In patients with metastatic breast cancer the identification of circulating tumor cells in epithelial-to-mesenchymal transition is associated with a poor prognosis.

BACKGROUND: Although recent models suggest that the detection of Circulating Tumor Cells (CTC) in epithelial-to-mesenchymal transition (EM CTC) might be related to disease progression in metastatic breast cancer (MBC) patients, current detection methods are not efficient in identifying this subpopulation of cells. Furthermore, the possible association of EM CTC with both clinicopathological features and prognosis of MBC patients has still to be demonstrated. Aims of this study were: first, to optimize a DEPArray-based protocol meant to identify, quantify and sort single, viable EM CTC and, subsequently, to test the association of EM CTC frequency with clinical data. METHODS: This prospective observational study enrolled 56 MBC patients regardless of the line of treatment. Blood samples, depleted of CD45(pos) leukocytes, were stained with an antibody cocktail recognizing both epithelial and mesenchymal markers. Four CD45(neg) cell subpopulations were identified: cells expressing only epithelial markers (E CTC), cells co-expressing epithelial and mesenchymal markers (EM CTC), cells expressing only mesenchymal markers (MES) and cells negative for every tested marker (NEG). CTC subpopulations were quantified as both absolute cell count and relative frequency. The association of CTC subpopulations with clinicopathological features, progression free survival (PFS), and overall survival (OS) was explored by Wilcoxon-Mann-Whitney test and Univariate Cox Regression Analysis, respectively. RESULTS: By employing the DEPArray-based strategy, we were able to assess the presence of cells pertaining to the above-described classes in every MBC patient. We observed a significant association between specific CD45(neg) subpopulations and tumor subtypes (e.g. NEG and triple negative), proliferation (NEG and Ki67 expression) and sites of metastatic spread (e.g. E CTC and bone; NEG and brain). Importantly, the fraction of CD45(neg) cells co-expressing epithelial and mesenchymal markers (EM CTC) was significantly associated with poorer PFS and OS, computed, this latter, both from the diagnosis of a stage IV disease and from the initial CTC assessment. CONCLUSION: This study suggests the importance of dissecting the heterogeneity of CTC in MBC. Precise characterization of CTC could help in estimating both metastatization pattern and outcome, driving clinical decision-making and surveillance strategies.

Dissecting the Heterogeneity of Circulating Tumor Cells in Metastatic Breast Cancer: Going Far Beyond the Needle in the Haystack.

Although the enumeration of circulating tumor cells (CTC) defined as expressing both epithelial cell adhesion molecule and cytokeratins (EpCAM⁺/CK⁺) can predict prognosis and response to therapy in metastatic breast, colon and prostate cancer, its clinical utility (i.e., the ability to improve patient outcome by guiding therapy) has not yet been proven in clinical trials. Therefore, scientists are now focusing on the molecular characterization of CTC as a way to explore its possible use as a "surrogate" of tumor tissues to non-invasively assess the genomic landscape of the cancer and its evolution during treatment. Additionally, evidences confirm the existence of CTC in epithelial-to-mesenchymal transition (EMT) characterized by a variable loss of epithelial markers. Since the EMT process can originate cells with enhanced invasiveness, stemness and drug-resistance, the enumeration and characterization of this population, perhaps the one truly responsible of tumor recurrence and progression, could be more clinically useful. For these reasons, several devices able to capture CTC independently from the expression of epithelial markers have been developed. In this review, we will describe the types of heterogeneity so far identified and the key role played by the epithelial-to-mesenchymal transition in driving CTC heterogeneity. The clinical relevance of detecting CTC-heterogeneity will be discussed as well.

A Method for Detecting Circulating Tumor Cells Based on the Measurement of Single-Cell Metabolism in Droplet-Based Microfluidics.

The number of circulating tumor cells (CTCs) in blood is strongly correlated with the progress of metastatic cancer. Current methods to detect CTCs are based on immunostaining or discrimination of physical properties. Herein, a label-free method is presented exploiting the abnormal metabolic behavior of cancer cells. A single-cell analysis technique is used to measure the secretion of acid from individual living tumor cells compartmentalized in microfluidically prepared, monodisperse, picoliter (pL) droplets. As few as 10 tumor cells can be detected in a background of 200 000 white blood cells and proof-of-concept data is shown on the detection of CTCs in the blood of metastatic patients.

