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.

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.

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.

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.

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).

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.

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.

Accurate prediction of pathological rectal tumor response after two weeks of preoperative radiochemotherapy using (18)F-fluorodeoxyglucose-positron emission tomography-computed tomography imaging

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.

Altered calcium handling is an early sign of streptozotocin-induced diabetic cardiomyopathy

The main objective of the present study was to determine alterations of calcium handling in the diabetic rat heart during the transition from adaptive to maladaptive phase of cardiomyopathy. By inhibiting the nuclear enzyme poly(ADP-ribose) polymerase (PARP), we also investigated the possible role of this enzyme in the sequence of pathological events. Six weeks after induction of type I diabetes by injection of streptozotocin in rats, the hearts were perfused according to Langendorff. Intracellular-free calcium (Ca(2+)(i)) levels were measured by surface fluorometry using Indo-1 AM. Cyclic changes in Ca(2+)(i) concentrations and hemodynamic parameters were measured simultaneously. The hearts were challenged by infusion of isoproterenol. Six weeks of diabetes resulted in reduced inotropy and lusitropy. The diabetic hearts (DM) expressed a significantly elevated end-diastolic Ca(2+)(i) level (control, 111-/+20 vs DM, 221-/+35 nM). The maximal transport capacity of SERCA2a and conductance of RyR2 were reduced. These changes were not accompanied by major alterations in the tissue content of SERCA2a, RyR2, phospholamban and Na(+)/Ca(2+) exchanger. In response to beta-adrenergic activation, SERCA2a transport capacity and RyR2 conductance were stunted in the DM hearts. Inhibition of PARP induced minor changes in the mechanical function and calcium handling of the DM hearts. In conclusion, the observed changes in contractility and in Ca(2+)(i) handling are most likely attributable to functional disturbances of SERCA2a and RyR2 in this transitional phase of diabetes. At this stage of diabetes, PARP does not appear to play a significant pathogenetic role in the alterations in contractile function and calcium handling.