Francesca Giunchi
@virgilio.it
MD pathology department
IRCCS Azienda Ospedaliero-Universitaria di Bologna
RESEARCH INTERESTS
Lung and genitourinary pathology.
molecolar biology
Scopus Publications
Scopus Publications
Matteo Rosellini, Veronica Mollica, Andrea Marchetti, Sara Coluccelli, Francesca Giunchi, Elisa Tassinari, Costantino Ricci, Michelangelo Fiorentino, Giovanni Tallini, Dario De Biase,et al.
Elsevier BV
Veronica Mollica, Elisa Tassinari, Matteo Santoni, Paola Valeria Marchese, Francesca Giunchi, Thais Maloberti, Valentina Tateo, Costantino Ricci, Matteo Rosellini, Andrea Marchetti,et al.
Elsevier BV
Costantino Ricci, Francesca Ambrosi, Tania Franceschini, Francesca Giunchi, Alessia Grillini, Eugenia Franchini, Marco Grillini, Riccardo Schiavina, Francesco Massari, Veronica Mollica,et al.
Springer Science and Business Media LLC
AbstractThe 2022 WHO classification of urinary and male genital tumors introduced several novel kidney entities exhibiting eosinophilic/oncocytic features with specific mutational backgrounds. Thus, molecular techniques, such as next-generation sequencing (NGS), became more commonly used for their evaluation. We studied 12 low-grade oncocytic tumors (LOT) of the kidney (from 11 patients), identified in a cohort of 210 eosinophilic/oncocytic renal tumors, diagnosed in our institution between October 2019 and May 2023, which represented 5.7% (12/210) of all eosinophilic/oncocytic renal tumors during this period. We reviewed their clinicopathologic, histologic, and immunohistochemical features, as well as their mutational profiles. We also reviewed the literature on NGS-derived data of LOT, by selecting papers in which LOT diagnosis was rendered according to the criteria proposed initially. Median age was 65 years (mean: 63.5; range 43–79) and median tumor size was 2.0 cm (mean: 2.2; range: 0.9–3.1). All tumors were positive for PAX8, CK7, and GATA3, and negative or focally positive for CD117/KIT. We found the following gene mutations: MTOR ((6/11), 54.5%)), TSC1 ((2/11), 18.2%)), and 1 had both NOTCH1 and NOTCH4 ((1/11), 9.1%)). Wild-type status was found in 2/11 (18.2%) patients and one tumor was not analyzable. A review of 8 previous studies that included 79 LOTs revealed frequent mutations in the genes that regulate the mammalian target of rapamycin (mTOR) pathway: MTOR (32/79 (40.5%)), TSC1 (21/79 (26.6%)), and TSC2 (9/79 (11.4%)). Other mutated genes included PIK3CA, NF2, and PTEN, not typically known to affect the mTOR pathway, but potentially acting as upstream and downstream effectors. Our study shows that LOT is increasingly diagnosed in routine practice when applying the appropriate diagnostic criteria. We also confirm that the mTOR pathway is strongly implicated in the pathogenesis of this tumor mainly through MTOR, TCS1, and TSC2 mutations, but other genes could also be involved in the pathway activation, especially in LOTs without “canonical” mutations.
Costantino Ricci, Francesca Ambrosi, Tania Franceschini, Francesca Giunchi, Giorgia Di Filippo, Eugenia Franchini, Francesco Massari, Veronica Mollica, Valentina Tateo, Federico Mineo Bianchi,et al.
Wiley
AIMS
Yolk sac tumour postpubertal-type (YSTpt) shows a wide range of histological patterns and is challenging to diagnose. Recently, forkhead box transcription factor A2 (FoxA2) emerged as a driver of YSTpt formation and a promising marker for diagnosing YSTpt. However, FoxA2 has not been tested in the different patterns of YSTpt. This study aimed to assess the staining pattern of FoxA2 in te different patterns of YSTpt and other germ cell tumours of the testis (GCTT), comparing it with glypican-3 (GPC3) and α-fetoprotein (AFP).
