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Hypoxia and a suppressive tumour microenvironment (TME) are both independent negative prognostic factors for muscle-invasive bladder cancer (MIBC) that contribute to treatment resistance. Hypoxia has been shown to induce an immune suppressive TME by recruiting myeloid cells that inhibit anti-tumour T cell responses. Recent transcriptomic analyses show hypoxia increases suppressive and anti-tumour immune signalling and infiltrates in bladder cancer. This study sought to investigate the relationship between hypoxia-inducible factor (HIF)-1 and -2, hypoxia, and immune signalling and infiltrates in MIBC. ChIP-seq was performed to identify HIF1α, HIF2α, and HIF1β binding in the genome of the MIBC cell line T24 cultured in 1% and 0.1% oxygen for 24 h. Microarray data from four MIBC cell lines (T24, J82, UMUC3, and HT1376) cultured under 1%, 0.2%, and 0.1% oxygen for 24 h were used. Differences in the immune contexture between high- and low-hypoxia tumours were investigated using in silico analyses of two bladder cancer cohorts (BCON and TCGA) filtered to only include MIBC cases. GO and GSEA were used with the R packages “limma” and “fgsea”. Immune deconvolution was performed using ImSig and TIMER algorithms. RStudio was used for all analyses. Under hypoxia, HIF1α and HIF2α bound to ~11.5–13.5% and ~4.5–7.5% of immune-related genes, respectively (1–0.1% O2). HIF1α and HIF2α both bound to genes associated with T cell activation and differentiation signalling pathways. HIF1α and HIF2α had distinct roles in immune-related signalling. HIF1 was associated with interferon production specifically, whilst HIF2 was associated with generic cytokine signalling as well as humoral and toll-like receptor immune responses. Neutrophil and myeloid cell signalling was enriched under hypoxia, alongside hallmark pathways associated with Tregs and macrophages. High-hypoxia MIBC tumours had increased expression of both suppressive and anti-tumour immune gene signatures and were associated with increased immune infiltrates. Overall, hypoxia is associated with increased inflammation for both suppressive and anti-tumour-related immune signalling and immune infiltrates, as seen in vitro and in situ using MIBC patient tumours.

Purpose:Intratumoral hypoxia and immunity have been correlated with patient outcome in various tumor settings. However, these factors are not currently considered for treatment selection in head and neck cancer (HNC) due to lack of validated biomarkers. Here we sought to develop a hypoxia-immune classifier with potential application in patient prognostication and prediction of response to targeted therapy.Experimental Design:A 54-gene hypoxia-immune signature was constructed on the basis of literature review. Gene expression was analyzed in silico using the The Cancer Genome Atlas (TCGA) HNC dataset (n = 275) and validated using two independent cohorts (n = 130 and 123). IHC was used to investigate the utility of a simplified protein signature. The spatial distribution of hypoxia and immune markers was examined using multiplex immunofluorescence staining.Results:Unsupervised hierarchical clustering of TCGA dataset (development cohort) identified three patient subgroups with distinct hypoxia-immune phenotypes and survival profiles: hypoxialow/immunehigh, hypoxiahigh/immunelow, and mixed, with 5-year overall survival (OS) rates of 71%, 51%, and 49%, respectively (P = 0.0015). The prognostic relevance of the hypoxia-immune gene signature was replicated in two independent validation cohorts. Only PD-L1 and intratumoral CD3 protein expression were associated with improved OS on multivariate analysis. Hypoxialow/immunehigh and hypoxiahigh/immunelow tumors were overrepresented in “inflamed” and “immune-desert” microenvironmental profiles, respectively. Multiplex staining demonstrated an inverse correlation between CA-IX expression and prevalence of intratumoral CD3+ T cells (r = −0.5464; P = 0.0377), further corroborating the transcription-based classification.Conclusions:We developed and validated a hypoxia-immune prognostic transcriptional classifier, which may have clinical application to guide the use of hypoxia modification and targeted immunotherapies for the treatment of HNC.

Figure S1. Gene order within the cluster defined groups. Figure S2. Analysis of individual hypoxia and immune gene signatures. Figure S3. Flow diagram showing progress of patient samples through study for Correlate cohort Figure S4. Relationship between heatmap defined hypoxia-immune subgroups and T stage. Figure S5 Expression of individual hypoxia- or immune-response related genes within the heat-map defined subgroups. Figure S6. Correlation of PD-L1 or CA-IX protein expression with survival. Figure S7 Correlation of gene and protein expression. Figure S8. Spatial interactions of hypoxia and immune cells: Vectra images of multiplex stained HNC sections. Table S1 Treatment schedules Table S2. Genes in the RNASeq panel/TCGA dataset Table S3. Relationship of hypoxia-immune classifier to other prognostic variables Table S4. Summary of immunohistochemistry staining Table S5. Summary of statistical analyses for IHC staining

