Binary classification of copy number alteration profiles in liquid biopsy with potential clinical impact in advanced NSCLC.

Liquid biopsy has recently emerged as an important tool in clinical practice particularly for lung cancer patients. We retrospectively evaluated cell-free DNA analyses performed at our Institution by next generation sequencing methodology detecting the major classes of genetic alterations. Starting from the graphical representation of chromosomal alterations provided by the analysis software, we developed a support vector machine classifier to automatically classify chromosomal profiles as stable (SCP) or unstable (UCP). High concordance was found between our binary classification and tumor fraction evaluation performed using shallow whole genome sequencing. Among clinical features, UCP patients were more likely to have ≥ 3 metastatic sites and liver metastases. Longitudinal assessment of chromosomal profiles in 33 patients with lung cancer receiving immune checkpoint inhibitors (ICIs) showed that only patients that experienced early death or hyperprogressive disease retained or acquired an UCP within 3 weeks from the beginning of ICIs. UCP was not observed following ICIs among patients that experienced progressive disease or clinical benefit. In conclusion, our binary classification, applied to whole copy number alteration profiles, could be useful for clinical risk stratification during systemic treatment for non-small cell lung cancer patients.

Machine learning to improve interpretability of clinical, radiological and panel-based genomic data of glioma grade 4 patients undergoing surgical resection.

BACKGROUND: Glioma grade 4 (GG4) tumors, including astrocytoma IDH-mutant grade 4 and the astrocytoma IDH wt are the most common and aggressive primary tumors of the central nervous system. Surgery followed by Stupp protocol still remains the first-line treatment in GG4 tumors. Although Stupp combination can prolong survival, prognosis of treated adult patients with GG4 still remains unfavorable. The introduction of innovative multi-parametric prognostic models may allow refinement of prognosis of these patients. Here, Machine Learning (ML) was applied to investigate the contribution in predicting overall survival (OS) of different available data (e.g. clinical data, radiological data, or panel-based sequencing data such as presence of somatic mutations and amplification) in a mono-institutional GG4 cohort. METHODS: By next-generation sequencing, using a panel of 523 genes, we performed analysis of copy number variations and of types and distribution of nonsynonymous mutations in 102 cases including 39 carmustine wafer (CW) treated cases. We also calculated tumor mutational burden (TMB). ML was applied using eXtreme Gradient Boosting for survival (XGBoost-Surv) to integrate clinical and radiological information with genomic data. RESULTS: By ML modeling (concordance (c)- index = 0.682 for the best model), the role of predicting OS of radiological parameters including extent of resection, preoperative volume and residual volume was confirmed. An association between CW application and longer OS was also showed. Regarding gene mutations, a role in predicting OS was defined for mutations of BRAF and of other genes involved in the PI3K-AKT-mTOR signaling pathway. Moreover, an association between high TMB and shorter OS was suggested. Consistently, when a cutoff of 1.7 mutations/megabase was applied, cases with higher TMB showed significantly shorter OS than cases with lower TMB. CONCLUSIONS: The contribution of tumor volumetric data, somatic gene mutations and TBM in predicting OS of GG4 patients was defined by ML modeling.

Machine Learning Application Identifies Germline Markers of Hypertension in Patients With Ovarian Cancer Treated With Carboplatin, Taxane, and Bevacizumab.

Pharmacogenomics studies how genes influence a person's response to treatment. When complex phenotypes are influenced by multiple genetic variations with little effect, a single piece of genetic information is often insufficient to explain this variability. The application of machine learning (ML) in pharmacogenomics holds great potential - namely, it can be used to unravel complicated genetic relationships that could explain response to therapy. In this study, ML techniques were used to investigate the relationship between genetic variations affecting more than 60 candidate genes and carboplatin-induced, taxane-induced, and bevacizumab-induced toxicities in 171 patients with ovarian cancer enrolled in the MITO-16A/MaNGO-OV2A trial. Single-nucleotide variation (SNV, formerly SNP) profiles were examined using ML to find and prioritize those associated with drug-induced toxicities, specifically hypertension, hematological toxicity, nonhematological toxicity, and proteinuria. The Boruta algorithm was used in cross-validation to determine the significance of SNVs in predicting toxicities. Important SNVs were then used to train eXtreme gradient boosting models. During cross-validation, the models achieved reliable performance with a Matthews correlation coefficient ranging from 0.375 to 0.410. A total of 43 SNVs critical for predicting toxicity were identified. For each toxicity, key SNVs were used to create a polygenic toxicity risk score that effectively divided individuals into high-risk and low-risk categories. In particular, compared with low-risk individuals, high-risk patients were 28-fold more likely to develop hypertension. The proposed method provided insightful data to improve precision medicine for patients with ovarian cancer, which may be useful for reducing toxicities and improving toxicity management.

