Extracellular vesicles as a promising source of lipid biomarkers for breast cancer detection in blood plasma.

Extracellular vesicles (EVs), including exosomes and microvesicles, mediate intercellular communication in cancer, from development to metastasis. EV-based liquid biopsy is a promising strategy for cancer diagnosis as EVs can be found in cancer patients' body fluids. In this study, the lipid composition of breast cancer-derived EVs was studied as well as the potential of blood plasma EVs for the identification of lipid biomarkers for breast cancer detection. Initially, an untargeted lipidomic analysis was carried out for a panel of cancerous and non-cancerous mammary epithelial cells and their secreted EVs. We found that breast cancer-derived EVs are enriched in sphingolipids and glycerophospholipids compared to their parental cells. The initial in vitro study showed that EVs and their parental cells can be correctly classified (100% accuracy) between cancerous and non-cancerous, as well as into their respective breast cancer subtypes, based on their lipid composition. Subsequently, an untargeted lipidomic analysis was carried out for blood plasma EVs from women diagnosed with breast cancer (primary or progressive metastatic breast cancer) as well as healthy women. Correspondingly, when blood plasma EVs were analysed, breast cancer patients and healthy women were correctly classified with an overall accuracy of 93.1%, based on the EVs' lipid composition. Similarly, the analysis of patients with primary breast cancer and healthy women showed an overall accuracy of 95% for their correct classification. Furthermore, primary and metastatic breast cancers were correctly classified with an overall accuracy of 89.5%. This reveals that the blood plasma EVs' lipids may be a promising source of biomarkers for detection of breast cancer. Additionally, this study demonstrates the usefulness of untargeted lipidomics in the study of EV lipid composition and EV-associated biomarker discovery studies. This is a proof-of-concept study and a starting point for further analysis on the identification of EV-based biomarkers for breast cancer.

Lipidomic Profiling of Colorectal Lesions for Real-Time Tissue Recognition and Risk-Stratification Using Rapid Evaporative Ionization Mass Spectrometry.

OBJECTIVE: Rapid evaporative ionization mass spectrometry (REIMS) is a metabolomic technique analyzing tissue metabolites, which can be applied intraoperatively in real-time. The objective of this study was to profile the lipid composition of colorectal tissues using REIMS, assessing its accuracy for real-time tissue recognition and risk-stratification. SUMMARY BACKGROUND DATA: Metabolic dysregulation is a hallmark feature of carcinogenesis; however, it remains unknown if this can be leveraged for real-time clinical applications in colorectal disease. METHODS: Patients undergoing colorectal resection were included, with carcinoma, adenoma and paired-normal mucosa sampled. Ex vivo analysis with REIMS was conducted using monopolar diathermy, with the aerosol aspirated into a Xevo G2S QToF mass spectrometer. Negatively charged ions over 600 to 1000 m/z were used for univariate and multivariate functions including linear discriminant analysis. RESULTS: A total of 161 patients were included, generating 1013 spectra. Unique lipidomic profiles exist for each tissue type, with REIMS differentiating samples of carcinoma, adenoma, and normal mucosa with 93.1% accuracy and 96.1% negative predictive value for carcinoma. Neoplasia (carcinoma or adenoma) could be predicted with 96.0% accuracy and 91.8% negative predictive value. Adenomas can be risk-stratified by grade of dysplasia with 93.5% accuracy, but not histological subtype. The structure of 61 lipid metabolites was identified, revealing that during colorectal carcinogenesis there is progressive increase in relative abundance of phosphatidylglycerols, sphingomyelins, and mono-unsaturated fatty acid-containing phospholipids. CONCLUSIONS: The colorectal lipidome can be sampled by REIMS and leveraged for accurate real-time tissue recognition, in addition to riskstratification of colorectal adenomas. Unique lipidomic features associated with carcinogenesis are described.

High Resolution Ambient MS Imaging of Biological Samples by Desorption Electro-Flow Focussing Ionization.

