Cancer Surgery 2.0: Guidance by Real-Time Molecular Technologies

Radioactive hybrid semiconducting polymer nanoparticles for imaging-guided tri-modal therapy of breast cancer

Due to the rapid progression and aggressive metastasis of breast cancer, its diagnosis and treatment remain a great challenge. The simultaneous inhibition of tumor growth and metastasis is necessary for breast cancer to obtain ideal therapeutic outcomes. We herein report the development of radioactive hybrid semiconducting polymer nanoparticles (SPNH) for imaging-guided tri-modal therapy of breast cancer. Two semiconducting polymers are used to form SPNH with a diameter of around 60 nm via nano-coprecipitation and they are also labeled with iodine-131 (131I) to enhance the imaging functions. The formed SPNH show good radiolabeling stability and excellent photodynamic and photothermal effects under 808 nm laser irradiation to produce singlet oxygen (1O2) and heat. Moreover, SPNH can generate 1O2 with ultrasound irradiation via their sonodynamic properties. After intravenous tail vein injection, SPNH can effectively accumulate in the subcutaneous 4T1 tumors of living mice as verified via fluorescence and single photon emission computed tomography (SPECT) imaging. With the irradiation of tumors using an 808 nm laser and US, SPNH mediate photodynamic therapy (PDT), photothermal therapy (PTT) and sonodynamic therapy (SDT) to kill tumor cells. Such a tri-modal therapy leads to an improved efficacy in inhibiting tumor growth and suppressing tumor metastasis compared to the sole SDT and combinational PDT-PTT. This study thus demonstrates the applications of SPNH to diagnose tumors and combine different therapies for effective breast cancer treatment.

Rapid detection of IDH mutations in gliomas by intraoperative mass spectrometry

The development and performance of two mass spectrometry (MS) workflows for the intraoperative diagnosis of isocitrate dehydrogenase (IDH) mutations in glioma is implemented by independent teams at Mayo Clinic, Jacksonville, and Huashan Hospital, Shanghai. The infiltrative nature of gliomas makes rapid diagnosis necessary to guide the extent of surgical resection of central nervous system (CNS) tumors. The combination of tissue biopsy and MS analysis used here satisfies this requirement. The key feature of both described methods is the use of tandem MS to measure the oncometabolite 2-hydroxyglutarate (2HG) relative to endogenous glutamate (Glu) to characterize the presence of mutant tumor. The experiments i) provide IDH mutation status for individual patients and ii) demonstrate a strong correlation of 2HG signals with tumor infiltration. The measured ratio of 2HG to Glu correlates with IDH-mutant (IDH-mut) glioma (P < 0.0001) in the tumor core data of both teams. Despite using different ionization methods and different mass spectrometers, comparable performance in determining IDH mutations from core tumor biopsies was achieved with sensitivities, specificities, and accuracies all at 100%. None of the 31 patients at Mayo Clinic or the 74 patients at Huashan Hospital were misclassified when analyzing tumor core biopsies. Robustness of the methodology was evaluated by postoperative re-examination of samples. Both teams noted the presence of high concentrations of 2HG at surgical margins, supporting future use of intraoperative MS to monitor for clean surgical margins. The power of MS diagnostics is shown in resolving contradictory clinical features, e.g., in distinguishing gliosis from IDH-mut glioma.

Cutaneous squamous cell carcinoma characterized by MALDI mass spectrometry imaging in combination with machine learning

Cutaneous squamous cell carcinoma (SCC) is an increasingly prevalent global health concern. Current diagnostic and surgical methods are reliable, but they require considerable resources and do not provide metabolomic insight. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) enables detailed, spatially resolved metabolomic analysis of tissue samples. Integrated with machine learning, MALDI-MSI could yield detailed information pertaining to the metabolic alterations characteristic for SCC. These insights have the potential to enhance SCC diagnosis and therapy, improving patient outcomes while tackling the growing disease burden. This study employs MALDI-MSI data, labelled according to histology, to train a supervised machine learning model (logistic regression) for the recognition and delineation of SCC. The model, based on data acquired from discrete tumor sections (n = 25) from a mouse model of SCC, achieved a predictive accuracy of 92.3% during cross-validation on the labelled data. A pathologist unacquainted with the dataset and tasked with evaluating the predictive power of the model in the unlabelled regions, agreed with the model prediction for over 99% of the tissue areas. These findings highlight the potential value of integrating MALDI-MSI with machine learning to characterize and delineate SCC, suggesting a promising direction for the advancement of mass spectrometry techniques in the clinical diagnosis of SCC and related keratinocyte carcinomas.