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges.

Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.

Colorectal cancer development is affected by the ECM molecule EMILIN-2 hinging on macrophage polarization via the TLR-4/MyD88 pathway.

BACKGROUND: Colorectal cancer is one of the most frequent and deadly tumors. Among the key regulators of CRC growth and progression, the microenvironment has emerged as a crucial player and as a possible route for the development of new therapeutic opportunities. More specifically, the extracellular matrix acts directly on cancer cells and indirectly affecting the behavior of stromal and inflammatory cells, as well as the bioavailability of growth factors. Among the ECM molecules, EMILIN-2 is frequently down-regulated by methylation in CRC and the purpose of this study was to verify the impact of EMILIN-2 loss in CRC development and its possible value as a prognostic biomarker. METHODS: The AOM/DSS CRC protocol was applied to Emilin-2 null and wild type mice. Tumor development was monitored by endoscopy, the molecular analyses performed by IHC, IF and WB and the immune subpopulations characterized by flow cytometry. Ex vivo cultures of monocyte/macrophages from the murine models were used to verify the molecular pathways. Publicly available datasets were exploited to determine the CRC patients' expression profile; Spearman's correlation analyses and Cox regression were applied to evaluate the association with the inflammatory response; the clinical outcome was predicted by Kaplan-Meier survival curves. Pearson correlation analyses were also applied to a cohort of patients enrolled in our Institute. RESULTS: In preclinical settings, loss of EMILIN-2 associated with an increased number of tumor lesions upon AOM/DSS treatment. In addition, in the early stages of the disease, the Emilin-2 knockout mice displayed a myeloid-derived suppressor cells-rich infiltrate. Instead, in the late stages, lack of EMILIN-2 associated with a decreased number of M1 macrophages, resulting in a higher percentage of the tumor-promoting M2 macrophages. Mechanistically, EMILIN-2 triggered the activation of the Toll-like Receptor 4/MyD88/NF-κB pathway, instrumental for the polarization of macrophages towards the M1 phenotype. Accordingly, dataset and immunofluorescence analyses indicated that low EMILIN-2 expression levels correlated with an increased M2/M1 ratio and with poor CRC patients' prognosis. CONCLUSIONS: These novel results indicate that EMILIN-2 is a key regulator of the tumor-associated inflammatory environment and may represent a promising prognostic biomarker for CRC patients.

Machine Learning Application in a Phase I Clinical Trial Allows for the Identification of Clinical-Biomolecular Markers Significantly Associated With Toxicity.

Machine learning (ML) algorithms have been used to forecast clinical outcomes or drug adverse effects by analyzing different data sets such as electronic health records, diagnostic data, and molecular data. However, ML implementation in phase I clinical trial is still an unexplored strategy that implies challenges such as the selection of the best development strategy when dealing with limited sample size. In the attempt to better define prechemotherapy baseline clinical and biomolecular predictors of drug toxicity, we trained and compared five ML algorithms starting from clinical, blood biochemistry, and genotype data derived from a previous phase Ib study aimed to define the maximum tolerated dose of irinotecan (FOLFIRI (folinic acid, fluorouracil, and irinotecan) plus bevacizumab regimen) in patients with metastatic colorectal cancer. During cross-validation the Random Forest algorithm achieved the best performance with a mean Matthews correlation coefficient of 0.549 and a mean accuracy of 80.4%; the best predictors of dose-limiting toxicity at baseline were hemoglobin, serum glutamic oxaloacetic transaminase (SGOT), and albumin. The feasibility of a prediction model prototype was in principle assessed using the two distinct dose escalation cohorts, where in the validation cohort the model scored a Matthews correlation coefficient of 0.59 and an accuracy of 82.0%. Moreover, we found a strong relationship between SGOT and irinotecan pharmacokinetics, suggesting its role as surrogates' estimators of the irinotecan metabolism equilibrium. In conclusion, the potential application of ML techniques to phase I study could provide clinicians with early prediction tools useful both to ameliorate the management of clinical trials and to make more adequate treatment decisions.

