Integrating metabolic profiling of pancreatic juice with transcriptomic analysis of pancreatic cancer tissue identifies distinct clinical subgroups.

Introduction: Metabolic reprogramming is a hallmark feature of pancreatic ductal adenocarcinoma (PDAC). A pancreatic juice (PJ) metabolic signature has been reported to be prognostic of oncological outcome for PDAC. Integration of PJ profiling with transcriptomic and spatial characterization of the tumor microenvironment would help in identifying PDACs with peculiar vulnerabilities. Methods: We performed a transcriptomic analysis of 26 PDAC samples grouped into 3 metabolic clusters (M_CL) according to their PJ metabolic profile. We analyzed molecular subtypes and transcriptional differences. Validation was performed by multidimensional imaging on tumor slides. Results: Pancreatic juice metabolic profiling was associated with PDAC transcriptomic molecular subtypes (p=0.004). Tumors identified as M_CL1 exhibited a non-squamous molecular phenotype and demonstrated longer survival. Enrichment analysis revealed the upregulation of immune genes and pathways in M_CL1 samples compared to M_CL2, the group with worse prognosis, a difference confirmed by immunofluorescence on tissue slides. Enrichment analysis of 39 immune signatures by xCell confirmed decreased immune signatures in M_CL2 compared to M_CL1 and allowed a stratification of patients associated with longer survival. Discussion: PJ metabolic fingerprints reflect PDAC molecular subtypes and the immune microenvironment, confirming PJ as a promising source of biomarkers for personalized therapy.

Multiplexed Imaging Mass Cytometry Analysis in Preclinical Models of Pancreatic Cancer.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers. PDAC is characterized by a complex tumor microenvironment (TME), that plays a pivotal role in disease progression and resistance to therapy. Investigating the spatial distribution and interaction of TME cells with the tumor is the basis for understanding the mechanisms underlying disease progression and represents a current challenge in PDAC research. Imaging mass cytometry (IMC) is the major multiplex imaging technology for the spatial analysis of tumor heterogeneity. However, there is a dearth of reports of multiplexed IMC panels for different preclinical mouse models, including pancreatic cancer. We addressed this gap by utilizing two preclinical models of PDAC: the genetically engineered, bearing KRAS-TP53 mutations in pancreatic cells, and the orthotopic, and developed a 28-marker panel for single-cell IMC analysis to assess the abundance, distribution and phenotypes of cells involved in PDAC progression and their reciprocal functional interactions. Herein, we provide an unprecedented definition of the distribution of TME cells in PDAC and compare the diversity between transplanted and genetic disease models. The results obtained represent an important and customizable tool for unraveling the complexities of PDAC and deciphering the mechanisms behind therapy resistance.

The Effect of Positive Pressure Ventilation on Acute Kidney Injury in COVID-19 Patients with Acute Respiratory Distress Syndrome: An Observational Study.

INTRODUCTION: Acute kidney injury (AKI) is frequent in critically ill COVID-19 patients and is associated with a higher mortality risk. By increasing intrathoracic pressure, positive pressure ventilation (PPV) may reduce renal perfusion pressure by reducing venous return to the heart or by increasing renal venous congestion. This study's aim was to evaluate the association between AKI and haemodynamic and ventilatory parameters in COVID-19 patients with ARDS. METHODS: This is a single-centre retrospective observational study. Consecutive patients diagnosed with COVID-19 who met ARDS criteria and required invasive mechanical ventilation were enrolled. The relationship between respiratory and haemodynamic parameters influenced by PPV and AKI development was evaluated. AKI was defined according to KDIGO criteria. AKI recovery was evaluated a month after ICU admission and patients were classified as "recovered," if serum creatinine (sCr) value returned to baseline, or as having "acute kidney disease" (AKD), if criteria for AKI stage 1 or greater persisted. The 6-month all-cause mortality was collected. RESULTS: A total of 144 patients were included in the analysis. AKI occurred in 69 (48%) patients and 26 (18%) required renal replacement therapy. In a multivariate logistic regression analysis, sex, hypertension, cumulative dose of furosemide, fluid balance, and plateau pressure were independently associated with AKI. Mortality at 6 months was 50% in the AKI group and 32% in the non-AKI group (p = 0.03). Among 36 patients who developed AKI and were discharged alive from the hospital, 56% had a full renal recovery after a month, while 14%, 6%, and 14% were classified as having an AKD of stage 0, 2, and 3, respectively. CONCLUSIONS: In our cohort, AKI was independently associated with multiple variables, including high plateau pressure, suggesting a possible role of PPV on AKI development. Further studies are needed to clarify the role of mechanical ventilation on renal function.

