المؤلفون: Poletto, Stefano, Paruzzo, Luca, Nepote, Alessandro, Caravelli, Daniela, Sangiolo, Dario, Carnevale-Schianca, Fabrizio

المصدر: Cancers; Jan2024, Vol. 16 Issue 1, p101, 31p

مصطلحات موضوعية: MELANOMA prognosis, C-reactive protein, IMMUNE checkpoint inhibitors, MELANOMA, GUT microbiome, AGE distribution, METASTASIS, CANCER patients, LACTATE dehydrogenase, GENE expression profiling, EXTRACELLULAR space, TUMOR markers, MYELOID cells, BODY mass index, NUCLEIC acids

مستخلص: Simple Summary: Metastatic melanoma treatment has greatly changed in the last decade due to the introduction of target therapies and immune checkpoint inhibitors. The combination of immune checkpoint inhibitors led to an unprecedented median overall survival of 72 months, but still, an important portion of patients did not significantly benefit from this approach. In this scenario, the identification of predictive factors is mandatory to improve treatment choices in daily practice. In this review, we summarize the most updated data on trials evaluating predictive factors in metastatic melanoma patients treated with immune checkpoint inhibitors, providing information to support daily practice decisions, and at the same time, highlighting the most promising future perspectives. The introduction of immunotherapy revolutionized the treatment landscape in metastatic melanoma. Despite the impressive results associated with immune checkpoint inhibitors (ICIs), only a portion of patients obtain a response to this treatment. In this scenario, the research of predictive factors is fundamental to identify patients who may have a response and to exclude patients with a low possibility to respond. These factors can be host-associated, immune system activation-related, and tumor-related. Patient-related factors can vary from data obtained by medical history (performance status, age, sex, body mass index, concomitant medications, and comorbidities) to analysis of the gut microbiome from fecal samples. Tumor-related factors can reflect tumor burden (metastatic sites, lactate dehydrogenase, C-reactive protein, and circulating tumor DNA) or can derive from the analysis of tumor samples (driver mutations, tumor-infiltrating lymphocytes, and myeloid cells). Biomarkers evaluating the immune system activation, such as IFN-gamma gene expression profile and analysis of circulating immune cell subsets, have emerged in recent years as significantly correlated with response to ICIs. In this manuscript, we critically reviewed the most updated literature data on the landscape of predictive factors in metastatic melanoma treated with ICIs. We focus on the principal limits and potentiality of different methods, shedding light on the more promising biomarkers. [ABSTRACT FROM AUTHOR]

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المؤلفون: Detroit, Marion, Collier, Mathis, Beeker, Nathanaël, Willems, Lise, Decroocq, Justine, Deau-Fischer, Bénédicte, Vignon, Marguerite, Birsen, Rudy, Moufle, Frederique, Leclaire, Clément, Balladur, Elisabeth, Deschamps, Paul, Chauchet, Adrien, Batista, Rui, Limat, Samuel, Treluyer, Jean-Marc, Ricard, Laure, Stocker, Nicolas, Hermine, Olivier, Choquet, Sylvain

المصدر: Cancers; Aug2023, Vol. 15 Issue 16, p4028, 16p

مصطلحات موضوعية: HODGKIN'S disease, RESEARCH, C-reactive protein, IMMUNE checkpoint inhibitors, ACADEMIC medical centers, HEMOGLOBINS, B cell lymphoma, CELL receptors, RETROSPECTIVE studies, TREATMENT effectiveness, SEX distribution, ANALYSIS of covariance, LACTATE dehydrogenase, LYMPHOMAS, PROGRESSION-free survival, PREDICTION models, HYPERKALEMIA, HYPOKALEMIA, IMMUNOTHERAPY