In this study, we examine the suitability of desorption electro-flow focusing ionization (DEFFI) for mass spectrometry imaging (MSI) of biological tissue. We also compare the performance of desorption electrospray ionization (DESI) with and without the flow focusing setup. The main potential advantages of applying the flow focusing mechanism in DESI is its rotationally symmetric electrospray jet, higher intensity, more controllable parameters, and better portability due to the robustness of the sprayer. The parameters for DEFFI have therefore been thoroughly optimized, primarily for spatial resolution but also for intensity. Once the parameters have been optimized, DEFFI produces similar images to the existing DESI. MS images for mouse brain samples, acquired at a nominal pixel size of 50 µm, are comparable for both DESI setups, albeit the new sprayer design yields better sensitivity. Furthermore, the two methods are compared with regard to spectral intensity as well as the area of the desorbed crater on rhodamine-coated slides. Overall, the implementation of a flow focusing mechanism in DESI is shown to be highly suitable for imaging biological tissue and has potential to overcome some of the shortcomings experienced with the current geometrical design of DESI.

Automated Cancer Diagnostics via Analysis of Optical and Chemical Images by Deep and Shallow Learning.

Optical microscopy has long been the gold standard to analyse tissue samples for the diagnostics of various diseases, such as cancer. The current diagnostic workflow is time-consuming and labour-intensive, and manual annotation by a qualified pathologist is needed. With the ever-increasing number of tissue blocks and the complexity of molecular diagnostics, new approaches have been developed as complimentary or alternative solutions for the current workflow, such as digital pathology and mass spectrometry imaging (MSI). This study compares the performance of a digital pathology workflow using deep learning for tissue recognition and an MSI approach utilising shallow learning to annotate formalin-fixed and paraffin-embedded (FFPE) breast cancer tissue microarrays (TMAs). Results show that both deep learning algorithms based on conventional optical images and MSI-based shallow learning can provide automated diagnostics with F1-scores higher than 90%, with the latter intrinsically built on biochemical information that can be used for further analysis.

Enhanced triacylglycerol catabolism by carboxylesterase 1 promotes aggressive colorectal carcinoma.

The ability to adapt to low-nutrient microenvironments is essential for tumor cell survival and progression in solid cancers, such as colorectal carcinoma (CRC). Signaling by the NF-κB transcription factor pathway associates with advanced disease stages and shorter survival in patients with CRC. NF-κB has been shown to drive tumor-promoting inflammation, cancer cell survival, and intestinal epithelial cell (IEC) dedifferentiation in mouse models of CRC. However, whether NF-κB affects the metabolic adaptations that fuel aggressive disease in patients with CRC is unknown. Here, we identified carboxylesterase 1 (CES1) as an essential NF-κB-regulated lipase linking obesity-associated inflammation with fat metabolism and adaptation to energy stress in aggressive CRC. CES1 promoted CRC cell survival via cell-autonomous mechanisms that fuel fatty acid oxidation (FAO) and prevent the toxic build-up of triacylglycerols. We found that elevated CES1 expression correlated with worse outcomes in overweight patients with CRC. Accordingly, NF-κB drove CES1 expression in CRC consensus molecular subtype 4 (CMS4), which is associated with obesity, stemness, and inflammation. CES1 was also upregulated by gene amplifications of its transcriptional regulator HNF4A in CMS2 tumors, reinforcing its clinical relevance as a driver of CRC. This subtype-based distribution and unfavorable prognostic correlation distinguished CES1 from other intracellular triacylglycerol lipases and suggest CES1 could provide a route to treat aggressive CRC.

De Novo Lipogenesis Alters the Phospholipidome of Esophageal Adenocarcinoma.

The incidence of esophageal adenocarcinoma is rising, survival remains poor, and new tools to improve early diagnosis and precise treatment are needed. Cancer phospholipidomes quantified with mass spectrometry imaging (MSI) can support objective diagnosis in minutes using a routine frozen tissue section. However, whether MSI can objectively identify primary esophageal adenocarcinoma is currently unknown and represents a significant challenge, as this microenvironment is complex with phenotypically similar tissue-types. Here, we used desorption electrospray ionization-MSI (DESI-MSI) and bespoke chemometrics to assess the phospholipidomes of esophageal adenocarcinoma and relevant control tissues. Multivariate models derived from phospholipid profiles of 117 patients were highly discriminant for esophageal adenocarcinoma both in discovery (AUC = 0.97) and validation cohorts (AUC = 1). Among many other changes, esophageal adenocarcinoma samples were markedly enriched for polyunsaturated phosphatidylglycerols with longer acyl chains, with stepwise enrichment in premalignant tissues. Expression of fatty acid and glycerophospholipid synthesis genes was significantly upregulated, and characteristics of fatty acid acyls matched glycerophospholipid acyls. Mechanistically, silencing the carbon switch ACLY in esophageal adenocarcinoma cells shortened glycerophospholipid chains, linking de novo lipogenesis to the phospholipidome. Thus, DESI-MSI can objectively identify invasive esophageal adenocarcinoma from a number of premalignant tissues and unveils mechanisms of phospholipidomic reprogramming. SIGNIFICANCE: These results call for accelerated diagnosis studies using DESI-MSI in the upper gastrointestinal endoscopy suite, as well as functional studies to determine how polyunsaturated phosphatidylglycerols contribute to esophageal carcinogenesis.