A fully interpretable machine learning model for increasing the effectiveness of urine screening.

OBJECTIVES: This article addresses the need for effective screening methods to identify negative urine samples before urine culture, reducing the workload, cost, and release time of results in the microbiology laboratory. We try to overcome the limitations of current solutions, which are either too simple, limiting effectiveness (1 or 2 parameters), or too complex, limiting interpretation, trust, and real-world implementation ("black box" machine learning models). METHODS: The study analyzed 15,312 samples from 10,534 patients with clinical features and the Sysmex Uf-1000i automated analyzer data. Decision tree (DT) models with or without lookahead strategy were used, as they offer a transparent set of logical rules that can be easily understood by medical professionals and implemented into automated analyzers. RESULTS: The best model achieved a sensitivity of 94.5% and classified negative samples based on age, bacteria, mucus, and 2 scattering parameters. The model reduced the workload by an additional 16% compared to the current procedure in the laboratory, with an estimated financial impact of €40,000/y considering 15,000 samples/y. Identified logical rules have a scientific rationale matched to existing knowledge in the literature. CONCLUSIONS: Overall, this study provides an effective and interpretable screening method for urine culture in microbiology laboratories, using data from the Sysmex UF-1000i automated analyzer. Unlike other machine learning models, our model is interpretable, generating trust and enabling real-world implementation.

Anti-VEGF therapy selects for clones resistant to glucose starvation in ovarian cancer xenografts.

BACKGROUND: Genetic and metabolic heterogeneity are well-known features of cancer and tumors can be viewed as an evolving mix of subclonal populations, subjected to selection driven by microenvironmental pressures or drug treatment. In previous studies, anti-VEGF therapy was found to elicit rewiring of tumor metabolism, causing marked alterations in glucose, lactate ad ATP levels in tumors. The aim of this study was to evaluate whether differences in the sensitivity to glucose starvation existed at the clonal level in ovarian cancer cells and to investigate the effects induced by anti-VEGF therapy on this phenotype by multi-omics analysis. METHODS: Clonal populations, obtained from both ovarian cancer cell lines (IGROV-1 and SKOV3) and tumor xenografts upon glucose deprivation, were defined as glucose deprivation resistant (GDR) or glucose deprivation sensitive (GDS) clones based on their in vitro behaviour. GDR and GDS clones were characterized using a multi-omics approach, including genetic, transcriptomic and metabolic analysis, and tested for their tumorigenic potential and reaction to anti-angiogenic therapy. RESULTS: Two clonal populations, GDR and GDS, with strikingly different viability following in vitro glucose starvation, were identified in ovarian cancer cell lines. GDR clones survived and overcame glucose starvation-induced stress by enhancing mitochondrial oxidative phosphorylation (OXPHOS) and both pyruvate and lipids uptake, whereas GDS clones were less able to adapt and died. Treatment of ovarian cancer xenografts with the anti-VEGF drug bevacizumab positively selected for GDR clones that disclosed increased tumorigenic properties in NOD/SCID mice. Remarkably, GDR clones were more sensitive than GDS clones to the mitochondrial respiratory chain complex I inhibitor metformin, thus suggesting a potential therapeutic strategy to target the OXPHOS-metabolic dependency of this subpopulation. CONCLUSION: A glucose-deprivation resistant population of ovarian cancer cells showing druggable OXPHOS-dependent metabolic traits is enriched in experimental tumors treated by anti-VEGF therapy.

Integration of Cellular and Humoral Immune Responses as an Immunomonitoring Tool for SARS-CoV-2 Vaccination in Healthy and Fragile Subjects.

