Overexpression of fatty acid synthase (FASN), a key regulator of the de novo synthesis of fatty acids, has been demonstrated in a variety of cancers and is associated with poor prognosis and ...increased multidrug resistance. Inhibition of FASN with the anti‐obesity drug orlistat has been shown to have significant anti‐tumourigenic effects in many cancers, notably breast and prostate. In our study, we investigated whether FASN inhibition using orlistat is an effective adjunctive treatment for ovarian cancers that have become platinum resistant using a cisplatin‐resistant ovarian tumour xenograft model in mice. Mice were treated with orlistat or cisplatin or a combination and metabolite analysis and histopathology were performed on the tumours ex vivo. Orlistat decreased tumour fatty acid metabolism by inhibiting FASN, cisplatin reduced fatty acid β‐oxidation, and combination treatment delayed tumour growth and induced apoptotic and necrotic cell death in cisplatin‐resistant ovarian cancer cells over and above that with either treatment alone. Combination treatment also decreased glutamine metabolism, nucleotide and glutathione biosynthesis and fatty acid β‐oxidation. Our data suggest that orlistat chemosensitised platinum‐resistant ovarian cancer to treatment with platinum and resulted in enhanced efficacy.
What's new?
Targeting metabolic dependencies of tumor cells has emerged as a promising new therapeutic approach. Here the authors used an approved anti‐obesity drug, orlistat, as an adjuvant treatment for platinum‐resistant ovarian cancer. They show that in mice addition of orlistat re‐sensitises tumors to cisplatin therapy and speculate that the combination of orlistat and cisplatin could be used clinically to treat patients with platinum‐refractory ovarian cancer.
Pre-clinical models have shown that targeting pancreatic stellate cells with all-trans-retinoic-acid (ATRA) reprograms pancreatic stroma to suppress pancreatic ductal adenocarcinoma (PDAC) growth. ...Here, in a phase Ib, dose escalation and expansion, trial for patients with advanced, unresectable PDAC (n = 27), ATRA is re-purposed as a stromal-targeting agent in combination with gemcitabine-nab-paclitaxel chemotherapy using a two-step adaptive continual re-assessment method trial design. The maximum tolerated dose (MTD) and recommended phase 2 dose (RP2D, primary outcome) is the FDA/EMEA approved dose of gemcitabine-nab-paclitaxel along-with ATRA (45 mg/m
orally, days 1-15/cycle). Dose limiting toxicity (DLT) is grade 4 thrombocytopenia (n = 2). Secondary outcomes show no detriment to ATRA pharmacokinetics.. Median overall survival for RP2D treated evaluable population, is 11.7 months (95%CI 8.6-15.7 m, n = 15, locally advanced (2) and metastatic (13)). Exploratory pharmacodynamics studies including changes in diffusion-weighted (DW)-MRI measured apparent diffusion coefficient after one cycle, and, modulation of cycle-specific serum pentraxin 3 levels over various cycles indicate stromal modulation. Baseline stromal-specific retinoid transport protein (FABP5, CRABP2) expression may be predicitve of response. Re-purposing ATRA as a stromal-targeting agent with gemcitabine-nab-paclitaxel is safe and tolerable. This combination will be evaluated in a phase II randomized controlled trial for locally advanced PDAC. Clinical trial numbers: EudraCT: 2015-002662-23; NCT03307148. Trial acronym: STARPAC.
Patients with oligometastatic disease (OMD) often have controllable symptoms, and cures are possible. Technical improvements in surgery and radiotherapy have introduced the option of ...metastasis-directed ablative therapies as an adjunct or alternative to standard-of-care systemic therapies. Several clinical trials and registries are investigating the benefit of these therapeutic approaches across several cancer sites. This requires that patients are correctly included and followed with appropriate imaging. This article discusses the evidence and offers recommendations for the implementation of standard-of-care (Response Evaluation Criteria in Solid Tumours measurements on computed tomography CT, magnetic resonance imaging MRI and bone scintigraphy) and advanced imaging modalities (functional, metabolic and radionuclide targeted) for identifying and following up patients with OMD.