METHODS AND RESULTS
FOXA2, GPC3 and AFP immunohistochemistry was performed on 24 YSTpt (24 microcystic/reticular, 10 myxoid, two macrocystic, five glandular/alveolar, two endodermal sinus/perivascular, four solid, two polyembryoma/embryoid body and two polyvesicular vitelline) and 81 other GCTT. The percentage of positive cells (0, 1+, 2+, 3+) and the intensity (0, 1, 2, 3) were evaluated regardless of and within each YSTpt pattern. FoxA2 was positive in all YSTpt (24 of 24) and all but one (23 of 24) exhibited 2+/3+ stain, with higher intensity [median value (mv): 2.6] than AFP (1.8) and GPC3 (2.5). Both FoxA2 and GPC3 were positive in all microcystic/reticular (24 of 24), myxoid (10 of 10), macrocystic (two of two), endodermal sinus/perivascular (four of four) and polyembryoma/embryoid body (two of two) patterns. Nevertheless, only FoxA2 was positive in all glandular/alveolar (five of five), solid (four of four) and polyvesicular vitelline (two of two) patterns. The intensity of FoxA2 was higher than AFP and GPC3 in almost all YST patterns. In the other GCTT, FoxA2 was positive only in teratoma postpubertal-type (Tpt) [13 of 20 (65%)], with staining almost exclusively confined to the mature gastrointestinal/respiratory tract epithelium.
CONCLUSIONS
FoxA2 is a highly sensitive and specific biomarker that supports the diagnosis of YSTpt. FoxA2 is superior to GPC3 and AFP, especially in rare and difficult-to-diagnose histological patterns of YSTpt, but mature glands of Tpt could represent a potential diagnostic pitfall.
Caterina Gaudiano, Margherita Mottola, Lorenzo Bianchi, Beniamino Corcioni, Lorenzo Braccischi, Makoto Taninokuchi Tomassoni, Arrigo Cattabriga, Maria Adriana Cocozza, Francesca Giunchi, Riccardo Schiavina,et al.
MDPI AG
The Prostate Imaging and Reporting Data System (PI-RADS) has a key role in the management of prostate cancer (PCa). However, the clinical interpretation of PI-RADS 3 score lesions may be challenging and misleading, thus postponing PCa diagnosis to biopsy outcome. Multiparametric magnetic resonance imaging (mpMRI) radiomic analysis may represent a stand-alone noninvasive tool for PCa diagnosis. Hence, this study aims at developing a mpMRI-based radiomic PCa diagnostic model in a cohort of PI-RADS 3 lesions. We enrolled 133 patients with 155 PI-RADS 3 lesions, 84 of which had PCa confirmation by fusion biopsy. Local radiomic features were generated from apparent diffusion coefficient maps, and the four most informative were selected using LASSO, the Wilcoxon rank-sum test (p < 0.001), and support vector machines (SVMs). The selected features where augmented and used to train an SVM classifier, externally validated on a holdout subset. Linear and second-order polynomial kernels were exploited, and their predictive performance compared through receiver operating characteristics (ROC)-related metrics. On the test set, the highest performance, equally for both kernels, was specificity = 76%, sensitivity = 78%, positive predictive value = 80%, and negative predictive value = 74%. Our findings substantially improve radiologist interpretation of PI-RADS 3 lesions and let us advance towards an image-driven PCa diagnosis.
Sofia Melotti, Francesca Ambrosi, Tania Franceschini, Francesca Giunchi, Giorgia Di Filippo, Eugenia Franchini, Francesco Massari, Veronica Mollica, Valentina Tateo, Federico Mineo Bianchi,et al.
Elsevier BV
Pietro PIAZZA, Lorenzo BIANCHI, Michelangelo FIORENTINO, Caterina GAUDIANO, Francesca GIUNCHI, Eugenio BRUNOCILLA, and Riccardo SCHIAVINA
Edizioni Minerva Medica
Thais Maloberti, Antonio De Leo, Sara Coluccelli, Viviana Sanza, Elisa Gruppioni, Annalisa Altimari, Stefano Zagnoni, Francesca Giunchi, Francesco Vasuri, Michelangelo Fiorentino,et al.