The surgical intelligent knife distinguishes normal, borderline and malignant gynaecological tissues using rapid evaporative ionisation mass spectrometry (REIMS).

BACKGROUND: Survival from ovarian cancer (OC) is improved with surgery, but surgery can be complex and tumour identification, especially for borderline ovarian tumours (BOT), is challenging. The Rapid Evaporative Ionisation Mass Spectrometric (REIMS) technique reports tissue histology in real-time by analysing aerosolised tissue during electrosurgical dissection. METHODS: Aerosol produced during diathermy of tissues was sampled with the REIMS interface. Histological diagnosis and mass spectra featuring complex lipid species populated a reference database on which principal component, linear discriminant and leave-one-patient-out cross-validation analyses were performed. RESULTS: A total of 198 patients provided 335 tissue samples, yielding 3384 spectra. Cross-validated OC classification vs separate normal tissues was high (97·4% sensitivity, 100% specificity). BOT were readily distinguishable from OC (sensitivity 90.5%, specificity 89.7%). Validation with fresh tissue lead to excellent OC detection (100% accuracy). Histological agreement between iKnife and histopathologist was very good (kappa 0.84, P < 0.001, z = 3.3). Five predominantly phosphatidic acid (PA(36:2)) and phosphatidyl-ethanolamine (PE(34:2)) lipid species were identified as being significantly more abundant in OC compared to normal tissue or BOT (P < 0.001, q < 0.001). CONCLUSIONS: The REIMS iKnife distinguishes gynaecological tissues by analysing mass-spectrometry-derived lipidomes from tissue diathermy aerosols. Rapid intra-operative gynaecological tissue diagnosis may improve surgical care when histology is unknown, leading to personalised operations tailored to the individual.

Correlated Heterospectral Lipidomics for Biomolecular Profiling of Remyelination in Multiple Sclerosis.

Analyzing lipid composition and distribution within the brain is important to study white matter pathologies that present focal demyelination lesions, such as multiple sclerosis. Some lesions can endogenously re-form myelin sheaths. Therapies aim to enhance this repair process in order to reduce neurodegeneration and disability progression in patients. In this context, a lipidomic analysis providing both precise molecular classification and well-defined localization is crucial to detect changes in myelin lipid content. Here we develop a correlated heterospectral lipidomic (HSL) approach based on coregistered Raman spectroscopy, desorption electrospray ionization mass spectrometry (DESI-MS), and immunofluorescence imaging. We employ HSL to study the structural and compositional lipid profile of demyelination and remyelination in an induced focal demyelination mouse model and in multiple sclerosis lesions from patients ex vivo. Pixelwise coregistration of Raman spectroscopy and DESI-MS imaging generated a heterospectral map used to interrelate biomolecular structure and composition of myelin. Multivariate regression analysis enabled Raman-based assessment of highly specific lipid subtypes in complex tissue for the first time. This method revealed the temporal dynamics of remyelination and provided the first indication that newly formed myelin has a different lipid composition compared to normal myelin. HSL enables detailed molecular myelin characterization that can substantially improve upon the current understanding of remyelination in multiple sclerosis and provides a strategy to assess remyelination treatments in animal models.

Faster, More Reproducible DESI-MS for Biological Tissue Imaging.

