DNA mutations are a well-characterized source of neoepitopes in immunotherapy. Here, we examined the contribution of dysregulated RNA processing to neoantigen production. Leveraging multi-omics and checkpoint inhibitor (CPI) response data from >1,000 patients, we identified reduced activity of the nonsense-mediated mRNA decay (NMD) pathway kinase SMG1 as a predictor of improved CPI response. NMD inhibition through SMG1 targeting stabilized transcripts containing premature termination codons, most of which were of non-mutational origin. This reshaped the major histocompatibility complex class I (MHC class I)-bound immunopeptidome and increased neoantigen abundance to levels comparable to high mutation burden tumors. Functionally, NMD inhibition drove antigen-dependent T cell-mediated tumor cell killing in vitro, promoted activation of tissue-resident T cells in patient-derived models ex vivo, and improved CPI efficacy in vivo. Our findings establish NMD inhibition as a strategy to harness a previously inaccessible source of canonical and non-canonical neoantigens, with the potential to increase tumor immunogenicity across cancers.

Immunotherapy has revolutionized cancer treatment, yet only a minority of individuals respond clinically, necessitating alternative strategies that can benefit these patients. Novel immuno-oncology targets may achieve this through bypassing resistance mechanisms to standard therapies. We introduce Mining Immunotherapy Drug tArgetS (MIDAS), a multimodal graph neural network system for immuno-oncology target discovery. MIDAS leverages gene interactions, multi-omic patient profiles, immune cell biology, antigen processing, disease associations and phenotypic consequences of genetic perturbations. It generalizes to time-sliced data, outcompetes state-of-the-art baselines (including OpenTargets) and ranks approved targets above those in clinical development. Moreover, MIDAS recovers immunotherapy-response-associated genes in unseen patients, thereby capturing immunotherapy response determinants. Interpretability analyses reveal a reliance on autoimmunity, regulatory networks and immuno-oncology pathways. Functionally perturbing oncostatin M-oncostatin M receptor signalling, a proposed MIDAS target, in TRACERx melanoma-patient-derived explants yielded reduced dysfunctional CD8(+) T cells, which associate with immunotherapy response, and reduced CCL4levels. Furthermore, oncostatin M and oncostatin M receptor expression is associated with altered T cell and macrophage profiles in bulk transcriptomic data from patient samples. These data are consistent with a role for oncostatin M-oncostatin M in modulating the tumour microenvironment towards immunosuppressive, tumour-promoting phenotypes. Our results present a machine learning framework for analysing multimodal data for immuno-oncology target discovery.

Abstract Introduction: Neoantigens from somatic tumor mutations are essential for effective anti-tumor immune responses. Frameshift insertions and deletions (fs-indels) represent a rare but highly immunogenic mutation subtype, as they create novel open reading frames (neoORFs) that generate peptides that are significantly distinct from self-antigens. Nevertheless, fs-indels often introduce premature termination codons, leading to transcript degradation via the nonsense-mediated mRNA decay (NMD) pathway, leading to loss of immunogenic neoantigen. Approach: For the first time, we pharmacologically inhibited SMG1, a core component of the NMD pathway, across a range of preclinical models, including human and mouse cancer cell lines, patient-derived tumor organoids (PDTOs), patient-derived tumor fragments (PDTFs), and syngeneic mouse xenografts. We analyzed the changes in transcriptome, proteome, and immunopeptidome following SMG1 inhibition (SMG1i) and peptide reactivity in in vitro priming experiments. We then combined tumor-T cell co-cultures and PDTFs to assess the anti-tumor immunogenicity induced by SMG1i. Ex vivo and in vivo immunological responses were assessed by high-dimensional flow cytometry, cytometric bead array, and single-cell RNA- and TCR-sequencing. Results: Using multi-omic and checkpoint inhibitor (CPI) response data from over 1,000 patients, we show that decreased expression of the key NMD mediator, SMG1, correlates with improved CPI response. Inhibiting SMG1 ex vivo and in vivo activates and expands tumor-reactive T cells and sensitizes CPI efficacy. Mechanistically, SMG1 inhibition stabilizes frameshift-derived transcripts, increasing the abundance and surface presentation of immunogenic neoantigens. This results in an increase in neoepitope burden in tumors, similar to that seen in tumors with high tumor mutational burden (TMB), without inducing DNA damage. Co-culturing tumor cells and PDTOs with CD8+ T cells after SMG1i results in strong MHC class I antigen-dependent T cell activation and tumor cell killing. Conclusion: Our findings highlight SMG1 inhibition as a promising strategy to exploit an untapped source of highly immunogenic peptides. It enhances anti-tumor immunogenicity without introducing DNA mutations, regardless of tumor type or TMB status, providing translational evidence for sensitizing ICB responses. Citation Format: Hongchang Fu, Roberto Vendramin, Shanila Fernandez Patel, Yue Zhao, Danwen Qian, Lorena Ligammari, Osnat Bartok, Polina Greenberg, Ronen Levy, Andrea Castro, Krupa Thakkar, Jun Murai, Wei-ting Lu, Christopher C. Sng, Chen Weller, Gordon Beattie, Amandeep Bhamra, Roc Farriol-Duran, Despoina Karagianni, Marcellus Augustine, Krijn Djikstra, Christopher L. Pinder, Benjamin S. Simpson, Gordon Weng-Kit Cheung, TRACERx Consortium, Felipe Galvez Cancino, Petra Vlckova, Silvia Surinova, Manuel Rodriguez-Justo, Mansi Shah, Nicholas McGranahan, Jeremy G. Carlton, Eva Camilla Gronroos, Sergio Quezada, James Luke Reading, Samra Turajlic, Yardena Samuels, Charles Swanton, Kevin Litchfield. Nonsense-mediated mRNA decay inhibition augments in vitro, in vivo, and ex vivo anti-tumor immunity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6742.