Background: As hypoxia can drive an immunosuppressive tumour microenvironment and inhibit CD8+ T cells, we investigated if patients with low tumour CD8+ T cells benefitted from hypoxia-modifying therapy. Methods: BCON was a phase III trial that randomised patients with muscle-invasive bladder cancer (MIBC) to radiotherapy alone or with hypoxia-modifying carbogen plus nicotinamide (CON). Tissue microarrays of diagnostic biopsies from 116 BCON patients were stained using multiplex immunohistochemistry (IHC) with the markers CD8, CD4, FOXP3, CD68 and PD-L1, plus DAPI. Hypoxia was assessed using CA9 IHC (n = 111). Linked transcriptomic data (n = 80) identified molecular subtype. Relationships with overall survival (OS) were investigated using Cox proportional hazard models. Results: High (upper quartile) vs. low CD8 T cell counts associated with a better OS across the whole cohort at 16 years (n = 116; HR 0.47, 95% CI 0.28–0.78, p = 0.003) and also in the radiotherapy alone group (n = 61; HR 0.39, 95% CI 0.19–0.76, p = 0.005). Patients with low CD8+ T cells benefited from CON (n = 87; HR 0.63, 95% CI 0.4–1.0, p = 0.05), but those with high CD8 T cells did not (n = 27; p = 0.95). CA9 positive tumours had fewer CD8+ T cells (p = 0.03). Prognostic significance of low CD8+ T cells in the whole cohort remained after adjusting for clinicopathologic variables. Basal vs. luminal subtype had more CD8+ cells (p = 0.02) but was not prognostic (n = 80; p = 0.26). Exploratory analyses with other immune markers did not improve on findings obtained with CD8 counts. Conclusions: MIBC with low CD8+ T cell counts may benefit from hypoxia-modifying treatment.

Owing to advances in radiotherapy, the physical properties of radiation can be optimized to enable individualized treatment; however, optimization is rarely based on biological properties and, therefore, treatments are generally planned with the assumption that all tumours respond similarly to radiation. Radiation affects multiple cellular pathways, including DNA damage, hypoxia, proliferation, stem cell phenotype and immune response. In this Review, we summarize the effect of these pathways on tumour responses to radiotherapy and the current state of research on genomic classifiers designed to exploit these variations to inform treatment decisions. We also discuss whether advances in genomics have generated evidence that could be practice changing and whether advances in genomics are now ready to be used to guide the delivery of radiotherapy alone or in combination.

Introduction Auto contouring models help consistently define volumes and reduce clinical workload. This study aimed to evaluate the cross acquisition of a Magnetic Resonance (MR) deep learning auto contouring model for organ at risk (OAR) delineation in head and neck radiotherapy. Methods Two auto contouring models were evaluated using deep learning contouring expert (DLCExpert) for OAR delineation: a CT model (modelCT) and an MR model (modelMRI). Models were trained to generate auto contours for the bilateral parotid glands and submandibular glands. Auto-contours for modelMRI were trained on diagnostic images and tested on 10 diagnostic, 10 MR radiotherapy planning (RTP), eight MR-Linac (MRL) scans and, by modelCT, on 10 CT planning scans. Goodness of fit scores, dice similarity coefficient (DSC) and distance to agreement (DTA) were calculated for comparison. Results ModelMRI contours improved the mean DSC and DTA compared with manual contours for the bilateral parotid glands and submandibular glands on the diagnostic and RTP MRs compared with the MRL sequence. There were statistically significant differences seen for modelMRI compared to modelCT for the left parotid (mean DTA 2.3 v 2.8 mm), right parotid (mean DTA 1.9 v 2.7 mm), left submandibular gland (mean DTA 2.2 v 2.4 mm) and right submandibular gland (mean DTA 1.6 v 3.2 mm). Conclusion A deep learning MR auto-contouring model shows promise for OAR auto-contouring with statistically improved performance vs a CT based model. Performance is affected by the method of MR acquisition and further work is needed to improve its use with MRL images.

Background miRNAs are promising biomarkers in oncology as their small size makes them less susceptible to degradation than mRNA in FFPE tissue. We aimed to derive a hypoxia-associated miRNA signature for bladder cancer. Methods Taqman miRNA array cards identified miRNA seed genes induced under hypoxia in bladder cancer cell lines. A signature was derived using feature selection methods in a TCGA BLCA training data set. miRNA expression data were generated for 190 tumours from the BCON Phase 3 trial and used for independent validation. Results A 14-miRNA hypoxia signature was derived, which was prognostic for poorer overall survival in the TCGA BLCA cohort (n = 403, p = 0.001). Univariable analysis showed that the miRNA signature predicted an overall survival benefit from having carbogen–nicotinamide with radiotherapy (HR = 0.30, 95% CI 0.094–0.95, p = 0.030) and performed similarly to a 24-gene mRNA signature (HR = 0.47, 95% CI 0.24–0.92, p = 0.025). Combining the signatures improved performance (HR = 0.26, 95% CI 0.08–0.82, p = 0.014) with borderline significance for an interaction test (p = 0.065). The interaction test was significant for local relapse-free survival LRFS (p = 0.033). Conclusion A 14-miRNA hypoxia signature can be used with an mRNA hypoxia signature to identify bladder cancer patients benefitting most from having carbogen and nicotinamide with radiotherapy.

Muscle-invasive bladder cancer can be treated with either radical cystectomy or bladder preservation approaches, and there is a need for reliable biomarkers to guide the optimal choice of therapy. The recent elucidation of the genomic landscape and biological drivers of bladder cancer has enabled the identification of tumor molecular features that may be helpful in driving clinical decision-making. Here, we summarize recent efforts to develop molecular biomarkers that could be leveraged to guide therapeutic decisions, post-treatment monitoring, and the optimal use of bladder preservation approaches for the effective treatment of muscle-invasive bladder cancer.