MDPI AG
Despite significant therapeutic advances, metastatic CRPC (mCRPC) remains a lethal disease. Mutations in homologous recombination repair (HRR) genes are frequent in mCRPC, and tumors harboring these mutations are known to be sensitive to PARP inhibitors. The aim of this study was to verify the technical effectiveness of this panel in the analysis of mCRPC, the frequency and type of mutations in the BRCA1/BRCA2 genes, as well as in the homologous recombination repair (HRR) genes. A total of 50 mCRPC cases were analyzed using a multi-gene next-generation sequencing panel evaluating a total of 1360 amplicons in 24 HRR genes. Of the 50 cases, 23 specimens (46.0%) had an mCRPC harboring a pathogenic variant or a variant of uncertain significance (VUS), whereas in 27 mCRPCs (54.0%), no mutations were detected (wild-type tumors). BRCA2 was the most commonly mutated gene (14.0% of samples), followed by ATM (12.0%), and BRCA1 (6.0%). In conclusion, we have set up an NGS multi-gene panel that is capable of analyzing BRCA1/BRCA2 and HRR alterations in mCRPC. Moreover, our clinical algorithm is currently being used in clinical practice for the management of patients with mCRPC.
Gianluca Carlini, Caterina Gaudiano, Rita Golfieri, Nico Curti, Riccardo Biondi, Lorenzo Bianchi, Riccardo Schiavina, Francesca Giunchi, Lorenzo Faggioni, Enrico Giampieri,et al.
MDPI AG
Background: Benign renal tumors, such as renal oncocytoma (RO), can be erroneously diagnosed as malignant renal cell carcinomas (RCC), because of their similar imaging features. Computer-aided systems leveraging radiomic features can be used to better discriminate benign renal tumors from the malignant ones. The purpose of this work was to build a machine learning model to distinguish RO from clear cell RCC (ccRCC). Method: We collected CT images of 77 patients, with 30 cases of RO (39%) and 47 cases of ccRCC (61%). Radiomic features were extracted both from the tumor volumes identified by the clinicians and from the tumor’s zone of transition (ZOT). We used a genetic algorithm to perform feature selection, identifying the most descriptive set of features for the tumor classification. We built a decision tree classifier to distinguish between ROs and ccRCCs. We proposed two versions of the pipeline: in the first one, the feature selection was performed before the splitting of the data, while in the second one, the feature selection was performed after, i.e., on the training data only. We evaluated the efficiency of the two pipelines in cancer classification. Results: The ZOT features were found to be the most predictive by the genetic algorithm. The pipeline with the feature selection performed on the whole dataset obtained an average ROC AUC score of 0.87 ± 0.09. The second pipeline, in which the feature selection was performed on the training data only, obtained an average ROC AUC score of 0.62 ± 0.17. Conclusions: The obtained results confirm the efficiency of ZOT radiomic features in capturing the renal tumor characteristics. We showed that there is a significant difference in the performances of the two proposed pipelines, highlighting how some already published radiomic analyses could be too optimistic about the real generalization capabilities of the models.
Luigia Vetrone, Riccardo Mei, Lorenzo Bianchi, Francesca Giunchi, Andrea Farolfi, Paolo Castellucci, Matteo Droghetti, Massimiliano Presutti, Alessio Degiovanni, Riccardo Schiavina,et al.
