Solitary fibrous tumor with IGF-II-induced non-islet cell tumor hypoglycemia: a case report and molecular characterization by next-generation sequencing

Background

Non-islet cell tumor-induced hypoglycemia (NICTH) is a rare, life-threatening medical condition caused by excessive insulin-like growth factor II (IGF-II) secretion from tumors of most commonly mesenchymal origin. Using next-generation sequencing, we have characterized the genome and transcriptome of the resected IGF-II-secreting solitary fibrous tumor from a patient with severe hypoglycemia accompanied by hypoglycemia unawareness.

Case presentation

A 69-year-old male patient presenting with abdominal discomfort was examined using computer tomography, revealing a large lesion at the lesser pelvis extending above the umbilicus. As no bone and lymph node metastases were detected, the patient was scheduled for laparotomy. Before surgery, the patient presented with symptoms of severe hypoglycemia. Suppressed C-peptide levels and subsequent hypokalemia indicated a possible case of NICTH. The patient was treated with methylprednisolone (8 mg) to assess hypoglycemia. After the surgery, mild hypoglycemia was present for the postoperative period, and no radiological recurrences were observed 3 and 12 months after discharge. Histopathological examination results were consistent with the diagnosis of malignant solitary fibrous tumor (SFT). Overexpression of IGF-II was confirmed by both immunohistochemistry and RNA sequencing. Further NGS analysis revealed an SFT characteristic alteration-NAB2-STAT6 fusion. Additionally, three deleterious missense variants were detected in oncogenes BIRC6, KIT, and POLQ, and one homozygous in-frame deletion in the RBM10 tumor suppressor gene.

Conclusion

While the NAB2-STAT6 fusions are well characterized, the mutational landscape of SFTs remains understudied. This study reports the importance of NGS to characterize SFTs as we detected four coding variants in genes (BIRC6, KIT, POLQ, and RBM10) associated with tumorigenesis that could potentially contribute to the overall pathogenesis of SFT.

Clinicopathological characteristics of Epstein-Barr virus-positive gastric cancer in Latvia

OBJECTIVE

Epstein-Barr virus (EBV)-associated gastric cancer has been proposed to be a distinct gastric cancer molecular subtype. The prognostic significance of EBV infection in gastric cancer remains unclear and needs further investigation. Our study aimed to analyze EBV-positive and EBV-negative gastric cancer patients regarding their personal and tumor-related characteristics, and compare their overall survival.

METHODS

Gastric cancer patients consecutively treated at the Riga East University Hospital during 2009-2016 were identified retrospectively. Tumor EBV status was determined by in-situ hybridization for EBV-encoded RNA (EBER). Information about clinicopathological characteristics was obtained from patient questionnaires, hospital records. Overall survival was ascertained through 30 July 2017. Cox proportional hazard regression models adjusted for personal and tumor-related covariates compared survival between EBV-positive and EBV-negative patients.

RESULTS

There were a total of 302 gastric cancer patients (61% males) with mean and SD age 63.6 ± 11.5 years. EBER positivity was present in 8.6% of tumors. EBV-positive gastric cancer patients had better survival at 80 months [adjusted hazard ratio = 0.37, 95% confidence interval (CI) = 0.19-0.72] compared to EBV-negative patients. Worse survival was observed for patients with stage III (hazard ratio = 2.76, 95% CI = 1.67-4.56) and stage IV (hazard ratio = 10.02, 95% CI = 5.72-17.57) compared to stage I gastric cancer, and overlapping and unspecified subsite (hazard ratio = 1.85; 95% CI = 1.14; 3.00) compared to distal tumors.

CONCLUSION

Tumor EBV positivity is a favorable prognostic factor in gastric cancer.

Screening for gastric cancer using exhaled breath samples

BACKGROUND

The aim was to derive a breath-based classifier for gastric cancer using a nanomaterial-based sensor array, and to validate it in a large screening population.

METHODS

A new training algorithm for the diagnosis of gastric cancer was derived from previous breath samples from patients with gastric cancer and healthy controls in a clinical setting, and validated in a blinded manner in a screening population.

RESULTS

The training algorithm was derived using breath samples from 99 patients with gastric cancer and 342 healthy controls, and validated in a population of 726 people. The calculated training set algorithm had 82 per cent sensitivity, 78 per cent specificity and 79 per cent accuracy. The algorithm correctly classified all three patients with gastric cancer and 570 of the 723 cancer-free controls in the screening population, yielding 100 per cent sensitivity, 79 per cent specificity and 79 per cent accuracy. Further analyses of lifestyle and confounding factors were not associated with the classifier.

CONCLUSION

This first validation of a nanomaterial sensor array-based algorithm for gastric cancer detection from breath samples in a large screening population supports the potential of this technology for the early detection of gastric cancer.

