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Purpose: Nucleoside analogues form the backbone of many therapeutic regimens in oncology and require the presence of intracellular enzymes for their activation. A ProTide is comprised of a nucleoside fused to a protective phosphoramidate cap. ProTides are easily incorporated into cells whereupon the cap is cleaved and a preactivated nucleoside released. 3′-Deoxyadenosine (3′-dA) is a naturally occurring adenosine analogue with established anticancer activity in vitro but limited bioavailability due to its rapid in vivo deamination by the circulating enzyme adenosine deaminase, poor uptake into cells, and reliance on adenosine kinase for its activation. In order to overcome these limitations, 3′-dA was chemically modified to create the novel ProTide NUC-7738. Experimental Design: We describe the synthesis of NUC-7738. We determine the IC50 of NUC-7738 using pharmacokinetics (PK) and conduct genome-wide analyses to identify its mechanism of action using different cancer model systems. We validate these findings in patients with cancer. Results: We show that NUC-7738 overcomes the cancer resistance mechanisms that limit the activity of 3′-dA and that its activation is dependent on ProTide cleavage by the enzyme histidine triad nucleotide-binding protein 1. PK and tumor samples obtained from the ongoing first-in-human phase I clinical trial of NUC-7738 further validate our in vitro findings and show NUC-7738 is an effective proapoptotic agent in cancer cells with effects on the NF-κB pathway. Conclusions: Our study provides proof that NUC-7738 overcomes cellular resistance mechanisms and supports its further clinical evaluation as a novel cancer treatment within the growing pantheon of anticancer ProTides.

Introduction: Cordycepin or 3′-deoxyadenosine is an extract from the fungus Cordyceps sinensis, a popular traditional medicine in the Far East. Although its active metabolite 3′-deoxydenosine 5′-triphosphate (3’dATP) has potent in vitro anti-cancer activity, it has shown limited efficacy in clinical trials. This is due to its poor bioavailability and rapid degradation by the ubiquitous catabolic enzyme adenosine deaminase (ADA). Here, using genetic and cellular cancer model systems, we investigated the biological impact of cordycepin in cancer cells. Experimental Procedures: Cell proliferation was measured in the presence of cordycepin and antagonists to determine its IC50. A haploid leukaemia-derived cell line, Hap1, was mutagenized using retroviral gene trapping and the mutant pool was selected for clones resistant to cordycepin which were sequenced by NGS. CRISP/Cas9 knock out combined with pharmacokinetic determination of the IC50 enabled us to validate the top hits. RNA deep sequencing-based transcriptomic analysis and gene set enrichment were used to identify the signalling pathways most affected by cordycepin and to define possible mechanisms of action. Results: We found 106 significant gene hits using the haploid genetic screen. The intracellular antiproliferative activity of cordycepin depended on both its uptake by human equilibrative nucleoside transporter 1 (hENT1) and its rate-limiting phosphorylation by the intracellular enzyme adenosine kinase (ADK). CRISPR/Cas9 knock out or selective inhibition of hENT1 or ADK led to a greater than 10-fold reduction in the activity of cordycepin. When extracellular degradation by ADA was inhibited, cordycepin showed up to 78x greater anti-proliferative potency in cancer cells. Furthermore, after 6h of treatment with cordycepin, cells showed significant differential expression of more 1000 genes. Integrating both datasets, we demonstrated by gene set enrichment analysis that, once successfully activated within the cell, 3’dATP induces cell death by a dual mechanism; by both activating the intrinsic apoptosis pathway and suppressing the TNFa-NFkB pro-survival signalling axis. Conclusions: We demonstrate here that, in the absence of ADA and in the presence of abundant hENT1 and ADK, the active metabolite of cordycepin, 3’ dATP, works as a dual driver of apoptosis in cancer cells. This suggests that a strategy of bypassing ADA and hENT1 while administering cordycepin or a chemically-modified version of cordycepin (that overcomes these resistance mechanisms) is essential to elicit any clinical anti-cancer effect. We are currently conducting a phase I trial of NUC-7738, a ProTide form of cordycepin designed to overcome these resistance mechanisms. Agents like NUC-7738 can enhance the stability and bioavailability of cordycepin, enabling it to effectively deliver its dualistic anticancer effect in patients. Citation Format: Hagen Schwenzer, Erica De Zan, Ruud van Stiphout, Cornelia H de Moor, Sebastian Nijman, Gareth Bond, Sarah P Blagden. Preclinical study of the fungal derivative cordycepin as an anti-cancer agent [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr A062. doi:10.1158/1535-7163.TARG-19-A062

The impact of new treatments for rectal cancer has substantially improved patients clinical outcomes in the last years, but still worldwide colorectal cancer remains the third most common. In 2000, colorectal cancer was responsible for 7.9% of the world’s cancer deaths, with 492,000 deaths; it accounted for 9.4% of the world’s new cancers registered, with 945,000 cases diagnosed [1, 2].

