Genomic characterization of metastatic patterns from prospective clinical sequencing of 25,000 patients.

Metastatic progression is the main cause of death in cancer patients, whereas the underlying genomic mechanisms driving metastasis remain largely unknown. Here, we assembled MSK-MET, a pan-cancer cohort of over 25,000 patients with metastatic diseases. By analyzing genomic and clinical data from this cohort, we identified associations between genomic alterations and patterns of metastatic dissemination across 50 tumor types. We found that chromosomal instability is strongly correlated with metastatic burden in some tumor types, including prostate adenocarcinoma, lung adenocarcinoma, and HR+/HER2+ breast ductal carcinoma, but not in others, including colorectal cancer and high-grade serous ovarian cancer, where copy-number alteration patterns may be established early in tumor development. We also identified somatic alterations associated with metastatic burden and specific target organs. Our data offer a valuable resource for the investigation of the biological basis for metastatic spread and highlight the complex role of chromosomal instability in cancer progression.

Interpreting and integrating genomic tests results in clinical cancer care: Overview and practical guidance.

The last decade has seen rapid progress in the use of genomic tests, including gene panels, whole-exome sequencing, and whole-genome sequencing, in research and clinical cancer care. These advances have created expansive opportunities to characterize the molecular attributes of cancer, revealing a subset of cancer-associated aberrations called driver mutations. The identification of these driver mutations can unearth vulnerabilities of cancer cells to targeted therapeutics, which has led to the development and approval of novel diagnostics and personalized interventions in various malignancies. The applications of this modern approach, often referred to as precision oncology or precision cancer medicine, are already becoming a staple in cancer care and will expand exponentially over the coming years. Although genomic tests can lead to better outcomes by informing cancer risk, prognosis, and therapeutic selection, they remain underutilized in routine cancer care. A contributing factor is a lack of understanding of their clinical utility and the difficulty of results interpretation by the broad oncology community. Practical guidelines on how to interpret and integrate genomic information in the clinical setting, addressed to clinicians without expertise in cancer genomics, are currently limited. Building upon the genomic foundations of cancer and the concept of precision oncology, the authors have developed practical guidance to aid the interpretation of genomic test results that help inform clinical decision making for patients with cancer. They also discuss the challenges that prevent the wider implementation of precision oncology.

Clinical management of metastatic colorectal cancer in the era of precision medicine.

Colorectal cancer (CRC) represents approximately 10% of all cancers and is the second most common cause of cancer deaths. Initial clinical presentation as metastatic CRC (mCRC) occurs in approximately 20% of patients. Moreover, up to 50% of patients with localized disease eventually develop metastases. Appropriate clinical management of these patients is still a challenging medical issue. Major efforts have been made to unveil the molecular landscape of mCRC. This has resulted in the identification of several druggable tumor molecular targets with the aim of developing personalized treatments for each patient. This review summarizes the improvements in the clinical management of patients with mCRC in the emerging era of precision medicine. In fact, molecular stratification, on which the current treatment algorithm for mCRC is based, although it does not completely represent the complexity of this disease, has been the first significant step toward clinically informative genetic profiling for implementing more effective therapeutic approaches. This has resulted in a clinically relevant increase in mCRC disease control and patient survival. The next steps in the clinical management of mCRC will be to integrate the comprehensive knowledge of tumor gene alterations, of tumor and microenvironment gene and protein expression profiling, of host immune competence as well as the application of the resulting dynamic changes to a precision medicine-based continuum of care for each patient. This approach could result in the identification of individual prognostic and predictive parameters, which could help the clinician in choosing the most appropriate therapeutic program(s) throughout the entire disease journey for each patient with mCRC. CA Cancer J Clin. 2022;72:000-000.

The current state of molecular testing in the treatment of patients with solid tumors, 2019.

The world of molecular profiling has undergone revolutionary changes over the last few years as knowledge, technology, and even standard clinical practice have evolved. Broad molecular profiling is now nearly essential for all patients with metastatic solid tumors. New agents have been approved based on molecular testing instead of tumor site of origin. Molecular profiling methodologies have likewise changed such that tests that were performed on patients a few years ago are no longer complete and possibly inaccurate today. As with all rapid change, medical providers can quickly fall behind or struggle to find up-to-date sources to ensure he or she provides optimum care. In this review, the authors provide the current state of the art for molecular profiling/precision medicine, practice standards, and a view into the future ahead.