MDPI AG
PSMA-PET/CT is a suitable replacement for conventional imaging in the primary staging of PCa. The aim of this retrospective study was to assess the correlation between parameters discovered by PSMA PET/CT in primary staging and either prostate histopathology (pT) findings or PSMA-IHC expression in a cohort of biopsy-proven high-risk PCa candidates for surgery. Clinical information (age, iPSA-value, and grading group) and PSMA-PET/CT parameters (SUVmax, PSMA tumor volume [PSMA-TV], and total lesion [PSMA-TL]) were compared with pT (including histologic pattern, Gleason grade, and lymphovascular invasion [LVI]) and PSMA-IHC features, including visual quantification (VS) with a four-tiered score (0 = negative, 1+ = weak, 2+ = moderate, 3+ = strong), growth pattern (infiltrative vs expansive), and visual pattern (cytoplasmic vs membranous). In total, 44 patients were enrolled, with a median age of 67 (IQR 57-77); the median iPSA was 9.4 ng/dL (IQR 12.5-6.0). One patient (3%) was grading group (GG) 3, 27/44 (61%) were GG4, and 16/44 (36%) were GG5. PSMA-PET/CT detection rate for the presence of primary prostate cancer was 100%. Fused/poorly formed Gleason grade 4 features were predominant (22/44–50%); a cribriform pattern was present in 18/44 (41%) and acinar in 4/44 (9%). We found that lower PSMA-TVs were mostly related to acinar, while higher PSMA-TVs correlated with a higher probability to have a cribriform pattern (p-value 0.04). LVI was present in 21/44(48%) patients. We found that higher PSMA-TV and PSMA-TL are predictive of LVI p-value 0.002 and p-value 0.01, respectively. There was no correlation between PET-parameters and perineural invasion (PNI), probably because this was present in almost all the patients. Moreover, patients with high PSMA-TL values displayed the highest PSMA-IHC expression (VS3+) with a membranous pattern. In conclusion, PSMA-TV and PSMA-TL are predictors of a cribriform pattern and LVI. These conditions are mostly related to higher aggressiveness and worse outcomes.
Luigia Vetrone, Giulia Cuzzani, Riccardo Mei, Lucia Zanoni, Alessandro Bertaccini, Lorenzo Bianchi, Paolo Castellucci, Caterina Gaudiano, Alberta Cappelli, Francesca Giunchi,et al.
Frontiers Media SA
This is a case of [68 Ga]Ga-Prostate-specific membrane antigen (PSMA)-11 PET/CT in a 73-years old patient presenting high Prostate Specific Antigen (PSA) levels despite both multi-parametric magnetic resonance imaging (mpMRI) and 12-core saturation biopsy negative for prostate cancer (Pca). This is a highly interesting case because, despite the advanced metastatic spread at initial presentation as showed by [68Ga]Ga-PSMA-PET/CT, the primary Pca was detected by none of the diagnostic techniques (12 random sample biopsy, mpMRI, PSMA PET/CT). However, [68Ga]Ga-PSMA-PET/CT showed a suspicious axillary lesion suitable for biopsy, which finally resulted as Pca metastasis. This case report is therefore a brilliant example of how [68Ga]Ga-PSMA-PET/CT optimized patient’s management.
Costantino Ricci, Francesca Ambrosi, Tania Franceschini, Francesca Giunchi, Maria Eugenia Maracci, Maria Sirolli, Agnese Orsatti, Federico Chiarucci, Eugenia Franchini, Matteo Borsato,et al.
Elsevier BV
Matteo DROGHETTI, Lorenzo BIANCHI, Caterina GAUDIANO, Beniamino CORCIONI, Arianna RUSTICI, Pietro PIAZZA, Carlo BERETTA, Eleonora BALESTRAZZI, Francesco COSTA, Alberto FERUZZI,et al.
Edizioni Minerva Medica
BACKGROUND
Latest changes in European guidelines on prostate cancer determined a widespread of multiparametric magnetic resonance imaging (mpMRI) even in less experienced centers due to an increased demand. This could decrease diagnostic accuracy of targeted biopsy (TB) since image interpretation can be challenging and requires adequate and supervised training. Therefore we aimed to evaluate the PCa detection rate on TB according to mpMRI center's volume and experience.
METHODS
We retrospectively analyzed data of 737 patients who underwent mpMRI-TB at our institution. Patients were stratified according to mpMRI center: Hub (high volume>100 exams/year with dedicated radiologists and supervised training) and Spoke center (low volume<100 exams/year without dedicated radiologists and/or supervised training). Detection rate of PCa at TB and possible predictors of clinically significant PCa (csPCa) at TB. Differences in detection rate were explored using Chi-square test. Predictors of csPCa were evaluated through uni and multivariable logistic regression. The adjustment for casemix included: age, PSA, mpMRI center,lesion's location, PSA density, PI-RADS score and index lesion's size.