Background Follicular lymphoma (FL) prognosis is influenced by the composition of the tumour microenvironment. We tested an automated approach to quantitatively assess the phenotypic and spatial immune infiltrate diversity as a prognostic biomarker for FL patients. Methods Diagnostic biopsies were collected from 127 FL patients initially treated with rituximab-based therapy (52%), radiotherapy (28%), or active surveillance (20%). Tissue microarrays were constructed and stained using multiplex immunofluorescence (CD4, CD8, FOXP3, CD21, PD-1, CD68, and DAPI). Subsequently, sections underwent automated cell scoring and analysis of spatial interactions, defined as cells co-occurring within 30 μm. Shannon’s entropy, a metric describing species biodiversity in ecological habitats, was applied to quantify immune infiltrate diversity of cell types and spatial interactions. Immune infiltrate diversity indices were tested in multivariable Cox regression and Kaplan–Meier analysis for overall (OS) and progression-free survival (PFS). Results Increased diversity of cell types (HR = 0.19 95% CI 0.06–0.65, p = 0.008) and cell spatial interactions (HR = 0.39, 95% CI 0.20–0.75, p = 0.005) was associated with favourable OS, independent of the Follicular Lymphoma International Prognostic Index. In the rituximab-treated subset, the favourable trend between diversity and PFS did not reach statistical significance. Conclusion Multiplex immunofluorescence and Shannon’s entropy can objectively quantify immune infiltrate diversity and generate prognostic information in FL. This automated approach warrants validation in additional FL cohorts, and its applicability as a pre-treatment biomarker to identify high-risk patients should be further explored. The multiplex image dataset generated by this study is shared publicly to encourage further research on the FL microenvironment.

Background Prostate cancer risk stratification using single-nucleotide polymorphisms (SNPs) demonstrates considerable promise in men of European, Asian, and African genetic ancestries, but there is still need for increased accuracy. We evaluated whether including additional SNPs in a prostate cancer polygenic hazard score (PHS) would improve associations with clinically significant prostate cancer in multi-ancestry datasets. Methods In total, 299 SNPs previously associated with prostate cancer were evaluated for inclusion in a new PHS, using a LASSO-regularized Cox proportional hazards model in a training dataset of 72,181 men from the PRACTICAL Consortium. The PHS model was evaluated in four testing datasets: African ancestry, Asian ancestry, and two of European Ancestry—the Cohort of Swedish Men (COSM) and the ProtecT study. Hazard ratios (HRs) were estimated to compare men with high versus low PHS for association with clinically significant, with any, and with fatal prostate cancer. The impact of genetic risk stratification on the positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was also measured. Results The final model (PHS290) had 290 SNPs with non-zero coefficients. Comparing, for example, the highest and lowest quintiles of PHS290, the hazard ratios (HRs) for clinically significant prostate cancer were 13.73 [95% CI: 12.43–15.16] in ProtecT, 7.07 [6.58–7.60] in African ancestry, 10.31 [9.58–11.11] in Asian ancestry, and 11.18 [10.34–12.09] in COSM. Similar results were seen for association with any and fatal prostate cancer. Without PHS stratification, the PPV of PSA testing for clinically significant prostate cancer in ProtecT was 0.12 (0.11–0.14). For the top 20% and top 5% of PHS290, the PPV of PSA testing was 0.19 (0.15–0.22) and 0.26 (0.19–0.33), respectively. Conclusions We demonstrate better genetic risk stratification for clinically significant prostate cancer than prior versions of PHS in multi-ancestry datasets. This is promising for implementing precision-medicine approaches to prostate cancer screening decisions in diverse populations.