Purpose: Novel molecular stratification of the breast cancer population based on genomic aberrations has been recently reported.[1] The main focus has been on deriving prognostic classifiers by integrating gene expression and copy number aberrations (CNAs) data, but the role of other events, such as loss-of-heterozygosity (LOH), has not been extensively characterized in this integrated landscape. Here we consider the respective role of CNAs and LOH in breast cancer, in the context of an integrated genomic analysis, and their impact on clinical outcome. Methods: Currently, retrospective data with gene expression, CNA and LOH were used from 219 patients with early primary breast cancer who were resected in the period 1989-2003.[2] CNA and LOH calls were made using OncoSNP v1.4, a Bayesian model driven genomic analysis tool to call genomic aberrations and characterize LOH status, and statistical analyses were performed in R v3.0.1. The evaluated clinical outcome was distant-recurrence free survival (DRFS). Two gene sets were tested: A) general cancer drivers from the COSMIC cancer gene census and B) all genes across the genome. Significantly amplified (increased DNA copy number with respect to normal tissue), copy neutral (no change in copy number) and deleted cancer drivers were identified by deriving null distributions from permutation analysis. Survival analysis was done using both LogRank and Cox Proportional Hazards methods. The effect of LOH on the gene expression was tested using Mann-Whitney. Benjamini and Hochberg method was used to adjust for multiple testing. Results: A higher than expected LOH occurrence was detected in 168 out of 523 COSMIC defined cancer drivers. Of these, 10 were significantly amplified, 94 were copy neutral and 64 deleted. Within these three copy number groups, patients with LOH vs no LOH were compared for DRFS. LOH was a prognostic event in 14 out of 94 copy neutral group of genes, while 4 and 2 genes were found in the amplified and deleted genes respectively. Furthermore, tumors showing copy neutral LOH events had worse outcome than tumors where these genes are amplified (no LOH) or deleted (LOH). A whole genome analysis showed that a higher global LOH frequency, across the entire genome, is associated with poor prognosis as measured by a decreased DRFS, irrespective of the global level of CNAs in the tumour. Furthermore, 386 of the 5184 genes that showed a higher than expected LOH occurrence, were significantly associated with DRFS with respect to LOH status, and 51 of these were so independently of CNAs and global LOH. Conclusions: LOH in copy neutral patients has an effect on clinical outcome in a subset of cancer drivers and overall genes, irrespective of the global genomic alteration status. This finding supports an approach of measuring and integrating multiple genomic data information, which expectantly improves the accuracy of identifying subgroups of patients, resulting in improved treatment selection. Ongoing work, which will be presented at the meeting, is focused on characterizing these events, and confirming these analyses in the large TCGA dataset[3] with multiple cancer types. References: [1] Curtis C, Shah SP, Chin SF, Turashvili G, Rueda OM, Dunning MJ, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature. 2012; 486(7403): 346-52. [2] Buffa FM, Camps C, Winchester L, Snell CE, Gee HE, Sheldon H, et al. microRNA-associated progression pathways and potential therapeutic targets identified by integrated mRNA and microRNA expression profiling in breast cancer. Cancer Res. 2011; 71(17): 5635-45. [3] Network TCGA. Comprehensive molecular portraits of human breast tumours. Nature. 2012; 490(7418): 61-70. Citation Format: Ruud GPM van Stiphout, Antoine deWeck, Laura Winchester, Syed A. Haider, Jiannis Ragoussis, Adrian L. Harris, Chris Holmes, Francesca M. Buffa. Distinct roles of copy number and loss-of-heterozygosity in predicting prognosis for breast cancer patients. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-56.

Purpose Personalized treatments based on predictions for patient outcome require early characterization of a rectal cancer patient’s sensitivity to treatment. This study has two aims: (1) identify the main patterns of recurrence and response to the treatments (2) evaluate pathologic complete response (pCR) and two-year disease-free survival (2yDFS) for overall survival (OS) and their potential to be relevant intermediate endpoints to predict. Methods Pooled and treatment subgroup analyses were performed on five large European rectal cancer trials (2795 patients), who all received long-course radiotherapy with or without concomitant and/or adjuvant chemotherapy. The ratio of distant metastasis (DM) and local recurrence (LR) rates was used to identify patient characteristics that increase the risk of recurrences. Findings The DM/LR ratio decreased to a plateau in the first 2 years, revealing it to be a critical follow-up period. According to the patterns of recurrences, three patient groups were identified: 5–15% had pCR and were disease free after 2 years (excellent prognosis), 65–75% had no pCR but were disease free (good prognosis) and 15–30% had neither pCR nor 2yDFS (poor prognosis). Interpretation Compared with pCR, 2yDFS is a stronger predictor of OS. To adapt treatment most efficiently, accurate prediction models should be developed for pCR to select patients for organ preservation and for 2yDFS to select patients for more intensified treatment strategies.

