper cells, antibodies, and cytokines and are also involved in maintaining homeostasis of the mucosal system \[(Ríos-Covián et al., 2016; Corrêa-Oliveira et al., 2016)\].

Dietary and microbiome-based therapies are being explored for the potential to support recovery of healthy gut commensal populations during and after critical illness. In the paediatric population interest is growing in the use of a blended diet for the management of feed intolerances \[(Schmitz É et al., 2021)\]. Industry have responded to this shift in feeding practices and developed a high fiber enteral formula with food derived ingredients, which has been shown to improve enteral feed tolerance \[(O'Connor et al., 2022)\].

This is the first study to observe the impact of AML treatment on the gut microbiome of children on different modes of feeding including parenteral nutrition and food-based formulas. Finally, this study will also observe the recovery phase of the gut microbiota after AML treatment and compare with subsequent infections and relapse.

Cases: children with AML: Stool specimens will be obtained at three predefined time points for microbiome analysis: baseline start of AML treatment (within 2 weeks of admission); after induction chemotherapy initiation (6-8weeks); recovery phase 6-8 months.

Paediatric stool samples will be collected using the stool sample collection kit. Each participant will be provided with 4 home collection kits. Each kit contains:

* • Fe-Col® collection kit * • Stool collection tube (containing stability buffer) * • Biohazard bag * • Bubble mailer bag * • Instructions.

The samples will then be posted back to PeploBio's laboratory (Royal Mail Priority, Track24). Upon arrival, PeploBio personnel will proceed to sample reception, including verification of sample integrity and accessioning. Samples will be homogenized and split into 200ul aliquots and stored for up to 7 days at -20ºC.

Controls: match control stool samples will be sent to PeploBio for microbiome analysis

Assay Summary:

Both case and control stool samples will be sent to PeploBio's Advanced Gut Microbiome Panel offer. This qPCR multiplex assay targets the 16s gene for 10 bacterial targets. These targets will specifically be chosen based on clinical, peer-reviewed correlation studies implicating their involvement in metabolic and inflammatory diseases, such as cancer. The results will provide an absolute quantification data within as little as 1-2 days from sample arrival at the laboratory.

Bioinformatic processing We plan to sequence the V4 region of the bacterial 16S rRNA gene using the Illumina MiSeq platform according to the manufacturer's specifications. Reads will be demultiplexed into fastq files for each sample using sequence barcodes. Forward and reverse reads will be joined with PANDAseq. After samples with fewer than 3000 reads will be excluded. The joined sequence files will be formatted using a Python script to add QIIME headers with the respective sample ID to each sequence before concatenating into one file for input into QIIME 1.8.0A.

The Shannon alpha diversity will be calculated on the unfiltered biom table using the alpha\_diversity.py script, and weighted UniFrac distances were calculated with the beta\_diversity.py script. The microbial dysbiosis index (initially described by Gevers et al. will be calculated in R for each sample. The microbial dysbiosis index will be defined as the log10 of the total abundance in organisms increased in CD divided by the total abundance of organisms decreased in CD. The increased-in-CD taxa comprise Enterobacteriaceae, Pasteurellaceae, Fusobacteriaceae, Neisseriaceae, Veillonellaceae, and Gemellaceae. Decreased-in-CD taxa are Bacteroidales, Clostridiales (excluding Veillonellaceae), Erysipelotrichaceae, and Bifidobacteriaceae

Gut microbiome composition analysis:

Microbial diversity will be expressed as the number of distinct species in a community (richness), the even distribution of their abundances (evenness) or a combination of both aspects, commonly termed alpha diversity. Microbial alpha diversity is estimated using the Shannon and Simpson indices, whereas microbial richness is estimated using the Chao1 index, or number of observed species/operational taxonomic units (OTUs).

For the extraction of the nucleic acids, the samples are lysed, and nucleic acid extracted using MagMAX™ Viral/Pathogen II (MVP II) Nucleic Acid Isolation Kit, following the manufacturer's IFU (Thermo Scientific). qRT-PCR will be executed using primers designed for the 10 bacterial targets (16s gene), and quantitative absolute abundance is determined using the standard curve method.

Anthropometry (data collection: T0 baseline; T1 end of consolidation 6-8months; T2 Recovery 10-12months) Weight will be determined to the nearest 0.1 kg, with subjects dressed in light clothing or ward gown among patients, using a Model 880 electronic scale (Seca, Hamburg, Germany) after voiding. Height will be measured to the nearest 0.1 cm without socks or shoes, using a Model 206 wall-mounted stadiometer (Seca). Body mass index (BMI) will calculate using the standard formula (kg m-2). Weight and height will be measured following standard procedures. Body mass index will be calculated as weight in kilograms divided by height in meters squared, and BMI SDS will be obtained from the WHO reference curves. BMI and standard deviations scores (SDS) will be calculated using WHO as reference data.

Obesity will be confirmed when the percentile is higher than 99.9th on the WHO Child Growth Standards Curve, or the z-score was higher than +3 (Cole et al., 2000). Children older than 5 years, with BMI between the 85th and 97th percentiles on the WHO Child Growth Standards Curve, will be classified as overweight, and as obese when the percentile is higher than the 97th or the z-score is higher than +2SD. Conversely, underweight will be confirmed when the z-score is lower than -2SD Mid upper arm circumference (MUAC) will be measured halfway between the tip of the acromion and olecranon process using a non-stretchable measuring tape SECA 212 to the nearest 0.1 cm Dietary data collection (data collection: T0 baseline; T1 end of consolidation 6-8months; T2 Recovery 10-12months) Three-day food records will be used to assess dietary intake and enteral nutrition of children. Parents or caregivers will be instructed on how to record the food and fluid consumption of their child using household measures to estimate portion sizes and food type (including brand names of foods, if applicable) in a diary provided. Dietary records will be kept for two weekdays and one weekend day. Analysis of dietary intake will be performed using Nutritics nutritional analysis software (Dublin, Republic of Ireland). Energy and nutrient intake of children will be compared against the SAGN 2017 guidelines (SACN, 2011).

Gastrointestinal Tolerability (data collection: T0 baseline; T1 end of consolidation 6-8months; T2 Recovery 10-12months) Stool consistency is a central component in the description of normal or altered bowel habit. Stool form can be considered as a proxy measure for stool consistency and refers to the shape and apparent texture of the stool, which can be assessed visually. Stool form sc

• Anthropometry [Срок оценки: 6 months]