A new, more robust sprayer for desorption electrospray ionization (DESI) mass spectrometry imaging is presented. The main source of variability in DESI is thought to be the uncontrolled variability of various geometric parameters of the sprayer, primarily the position of the solvent capillary, or more specifically, its positioning within the gas capillary or nozzle. If the solvent capillary is off-center, the sprayer becomes asymmetrical, making the geometry difficult to control and compromising reproducibility. If the stiffness, tip quality, and positioning of the capillary are improved, sprayer reproducibility can be improved by an order of magnitude. The quality of the improved sprayer and its potential for high spatial resolution imaging are demonstrated on human colorectal tissue samples by acquisition of images at pixel sizes of 100, 50, and 20 µm, which corresponds to a lateral resolution of 40-60 µm, similar to the best values published in the literature. The high sensitivity of the sprayer also allows combination with a fast scanning quadrupole time-of-flight mass spectrometer. This provides up to 30 times faster DESI acquisition, reducing the overall acquisition time for a 10 mm × 10 mm rat brain sample to approximately 1 h. Although some spectral information is lost with increasing analysis speed, the resulting data can still be used to classify tissue types on the basis of a previously constructed model. This is particularly interesting for clinical applications, where fast, reliable diagnosis is required. Graphical Abstract ᅟ.

Rapid evaporative ionisation mass spectrometry of electrosurgical vapours for the identification of breast pathology: towards an intelligent knife for breast cancer surgery.

BACKGROUND: Re-operation for positive resection margins following breast-conserving surgery occurs frequently (average = 20-25%), is cost-inefficient, and leads to physical and psychological morbidity. Current margin assessment techniques are slow and labour intensive. Rapid evaporative ionisation mass spectrometry (REIMS) rapidly identifies dissected tissues by determination of tissue structural lipid profiles through on-line chemical analysis of electrosurgical aerosol toward real-time margin assessment. METHODS: Electrosurgical aerosol produced from ex-vivo and in-vivo breast samples was aspirated into a mass spectrometer (MS) using a monopolar hand-piece. Tissue identification results obtained by multivariate statistical analysis of MS data were validated by histopathology. Ex-vivo classification models were constructed from a mass spectral database of normal and tumour breast samples. Univariate and tandem MS analysis of significant peaks was conducted to identify biochemical differences between normal and cancerous tissues. An ex-vivo classification model was used in combination with bespoke recognition software, as an intelligent knife (iKnife), to predict the diagnosis for an ex-vivo validation set. Intraoperative REIMS data were acquired during breast surgery and time-synchronized to operative videos. RESULTS: A classification model using histologically validated spectral data acquired from 932 sampling points in normal tissue and 226 in tumour tissue provided 93.4% sensitivity and 94.9% specificity. Tandem MS identified 63 phospholipids and 6 triglyceride species responsible for 24 spectral differences between tissue types. iKnife recognition accuracy with 260 newly acquired fresh and frozen breast tissue specimens (normal n = 161, tumour n = 99) provided sensitivity of 90.9% and specificity of 98.8%. The ex-vivo and intra-operative method produced visually comparable high intensity spectra. iKnife interpretation of intra-operative electrosurgical vapours, including data acquisition and analysis was possible within a mean of 1.80 seconds (SD ±0.40). CONCLUSIONS: The REIMS method has been optimised for real-time iKnife analysis of heterogeneous breast tissues based on subtle changes in lipid metabolism, and the results suggest spectral analysis is both accurate and rapid. Proof-of-concept data demonstrate the iKnife method is capable of online intraoperative data collection and analysis. Further validation studies are required to determine the accuracy of intra-operative REIMS for oncological margin assessment.

Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer.

Visual inspection of tumour tissues does not reveal the complex metabolic changes that differentiate cancer and its sub-types from healthy tissues. Mass spectrometry imaging, which quantifies the underlying chemistry, represents a powerful tool for the molecular exploration of tumour tissues. A 3-dimensional topological description of the chemical properties of the tumour permits the formulation of hypotheses about the biological composition and interactions and the possible causes of its heterogeneous structure. The large amount of information contained in such datasets requires powerful tools for its analysis, visualisation and interpretation. Linear methods for unsupervised dimensionality reduction, such as PCA, are inadequate to capture the complex non-linear relationships present in these data. For this reason, a deep unsupervised neural network based technique, parametric t-SNE, is adopted to map a 3D-DESI-MS dataset from a human colorectal adenocarcinoma biopsy onto a 2-dimensional manifold. This technique allows the identification of clusters not visible with linear methods. The unsupervised clustering of the tumour tissue results in the identification of sub-regions characterised by the abundance of identified metabolites, making possible the formulation of hypotheses to account for their significance and the underlying biological heterogeneity in the tumour.