BackgroundNeoantigens, mutated tumour-specific antigens, are key targets of anti-tumour immunity during checkpoint inhibitor (CPI) treatment. Their identification is fundamental to designing neoantigen-directed therapy. Non-canonical neoantigens arising from the untranslated regions (UTR) of the genome are an overlooked source of immunogenic neoantigens. Here, we describe the landscape of UTR-derived neoantigens and release a computational tool, PrimeCUTR, to predict UTR neoantigens generated by start-gain and stop-loss mutations.MethodsWe applied PrimeCUTR to a whole genome sequencing dataset of pre-treatment tumour samples from CPI-treated patients (n = 341). Cancer immunopeptidomic datasets were interrogated to identify MHC class I presentation of UTR neoantigens.ResultsStart-gain neoantigens were predicted in 72.7% of patients, while stop-loss mutations were found in 19.3% of patients. While UTR neoantigens only accounted 2.6% of total predicted neoantigen burden, they contributed 12.4% of neoantigens with high dissimilarity to self-proteome. More start-gain neoantigens were found in CPI responders, but this relationship was not significant when correcting for tumour mutational burden. While most UTR neoantigens are private, we identified two recurrent start-gain mutations in melanoma. Using immunopeptidomic datasets, we identify two distinct MHC class I-presented UTR neoantigens: one from a recurrent start-gain mutation in melanoma, and one private to Jurkat cells.ConclusionPrimeCUTR is a novel tool which complements existing neoantigen discovery approaches and has potential to increase the detection yield of neoantigens in personalised therapeutics, particularly for neoantigens with high dissimilarity to self. Further studies are warranted to confirm the expression and immunogenicity of UTR neoantigens.

Immunotherapy has revolutionised cancer therapy but current immune checkpoint inhibitors (ICI) produce low response rates in most cancers, indicating that new therapeutic options are needed. Conventional immune-oncology (IO) discovery uses preclinical models with limited translation capacity as they do not fully recapitulate human tumour complexity. We use multimodal patient molecular data with modern machine learning (ML) methods to identify new IO targets with improved clinical potential.

Immunotherapy has revolutionised cancer treatment, yet few patients respond clinically, necessitating alternative strategies that can benefit these patients. Novel immune-oncology targets can achieve this through bypassing resistance mechanisms to standard therapies. To address this, we introduce MIDAS, a multimodal graph neural network system for immune-oncology target discovery that leverages gene interactions, multi-omic patient profiles, immune cell biology, antigen processing, disease associations, and phenotypic consequences of genetic perturbations. MIDAS generalises to time-sliced data, outcompetes existing methods, including OpenTargets, and distinguishes approved from prospective targets. Moreover, MIDAS recovers immunotherapy response-associated genes in unseen trials, thus capturing tumour-immune dynamics within human tumours. Interpretability analyses reveal a reliance on autoimmunity, regulatory networks, and relevant biological pathways. Functionally perturbing the OSM-OSMR axis, a proposed target, in TRACERx melanoma patient-derived explants yielded reduced dysfunctional CD8+ T cells, which associate with immunotherapy response. Our results present a machine learning framework for analysing multimodal data for immune-oncology discovery.