RESULTS
449 (60.9%) and 288 (39.1%) patients underwent mpMRI at a Hub or Spoke center, respectively. Hub group had higher detection rate for both any (60.3% vs 48.1%) and csPCa (46.9% vs 38.7%; all p≤0.001). After stratifying for PI-RADS score, Hub group had higher detection rate for PI-RADS score 3 (csPCA 25.2% vs. 15.7%; p 0.04) and 4 (csPCa 65.7% vs. 45.7%; p 0.001). At multivariable analyses, receiving an mpMRI scan at a Spoke center was an independent predictor for csPCa on TB (OR 0.65; p 0.04).
CONCLUSIONS
mpMRI performed in Hub centers provided a significantly higher PCa yield on TB. A dedicated team of experienced radiologist, a supervised training for mpMRI and a central revision of mpMRI performed in non-experienced centres are essential to avoid unnecessary and potentially harmful procedures.
Nicolas Destefanis, Valentina Fiano, Lorenzo Milani, Paolo Vasapolli, Michelangelo Fiorentino, Francesca Giunchi, Luca Lianas, Mauro Del Rio, Francesca Frexia, Luca Pireddu,et al.
Frontiers Media SA
IntroductionProstate cancer (PCa) is the most frequent tumor among men in Europe and has both indolent and aggressive forms. There are several treatment options, the choice of which depends on multiple factors. To further improve current prognostication models, we established the Turin Prostate Cancer Prognostication (TPCP) cohort, an Italian retrospective biopsy cohort of patients with PCa and long-term follow-up. This work presents this new cohort with its main characteristics and the distributions of some of its core variables, along with its potential contributions to PCa research.MethodsThe TPCP cohort includes consecutive non-metastatic patients with first positive biopsy for PCa performed between 2008 and 2013 at the main hospital in Turin, Italy. The follow-up ended on December 31st 2021. The primary outcome is the occurrence of metastasis; death from PCa and overall mortality are the secondary outcomes. In addition to numerous clinical variables, the study’s prognostic variables include histopathologic information assigned by a centralized uropathology review using a digital pathology software system specialized for the study of PCa, tumor DNA methylation in candidate genes, and features extracted from digitized slide images via Deep Neural Networks.ResultsThe cohort includes 891 patients followed-up for a median time of 10 years. During this period, 97 patients had progression to metastatic disease and 301 died; of these, 56 died from PCa. In total, 65.3% of the cohort has a Gleason score less than or equal to 3 + 4, and 44.5% has a clinical stage cT1. Consistent with previous studies, age and clinical stage at diagnosis are important prognostic factors: the crude cumulative incidence of metastatic disease during the 14-years of follow-up increases from 9.1% among patients younger than 64 to 16.2% for patients in the age group of 75-84, and from 6.1% for cT1 stage to 27.9% in cT3 stage.DiscussionThis study stands to be an important resource for updating existing prognostic models for PCa on an Italian cohort. In addition, the integrated collection of multi-modal data will allow development and/or validation of new models including new histopathological, digital, and molecular markers, with the goal of better directing clinical decisions to manage patients with PCa.
Caterina Gaudiano, Benedetta Renzetti, Cristina De Fino, Beniamino Corcioni, Federica Ciccarese, Lorenzo Bianchi, Riccardo Schiavina, Matteo Droghetti, Francesca Giunchi, Eugenio Brunocilla,et al.
Frontiers Media SA
Multiparametric magnetic resonance imaging (mpMRI) is currently the most effective diagnostic tool for detecting prostate cancer (PCa) and evaluating adenocarcinoma-mimicking lesions of the prostate gland, among which granulomatous prostatitis (GP) represents the most interesting diagnostic challenge. GP consists of a heterogeneous group of chronic inflammatory lesions that can be differentiated into four types: idiopathic, infective, iatrogenic, and associated with systemic granulomatous disease. The incidence of GP is growing due to the increase in endourological surgical interventions and the adoption of intravesical instillation of Bacillus Calmette-Guerin in patients with non-muscle invasive bladder cancer; therefore, the difficulty lies in identifying specific features of GP on mpMRI to avoid the use of transrectal prostate biopsy as much as possible.