Epithelial ovarian carcinoma diagnosis by desorption electrospray ionization mass spectrometry imaging.

Ovarian cancer is highly prevalent among European women, and is the leading cause of gynaecological cancer death. Current histopathological diagnoses of tumour severity are based on interpretation of, for example, immunohistochemical staining. Desorption electrospray mass spectrometry imaging (DESI-MSI) generates spatially resolved metabolic profiles of tissues and supports an objective investigation of tumour biology. In this study, various ovarian tissue types were analysed by DESI-MSI and co-registered with their corresponding haematoxylin and eosin (H&E) stained images. The mass spectral data reveal tissue type-dependent lipid profiles which are consistent across the n = 110 samples (n = 107 patients) used in this study. Multivariate statistical methods were used to classify samples and identify molecular features discriminating between tissue types. Three main groups of samples (epithelial ovarian carcinoma, borderline ovarian tumours, normal ovarian stroma) were compared as were the carcinoma histotypes (serous, endometrioid, clear cell). Classification rates >84% were achieved for all analyses, and variables differing statistically between groups were determined and putatively identified. The changes noted in various lipid types help to provide a context in terms of tumour biochemistry. The classification of unseen samples demonstrates the capability of DESI-MSI to characterise ovarian samples and to overcome existing limitations in classical histopathology.

Shotgun Lipidomic Profiling of the NCI60 Cell Line Panel Using Rapid Evaporative Ionization Mass Spectrometry.

Rapid evaporative ionization mass spectrometry (REIMS) was used for the rapid mass spectrometric profiling of cancer cell lines. Spectral reproducibility was assessed for three different cell lines, and the extent of interclass differences and intraclass variance was found to allow the identification of these cell lines based on the REIMS data. Subsequently, the NCI60 cell line panel was subjected to REIMS analysis, and the resulting data set was investigated for its distinction of individual cell lines and different tissue types of origin. Information content of REIMS spectral profiles of cell lines were found to be similar to those obtained from mammalian tissues although pronounced differences in relative lipid intensity were observed. Ultimately, REIMS was shown to detect changes in lipid content of cell lines due to mycoplasma infection. The data show that REIMS is an attractive means to study cell lines involving minimal sample preparation and analysis times in the range of seconds.

Imaging of Esophageal Lymph Node Metastases by Desorption Electrospray Ionization Mass Spectrometry.

Histopathological assessment of lymph node metastases (LNM) depends on subjective analysis of cellular morphology with inter-/intraobserver variability. In this study, LNM from esophageal adenocarcinoma was objectively detected using desorption electrospray ionization-mass spectrometry imaging (DESI-MSI). Ninety lymph nodes (LN) and their primary tumor biopsies from 11 esophago-gastrectomy specimens were examined and analyzed by DESI-MSI. Images from mass spectrometry and corresponding histology were coregistered and analyzed using multivariate statistical tools. The MSIs revealed consistent lipidomic profiles of individual tissue types found within LNs. Spatial mapping of the profiles showed identical distribution patterns as per the tissue types in matched IHC images. Lipidomic profile comparisons of LNM versus the primary tumor revealed a close association in contrast to benign LN tissue types. This similarity was used for the objective prediction of LNM in mass spectrometry images utilizing the average lipidomic profile of esophageal adenocarcinoma. The multivariate statistical algorithm developed for LNM identification demonstrated a sensitivity, specificity, positive predictive value, and negative predictive value of 89.5%, 100%, 100%, and 97.2%, respectively, when compared with gold-standard IHC. DESI-MSI has the potential to be a diagnostic tool for perioperative identification of LNM and compares favorably with techniques currently used by histopathology experts. Cancer Res; 76(19); 5647-56. ©2016 AACR.

Investigation of the Impact of Desorption Electrospray Ionization Sprayer Geometry on Its Performance in Imaging of Biological Tissue.