The identification of recurrent founder variants in cancer predisposing genes may have important implications for implementing cost-effective targeted genetic screening strategies. In this study, we evaluated the prevalence and relative risk of the CHEK2 recurrent variant c.349A>G in a series of 462 Portuguese patients with early-onset and/or familial/hereditary prostate cancer (PrCa), as well as in the large multicentre PRACTICAL case-control study comprising 55,162 prostate cancer cases and 36,147 controls. Additionally, we investigated the potential shared ancestry of the carriers by performing identity-by-descent, haplotype and age estimation analyses using high-density SNP data from 70 variant carriers belonging to 11 different populations included in the PRACTICAL consortium. The CHEK2 missense variant c.349A>G was found significantly associated with an increased risk for PrCa (OR 1.9; 95% CI: 1.1-3.2). A shared haplotype flanking the variant in all carriers was identified, strongly suggesting a common founder of European origin. Additionally, using two independent statistical algorithms, implemented by DMLE+2.3 and ESTIAGE, we were able to estimate the age of the variant between 2300 and 3125 years. By extending the haplotype analysis to 14 additional carrier families, a shared core haplotype was revealed among all carriers matching the conserved region previously identified in the high-density SNP analysis. These findings are consistent with CHEK2 c.349A>G being a founder variant associated with increased PrCa risk, suggesting its potential usefulness for cost-effective targeted genetic screening in PrCa families. We thank the funding support from IPO-Porto Research Center (CI-IPOP-16-2012) and from Fundação para a Ciência e a Tecnologia (FCT; PEst-OE/SAU/ UI0776/2014 and PTDC/DTP-PIC/1308/2014). The following authors were awarded with grants from FCT: PPa (UID/DTP/00776/2013/POCI-01-0145-FEDER-006868), SM (SFRH/BD/71397/2010) and PPi (SFRH/BD/73719/2010). The PRACTICAL consortium (http://practical.icr.ac.uk/) was supported by the Canadian Institutes of Health Research, European Commission’s Seventh Framework Programme grant agreement n° 223175 (HEALTH-F2-2009-223175), Cancer Research UK Grants C5047/A7357, C1287/A10118, C1287/A16563, C5047/A3354, C5047/A10692, C16913/A6135, and The National Institute of Health (NIH) Cancer Post-Cancer GWAS initiative grant: No. 1 U19 CA 148537-01 (the GAME-ON initiative). Genotyping of the OncoArray was funded by the US National Institutes of Health (NIH) [U19 CA 148537 for ELucidating Loci Involved in Prostate cancer SuscEptibility (ELLIPSE) project and X01HG007492 to the Center for Inherited Disease Research (CIDR) under contract number HHSN268201200008I] and by Cancer Research UK grant A8197/A16565. Additional analytic support was provided by NIH NCI U01 CA188392 (PI: Schumacher). We would also like to thank the following for funding support: The Institute of Cancer Research and The Everyman Campaign, The Prostate Cancer Research Foundation, Prostate Research Campaign UK (now PCUK), The Orchid Cancer Appeal, Rosetrees Trust, The National Cancer Research Network UK, The National Cancer Research Institute (NCRI) UK. We are grateful for support of NIHR funding to the NIHR Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, NIHR funding to the Manchester Biomedical Research Centre and the Manchester Academic Health Sciences Centre.

Background Fulfilling the promise of cancer immunotherapy requires novel predictive biomarkers to characterise the host immune microenvironment. Deciphering the complexity of immune cell interactions requires an automated multiplex approach to histological analysis of tumour sections. We tested a new automatic approach to select tissue and quantify the frequencies of cell-cell spatial interactions occurring in the PD1/PD-L1 pathway, hypothesised to reflect immune escape in oropharyngeal squamous cell carcinoma (OPSCC). Methods Single sections of diagnostic biopsies from 72 OPSCC patients were stained using multiplex immunofluorescence (CD8, PD1, PD-L1, CD68). Following multispectral scanning and automated regions-of-interest selection, the Hypothesised Interaction Distribution (HID) method quantified spatial proximity between cells. Method applicability was tested by investigating the prognostic significance of co-localised cells (within 30 μm) in patients stratified by HPV status. Results High frequencies of proximal CD8+ and PD-L1+ (HR 2.95, p = 0.025) and PD1+ and PD-L1+ (HR 2.64, p = 0.042) cells were prognostic for poor overall survival in patients with HPV negative OPSCC (n = 31). Conclusion The HID method can quantify spatial interactions considered to reflect immune escape and generate prognostic information in OPSCC. The new automated approach is ready to test in additional cohorts and its applicability should be explored in research and clinical studies.

There is a need to identify patients with early stage breast cancer for whom radiotherapy and its associated toxicities might be avoided. Tutzauer et al. hypothesised that a hypoxia biomarker might be used but found that adjuvant radiotherapy following surgery reduces risk of recurrence irrespective of hypoxia status.

Introduction Recent data has demonstrated that hypoxia drives an immunosuppressive tumour microenvironment (TME) via various mechanisms including hypoxia inducible factor (HIF)-dependent upregulation of programmed death ligand 1 (PD-L1). Both hypoxia and an immunosuppressive TME are targetable independent negative prognostic factors for bladder cancer. Therefore we sought to investigate whether hypoxia is associated with upregulation of PD-L1 in the disease. Materials and methods Three human muscle-invasive bladder cancer cell lines (T24, J82, UMUC3) were cultured in normoxia (20% oxygen) or hypoxia (1 and 0.1% oxygen) for 24 h. Differences in PD-L1 expression were measured using Western blotting, quantitative polymerase chain reaction (qPCR) and flow cytometry (≥3 independent experiments). Statistical tests performed were unpaired t tests and ANOVA. For in silico work an hypoxia signature was used to apply hypoxia scores to muscle-invasive bladder cancers from a clinical trial (BCON; n = 142) and TCGA (n = 404). Analyses were carried out using R and RStudio and statistical tests performed were linear models and one-way ANOVA. Results When T24 cells were seeded at p = 0.009) and mRNA (p p = 0.009 and 0.001), J82 (p = 0.008 and 0.013) and UMUC3 (p = 0.003 and 0.289). Increasing seeding density decreased PD-L1 protein (p p = 0.001) expression in T24 cells grown in both 20 and 1% oxygen. Only when cells were 100% confluent, were PD-L1 protein and mRNA levels higher in 1% versus 20% oxygen (p = 0.056 and p = 0.037). In silico analyses showed a positive correlation between hypoxia signature scores and PD-L1 expression in both BCON (p = 0.003) and TCGA (p p Conclusion Tumour hypoxia correlates with increased PD-L1 expression in patient derived bladder cancer tumours. In vitro PD-L1 expression was affected by cell density and decreased PD-L1 expression was observed after culture in hypoxia in muscle-invasive bladder cancer cell lines. As cell density has such an important effect on PD-L1 expression, it should be considered when investigating PD-L1 expression in vitro.