Caterina Gaudiano, Lorenzo Braccischi, Makoto Taninokuchi Tomassoni, Alexandro Paccapelo, Lorenzo Bianchi, Beniamino Corcioni, Federica Ciccarese, Riccardo Schiavina, Matteo Droghetti, Francesca Giunchi,et al.
Frontiers Media SA
BackgroundTo evaluate multiparametric magnetic resonance imaging (mpMRI) parameters, such as TransPA (transverse prostate maximum sectional area), TransCGA (transverse central gland sectional area), TransPZA (transverse peripheral zone sectional area), and TransPAI (TransPZA/TransCGA ratio) in predicting prostate cancer (PCa) in prostate imaging reporting and data system (PI-RADS) 3 lesions.MethodsSensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV), the area under the receiver operating characteristic curve (AUC), and the best cut-off, were calculated. Univariate and multivariate analyses were carried out to evaluate the capability to predict PCa.ResultsOut of 120 PI-RADS 3 lesions, 54 (45.0%) were PCa with 34 (28.3%) csPCas. Median TransPA, TransCGA, TransPZA and TransPAI were 15.4cm2, 9.1cm2, 5.5cm2 and 0.57, respectively. At multivariate analysis, location in the transition zone (OR=7.92, 95% CI: 2.70-23.29, P&lt;0.001) and TransPA (OR=0.83, 95% CI: 0.76-0.92, P&lt;0.001) were independent predictors of PCa. The TransPA (OR=0.90, 95% CI: 0.082-0.99, P=0.022) was an independent predictor of csPCa. The best cut-off of TransPA for csPCa was 18 (Sensitivity 88.2%, Specificity 37.2%, PPV 35.7%, NPV 88.9%). The discrimination (AUC) of the multivariate model was 0.627 (95% CI: 0.519-0.734, P&lt;0.031).ConclusionsIn PI-RADS 3 lesions, the TransPA could be useful in selecting patients requiring biopsy.
Costantino Ricci, Francesca Ambrosi, Tania Franceschini, Francesca Giunchi, Eugenia Franchini, Francesco Massari, Veronica Mollica, Federico Mineo Bianchi, Maurizio Colecchia, Andres Martin Acosta,et al.
Elsevier BV
Matteo Droghetti, Lorenzo Bianchi, Carlo Beretta, Eleonora Balestrazzi, Francesco Costa, Alberto Feruzzi, Pietro Piazza, Carlo Roveroni, Caterina Gaudiano, Beniamino Corcioni,et al.
Springer Science and Business Media LLC
Caterina Gaudiano, Margherita Mottola, Lorenzo Bianchi, Beniamino Corcioni, Arrigo Cattabriga, Maria Adriana Cocozza, Antonino Palmeri, Francesca Coppola, Francesca Giunchi, Riccardo Schiavina,et al.
MDPI AG
The risk of misclassifying clinically significant prostate cancer (csPCa) by multiparametric magnetic resonance imaging is consistent, also using the updated PIRADS score and although different definitions of csPCa, patients with Gleason Grade group (GG) ≥ 3 have a significantly worse prognosis. This study aims to develop a machine learning model predicting csPCa (i.e., any GG ≥ 3 lesion at target biopsy) by mpMRI radiomic features and analyzing similarities between GG groups. One hundred and two patients with 117 PIRADS ≥ 3 lesions at mpMRI underwent target+systematic biopsy, providing histologic diagnosis of PCa, 61 GG < 3 and 56 GG ≥ 3. Features were generated locally from an apparent diffusion coefficient and selected, using the LASSO method and Wilcoxon rank-sum test (p < 0.001), to achieve only four features. After data augmentation, the features were exploited to train a support vector machine classifier, subsequently validated on a test set. To assess the results, Kruskal–Wallis and Wilcoxon rank-sum tests (p < 0.001) and receiver operating characteristic (ROC)-related metrics were used. GG1 and GG2 were equivalent (p = 0.26), whilst clear separations between either GG[1,2] and GG ≥ 3 exist (p < 10−6). On the test set, the area under the curve = 0.88 (95% CI, 0.68–0.94), with positive and negative predictive values being 84%. The features retain a histological interpretation. Our model hints at GG2 being much more similar to GG1 than GG ≥ 3.