In this study, the impact of sprayer design and geometry on performance in desorption electrospray ionization mass spectrometry (DESI-MS) is assessed, as the sprayer is thought to be a major source of variability. Absolute intensity repeatability, spectral composition, and classification accuracy for biological tissues are considered. Marked differences in tissue analysis performance are seen between the commercially available and a lab-built sprayer. These are thought to be associated with the geometry of the solvent capillary and the resulting shape of the primary electrospray. Experiments with a sprayer with a fixed solvent capillary position show that capillary orientation has a crucial impact on tissue complex lipid signal and can lead to an almost complete loss of signal. Absolute intensity repeatability is compared for five lab-built sprayers using pork liver sections. Repeatability ranges from 1 to 224% for individual sprayers and peaks of different spectral abundance. Between sprayers, repeatability is 16%, 9%, 23%, and 34% for high, medium, low, and very low abundance peaks, respectively. To assess the impact of sprayer variability on tissue classification using multivariate statistical tools, nine human colorectal adenocarcinoma sections are analyzed with three lab-built sprayers, and classification accuracy for adenocarcinoma versus the surrounding stroma is assessed. It ranges from 80.7 to 94.5% between the three sprayers and is 86.5% overall. The presented results confirm that the sprayer setup needs to be closely controlled to obtain reliable data, and a new sprayer setup with a fixed solvent capillary geometry should be developed.

Benchmark datasets for 3D MALDI- and DESI-imaging mass spectrometry.

BACKGROUND: Three-dimensional (3D) imaging mass spectrometry (MS) is an analytical chemistry technique for the 3D molecular analysis of a tissue specimen, entire organ, or microbial colonies on an agar plate. 3D-imaging MS has unique advantages over existing 3D imaging techniques, offers novel perspectives for understanding the spatial organization of biological processes, and has growing potential to be introduced into routine use in both biology and medicine. Owing to the sheer quantity of data generated, the visualization, analysis, and interpretation of 3D imaging MS data remain a significant challenge. Bioinformatics research in this field is hampered by the lack of publicly available benchmark datasets needed to evaluate and compare algorithms. FINDINGS: High-quality 3D imaging MS datasets from different biological systems at several labs were acquired, supplied with overview images and scripts demonstrating how to read them, and deposited into MetaboLights, an open repository for metabolomics data. 3D imaging MS data were collected from five samples using two types of 3D imaging MS. 3D matrix-assisted laser desorption/ionization imaging (MALDI) MS data were collected from murine pancreas, murine kidney, human oral squamous cell carcinoma, and interacting microbial colonies cultured in Petri dishes. 3D desorption electrospray ionization (DESI) imaging MS data were collected from a human colorectal adenocarcinoma. CONCLUSIONS: With the aim to stimulate computational research in the field of computational 3D imaging MS, selected high-quality 3D imaging MS datasets are provided that could be used by algorithm developers as benchmark datasets.

Addressing the efficacy of dihydrocodeine versus methadone as an alternative maintenance treatment for opiate dependence: A randomized controlled trial.

AIM: The aim of this study is to define the efficacy of dihydrocodeine as an alternative to methadone in the maintenance treatment of opiate dependence. DESIGN: A pragmatic open-label randomized controlled study of patients recommended for opiate maintenance treatment to test equivalence of the two treatment options with follow-up continuing for up to 42 months after recruitment. SETTING: Assessment at either Edinburgh's Community Drug Problem Service or at two general practitioner practices with specialist drug community psychiatric nurses, then with shared care follow-up. PARTICIPANTS: Two hundred and thirty-five subjects (168 male, 67 female) with opiate dependence syndrome were recruited. Subjects selected were suitable for opiate maintenance treatment. Routine treatment was offered throughout. INTERVENTION: Patients were randomized to receive either methadone mixture 1 mg/ml or dihydrocodeine, 30 mg or 60 mg tablets. MEASUREMENTS: The primary outcome measure was retention in treatment. Eight secondary outcomes included total illicit opiate use, reported crime, physical health, mental health, injecting drug use, overdoses, selling drugs and being in education or work. Measures were compared over 42 months follow-up. FINDINGS: There was no difference in groups for retention in treatment at follow-up and there was improvement in all secondary outcomes from baseline. No significant difference in outcomes was found between randomized groups over time. Compliance with randomized treatment differed by randomized group and was affected by experiences in custody during follow-up. Those randomized to dihydrocodeine were more likely to switch treatments. CONCLUSIONS: These results, combined with existing clinical experience, provide evidence that dihydrocodeine is a viable alternative to methadone as a maintenance treatment for opiate dependence. Indirect comparisons with other studies show dihydrocodeine (and methadone) to be superior to placebo.