Purpose To develop and externally validate a predictive model for pathologic complete response (pCR) for locally advanced rectal cancer (LARC) based on clinical features and early sequential 18 F-FDG PETCT imaging. Materials and methods Prospective data (i.a. THUNDER trial) were used to train ( N =112, MAASTRO Clinic) and validate ( N =78, Universita Cattolica del S. Cuore) the model for pCR (ypT0N0). All patients received long-course chemoradiotherapy (CRT) and surgery. Clinical parameters were age, gender, clinical tumour (cT) stage and clinical nodal (cN) stage. PET parameters were SUV max , SUV mean , metabolic tumour volume (MTV) and maximal tumour diameter, for which response indices between pre-treatment and intermediate scan were calculated. Using multivariate logistic regression, three probability groups for pCR were defined. Results The pCR rates were 21.4% (training) and 23.1% (validation). The selected predictive features for pCR were cT-stage, cN-stage, response index of SUV mean and maximal tumour diameter during treatment. The models' performances (AUC) were 0.78 (training) and 0.70 (validation). The high probability group for pCR resulted in 100% correct predictions for training and 67% for validation. The model is available on the website www.predictcancer.org. Conclusions The developed predictive model for pCR is accurate and externally validated. This model may assist in treatment decisions during CRT to select complete responders for a wait-and-see policy, good responders for extra RT boost and bad responders for additional chemotherapy.

PURPOSE/OBJECTIVE: Chemoradiation (CRT) has been shown to lead to downsizing of an important portion of rectal cancers. In order to tailor treatment at an earlier stage during treatment, predictive models are being developed. Adding blood biomarkers may be attractive for prediction, as they can be collected very easily and determined with excellent reproducibility in clinical practice. The hypothesis of this study was that blood biomarkers related to tumor load, hypoxia and inflammation can help to predict response to CRT in rectal cancer. MATERIAL/METHODS: 295 patients with locally advanced rectal cancer who were planned to undergo CRT were prospectively entered into a biobank protocol (NCT01067872). Blood samples were drawn before start of CRT. Nine biomarkers were selected, based on a previously defined hypothesis, and measured in a standardized way by a certified lab: CEA, CA19-9, LDH, CRP, IL-6, IL-8, CA IX, osteopontin and 25-OH-vitamin D. Outcome was analyzed in two ways: pCR vs. non-pCR and responders (defined as ypT0-2N0) vs. non-responders (all other ypTN stages). RESULTS: 276 patients could be analyzed. 20.7% developed a pCR and 47.1% were classified as responders. In univariate analysis CEA (p=0.001) and osteopontin (p=0.012) were significant predictors for pCR. Taking response as outcome CEA (p

International audience; Background. The objectives of this study were to investigate the predictive value of sequential (18)F-FDG PET scans for pathological tumor response grade (TRG) after preoperative chemoradiotherapy (PCRT) in locally advanced rectal cancer (LARC) and the impact of partial volume effects correction (PVC). Methods. Twenty-eight LARC patients were included. Responders and non-responders status were determined in histopathology. PET indices [SUV max and mean, volume and total lesion glycolysis (TLG)] at baseline and their evolution after one and two weeks of PCRT were extracted by delineation of the PET images, with or without PVC. Their predictive value was investigated using Mann-Whitney-U tests and ROC analysis. Results. Within baseline parameters, only SUV(mean) was correlated with response. No evolution after one week was predictive of the response, whereas after two weeks all the parameters except volume were, the best prediction being obtained with TLG (AUC 0.79, sensitivity 63%, specificity 92%). PVC had no significant impact on these results. Conclusion. Several PET indices at baseline and their evolution after two weeks of PCRT are good predictors of response in LARC, with or without PVC, whereas results after one week are suboptimal. Best predictor was TLG reduction after two weeks, although baseline SUV(mean) had smaller but similar predictive power.