From morphology to methylome: epigenetic studies of Müllerian mesonephric-like adenocarcinoma reveal similarities to cervical mesonephric adenocarcinoma†.

Mesonephric adenocarcinomas (MAs) and mesonephric-like adenocarcinomas (MLAs) are rare, aggressive neoplasms that arise in the gynecologic tract and show overlapping morphologic, immunohistochemical, and molecular features. While MAs occur in the cervix and are thought to arise from mesonephric remnants, MLAs occur in the endometrium and ovary and are believed to originate from transdifferentiation of Müllerian lesions. Both MAs and MLAs show a variety of architectural patterns, exhibit frequent expression of GATA3 by immunohistochemistry, and harbor KRAS mutations. In a recent article published in The Journal of Pathology, Kommoss and colleagues used DNA methylation profiling to extend these similarities and showed that MLAs and MAs cluster together based on their epigenetic signatures and are epigenetically distinct from other Müllerian adenocarcinomas. They also showed that MLAs and MAs harbor a high number of global copy number alterations. This study provides evidence that MLAs more closely resemble MAs than Müllerian carcinomas on an epigenetic level. As a result, the authors argue that MLA should be renamed 'mesonephric-type adenocarcinoma.' Further research is needed to establish the relationship between these two entities, their etiology, and pathogenesis. © 2024 The Pathological Society of Great Britain and Ireland.

Artificial intelligence-based risk stratification, accurate diagnosis and treatment prediction in gynecologic oncology.

As data-driven science, artificial intelligence (AI) has paved a promising path toward an evolving health system teeming with thrilling opportunities for precision oncology. Notwithstanding the tremendous success of oncological AI in such fields as lung carcinoma, breast tumor and brain malignancy, less attention has been devoted to investigating the influence of AI on gynecologic oncology. Hereby, this review sheds light on the ever-increasing contribution of state-of-the-art AI techniques to the refined risk stratification and whole-course management of patients with gynecologic tumors, in particular, cervical, ovarian and endometrial cancer, centering on information and features extracted from clinical data (electronic health records), cancer imaging including radiological imaging, colposcopic images, cytological and histopathological digital images, and molecular profiling (genomics, transcriptomics, metabolomics and so forth). However, there are still noteworthy challenges beyond performance validation. Thus, this work further describes the limitations and challenges faced in the real-word implementation of AI models, as well as potential solutions to address these issues.

Molecular tumor board: molecularly adjusted therapy upon identification and functional validation of a novel ALK resistance mutation in a case of lung adenocarcinoma.

We report a case of a long-term surviving patient with EML4/ALK translocated non-small cell adenocarcinoma of the lung in UICC8 stage IVA. During recurrence under continuous crizotinib therapy, a hitherto insufficiently characterized missense mutation in the ALK gene (Arg1181His) was identified through targeted sequencing. The aforementioned EML4/ALK translocation could still be detected in this situation. Employing a 3D reconstruction of the ALK tertiary structure, considering its interaction with various ALK inhibitors at the molecular binding site, our analysis indicated the presence of a mutation associated with crizotinib resistance. To validate the biological relevance of this previously unknown mutation, we carried out an in vitro validation approach in cell culture in addition to the molecular diagnostics accompanied by the molecular tumor board. The tumor scenario was mimicked through retroviral transfection. Our comparative in vitro treatment regimen paired with the clinical trajectory of the patient, corroborated our initial clinical and biochemical suspicions. Our approach demonstrates preclinical, in silico, and clinical evidence of a novel crizotinib resistance mutation in ALK as well as sensitivity toward brigatinib and potentially lorlatinib. In future cases, this procedure represents an important contribution to functional diagnostics in the context of molecular tumor boards.

Next-generation sequencing-based evaluation of the actionable landscape of genomic alterations in solid tumors: the "MOZART" prospective observational study.