Non-contact breath sampling for sensor-based breath analysis

Breath analysis holds great promise for real-time and non-invasive medical diagnosis. Thus, there is a considerable need for simple-in-use and portable analyzers for rapid detection of breath indicators for different diseases in their early stages. Sensor technology meets all of these demands. However, miniaturized breath analyzers require adequate breath sampling methods. In this context, we propose non-contact sampling; namely the collection of breath samples by exhalation from a distance into a miniaturized collector without bringing the mouth into direct contact with the analyzing device. To evaluate this approach different breathing maneuvers have been tested in a real-time regime on a cohort of 23 volunteers using proton transfer reaction mass spectrometry. The breathing maneuvers embraced distinct depths of respiration, exhalation manners, size of the mouth opening and different sampling distances. Two inhalation modes (normal, relaxed breathing and deep breathing) and two exhalation manners (via smaller and wider lips opening) forming four sampling scenarios were selected. A sampling distance of approximately 2 cm was found to be a reasonable trade-off between sample dilution and requirement of no physical contact of the subject with the analyzer. All four scenarios exhibited comparable measurement reproducibility spread of around 10%. For normal, relaxed inspiration both dead-space and end-tidal phases of exhalation lasted approximately 1.5 s for both expiration protocols. Deep inhalation prolongs the end-tidal phase to about 3 s in the case of blowing via a small lips opening, and by 50% when the air is exhaled via a wide one. In conclusion, non-contact breath sampling can be considered as a promising alternative to the existing breath sampling methods, being relatively close to natural spontaneous breathing.

Ex vivo emission of volatile organic compounds from gastric cancer and non-cancerous tissue

The presence of certain volatile organic compounds (VOCs) in the breath of patients with gastric cancer has been reported by a number of research groups; however, the source of these compounds remains controversial. Comparison of VOCs emitted from gastric cancer tissue to those emitted from non-cancerous tissue would help in understanding which of the VOCs are associated with gastric cancer and provide a deeper knowledge on their generation. Gas chromatography with mass spectrometric detection (GC-MS) coupled with head-space needle trap extraction (HS-NTE) as the pre-concentration technique, was used to identify and quantify VOCs released by gastric cancer and non-cancerous tissue samples collected from 41 patients during surgery. Excluding contaminants, a total of 32 VOCs were liberated by the tissue samples. The emission of four of them (carbon disulfide, pyridine, 3-methyl-2-butanone and 2-pentanone) was significantly higher from cancer tissue, whereas three compounds (isoprene, γ-butyrolactone and dimethyl sulfide) were in greater concentration from the non-cancerous tissues (Wilcoxon signed-rank test, p < 0.05). Furthermore, the levels of three VOCs (2-methyl-1-propene, 2-propenenitrile and pyrrole) were correlated with the occurrence of H. pylori; and four compounds (acetonitrile, pyridine, toluene and 3-methylpyridine) were associated with tobacco smoking. Ex vivo analysis of VOCs emitted by human tissue samples provides a unique opportunity to identify chemical patterns associated with a cancerous state and can be considered as a complementary source of information on volatile biomarkers found in breath, blood or urine.

Diagnosis and Classification of 17 Diseases from 1404 Subjects via Pattern Analysis of Exhaled Molecules

We report on an artificially intelligent nanoarray based on molecularly modified gold nanoparticles and a random network of single-walled carbon nanotubes for noninvasive diagnosis and classification of a number of diseases from exhaled breath. The performance of this artificially intelligent nanoarray was clinically assessed on breath samples collected from 1404 subjects having one of 17 different disease conditions included in the study or having no evidence of any disease (healthy controls). Blind experiments showed that 86% accuracy could be achieved with the artificially intelligent nanoarray, allowing both detection and discrimination between the different disease conditions examined. Analysis of the artificially intelligent nanoarray also showed that each disease has its own unique breathprint, and that the presence of one disease would not screen out others. Cluster analysis showed a reasonable classification power of diseases from the same categories. The effect of confounding clinical and environmental factors on the performance of the nanoarray did not significantly alter the obtained results. The diagnosis and classification power of the nanoarray was also validated by an independent analytical technique, i.e., gas chromatography linked with mass spectrometry. This analysis found that 13 exhaled chemical species, called volatile organic compounds, are associated with certain diseases, and the composition of this assembly of volatile organic compounds differs from one disease to another. Overall, these findings could contribute to one of the most important criteria for successful health intervention in the modern era, viz. easy-to-use, inexpensive (affordable), and miniaturized tools that could also be used for personalized screening, diagnosis, and follow-up of a number of diseases, which can clearly be extended by further development.