Abstract Overview: Immunotherapy has revolutionized cancer treatment. However, existing immune checkpoint inhibitors (CPI) yield low response rates in most cancers, highlighting the need for new therapeutic options. Traditional immune-oncology (IO) target discovery relies on preclinical models, which struggle to recapitulate human tumor complexity, limiting translation potential. Attention is thus directed at harnessing multimodal patient molecular data with modern machine learning (ML) techniques to identify new IO targets which may have higher clinical viability. Approach: We posed IO target discovery as a binary classification task, training ML systems on known candidate drug targets that have progressed to stage I or higher clinical trials. We constructed a rich knowledge graph database to support model development. Graph nodes (n=11,919) comprised genes with edges linking genes involved in n=99,275 protein-protein interactions (PPIs; PMID: 28936969). Genes were labelled with n=6,387 gene-disease associations alongside bulk exome and transcriptome (RNAseq) features from n=1,317 CPI-treated patients (PMID: 33508232). We considered the role of different immune subsets by incorporating cell type-specific PPIs from single cell RNAseq atlases of n=350 samples (PMID: 31786210). To capture how antigen processing affects immunotherapy response, we also examined the immunopeptidome of n=60 patients (PMID: 30556813). Causal data on immune responses to genetic perturbation stemmed from n=7 publicly available CRISPR tumor-T cell co-cultures and n=15,442 SNP-phenotype links. Lastly, we used interpretability analysis to understand drivers of model predictions and elucidate critical genes that influence multiple IO target pathways. Results: Firstly, we developed an ensemble ML approach which achieved test ROC-AUC>0.75. Next, we used a graph-based ML framework which yielded superior performance (test ROC-AUC>0.90). Orthogonal validation confirmed these models can discriminate known targets by trial phase (p<0.001), predict patient response in new CPI trials (p<0.05), and identify genes that rank highly in unseen genome-wide CRISPR screens. Targets from both methods have entered experimental validation in patient-derived explants and organoid-immune co-cultures. Already, we have identified 2 novel targets predicted to relate to macrophage activity. Early data suggest that perturbing them leads to macrophage repolarization. Further validation experiments are ongoing. Conclusions: We will reveal candidate targets, showing that our method is effective at uncovering new IO targets and can identify critical nodes in biological networks that might be attractive hits. In addition, deciphering data types that drive model predictions provides a broader immunobiological understanding of anti-tumor immune responses. Thus, our results endorse using ML and multimodal data for novel IO target discovery. Citation Format: Marcellus Augustine, Nuno Rocha Nene, Krupa Thakkar, Danwen Qian, Evelyn Fitzsimons, Benjamin S. Simpson, Chris Watkins, Charles Swanton, Kevin Litchfield. Identifying new immunotherapy targets using machine learning and ex vivo validation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5887.

While the presence of intratumoural microbes has been well described, their role in tumour evolution is unknown. Here we conducted microbial sequencing of 567 samples from non-small cell lung cancer (NSCLC) patients in the TRACERx 421 cohort, spanning matched primary tumour, metastasis and normal adjacent tissue samples. We demonstrate the presence of cancer-associated bacteria shared across multiple primary tumour regions, enriched for common lung pathogens. Using spatially resolved digital histology data in combination with the bacterial load within the tumour, we show that patients with a high bacterial load and low infiltration of lymphocytes have an overall worse survival. Lastly, signature analysis of mutations in the bacterial genome revealed a predominantly endogenous processes contributing to the mutagenesis of intratumoural bacteria in NSCLC. We present a spatially and longitudinally resolved study of the cancer associated bacteriome in lung cancer.