With the emergence of individualized medicine and the increasing amount and complexity of available medical data, a growing need exists for the development of clinical decision-support systems based on prediction models of treatment outcome. In radiation oncology, these models combine both predictive and prognostic data factors from clinical, imaging, molecular and other sources to achieve the highest accuracy to predict tumour response and follow-up event rates. In this Review, we provide an overview of the factors that are correlated with outcome-including survival, recurrence patterns and toxicity-in radiation oncology and discuss the methodology behind the development of prediction models, which is a multistage process. Even after initial development and clinical introduction, a truly useful predictive model will be continuously re-evaluated on different patient datasets from different regions to ensure its population-specific strength. In the future, validated decision-support systems will be fully integrated in the clinic, with data and knowledge being shared in a standardized, instant and global manner. Lambin, P. et al. Nat. Rev. Clin. Oncol. 10, 27-40 (2013); published online 20 November 2012; doi:10.1038/nrclinonc.2012.196

Purpose An overview of the Rapid Learning methodology, its results, and the potential impact on radiotherapy. Material and results Rapid Learning methodology is divided into four phases. In the data phase, diverse data are collected about past patients, treatments used, and outcomes. Innovative information technologies that support semantic interoperability enable distributed learning and data sharing without additional burden on health care professionals and without the need for data to leave the hospital. In the knowledge phase, prediction models are developed for new data and treatment outcomes by applying machine learning methods to data. In the application phase, this knowledge is applied in clinical practice via novel decision support systems or via extensions of existing models such as Tumour Control Probability models. In the evaluation phase, the predictability of treatment outcomes allows the new knowledge to be evaluated by comparing predicted and actual outcomes. Conclusion Personalised or tailored cancer therapy ensures not only that patients receive an optimal treatment, but also that the right resources are being used for the right patients. Rapid Learning approaches combined with evidence based medicine are expected to improve the predictability of outcome and radiotherapy is the ideal field to study the value of Rapid Learning. The next step will be to include patient preferences in the decision making. ?? 2013 The Authors. Published by Elsevier Ireland Ltd.

Gemcitabine constitutes one of the backbones for chemotherapy treatment in pancreatic ductal adenocarcinoma (PDAC), but patients often respond poorly to this agent. Molecular markers downstream of gemcitabine treatment in preclinical models may provide an insight into resistance mechanisms. Using cytokine arrays, we identified potential secretory biomarkers of gemcitabine resistance (response) in the transgenic KRasG12D; Trp53R172H; Pdx-1 Cre (KPC) mouse model of PDAC. We verified the oncogenic role of the cytokine tissue inhibitor of matrix metalloproteinases 1 (TIMP1) in primary pancreatic tumors and metastases using both in vitro techniques and animal models. We identified potential pathways affected downstream of TIMP1 using the Illumina Human H12 array. Our findings were validated in both primary and metastatic models of pancreatic cancer. Gemcitabine increased inflammatory cytokines including TIMP1 in the KPC mouse model. TIMP1 was upregulated in patients with pancreatic intraepithelial neoplasias grade 3 and PDAC lesions relative to matched normal pancreatic tissue. In addition, TIMP1 played a role in tumor clonogenic survival and vascular density, while TIMP1 inhibition resensitized tumors to gemcitabine and radiotherapy. We observed a linear relationship between TIMP-1 expression, liver metastatic burden, and infiltration by CD11b+Gr1+ myeloid cells and CD4+CD25+FOXP3+ Tregs, whereas the presence of tumor cells was required for immune cell infiltration. Overall, our results identify TIMP1 upregulation as a resistance mechanism to gemcitabine and provide a rationale for combining chemo/radiotherapy with TIMP1 inhibitors in PDAC. Cancer Res; 77(21); 5952–62. ©2017 AACR.

Solid cancers are spatially and temporally heterogeneous. This limits the use of invasive biopsy based molecular assays but gives huge potential for medical imaging, which has the ability to capture intra-tumoural heterogeneity in a non-invasive way. During the past decades, medical imaging innovations with new hardware, new imaging agents and standardised protocols, allows the field to move towards quantitative imaging. Therefore, also the development of automated and reproducible analysis methodologies to extract more information from image-based features is a requirement. Radiomics - the high-throughput extraction of large amounts of image features from radiographic images - addresses this problem and is one of the approaches that hold great promises but need further validation in multi-centric settings and in the laboratory.

Background and purpose Previously, we showed a good correlation between pathology and an automatically generated PET-contour in rectal cancer. This study analyzed the effect of the use of PET–CT scan on the interobserver variation in GTV definition in rectal cancer and the influence of PET–CT on treatment volumes. Materials and methods Forty two patients diagnosed with rectal cancer underwent an FDG–PET–CT for radiotherapy planning. An automatic contour was created on PET-scan using the source-to-background ratio. The GTV was delineated by 5 observers in 3 rounds: using CT and MRI, using CT, MRI and PET and using CT, MRI and PET auto-contour. GTV volumes were compared and concordance indices (CI) were calculated. Since the GTV is only a small portion of the treatment volume in rectal cancer, a separate analysis was performed to evaluate the influence of PET on the definition of the CTV used in daily clinical practice and the caudal extension of the treatment volumes. Results GTV volumes based on PET were significantly smaller. CIs increased significantly using PET and the best interobserver agreement was observed using PET auto-contours. Furthermore, we found that in up to 29% of patients the CTV based on PET extended outside the CTV used in clinical practice. The caudal border of the treatment volume can be tailored using PET-scan in low seated tumors. Influence of PET on the position of the caudal border was most pronounced in high seated tumors. Conclusion PET–CT increases the interobserver agreement in the GTV definition in rectal cancer, helps to avoid geographical misses and allows tailoring the caudal border of the treatment volume.