Integrating AI-Powered Digital Pathology and Imaging Mass Cytometry Identifies Key Classifiers of Tumor Cells, Stroma, and Immune Cells in Non-Small Cell Lung Cancer.

Artificial intelligence (AI)-powered approaches are becoming increasingly used as histopathologic tools to extract subvisual features and improve diagnostic workflows. On the other hand, hi-plex approaches are widely adopted to analyze the immune ecosystem in tumor specimens. Here, we aimed at combining AI-aided histopathology and imaging mass cytometry (IMC) to analyze the ecosystem of non-small cell lung cancer (NSCLC). An AI-based approach was used on hematoxylin and eosin (H&E) sections from 158 NSCLC specimens to accurately identify tumor cells, both adenocarcinoma and squamous carcinoma cells, and to generate a classifier of tumor cell spatial clustering. Consecutive tissue sections were stained with metal-labeled antibodies and processed through the IMC workflow, allowing quantitative detection of 24 markers related to tumor cells, tissue architecture, CD45+ myeloid and lymphoid cells, and immune activation. IMC identified 11 macrophage clusters that mainly localized in the stroma, except for S100A8+ cells, which infiltrated tumor nests. T cells were preferentially localized in peritumor areas or in tumor nests, the latter being associated with better prognosis, and they were more abundant in highly clustered tumors. Integrated tumor and immune classifiers were validated as prognostic on whole slides. In conclusion, integration of AI-powered H&E and multiparametric IMC allows investigation of spatial patterns and reveals tissue relevant features with clinical relevance. SIGNIFICANCE: Leveraging artificial intelligence-powered H&E analysis integrated with hi-plex imaging mass cytometry provides insights into the tumor ecosystem and can translate tumor features into classifiers to predict prognosis, genotype, and therapy response.

hMENA isoforms regulate cancer intrinsic type I IFN signaling and extrinsic mechanisms of resistance to immune checkpoint blockade in NSCLC.

BACKGROUND: Understanding how cancer signaling pathways promote an immunosuppressive program which sustains acquired or primary resistance to immune checkpoint blockade (ICB) is a crucial step in improving immunotherapy efficacy. Among the pathways that can affect ICB response is the interferon (IFN) pathway that may be both detrimental and beneficial. The immune sensor retinoic acid-inducible gene I (RIG-I) induces IFN activation and secretion and is activated by actin cytoskeleton disturbance. The actin cytoskeleton regulatory protein hMENA, along with its isoforms, is a key signaling hub in different solid tumors, and recently its role as a regulator of transcription of genes encoding immunomodulatory secretory proteins has been proposed. When hMENA is expressed in tumor cells with low levels of the epithelial specific hMENA11a isoform, identifies non-small cell lung cancer (NSCLC) patients with poor prognosis. Aim was to identify cancer intrinsic and extrinsic pathways regulated by hMENA11a downregulation as determinants of ICB response in NSCLC. Here, we present a potential novel mechanism of ICB resistance driven by hMENA11a downregulation. METHODS: Effects of hMENA11a downregulation were tested by RNA-Seq, ATAC-Seq, flow cytometry and biochemical assays. ICB-treated patient tumor tissues were profiled by Nanostring IO 360 Panel enriched with hMENA custom probes. OAK and POPLAR datasets were used to validate our discovery cohort. RESULTS: Transcriptomic and biochemical analyses demonstrated that the depletion of hMENA11a induces IFN pathway activation, the production of different inflammatory mediators including IFNß via RIG-I, sustains the increase of tumor PD-L1 levels and activates a paracrine loop between tumor cells and a unique macrophage subset favoring an epithelial-mesenchymal transition (EMT). Notably, when we translated our results in a clinical setting of NSCLC ICB-treated patients, transcriptomic analysis revealed that low expression of hMENA11a, high expression of IFN target genes and high macrophage score identify patients resistant to ICB therapy. CONCLUSIONS: Collectively, these data establish a new function for the actin cytoskeleton regulator hMENA11a in modulating cancer cell intrinsic type I IFN signaling and extrinsic mechanisms that promote protumoral macrophages and favor EMT. These data highlight the role of actin cytoskeleton disturbance in activating immune suppressive pathways that may be involved in resistance to ICB in NSCLC.