Real-time glioblastoma tumor microenvironment assessment by SpiderMass for improved patient management

Glioblastoma is a highly heterogeneous and infiltrative form of brain cancer associated with a poor outcome and limited therapeutic effectiveness. The extent of the surgery is related to survival. Reaching an accurate diagnosis and prognosis assessment by the time of the initial surgery is therefore paramount in the management of glioblastoma. To this end, we are studying the performance of SpiderMass, an ambient ionization mass spectrometry technology that can be used in vivo without invasiveness, coupled to our recently established artificial intelligence pipeline. We demonstrate that we can both stratify isocitrate dehydrogenase (IDH)-wild-type glioblastoma patients into molecular sub-groups and achieve an accurate diagnosis with over 90% accuracy after cross-validation. Interestingly, the developed method offers the same accuracy for prognosis. In addition, we are testing the potential of an immunoscoring strategy based on SpiderMass fingerprints, showing the association between prognosis and immune cell infiltration, to predict patient outcome.

Ultrasensitive Optical Detection and Elimination of Residual Microtumors with a Postoperative Implantable Hydrogel Sensor for Preventing Cancer Recurrence

In vivo optical imaging of trace biomarkers in residual microtumors holds significant promise for cancer prognosis but poses a formidable challenge. Here, a novel hydrogel sensor is designed for ultrasensitive and specific imaging of the elusive biomarker. This hydrogel sensor seamlessly integrates a molecular beacon nanoprobe with fibroblasts, offering both high tissue retention capability and an impressive signal-to-noise ratio for imaging. Signal amplification is accomplished through exonuclease I-mediated biomarker recycling. The resulting hydrogel sensor sensitively detects the biomarker carcinoembryonic antigen with a detection limit of 1.8 pg mL-1 in test tubes. Moreover, it successfully identifies residual cancer nodules with a median diameter of less than 2 mm in mice bearing partially removed primary triple-negative breast carcinomas (4T1). Notably, this hydrogel sensor is proven effective for the sensitive diagnosis of invasive tumors in post-surgical mice with infiltrating 4T1 cells, leveraging the role of fibroblasts in locally enriching tumor cells. Furthermore, the residual microtumor is rapidly photothermal ablation by polydopamine-based nanoprobe under the guidance of visualization, achieving ≈100% suppression of tumor recurrence and lung metastasis. This work offers a promising alternative strategy for visually detecting residual microtumors, potentially enhancing the prognosis of cancer patients following surgical interventions.

Molecular Engineering of Bright NIR-I/NIR-II Nanofluorophores for High-Resolution Bioimaging and Tumor Detection in Vivo

A comprehensive approach for the construction of NIR-I/NIR-II nanofluorophores with exceptional brightness and excellent chemo- and photostability has been developed. This study first confirmed that the amphiphilic molecules with stronger hydrophobic moieties and weaker hydrophilic moieties are superior candidates for constructing brighter nanofluorophores, which are attributed to its higher efficiency in suppressing the intramolecular charge transfer/aggregation-caused fluorescence quenching of donor-acceptor-donor type fluorophores. The prepared nanofluorophore demonstrates a fluorescence quantum yield exceeding 4.5% in aqueous solution and exhibits a strong NIR-II tail emission up to 1300 nm. The superior performance of the nanofluorophore enabled the achievement of high-resolution whole-body vessel imaging and brain vessel imaging, as well as high-contrast fluorescence imaging of the lymphatic system in vivo. Furthermore, their potential for highly sensitive fluorescence detection of tiny tumors in vivo has been successfully confirmed, thus supporting their future applications in precise fluorescence imaging-guided surgery in the early stages of cancer.

Edaravone: A Novel Possible Drug for Cancer Treatment?