Detection of precancerous gastric lesions and gastric cancer through exhaled breath

OBJECTIVES

Timely detection of gastric cancer (GC) and the related precancerous lesions could provide a tool for decreasing both cancer mortality and incidence.

DESIGN

968 breath samples were collected from 484 patients (including 99 with GC) for two different analyses. The first sample was analysed by gas chromatography linked to mass spectrometry (GCMS) while applying t test with multiple corrections (p value<0.017); the second by cross-reactive nanoarrays combined with pattern recognition. For the latter, 70% of the samples were randomly selected and used in the training set while the remaining 30% constituted the validation set. The operative link on gastric intestinal metaplasia (OLGIM) assessment staging system was used to stratify the presence/absence and risk level of precancerous lesions. Patients with OLGIM stages III-IV were considered to be at high risk.

RESULTS

According to the GCMS results, patients with cancer as well as those at high risk had distinctive breath-print compositions. Eight significant volatile organic compounds (p value<0.017) were detected in exhaled breath in the different comparisons. The nanoarray analysis made it possible to discriminate between the patients with GC and the control group (OLGIM 0-IV) with 73% sensitivity, 98% specificity and 92% accuracy. The classification sensitivity, specificity, and accuracy between the subgroups was as follows: GC versus OLGIM 0-II-97%, 84% and 87%; GC versus OLGIM III-IV-93%, 80% and 90%; but OLGIM I-II versus OLGIM III-IV and dysplasia combined-83%, 60% and 61%, respectively.

CONCLUSIONS

Nanoarray analysis could provide the missing non-invasive screening tool for GC and related precancerous lesions as well as for surveillance of the latter.

TRIAL REGISTRATION NUMBER

Clinical Trials.gov number, NCT01420588 (3/11/2013).

Breath testing as potential colorectal cancer screening tool

Although colorectal cancer (CRC) screening is included in organized programs of many countries worldwide, there is still a place for better screening tools. In this study, 418 breath samples were collected from 65 patients with CRC, 22 with advanced or nonadvanced adenomas, and 122 control cases. All patients, including the controls, had undergone colonoscopy. The samples were analysed with two different techniques. The first technique relied on gas chromatography coupled with mass spectrometry (GC-MS) for identification and quantification of volatile organic compounds (VOCs). The T-test was used to identify significant VOCs (p values < 0.017). The second technique relied on sensor analysis with a pattern recognition method for building a breath pattern to identify different groups. Blind analysis or leave-one-out cross validation was conducted for validation. The GC-MS analysis revealed four significant VOCs that identified the tested groups; these were acetone and ethyl acetate (higher in CRC), ethanol and 4-methyl octane (lower in CRC). The sensor-analysis distinguished CRC from the control group with 85% sensitivity, 94% specificity and 91% accuracy. The performance of the sensors in identifying the advanced adenoma group from the non-advanced adenomas was 88% sensitivity, 100% specificity, and 94% accuracy. The performance of the sensors in identifying the advanced adenoma group was distinguished from the control group was 100% sensitivity, 88% specificity, and 94% accuracy. For summary, volatile marker testing by using sensor analysis is a promising noninvasive approach for CRC screening.

Geographical variation in the exhaled volatile organic compounds

Breath-gas analysis has demonstrated that concentration profiles of volatile organic compounds (VOCs) could be used for detecting a variety of diseases, among them gastric cancer (GC) and peptic ulcer disease (PUD). Here, we explore how geographical variation affects the disease-specific changes in the chemical composition of breath samples, as compared to control states (less severe gastric conditions). Alveolar exhaled breath samples from 260 patients were collected at two remotely different geographic locations (China and Latvia), following similar breath-collection protocols. Each cohort included 130 patients that were matched in terms of diagnosis (37 GC/32 PUD/61 controls), average age, gender ratio and smoking habits. Helicobacter Pylori infection, which is a major cause for GC and PUD, was found in part of the patients, as well as in part of the controls, at both locations. The breath samples were analyzed by gas chromatography/mass spectrometry, using the same equipment and protocol-of-experiment. We observed similar characteristic differences in the chemical composition of the breath samples between the study groups at the two locations, even though the exact composition of the breath samples differed. Both in China and Latvia, the GC patients and controls could be distinguished by differences in the average levels of 6-methyl-5-hepten-2-one; PUD patients were distinguished from controls by the levels of aromatic compounds and alcohols; GC and PUD patients could not be distinguished at either site. This pilot study indicates the limitations of chemical breath-gas analysis alone for identifying gastric diseases based on the concentration profiles of separate VOCs in international patient cohorts. We assume that these limitations would apply to other diseases as well. The presented data could potentially be useful for developing an alternative, universally applicable diagnostic method that relies on the detection of changes in the collective patterns of the disease-specific classes of exhaled VOCs.