Clinical frailty scale as a predictor of outcome in elderly patients affected by moderate or severe traumatic brain injury.

Background: Older age is a well-known risk factor for unfavorable outcome in traumatic brain injury (TBI). However, many older people with TBI respond well to aggressive treatments, suggesting that chronological age and TBI severity alone may be inadequate prognostic markers. Frailty is an age-related homeostatic imbalance of loss of physiologic and cognitive reserve resulting in both limitation in autonomy of activities of daily living and vulnerability to adverse events. We hypothesized that frailty would be associated with 6-month adverse functional outcome in older people affected by moderate or severe TBI. Methods: This was a single-center prospective observational study. We enrolled consecutive patients aged ≥65 years after TBI with Glasgow Coma Scale ≤13 and admitted to our Neurosurgical Intensive Care Unit. Frailty was evaluated by Clinical Frailty Scale (CFS). Relationships between TBI severity, frailty and extended Glasgow Outcome Scale (GOSE) at 6-month were evaluated. Results: Sixty patients were studied, 65% were males, their age was 76 years (IQR 70-80) and their admission GCS was 8 (IQR 6-11) with a GCS motor score of 5 (IQR 4-5). Twenty eight were vulnerable-frail (defined as CFS ≥ 4). Vulnerable-frail patients showed greater 6-month mortality and unfavorable outcome compared to non-frail [87% vs. 30% OR and 95% CI: 15.7 (3.9-55.2), p < 0.0001 and 92% vs. 51% OR and 95% CI: 9.9 (2.1-46.3), p = 0.002]. In univariate analysis patients with unfavorable outcome were more frequently male and vulnerable-frail, had a higher prevalence of pre-existing neurodegenerative disease, abnormal pupil, lower GCS and had worst CT scan characteristics. At multivariate analysis, only CFS ≥ 4 and traumatic subarachnoid hemorrhage remained associated to 6-month outcome. Conclusion: Frailty was associated with 6 month-outcome, suggesting that the pre-injury functional status could represent an additional indicator to stratify patient's severity and to predict outcome.

Macrophage morphology and distribution are strong predictors of prognosis in resected colorectal liver metastases: results from an external retrospective observational study.

INTRODUCTION: Tumor-associated macrophages (TAMs) are key components of a tumoral microenvironment and have been shown to impact prognosis in different cancers. Previously reported data showed that TAM morphology correlates with prognosis in colorectal liver metastases (CLMs) after hepatectomy, with smaller TAMs (S-TAMs) conferring a more favorable prognosis than larger ones (L-TAMs). This study aims to externally validate this finding. MATERIAL AND METHODS: The external cohort consisted of 84 formalin-fixed and paraffin-embedded surgical samples of CLMs and peritumoral tissue. Two-micrometer-section slides were obtained; the area and perimeter of 21 macrophages in each slide were recorded. The endpoints were TAMs morphometrics and their prognostic significance in relation to disease-free survival (DFS). RESULTS: The average macrophage perimeter was 71.5±14.1 µm whilst the average area was 217.7±67.8 µm 2 . At univariate analysis, the TAM area demonstrated a statistically significant association with DFS ( P =0.0006). Optimal area cutoff value was obtained, showing a sensitivity and specificity of 92 and 56%, respectively. S-TAMs and L-TAMs were associated with 3-year DFS rates of 60 and 8.5%, respectively ( P <0.001). Multivariate analysis confirmed the predictive role of TAM area for DFS [hazard ratio (HR)=5.03; 95% CI=1.70-14.94; P =0.003]. Moreover, in a subset of patients ( n =12) characterized by unfavorable ( n =6, recurrence within 3 months) or favorable ( n =6, no recurrence after 48 months) prognosis, TAMs showed a different distribution: L-TAMs were more abundant and closer to the tumor invasive margin in patients that encountered early recurrence and tended to cluster in foci significantly larger ( P =0.02). CONCLUSIONS: This external validation confirms that morphometric characterization of TAMs can serve as a simple readout of their diversity and allows to reliably stratify patient outcomes and predict disease recurrence after hepatectomy for CLMs.

The Impact of Molecular Subtyping on Pathological Staging of Pancreatic Cancer.

BACKGROUND: The long-term outcomes following surgical resection for pancreatic ductal adenocarcinoma (PDAC) remains poor, with only 20% of patients surviving 5 years after pancreatectomy. Patient selection for surgery remains suboptimal largely due to the absence of consideration of aggressive tumor biology. OBJECTIVE: The aim of this study was to evaluate traditional staging criteria for PDAC in the setting of molecular subtypes. METHODS: Clinicopathological data were obtained for 5 independent cohorts of consecutive unselected patients, totaling n = 1298, including n = 442 that underwent molecular subtyping. The main outcome measure was disease-specific survival following surgical resection for PDAC stratified according to the American Joint Commission for Cancer (TNM) staging criteria, margin status, and molecular subtype. RESULTS: TNM staging criteria and margin status confers prognostic value only in tumors with classical pancreatic subtype. Patients with tumors that are of squamous subtype, have a poor outcome irrespective of favorable traditional pathological staging [hazard ratio (HR) 1.54, 95% confidence interval (CI) 1.04-2.28, P = 0.032]. Margin status has no impact on survival in the squamous subtype (16.0 vs 12.1 months, P = 0.374). There were no differences in molecular subtype or gene expression of tumors with positive resection margin status. CONCLUSIONS: Aggressive tumor biology as measured by molecular subtype predicts poor outcome following pancreatectomy for PDAC and should be utilized to inform patient selection for surgery.

High-Resolution Analysis of Mononuclear Phagocytes Reveals GPNMB as a Prognostic Marker in Human Colorectal Liver Metastasis.

Patients with colorectal liver metastasis (CLM) present with heterogenous clinical outcomes and improved classification is needed to ameliorate the therapeutic output. Macrophages (Mϕ) hold promise as prognostic classifiers and therapeutic targets. Here, stemming from a single-cell analysis of mononuclear phagocytes infiltrating human CLM, we identified two Mϕ markers associated with distinct populations with opposite clinical relevance. The invasive margin of CLM was enriched in pro-inflammatory monocyte-derived Mϕ (MoMϕ) expressing the monocytic marker SERPINB2, and a more differentiated population, tumor-associated Mϕ (TAM), expressing glycoprotein nonmetastatic melanoma protein B (GPNMB). SERPINB2+ MoMϕ had an early inflammatory profile, whereas GPNMB+ TAMs were enriched in pathways of matrix degradation, angiogenesis, and lipid metabolism and were found closer to the tumor margin, as confirmed by spatial transcriptomics on CLM specimens. In a cohort of patients, a high infiltration of SERPINB2+ cells independently associated with longer disease-free survival (DFS; P = 0.033), whereas a high density of GPNMB+ cells correlated with shorter DFS (P = 0.012) and overall survival (P = 0.002). Cell-cell interaction analysis defined opposing roles for MoMϕ and TAMs, suggesting that SERPINB2+ and GPNMB+ cells are discrete populations of Mϕ and may be exploited for further translation to an immune-based stratification tool. This study provides evidence of how multi-omics approaches can identify nonredundant, clinically relevant markers for further translation to immune-based patient stratification tools and therapeutic targets. GPNMB has been shown to set Mϕ in an immunosuppressive mode. Our high dimensional analyses provide further evidence that GPNMB is a negative prognostic indicator and a potential player in the protumor function of Mϕ populations.

Distinct responses of newly identified monocyte subsets to advanced gastrointestinal cancer and COVID-19.

Monocytes are critical cells of the immune system but their role as effectors is relatively poorly understood, as they have long been considered only as precursors of tissue macrophages or dendritic cells. Moreover, it is known that this cell type is heterogeneous, but our understanding of this aspect is limited to the broad classification in classical/intermediate/non-classical monocytes, commonly based on their expression of only two markers, i.e. CD14 and CD16. We deeply dissected the heterogeneity of human circulating monocytes in healthy donors by transcriptomic analysis at single-cell level and identified 9 distinct monocyte populations characterized each by a profile suggestive of specialized functions. The classical monocyte subset in fact included five distinct populations, each enriched for transcriptomic gene sets related to either inflammatory, neutrophil-like, interferon-related, and platelet-related pathways. Non-classical monocytes included two distinct populations, one of which marked specifically by elevated expression levels of complement components. Intermediate monocytes were not further divided in our analysis and were characterized by high levels of human leukocyte antigen (HLA) genes. Finally, we identified one cluster included in both classical and non-classical monocytes, characterized by a strong cytotoxic signature. These findings provided the rationale to exploit the relevance of newly identified monocyte populations in disease evolution. A machine learning approach was developed and applied to two single-cell transcriptome public datasets, from gastrointestinal cancer and Coronavirus disease 2019 (COVID-19) patients. The dissection of these datasets through our classification revealed that patients with advanced cancers showed a selective increase in monocytes enriched in platelet-related pathways. Of note, the signature associated with this population correlated with worse prognosis in gastric cancer patients. Conversely, after immunotherapy, the most activated population was composed of interferon-related monocytes, consistent with an upregulation in interferon-related genes in responder patients compared to non-responders. In COVID-19 patients we confirmed a global activated phenotype of the entire monocyte compartment, but our classification revealed that only cytotoxic monocytes are expanded during the disease progression. Collectively, this study unravels an unexpected complexity among human circulating monocytes and highlights the existence of specialized populations differently engaged depending on the pathological context.

Macrophages as tools and targets in cancer therapy.

Tumour-associated macrophages are an essential component of the tumour microenvironment and have a role in the orchestration of angiogenesis, extracellular matrix remodelling, cancer cell proliferation, metastasis and immunosuppression, as well as in resistance to chemotherapeutic agents and checkpoint blockade immunotherapy. Conversely, when appropriately activated, macrophages can mediate phagocytosis of cancer cells and cytotoxic tumour killing, and engage in effective bidirectional interactions with components of the innate and adaptive immune system. Therefore, they have emerged as therapeutic targets in cancer therapy. Macrophage-targeting strategies include inhibitors of cytokines and chemokines involved in the recruitment and polarization of tumour-promoting myeloid cells as well as activators of their antitumorigenic and immunostimulating functions. Early clinical trials suggest that targeting negative regulators (checkpoints) of myeloid cell function indeed has antitumor potential. Finally, given the continuous recruitment of myelomonocytic cells into tumour tissues, macrophages are candidates for cell therapy with the development of chimeric antigen receptor effector cells. Macrophage-centred therapeutic strategies have the potential to complement, and synergize with, currently available tools in the oncology armamentarium.

Oncogenic KRAS-Induced Protein Signature in the Tumor Secretome Identifies Laminin-C2 and Pentraxin-3 as Useful Biomarkers for the Early Diagnosis of Pancreatic Cancer.

KRAS mutations characterize pancreatic cell transformation from the earliest stages of carcinogenesis, and are present in >95% of pancreatic ductal adenocarcinoma (PDAC) cases. In search of novel biomarkers for the early diagnosis of PDAC, we identified the proteins secreted by the normal human pancreatic cell line (HPDE) recently transformed by inducing the overexpression of the KRASG12V oncogene. We report a proteomic signature of KRAS-induced secreted proteins, which was confirmed in surgical tumor samples from resected PDAC patients. The putative diagnostic performance of three candidates, Laminin-C2 (LAMC2), Tenascin-C (TNC) and Pentraxin-3 (PTX3), was investigated by ELISA quantification in two cohorts of PDAC patients (n = 200) eligible for surgery. Circulating levels of LAMC2, TNC and PTX3 were significantly higher in PDAC patients compared to the healthy individuals (p < 0.0001). The Receiver Operating Characteristics (ROC) curve showed good sensitivity (1) and specificity (0.63 and 0.85) for LAMC2 and PTX3, respectively, but not for TNC, and patients with high levels of LAMC2 had significantly shorter overall survival (p = 0.0007). High levels of LAMC2 and PTX3 were detected at early stages (I−IIB) and in CA19-9-low PDAC patients. In conclusion, pancreatic tumors release LAMC2 and PTX3, which can be quantified in the systemic circulation, and may be useful in selecting patients for further diagnostic imaging.

A topology perspective on macrophages in melanoma metastasis.

In this issue of Cell Reports Medicine, Martinek et al.1 provide a window into the regional specialization of macrophages infiltrating metastatic melanoma. Combining histo-cytometry and transcriptomics, they identify a signature of stromal macrophages with dendritic cell features and clinical relevance.

Development of a Deep-Learning Pipeline to Recognize and Characterize Macrophages in Colo-Rectal Liver Metastasis.

Quantitative analysis of Tumor Microenvironment (TME) provides prognostic and predictive information in several human cancers but, with few exceptions, it is not performed in daily clinical practice since it is extremely time-consuming. We recently showed that the morphology of Tumor Associated Macrophages (TAMs) correlates with outcome in patients with Colo-Rectal Liver Metastases (CLM). However, as for other TME components, recognizing and characterizing hundreds of TAMs in a single histopathological slide is unfeasible. To fasten this process, we explored a deep-learning based solution. We tested three Convolutional Neural Networks (CNNs), namely UNet, SegNet and DeepLab-v3, with three different segmentation strategies, semantic segmentation, pixel penalties and instance segmentation. The different experiments are compared according to the Intersection over Union (IoU), a metric describing the similarity between what CNN predicts as TAM and the ground truth, and the Symmetric Best Dice (SBD), which indicates the ability of CNN to separate different TAMs. UNet and SegNet showed intrinsic limitations in discriminating single TAMs (highest SBD 61.34±2.21), whereas DeepLab-v3 accurately recognized TAMs from the background (IoU 89.13±3.85) and separated different TAMs (SBD 79.00±3.72). This deep-learning pipeline to recognize TAMs in digital slides will allow the characterization of TAM-related metrics in the daily clinical practice, allowing the implementation of prognostic tools.

Heterogeneity of Colorectal Cancer Progression: Molecular Gas and Brakes.

The review begins with molecular genetics, which hit the field unveiling the involvement of oncogenes and tumor suppressor genes in the pathogenesis of colorectal cancer (CRC) and uncovering genetic predispositions. Then the notion of molecular phenotypes with different clinical behaviors was introduced and translated in the clinical arena, paving the way to next-generation sequencing that captured previously unrecognized heterogeneity. Among other molecular regulators of CRC progression, the extent of host immune response within the tumor micro-environment has a critical position. Translational sciences deeply investigated the field, accelerating the pace toward clinical transition, due to its strong association with outcomes. While the perturbation of gut homeostasis occurring in inflammatory bowel diseases can fuel carcinogenesis, micronutrients like vitamin D and calcium can act as brakes, and we discuss underlying molecular mechanisms. Among the components of gut microbiota, Fusobacterium nucleatum is over-represented in CRC, and may worsen patient outcome. However, any translational knowledge tracing the multifaceted evolution of CRC should be interpreted according to the prognostic and predictive frame of the TNM-staging system in a perspective of clinical actionability. Eventually, we examine challenges and promises of pharmacological interventions aimed to restrain disease progression at different disease stages.

Histopathological and Immune Prognostic Factors in Colo-Rectal Liver Metastases.

Prognostic studies are increasingly providing new tools to stratify colo-rectal liver metastasis patients into clinical subgroups, with remarkable implications in terms of clinical management and therapeutic choice. Here, the strengths and hurdles of current prognostic tools in colo-rectal liver metastasis are discussed. Alongside more classic histopathological parameters, which capture features related to the tumor component, such as tumor invasion, tumor growth pattern and regression score, we will discuss immune mediators, which are starting to be considered important features. Their objective quantification has shown significant results in prognostication studies, with most of the work focused on adaptive immune cells, namely T cells. As for macrophages, they are only starting to be appreciated and we will present recent advances in evaluation of macrophage morphological features. Deeper knowledge acquired by multiparametric analyses is rapidly uncovering the variety of immune players that should be assessed. The future projection is to implement deep-learning histopathological tools and to integrate histopathological and immune metrics in multiparametric scores, with the ultimate objective to achieve a deeper resolution of the tumor features and their relevance for colo-rectal liver metastasis.

Tumor-associated myeloid cells: diversity and therapeutic targeting.

Myeloid cells in tumor tissues constitute a dynamic immune population characterized by a non-uniform phenotype and diverse functional activities. Both tumor-associated macrophages (TAMs), which are more abundantly represented, and tumor-associated neutrophils (TANs) are known to sustain tumor cell growth and invasion, support neoangiogenesis and suppress anticancer adaptive immune responses. In recent decades, several therapeutic approaches have been implemented in preclinical cancer models to neutralize the tumor-promoting roles of both TAMs and TANs. Some of the most successful strategies have now reached the clinic and are being investigated in clinical trials. In this review, we provide an overview of the recent literature on the ever-growing complexity of the biology of TAMs and TANs and the development of the most promising approaches to target these populations therapeutically in cancer patients.

Isolation of Proximal Fluids to Investigate the Tumor Microenvironment of Pancreatic Adenocarcinoma.

Pancreatic adenocarcinoma (PDAC) is the fourth leading cause of cancer-related death, and soon to become the second. There is an urgent need of variables associated to specific pancreatic pathologies to help preoperative differential diagnosis and patient profiling. Pancreatic juice is a relatively unexplored body fluid, which, due to its close proximity to the tumor site, reflects changes in the surrounding tissue. Here we describe in detail the intraoperative collection procedure. Unfortunately, translating pancreatic juice collection to murine models of PDAC, to perform mechanistic studies, is technically very challenging. Tumor interstitial fluid (TIF) is the extracellular fluid, outside blood and plasma, which bathes tumor and stromal cells. Similarly to pancreatic juice, for its property to collect and concentrate molecules that are found diluted in plasma, TIF can be exploited as an indicator of microenvironmental alterations and as a valuable source of disease-associated biomarkers. Since TIF is not readily accessible, various techniques have been proposed for its isolation. We describe here two simple and technically undemanding methods for its isolation: tissue centrifugation and tissue elution.