Selective Targeting of Bromodomains of the Bromodomain-PHD Fingers Family Impairs Osteoclast Differentiation.

Histone acetyltransferases of the MYST family are recruited to chromatin by BRPF scaffolding proteins. We explored functional consequences and the therapeutic potential of inhibitors targeting acetyl-lysine dependent protein interaction domains (bromodomains) present in BRPF1-3 in bone maintenance. We report three potent and selective inhibitors: one (PFI-4) with high selectivity for the BRPF1B isoform and two pan-BRPF bromodomain inhibitors (OF-1, NI-57). The developed inhibitors displaced BRPF bromodomains from chromatin and did not inhibit cell growth and proliferation. Intriguingly, the inhibitors impaired RANKL-induced differentiation of primary murine bone marrow cells and human primary monocytes into bone resorbing osteoclasts by specifically repressing transcriptional programs required for osteoclastogenesis. The data suggest a key role of BRPF in regulating gene expression during osteoclastogenesis, and the excellent druggability of these bromodomains may lead to new treatment strategies for patients suffering from bone loss or osteolytic malignant bone lesions.

Genomic alterations underlie a pan-cancer metabolic shift associated with tumour hypoxia.

BACKGROUND: Altered metabolism is a hallmark of cancer. However, the role of genomic changes in metabolic genes driving the tumour metabolic shift remains to be elucidated. Here, we have investigated the genomic and transcriptomic changes underlying this shift across ten different cancer types. RESULTS: A systematic pan-cancer analysis of 6538 tumour/normal samples covering ten major cancer types identified a core metabolic signature of 44 genes that exhibit high frequency somatic copy number gains/amplifications (>20 % cases) associated with increased mRNA expression (ρ > 0.3, q < 10(-3)). Prognostic classifiers using these genes were confirmed in independent datasets for breast and kidney cancers. Interestingly, this signature is strongly associated with hypoxia, with nine out of ten cancer types showing increased expression and five out of ten cancer types showing increased gain/amplification of these genes in hypoxic tumours (P ≤ 0.01). Further validation in breast and colorectal cancer cell lines highlighted squalene epoxidase, an oxygen-requiring enzyme in cholesterol biosynthesis, as a driver of dysregulated metabolism and a key player in maintaining cell survival under hypoxia. CONCLUSIONS: This study reveals somatic genomic alterations underlying a pan-cancer metabolic shift and suggests genomic adaptation of these genes as a survival mechanism in hypoxic tumours.

Adjuvant Chemoradiation in Pancreatic Cancer: A Pooled Analysis in Elderly (≥75 years) Patients.

AIM: To determine the impact of postoperative chemoradiation (POCR) on overall survival (OS) after resection of pancreatic adenocarcinoma (PAC) in elderly (≥75 years) patients. MATERIALS AND METHODS: A multi-center retrospective review of 1248 patients who underwent complete resection with macroscopically negative margins (R0-1) for invasive PAC was performed. Exclusion criteria included age <75 years, metastatic or unresectable disease at surgery, macroscopic residual disease (R2), treatment with intraoperative radiotherapy (IORT) and postoperative death. RESULTS: A total of 98 patients were included in the analysis (males=39.8%, females=60.2%; R1 resections=33.7%; pN1=61.2%); 63 patients received POCR and 26 patients received adjuvant chemotherapy alone. The median follow-up was 25.6 months. The mean age for the entire cohort of patients was 78.1±2.9 (SD) years. No differences were observed between patients receiving or not receiving POCR in terms of age (p=0.081), tumor diameter (p=0.412), rate of R1 resection (p=0.331) and incidence of lymph node-positive disease (p=0.078). The only factor predicting an improved OS was POCR. The median OS was 69.0 months in patients treated by POCR and 23.0 months in patients treated without POCR (p=0.008). Even by Cox multivariate analysis, the only significant predictor of OS was POCR (hazard ratio=0.449; 95% confidence interval=0.212-0.950; p=0.036). CONCLUSION: The study represents the first comparative approach on POCR in elderly patients after resection of PAC. OS was higher in patients who received POCR. Further analyses are warranted to evaluate the toxicity rate/grade and the impact of POCR on patient quality of life.

TH-302 in Combination with Radiotherapy Enhances the Therapeutic Outcome and Is Associated with Pretreatment [18F]HX4 Hypoxia PET Imaging.

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 [(18)F]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 [(18)F]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 [(18)F]HX4 hypoxia PET imaging for patient selection.

Nomograms for prediction of outcome with or without adjuvant radiation therapy for patients with endometrial cancer: a pooled analysis of PORTEC-1 and PORTEC-2 trials.

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.

Selection of appropriate end-points (pCR vs 2yDFS) for tailoring treatments with prediction models in locally advanced rectal cancer.