BACKGROUND:Serum PSA and digital rectal examination remain the key diagnostic tools for detecting prostate cancer. However, due to the limited specificity of serum PSA, the applicability of this marker continues to be controversial. Recent use of image-guided biopsy along with pathological assessment and the use of biomarkers has dramatically improved the diagnosis of clinically significant cancer. Despite the two modalities working together for diagnosis biomarker research often fails to correlate findings with imaging. METHODS AND RESULTS:We looked at 21 prostate cancer biomarkers correlating our results with mpMRI data to investigate the hypothesis that biomarkers along with mpMRI data make a powerful tool to detect clinically significant prostate cancer. Biomarkers were selected based on the existing literature. Using a tissue microarray comprised of samples from the PICTURE study, with biopsies at 5 mm intervals and mpMRI data we analysed which biomarkers could differentiate benign and malignant tissue. Biomarker data were also correlated with pathological grading, mpMRI, serum PSA, age and family history. AGR2, CD10 and EGR protein expression was significantly different in both matched malignant and benign tissues. AMACR, ANPEP, GDF15, MSMB, PSMA, PTEN, TBL1XR1, TP63, VPS13A and VPS28 showed significantly different expression between Gleason grades in malignant tissue. The majority of the biomarkers tested did not correlate with mpMRI data. However, CD10, KHDRBS3, PCLAF, PSMA, SIK2 and GDF15 were differentially expressed with prostate cancer progression. AMACR and PTEN were identified in both pathological and image data evaluation. CONCLUSIONS:There is a high demand to develop biomarkers that would help the diagnosis and prognosis of prostate cancer. Tissue biomarkers are of particular interest since immunohistochemistry remains a cheap, reliable method that is widely available in pathology departments. These results demonstrate that testing biomarkers in a cohort consistent with the current diagnostic pathway is crucial to identifying biomarker with potential clinical utility.

Introduction Multiparametric MRI (mpMRI) has transformed the prostate cancer diagnostic pathway, allowing for improved risk stratification and more targeted subsequent management. However, concerns exist over the interobserver variability of images and the applicability of this model long term, especially considering the current shortage of radiologists and the growing ageing population. Artificial intelligence (AI) is being integrated into clinical practice to support diagnostic and therapeutic imaging analysis to overcome these concerns. The following report details a protocol for a systematic review and meta-analysis investigating the accuracy of AI in predicting primary prostate cancer on mpMRI.Methods and analysis A systematic search will be performed using PubMed, MEDLINE, Embase and Cochrane databases. All relevant articles published between January 2016 and February 2023 will be eligible for inclusion. To be included, articles must use AI to study MRI prostate images to detect prostate cancer. All included articles will be in full-text, reporting original data and written in English. The protocol follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols 2015 checklist. The QUADAS-2 score will assess the quality and risk of bias across selected studies.Ethics and dissemination Ethical approval will not be required for this systematic review. Findings will be disseminated through peer-reviewed publications and presentations at both national and international conferences.PROSPERO registration number CRD42021293745.

Abstract Checkpoint inhibitors (CPI), ameliorate the anti-tumour response by blocking inhibitory immune checkpoint receptors, and have revolutionised the treatment of advanced cancers. However, the prediction of treatment response is suboptimal, and there remains a strong reliance on tumour mutation burden (TMB). Studies to date are limited to whole exome sequencing (WES), with no data yet reported on the utility of whole genome sequencing (WGS) in a pan-cancer cohort. Here we report a pan-cancer cohort of 318 tumour/normal genomes from the Genomics England 100,000 Genomes Project cohort treated with CPIs. Pan-cancer biomarkers previously reported from WES such as clonal TMB, total neoantigen burden and TMB had continued utility in predicting treatment response. Clonal TMB remained the strongest univariate predictor of positive treatment outcome, followed by infiltrating T cell fraction, and tobacco/UV mutational signatures. using whole genome assay, we additionally detected novel signatures associated with poor outcomes, including markers reflecting chemotherapy-induced mutations. Patients treated with chemotherapy prior to CPI displayed reduced survival irrespective of tumour type and had more subclonal mutations. Structural variants (SVs) were also predictive of poor therapeutic response and were enriched with non-coding intronic breakpoints, generating significantly fewer neoantigens than expected by chance. Global genomic features such as telomere length were associated with poor survival following CPI treatment, particularly in renal and bladder cancers. Together, these validated and novel biomarkers showed collective utility when combined to predict CPI outcomes. Our results highlight the value of WGS in detecting biomarkers of treatment resistance and highlight the promise of WGS for use in clinical practice.