مصطلحات جغرافية: FRANCE

مستخلص: Simple Summary: Immunotherapy is increasingly used in lymphoma strategy. Risk-adapted therapeutical management and set-up scores to -stratify the most vulnerable patients by risk are becoming major concerns. With the continuing upward trend of real-world data usage in addition to clinical trial data, it is possible to test the feasibility of using data from clinical data warehouses (CDWs) to identify new predictive factors for response or toxicity to immunotherapy. Based on a large set of biological and clinical factors, our results confirm already known predictors factors of CAR T (chimeric antigen receptor T) cells: age, elevated lactate dehydrogenase, and C-Reactive Protein at the time of infusion. Additionally male gender, low hemoglobin, and hypo- or hyperkalemia are demonstrated to be predictive factors for progression after CAR T cell therapy. Thus, the attractiveness of CDW for generating data by building ever larger cohorts is proven, enabling significant results to be obtained in line with those previously described in the literature. Immunotherapy (IT) is a major therapeutic strategy for lymphoma, significantly improving patient prognosis. IT remains ineffective for a significant number of patients, however, and exposes them to specific toxicities. The identification predictive factors around efficacy and toxicity would allow better targeting of patients with a higher ratio of benefit to risk. PRONOSTIM is a multicenter and retrospective study using the Clinical Data Warehouse (CDW) of the Greater Paris University Hospitals network. Adult patients with Hodgkin lymphoma or diffuse large-cell B lymphoma treated with immune checkpoint inhibitors or CAR T (Chimeric antigen receptor T) cells between 2017 and 2022 were included. Analysis of covariates influencing progression-free survival (PFS) or the occurrence of grade ≥3 toxicity was performed. In total, 249 patients were included. From this study, already known predictors for response or toxicity of CAR T cells such as age, elevated lactate dehydrogenase, and elevated C-Reactive Protein at the time of infusion were confirmed. In addition, male gender, low hemoglobin, and hypo- or hyperkalemia were demonstrated to be potential predictive factors for progression after CAR T cell therapy. These findings prove the attractiveness of CDW in generating real-world data, and show its essential contribution to identifying new predictors for decision support before starting IT. [ABSTRACT FROM AUTHOR]

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المؤلفون: van Not, Olivier J., Wind, Thijs T., Ismail, Rawa K., Bhattacharya, Arkajyoti, Jalving, Mathilde, Blank, Christian U., Aarts, Maureen J. B., van den Berkmortel, Franchette W. P. J., Boers-Sonderen, Marye J., van den Eertwegh, Alfonsus J. M., de Groot, Jan Willem B., Haanen, John B., Kapiteijn, Ellen, Bloem, Manja, Piersma, Djura, van Rijn, Rozemarijn S., Stevense-den Boer, Marion, van der Veldt, Astrid A. M., Vreugdenhil, Gerard, Wouters, Michel W. J. M.

المصدر: Cancers; Jun2023, Vol. 15 Issue 11, p2922, 12p

مصطلحات موضوعية: MELANOMA prognosis, THERAPEUTIC use of antineoplastic agents, IMMUNE checkpoint inhibitors, GENETIC mutation, CONFIDENCE intervals, AGE distribution, MULTIVARIATE analysis, METASTASIS, IPILIMUMAB, HEALTH status indicators, BRAIN tumors, CANCER patients, SURVIVAL analysis (Biometry), NIVOLUMAB, DESCRIPTIVE statistics, KAPLAN-Meier estimator, LACTATE dehydrogenase, DATA analysis software, RADIOSURGERY, OVERALL survival, LONGITUDINAL method