Cellular and humoral immunity are both required for SARS-CoV-2 infection recovery and vaccine efficacy. The factors affecting mRNA vaccination-induced immune responses, in healthy and fragile subjects, are still under investigation. Thus, we monitored the vaccine-induced cellular and humoral immunity in healthy subjects and cancer patients after vaccination to define whether a different antibody titer reflected similar rates of cellular immune responses and if cancer has an impact on vaccination efficacy. We found that higher titers of antibodies were associated with a higher probability of positive cellular immunity and that this greater immune response was correlated with an increased number of vaccination side effects. Moreover, active T-cell immunity after vaccination was associated with reduced antibody decay. The vaccine-induced cellular immunity appeared more likely in healthy subjects rather than in cancer patients. Lastly, after boosting, we observed a cellular immune conversion in 20% of subjects, and a strong correlation between pre- and post-boosting IFN-γ levels, while antibody levels did not display a similar association. Finally, our data suggested that integrating humoral and cellular immune responses could allow the identification of SARS-CoV-2 vaccine responders and that T-cell responses seem more stable over time compared to antibodies, especially in cancer patients.

An antivenin resistant, IVIg-corticosteroids responsive viper induced thrombocytopenia.

In this case report the hospital management of an acute, severe thrombocytopenia in a 57-year-old man in the north-east of Italy is reported. Thrombocytopenia developed immediately after the viper bite, despite the absence of clinical signs of envenomation. No hemorrhage, ecchymoses or other signs of coagulopathy developed during the hospitalization; two doses of antivenin FAB-Fragments had no effect on thrombocytopenia, which instead responded promptly to intravenous immunoglobulins (IVIg) and glucocorticoids. Direct and indirect anti-platelet antibodies against anti-GP IIb/IIIa and Ia/IIa were detected during the treatment and turned negative after 20 weeks. The rationale of such off-label treatment is the interpretation of the thrombocytopenia as a venom-induced immune thrombocytopenia which led to splenic sequestration of platelets. To our knowledge, there is no literature about venom-induced immune thrombocytopenia against GP IIb/IIIa and Ia/IIa protein in European countries and subsequent response to IVIg and corticosteroids.

Image Analysis of Circulating Tumor Cells and Leukocytes Predicts Survival and Metastatic Pattern in Breast Cancer Patients.

BACKGROUND: The purpose of the present work was to test whether quantitative image analysis of circulating cells can provide useful clinical information targeting bone metastasis (BM) and overall survival (OS >30 months) in metastatic breast cancer (MBC). METHODS: Starting from cell images of epithelial circulating tumor cells (eCTC) and leukocytes (CD45pos) obtained with DEPArray, we identified the most significant features and applied single-variable and multi-variable methods, screening all combinations of four machine-learning approaches (Naïve Bayes, Logistic regression, Decision Trees, Random Forest). RESULTS: Best predictive features were circularity (OS) and diameter (BM), in both eCTC and CD45pos. Median difference in OS was 15 vs. 43 (months), p = 0.03 for eCTC and 19 vs. 36, p = 0.16 for CD45pos. Prediction for BM showed low accuracy (64%, 53%) but strong positive predictive value PPV (79%, 91%) for eCTC and CD45, respectively. Best machine learning model was Naïve Bayes, showing 46 vs 11 (months), p <0.0001 for eCTC; 12.5 vs. 45, p = 0.0004 for CD45pos and 11 vs. 45, p = 0.0003 for eCTC + CD45pos. BM prediction reached 91% accuracy with eCTC, 84% with CD45pos and 91% with combined model. CONCLUSIONS: Quantitative image analysis and machine learning models were effective methods to predict survival and metastatic pattern, with both eCTC and CD45pos containing significant and complementary information.

Clinical relevance of the combined analysis of circulating tumor cells and anti-tumor T-cell immunity in metastatic breast cancer patients.