IntroductionProstate cancer risk stratification using single-nucleotide polymorphisms (SNPs) demonstrates considerable promise in men of European, Asian, and African genetic ancestries, but there is still need for increased accuracy. We evaluated whether including additional SNPs in a prostate cancer polygenic hazard score (PHS) would improve associations with clinically significant prostate cancer in multi-ancestry datasets.MethodsIn total, 299 SNPs previously associated with prostate cancer were evaluated for inclusion in a new PHS, using a LASSO-regularized Cox proportional hazards model in a training dataset of 72,181 men from the PRACTICAL Consortium. The PHS model was evaluated in four testing datasets: African ancestry, Asian ancestry, and two of European Ancestry—the Cohort of Swedish Men (COSM) and the ProtecT study. Hazard ratios (HRs) were estimated to compare men with high versus low PHS for association with clinically significant, with any, and with fatal prostate cancer. The impact of genetic risk stratification on the positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was also measured.ResultsThe final model (PHS290) had 290 SNPs with non-zero coefficients. Comparing, for example, the highest and lowest quintiles of PHS290, the hazard ratios (HRs) for clinically significant prostate cancer were 13.73 [95% CI: 12.43-15.16] in ProtecT, 7.07 [6.58-7.60] in African ancestry, 10.31 [9.58-11.11] in Asian ancestry, and 11.18 [10.34-12.09] in COSM. Similar results were seen for association with any and fatal prostate cancer. Without PHS stratification, the PPV of PSA testing for clinically significant prostate cancer in ProtecT was 0.12 (0.11-0.14). For the top 20% and top 5% of PHS290, the PPV was 0.19 (0.15-0.22) and 0.26 (0.19-0.33), respectively.ConclusionWe demonstrate better genetic risk stratification for clinically significant prostate cancer than prior versions of PHS in multi-ancestry datasets. This is promising for implementing precision-medicine approaches to prostate cancer screening decisions in diverse populations.

Background The presence of hypoxia is a poor prognostic factor in prostate cancer and the hypoxic tumor microenvironment promotes radioresistance. There is potential for drug radiotherapy combinations to improve the therapeutic ratio. We aimed to investigate whether hypoxia-associated genes could be used to identify FDA approved drugs for repurposing for the treatment of hypoxic prostate cancer. Methods Hypoxia associated genes were identified and used in the connectivity mapping software QUADrATIC to identify FDA approved drugs as candidates for repurposing. Drugs identified were tested in vitro in prostate cancer cell lines (DU145, PC3, LNCAP). Cytotoxicity was investigated using the sulforhodamine B assay and radiosensitization using a clonogenic assay in normoxia and hypoxia. Results Menadione and gemcitabine had similar cytotoxicity in normoxia and hypoxia in all three cell lines. In DU145 cells, the radiation sensitizer enhancement ratio (SER) of menadione was 1.02 in normoxia and 1.15 in hypoxia. The SER of gemcitabine was 1.27 in normoxia and 1.09 in hypoxia. No radiosensitization was seen in PC3 cells. Conclusion Connectivity mapping can identify FDA approved drugs for potential repurposing that are linked to a radiobiologically relevant phenotype. Gemcitabine and menadione could be further investigated as potential radiosensitizers in prostate cancer.

AIM: To identify the effect of single nucleotide polymorphism (SNP) interactions on the risk of toxicity following radiotherapy (RT) for prostate cancer (PCa) and propose a new method for polygenic risk score incorporating SNP-SNP interactions (PRSi). MATERIALS AND METHODS: Analysis included the REQUITE PCa cohort that received external beam RT and was followed for 2 years. Late toxicity endpoints were: rectal bleeding, urinary frequency, haematuria, nocturia, decreased urinary stream. Among 43 literature-identified SNPs, the 30% most strongly associated with each toxicity were tested. SNP-SNP combinations (named SNP-allele sets) seen in ≥10% of the cohort were condensed into risk (RS) and protection (PS) scores, respectively indicating increased or decreased toxicity risk. Performance of RS and PS was evaluated by logistic regression. RS and PS were then combined into a single PRSi evaluated by area under the receiver operating characteristic curve (AUC). RESULTS: Among 1,387 analysed patients, toxicity rates were 11.7% (rectal bleeding), 4.0% (urinary frequency), 5.5% (haematuria), 7.8% (nocturia) and 17.1% (decreased urinary stream). RS and PS combined 8 to 15 different SNP-allele sets, depending on the toxicity endpoint. Distributions of PRSi differed significantly in patients with/without toxicity with AUCs ranging from 0.61 to 0.78. PRSi was better than the classical summed PRS, particularly for the urinary frequency, haematuria and decreased urinary stream endpoints. CONCLUSIONS: Our method incorporates SNP-SNP interactions when calculating PRS for radiotherapy toxicity. Our approach is better than classical summation in discriminating patients with toxicity and should enable incorporating genetic information to improve normal tissue complication probability models. ispartof: RADIOTHERAPY AND ONCOLOGY vol:159 pages:241-248 ispartof: location:Ireland status: published

Background Polygenic hazard scores (PHS) can identify individuals with increased risk of prostate cancer. We estimated the benefit of additional SNPs on performance of a previously validated PHS (PHS46). Materials and method 180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy. Results 166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer. Conclusions Incorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.