مصطلحات جغرافية: NETHERLANDS

مستخلص: Simple Summary: Up to 50% of patients diagnosed with advanced melanoma develop brain metastases during the course of their disease. The prognosis of melanoma patients is heavily affected by the presence of brain metastases. Unfortunately, there is a lack of data on prognostic factors for these patients. Many of these patients are treated with immune checkpoint inhibitors. Therefore, the aim of our study was to identify prognostic factors in melanoma patients with brain metastases treated with immune checkpoint inhibitors. In a population of 1278 advanced melanoma patients, we found that serum lactate dehydrogenase levels were the strongest clinical parameter associated with survival. This information is useful for both doctors and patients to provide more insight into patients' prognoses. The efficacy of immune checkpoint inhibitors (ICIs) in patients with advanced melanoma that develop brain metastases (BM) remains unpredictable. In this study, we aimed to identify prognostic factors in patients with melanoma BM who are treated with ICIs. Data from advanced melanoma patients with BM treated with ICIs in any line between 2013 and 2020 were obtained from the Dutch Melanoma Treatment Registry. Patients were included from the time of the treatment of BM with ICIs. Survival tree analysis was performed with clinicopathological parameters as potential classifiers and overall survival (OS) as the response variable. In total, 1278 patients were included. Most patients were treated with ipilimumab–nivolumab combination therapy (45%). The survival tree analysis resulted in 31 subgroups. The median OS ranged from 2.7 months to 35.7 months. The strongest clinical parameter associated with survival in advanced melanoma patients with BM was the serum lactate dehydrogenase (LDH) level. Patients with elevated LDH levels and symptomatic BM had the worst prognosis. The clinicopathological classifiers identified in this study can contribute to optimizing clinical studies and can aid doctors in giving an indication of the patients' survival based on their baseline and disease characteristics. [ABSTRACT FROM AUTHOR]

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المؤلفون: Tabari, Azadeh, Cox, Meredith, D'Amore, Brian, Mansur, Arian, Dabbara, Harika, Boland, Genevieve, Gee, Michael S., Daye, Dania

المصدر: Cancers; May2023, Vol. 15 Issue 10, p2700, 13p

مصطلحات موضوعية: MELANOMA prognosis, IMMUNE checkpoint inhibitors, MELANOMA, MACHINE learning, METASTASIS, TREATMENT effectiveness, CANCER patients, LACTATE dehydrogenase, RESEARCH funding, PREDICTION models, COMPUTED tomography, LOGISTIC regression analysis, ALGORITHMS, IMMUNOTHERAPY, PHARMACODYNAMICS

مستخلص: Simple Summary: Lactate dehydrogenase (LDH) levels prior to treatment are a known biomarker to predict advanced melanoma's response to immune checkpoint inhibitors (ICI). In this study, we evaluated the ability of machine learning-based models to predict responses to ICI and complement LDH for in predicting the outcomes of metastatic melanoma. A machine learning algorithm was developed using radiomics, and further analysis helped select the most important predictive features and variables. The machine learning model that combined both features extracted from images (radiomics) and pretreatment LDH levels resulted in better predictions of cancer response to ICI than models that only use radiomics features or LDH levels alone. Pretreatment LDH is a standard prognostic biomarker for advanced melanoma and is associated with response to ICI. We assessed the role of machine learning-based radiomics in predicting responses to ICI and in complementing LDH for prognostication of metastatic melanoma. From 2008–2022, 79 patients with 168 metastatic hepatic lesions were identified. All patients had arterial phase CT images 1-month prior to initiation of ICI. Response to ICI was assessed on follow-up CT at 3 months using RECIST criteria. A machine learning algorithm was developed using radiomics. Maximum relevance minimum redundancy (mRMR) was used to select features. ROC analysis and logistic regression analyses evaluated performance. Shapley additive explanations were used to identify the variables that are the most important in predicting a response. mRMR selection revealed 15 features that are associated with a response to ICI. The machine learning model combining both radiomics features and pretreatment LDH resulted in better performance for response prediction compared to models that included radiomics or LDH alone (AUC of 0.89 (95% CI: [0.76–0.99]) vs. 0.81 (95% CI: [0.65–0.94]) and 0.81 (95% CI: [0.72–0.91]), respectively). Using SHAP analysis, LDH and two GLSZM were the most predictive of the outcome. Pre-treatment CT radiomic features performed equally well to serum LDH in predicting treatment response. [ABSTRACT FROM AUTHOR]