Despite significant advancements in understanding the causes and progression of tumors, cancer remains one of the leading causes of death worldwide. In light of advances in cancer therapy, there has been a growing interest in drug repurposing, which involves exploring new uses for medications that are already approved for clinical use. One such medication is edaravone, which is currently used to manage patients with cerebral infarction and amyotrophic lateral sclerosis. Due to its antioxidant and anti-inflammatory properties, edaravone has also been investigated for its potential activities in treating cancer, notably as an anti-proliferative and cytoprotective drug against side effects induced by traditional cancer therapies. This comprehensive review aims to provide updates on the various applications of edaravone in cancer therapy. It explores its potential as a standalone antitumor drug, either used alone or in combination with other medications, as well as its role as an adjuvant to mitigate the side effects of conventional anticancer treatments.

Lipidomic-Based Approach to 10 s Classification of Major Pediatric Brain Cancer Types with Picosecond Infrared Laser Mass Spectrometry

Picosecond infrared laser mass spectrometry (PIRL-MS) is shown, through a retrospective patient tissue study, to differentiate medulloblastoma cancers from pilocytic astrocytoma and two molecular subtypes of ependymoma (PF-EPN-A, ST-EPN-RELA) using laser-extracted lipids profiled with PIRL-MS in 10 s of sampling and analysis time. The average sensitivity and specificity values for this classification, taking genomic profiling data as standard, were 96.41 and 99.54%, and this classification used many molecular features resolvable in 10 s PIRL-MS spectra. Data analysis and liquid chromatography coupled with tandem high-resolution mass spectrometry (LC-MS/MS) further allowed us to reduce the molecular feature list to only 18 metabolic lipid markers most strongly involved in this classification. The identified 'metabolite array' was comprised of a variety of phosphatidic and fatty acids, ceramides, and phosphatidylcholine/ethanolamine and could mediate the above-mentioned classification with average sensitivity and specificity values of 94.39 and 98.78%, respectively, at a 95% confidence in prediction probability threshold. Therefore, a rapid and accurate pathology classification of select pediatric brain cancer types from 10 s PIRL-MS analysis using known metabolic biomarkers can now be available to the neurosurgeon. Based on retrospective mining of 'survival' versus 'extent-of-resection' data, we further identified pediatric cancer types that may benefit from actionable 10 s PIRL-MS pathology feedback. In such cases, aggressiveness of the surgical resection can be optimized in a manner that is expected to benefit the patient's overall or progression-free survival. PIRL-MS is a promising tool to drive such personalized decision-making in the operating theater.

The role of N6-methyladenosine RNA modification in platinum resistance

N6-methyladenosine (m6A) RNA methylation, a dynamic regulator of transcript expression, plays a pivotal role in cancer by influencing diverse mRNA processes, including nuclear export, splicing, translation and decay. It intersects with cancer biology, impacting progression, treatment sensitivity and prognosis. Platinum-based compounds are essential in cancer treatment, while intrinsic or acquired resistance poses a formidable challenge, limiting therapeutic efficacy. Recent breakthroughs have established a direct association between m6A RNA methylation and platinum resistance in various cancer types. This review summarized related studies, aiming to provide profound insights into the interplay between m6A-associated regulation and platinum-resistance mechanisms in cancer. It explores therapeutic approaches, including personalized treatments based on m6A profiles, guiding future research to enhance clinical strategies for oncological prognostic outcomes.

Intraoperative Identification of Thyroid and Parathyroid Tissues During Human Endocrine Surgery Using the MasSpec Pen

Importance

Intraoperative identification of tissues through gross inspection during thyroid and parathyroid surgery is challenging yet essential for preserving healthy tissue and improving outcomes for patients.

Objective

To evaluate the performance and clinical applicability of the MasSpec Pen (MSPen) technology for discriminating thyroid, parathyroid, and lymph node tissues intraoperatively.

