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

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.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.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.

Abstract 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

Gemcitabine-induced TIMP1 attenuates therapy response and promotes tumor growth and liver metastasis in pancreatic cancer

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

'Rapid Learning health care in oncology' – An approach towards decision support systems enabling customised radiotherapy' ☆ ☆☆

AbstractPurposeAn overview of the Rapid Learning methodology, its results, and the potential impact on radiotherapy.Material and resultsRapid 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.ConclusionPersonalised 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

Early prediction of pathological response in locally advanced rectal cancer based on sequential (18)F-FDG PET.

International audienceBackground. 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

Predicting resilience from psychological and physiological daily-life measures

Monitoring well-being with mobile and wearable devices has become an important component for the development of preventive interventions for stress-related psychopathology. Here, we investigated the potential of daily-life psychological and physiological measures from Ecological Momentary Assessments (EMA) and Ecological Physiological Assessments (EPA), as well as their combination, for predicting long-term stress resilience. We operationalized resilience as inverse stressor reactivity (SR) at multiple measurement time points across a six-month period of longitudinal assessments. This allowed us to explicitly separate the contributions from between-subject and within-subject variances in EMA and EPA measures to interindividual differences in SR and intraindividual fluctuations in SR over time. We first used linear mixed models to understand how individual EMA items and EPA features are associated with SR, after which we trained machine learning models (random forest regression) to predict either a participant’s average SR score or their weekly individual SR scores from EMA, EPA or combined EMA and EPA data. We identified significant associations between changes in SR and various psychological and physiological measures from EMA and EPA, respectively – both between-subject and within-subject – suggesting that these measures can be used for monitoring resilience in daily life. We furthermore successfully demonstrate that SR scores can be predicted with moderate accuracy using machine learning models that are trained on EMA data, and that these models perform best when considering within-subject variance by predicting weekly SR scores. Our findings may have implications for future research on daily-life measures and stress resilience, as well as the development of clinical applications targeting the early detection and prevention of stress-related disorders through personalized just-in-time adaptive interventions