Purpose To develop a positron emission tomography (PET)-based response prediction model to differentiate pathological responders from nonresponders. The predictive strength of the model was validated in a second patient group, treated and imaged identical to the patients on which the predictive model was based. Methods and Materials Fifty-one rectal cancer patients were prospectively included in this study. All patients underwent fluorodeoxyglucose (FDG) PET-computed tomography (CT) imaging both before the start of chemoradiotherapy (CRT) and after 2 weeks of treatment. Preoperative treatment with CRT was followed by a total mesorectal excision. From the resected specimen, the tumor regression grade (TRG) was scored according to the Mandard criteria. From one patient group (n = 30), the metabolic treatment response was correlated with the pathological treatment response, resulting in a receiver operating characteristic (ROC) curve based cutoff value for the reduction of maximum standardized uptake value (SUVmax) within the tumor to differentiate pathological responders (TRG 1–2) from nonresponders (TRG 3–5). The applicability of the selected cutoff value for new patients was validated in a second patient group (n = 21). Results When correlating the metabolic and pathological treatment response for the first patient group using ROC curve analysis (area under the curve = 0.98), a cutoff value of 48% SUVmax reduction was selected to differentiate pathological responders from nonresponders (specificity of 100%, sensitivity of 64%). Applying this cutoff value to the second patient group resulted in a specificity and sensitivity of, respectively, 93% and 83%, with only one of the pathological nonresponders being false positively predicted as pathological responding. Conclusions For rectal cancer, an accurate PET-based prediction of the pathological treatment response is feasible already after 2 weeks of CRT. The presented predictive model could be used to select patients to be considered for less invasive surgical interventions or even a “wait and see” policy. Also, based on the predicted response, early modifications of the treatment protocol are possible, which might result in an improved clinical outcome.

Purpose To determine the optimal time point for repeated 18 F-fluorodeoxyglucose-positron emission tomography (PET)-CT imaging during preoperative radiochemotherapy (RCT) and the best predictive factor for the prediction of pathological treatment response in patients with locally advanced rectal cancer. Methods and Materials A total of 30 patients referred for preoperative RCT treatment were included in this prospective study. All patients underwent sequential PET-CT imaging at four time points: prior to therapy, at day 8 and 15 during RCT, and shortly before surgery. Tumor metabolic treatment responses were correlated with the pathological responses by evaluation of the tumor regression grade (TRG) and the pathological TN (ypT) stage of the resected specimen. Results Based on their TRG evaluations, 13 patients were classified as pathological responders, whereas 17 patients were classified as pathological nonresponders. The response index (RI) for the maximum standardized uptake value (SUV max ) on day 15 of RCT was found to be the best predictive factor for the pathological response (area under the curve [AUC] = 0.87) compared to the RI on day 8 (AUC = 0.78) or the RI of presurgical PET imaging (AUC = 0.66). A cutoff value of 43% for the reduction of SUV max resulted in a sensitivity of 77% and a specificity of 93%. Conclusions The SUV max -based RI calculated after the first 2 weeks of RCT provided the best predictor of pathological treatment response, reaching AUCs of 0.87 and 0.84 for the TRG and the ypT stage, respectively. However, a few patients presented with peritumoral inflammatory reactions, which led to mispredictions. Exclusion of these patients further enhanced the predictive accuracy of PET imaging to AUCs of 0.97 and 0.89 for TRG and ypT, respectively.

Purpose: To quantify the influence of fluctuating blood glucose level (BGLs) and the timing of PET acquisition on PET-based predictions of the pathological treatment response in rectal cancer. Material and methods: Thirty patients, diagnosed with locally advanced-rectal-cancer (LARC), were included in this prospective study. Sequential FDG-PET-CT investigations were performed at four time points during and after pre-operative radiochemotherapy (RCT). All PET-data were normalized for the BGL measured shortly before FDG injection. The metabolic treatment response of the tumor was correlated with the pathological treatment response. Results: During RCT, strong intra-patient BGL-fluctuations were observed, ranging from -38.7 to 95.6%. BGL-normalization of the SUVs revealed differences ranging from -54.7 to 34.7% (p