Background: Metastatic breast cancer (mBC) is a heterogeneous disease with varying responses to treatments and clinical outcomes, still requiring the identification of reliable predictive biomarkers. In this context, liquid biopsy has emerged as a powerful tool to assess in real-time the evolving landscape of cancer, which is both orchestrated by the metastatic process and immune-surveillance mechanisms. Thus, we investigated circulating tumor cells (CTCs) coupled with peripheral T-cell immunity to uncover their potential clinical relevance in mBC. Methods: A cohort of 20 mBC patients was evaluated, before and one month after starting therapy, through the following liquid biopsy approaches: CTCs enumerated by a metabolism-based assay, T-cell responses against tumor-associated antigens (TAA) characterized by interferon-γ enzyme-linked immunosorbent spot (ELISpot), and the T-cell receptor (TCR) repertoire investigated by a targeted next-generation sequencing technique. TCR repertoire features were characterized by the Morisita's overlap and the Productive Simpson Clonality indexes, and the TCR richness. Differences between groups were calculated by Fisher's, Mann-Whitney or Kruskal-Wallis test, as appropriate. Prognostic data analysis was estimated by Kaplan-Meier method. Results: Stratifying patients for their prognostic level of 6 CTCs before therapy, TAA specific T-cell responses were detected only in patients with a low CTC level. By analyzing the TCR repertoire, the highest TCR clonality was observed in the case of CTCs under the cut-off and a positive ELISpot response (p=0.03). Whereas, at follow-up, patients showing a good clinical response coupled with a low number of CTCs were characterized by the most elevated TCR clonality (p<0.05). The detection of CTCs≥6 in at least one time-point was associated with a lower TCR clonality (p=0.02). Intriguingly, by combining overall survival analysis with TCR repertoire, we highlighted a potential prognostic role of the TCR clonality measured at follow-up (p=0.03). Conclusion: These data, whether validated in a larger cohort of patients, suggest that the combined analysis of CTCs and circulating anti-tumor T-cell immunity could represent a valuable immune-oncological biomarker for the liquid biopsy field. The clinical application of this promising tool could improve the management of mBC patients, especially in the setting of immunotherapy, a rising approach for BC treatment requiring reliable predictive biomarkers.

IgG antibodies against SARS-CoV-2 decay but persist 4 months after vaccination in a cohort of healthcare workers.

BACKGROUND AND AIMS: Monitoring the immune response against SARS-CoV-2 is pivotal in the evaluation of long-term vaccine efficacy. Immunoglobulin G (IgG) antibodies represent an advisable tool to reach this goal, especially for the still poorly defined antibody trend induced by the new class of mRNA vaccines against SARS-CoV-2. MATERIALS AND METHODS: Anti-Spike RBD IgG antibodies were monitored in a cohort of healthcare workers at CRO Aviano, National Cancer Institute, through MAGLUMI® chemiluminescence assay, at 1 and 4 months after full-schedule of BNT162b2 or mRNA-1273 vaccination. RESULTS: At 1 month after vaccination, 99.9% of 767 healthcare workers showed a reactive antibody response, which was inversely correlated with age, and positively associated with a previous history of COVID-19, and mRNA-1273 vaccination. Serological response was maintained in 99.6% of the 516 subjects monitored also at follow-up. An antibody decay from 559.8 AU/mL (IQR 359.7-845.7) to 92.7 AU/mL (IQR 65.1-148.6; p < 0.001) was observed, independently from age and sex. CONCLUSION: Our data supported the ability of SARS-CoV-2 mRNA vaccines to induce at least a 4 months-lasting IgG response, even outside the rules of clinical trials. The antibody decay observed at follow-up suggested to deepen the immune response characterization to identify subjects with low anti-SARS-CoV-2 immunity possibly requiring a vaccination boost.

Dysmetabolic Circulating Tumor Cells Are Prognostic in Metastatic Breast Cancer.

Circulating tumor cells (CTCs) belong to a heterogeneous pool of rare cells, and a unequivocal phenotypic definition of CTC is lacking. Here, we present a definition of metabolically-altered CTC (MBA-CTCs) as CD45-negative cells with an increased extracellular acidification rate, detected with a single-cell droplet microfluidic technique. We tested the prognostic value of MBA-CTCs in 31 metastatic breast cancer patients before starting a new systemic therapy (T0) and 3-4 weeks after (T1), comparing results with a parallel FDA-approved CellSearch (CS) approach. An increased level of MBA-CTCs was associated with: i) a shorter median PFS pre-therapy (123 days vs. 306; p < 0.0001) and during therapy (139 vs. 266 days; p = 0.0009); ii) a worse OS pre-therapy (p = 0.0003, 82% survival vs. 20%) and during therapy (p = 0.0301, 67% survival vs. 38%); iii) good agreement with therapy response (kappa = 0.685). The trend of MBA-CTCs over time (combining data at T0 and T1) added information with respect to separate evaluation of T0 and T1. The combined results of the two assays (MBA and CS) increased stratification accuracy, while correlation between MBA and CS was not significant, suggesting that the two assays are detecting different CTC subsets. In conclusion, this study suggests that MBA allows detection of both EpCAM-negative and EpCAM-positive, viable and label-free CTCs, which provide clinical information apparently equivalent and complementary to CS. A further validation of proposed method and cut-offs is needed in a larger, separate study.