BACKGROUND: The identification of the most appropriate targeted therapies for advanced cancers is challenging. We performed a molecular profiling of metastatic solid tumors utilizing a comprehensive next-generation sequencing (NGS) assay to determine genomic alterations' type, frequency, actionability, and potential correlations with PD-L1 expression. METHODS: A total of 304 adult patients with heavily pretreated metastatic cancers treated between January 2019 and March 2021 were recruited. The CLIA-/UKAS-accredit Oncofocus assay targeting 505 genes was used on newly obtained or archived biopsies. Chi-square, Kruskal-Wallis, and Wilcoxon rank-sum tests were used where appropriate. Results were significant for P < .05. RESULTS: A total of 237 tumors (78%) harbored potentially actionable genomic alterations. Tumors were positive for PD-L1 in 68.9% of cases. The median number of mutant genes/tumor was 2.0 (IQR: 1.0-3.0). Only 34.5% were actionable ESCAT Tier I-II with different prevalence according to cancer type. The DNA damage repair (14%), the PI3K/AKT/mTOR (14%), and the RAS/RAF/MAPK (12%) pathways were the most frequently altered. No association was found among PD-L1, ESCAT, age, sex, and tumor mutational status. Overall, 62 patients underwent targeted treatment, with 37.1% obtaining objective responses. The same molecular-driven treatment for different cancer types could be associated with opposite clinical outcomes. CONCLUSIONS: We highlight the clinical value of molecular profiling in metastatic solid tumors using comprehensive NGS-based panels to improve treatment algorithms in situations of uncertainty and facilitate clinical trial recruitment. However, interpreting genomic alterations in a tumor type-specific manner is critical.

The multiple facets of Cajal-Retzius neurons.

Cajal-Retzius neurons (CRs) are among the first-born neurons in the developing cortex of reptiles, birds and mammals, including humans. The peculiarity of CRs lies in the fact they are initially embedded into the immature neuronal network before being almost completely eliminated by cell death at the end of cortical development. CRs are best known for controlling the migration of glutamatergic neurons and the formation of cortical layers through the secretion of the glycoprotein reelin. However, they have been shown to play numerous additional key roles at many steps of cortical development, spanning from patterning and sizing functional areas to synaptogenesis. The use of genetic lineage tracing has allowed the discovery of their multiple ontogenetic origins, migratory routes, expression of molecular markers and death dynamics. Nowadays, single-cell technologies enable us to appreciate the molecular heterogeneity of CRs with an unprecedented resolution. In this Review, we discuss the morphological, electrophysiological, molecular and genetic criteria allowing the identification of CRs. We further expose the various sources, migration trajectories, developmental functions and death dynamics of CRs. Finally, we demonstrate how the analysis of public transcriptomic datasets allows extraction of the molecular signature of CRs throughout their transient life and consider their heterogeneity within and across species.

Pathologic Assessment and Staging of Multiple Non-Small Cell Lung Carcinomas: A Paradigm Shift with the Emerging Role of Molecular Methods.

Non-small cell lung carcinomas (NSCLCs) commonly present as 2 or more separate tumors. Biologically, this encompasses 2 distinct processes: separate primary lung carcinomas (SPLCs), representing independently arising tumors, and intrapulmonary metastases (IPMs), representing intrapulmonary spread of a single tumor. The advent of computed tomography imaging has substantially increased the detection of multifocal NSCLCs. The strategies and approaches for distinguishing between SPLCs and IPMs have evolved significantly over the years. Recently, genomic sequencing of somatic mutations has been widely adopted to identify targetable alterations in NSCLC. These molecular techniques have enabled pathologists to reliably discern clonal relationships among multiple NSCLCs in clinical practice. However, a standardized approach to evaluating and staging multiple NSCLCs using molecular methods is still lacking. Here, we reviewed the historical context and provided an update on the growing applications of genomic testing as a clinically relevant benchmark for determining clonal relationships in multiple NSCLCs, a practice we have designated "comparative molecular profiling." We examined the strengths and limitations of the morphology-based distinction of SPLCs vs IPMs and highlighted pivotal clinical and pathologic insights that have emerged from studying multiple NSCLCs using genomic approaches as a gold standard. Lastly, we suggest a practical approach for evaluating multiple NSCLCs in the clinical setting, considering the varying availability of molecular techniques.