The introduction of checkpoint inhibitors (CPI) has revolutionised the treatment of advanced cancers, which act to ameliorate the anti-tumour response through blocking inhibitive immune checkpoint receptors. Whole-exome sequencing (WES) based estimates of tumour mutational burden (TMB) has been robustly associated with response to CPI therapy across multiple tumour types and a threshold of ≥10 mutations per megabase (Mb) is now an FDA-approved biomarker for treatment with pembrolizumab. However, studies to date have been limited to whole exome sequencing (WES), with no data yet reported on the utility of whole genome sequencing (WGS) as a biomarker to predict CPI response. WGS data can identify non-coding alterations which impact the expression of immune regulatory genes, variants likely to form non-coding epitopes, including novel open reading frame mutations especially structure variants (SV). Here, we have identified a pan-cancer cohort of n=364 patients treated with CPI from Genomics England 100,000 Genomes Project (GEL). We find that SV burden is highly associated with worse CPI response (HR =1.3 (1.0-1.8) , p-value = 0.037) and high SV burden group have low tumour-infiltrating lymphocyte (TIL) score (P-value < 0.0001). However, we found that patients who have high proportion of SV derived neoantigen group improved overall survival (HR = 0.44 (0.27-0.73) , p-value = 0.002). In addition, enhancer region alteration of antigen present gene was observed in 43 patients (12%) and was associated with worse CPI response. (HR = 1.5 (1.0-2.2), P-value = 0.034). We further explored the SV derived neoantigen generate neoantigen-specific T cell response. Summary of Cox Model from structure variant Variables No. patients HR (95% CI) P Sex Female 164 Male 200 1.15 ( 0.87-1.52) 0.3285 Cancer Type Others 59 Lung 88 1.39 (0.91-2.14) 0.1255 Melanoma 155 0.84 (0.56-1.28) 0.4267 Renal 62 0.84 (0.52-1.38) 0.495 TMB estimated WGS 364 0.98 (0.97-0.99) 0.0036 SV burden Low 182 High 182 1.36 (1.02-1.81) 0.0358 Proportion of anti-tumour antigen from SV Low 49 High 47 0.48 (0.29-0.8) 0.0045 antigen non-generate 268 0.49 (0.35-0.7) < 0.0001 Citation Format: Hongui Cha, Benjamin Simpson, Charles Swanton, Kevin Litchfield, Genomics England Research Consortium. Somatic structure variants as a biomarker to predict immune checkpoint inhibitor response by generating anti-tumour antigen [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3137.

Checkpoint inhibitors (CPI), ameliorate the anti-tumour response by blocking inhibitory immune checkpoint receptors, and have revolutionised the treatment of advanced cancers. However, the prediction of treatment response is suboptimal, and there remains a strong reliance on tumour mutation burden (TMB). Studies to date are limited to whole exome sequencing (WES), with no data yet reported on the utility of whole genome sequencing (WGS) in a pan-cancer cohort. Here we report a pan-cancer cohort of 318 tumour/normal genomes from the Genomics England 100,000 Genomes Project cohort treated with CPIs. Pan-cancer biomarkers previously reported from WES such as clonal TMB, total neoantigen burden and TMB had continued utility in predicting treatment response. Clonal TMB remained the strongest univariate predictor of positive treatment outcome, followed by infiltrating T cell fraction, and tobacco/UV mutational signatures. using whole genome assay, we additionally detected novel signatures associated with poor outcomes, including markers reflecting chemotherapy-induced mutations. Patients treated with chemotherapy prior to CPI displayed reduced survival irrespective of tumour type and had more subclonal mutations. Structural variants (SVs) were also predictive of poor therapeutic response and were enriched with non-coding intronic breakpoints, generating significantly fewer neoantigens than expected by chance. Global genomic features such as telomere length were associated with poor survival following CPI treatment, particularly in renal and bladder cancers. Together, these validated and novel biomarkers showed collective utility when combined to predict CPI outcomes. Our results highlight the value of WGS in detecting biomarkers of treatment resistance and highlight the promise of WGS for use in clinical practice.