Application of an Artificial Intelligence Algorithm to Prognostically Stratify Grade II Gliomas.

(1) Background: Recently, it has been shown that the extent of resection (EOR) and molecular classification of low-grade gliomas (LGGs) are endowed with prognostic significance. However, a prognostic stratification of patients able to give specific weight to the single parameters able to predict prognosis is still missing. Here, we adopt classic statistics and an artificial intelligence algorithm to define a multiparametric prognostic stratification of grade II glioma patients. (2) Methods: 241 adults who underwent surgery for a supratentorial LGG were included. Clinical, neuroradiological, surgical, histopathological and molecular data were assessed for their ability to predict overall survival (OS), progression-free survival (PFS), and malignant progression-free survival (MPFS). Finally, a decision-tree algorithm was employed to stratify patients. (3) Results: Classic statistics confirmed EOR, pre-operative- and post-operative tumor volumes, Ki67, and the molecular classification as independent predictors of OS, PFS, and MPFS. The decision tree approach provided an algorithm capable of identifying prognostic factors and defining both the cut-off levels and the hierarchy to be used in order to delineate specific prognostic classes with high positive predictive value. Key results were the superior role of EOR on that of molecular class, the importance of second surgery, and the role of different prognostic factors within the three molecular classes. (4) Conclusions: This study proposes a stratification of LGG patients based on the different combinations of clinical, molecular, and imaging data, adopting a supervised non-parametric learning method. If validated in independent case studies, the clinical utility of this innovative stratification approach might be proved.

Sleeping beauty genetic screen identifies miR-23b::BTBD7 gene interaction as crucial for colorectal cancer metastasis.

BACKGROUND: Metastatic colorectal cancer (CRC) remains a deadly disease. Identifying locally advanced CRC patients with high risk of developing metastasis and improving outcome of metastatic CRC patients require discovering master regulators of metastasis. In this context, the non-coding part of the human genome is still largely unexplored. METHODS: To interrogate the non-coding part of the human genome and disclose regulators of CRC metastasis, we combined a transposon-based forward genetic screen with a novel in vitro assay, which forces cells to grow deprived of cell-substrate and cell-cell contacts (i.e. forced single cell suspension assay - fSCS). FINDINGS: We proved that fSCS selects CRC cells with mesenchymal and pro-metastatic traits. Moreover, we found that the transposon insertions conferred CRC cells resistance to fSCS and thus metastatic advantage. Among the retrieved transposon insertions, we demonstrated that the one located in the 3'UTR of BTBD7 disrupts miR-23b::BTBD7 interaction and contributes to pro-metastatic traits. In addition, miR-23b and BTBD7 correlate with CRC metastasis both in preclinical experiments and in clinical samples. INTERPRETATION: fSCS is a simple and scalable in vitro assay to investigate pro-metastatic traits and transposon-based genetic screens can interrogate the non-coding part of the human genome (e.g. miRNA::target interactions). Finally, both Btbd7 and miR-23b represent promising prognostic biomarkers and therapeutic targets in CRC. FUND: This work was supported by Marie Curie Actions (CIG n. 303877) and Friuli Venezia Giulia region (Grant Agreement n°245574), Italian Association for Cancer Research (AIRC, MFAG n°13589), Italian Ministry of Health (GR-2010-2319387 and PE-2016-02361040) and 5x1000 to CRO Aviano.

Assessment of the Mutational Status of NSCLC Using Hypermetabolic Circulating Tumor Cells.