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المؤلفون: Internò, Valeria, Sergi, Maria Chiara, Metta, Maria Elvira, Guida, Michele, Trerotoli, Paolo, Strippoli, Sabino, Circelli, Salvatore, Porta, Camillo, Tucci, Marco

المصدر: Cancers; Mar2023, Vol. 15 Issue 5, p1542, 12p

مصطلحات موضوعية: MELANOMA prognosis, BRAIN tumor treatment, MELANOMA treatment, DISEASE progression, METASTASIS, RETROSPECTIVE studies, BRAIN tumors, RESEARCH funding, LACTATE dehydrogenase, COMBINED modality therapy, RADIOTHERAPY, OVERALL survival, IMMUNOTHERAPY

مستخلص: Simple Summary: The treatment strategies of patients with melanoma brain metastases are continually evolving, although this remains a poor prognostic subset. We report a real-life retrospective analysis of 105 patients with melanoma brain metastases aiming to analyze the impact of clinical–pathological features and multimodal therapies, such as neurological symptom occurrence, on overall survival in the pre-combined immunotherapy era. We observed a significant improvement in the survival of patients treated with encephalic radiotherapy (eRT) despite the type of systemic treatment performed. The only subset of patients that did not experience survival improvement from eRT was identified by LDH levels higher than two times the upper limit normal. In our opinion, our results, if confirmed by prospective analysis, may help to identify the correct therapeutic strategy for the worst prognostic subgroup of patients with melanoma brain metastases. Brain metastasis in cutaneous melanoma (CM) has historically been considered to be a dismal prognostic feature, although recent evidence has highlighted the intracranial activity of combined immunotherapy (IT). Herein, we completed a retrospective study to investigate the impact of clinical–pathological features and multimodal therapies on the overall survival (OS) of CM patients with brain metastases. A total of 105 patients were evaluated. Nearly half of the patients developed neurological symptoms leading to a negative prognosis (p = 0.0374). Both symptomatic and asymptomatic patients benefited from encephalic radiotherapy (eRT) (p = 0.0234 and p = 0.011). Lactate dehydrogenase (LDH) levels two times higher than the upper limit normal (ULN) at the time of brain metastasis onset was associated with poor prognosis (p = 0.0452) and identified those patients who did not benefit from eRT. Additionally, the poor prognostic role of LDH levels was confirmed in patients treated with targeted therapy (TT) (p = 0.0015) concerning those who received immunotherapy (IT) (p = 0.16). Based on these results, LDH levels higher than two times the ULN at the time of the encephalic progression identify those patients with a poor prognosis who did not benefit from eRT. The negative prognostic role of LDH levels on eRT observed in our study will require prospective evaluations. [ABSTRACT FROM AUTHOR]

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المؤلفون: Hakozaki, Taiki, Nolin-Lapalme, Alexis, Kogawa, Masato, Okuma, Yusuke, Nakamura, Shohei, Moreau-Amaru, Danielle, Tamura, Taichi, Hosomi, Yukio, Takeyama, Haruko, Richard, Corentin, Hosokawa, Masahito, Routy, Bertrand

المصدر: Cancers; Nov2022, Vol. 14 Issue 21, p5405, 13p

مصطلحات موضوعية: RNA analysis, LUNG cancer prognosis, LUNG cancer, DRUG efficacy, IMMUNE checkpoint inhibitors, SCIENTIFIC observation, GUT microbiome, MULTIVARIATE analysis, INFLAMMATION, CANCER patients, COMPARATIVE studies, WEIGHT gain, NEUTROPHIL lymphocyte ratio, DISEASE prevalence, LACTATE dehydrogenase, DESCRIPTIVE statistics, CACHEXIA, PROGRESSION-free survival, LONGITUDINAL method, ESCHERICHIA, GRAM-positive bacteria, ANAEROBIC bacteria, EVALUATION