Pluripotency defines the unlimited potential of individual cells of vertebrate embryos, from which all adult somatic cells and germ cells are derived. Understanding how the programming of pluripotency evolved has been obscured in part by a lack of data from lower vertebrates; in model systems such as frogs and zebrafish, the function of the pluripotency genes NANOG and POU5F1 have diverged. Here, we investigated how the axolotl ortholog of NANOG programs pluripotency during development. Axolotl NANOG is absolutely required for gastrulation and germ-layer commitment. We show that in axolotl primitive ectoderm (animal caps; ACs) NANOG and NODAL activity, as well as the epigenetic modifying enzyme DPY30, are required for the mass deposition of H3K4me3 in pluripotent chromatin. We also demonstrate that all 3 protein activities are required for ACs to establish the competency to differentiate toward mesoderm. Our results suggest the ancient function of NANOG may be establishing the competence for lineage differentiation in early cells. These observations provide insights into embryonic development in the tetrapod ancestor from which terrestrial vertebrates evolved.

Multiparametric magnetic-resonance imaging (mpMRI) has proven utility in diagnosing primary prostate cancer. However, the diagnostic potential of prostate-specific membrane antigen positron-emission tomography (PSMA PET) has yet to be established. This study aims to systematically review the current literature comparing the diagnostic performance of mpMRI and PSMA PET imaging to diagnose primary prostate cancer. A systematic literature search was performed up to December 2021. Quality analyses were conducted using the QUADAS-2 tool. The reference standard was whole-mount prostatectomy or prostate biopsy. Statistical analysis involved the pooling of the reported diagnostic performances of each modality, and differences in per-patient and per-lesion analysis were compared using a Fisher’s exact test. Ten articles were included in the meta-analysis. At a per-patient level, the pooled values of sensitivity, specificity, and area under the curve (AUC) for mpMRI and PSMA PET/CT were 0.87 (95% CI: 0.83–0.91) vs. 0.93 (95% CI: 0.90–0.96, p < 0.01); 0.47 (95% CI: 0.23–0.71) vs. 0.54 (95% CI: 0.23–0.84, p > 0.05); and 0.84 vs. 0.91, respectively. At a per-lesion level, the pooled sensitivity, specificity, and AUC value for mpMRI and PSMA PET/CT were lower, at 0.63 (95% CI: 0.52–0.74) vs. 0.79 (95% CI: 0.62–0.92, p < 0.001); 0.88 (95% CI: 0.81–0.95) vs. 0.71 (95% CI: 0.47–0.90, p < 0.05); and 0.83 vs. 0.84, respectively. High heterogeneity was observed between studies. PSMA PET/CT may better confirm the presence of prostate cancer than mpMRI. However, both modalities appear comparable in determining the localisation of the lesions.

Pluripotency defines the unlimited potential of cells in the primitive ectoderm of vertebrate embryos, from which all adult somatic cells and germ cells are derived. Understanding how the programing of pluripotency evolved has been obscured by the study of early development in models from lower vertebrates in which pluripotency is not conserved. Here we investigated how the axolotl ortholog of the mammalian core pluripotency factor NANOG , programs pluripotency during axolotl development to model the tetrapod ancestor from which terrestrial vertebrates evolved. We show that in axolotl primitive ectoderm (animal caps; AC) NANOG synergizes with NODAL activity and the epigenetic modifying enzyme DPY30 to direct the deposition of H3K4me3 in chromatin prior to the waves of transcription required for lineage commitment and developmental progression. We show that the interaction of NANOG and NODAL with DPY30 is required to direct development downstream of pluripotency and this is conserved in axolotls and human. These data demonstrate that the interaction of NANOG and NODAL signaling represents the basal state of vertebrate pluripotency.

Tumour-specific antigens are one of the principal targets in immune checkpoint inhibitor (CPI) treatment and fundamental to the development of personalised immunotherapy. The search for immunogenic neoantigens has primarily focused on mutations in protein-coding regions of genes. In this study, we investigate how novel open-reading frames (neoORFs) in the 5' and 3' untranslated region (UTR) of genes (generated by premature start-gain and stop-loss mutations respectively) contribute to the immune landscape of cancer. We analysed two pan-cancer CPI-treated patient cohorts from the Hartwig Medical Foundation (HMF, n = 384), and Genomics England (GEL, n = 364). The frequency and length of UTR neoORFs for each patient tumour were predicted from whole genome sequencing data using PrimeCUTR, an R-based bioinformatics tool we developed. CPI treatment response was determined based on RECIST 1.1 criteria. Start-gain neoORFs occurred in 65.4–75.5% (GEL–HMF) of patients, while stop-loss neoORFs were found in 18.6–30.5%. 53.2–65.6% had start-gain neoORFs spanning 20 amino acids or more, with an overall median length of 28 amino acids. Among prevalent cancer groups, lung and head and neck cancer had the longest start-gain neoORFs. CPI response in HMF was associated with longer total length of start-gain (but not frameshift or stop-loss) neoORFs (P = 0.005). This association was also seen subsetting for low tumour mutational burden (TMB) (P = 0.014). In GEL, survival benefit was specifically seen in melanoma (n = 155), where patients with start-gain neoORFs longer than 28 amino acids (n = 60) had greater OS compared to those without (HR 0.59, 95% CI 0.36–0.95, P = 0.03). UTR neoORFs occur frequently in cancer, yielding a promising source of neoantigens. We designed a computational pipeline for identifying these neoORFs, which will be made available upon peer-review. We show that 5' UTR start-gain neoORFs were associated with clinical response to CPI, even in the low TMB setting which is typically linked to reduced CPI response. These findings warrant further study to validate UTR neoORFs as a biomarker and target for personalised immunotherapy.