Purpose: Intratumoral hypoxia and immunity have been correlated with patient outcome in various tumor settings. However, these factors are not currently considered for treatment selection in head and neck cancer (HNC) due to lack of validated biomarkers. Here we sought to develop a hypoxia-immune classifier with potential application in patient prognostication and prediction of response to targeted therapy. Experimental Design: A 54-gene hypoxia-immune signature was constructed on the basis of literature review. Gene expression was analyzed in silico using the The Cancer Genome Atlas (TCGA) HNC dataset (n = 275) and validated using two independent cohorts (n = 130 and 123). IHC was used to investigate the utility of a simplified protein signature. The spatial distribution of hypoxia and immune markers was examined using multiplex immunofluorescence staining. Results: Unsupervised hierarchical clustering of TCGA dataset (development cohort) identified three patient subgroups with distinct hypoxia-immune phenotypes and survival profiles: hypoxialow/immunehigh, hypoxiahigh/immunelow, and mixed, with 5-year overall survival (OS) rates of 71%, 51%, and 49%, respectively (P = 0.0015). The prognostic relevance of the hypoxia-immune gene signature was replicated in two independent validation cohorts. Only PD-L1 and intratumoral CD3 protein expression were associated with improved OS on multivariate analysis. Hypoxialow/immunehigh and hypoxiahigh/immunelow tumors were overrepresented in “inflamed” and “immune-desert” microenvironmental profiles, respectively. Multiplex staining demonstrated an inverse correlation between CA-IX expression and prevalence of intratumoral CD3+ T cells (r = −0.5464; P = 0.0377), further corroborating the transcription-based classification. Conclusions: We developed and validated a hypoxia-immune prognostic transcriptional classifier, which may have clinical application to guide the use of hypoxia modification and targeted immunotherapies for the treatment of HNC.

Highlights • The RILA assay is the leading candidate biomarker for radiotherapy toxicity. • We describe work to standardise its use across multiple centres. • Patient factors including smoking and arthritis were found to affect RILA score. • RILA predicts acute breast pain but not other acute end-points. • This work establishes the basis for implementing the assay clinically., Predicting which patients will develop adverse reactions to radiotherapy is important for personalised treatment. Prediction will require an algorithm or nomogram combining clinical and biological data. The radiation-induced lymphocyte apoptosis (RILA) assay is the leading candidate as a biological predictor of radiotherapy toxicity. In this study we tested the potential of the assay for standardisation and use in multiple testing laboratories. The assay was standardised and reproducibility determined using samples from healthy volunteers assayed concurrently in three laboratories in Leicester (UK), Mannheim (Germany) and Montpellier (France). RILA assays were performed on samples taken prior to radiotherapy from 1319 cancer patients enrolled in the REQUITE project at multiple centres. The patients were being treated for breast (n = 753), prostate (n = 506) or lung (n = 60) cancer. Inter-laboratory comparisons identified several factors affecting results: storage time, incubation periods and type of foetal calf serum. Following standardisation, there was no significant difference in results between the centres. Significant differences were seen in RILA scores between cancer types (prostate > breast > lung), by smoking status (non-smokers > smokers) and co-morbidity with rheumatoid arthritis (arthritics > non-arthritics). An analysis of acute radiotherapy toxicity showed as expected that RILA assay does not predict most end-points, but unexpectedly did predict acute breast pain. This result may elucidate the mechanism by which the RILA assay predicts late radiotherapy toxicity. The work shows clinical trials involving multiple laboratory measurement of the RILA assay are feasible and the need to account for tumour type and other variables when applying to predictive models.

AIMS:There is a need for predictive and surrogate response biomarkers to support treatment with antiangiogenic vascular endothelial growth factor (VEGF) inhibitors. We aimed to identify a minimally-invasive biomarker predicting benefit from cediranib pretreatment or early during treatment in patients with recurrent or metastatic cervical cancer.METHODS:Blood samples were collected before treatment, during treatment and upon disease progression where appropriate from patients enrolled in CIRCCa, a randomised phase II trial of carboplatin and paclitaxel with or without cediranib. Plasma concentrations of VEGF-A, VEGF-receptor 2, Ang1 and Tie2 were measured using multiplex enzyme-linked immunosorbent assay. Pretreatment and temporal changes of the biomarkers were investigated using proportional hazard regression and unsupervised clustering analysis.RESULTS:Samples (n = 556) from 52 patients were analysed. VEGF-receptor 2 (P = .0006) and Tie2 (P = .04) were downregulated following cediranib, while VEGF-A (P = .0025) was upregulated. High Eastern Cooperative Oncology Group performance status (P = .02, hazard ratio [HR] = 2.15, 95% confidence interval [CI] 1.13-4.09) and low pretreatment Tie2 concentrations (P = .003, HR = 0.57, 95%CI 0.39-0.83) were independent prognostic factors associated with reduced progression-free survival. Two patterns of changes in VEGF-A following cediranib were identified. Patients with elevated VEGF-A in the first 3 treatment cycles, regardless of magnitude, had reduced progression-free survival in the placebo arm but improved survival with the addition of cediranib (P = .019, HR = 0.13, 95% CI 0.02-0.71).CONCLUSION:Patterns of early elevation in plasma VEGF-A should be studied further as a potential biomarker to predict treatment benefit from cediranib.

