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- Hoshino, Ayuko, et al.
(författare)
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Extracellular Vesicle and Particle Biomarkers Define Multiple Human Cancers
- 2020
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Ingår i: Cell. - : CELL PRESS. - 0092-8674 .- 1097-4172. ; 182:4, s. 1044-
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Tidskriftsartikel (refereegranskat)abstract
- There is an unmet clinical need for improved tissue and liquid biopsy tools for cancer detection. We investigated the proteomic profile of extracellular vesicles and particles (EVPs) in 426 human samples from tissue explants (TEs), plasma, and other bodily fluids. Among traditional exosome markers, CD9, HSPA8, ALIX, and HSP90AB1 represent pan-EVP markers, while ACTB, MSN, and RAP1B are novel pan-EVP markers. To confirm that EVPs are ideal diagnostic tools, we analyzed proteomes of TE- (n =151) and plasma-derived (n =120) EVPs. Comparison of TE EVPs identified proteins (e.g., VCAN, TNC, and THBS2) that distinguish tumors from normal tissues with 90% sensitivity/94% specificity. Machine-learning classification of plasma-derived EVP cargo, including immunoglobulins, revealed 95% sensitivity/90% specificity in detecting cancer Finally, we defined a panel of tumor-type-specific EVP proteins in TEs and plasma, which can classify tumors of unknown primary origin. Thus, EVP proteins can serve as reliable biomarkers for cancer detection and determining cancer type.
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| 2. |
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| 3. |
- Mandelli, GE, et al.
(författare)
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Tumor Infiltrating Neutrophils Are Enriched in Basal-Type Urothelial Bladder Cancer
- 2020
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Ingår i: Cells. - : MDPI AG. - 2073-4409. ; 9:2
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Tidskriftsartikel (refereegranskat)abstract
- Background: Urothelial bladder cancers (UBCs) are distinct in two main molecular subtypes, namely basal and luminal type. Subtypes are also diverse in term of immune contexture, providing a rationale for patient selection to immunotherapy. Methods: By digital microscopy analysis of a muscle-invasive BC (MIBC) cohort, we explored the density and clinical significance of CD66b+ tumor-associated-neutrophils (TAN) and CD3+ T cells. Bioinformatics analysis of UBC datasets and gene expression analysis of UBC cell lines were additionally performed. Results: Basal type BC contained a significantly higher density of CD66b+ TAN compared to the luminal type. This finding was validated on TCGA, GSE32894 and GSE124305 datasets by computing a neutrophil signature. Of note, basal-type MIBC display a significantly higher level of chemokines (CKs) attracting neutrophils. Moreover, pro-inflammatory stimuli significantly up-regulate CXCL1, CXCL2 and CXCL8 in 5637 and RT4 UBC cell lines and induce neutrophil chemotaxis. In term of survival, a high density of T cells and TAN was significantly associated to a better outcome, with TAN density showing a more limited statistical power and following a non-linear predicting model. Conclusions: TAN are recruited in basal type MIBC by pro-inflammatory CKs. This finding establishes a groundwork for a better understanding of the UBC immunity and its relevance.
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| 4. |
- Madonna, G, et al.
(författare)
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Clinical Categorization Algorithm (CLICAL) and Machine Learning Approach (SRF-CLICAL) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-World Evidence from the Istituto Nazionale Tumori IRCCS Fondazione Pascale, Napoli, Italy
- 2021
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Ingår i: Cancers. - : MDPI AG. - 2072-6694. ; 13:16
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Tidskriftsartikel (refereegranskat)abstract
- The real-life application of immune checkpoint inhibitors (ICIs) may yield different outcomes compared to the benefit presented in clinical trials. For this reason, there is a need to define the group of patients that may benefit from treatment. We retrospectively investigated 578 metastatic melanoma patients treated with ICIs at the Istituto Nazionale Tumori IRCCS Fondazione “G. Pascale” of Napoli, Italy (INT-NA). To compare patients’ clinical variables (i.e., age, lactate dehydrogenase (LDH), neutrophil–lymphocyte ratio (NLR), eosinophil, BRAF status, previous treatment) and their predictive and prognostic power in a comprehensive, non-hierarchical manner, a clinical categorization algorithm (CLICAL) was defined and validated by the application of a machine learning algorithm—survival random forest (SRF-CLICAL). The comprehensive analysis of the clinical parameters by log risk-based algorithms resulted in predictive signatures that could identify groups of patients with great benefit or not, regardless of the ICI received. From a real-life retrospective analysis of metastatic melanoma patients, we generated and validated an algorithm based on machine learning that could assist with the clinical decision of whether or not to apply ICI therapy by defining five signatures of predictability with 95% accuracy.
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