The benefit of early surgery on overall survival in incidental low-grade glioma patients: A multicenter study.

BACKGROUND: The role of surgery for incidentally discovered diffuse incidental low-grade gliomas (iLGGs) is debatable and poorly documented in current literature. OBJECTIVE: The aim was to identify factors that influence survival for patients that underwent surgical resection of iLGGs in a large multicenter population. METHODS: Clinical, radiological, and surgical data were retrospectively analyzed in 267 patients operated for iLGG from 4 neurosurgical Centers. Univariate and multivariate analyses were performed to identify predictors of overall survival (OS) and tumor recurrence (TR). RESULTS: The OS rate was 92.41%. The 5- and 10-year estimated OS rates were 98.09% and 93.2%, respectively. OS was significantly longer for patients with a lower preoperative tumor volume (P = .001) and higher extent of resection (EOR) (P = .037), regardless the WHO-defined molecular class (P = .2). In the final model, OS was influenced only by the preoperative tumor volume (P = .006), while TR by early surgery (P = .028). A negative association was found between preoperative tumor volumes and EOR (rs = -0.44, P < .001). The median preoperative tumor volume was 15 cm3. The median EOR was 95%. Total or supratotal resection of T2-FLAIR abnormality was achieved in 61.62% of cases. Second surgery was performed in 26.22%. The median time between surgeries was 5.5 years. Histological evolution to high-grade glioma was detected in 22.85% of cases (16/70). Permanent mild deficits were observed in 3.08% of cases. CONCLUSIONS: This multicenter study confirms the results of previous studies investigating surgical management of iLGGs and thereby strengthens the evidence in favor of early surgery for these lesions.

Toward best practice in cancer mutation detection with whole-genome and whole-exome sequencing.

Clinical applications of precision oncology require accurate tests that can distinguish true cancer-specific mutations from errors introduced at each step of next-generation sequencing (NGS). To date, no bulk sequencing study has addressed the effects of cross-site reproducibility, nor the biological, technical and computational factors that influence variant identification. Here we report a systematic interrogation of somatic mutations in paired tumor-normal cell lines to identify factors affecting detection reproducibility and accuracy at six different centers. Using whole-genome sequencing (WGS) and whole-exome sequencing (WES), we evaluated the reproducibility of different sample types with varying input amount and tumor purity, and multiple library construction protocols, followed by processing with nine bioinformatics pipelines. We found that read coverage and callers affected both WGS and WES reproducibility, but WES performance was influenced by insert fragment size, genomic copy content and the global imbalance score (GIV; G > T/C > A). Finally, taking into account library preparation protocol, tumor content, read coverage and bioinformatics processes concomitantly, we recommend actionable practices to improve the reproducibility and accuracy of NGS experiments for cancer mutation detection.

The Efficacy of Anti-PD-L1 Treatment in Melanoma Is Associated with the Expression of the ECM Molecule EMILIN2.

The use of immune checkpoint inhibitors has revolutionized the treatment of melanoma patients, leading to remarkable improvements in the cure. However, to ensure a safe and effective treatment, there is the need to develop markers to identify the patients that would most likely respond to the therapies. The microenvironment is gaining attention in this context, since it can regulate both the immunotherapy efficacyand angiogenesis, which is known to be affected by treatment. Here, we investigated the putative role of the ECM molecule EMILIN-2, a tumor suppressive and pro-angiogenic molecule. We verified that the EMILIN2 expression is variable among melanoma patients and is associated with the response to PD-L1 inhibitors. Consistently, in preclinical settings,the absence of EMILIN-2 is associated with higher PD-L1 expression and increased immunotherapy efficacy. We verified that EMILIN-2 modulates PD-L1 expression in melanoma cells through indirect immune-dependent mechanisms. Notably, upon PD-L1 blockage, Emilin2-/- mice displayed improved intra-tumoral vessel normalization and decreased tumor hypoxia. Finally, we provide evidence indicating that the inclusion of EMILIN2 in a number of gene expression signatures improves their predictive potential, a further indication that the analysis of this molecule may be key for the development of new markers to predict immunotherapy efficacy.