Purpose: Conventional anticancer treatments are often impaired by the presence of hypoxia. TH-302 selectively targets hypoxic tumor regions, where it is converted into a cytotoxic agent. This study assessed the efficacy of the combination treatment of TH-302 and radiotherapy in two preclinical tumor models. The effect of oxygen modification on the combination treatment was evaluated and the effect of TH-302 on the hypoxic fraction (HF) was monitored using [18F]HX4-PET imaging and pimonidazole IHC stainings. Experimental Design: Rhabdomyosarcoma R1 and H460 NSCLC tumor-bearing animals were treated with TH-302 and radiotherapy (8 Gy, single dose). The tumor oxygenation status was altered by exposing animals to carbogen (95% oxygen) and nicotinamide, 21% or 7% oxygen breathing during the course of the treatment. Tumor growth and treatment toxicity were monitored until the tumor reached four times its start volume (T4×SV). Results: Both tumor models showed a growth delay after TH-302 treatment, which further increased when combined with radiotherapy (enhancement ratio rhabdomyosarcoma 1.23; H460 1.49). TH-302 decreases the HF in both models, consistent with its hypoxia-targeting mechanism of action. Treatment efficacy was dependent on tumor oxygenation; increasing the tumor oxygen status abolished the effect of TH-302, whereas enhancing the HF enlarged TH-302′s therapeutic effect. An association was observed in rhabdomyosarcoma tumors between the pretreatment HF as measured by [18F]HX4-PET imaging and the T4×SV. Conclusions: The combination of TH-302 and radiotherapy is promising and warrants clinical testing, preferably guided by the companion biomarker [18F]HX4 hypoxia PET imaging for patient selection. Clin Cancer Res; 21(13); 2984–92. ©2015 AACR.

Background Postoperative radiation therapy for stage I endometrial cancer improves locoregional control but is without survival benefit. To facilitate treatment decision support for individual patients, accurate statistical models to predict locoregional relapse (LRR), distant relapse (DR), overall survival (OS), and disease-free survival (DFS) are required. Methods and Materials Clinical trial data from the randomized Post Operative Radiation Therapy for Endometrial Cancer (PORTEC-1; N=714 patients) and PORTEC-2 (N=427 patients) trials and registered group (grade 3 and deep invasion, n=99) were pooled for analysis (N=1240). For most patients (86%) pathology review data were available; otherwise original pathology data were used. Trial variables which were clinically relevant and eligible according to data constraints were age, stage, given treatment (pelvic external beam radiation therapy (EBRT), vaginal brachytherapy (VBT), or no adjuvant treatment, FIGO histological grade, depth of invasion, and lymph-vascular invasion (LVSI). Multivariate analyses were based on Cox proportional hazards regression model. Predictors were selected based on a backward elimination scheme. Model results were expressed by the c-index (0.5-1.0; random to perfect prediction). Two validation sets (n=244 and 291 patients) were used. Results Accuracy of the developed models was good, with training accuracies between 0.71 and 0.78. The nomograms validated well for DR (0.73), DFS (0.69), and OS (0.70), but validation was only fair for LRR (0.59). Ranking of variables as to their predictive power showed that age, tumor grade, and LVSI were highly predictive for all outcomes, and given treatment for LRR and DFS. The nomograms were able to significantly distinguish low- from high-probability patients for these outcomes. Conclusions The nomograms are internally validated and able to accurately predict long-term outcome for endometrial cancer patients with observation, pelvic EBRT, or VBT after surgery. These models facilitate decision support in daily clinical practice and can be used for patient counseling and shared decision making, selecting patients who benefit most from adjuvant treatment, and generating new hypotheses.

Purpose: To determine the impact of chemoradiation therapy (CRT) on overall survival (OS) after resection of pancreatic adenocarcinoma. Methods and Materials: A multicenter retrospective review of 955 consecutive patients who underwent complete resection with macroscopically negative margins (R0-1) for invasive carcinoma (T1-4; N0-1; M0) of the pancreas was performed. Exclusion criteria included metastatic or unresectable disease at surgery, macroscopic residual disease (R2), treatment with intraoperative radiation therapy (IORT), and a histological diagnosis of no ductal carcinoma, or postoperative death (within 60 days of surgery). In all, 623 patients received postoperative radiation therapy (RT), 575 patients received concurrent chemotherapy (CT), and 462 patients received adjuvant CT. Results: Median follow-up was 21.0 months. Median OS after adjuvant CRT was 39.9 versus 24.8 months after no adjuvant CRT (P20 mm (HR = 1.14; CI = 1.05-1.23; P=.002). Multivariate analysis also showed a better prognosis in patients treated in centers with >10 pancreatic resections per year (HR = 0.87; CI = 0.78-0.97; P=.014). Conclusion: This study represents the largest comparative study on adjuvant therapy in patients after resection of carcinoma of the pancreas. Overall survival was better in patients who received adjuvant CRT.