مصطلحات جغرافية: JAPAN

مستخلص: Simple Summary: Immunotherapy has revolutionized the therapeutic options for patients living with non-small-cell lung cancer (NSCLC). Despite the unprecedented results achieved through immunotherapy, a low body mass index, which is referred to as cachexia, and the bacterial composition of the gut microbiota are known factors associated with resistance. In this paper, we enrolled 113 Japanese patients with NSCLC and demonstrated that cachexia was associated with poor outcomes. Moreover, microbiota sequencing revealed that patients without cachexia had abundant bacteria that correlated with a beneficial outcome. Altogether, our results demonstrated an association between the gut microbiota and cachexia. This study provides a rationale to launch clinical trials on the outcome of shifting the microbiota composition of patients with cachexia that are receiving immunotherapy. Cancer cachexia exerts a negative clinical influence on patients with advanced non-small-cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICI). The prognostic impact of body weight change during ICI treatment remains unknown. The gut microbiota (GM) is a key contributor to the response to ICI therapy in cancer patients. However, the association between cancer cachexia and GM and their association with the response to ICIs remains unexplored. This study examined the association of cancer cachexia with GM composition and assessed the impact of GM on clinical outcomes in patients with NSCLC treated with ICIs. In this observational, prospective study, which included 113 Japanese patients with advanced NSCLC treated with ICIs, the prevalence of cachexia was 50.4% (57/113). The median progression-free survival (PFS) and overall survival (OS) were significantly shorter in the cachexia group than in the non-cachexia group (4.3 vs. 11.6 months (p = 0.003) and 12.0 months vs. not reached (p = 0.02), respectively). A multivariable analysis revealed that baseline cachexia was independently associated with a shorter PFS. Moreover, a gain in body weight from the baseline (reversible cachexia) was associated with a significantly longer PFS and OS compared to irreversible cachexia. Microbiome profiling with 16S rRNA analysis revealed that the cachexia group presented an overrepresentation of the commensal bacteria, Escherichia-Shigella and Hungatella, while the non-cachexia group had a preponderance of Anaerostipes, Blautia, and Eubacterium ventriosum. Anaerostipes and E. ventriosum were associated with longer PFS and OS. Moreover, a cachexia status correlated with the systemic inflammatory marker-derived-neutrophil-to-lymphocytes ratio (dNLR) and Lung Immune Prognostic Index (LIPI) indexes. Our study demonstrates that cachexia and longitudinal bodyweight change have a prognostic impact on patients with advanced NSCLC treated with ICI therapy. Moreover, our study demonstrates that bacteria associated with ICI resistance are also linked to cachexia. Targeted microbiota interventions may represent a new type of treatment to overcome cachexia in patients with NSCLC. [ABSTRACT FROM AUTHOR]

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المؤلفون: Rebecca Gross, Negin Rahimzadeh, Stacey L. Stern, Linh T Tran, Hunter Cole, Ling Takeshima, Dave S.B. Hoon, Steven J. O'Day, Matias A. Bustos, Kevin Tran

المصدر: Cancers, Vol 12, Iss 3361, p 3361 (2020)
Cancers
Volume 12
Issue 11

مصطلحات موضوعية: 0301 basic medicine, Oncology, Cancer Research, medicine.medical_specialty, medicine.medical_treatment, lcsh:RC254-282, Article, immune checkpoint inhibitors, Transcriptome, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Lactate dehydrogenase, Internal medicine, microRNA, Medicine, Stage (cooking), neoplasms, plasma, miRNA, business.industry, Melanoma, Immunotherapy, medicine.disease, lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Circulating MicroRNA, 030104 developmental biology, chemistry, 030220 oncology & carcinogenesis, serum LDH, circulating microRNA, immunotherapy, blood biomarker, business, Progressive disease, metastatic melanoma