A New Epigenetic Model to Stratify Glioma Patients According to Their Immunosuppressive State.

Gliomas are the most common primary neoplasm of the central nervous system. A promising frontier in the definition of glioma prognosis and treatment is represented by epigenetics. Furthermore, in this study, we developed a machine learning classification model based on epigenetic data (CpG probes) to separate patients according to their state of immunosuppression. We considered 573 cases of low-grade glioma (LGG) and glioblastoma (GBM) from The Cancer Genome Atlas (TCGA). First, from gene expression data, we derived a novel binary indicator to flag patients with a favorable immune state. Then, based on previous studies, we selected the genes related to the immune state of tumor microenvironment. After, we improved the selection with a data-driven procedure, based on Boruta. Finally, we tuned, trained, and evaluated both random forest and neural network classifiers on the resulting dataset. We found that a multi-layer perceptron network fed by the 338 probes selected by applying both expert choice and Boruta results in the best performance, achieving an out-of-sample accuracy of 82.8%, a Matthews correlation coefficient of 0.657, and an area under the ROC curve of 0.9. Based on the proposed model, we provided a method to stratify glioma patients according to their epigenomic state.

Long Noncoding RNA NEAT1 Acts as a Molecular Switch for BRD4 Transcriptional Activity and Mediates Repression of BRD4/WDR5 Target Genes.

BRD4 is an epigenome reader known to exert key roles at the interface between chromatin remodeling and transcriptional regulation, and is primarily known for its role in promoting gene expression. In selective contexts, however, BRD4 may work as negative regulator of transcription. Here, we reported that BRD4 binds several long noncoding RNAs (lncRNA). Among these, the lncRNA NEAT1 was found to interfere with BRD4 transcriptional activity. Mechanistically, lncNEAT1 forms a complex with BRD4 and WDR5 and maintains them in a low-activity state. Treatment with Bromodomains and Extraterminal (BET) inhibitor caused the lncRNA NEAT1 to dissociate from the BRD4/WDR5 complex, restored the acetyl-transferase capacity of BRD4, and restored the availability of WDR5 to promote histone trimethylation, thereby promoting BRD4/WDR5 transcriptional activity and activation of target gene expression. In addition, the lncRNA NEAT1 then became available to bind and to inhibit EZH2, cooperatively increasing transcriptional activation. IMPLICATIONS: Our results revealed an epigenetic program that involves the interaction between the lncRNA NEAT1 and BRD4, functioning as a molecular switch between BRD4's activator and repressor chromatin complexes.

A Novel Epigenetic Machine Learning Model to Define Risk of Progression for Hepatocellular Carcinoma Patients.

Although extensive advancements have been made in treatment against hepatocellular carcinoma (HCC), the prognosis of HCC patients remains unsatisfied. It is now clearly established that extensive epigenetic changes act as a driver in human tumors. This study exploits HCC epigenetic deregulation to define a novel prognostic model for monitoring the progression of HCC. We analyzed the genome-wide DNA methylation profile of 374 primary tumor specimens using the Illumina 450 K array data from The Cancer Genome Atlas. We initially used a novel combination of Machine Learning algorithms (Recursive Features Selection, Boruta) to capture early tumor progression features. The subsets of probes obtained were used to train and validate Random Forest models to predict a Progression Free Survival greater or less than 6 months. The model based on 34 epigenetic probes showed the best performance, scoring 0.80 accuracy and 0.51 Matthews Correlation Coefficient on testset. Then, we generated and validated a progression signature based on 4 methylation probes capable of stratifying HCC patients at high and low risk of progression. Survival analysis showed that high risk patients are characterized by a poorer progression free survival compared to low risk patients. Moreover, decision curve analysis confirmed the strength of this predictive tool over conventional clinical parameters. Functional enrichment analysis highlighted that high risk patients differentiated themselves by the upregulation of proliferative pathways. Ultimately, we propose the oncogenic MCM2 gene as a methylation-driven gene of which the representative epigenetic markers could serve both as predictive and prognostic markers. Briefly, our work provides several potential HCC progression epigenetic biomarkers as well as a new signature that may enhance patients surveillance and advances in personalized treatment.