Extensive, multifactorial data sharing is a crucial prerequisite for current and future (radiotherapy) research. However, the cost, time and effort to achieve this are often a roadblock. We present an open-source based data-sharing infrastructure between two radiotherapy departments, allowing seamless exchange of de-identified, automatically translated clinical and biomedical treatment data.

Purpose The purpose of this study was to develop accurate models and nomograms to predict local recurrence, distant metastases, and survival for patients with locally advanced rectal cancer treated with long-course chemoradiotherapy (CRT) followed by surgery and to allow for a selection of patients who may benefit most from postoperative adjuvant chemotherapy and close follow-up. Patients and Methods All data (N = 2,795) from five major European clinical trials for rectal cancer were pooled and used to perform an extensive survival analysis and to develop multivariate nomograms based on Cox regression. Data from one trial was used as an external validation set. The variables used in the analysis were sex, age, clinical tumor stage stage, tumor location, radiotherapy dose, concurrent and adjuvant chemotherapy, surgery procedure, and pTNM stage. Model performance was evaluated by the concordance index (c-index). Risk group stratification was proposed for the nomograms. Results The nomograms are able to predict events with a c-index for external validation of local recurrence (LR; 0.68), distant metastases (DM; 0.73), and overall survival (OS; 0.70). Pathologic staging is essential for accurate prediction of long-term outcome. Both preoperative CRT and adjuvant chemotherapy have an added value when predicting LR, DM, and OS rates. The stratification in risk groups allows significant distinction between Kaplan-Meier curves for outcome. Conclusion The easy-to-use nomograms can predict LR, DM, and OS over a 5-year period after surgery. They may be used as decision support tools in future trials by using the three defined risk groups to select patients for postoperative chemotherapy and close follow-up ( http://www.predictcancer.org ).

Purpose: To develop and validate an accurate predictive model and a nomogram for pathologic complete response (pCR) after chemoradiotherapy (CRT) for rectal cancer based on clinical and sequential PET-CT data. Accurate prediction could enable more individualised surgical approaches, including less extensive resection or even a wait-and-see policy. Methods and materials: Population based databases from 953 patients were collected from four different institutes and divided into three groups: clinical factors (training: 677 patients, validation: 85 patients), pre-CRT PET-CT (training: 114 patients, validation: 37 patients) and post-CRT PET-CT (training: 107 patients, validation: 55 patients). A pCR was defined as ypT0N0 reported by pathology after surgery. The data were analysed using a linear multivariate classification model (support vector machine), and the model's performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Results: The occurrence rate of pCR in the datasets was between 15% and 31%. The model based on clinical variables (AUC(train) = 0.61 +/- 0.03, AUC(validation) = 0.69 +/- 0.08) resulted in the following predictors: cT- and cN-stage and tumour length. Addition of pre-CRT PET data did not result in a significantly higher performance (AUC(train) = 0.68 +/- 0.08, AUC(validation) = 0.68 +/- 0.10) and revealed maximal radioactive isotope uptake (SUV(max)) and tumour location as extra predictors. The best model achieved was based on the addition of post-CRT PET-data (AUC(train) = 0.83 +/- 0.05, AUC(validation) = 0.86 +/- 0.05) and included the following predictors: tumour length, post-CRT SUV(max) and relative change of SUV(max). This model performed significantly better than the clinical model (p(train)

Evidence is accumulating that radiotherapy of non-small cell lung cancer patients can be optimized by escalating the tumour dose until the normal tissue tolerances are met. To further improve the therapeutic ratio between tumour control probability and the risk of normal tissue complications, we firstly need to exploit inter patient variation. This variation arises, e.g. from differences in tumour shape and size, lung function and genetic factors. Secondly improvement is achieved by taking into account intra-tumour and intra-organ heterogeneity derived from molecular and functional imaging. Additional radiation dose must be delivered to those parts of the tumour that need it the most, e.g. because of increased radio-resistance or reduced therapeutic drug uptake, and away from regions inside the lung that are most prone to complication. As the delivery of these treatments plans is very sensitive for geometrical uncertainties, probabilistic treatment planning is needed to generate robust treatment plans. The administration of these complicated dose distributions requires a quality assurance procedure that can evaluate the treatment delivery and, if necessary, adapt the treatment plan during radiotherapy.