Molecular characterization is currently a key step in NSCLC therapy selection. Circulating tumor cells (CTC) are excellent candidates for downstream analysis, but technology is still lagging behind. In this work, we show that the mutational status of NSCLC can be assessed on hypermetabolic CTC, detected by their increased glucose uptake. We validated the method in 30 Stage IV NSCLC patients: peripheral blood samples were incubated with a fluorescent glucose analog (2-NBDG) and analyzed by flow cytometry. Cells with the highest glucose uptake were sorted out. EGFR and KRAS mutations were detected by ddPCR. In sorted cells, mutated DNA was found in 85% of patients, finding an exact match with primary tumor in 70% of cases. Interestingly, in two patients multiple KRAS mutations were detected. Two patients displayed different mutations with respect to the primary tumor, and in two out of the four patients with a wild type primary tumor, new mutations were highlighted: EGFR p.746_750del and KRAS p.G12V. Hypermetabolic CTC can be enriched without the need of dedicated equipment and their mutational status can successfully be assessed by ddPCR. Finally, the finding of new mutations supports the possibility of probing tumor heterogeneity.

Microfluidic droplets content classification and analysis through convolutional neural networks in a liquid biopsy workflow.

In a recent paper we presented an innovative method of liquid biopsy, for the detection of circulating tumor cells (CTC) in the peripheral blood. Using microfluidics, CTC are individually encapsulated in water-in-oil droplets and selected by their increased rate of extracellular acidification (ECAR). During the analysis, empty or debris-containing droplets are discarded manually by screening images of positive droplets, increasing the operator-dependency and time-consumption of the assay. In this work, we addressed the limitations of the current method integrating computer vision techniques in the analysis. We implemented an automatic classification of droplets using convolutional neural networks, correctly classifying more than 96% of droplets. A second limitation of the technique is that ECAR is computed using an average droplet volume, without considering small variations in extracellular volume which can occur due to the normal variability in the size of the droplets or cells. Here, with the use of neural networks for object detection, we segmented the images of droplets and cells to measure their relative volumes, correcting over- or under-estimation of ECAR, which was present up to 20%. Finally, we evaluated whether droplet images contained additional information. We preliminarily gave a proof-of-concept demonstration showing that white blood cells expression of CD45 can be predicted with 82.9% accuracy, based on bright-field cell images alone. Then, we applied the method to classify acid droplets as coming from metastatic breast cancer patients or healthy donors, obtaining an accuracy of 90.2%.

Emerging Technologies for Cancer Research: Towards Personalized Medicine with Microfluidic Platforms and 3D Tumor Models.

In the present review, we describe three hot topics in cancer research such as circulating tumor cells, exosomes, and 3D environment models. The first section is dedicated to microfluidic platforms for detecting circulating tumor cells, including both affinity-based methods that take advantage of antibodies and aptamers, and "label-free" approaches, exploiting cancer cells physical features and, more recently, abnormal cancer metabolism. In the second section, we briefly describe the biology of exosomes and their role in cancer, as well as conventional techniques for their isolation and innovative microfluidic platforms. In the third section, the importance of tumor microenvironment is highlighted, along with techniques for modeling it in vitro. Finally, we discuss limitations of two-dimensional monolayer methods and describe advantages and disadvantages of different three-dimensional tumor systems for cell-cell interaction analysis and their potential applications in cancer management.

Glucose is a key driver for GLUT1-mediated nanoparticles internalization in breast cancer cells.

The mesenchymal state in cancer is usually associated with poor prognosis due to the metastatic predisposition and the hyper-activated metabolism. Exploiting cell glucose metabolism we propose a new method to detect mesenchymal-like cancer cells. We demonstrate that the uptake of glucose-coated magnetic nanoparticles (MNPs) by mesenchymal-like cells remains constant when the glucose in the medium is increased from low (5.5 mM) to high (25 mM) concentration, while the MNPs uptake by epithelial-like cells is significantly reduced. These findings reveal that the glucose-shell of MNPs plays a major role in recognition of cells with high-metabolic activity. By selectively blocking the glucose transporter 1 channels we showed its involvement in the internalization process of glucose-coated MNPs. Our results suggest that glucose-coated MNPs can be used for metabolic-based assays aimed at detecting cancer cells and that can be used to selectively target cancer cells taking advantage, for instance, of the magnetic-thermotherapy.