Prognostic role of visceral fat for overall survival in metastatic colorectal cancer: A pilot study.

BACKGROUND: Body composition, has been established as a risk factor for colorectal cancer diagnosis and disease progression. Aim of this study was to investigate the prognostic role of adiposity, especially visceral fat (VAT), in patients (pts) with metastatic colorectal cancer (MCRC). MATERIAL AND METHODS: A retrospective cohort of 71 MCRC pts treated between 2013 and 2017 was evaluated. VAT was measured as cross-sectional (cm2) area at the L3 level divided by the square of the height (m2). A ROC analysis was performed to define a prognostic threshold according to VAT. RESULTS: Before first-line therapy start, 40 pts (56%) had a body mass index (BMI) > 25 kg/m2. The obtained cut-off value for VAT was 44. Median OS was 30.97 months. At univariate analysis, primary tumor resection (HR 0.40, p = 0.029), VAT>44 (HR 2.85, p = 0.011) and metastasectomy (HR 0.22, p = 0.005) were significantly associated with OS. By multivariate analysis, VAT>44 (HR 2.6; p = 0.020) and metastasectomy were still significantly associated with OS. CONCLUSION: This exploratory study suggests a prognostic role for VAT in MCRC pts, with higher VAT values predicting worse outcome.

Understanding the organ tropism of metastatic breast cancer through the combination of liquid biopsy tools.

BACKGROUND: Liquid biopsy provides real-time data about prognosis and actionable mutations in metastatic breast cancer (MBC). The aim of this study was to explore the combination of circulating tumour DNA (ctDNA) analysis and circulating tumour cells (CTCs) enumeration in estimating target organs more susceptible to MBC involvement. METHODS: This retrospective study analysed 88 MBC patients characterised for both CTCs and ctDNA at baseline. CTCs were isolated through the CellSearch kit, while ctDNA was analysed using the Guardant360 NGS-based assay. Sites of disease were collected on the basis of imaging. Associations were explored both through uni- and multivariate logistic regression and Fisher's exact test and the random forest machine learning algorithm. RESULTS: After multivariate logistic regression, ESR1 mutation was the only significant factor associated with liver metastases (OR 8.10), while PIK3CA was associated with lung localisations (OR 3.74). CTC enumeration was independently associated with bone metastases (OR 10.41) and TP53 was associated with lymph node localisations (OR 2.98). The metastatic behaviour was further investigated through a random forest machine learning algorithm. Bone involvement was described by CTC enumeration and alterations in ESR1, GATA3, KIT, CDK4 and ERBB2, while subtype, CTC enumeration, inflammatory BC diagnosis, ESR1 and KIT aberrations described liver metastases. PIK3CA, MET, AR, CTC enumeration and TP53 were associated with lung organotropism. The model, moreover, showed that AR, CCNE1, ESR1, MYC and CTC enumeration were the main drivers in HR positive MBC metastatic pattern. CONCLUSIONS: These results indicate that ctDNA and CTCs enumeration could provide useful insights regarding MBC organotropism, suggesting a possible role for future monitoring strategies that dynamically focus on high-risk organs defined by tumourbiology.

The Molecular and Microenvironmental Landscape of Glioblastomas: Implications for the Novel Treatment Choices.

Glioblastoma (GBM) is the most frequent and aggressive primary central nervous system tumor. Surgery followed by radiotherapy and chemotherapy with alkylating agents constitutes standard first-line treatment of GBM. Complete resection of the GBM tumors is generally not possible given its high invasive features. Although this combination therapy can prolong survival, the prognosis is still poor due to several factors including chemoresistance. In recent years, a comprehensive characterization of the GBM-associated molecular signature has been performed. This has allowed the possibility to introduce a more personalized therapeutic approach for GBM, in which novel targeted therapies, including those employing tyrosine kinase inhibitors (TKIs), could be employed. The GBM tumor microenvironment (TME) exerts a key role in GBM tumor progression, in particular by providing an immunosuppressive state with low numbers of tumor-infiltrating lymphocytes (TILs) and other immune effector cell types that contributes to tumor proliferation and growth. The use of immune checkpoint inhibitors (ICIs) has been successfully introduced in numerous advanced cancers as well as promising results have been shown for the use of these antibodies in untreated brain metastases from melanoma and from non-small cell lung carcinoma (NSCLC). Consequently, the use of PD-1/PD-L1 inhibitors has also been proposed in several clinical trials for the treatment of GBM. In the present review, we will outline the main GBM molecular and TME aspects providing also the grounds for novel targeted therapies and immunotherapies using ICIs for GBM.