Background and purpose: The purpose of this study was to prospectively investigate metabolic changes of rectal tumors after 1 week of treatment of either radiochemotherapy (28 x 1.8 Gy + Capecitabine) (RCT) or hypofractionated radiotherapy (5 x 5 Gy) alone (RT). Materials and methods: Fourty-six rectal cancer patients, 25 RCT- and 21 RT-patients, were included in this study. Sequential FDG-PET-CT scans were performed for each of the included patients both prior to treatment and after the first week of treatment. Consecutively, the metabolic treatment response of the tumor was evaluated. Results: For the patients referred for pre-operative RCT, significant reductions of SUV(mean) (p

Background Hypoxia is a key physiological and microenvironmental difference between tumour and normal tissues. Hypoxia induces DNA damage, genomic aberrations and reprogramming of DNA repair mechanisms. A crucial mechanism in a highly dynamic hypoxic tumour microenvironment is metabolic reprogramming of tumour cells. Genome-wide multi-modal molecular profiles have been generated, however it remains unclear whether upregulation of metabolic genes is substantially enriched with somatically acquired alterations. To answer this, we performed a pan-cancer integrative analysis of genomic and transcriptomic profiles of 10 TCGA tumour types (n = 5,500). Methods 2,750 metabolic genes were extracted from KEGG pathways, and differentially upregulated genes were identified for each tumour type. High-ranking (10%) upregulated genes were assessed for correlation between mRNA abundance and DNA copy-number changes, and subsequently tested for metabolic-enrichment using genome-wide permutation analysis. For each tumour type, highly correlated genesets were subsequently examined for their potential as drug targets and biomarkers of disease relapse. Results Integrative analysis of mRNA and copy-number data revealed clusters of tumours that exhibit similar landscape of metabolic gene profiles, and those having unique metabolic copy number aberrations. Within these profiles, we identify a core set of genes that exhibit strong correlation (Spearman's ρ>0.3; q Conclusions We isolate tumour-type specific and core pan-cancer metabolic gene signatures revealing within and cross disease heterogeneity in metabolic profiles. We highlight a core set of pan-cancer metabolic candidate drivers which are recurrently over-expressed due to genomic amplifications induced by hypoxia. Citation Format: Syed Haider, Alan McIntyre, Ruud GPM van Stiphout, Laura M. Winchester, Simon Wigfield, Adrian L. Harris, Francesca M. Buffa. Genomic alterations underlie a pan-cancer metabolic transcriptome shift. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3750. doi:10.1158/1538-7445.AM2015-3750

// Alessandro Valli 1,2,7 , Miguel Rodriguez 3,4 , Loukas Moutsianas 5 , Roman Fischer 2 , Vita Fedele 2 , Hong-Lei Huang 2 , Ruud Van Stiphout 1 , Dylan Jones 1 , Michael Mccarthy 2 , Maria Vinaxia 3,4 , Kaori Igarashi 6 , Maya Sato 6 , Tomoyoshi Soga 6 , Francesca Buffa 1 , James Mccullagh 7 , Oscar Yanes 3,4 , Adrian Harris 1,* and Benedikt Kessler 2,* 1 Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK 2 Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK 3 Centre for Omic Sciences, Rovira i Virgili University, Reus, Spain 4 Biomedical Research Centre in Diabetes and Associated Metabolic Disorders, Madrid, Spain 5 The Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, Oxford, UK 6 Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan 7 Mass Spectrometry Research Facility CRL, Department of Chemistry, University of Oxford, Oxford, UK * equal senior authors Correspondence: Alessandro Valli, email: // Keywords : cancer metabolism, fatty acids, HIF1α, HIF2α, hypoxia, Kennedy pathway, lipidomics, PAF Received : June 01, 2014 Accepted : December 10, 2014 Published : December 11, 2014 Abstract The biochemistry of cancer cells diverges significantly from normal cells as a result of a comprehensive reprogramming of metabolic pathways. A major factor influencing cancer metabolism is hypoxia, which is mediated by HIF1α and HIF2α. HIF1α represents one of the principal regulators of metabolism and energetic balance in cancer cells through its regulation of glycolysis, glycogen synthesis, Krebs cycle and the pentose phosphate shunt. However, less is known about the role of HIF1α in modulating lipid metabolism. Lipids serve cancer cells to provide molecules acting as oncogenic signals, energetic reserve, precursors for new membrane synthesis and to balance redox biological reactions. To study the role of HIF1α in these processes, we used HCT116 colorectal cancer cells expressing endogenous HIF1α and cells in which the hif1α gene was deleted to characterize HIF1α-dependent and independent effects on hypoxia regulated lipid metabolites. Untargeted metabolomics integrated with proteomics revealed that hypoxia induced many changes in lipids metabolites. Enzymatic steps in fatty acid synthesis and the Kennedy pathway were modified in a HIF1α-dependent fashion. Palmitate, stearate, PLD3 and PAFC16 were regulated in a HIF-independent manner. Our results demonstrate the impact of hypoxia on lipid metabolites, of which a distinct subset is regulated by HIF1α.