Scoring personalized molecular portraits identify Systemic Lupus Erythematosus subtypes and predict individualized drug responses, symptomatology and disease progression.

OBJECTIVES: Systemic Lupus Erythematosus is a complex autoimmune disease that leads to significant worsening of quality of life and mortality. Flares appear unpredictably during the disease course and therapies used are often only partially effective. These challenges are mainly due to the molecular heterogeneity of the disease, and in this context, personalized medicine-based approaches offer major promise. With this work we intended to advance in that direction by developing MyPROSLE, an omic-based analytical workflow for measuring the molecular portrait of individual patients to support clinicians in their therapeutic decisions. METHODS: Immunological gene-modules were used to represent the transcriptome of the patients. A dysregulation score for each gene-module was calculated at the patient level based on averaged z-scores. Almost 6100 Lupus and 750 healthy samples were used to analyze the association among dysregulation scores, clinical manifestations, prognosis, flare and remission events and response to Tabalumab. Machine learning-based classification models were built to predict around 100 different clinical parameters based on personalized dysregulation scores. RESULTS: MyPROSLE allows to molecularly summarize patients in 206 gene-modules, clustered into nine main lupus signatures. The combination of these modules revealed highly differentiated pathological mechanisms. We found that the dysregulation of certain gene-modules is strongly associated with specific clinical manifestations, the occurrence of relapses or the presence of long-term remission and drug response. Therefore, MyPROSLE may be used to accurately predict these clinical outcomes. CONCLUSIONS: MyPROSLE (https://myprosle.genyo.es) allows molecular characterization of individual Lupus patients and it extracts key molecular information to support more precise therapeutic decisions.

Establishment and characterization of 18 Sarcoma Cell Lines: Unraveling the Molecular Mechanisms of Doxorubicin Resistance in Sarcoma Cell Lines.

Sarcomas, malignant tumors from mesenchymal tissues, exhibit poor prognosis despite advancements in treatment modalities such as surgery, radiotherapy, and chemotherapy, with doxorubicin being a cornerstone treatment. Resistance to doxorubicin remains a significant hurdle in therapy optimization. This study aims to dissect the molecular bases of doxorubicin resistance in sarcoma cell lines, which could guide the development of tailored therapeutic strategies. Eighteen sarcoma cell lines from 14 patients were established under ethical approvals and classified into seven subtypes. Molecular, genomic, and transcriptomic analyses included whole-exome sequencing, RNA sequencing, drug sensitivity assays, and pathway enrichment studies to elucidate the resistance mechanisms. Variability in doxorubicin sensitivity was linked to specific genetic alterations, including mutations in TP53 and variations in the copy number of genomic loci like 11q24.2. Transcriptomic profiling divided cell lines into clusters by karyotype complexity, influencing drug responses. Additionally, pathway analyses highlighted the role of signaling pathways like WNT/BETA-CATENIN and HEDGEHOG in doxorubicin-resistant lines. Comprehensive molecular profiling of sarcoma cell lines has revealed complex interplays of genetic and transcriptomic factors dictating doxorubicin resistance, underscoring the need for personalized medicine approaches in sarcoma treatment. Further investigations into these resistance mechanisms could facilitate the development of more effective, customized therapy regimens.

Sequential tumor molecular profiling identifies likely germline variants.

PURPOSE: To identify likely germline DNA variants from sequential tumor profiling data from hematopoietic malignancies (HMs). METHODS: The coefficient of variance was calculated from variant allele frequency of next-generation sequencing assays. Variants' likelihood of being germline was ranked on a 1 to 5 scale. Outcomes were examined in patients with such variants. RESULTS: In a pilot set of 33 genes, 89% of grade 1, 77% of grade 2, 62% of grade 3, 52% of grade 4, and 21% of grade 5 variants were confirmed to be germline. Among those, 22% were pathogenic or likely pathogenic in genes recognized as conferring hereditary HM risk, including BRCA1/2, CHEK2, CSF3R, and DDX41. To determine if this approach identified genes with known autosomal dominant inheritance, we analyzed sequential data from 1336 genes in 1135 HM patients. Among unique variants, 16% occurred in hereditary HM genes, and 15% were deleterious. Patients with grade 1/2 alleles had decreased survival 2 years after initial molecular testing (78% versus 88%, P = .0037) and increased all-cause mortality compared with those without (hazard ratio 2.02, 95% CI 1.18-3.46, P = .019). CONCLUSION: Variant germline status may be predicted using sequential tumor profiling and patients with likely germline variants experience inferior outcomes compared with those without.