Design, Setting, and Participants

In this diagnostic/prognostic study, the MSPen was used to analyze 184 fresh-frozen thyroid, parathyroid, and lymph node tissues in the laboratory and translated to the operating room to enable in vivo and ex vivo tissue analysis by endocrine surgeons in 102 patients undergoing thyroidectomy and parathyroidectomy procedures. This diagnostic study was conducted between August 2017 and March 2020. Fresh-frozen tissues were analyzed in a laboratory. Clinical analyses occurred in an operating room at an academic medical center. Of the analyses performed on 184 fresh-frozen tissues, 131 were included based on sufficient signal and postanalysis pathologic diagnosis. From clinical tests, 102 patients undergoing surgery were included. A total of 1015 intraoperative analyses were performed, with 269 analyses subject to statistical classification. Statistical classifiers for discriminating thyroid, parathyroid, and lymph node tissues were generated using training sets comprising both laboratory and intraoperative data and evaluated on an independent test set of intraoperative data. Data were analyzed from July to December 2022.

Main Outcomes and Measures

Accuracy for each tissue type was measured for classification models discriminating thyroid, parathyroid, and lymph node tissues using MSPen data compared to gross analysis and final pathology results.

Results

Of the 102 patients in the intraoperative study, 80 were female (78%) and the median (IQR) age was 52 (42-66) years. For discriminating thyroid and parathyroid tissues, an overall accuracy, defined as agreement with pathology, of 92.4% (95% CI, 87.7-95.4) was achieved using MSPen data, with 82.6% (95% CI, 76.5-87.4) accuracy achieved for the independent test set. For distinguishing thyroid from lymph node and parathyroid from lymph node, overall training set accuracies of 97.5% (95% CI, 92.8-99.1) and 96.1% (95% CI, 91.2-98.3), respectively, were achieved.

Conclusions and Relevance

In this study, the MSPen showed high performance for discriminating thyroid, parathyroid, and lymph node tissues intraoperatively, suggesting this technology may be useful for providing near real-time feedback on tissue type to aid in surgical decision-making.

Identification of tumor tissue in thin pathological samples via femtosecond laser-induced breakdown spectroscopy and machine learning

In the treatment of most newly discovered solid cancerous tumors, surgery remains the first treatment option. An important factor in the success of these operations is the precise identification of oncological safety margins to ensure the complete removal of the tumor without affecting much of the neighboring healthy tissue. Here we report on the possibility of applying femtosecond Laser-Induced Breakdown Spectroscopy (LIBS) combined with Machine Learning algorithms as an alternative discrimination technique to differentiate cancerous tissue. The emission spectra following the ablation on thin fixed liver and breast postoperative samples were recorded with high spatial resolution; adjacent stained sections served as a reference for tissue identification by classical pathological analysis. In a proof of principle test performed on liver tissue, Artificial Neural Networks and Random Forest algorithms were able to differentiate both healthy and tumor tissue with a very high Classification Accuracy of around 0.95. The ability to identify unknown tissue was performed on breast samples from different patients, also providing a high level of discrimination. Our results show that LIBS with femtosecond lasers is a technique with potential to be used in clinical applications for rapid identification of tissue type in the intraoperative surgical field.

Intraoperative detection of IDH-mutant glioma using fluorescence lifetime imaging

Identifying isocitrate dehydrogenase (IDH)-mutation and glioma subtype during surgery instead of days later can aid in modifying tumor resection strategies for better survival outcomes. We report intraoperative identification of IDH-mutant glioma (N = 12 patients) with a clinically compatible fluorescence lifetime imaging (FLIm) device (excitation: 355 nm; emission spectral bands: 390/40 nm, 470/28 nm, 542/50 nm). The fluorescence-derived parameters were analyzed to study the optical contrast between IDH-mutant tumors and surrounding brain tissue. IDH-mutant oligodendrogliomas exhibited shorter lifetimes (3.3 ± 0.1 ns) than IDH-mutant astrocytomas (4.1 ± 0.1 ns). Both IDH-mutant glioma subtypes had shorter lifetimes than white matter (4.6 ± 0.4 ns) but had comparable lifetimes to cortex. Lifetimes also increased with malignancy grade within IDH-mutant oligodendrogliomas (grade 2: 2.96 ± 0.08 ns, grade 3: 3.4 ± 0.3 ns) but not within IDH-mutant astrocytomas. The current results support the feasibility of FLIm as a surgical adjuvant for identifying IDH-mutant glioma tissue.