PIM 1 and PI3K/mTOR pathways are frequently dysregulated in prostate cancer and may lead to decreased survival invasion and metastasis. Moreover, anti-tumour drug resistance has been associated with the interconnection of these pathways. Furthermore, current treatments exhibit issues with toxicity. Hence, these pathways were co-targeted with novel preclinical multikinase PIM/PI3K/mTOR inhibitor- AUM302, PI3K/mTOR inhibitor BEZ235 (Dactolisib) and PIM inhibitor, AZD-1208 in our laboratory using a cohort of cancer explants emanating from our PEOPLE: PatiEnt prOstate samPLes for rEsea ch study and our current SCREEN study. This cohort has a high Gleason grade score of ≥ 8. Therefore, this study aims to assess the effect of the combination therapy on the transcriptional landscape of ex vivo prostate cancer models derived from prostate cancer patients. Using the Nanostring GeoMX DSP technology, we aim to analyse the spatial transcriptomic profile of the co-targeted therapy treated ex vivo models to decipher the effects of heterogeneity on the co-targeted therapies' efficacy. Tissue microarrays of co-targeted treated twenty-five ex vivo 3mm cores derived from 4 patients will be analysed. Following RNA Scope analysis, morphology markers, including PAN CK positive and PAN CK negative, will be used to guide the selection of 270 regions of interest (ROI). ROI will be segmented and profiled using immunofluorescence. The morphological markers will define these segments into areas of illumination (AOIs) using a combination of the absence or presence of CD45 and pSTAT3. The AOIs will generate multiple expression profiles for the related ROI. We intend to use this flexible, high-dimensional spatial profiling to identify the spatial transcriptomic signatures and explore phosphorylation sites in cancer-targeted therapies. The spatial transcriptomics analysis of this study is in view. Our findings will contribute to understanding how the spatial landscape of the tumour microenvironment enhances the efficacy of anti-tumour drugs and what subset of patients are more likely to benefit from such therapy.

Introduction Multiparametric magnetic resonance imaging (mpMRI) has improved the triage of men with suspected prostate cancer, through precision prebiopsy identification of clinically significant disease. While multiple important characteristics, including tumour grade and size have been shown to affect conspicuity on mpMRI, tumour location and association with mpMRI visibility is an underexplored facet of this field. Therefore, the objective of this systematic review and meta-analysis is to collate the extant evidence comparing MRI performance between different locations within the prostate in men with existing or suspected prostate cancer. This review will help clarify mechanisms that underpin whether a tumour is visible, and the prognostic implications of our findings. Methods and analysis The databases MEDLINE, PubMed, Embase and Cochrane will be systematically searched for relevant studies. Eligible studies will be full-text English-language articles that examine the effect of zonal location on mpMRI conspicuity. Two reviewers will perform study selection, data extraction and quality assessment. A third reviewer will be involved if consensus is not achieved. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines will inform the methodology and reporting of the review. Study bias will be assessed using a modified Newcastle-Ottawa scale. A thematic approach will be used to synthesise key location-based factors associated with mpMRI conspicuity. A meta-analysis will be conducted to form a pooled value of the sensitivity and specificity of mpMRI at different tumour locations. Ethics and dissemination Ethical approval is not required as it is a protocol for a systematic review. Findings will be disseminated through peer-reviewed publications and conference presentations. PROSPERO registration number CRD42021228087.