Clonal Selection of a Novel Deleterious TP53 Somatic Mutation Discovered in ctDNA of a KIT/PDGFRA Wild-Type Gastrointestinal Stromal Tumor Resistant to Imatinib.

The standard of care for the first-line treatment of advanced gastrointestinal stromal tumor (GIST) is represented by imatinib, which is given daily at a standard dosage until tumor progression. Resistance to imatinib commonly occurs through the clonal selection of genetic mutations in the tumor DNA, and an increase in imatinib dosage was demonstrated to be efficacious to overcome imatinib resistance. Wild-type GISTs, which do not display KIT or platelet-derived growth factor receptor alpha (PDGFRA) mutations, are usually primarily insensitive to imatinib and tend to rapidly relapse in course of treatment. Here we report the case of a 53-year-old male patient with gastric GIST who primarily did not respond to imatinib and that, despite the administration of an increased imatinib dose, led to patient death. By using a deep next-generation sequencing barcode-aware approach, we analyzed a panel of actionable cancer-related genes in the patient cfDNA to investigate somatic changes responsible for imatinib resistance. We identified, in two serial circulating tumor DNA (ctDNA) samples, a sharp increase in the allele frequency of a never described TP53 mutation (c.560-7_560-2delCTCTTAinsT) located in a splice acceptor site and responsible for a protein loss of function. The same TP53 mutation was retrospectively identified in the primary tumor by digital droplet PCR at a subclonal frequency (0.1%). The mutation was detected at a very high allelic frequency (99%) in the metastatic hepatic lesion, suggesting a rapid clonal selection of the mutation during tumor progression. Imatinib plasma concentration at steady state was above the threshold of 760 ng/ml reported in the literature for the minimum efficacious concentration. The de novo TP53 (c.560-7_560-2delCTCTTAinsT) mutation was in silico predicted to be associated with an aberrant RNA splicing and with an aggressive phenotype which might have contributed to a rapid disease spread despite the administration of an increased imatinib dosage. This result underlies the need of a better investigation upon the role of TP53 in the pathogenesis of GISTs and sustains the use of next-generation sequencing (NGS) in cfDNA for the identification of novel genetic markers in wild-type GISTs.

A Novel Comprehensive Clinical Stratification Model to Refine Prognosis of Glioblastoma Patients Undergoing Surgical Resection.

Despite recent discoveries in genetics and molecular fields, glioblastoma (GBM) prognosis still remains unfavorable with less than 10% of patients alive 5 years after diagnosis. Numerous studies have focused on the research of biological biomarkers to stratify GBM patients. We addressed this issue in our study by using clinical/molecular and image data, which is generally available to Neurosurgical Departments in order to create a prognostic score that can be useful to stratify GBM patients undergoing surgical resection. By using the random forest approach [CART analysis (classification and regression tree)] on Survival time data of 465 cases, we developed a new prediction score resulting in 10 groups based on extent of resection (EOR), age, tumor volumetric features, intraoperative protocols and tumor molecular classes. The resulting tree was trimmed according to similarities in the relative hazard ratios amongst groups, giving rise to a 5-group classification tree. These 5 groups were different in terms of overall survival (OS) (p < 0.000). The score performance in predicting death was defined by a Harrell's c-index of 0.79 (95% confidence interval [0.76-0.81]). The proposed score could be useful in a clinical setting to refine the prognosis of GBM patients after surgery and prior to postoperative treatment.

A Pan-Cancer Approach to Predict Responsiveness to Immune Checkpoint Inhibitors by Machine Learning.