Deceptive learning in histopathology.

AIMS: Deep learning holds immense potential for histopathology, automating tasks that are simple for expert pathologists and revealing novel biology for tasks that were previously considered difficult or impossible to solve by eye alone. However, the extent to which the visual strategies learned by deep learning models in histopathological analysis are trustworthy or not has yet to be systematically analysed. Here, we systematically evaluate deep neural networks (DNNs) trained for histopathological analysis in order to understand if their learned strategies are trustworthy or deceptive. METHODS AND RESULTS: We trained a variety of DNNs on a novel data set of 221 whole-slide images (WSIs) from lung adenocarcinoma patients, and evaluated their effectiveness at (1) molecular profiling of KRAS versus EGFR mutations, (2) determining the primary tissue of a tumour and (3) tumour detection. While DNNs achieved above-chance performance on molecular profiling, they did so by exploiting correlations between histological subtypes and mutations, and failed to generalise to a challenging test set obtained through laser capture microdissection (LCM). In contrast, DNNs learned robust and trustworthy strategies for determining the primary tissue of a tumour as well as detecting and localising tumours in tissue. CONCLUSIONS: Our work demonstrates that DNNs hold immense promise for aiding pathologists in analysing tissue. However, they are also capable of achieving seemingly strong performance by learning deceptive strategies that leverage spurious correlations, and are ultimately unsuitable for research or clinical work. The framework we propose for model evaluation and interpretation is an important step towards developing reliable automated systems for histopathological analysis.

TFE3-rearranged nonmelanotic renal PEComa: a case series expanding their phenotypic and fusion landscape.

AIMS: A subset of exceptionally rare primary renal perivascular epithelioid cell tumours (PEComas) that harbour Xp11.2 translocation have been reported, but no larger series devoted to this topic have been published. METHODS AND RESULTS: We describe the clinicopathological and molecular features of 10 renal PEComas, collected from our routine and consultation files. There were five female and five male patients aged 14-65 (median: 32 years). One patient had a history of childhood neuroblastoma, but no patients were known to have a tuberous sclerosis complex or other hereditary disorder. Complete surgical excision was the treatment for all patients. The available follow-up in five patients indicated a favourable outcome in 4/5 cases. Tumour size ranged from 2.8 to 15.2 cm (median, 5.2 cm). Immunohistochemistry revealed consistently strong TFE3 expression in all tumours, whereas PAX8 and keratin cocktails were uniformly negative. Other positive markers included HMB45 (7/9 tumours), CathepsinK (7/9 tumours), and CD117 (KIT) (3/5 tumours). TFE3 rearrangements were detected in 8/9 tumours (by targeted RNA sequencing in seven and by FISH in one). The identified fusion partners included SFPQ (n = 2) and one tumour each with ASPSCR1, ZC3H4, MED15, RBMX, and PRCC. One tumour that lacked TFE3 rearrangement by next-generation sequencing (NGS) and fluorescence in situ hybridization (FISH) revealed a large intrachromosomal deletion involving PKD1 and TSC2 by DNA-based NGS. CONCLUSION: This study highlights the morphologic and genetic diversity of TFE3-rearranged primary renal PEComas and underlines the value of surrogate TFE3 immunohistochemistry in identifying them. The lack of PAX8 and keratin expression represents the mainstay for distinguishing these tumours from MiTF-associated renal cell carcinomas. In addition, we report rare (ZC3H4, RBMX) and novel (MED15) TFE3 fusion partners in PEComa.

Harnessing exosomes as cancer biomarkers in clinical oncology.