الوصف: Serum lactate dehydrogenase (LDH) is a standard prognostic biomarker for stage IV melanoma patients. Often, LDH levels do not provide real-time information about the metastatic melanoma patients&rsquo
disease status and treatment response. Therefore, there is a need to find reliable blood biomarkers for improved monitoring of metastatic melanoma patients who are undergoing checkpoint inhibitor immunotherapy (CII). The objective in this prospective pilot study was to discover circulating cell-free microRNA (cfmiR) signatures in the plasma that could assess melanoma patients&rsquo
responses during CII. The cfmiRs were evaluated by the next-generation sequencing (NGS) HTG EdgeSeq microRNA (miR) Whole Transcriptome Assay (WTA
2083 miRs) in 158 plasma samples obtained before and during the course of CII from 47 AJCC stage III/IV melanoma patients&rsquo
and 73 normal donors&rsquo
plasma samples. Initially, cfmiR profiles for pre- and post-treatment plasma samples of stage IV non-responder melanoma patients were compared to normal donors&rsquo
plasma samples. Using machine learning, we identified a 9 cfmiR signature that was associated with stage IV melanoma patients being non-responsive to CII. These cfmiRs were compared in pre- and post-treatment plasma samples from stage IV melanoma patients that showed good responses. Circulating miR-4649-3p, miR-615-3p, and miR-1234-3p demonstrated potential prognostic utility in assessing CII responses. Compared to LDH levels during CII, circulating miR-615-3p levels were consistently more efficient in detecting melanoma patients undergoing CII who developed progressive disease. By combining stage III/IV patients, 92 and 17 differentially expressed cfmiRs were identified in pre-treatment plasma samples from responder and non-responder patients, respectively. In conclusion, this pilot study demonstrated cfmiRs that identified treatment responses and could allow for real-time monitoring of patients receiving CII.

وصف الملف: application/pdf

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::36f4dd98399f8dea7db6fdd0d1f3486cTest
https://www.mdpi.com/2072-6694/12/11/3361Test

المؤلفون: Trommer, Maike, Adams, Anne, Celik, Eren, Fan, Jiaqi, Funken, Dominik, Herter, Jan M., Linde, Philipp, Morgenthaler, Janis, Wegen, Simone, Mauch, Cornelia, Franklin, Cindy, Galldiks, Norbert, Werner, Jan-Michael, Kocher, Martin, Rueß, Daniel, Ruge, Maximilian, Meißner, Anna-Katharina, Baues, Christian, Marnitz, Simone

المصدر: Cancers; Mar2022, Vol. 14 Issue 5, p1240, 1p

مصطلحات موضوعية: PROGRAMMED cell death 1 receptors, IMMUNE checkpoint inhibitors, ACADEMIC medical centers, MELANOMA, METASTASIS, IMMUNE system, RETROSPECTIVE studies, LUNG tumors, BRAIN tumors, TREATMENT effectiveness, SURVIVAL analysis (Biometry), LACTATE dehydrogenase, DESCRIPTIVE statistics, RADIOTHERAPY, RADIOSURGERY, PROGRESSION-free survival, IMMUNOTHERAPY, PROPORTIONAL hazards models