Immunotherapy by using immune checkpoint inhibitors (ICI) has dramatically improved the treatment options in various cancers, increasing survival rates for treated patients. Nevertheless, there are heterogeneous response rates to ICI among different cancer types, and even in the context of patients affected by a specific cancer. Thus, it becomes crucial to identify factors that predict the response to immunotherapeutic approaches. A comprehensive investigation of the mutational and immunological aspects of the tumor can be useful to obtain a robust prediction. By performing a pan-cancer analysis on gene expression data from the Cancer Genome Atlas (TCGA, 8055 cases and 29 cancer types), we set up and validated a machine learning approach to predict the potential for positive response to ICI. Support vector machines (SVM) and extreme gradient boosting (XGboost) models were developed with a 10×5-fold cross-validation schema on 80% of TCGA cases to predict ICI responsiveness defined by a score combining tumor mutational burden and TGF- ß signaling. On the remaining 20% validation subset, our SVM model scored 0.88 accuracy and 0.27 Matthews Correlation Coefficient. The proposed machine learning approach could be useful to predict the putative response to ICI treatment by expression data of primary tumors.

NR4A3 fusion proteins trigger an axon guidance switch that marks the difference between EWSR1 and TAF15 translocated extraskeletal myxoid chondrosarcomas.

Extraskeletal myxoid chondrosarcoma (EMC) is a rare sarcoma histotype with uncertain differentiation. EMC is hallmarked by the rearrangement of the NR4A3 gene, which in most cases fuses with EWSR1 or TAF15. TAF15-translocated EMC seem to feature a more aggressive course compared to EWSR1-positive EMCs, but whether the type of NR4A3 chimera impinges upon EMC biology is still largely undefined. To gain insights on this issue, a series of EMC samples (7 EWSR1-NR4A3 and 5 TAF15-NR4A3) were transcriptionally profiled. Our study unveiled that the two EMC variants display a distinct transcriptional profile and that the axon guidance pathway is a major discriminant. In particular, class 4-6 semaphorins and axonal guidance cues endowed with pro-tumorigenic activity were more expressed in TAF15-NR4A3 tumors; vice versa, class 3 semaphorins, considered to convey growth inhibitory signals, were more abundant in EWSR1-NR4A3 EMC. Intriguingly, the dichotomy in axon guidance signaling observed in the two tumor variants was recapitulated in in vitro cell models engineered to ectopically express EWSR1-NR4A3 or TAF15-NR4A3. Moreover, TAF15-NR4A3 cells displayed a more pronounced tumorigenic potential, as assessed by anchorage-independent growth. Overall, our results indicate that the type of NR4A3 chimera dictates an axon guidance switch and impacts on tumor cell biology. These findings may provide a framework for interpretation of the different clinical-pathological features of the two EMC variants and lay down the bases for the development of novel patient stratification criteria and therapeutic approaches. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Neurofibromin C terminus-specific antibody (clone NFC) is a valuable tool for the identification of NF1-inactivated GISTs.

An increasing body of evidence supports the involvement of NF1 mutations, constitutional or somatic, in the pathogenesis of gastrointestinal stromal tumors (GISTs). Due to the large size of the NF1 locus, the existence of multiple pseudogenes and the wide spectrum of mechanisms of gene inactivation, the analysis of NF1 gene status is still challenging for most laboratories. Here we sought to assess the efficacy of a recently developed neurofibromin-specific antibody (NFC) in detecting NF1-inactivated GISTs. NFC reactivity was analyzed in a series of 98 GISTs. Of these, 29 were 'NF1-associated' (17 with ascertained NF1 mutations and 12 arising in the context of clinically diagnosed Neurofibromatosis type 1 syndrome and thus considered bona fine NF1 inactivated); 38 were 'NF1-unrelated' (either wild-type or carrying non-pathogenic variants of NF1). Thirty-one additional GISTs with no available information on NF1 gene status or with NF1 gene variants of uncertain pathogenic significance were also included in the analysis. Cases were scored as NFC negative when, in the presence of NFC positive internal controls, no cytoplasmic staining was detected in the neoplastic cells. NFC immunoreactivity was lost in 24/29 (83%) NF1-associated GISTs as opposed to only 2/38 (5%) NF1-unrelated GISTs (P=3e-11). NFC staining loss significantly correlated (P=0.007) with the presence of biallelic NF1 inactivation, due essentially to large deletions or truncating mutations. NFC reactivity was instead retained in two cases in which the NF1 alteration was heterozygous and in one case where the pathogenic NF1 variant, although homo/hemizygous, was a missense mutation predicted not to affect neurofibromin half-life. Overall this study provides evidence that NFC is a valuable tool for identifying NF1-inactivated GISTs, thus serving as a surrogate for molecular analysis.