Defining the relevance of surgical margins. Part two: Strategies to improve prediction of recurrence risk

Due to the complex nature of tumour biology and the integration between host tissues and molecular processes of the tumour cells, a continued reliance on the status of the microscopic cellular margin should not remain our only determinant of the success of a curative-intent surgery for patients with cancer. Based on current evidence, relying on a purely cellular focus to provide a binary indication of treatment success can provide an incomplete interpretation of potential outcome. A more holistic analysis of the cancer margin may be required. If we are to move ahead from our current situation - and allow treatment plans to be more intelligently tailored to meet the requirements of each individual tumour - we need to improve our utilisation of techniques that either improve recognition of residual tumour cells within the surgical field or enable a more comprehensive interrogation of tumour biology that identifies a risk of recurrence. In the second article in this series on defining the relevance of surgical margins, the authors discuss possible alternative strategies for margin assessment and evaluation in the canine and feline cancer patient. These strategies include considering adoption of the residual tumour classification scheme; intra-operative imaging systems including fluorescence-guided surgery, optical coherence tomography and Raman spectroscopy; molecular analysis and whole transcriptome analysis of tissues; and the development of a biologic index (nomogram). These techniques may allow evaluation of individual tumour biology and the status of the resection margin in ways that are different to our current techniques. Ultimately, these techniques seek to better define the risk of tumour recurrence following surgery and provide the surgeon and patient with more confidence in margin assessment.

Principe et applicabilité de la chirurgie de précision aux cancers de la tête et du cou

La chirurgie est la modalité de traitement curatif la plus fréquemment utilisée dans les cancers de la tête et du cou. Elle est intégrée dans des schémas de stratification thérapeutique précis, mais la conduite de l’acte chirurgical et son évaluation ne tiennent, la plupart du temps, pas compte de la biologie tumorale. Nous présentons dans cette revue plusieurs études qui montrent comment les analyses de la biologie tumorale pourraient préciser les indications et le contour d’une résection chirurgicale, personnaliser la prise en charge péri-opératoire du patient, et faciliter la détection des récurrences tumorales. Ces études apportent ainsi une preuve de principe qu’une chirurgie de précision, c’est-à-dire adossée à la biologie tumorale, à la façon de la médecine de précision pour d’autres cancers, est applicable aux cancers de la tête et du cou.

Evaluation of the Sensitivity of Metabolic Profiling by Rapid Evaporative Ionization Mass Spectrometry: Toward More Radical Oral Cavity Cancer Resections

Radical resection for patients with oral cavity cancer remains challenging. Rapid evaporative ionization mass spectrometry (REIMS) of electrosurgical vapors has been reported for real-time classification of normal and tumor tissues for numerous surgical applications. However, the infiltrative pattern of invasion of oral squamous cell carcinomas (OSCC) challenges the ability of REIMS to detect low amounts of tumor cells. We evaluate REIMS sensitivity to determine the minimal amount of detected tumors cells during oral cavity cancer surgery. A total of 11 OSCC patients were included in this study. The tissue classification based on 185 REIMS ex vivo metabolic profiles from five patients was compared to histopathology classification using multivariate analysis and leave-one-patient-out cross-validation. Vapors were analyzed in vivo by REIMS during four glossectomies. Complementary desorption electrospray ionization–mass spectrometry imaging (DESI-MSI) was employed to map tissue heterogeneity on six oral cavity sections to support REIMS findings. REIMS sensitivity was assessed with a new cell-based assay consisting of mixtures of cell lines (tumor, myoblasts, keratinocytes). Our results depict REIMS classified tumor and soft tissues with 96.8% accuracy. In vivo REIMS generated intense mass spectrometric signals. REIMS detected 10% of tumor cells mixed with 90% myoblasts with 83% sensitivity and 82% specificity. DESI-MSI underlined distinct metabolic profiles of nerve features and a metabolic shift phosphatidylethanolamine PE(O-16:1/18:2))/cholesterol sulfate common to both mucosal maturation and OSCC differentiation. In conclusion, the assessment of tissue heterogeneity with DESI-MSI and REIMS sensitivity with cell mixtures characterized sensitive metabolic profiles toward in vivo tissue recognition during oral cavity cancer surgeries.