مستخلص: Simple Summary: Immune checkpoint inhibitors (ICIs) and radiotherapy (RT) are widely used for patients with brain metastasis (BM). To evaluate markers for treatment response and find a treatment concept which has the best outcome effects, we analyzed data of 93 patients with BM from different cancer types. Predictive markers for survival were good performance status, melanoma as cancer type, low metastasis volume, normal inflammatory blood parameters, and a stereotactic radiotherapy concept with high doses. We found that the best survival outcome can be achieved with the concurrent use of RT and ICI. Concurrent treatment was particularly beneficial in patients with low inflammatory status and more and larger metastases, and when high doses cannot be administered. In concurrently treated patients, therapeutic response was often delayed compared to sequential treatment. Specific immune responses such as pseudoprogression and abscopal effects were induced by concurrent treatment and associated with prolonged survival. While immune checkpoint inhibitors (ICIs) in combination with radiotherapy (RT) are widely used for patients with brain metastasis (BM), markers that predict treatment response for combined RT and ICI (RT-ICI) and their optimal dosing and sequence for the best immunogenic effects are still under investigation. The aim of this study was to evaluate prognostic factors for therapeutic outcome and to compare effects of concurrent and non-concurrent RT-ICI. We retrospectively analyzed data of 93 patients with 319 BMs of different cancer types who received PD-1 inhibitors and RT at the University Hospital Cologne between September/2014 and November/2020. Primary study endpoints were overall survival (OS), progression-free survival (PFS), and local control (LC). We included 66.7% melanoma, 22.8% lung, and 5.5% other cancer types with a mean follow-up time of 23.8 months. Median OS time was 12.19 months. LC at 6 months was 95.3% (concurrent) vs. 69.2% (non-concurrent; p = 0.008). Univariate Cox regression analysis detected following prognostic factors for OS: neutrophil-to-lymphocyte ratio NLR favoring <3 (low; HR 2.037 (1.184–3.506), p = 0.010), lactate dehydrogenase (LDH) favoring ≤ULN (HR 1.853 (1.059–3.241), p = 0.031), absence of neurological symptoms (HR 2.114 (1.285–3.478), p = 0.003), RT concept favoring SRS (HR 1.985 (1.112–3.543), p = 0.019), RT dose favoring ≥60 Gy (HR 0.519 (0.309–0.871), p = 0.013), and prior anti-CTLA4 treatment (HR 0.498 (0.271–0.914), p = 0.024). Independent prognostic factors for OS were concurrent RT-ICI application (HR 0.539 (0.299–0.971), p = 0.024) with a median OS of 17.61 vs. 6.83 months (non-concurrent), ECOG performance status favoring 0 (HR 7.756 (1.253–6.061), p = 0.012), cancer type favoring melanoma (HR 0.516 (0.288–0.926), p = 0.026), BM volume (PTV) favoring ≤3 cm³ (HR 1.947 (1.007–3.763), p = 0.048). Subgroups with the following factors showed significantly longer OS when being treated concurrently: RT dose <60 Gy (p = 0.014), PTV > 3 cm³ (p = 0.007), other cancer types than melanoma (p = 0.006), anti-CTLA4-naïve patients (p < 0.001), low NLR (p = 0.039), steroid intake ≤4 mg (p = 0.042). Specific immune responses, such as abscopal effects (AbEs), pseudoprogression (PsP), or immune-related adverse events (IrAEs), occurred more frequently with concurrent RT-ICI and resulted in better OS. Other toxicities, including radionecrosis, were not statistically different in both groups. The concurrent application of RT and ICI, the ECOG-PS, cancer type, and PTV had an independently prognostic impact on OS. In concurrently treated patients, treatment response (LC) was delayed and specific immune responses (AbE, PsP, IrAE) occurred more frequently with longer OS rates. Our results suggest that concurrent RT-ICI application is more beneficial than sequential treatment in patients with low pretreatment inflammatory status, more and larger BMs, and with other cancer types than melanoma. [ABSTRACT FROM AUTHOR]

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المؤلفون: Madonna, Gabriele, Masucci, Giuseppe V., Capone, Mariaelena, Mallardo, Domenico, Grimaldi, Antonio Maria, Simeone, Ester, Vanella, Vito, Festino, Lucia, Palla, Marco, Scarpato, Luigi, Tuffanelli, Marilena, D'angelo, Grazia, Villabona, Lisa, Krakowski, Isabelle, Eriksson, Hanna, Simao, Felipe, Lewensohn, Rolf, Ascierto, Paolo Antonio

المصدر: Cancers; Aug2021, Vol. 13 Issue 16, p4164, 1p

مصطلحات موضوعية: EOSINOPHILS, IMMUNE checkpoint inhibitors, MELANOMA, MACHINE learning, METASTASIS, RETROSPECTIVE studies, IPILIMUMAB, CANCER patients, NEUTROPHILS, LYMPHOCYTES, LACTATE dehydrogenase, DESCRIPTIVE statistics, ALGORITHMS, IMMUNOTHERAPY, THERAPEUTICS