Exosomes are extracellular vesicles well known for facilitating cell-to-cell communication by distributing essential macromolecules like proteins, DNA, mRNA, lipids, and miRNA. These vesicles are abundant in fluids distributed throughout the body, including urine, blood, saliva, and even bile. They are important diagnostic tools for breast, lung, gastrointestinal cancers, etc. However, their application as cancer biomarkers has not yet been implemented in most parts of the world. In this review, we discuss how OMICs profiling of exosomes can be practiced by substituting traditional imaging or biopsy methods for cancer detection. Previous methods like extensive imaging and biopsy used for screening were expensive, mostly invasive, and could not easily provide early detection for various types of cancer. Exosomal biomarkers can be utilized for routine screening by simply collecting body fluids from the individual. We anticipate that the use of exosomes will be brought to light by the success of clinical trials investigating their potential to enhance cancer detection and treatment in the upcoming years.

Characterizing the genomic landscape through the lens of FOLR1 status in low and high grade serous ovarian carcinoma.

OBJECTIVE: Targeted therapy in folate receptor alpha (FOLR1)-positive high grade serous ovarian carcinoma (HGSOC) is now a mainstay for platinum-resistant disease. However, the rate of FOLR1-positivity in low grade serous ovarian carcinoma (LGSOC) is not well documented. Less common than HGSOC, LGSOC tends to respond poorly to traditional platinum-based chemotherapeutic regimens, particularly in recurrence. Thus, there is an urgent need to identify molecular targets that may assist in identifying more efficacious treatments for LGSOC. In this work, we assessed the genomic and transcriptomic landscapes in FOLR1-positive/negative LGSOC compared to its high-grade counterpart. METHODS: Using a large precision oncology database, next-generation sequencing and immunohistochemistry was performed on a cohort of 281 LGSOC and 5086 HGSOC. Associated MAPK activation was calculated based on NGS results and patient survival analysis was completed stratified by molecular alteration. RESULTS: Compared with LGSOC (24.6 %), HGSOC tumors have significantly higher prevalence of FOLR1+ status (43.5 %) and significantly higher PD-L1+ status. Conversely, LGSOC had higher prevalence of KRAS and NRAS mutations, with a near exclusivity for BRAF mutation compared to HGSOC. FOLR1- LGSOC and HGSOC had similar prevalences of T cell-inflamed tumors, though FOLR1+ LGSOC had a significantly lower prevalence of T-Cell inflamed tumors than FOLR1+ HGSOC. MAPK activation, quantified via MAPK activation score (MPAS), was significantly higher in low-grade tumors compared to HGSOC, yet no difference between FOLR1+ vs FOLR1- LGSOC was observed. CONCLUSIONS: Though less than in high-grade disease, a notable portion of low-grade tumors were FOLR1+, suggesting FOLR1 expression in LGSOC could be a viable target for this rare histology, particularly in the recurrent setting.

Molecular profiling of gestational trophoblastic neoplasia: Identifying therapeutic targets.

OBJECTIVE: The treatment for high risk or recurrent gestational trophoblastic neoplasia (GTN) is a highly toxic multi-agent chemotherapy. For patients with progressive or recurrent GTN, checkpoint inhibitors have demonstrated anti-tumor activity; however, identification of novel therapies for GTN remain an unmet need. Therefore, we sought to characterize the molecular landscape of GTN to identify potential therapeutic targets. METHODS: GTN samples were analyzed using a combination of molecular - next-generation sequencing (NGS) or whole exome sequencing (WES)- and protein- Immunohistochemistry (IHC) analyses. GTN samples encompassed complete moles, choriocarcinoma, epithelioid trophoblastic tumors (ETT), and placental site trophoblastic tumors (PSTT). RESULTS: We analyzed 30 cases of GTN including 15 choriocarcinoma, 7 ETT, 5 PSTT, 1 invasive mole and 2 mixed histologies. The median age was 41.5. GTN samples were found to be PD-L1 positive (92.3%), tumor mutational burden (TMB) low (92.8%), and microsatellite stable (MSS) (100%). Forty-six percent of choriocarcinoma specimens contained a genomic alteration including TP53 (33%) and homologous recombination repair (HRR) (13%) genes. Alterations in RTK-RAS pathway signaling was present in 40% of ETT cases. CONCLUSIONS: The high rate of PD-L1 positivity in this real-world database and reported in prior literature support continued clinical trial development evaluating immunotherapy for treatment of GTN. Other potential targeted treatments identified include Wee1, PARP and MEK inhibitors based on molecular alterations in TP53, HRR genes, and RTK-RAS pathways respectively.