Most radiogenomics studies investigate how genetic variation can help to explain the differences in early and late radiotherapy toxicity between individuals. The field of radiogenomics in photon beam therapy has grown rapidly in recent years, carving out a unique translational discipline, which has progressed from candidate gene studies to larger scale genome-wide association studies, meta-analyses and now prospective validation studies. Genotyping is increasingly sophisticated and affordable, and whole-genome sequencing may soon become readily available as a diagnostic tool in the clinic. The ultimate aim of radiogenomics research is to tailor treatment to the individual with a test based on a combination of treatment, clinical and genetic factors. This personalisation would allow the greatest tumour control while minimising acute and long-term toxicity. Here we discuss the evolution of the field of radiogenomics with reference to the most recent developments and challenges.

PURPOSE/OBJECTIVE(S): Trismus is caused by injury to the masticatory muscles resulting from cancer or its treatment. Contouring these muscles to reduce dose and radiation related trismus can be problematic due to interobserver variability. This study aimed to evaluate the reduction in interobserver variability achievable with a new contouring atlas.MATERIALS/METHODS: The atlas included: medial and lateral pterygoids (MP, LP), masseter (M) and temporalis (T) muscles, and the temporo-mandibular joint (TMJ). Seven clinicians delineated five paired structures on CT scans from 5 patients without the atlas. After ≥5 weeks, contouring was repeated using the atlas. Using contours generated by the clinicians on the same 5 CT scans as reference, dice similarity coefficient (DSC), mean distance-to-agreement (DTA) and centre of mass (COM) difference were compared with and without the atlas. Comparison was also performed split by training grade. Mean and standard deviation (SD) values were measured.RESULTS: The atlas reduced interobserver variability for all structures. Mean DTA significantly improved for MP (p = 0.01), M (p CONCLUSION: A new atlas for contouring masticatory muscles during radiotherapy planning for head and neck cancer reduces interobserver variability and could be used as an educational tool.

Background: Whether associations between circulating metabolites and prostate cancer are causal is unknown. We report on the largest study of metabolites and prostate cancer (2,291 cases and 2,661 controls) and appraise causality for a subset of the prostate cancer–metabolite associations using two-sample Mendelian randomization (MR). Methods: The case–control portion of the study was conducted in nine UK centers with men ages 50–69 years who underwent prostate-specific antigen screening for prostate cancer within the Prostate Testing for Cancer and Treatment (ProtecT) trial. Two data sources were used to appraise causality: a genome-wide association study (GWAS) of metabolites in 24,925 participants and a GWAS of prostate cancer in 44,825 cases and 27,904 controls within the Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium. Results: Thirty-five metabolites were strongly associated with prostate cancer (P < 0.0014, multiple-testing threshold). These fell into four classes: (i) lipids and lipoprotein subclass characteristics (total cholesterol and ratios, cholesterol esters and ratios, free cholesterol and ratios, phospholipids and ratios, and triglyceride ratios); (ii) fatty acids and ratios; (iii) amino acids; (iv) and fluid balance. Fourteen top metabolites were proxied by genetic variables, but MR indicated these were not causal. Conclusions: We identified 35 circulating metabolites associated with prostate cancer presence, but found no evidence of causality for those 14 testable with MR. Thus, the 14 MR-tested metabolites are unlikely to be mechanistically important in prostate cancer risk. Impact: The metabolome provides a promising set of biomarkers that may aid prostate cancer classification.

The original version of this Article contained an error in the spelling of the author Manuela Gago-Dominguez, which was incorrectly given as Manuela G. Dominguez. This has now been corrected in both the PDF and HTML versions of the Article.

The annual global incidence of cervical cancer is approximately 604 000 cases/342 000 deaths, making it the fourth most common cancer in women. Cervical cancer is a major healthcare problem in low and middle income countries where 85% of new cases and deaths occur. Secondary prevention measures have reduced incidence and mortality in developed countries over the past 30 years, but cervical cancer remains a major cause of cancer deaths in women. For women who present with Fédération Internationale de Gynécologie et d’Obstétrique (FIGO 2018) stages IB3 or upwards, chemoradiation is the established treatment. Despite high rates of local control, overall survival is less than 50%, largely due to distant relapse. Reducing the health burden of cervical cancer requires greater individualization of treatment, identifying those at risk of relapse and progression for modified or intensified treatment. Hypoxia is a well known feature of solid tumors and an established therapeutic target. Low tumorous oxygenation increases the risk of local invasion, metastasis and treatment failure. While meta-analyses show benefit, many individual trials targeting hypoxia failed in part due to not selecting patients most likely to benefit. This review summarizes the available hypoxia-targeted strategies and identifies further research and new treatment paradigms needed to improve patient outcomes. The applications and limitations of hypoxia biomarkers for treatment selection and response monitoring are discussed. Finally, areas of greatest unmet clinical need are identified to measure and target hypoxia and therefore improve cervical cancer outcomes.