Mass Spectrometry-Based Differentiation of Oral Tongue Squamous Cell Carcinoma and Nontumor Regions With the SpiderMass Technology

Oral cavity cancers are the 15th most common cancer with more than 350,000 new cases and ~178,000 deaths each year. Among them, squamous cell carcinoma (SCC) accounts for more than 90% of tumors located in the oral cavity and on oropharynx. For the oral cavity SCC, the surgical resection remains the primary course of treatment. Generally, surgical margins are defined intraoperatively using visual and tactile elements. However, in 15-30% of cases, positive margins are found after histopathological examination several days postsurgery. Technologies based on mass spectrometry (MS) were recently developed to help guide surgical resection. The SpiderMass technology is designed for in-vivo real-time analysis under minimally invasive conditions. This instrument achieves tissue microsampling and real-time molecular analysis with the combination of a laser microprobe and a mass spectrometer. It ultimately acts as a tool to support histopathological decision-making and diagnosis. This pilot study included 14 patients treated for tongue SCC (T1 to T4) with the surgical resection as the first line of treatment. Samples were first analyzed by a pathologist to macroscopically delineate the tumor, dysplasia, and peritumoral areas. The retrospective and prospective samples were sectioned into three consecutive sections and thaw-mounted on slides for H&E staining (7 μm), SpiderMass analysis (20 μm), and matrix-assisted laser desorption ionization (MALDI) MS imaging (12 μm). The SpiderMass microprobe collected lipidometabolic profiles of the dysplasia, tumor, and peritumoral regions annotated by the pathologist. The MS spectra were then subjected to the multivariate statistical analysis. The preliminary data demonstrate that the lipidometabolic molecular profiles collected with the SpiderMass are significantly different between the tumor and peritumoral regions enabling molecular classification to be established by linear discriminant analysis (LDA). MALDI images of the different samples were submitted to segmentation for cross instrument validation and revealed additional molecular discrimination within the tumor and nontumor regions. These very promising preliminary results show the applicability of the SpiderMass to SCC of the tongue and demonstrate its interest in the surgical treatment of head and neck cancers.

Therapeutic Perspectives for the Perioperative Period in Oral Squamous Cell Carcinoma (OSCC)

The perioperative period is the relatively short window of time, usually measured in days or weeks, around the surgical procedure. Despite its short duration, this time period is of great importance for cancer patients. From a biological point of view, the perioperative period is complex. Synchronous with primary tumor removal, surgery has local and distant consequences, including systemic and local inflammation, coagulation and sympathetic activation. Furthermore, the patients often present comorbidities and receive several medical prescriptions (hypnotics, pain killers, anti-emetics, hemostatics, inotropes, antibiotics). Because of the complex nature of the perioperative period, it is often difficult to predict the oncological outcome of tumor resection. Here, we review the biological consequences of surgery of Oral Squamous Cell Carcinoma (OSCC), the most frequent form of primary head and neck tumors. We briefly address the specificities and the challenges of the surgical care of these tumors and highlight the biological and clinical studies that offer insight into the perioperative period. The recent trials examining neoadjuvant immunotherapy for OSCC illustrate the therapeutic opportunities offered by the perioperative period.

Above and Beyond Robotic Surgery and 3D Modelling in Paediatric Cancer Surgery

Although the survival rates for children's cancers have more than doubled in the last few decades, the surgical practise has not significantly changed. Among the most recent innovations introduced in the clinic, robotic surgery and augmented reality are two of the most promising, even if they are not widespread. The increased flexibility of the motion, the magnification of the surgical field and the tremor reduction provided by robotic surgery have been beneficial to perform complex oncological procedures in children. Besides, augmented reality has been proven helpful in planning for tumour removal, facilitating early discrimination between cancer and healthy organs. Nowadays, research in the field of surgical oncology is moving fast, and new technologies and innovations wich will help to shape a new way to perform cancer surgery. Paediatric surgeons need to be ready to adopt these novel devices and intraoperative techniques to allow more radical tumour resections with fewer complications. This review aims to present the mechanism of action and indications of several novel technologies such as optical imaging surgery, high definition cameras, and intraoperative loco-regional treatments. We hope this will enhance early adoption and more research on how to employ technology for the benefit of children.