The development of a custom RNA-sequencing panel for the identification of predictive and diagnostic biomarkers in glioma.

PURPOSE: Various molecular profiles are needed to classify malignant brain tumors, including gliomas, based on the latest classification criteria of the World Health Organization, and their poor prognosis necessitates new therapeutic targets. The Todai OncoPanel 2 RNA Panel (TOP2-RNA) is a custom-target RNA-sequencing (RNA-seq) using the junction capture method to maximize the sensitivity of detecting 455 fusion gene transcripts and analyze the expression profiles of 1,390 genes. This study aimed to classify gliomas and identify their molecular targets using TOP2-RNA. METHODS: A total of 124 frozen samples of malignant gliomas were subjected to TOP2-RNA for classification based on their molecular profiles and the identification of molecular targets. RESULTS: Among 55 glioblastoma cases, gene fusions were detected in 11 cases (20%), including novel MET fusions. Seven tyrosine kinase genes were found to be overexpressed in 15 cases (27.3%). In contrast to isocitrate dehydrogenase (IDH) wild-type glioblastoma, IDH-mutant tumors, including astrocytomas and oligodendrogliomas, barely harbor fusion genes or gene overexpression. Of the 34 overexpressed tyrosine kinase genes, MDM2 and CDK4 in glioblastoma, 22 copy number amplifications (64.7%) were observed. When comparing astrocytomas and oligodendrogliomas in gene set enrichment analysis, the gene sets related to 1p36 and 19q were highly enriched in astrocytomas, suggesting that regional genomic DNA copy number alterations can be evaluated by gene expression analysis. CONCLUSIONS: TOP2-RNA is a highly sensitive assay for detecting fusion genes, exon skipping, and aberrant gene expression. Alterations in targetable driver genes were identified in more than 50% of glioblastoma. Molecular profiling by TOP2-RNA provides ample predictive, prognostic, and diagnostic biomarkers that may not be identified by conventional assays and, therefore, is expected to increase treatment options for individual patients with glioma.

Lipid profiling of electrosurgical vapors for real-time assistance of soft tissue sarcoma resection.

BACKGROUND: Soft tissue sarcomas (STS) constitute a heterogeneous group of rare tumor entities. Treatment relies on challenging patient-tailored surgical resection. Real-time intraoperative lipid profiling of electrosurgical vapors by rapid evaporative ionization mass spectrometry (REIMS) may aid in achieving successful surgical R0 resection (i.e., microscopically negative-tumor margin resection). Here, we evaluate the ex vivo accuracy of REIMS to discriminate and identify various STS from normal surrounding tissue. METHODS: Twenty-seven patients undergoing surgery for STS at Maastricht University Medical Center+ were included in the study. Samples of resected STS specimens were collected and analyzed ex vivo using REIMS. Electrosurgical cauterization of tumor and surrounding was generated successively in both cut and coagulation modes. Resected specimens were subsequently processed for gold standard histopathological review. Multivariate statistical analysis (principal component analysis-linear discriminant analysis) and leave-one patient-out cross-validation were employed to compare the classifications predicted by REIMS lipid profiles to the pathology classifications. Electrosurgical vapors produced during sarcoma resection were analyzed in vivo using REIMS. RESULTS: In total, 1200 histopathologically-validated ex vivo REIMS lipid profiles were generated from 27 patients. Ex vivo REIMS lipid profiles classified STS and normal tissues with 95.5% accuracy. STS, adipose and muscle tissues were classified with 98.3% accuracy. Well-differentiated liposarcomas and adipose tissues could not be discriminated based on their respective lipid profiles. Distinction of leiomyosarcomas from other STS could be achieved with 96.6% accuracy. In vivo REIMS analyses generated intense mass spectrometric signals. CONCLUSION: Lipid profiling by REIMS is able to discriminate and identify STS with high accuracy and therefore constitutes a potential asset to improve surgical resection of STS in the future.