Systemic administration of the highly potent anticancer therapeutic, tumour necrosis factor alpha (TNFα) induces high levels of toxicity and is responsible for serious side effects. Consequently, tumour targeting is required in order to confine this toxicity within the locality of the tumour. Bacteria have a natural capacity to grow within tumours and deliver therapeutic molecules in a controlled fashion. The non-pathogenic E. coli strain MG1655 was investigated as a tumour targeting system in order to produce TNFα specifically within murine tumours. In vivo bioluminescence imaging studies and ex vivo immunofluorescence analysis demonstrated rapid targeting dynamics and prolonged survival, replication and spread of this bacterial platform within tumours. An engineered TNFα producing construct deployed in mouse models via either intra-tumoural (i.t.) or intravenous (i.v.) administration facilitated robust TNFα production, as evidenced by ELISA of tumour extracts. Tumour growth was impeded in three subcutaneous murine tumour models (CT26 colon, RENCA renal, and TRAMP prostate) as evidenced by tumour volume and survival analyses. A pattern of pro-inflammatory cytokine induction was observed in tumours of treated mice vs. controls. Mice remained healthy throughout experiments. This study indicates the therapeutic efficacy and safety of TNFα expressing bacteria in vivo, highlighting the potential of non-pathogenic bacteria as a platform for restricting the activity of highly potent cancer agents to tumours.

Protein engineering and synthetic biology stand to benefit immensely from recent advances in silico tools for structural and functional analyses of proteins. In the context of designing novel proteins, current in silico tools inform the user on individual parameters of a query protein, with output scores/metrics unique to each parameter. In reality, proteins feature multiple “parts”/functions and modification of a protein aimed at altering a given part, typically has collateral impact on other protein parts. A system for prediction of the combined effect of design parameters on the overall performance of the final protein does not exist. Function2Form Bridge (F2F‐Bridge) attempts to address this by combining the scores of different design parameters pertaining to the protein being analyzed into a single easily interpreted output describing overall performance. The strategy comprises of (a) a mathematical strategy combining data from a myriad of in silico tools into an OP‐score (a singular score informing on a user‐defined overall performance) and (b) the F2F Plot, a graphical means of informing the wetlab biologist holistically on designed construct suitability in the context of multiple parameters, highlighting scope for improvement. F2F predictive output was compared with wetlab data from a range of synthetic proteins designed, built, and tested for this study. Statistical/machine learning approaches for predicting overall performance, for use alongside the F2F plot, were also examined. Comparisons between wetlab performance and F2F predictions demonstrated close and reliable correlations. This user‐friendly strategy represents a pivotal enabler in increasing the accessibility of synthetic protein building and de novo protein design.

Bacterial inhabitants of the body have the potential to play a role in various stages of cancer initiation, progression, and treatment. These bacteria may be distal to the primary tumour, such as gut microbiota, or local to the tissue, before or after tumour growth. Breast cancer is well studied in this context. Amongst breast cancer types, Triple Negative Breast Cancer (TNBC) is more aggressive, has fewer treatment options than receptor-positive breast cancers, has an overall worse prognosis and higher rates of reoccurrence. Thus, an in-depth understanding of the bacterial influence on TNBC progression and treatment is of high value. In this regard, the Gut Microbiota (GM) can be involved in various stages of tumour progression. It may suppress or promote carcinogenesis through the release of carcinogenic metabolites, sustenance of proinflammatory environments and/or the promotion of epigenetic changes in our genome. It can also mediate metastasis and reoccurrence through interactions with the immune system and has been recently shown to influence chemo-, radio-, and immune-therapies. Furthermore, bacteria have also been found to reside in normal and malignant breast tissue. Several studies have now described the breast and breast tumour microbiome, with the tumour microbiota of TNBC having the least taxonomic diversity among all breast cancer types. Here, specific conditions of the tumour microenvironment (TME) - low O2, leaky vasculature and immune suppression - are supportive of tumour selective bacterial growth. This innate bacterial ability could enable their use as delivery agents for various therapeutics or as diagnostics. This review aims to examine the current knowledge on bacterial relevance to TNBC and potential uses while examining some of the remaining unanswered questions regarding mechanisms underpinning observed effects.

TPS819 Background: The gut microbiome (GM) is thought to influence host immunity by modulating multiple immunologic pathways. Studies have suggested that dysbiosis of the GM confers a predisposition to certain malignancies and influences response to immune checkpoint inhibitors. However, little is known about how the GM diversity influences complete pathological response to neoadjuvant therapy in gastrointestinal (GI) tumours. We hypothesize that a more diverse GM constitution at baseline will lead to improved pathological response at the time of definitive surgery. Methods: We designed a cross-institutional multi-center translational study investigating the impact of the GM diversity on the efficacy of neoadjuvant therapy in GI cancers by assessing its association with pathological response. The study population will consist of patients with an early-stage rectal or esophageal cancer due to commence neoadjuvant therapy (including chemotherapy and chemoradiation) and planned for definitive surgery. Patients who received prior chemotherapy/monoclonal antibodies/immune checkpoint inhibitors or radiation will be excluded. The study assessments will include fecal sampling of the GM prior to neoadjuvant therapy, upon completion and again six months post completion of therapy. Fecal samples will be analysed by 16S RNA sequencing. Pathological response will be examined at time of surgery and patients will be classified as responders (complete pathological response) or non-responders. The primary endpoint of the study is to examine the association between the GM diversity and pathological response. 120 patients will be recruited over 18 months. Results: Species richness (Alpha Diversity) will be analysed using the Shannon diversity index and Jaccard similarity index to calculate beta diversity. Classification and clustering analysis will be performed with Principal Component Analysis (PCA) and Random Forest analysis. Comparison of taxa or functions between clinical cohorts will be performed using the two tailed Z test and corrected using the false discovery rate to determine Q-values. The association between GM and complete pathological response will be examined using logistic regression analysis adjusting for potential confounding factors. Adjusted odds ratios (OR) and 95% confidence intervals will be presented. Conclusions: This study will show preliminary insights into the role of GM as a potential biomarker for neoadjuvant therapy efficacy in patients with GI cancers. Recruitment is on-going.