مصطلحات جغرافية: ITALY

مستخلص: Simple Summary: Immune checkpoint inhibitors have improved the prognosis for patients with advanced melanoma. Despite the recent success of immunotherapy, many patients still do not benefit from these treatments, and their real-life application may yield different outcomes compared to the advantage presented in clinical trials. There is therefore a need to select patients who can really benefit from these treatments. We have focused our study on a real-life retrospective analysis of metastatic melanoma patients treated with immunotherapy at a single institution—the Istituto Nazionale Tumori IRCCS Fondazione "G. Pascale" of Napoli, Italy. With the help of AI and machine learning we validated an algorithm based on clinical variables of patients—namely, the Clinical Categorization Algorithm (CLICAL)—that defines five predictable cohorts of benefit to immunotherapy with 95% accuracy. It can be a useful tool for the stratification of metastatic melanoma patients who may or may not improve from immunotherapy treatment. 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. [ABSTRACT FROM AUTHOR]

: Copyright of Cancers is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Galland, Loïck, Lecuelle, Julie, Favier, Laure, Fraisse, Cléa, Lagrange, Aurélie, Kaderbhai, Courèche, Truntzer, Caroline, Ghiringhelli, François

المصدر: Cancers; Jun2021, Vol. 13 Issue 12, p3020, 1p

مصطلحات موضوعية: LUNG cancer prognosis, LUNG cancer, DRUG efficacy, STATISTICS, IMMUNE checkpoint inhibitors, SPECIALTY hospitals, MULTIVARIATE analysis, METASTASIS, RETROSPECTIVE studies, CANCER patients, CANCER treatment, PRE-tests & post-tests, NEUTROPHILS, SPLEEN diseases, DESCRIPTIVE statistics, SURVIVAL analysis (Biometry), LACTATE dehydrogenase, LEUKOCYTE count, SPLEEN, TUMOR markers, IMMUNOTHERAPY, PROPORTIONAL hazards models, THERAPEUTICS, EVALUATION

مصطلحات جغرافية: FRANCE

مستخلص: Simple Summary: Monoclonal antibodies targeting PD1/PD-L1 are game changers in the treatment of advanced non-small cell lung cancer (NSCLC), but biomarkers are lacking. We previously reported the prognostic role of splenic volume in digestive cancer and its correlation with the presence of immunosuppressive cells. The aim of this study was to evaluate the prognostic role of splenic volume in NSCLC patients treated with immune checkpoint inhibitors (ICIs). Monoclonal antibodies targeting PD1/PD-L1 are game changers in advanced non-small cell lung cancer (NSCLC), but biomarkers are lacking. We previously reported the prognostic role of splenic volume in digestive cancer and its correlation with the presence of immunosuppressive cells. The aim of this study was to evaluate the prognostic role of splenic volume in NSCLC patients treated with immune checkpoint inhibitors (ICIs). We conducted a retrospective study of 276 patients receiving ICIs for advanced NSCLC in the Georges François Leclerc Cancer Center. The association between splenic volume at baseline and at two months of therapy and progression-free survival (PFS) during ICI treatment or overall survival (OS) from ICI initiation was evaluated using univariate and multivariable Cox analyses. Splenic volume during treatment and the change in splenic volume were associated with poor PFS (respectively p = 0.02 and p = 0.001) and with OS (respectively p < 1.10⁻³ and p < 1.10⁻³). Baseline splenic volume at the first evaluation was also associated with poor OS (p = 0.001). LDH rate and dNLR were positively correlated with splenic volume, as well as with its evolution. After the adjustment of clinical variables, splenic volumes remained a predictive marker of immunotherapy efficacy. Splenic volume is a prognostic biomarker in patients with advanced NSCLC treated with ICIs. [ABSTRACT FROM AUTHOR]

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