BREAst Cancer Personalised NuTrition (BREACPNT): dietary intervention in breast cancer survivors treated with endocrine therapy - a protocol for a randomised clinical trial.

INTRODUCTION: Breast cancer survivors treated with adjuvant endocrine therapy commonly experience weight gain, which has been associated with low adherence to therapy and worse breast cancer prognosis. We aim to assess whether a personalised postprandial glucose targeting diet will be beneficial for weight management as compared with the recommended Mediterranean diet in this patient population METHODS AND ANALYSIS: The BREAst Cancer Personalised NuTrition study is a phase-2 randomised trial in hormone receptor positive patients with breast cancer, treated with adjuvant endocrine therapy. The study objective is to assess whether dietary intervention intended to improve postprandial glycaemic response to meals results in better weight and glycaemic control in this population as compared with the standard recommended Mediterranean diet. Consenting participants will be assigned in a single blinded fashion to either of two dietary arms (Mediterranean diet or an algorithm-based personalised diet). They will be asked to provide a stool sample for microbiome analysis and will undergo continuous glucose monitoring for 2 weeks, at the initiation and termination of the intervention period. Microbiome composition data will be used to tailor personal dietary recommendations. After randomisation and provision of dietary recommendations, participants will be asked to continuously log their diet and lifestyle activities on a designated smartphone application during the 6-month intervention period, during which they will be monthly monitored by a certified dietitian. Participants' clinical records will be followed twice yearly for 5 years for treatment adherence, disease-free survival and recurrence. ETHICS AND DISSEMINATION: The study has been approved by the ethics committee in the Sheba medical centre (file 5725-18-SMC, Ramat Gan, Israel) and the Weizmann Institutional Review Board (file 693-2, Rehovot, Israel). The findings of this study will be published in a peer reviewed publication. TRIAL REGISTRATION NUMBER: NCT04079270.

Identification of bacteria-derived HLA-bound peptides in melanoma.

A variety of species of bacteria are known to colonize human tumours1-11, proliferate within them and modulate immune function, which ultimately affects the survival of patients with cancer and their responses to treatment12-14. However, it is not known whether antigens derived from intracellular bacteria are presented by the human leukocyte antigen class I and II (HLA-I and HLA-II, respectively) molecules of tumour cells, or whether such antigens elicit a tumour-infiltrating T cell immune response. Here we used 16S rRNA gene sequencing and HLA peptidomics to identify a peptide repertoire derived from intracellular bacteria that was presented on HLA-I and HLA-II molecules in melanoma tumours. Our analysis of 17 melanoma metastases (derived from 9 patients) revealed 248 and 35 unique HLA-I and HLA-II peptides, respectively, that were derived from 41 species of bacteria. We identified recurrent bacterial peptides in tumours from different patients, as well as in different tumours from the same patient. Our study reveals that peptides derived from intracellular bacteria can be presented by tumour cells and elicit immune reactivity, and thus provides insight into a mechanism by which bacteria influence activation of the immune system and responses to therapy.

A reference map of potential determinants for the human serum metabolome.

The serum metabolome contains a plethora of biomarkers and causative agents of various diseases, some of which are endogenously produced and some that have been taken up from the environment1. The origins of specific compounds are known, including metabolites that are highly heritable2,3, or those that are influenced by the gut microbiome4, by lifestyle choices such as smoking5, or by diet6. However, the key determinants of most metabolites are still poorly understood. Here we measured the levels of 1,251 metabolites in serum samples from a unique and deeply phenotyped healthy human cohort of 491 individuals. We applied machine-learning algorithms to predict metabolite levels in held-out individuals on the basis of host genetics, gut microbiome, clinical parameters, diet, lifestyle and anthropometric measurements, and obtained statistically significant predictions for more than 76% of the profiled metabolites. Diet and microbiome had the strongest predictive power, and each explained hundreds of metabolites-in some cases, explaining more than 50% of the observed variance. We further validated microbiome-related predictions by showing a high replication rate in two geographically independent cohorts7,8 that were not available to us when we trained the algorithms. We used feature attribution analysis9 to reveal specific dietary and bacterial interactions. We further demonstrate that some of these interactions might be causal, as some metabolites that we predicted to be positively associated with bread were found to increase after a randomized clinical trial of bread intervention. Overall, our results reveal potential determinants of more than 800 metabolites, paving the way towards a mechanistic understanding of alterations in metabolites under different conditions and to designing interventions for manipulating the levels of circulating metabolites.

The human tumor microbiome is composed of tumor type-specific intracellular bacteria.

Bacteria were first detected in human tumors more than 100 years ago, but the characterization of the tumor microbiome has remained challenging because of its low biomass. We undertook a comprehensive analysis of the tumor microbiome, studying 1526 tumors and their adjacent normal tissues across seven cancer types, including breast, lung, ovary, pancreas, melanoma, bone, and brain tumors. We found that each tumor type has a distinct microbiome composition and that breast cancer has a particularly rich and diverse microbiome. The intratumor bacteria are mostly intracellular and are present in both cancer and immune cells. We also noted correlations between intratumor bacteria or their predicted functions with tumor types and subtypes, patients' smoking status, and the response to immunotherapy.

A Systematic p53 Mutation Library Links Differential Functional Impact to Cancer Mutation Pattern and Evolutionary Conservation.

The TP53 gene is frequently mutated in human cancer. Research has focused predominantly on six major "hotspot" codons, which account for only ∼30% of cancer-associated p53 mutations. To comprehensively characterize the consequences of the p53 mutation spectrum, we created a synthetically designed library and measured the functional impact of ∼10,000 DNA-binding domain (DBD) p53 variants in human cells in culture and in vivo. Our results highlight the differential outcome of distinct p53 mutations in human patients and elucidate the selective pressure driving p53 conservation throughout evolution. Furthermore, while loss of anti-proliferative functionality largely correlates with the occurrence of cancer-associated p53 mutations, we observe that selective gain-of-function may further favor particular mutants in vivo. Finally, when combined with additional acquired p53 mutations, seemingly neutral TP53 SNPs may modulate phenotypic outcome and, presumably, tumor progression.

Environment dominates over host genetics in shaping human gut microbiota.

Human gut microbiome composition is shaped by multiple factors but the relative contribution of host genetics remains elusive. Here we examine genotype and microbiome data from 1,046 healthy individuals with several distinct ancestral origins who share a relatively common environment, and demonstrate that the gut microbiome is not significantly associated with genetic ancestry, and that host genetics have a minor role in determining microbiome composition. We show that, by contrast, there are significant similarities in the compositions of the microbiomes of genetically unrelated individuals who share a household, and that over 20% of the inter-person microbiome variability is associated with factors related to diet, drugs and anthropometric measurements. We further demonstrate that microbiome data significantly improve the prediction accuracy for many human traits, such as glucose and obesity measures, compared to models that use only host genetic and environmental data. These results suggest that microbiome alterations aimed at improving clinical outcomes may be carried out across diverse genetic backgrounds.