Integrative Metabolomics Reveals Deep Tissue and Systemic Metabolic Remodeling in Glioblastoma

(1) Background: Glioblastoma is the most common malignant brain tumor in adults. Its etiology remains unknown in most cases. Glioblastoma pathogenesis consists of a progressive infiltration of the white matter by tumoral cells leading to progressive neurological deficit, epilepsy, and/or intracranial hypertension. The mean survival is between 15 to 17 months. Given this aggressive prognosis, there is an urgent need for a better understanding of the underlying mechanisms of glioblastoma to unveil new diagnostic strategies and therapeutic targets through a deeper understanding of its biology. (2) Methods: To systematically address this issue, we performed targeted and untargeted metabolomics-based investigations on both tissue and plasma samples from patients with glioblastoma. (3) Results: This study revealed 176 differentially expressed lipids and metabolites, 148 in plasma and 28 in tissue samples. Main biochemical classes include phospholipids, acylcarnitines, sphingomyelins, and triacylglycerols. Functional analyses revealed deep metabolic remodeling in glioblastoma lipids and energy substrates, which unveils the major role of lipids in tumor progression by modulating its own environment. (4) Conclusions: Overall, our study demonstrates in situ and systemic metabolic rewiring in glioblastoma that could shed light on its underlying biological plasticity and progression to inform diagnosis and/or therapeutic strategies.

Tissue Sampling and Homogenization in the Sub-Microliter Scale with a Nanosecond Infrared Laser (NIRL) for Mass Spectrometric Proteomics

It was recently shown that ultrashort pulse infrared (IR) lasers, operating at the wavelength of the OH vibration stretching band of water, are highly efficient for sampling and homogenizing biological tissue. In this study we utilized a tunable nanosecond infrared laser (NIRL) for tissue sampling and homogenization with subsequent liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis for mass spectrometric proteomics. For the first time, laser sampling was performed with murine spleen and colon tissue. An ablation volume of 1.1 × 1.1 × 0.4 mm³ (approximately 0.5 µL) was determined with optical coherence tomography (OCT). The results of bottom-up proteomics revealed proteins with significant abundance differences for both tissue types, which are in accordance with the corresponding data of the Human Protein Atlas. The results demonstrate that tissue sampling and homogenization of small tissue volumes less than 1 µL for subsequent mass spectrometric proteomics is feasible with a NIRL.

Metabolic profiles of human brain parenchyma and glioma for rapid tissue diagnosis by targeted desorption electrospray ionization mass spectrometry

Desorption electrospray ionization mass spectrometry (DESI-MS) is well suited for intraoperative tissue analysis since it requires little sample preparation and offers rapid and sensitive molecular diagnostics. Currently, intraoperative assessment of the tumor cell percentage of glioma biopsies can be made by measuring a single metabolite, N-acetylaspartate (NAA). The inclusion of additional biomarkers will likely improve the accuracy when distinguishing brain parenchyma from glioma by DESI-MS. To explore this possibility, mass spectra were recorded for extracts from 32 unmodified human brain samples with known pathology. Statistical analysis of data obtained from full-scan and multiple reaction monitoring (MRM) profiles identified discriminatory metabolites, namely gamma-aminobutyric acid (GABA), creatine, glutamic acid, carnitine, and hexane-1,2,3,4,5,6-hexol (abbreviated as hexol), as well as the established biomarker NAA. Brain parenchyma was readily differentiated from glioma based on these metabolites as measured both in full-scan mass spectra and by the intensities of their characteristic MRM transitions. New DESI-MS methods (5 min acquisition using full scans and MS/MS), developed to measure ion abundance ratios among these metabolites, were tested using smears of 29 brain samples. Ion abundance ratios based on signals for GABA, creatine, carnitine, and hexol all had sensitivities > 90%, specificities > 80%, and accuracies > 85%. Prospectively, the implementation of diagnostic ion abundance ratios should strengthen the discriminatory power of individual biomarkers and enhance method robustness against signal fluctuations, resulting in an improved DESI-MS method of glioma diagnosis.