PURPOSE/OBJECTIVE(S) Genome-wide association studies (GWAS) identified single nucleotide polymorphisms (SNPs) near AGT associated with late hematuria following radiotherapy (RT) for prostate cancer. AGT encodes angiotensinogen and, in pre-clinical models, angiotensin pathway inhibition protects against late radiation toxicity in some tissues. Effects on the bladder have not been investigated. We tested the hypothesis that angiotensin converting enzyme inhibitors (ACEi) reduce risk of late hematuria following RT. We additionally investigated the effect of genetically-defined hypertension on hematuria. MATERIALS/METHODS Prostate cancer patients (N = 268) undergoing curative RT (alone or with surgery and/or androgen deprivation therapy) were enrolled from each of two cancer centers pre-RT and followed prospectively for development of hematuria for up to four years using patient reported outcome questionnaires. Hematuria was defined as gross bleeding or medication use for hematuria. Demographic, treatment, and medication data were abstracted from medical records. The cumulative probability of hematuria was estimated by Kaplan-Meier methods, and the log rank test assessed differences by ACEi/ARB, use stratified by enrollment center. Multivariable analyses used Cox models of time to onset of hematuria stratified by enrollment center. A polygenic risk score (PRS) comprising 882 SNPs previously associated with blood pressure was tested for association with time to onset of hematuria in our Radiogenomics Consortium GWAS cohorts (N = 3,608). RESULTS The cumulative probability of hematuria among all participants was 17.1% (N = 19) at four years (median follow-up of two years). Hematuria was seen in 1.4% of those taking vs 22.9% in those not taking ACEi (P = 0.01) at the start of RT, resulting in a hazard ratio (HR) of 0.11 (95% CI 0.01 to 0.83). The protective effect of ACEi on hematuria remained when adjusted for factors associated with ACEi use, including body mass index, hypertension, or diabetes. The protective effect also remained after stratifying by risk factors for hematuria, including prior transurethral prostate resection, smoking status, or prior prostatectomy. A blood pressure PRS was strongly associated with hypertension (odds ratio per standard deviation 1.40, 95% CI 1.30 to 1.50, P < 0.001) but not with hematuria (HR per standard deviation 0.97, 95% CI 0.87 to 1.07, P = 0.52). CONCLUSION Our study is the first to show a radioprotective effect of ACEi on bladder toxicity. This effect appears distinct from presence of hypertension, which is not itself a risk factor. These results point to a promising radioprotector readily available for testing in a randomized clinical trial. Mechanistic studies are needed to understand how targeting the angiotensin pathway protects normal tissues to reduce RT toxicity.

Genetic models for cancer have been evaluated using almost exclusively European data, which could exacerbate health disparities. A polygenic hazard score (PHS1) is associated with age at prostate cancer diagnosis and improves screening accuracy in Europeans. Here, we evaluate performance of PHS2 (PHS1, adapted for OncoArray) in a multi-ethnic dataset of 80,491 men (49,916 cases, 30,575 controls). PHS2 is associated with age at diagnosis of any and aggressive (Gleason score ≥ 7, stage T3-T4, PSA ≥ 10 ng/mL, or nodal/distant metastasis) cancer and prostate-cancer-specific death. Associations with cancer are significant within European (n = 71,856), Asian (n = 2,382), and African (n = 6,253) genetic ancestries (p, A polygenic hazard score (PHS1) improves prostate cancer screening accuracy in European patients. Here, the authors test the performance of a version compatible with OncoArray genotypes (PHS2) in a multi-ethnic dataset and find that it risk-stratifies men for any, aggressive, and fatal prostate cancer.

Radiotherapy plays an essential role in the curative treatment of muscle-invasive bladder cancer (MIBC). Hypoxia affects the response to MIBC radiotherapy, limiting radiocurability. Likewise, hypoxia influences MIBC genetic instability and malignant progression being associated with metastatic disease and a worse prognosis. Hypoxia identification in MIBC enables treatment stratification and the promise of improved survival. The most promising methods are histopathological markers such as necrosis; biomarkers of protein expression such as HIF-1α, GLUT-1 and CAIX; microRNAs; and novel mRNA signatures. Although hypoxia modification can take different forms, the gold standard remains carbogen and nicotinamide, which improve local control rates in bladder preservation and absolute overall survival with no significant increase in late toxicity. This is an exciting time for evolving therapies such as bioreductive agents, novel oxygen delivery techniques, immunotherapy and poly (ADP-ribose) polymerase 1 (PARP) inhibitors, all in development and representing upcoming trends in MIBC hypoxia modification. Whatever the future holds for hypoxia-modified radiotherapy, there is no doubt of its importance in MIBC. mRNA signatures provide an ideal platform for the selection of those with hypoxic tumours but are yet to qualified and integrated into the clinic. Future interventional trials will require biomarker stratification to ensure optimal treatment response to improve outcomes for patients with MIBC.

In the version of this article originally published, the names of the equally contributing authors and jointly supervising authors were switched. The correct affiliations are: “These authors contributed equally: David V. Conti, Burcu F. Darst. These authors jointly supervised this work: David V. Conti, Rosalind A. Eeles, Zsofia Kote-Jarai, Christopher A. Haiman.” The error has been corrected in the HTML and PDF versions of the article.

International audience; Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84-5.29) for men of European ancestry to 3.74 (95% CI, 3.36-4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14-2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71-0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction.