Unraveling the Connection: Pancreatic Cancer Cells and Schwann Cells.

Pancreatic ductal adenocarcinoma is one of the most lethal solid malignancies, characterized by its aggressiveness and metastatic potential, with a 5-year survival rate of only 13%. Progress in the management of metastatic disease has been modest. A robust connection between nervous system and tumor progression exists, with prominent neural alterations having been observed during pancreatic cancer's progression, including neural hypertrophy, neural density, and neural remodeling. The pancreatic tumor microenvironment includes s set of cells and structures that constantly dialogue with cancer cells, influencing its growth and behavior. The microglia is key cellular components of the tumor microenvironment, and Schwann cells are the principal glial cells in the peripheral neural system. Schwann cells can regulate changes in the tumor microenvironment and immune responses by secreting a variety of factors and can support a tumor's invasion of nerves and distant metastasis, with further pain exacerbation. Schwann cells secrete various pain-related molecules, such as the neural growth factor, to mediate the activation of primary sensory neurons, leading to pain induction. The binding of the neural growth factor to tropomyosin receptor kinase A is an important signaling mechanism for pain perception in humans. Consequently, directing efforts towards targeting neural invasion may provide an alternative strategy to improve the prognosis of and alleviate pain in patients with pancreatic cancer.

Integrating Red Blood Cell Features and Hemoglobin Levels in Metastatic Renal Cell Carcinoma Patients Treated with Pazopanib or Cabozantinib: An Easily Exploitable Prognostic Score.

BACKGROUND: The advent of immune checkpoint inhibitors (ICIs) has revolutionized the metastatic renal cell carcinoma (mRCC) therapeutic landscape. Nevertheless, tyrosine-kinase inhibitors (TKIs) targeting the vascular endothelial growth factor (VEGF) axis still play a key role. The aim of the present study was to explore the prognostic performance of an integrated blood score, based on hemoglobin (Hb) concentration, mean corpuscular volume (MCV), and red cell distribution width (RDW), in mRCC patients treated with anti-VEGF TKIs. The primary endpoint was to correlate Hb, MCV, and RDW with progression-free survival (PFS) and overall survival (OS). MATERIALS AND METHODS: Our multicenter retrospective observational study involved mRCC patients treated with pazopanib or cabozantinib from January 2012 to December 2020 in nine Italian centers. Clinical records and laboratory data, including Hb levels, MCV, and RDW, were collected at baseline. Descriptive statistics and univariate and multivariate analyses were performed. RESULTS: We enrolled 301 mRCC patients of which 179 (59%) underwent pazopanib, and 122 (41%) cabozantinib. We considered baseline Hb ≥12 g/dL, MCV >87 fL, and RDW ≤16% as good prognostic factors; hence, developing a multiparametric score capable of delineating 4 different categories. The number of good prognostic factors was associated with significantly longer PFS and OS (p < 0.001 for both). Therefore, we developed a red blood cell-based score by stratifying cases into two groups (2-3 versus 0-1, good factors). The impact on PFS and OS was even more striking (median PFS (mPFS): 16.3 vs 7.9 months; median OS (mOS): 33.7 vs 14.1 months)), regardless of the TKI agent. When challenged with univariate and multivariate analysis, the blood score maintained its high prognostic significance in terms of OS (multivariate analysis HR for OS: 0.53, 95% CI 0.39-0.75; p < 0.001, respectively), while the impact on PFS resulted in borderline significance. CONCLUSIONS: Our analyses demonstrate the prognostic role of a multiparametric score based on easily exploitable blood parameters, such as Hb concentration, MCV, and RDW. The red blood cell-based score may underlie the upregulation of the HIF-1α pathway and VEGF axis, thereby identifying a selected population who is likely to benefit from TKI therapy.

BONSAI-2 study: Nivolumab as therapeutic option after cabozantinib failure in metastatic collecting duct carcinoma patients.

BACKGROUND: The BONSAI phase II trial recently demonstrated the activity of cabozantinib in metastatic collecting duct patients. The outcomes of patients in this setting treated with immunotherapy as second-line is unknown. The aim of the present report was to describe outcomes of patients enrolled in the BONSAI trial that received nivolumab as second-line treatment. MATERIAL AND METHODS: We describe the oncological outcomes in terms of overall response rate, progression-free survival, overall survival and safety. We excluded patients that did not receive any second-line treatment or were treated with agents other than nivolumab. RESULTS: We identified five patients of whom one was excluded due to lack of data. Three patients obtained clinical benefit (one partial response, two stable disease); the second-line progression-free survival (nivolumab) ranged from 2.8 to 19.9 months to and second-line overall survival ranged from 5.1 to 26.5 months. No new safety signals were observed. CONCLUSIONS: Nivolumab may be considered as second-line therapy option after cabozantinib failure in selected metastatic collecting duct carcinoma patients.

Validation of a radiomic approach to decipher NSCLC immune microenvironment in surgically resected patients.

Radiomics has emerged as a noninvasive tool endowed with the potential to intercept tumor characteristics thereby predicting clinical outcome. In a recent study on resected non-small cell lung cancer (NSCLC), we identified highly prognostic computed tomography (CT) -derived radiomic features (RFs), which in turn were able to discriminate hot from cold tumor immune microenvironment (TIME). We aimed at validating a radiomic model capable of dissecting specific TIME profiles bearing prognostic power in resected NSCLC. The validation cohort included 31 radically resected NSCLCs clinicopathologically matched with the training set (n = 69). TIME was classified in hot and cold according to a multiparametric immunohistochemical analysis involving PD-L1 score and incidence of immune effector phenotypes among tumor infiltrating lymphocytes (TILs). High- throughput radiomic features (n = 841) extracted from CT images were correlated to TIME parameters to ultimately define prognostic classes. We confirmed PD-1 to CD8 ratio as best predictor of clinical outcome among TIME characteristics. Significantly prolonged overall survival (OS) was observed in patients carrying hot (median OS not reached) vs cold (median OS 22 months; hazard ratio 0.28, 95% confidence interval 0.09 -0.82; p = 0.015) immune background, thus validating the prognostic impact of these two TIME categories in resected NSCLC. Importantly, in the validation setting, three out of eight previously identified RFs sharply distinguishing hot from cold TIME were endorsed. Among signature-related RFs, Wavelet-HHH_gldm_HighGrayLevelEmphasis highly performed as descriptor of hot immune contexture (area under the receiver operating characteristic curve 0.94, 95% confidence interval 0.81 -1.00; p = 0.01). Based on our findings, Radiomics may decipher specific TIME profiles providing a noninvasive prognostic approach in resected NSCLC and an exploitable predictive strategy in advanced cases.

Dataset on the identification of a prognostic radio-immune signature in surgically resected Non Small Cell Lung Cancer.

The immune regulation of cancer growth and regression has been underscored by the recent success of immunotherapy. The possibility that immune microenvironmental factors may impact on clinical outcome and treatment response still requires intense investigations. Hereby, supporting data of the research article "Integrated CT Imaging and Tissue Immune Features Disclose a Radio-Immune Signature with High Prognostic Impact on Surgically Resected NSCLC" [1], are presented. With the ultimate aim to provide non-invasive prognostic scores, we report on our approach to correlate different Tumor Immune Microenvironment (TIME) profiles with CT imaging-derived qualitative (semantic, CT-SFs) and quantitative (radiomic, CT-RFs) features in a cohort of 60 surgically resected NSCLC. The renowned characterization of TIME, essentially based on the score evaluation of Programme Death Ligand-1 (PD-L1) and Tumor Infiltrating Lymphocytes (TILs), was implemented here by the assessment of effector and suppressor phenotypes including the analysis of Programme Death receptor 1 (PD-1). Thus, we defined two main TIME categories: hot inflamed (PD-L1high, CD8/CD3high and PD-1/CD8low) as opposed to cold inactive (PD-L1low, CD8/CD3lowand PD-1/CD8high). Importantly, as reported in the extended publication [1], these distinctive immune contextures identified different prognostic classes and were decoded by radiomics. To corroborate our radiomic approach, a comparative estimation of CT-RFs extracted from 60 NSCLC and 13 non neoplastic tissues was undertaken, documenting high discrimination ability. Moreover, we tested the potential association of qualitative radiologic features with clinico-pathological and TIME parameters. Taken together, our findings suggest that CT-SFs and CT-RFs may underlay specific patterns of lung cancer.

Integrated CT imaging and tissue immune features disclose a radio-immune signature with high prognostic impact on surgically resected NSCLC.

OBJECTIVES: Qualitative and quantitative CT imaging features might intercept the multifaceted tumor immune microenvironment (TIME), providing a non-invasive approach to design new prognostic models in NSCLC patients. MATERIALS AND METHODS: Our study population consisted of 100 surgically resected NSCLC patients among which 31 served as a validation cohort for quantitative image analysis. TIME was classified according to PD-L1 expression and the magnitude of Tumor Infiltrating Lymphocytes (TILs) and further defined as hot or cold by the tissue analysis of effector (CD8-to-CD3high/PD-1-to-CD8low) or inert (CD8-to-CD3low/PD-1-to-CD8high) phenotypes. CT datasets acted as source for qualitative (semantic, CT-SFs) and quantitative (radiomic, CT-RFs) features which were correlated with clinico-pathological and TIME profiles to determine their impact on survival outcome. RESULTS: Specific CT-SFs (texture [TXT], effect [EFC] and margins [MRG]) strongly correlated to PD-L1 and TILs status and showed significant impact on survival outcome (TXT, HR:3.39, 95 % CI 1.12-10-27, P < 0.05; EFC, HR:0.41, 95 % CI 0.18-0.93, P < 0.05; MRG, HR:1.93, 95 % CI 0.88-4.25, P = 0.09). Seven CT derived radiomic features were able to sharply discriminate cases with hot (inflamed) vs cold (desert) TIME, which also exhibited opposite OS (long vs short, HR:0.09, 95 % CI 0.04-0.23, P < 0.001) and DFS (long vs short, HR:0.31, 95 % CI 0.16-0.58, P < 0.001). Moreover, we identified 6 prognostic radiomic features among which ClusterProminence displayed the highest statistical significance (HR:0.13, 95 % CI 0.06-0.31, P < 0.001). These findings were independently validated in an additional cohort of NSCLC (HR:0.11, 95 % CI 0.03-0.40, P = 0.001). Finally, in our training cohort we developed a multiparametric prognostic model, interlacing TIME and clinico-pathological characteristics with CT-SFs (ROC curve AUC:0.83, 95 % CI 0.71-0.92, P < 0.001) or CT-RFs (AUC: 0.91, 95 % CI 0.83-0.99, P < 0.001), which appeared to outperform pTNM staging (AUC: 0.66, 95 % CI 0.51-0.80, P < 0.05) in the risk assessment of NSCLC. CONCLUSION: Higher order CT extracted features associated with specific TIME profiles may reveal a radio-immune signature with prognostic impact on resected NSCLC.