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.

Nomogram predicting response after chemoradiotherapy in rectal cancer using sequential PETCT imaging: a multicentric prospective study with external validation.

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, Università 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 SUVmax, SUVmean, 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 SUVmean 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.

Multi-institutional pooled analysis on adjuvant chemoradiation in pancreatic cancer.

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 (P<.001) and 27.8 months after CT alone (P<.001). Five-year OS was 41.2% versus 24.8% with and without postoperative CRT, respectively. The positive impact of CRT was confirmed by multivariate analysis (hazard ratio [HR] = 0.72; confidence interval [CI], 0.60-0.87; P=.001). Adverse prognostic factors identified by multivariate analysis included the following: R1 resection (HR = 1.17; CI = 1.07-1.28; P<.001), higher pT stage (HR = 1.23; CI = 1.11-1.37; P<.001), positive lymph nodes (HR = 1.27; CI = 1.15-1.41; P<.001), and tumor diameter >20 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.

Blood biomarkers are helpful in the prediction of response to chemoradiation in rectal cancer: a prospective, hypothesis driven study on patients with locally advanced rectal cancer.

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<0.001), IL-8 (p<0.001) and osteopontin (p=0.004) were significant predictors. In multivariate analysis CEA was the strongest predictor for pCR (OR 0.92, p=0.019) and CEA and IL-8 predicted for response (OR 0.97, p=0.029 and OR 0.94, p=0.036). The model based on biomarkers only had an AUC of 0.65 for pCR and 0.68 for response; the strongest model included clinical data, PET-data and biomarkers and had an AUC of 0.81 for pCR and 0.78 for response. CONCLUSION: CEA and IL-8 were identified as predictive biomarkers for tumor response and PCR after CRT in rectal cancer. Incorporation of these blood biomarkers leads to an additional accuracy of earlier developed prediction models using clinical variables and PET-information. The new model could help to an early adaptation of treatment in rectal cancer patients.

International data-sharing for radiotherapy research: an open-source based infrastructure for multicentric clinical data mining.

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.

'Rapid Learning health care in oncology' - an approach towards decision support systems enabling customised radiotherapy'.

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.

Predicting outcomes in radiation oncology--multifactorial decision support systems.

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.

Radiomics: extracting more information from medical images using advanced feature analysis.

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.

PET-based treatment response evaluation in rectal cancer: prediction and validation.

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 (SUV(max)) 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% SUV(max) 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.

Nomograms for predicting local recurrence, distant metastases, and overall survival for patients with locally advanced rectal cancer on the basis of European randomized clinical trials.

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).

Development and external validation of a predictive model for pathological complete response of rectal cancer patients including sequential PET-CT imaging.

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)<0.001, p(validation)=0.056). CONCLUSIONS: The model and the nomogram developed based on clinical and sequential PET-CT data can accurately predict pCR, and can be used as a decision support tool for surgery after prospective validation.

The ESTRO Breur Lecture 2009. From population to voxel-based radiotherapy: exploiting intra-tumour and intra-organ heterogeneity for advanced treatment of non-small cell lung cancer.

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.

Evaluation of early metabolic responses in rectal cancer during combined radiochemotherapy or radiotherapy alone: sequential FDG-PET-CT findings.

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<0.001) and SUV(max) (p<0.001) within the tumor were found already after the first week of treatment (8 Gy biological equivalent dose (BED). In contrast, 1 week of treatment with RT alone did not result in significant changes in the metabolic activity of the tumor (p=0.767, p=0.434), despite the higher applied RT dose of 38.7 Gy BED. CONCLUSIONS: Radiochemotherapy of rectal cancer leads to significant early changes in the metabolic activity of the tumor, which was not the case early after hypofractionated radiotherapy alone, despite the higher radiotherapy dose given. Thus, the chemotherapeutic agent Capecitabine might be responsible for the early metabolic treatment responses during radiochemotherapy in rectal cancer.

Blood glucose level normalization and accurate timing improves the accuracy of PET-based treatment response predictions in rectal cancer.

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 < 0.001). Also, a SUV(max) time-dependency of 1.30 +/- 0.66 every 10 min (range: 0.39-2.58) was found during the first 60 min of acquisition. When correlating the percent reduction of SUV(max) after 2 weeks of RCT with the pathological treatment response, a significant increase (p = 0.027) in the area under the curve of ROC-curve analysis was found when normalizing the PET-data for the measured BGLs, indicating an increase of the predictive strength. CONCLUSIONS: This study strongly underlines the necessity of BGL-normalization of PET-data and a precise time-management between FDG injection and the start of PET acquisition when using sequential FDG-PET-CT imaging for the prediction of pathological treatment response.