Imaging requirements for recognising OMD vary with tumour type, metastatic location, and timing of measurement in relation to previous treatment. At each point in the disease cycle (diagnosis, response assessment and follow-up), imaging must be tailored to the clinical question and the context of prior treatment. The differential use of whole-body approaches such as 18F-FDG-positron emission tomography (PET)/CT, diffusion-weighted MRI, 18F-Choline-PET/CT and 68Ga-prostate specific membrane antigen–PET/CT require rationalisation depending on clinical risk assessment. Optimal standardised imaging approaches will enable OMD trials to document patterns of disease progression and outcomes of treatment. Quality assured and quality controlled imaging data included in databases such as the European Organisation for Research and Treatment of Cancer Imaging platform for the Oligocare trial (a prospective, large-scale observational basket study being set up to collect outcome data from patients with OMD treated with radiation therapy) will establish a large and high-quality imaging warehouse for future research.
•The imaging requirements for identifying patients with oligometastases are discussed.•Recommendations are made for imaging oligometastases in four cancer types.•Relevance of specific imaging at various points in the disease cycle is highlighted.
Oligometastatic disease has been proposed as an intermediate state between localised and systemically metastasised disease. In the absence of randomised phase 3 trials, early clinical studies show ...improved survival when radical local therapy is added to standard systemic therapy for oligometastatic disease. However, since no biomarker for the identification of patients with true oligometastatic disease is clinically available, the diagnosis of oligometastatic disease is based solely on imaging findings. A small number of metastases on imaging could represent different clinical scenarios, which are associated with different prognoses and might require different treatment strategies. 20 international experts including 19 members of the European Society for Radiotherapy and Oncology and European Organisation for Research and Treatment of Cancer OligoCare project developed a comprehensive system for characterisation and classification of oligometastatic disease. We first did a systematic review of the literature to identify inclusion and exclusion criteria of prospective interventional oligometastatic disease clinical trials. Next, we used a Delphi consensus process to select a total of 17 oligometastatic disease characterisation factors that should be assessed in all patients treated with radical local therapy for oligometastatic disease, both within and outside of clinical trials. Using a second round of the Delphi method, we established a decision tree for oligometastatic disease classification together with a nomenclature. We agreed oligometastatic disease as the overall umbrella term. A history of polymetastatic disease before diagnosis of oligometastatic disease was used as the criterion to differentiate between induced oligometastatic disease (previous history of polymetastatic disease) and genuine oligometastatic disease (no history of polymetastatic disease). We further subclassified genuine oligometastatic disease into repeat oligometastatic disease (previous history of oligometastatic disease) and de-novo oligometastatic disease (first time diagnosis of oligometastatic disease). In de-novo oligometastatic disease, we differentiated between synchronous and metachronous oligometastatic disease. We did a final subclassification into oligorecurrence, oligoprogression, and oligopersistence, considering whether oligometastatic disease is diagnosed during a treatment-free interval or during active systemic therapy and whether or not an oligometastatic lesion is progressing on current imaging. This oligometastatic disease classification and nomenclature needs to be prospectively evaluated by the OligoCare study.
To determine the feasibility of whole-body diffusion-weighted (DW) magnetic resonance (MR) imaging for assessment of treatment response in myeloma.
This prospective single-institution study was ...HIPAA-compliant with local research ethics committee approval. Written informed consent was obtained from each subject. Eight healthy volunteers (cohort 1a) and seven myeloma patients (cohort 1b) were imaged twice to assess repeatability of quantitative apparent diffusion coefficient (ADC) estimates. Thirty-four additional myeloma patients (cohort 2) underwent whole-body DW imaging before treatment; 26 completed a posttreatment imaging. Whole-body DW data were compared before and after treatment by using qualitative (ie, observer scores) and quantitative (ie, whole-body segmentation of marrow ADC) methods. Serum paraproteins and/or light chains or bone marrow biopsy defined response.
Whole-body DW imaging scores were significantly different between observers (P < .001), but change in scores between observers after treatment was not (P = .49). Sensitivity and specificity for detecting response according to observer scores were 86% (18 of 21 patients) and 80% (4 of 5 patients) for both observers. ADC measurement was repeatable: mean coefficient of variation was 3.8% in healthy volunteers and 2.8% in myeloma patients. Pretreatment ADC in cohort 2 was significantly different from that in cohort 1a (P = .03), but not from that in cohort 1b (P = .2). Mean ADC increased in 95% (19 of 20) of responding patients and decreased in all (five of five) nonresponders (P = .002). A 3.3% increase in ADC helped identify response with 90% sensitivity and 100% specificity; an 8% increase (greater than repeatability of cohort 1b) resulted in 70% sensitivity and 100% specificity. There was a significant negative correlation between change in ADC and change in laboratory markers of response (r = -0.614; P = .001).
Preliminary work demonstrates whole-body DW imaging is a repeatable, quantifiable technique for assessment of treatment response in myeloma.
Oligometastatic disease represents a clinical and anatomical manifestation between localised and polymetastatic disease. In prostate cancer, as with other cancers, recognition of oligometastatic ...disease enables focal, metastasis-directed therapies. These therapies potentially shorten or postpone the use of systemic treatment and can delay further metastatic progression, thus increasing overall survival. Metastasis-directed therapies require imaging methods that definitively recognise oligometastatic disease to validate their efficacy and reliably monitor response, particularly so that morbidity associated with inappropriately treating disease subsequently recognised as polymetastatic can be avoided. In this Review, we assess imaging methods used to identify metastatic prostate cancer at first diagnosis, at biochemical recurrence, or at the castration-resistant stage. Standard imaging methods recommended by guidelines have insufficient diagnostic accuracy for reliably diagnosing oligometastatic disease. Modern imaging methods that use PET-CT with tumour-specific radiotracers (choline or prostate-specific membrane antigen ligand), and increasingly whole-body MRI with diffusion-weighted imaging, allow earlier and more precise identification of metastases. The European Organisation for Research and Treatment of Cancer (EORTC) Imaging Group suggests clinical algorithms to integrate modern imaging methods into the care pathway at the various stages of prostate cancer to identify oligometastatic disease. The EORTC proposes clinical trials that use modern imaging methods to evaluate the benefits of metastasis-directed therapies.
Recognition of <3 metastases in <2 organs, particularly in cancers with a known predisposition to oligometastatic disease (OMD) (colorectal, prostate, renal, sarcoma and lung), offers the opportunity ...to focally treat the lesions identified and confers a survival advantage. The reliability with which OMD is identified depends on the sensitivity of the imaging technique used for detection and may be predicted from phenotypic and genetic factors of the primary tumour, which determine metastatic risk. Whole‐body or organ‐specific imaging to identify oligometastases requires optimization to achieve maximal sensitivity. Metastatic lesions at multiple locations may require a variety of imaging modalities for best visualisation because the optimal image contrast is determined by tumour biology. Newer imaging techniques used for this purpose require validation. Additionally, rationalisation of imaging strategies is needed, particularly with regard to timing of imaging and follow‐up studies. This article reviews the current evidence for the use of imaging for recognising OMD and proposes a risk‐based roadmap for identifying patients with true OMD, or at risk of metastatic disease likely to be OM.
Quantitative radiomics features, extracted from medical images, characterize tumour-phenotypes and have been shown to provide prognostic value in predicting clinical outcomes. Stability of radiomics ...features extracted from apparent diffusion coefficient (ADC)-maps is essential for reliable correlation with the underlying pathology and its clinical applications. Within a multicentre, multi-vendor trial we established a method to analyse radiomics features from ADC-maps of ovarian (n = 12), lung (n = 19), and colorectal liver metastasis (n = 30) cancer patients who underwent repeated (<7 days) diffusion-weighted imaging at 1.5 T and 3 T. From these ADC-maps, 1322 features describing tumour shape, texture and intensity were retrospectively extracted and stable features were selected using the concordance correlation coefficient (CCC > 0.85). Although some features were tissue- and/or respiratory motion-specific, 122 features were stable for all tumour-entities. A large proportion of features were stable across different vendors and field strengths. By extracting stable phenotypic features, fitting-dimensionality is reduced and reliable prognostic models can be created, paving the way for clinical implementation of ADC-based radiomics.
Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before ...incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials.
Key Points
• Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting.
• Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes.
• Biological correlation may be established after clinical validation but is not mandatory.
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