Francesca Ruzzi, Stefania Angelicola, Lorena Landuzzi, Elena Nironi, Maria Sofia Semprini, Laura Scalambra, Annalisa Altimari, Elisa Gruppioni, Michelangelo Fiorentino, Francesca Giunchi,et al.
AME Publishing Company
Background ROS1 fusions are driver molecular alterations in 1–2% of non-small cell lung cancers (NSCLCs). Several tyrosine kinase inhibitors (TKIs) have shown high efficacy in patients whose tumors harbour a ROS1 fusion. However, the limited availability of preclinical models of ROS1-positive NSCLC hinders the discovery of new drugs and the understanding of the mechanisms underlying drug resistance and strategies to overcome it. Methods The ADK-VR2 cell line was derived from the pleural effusion of a treatment-naïve NSCLC patient bearing SDC4-ROS1 gene fusion. The sensitivity of ADK-VR2 and its crizotinib-resistant clone ADK-VR2 AG143 (selected in 3D culture in the presence of crizotinib) to different TKIs was tested in vitro, in both 2D and 3D conditions. Tumorigenic and metastatic ability was assessed in highly immunodeficient mice. In addition, crizotinib efficacy on ADK-VR2 was evaluated in vivo. Results 2D-growth of ADK-VR2 cells was partially inhibited by crizotinib. On the contrary, the treatment with other TKIs, such as lorlatinib, entrectinib and DS-6051b, did not result in cell growth inhibition. TKIs showed dramatically different efficacy on ADK-VR2 cells, depending on the cell culture conditions. In 3D culture, ADK-VR2 growth was indeed almost totally inhibited by lorlatinib and DS-6051b. The clone ADK-VR2 AG143 showed higher resistance to crizotinib treatment in vitro, compared to its parental cell line, in both 2D and 3D cultures. Similarly to ADK-VR2, ADK-VR2 AG143 growth was strongly inhibited by lorlatinib in 3D conditions. Nevertheless, ADK-VR2 AG143 sphere formation was less affected by TKIs treatment, compared to the parental cell line. In vivo experiments highlighted the high tumorigenic and metastatic ability of ADK-VR2 cell line, which, once injected in immunodeficient mice, gave rise to both spontaneous and experimental lung metastases while the crizotinib-resistant clone ADK-VR2 AG143 showed a slower growth in vivo. In addition, ADK-VR2 tumor growth was significantly reduced but not eradicated by crizotinib treatment. Conclusions The ADK-VR2 cell line is a promising NSCLC preclinical model for the study of novel targeted therapies against ROS1 fusions and the mechanisms of resistance to TKI therapies.
Agnese Orsatti, Maria Sirolli, Francesca Ambrosi, Tania Franceschini, Francesca Giunchi, Eugenia Franchini, Marco Grillini, Francesco Massari, Veronica Mollica, Federico Mineo Bianchi,et al.
Elsevier BV
Paola Valeria Marchese, Veronica Mollica, Dario De Biase, Francesca Giunchi, Elisa Tassinari, Andrea Marchetti, Matteo Rosellini, Giacomo Nuvola, Thais Maloberti, Michelangelo Fiorentino,et al.
Elsevier BV
Alessandro Di Federico, Andrea De Giglio, Francesco Gelsomino, Dario De Biase, Francesca Giunchi, Arianna Palladini, Francesca Sperandi, Barbara Melotti, and Andrea Ardizzoni
MDPI AG
Background: In non-small cell lung cancer (NSCLC), BRAF class 1 alterations are effectively targeted by BRAF inhibitors. Conversely, targeted therapies have very low or absent activity in patients carrying class 2 and 3 alterations. The spectrum of BRAF alterations in NSCLC patients, and their accompanying clinical features, genomic landscape and treatment outcomes have been poorly reported. Patients and methods: We identified BRAF alterations of defined functional class across different tumors through a systematic review. Then, we selected NSCLC patients carrying BRAF alterations, according to the systematic review, in the cBioPortal (cBioPortal cohort) to collect and analyze clinical, biomolecular and survival data. Finally, we identified NSCLC patients carrying BRAF non-V600 mutations enrolled in POPLAR and OAK trials (POPLAR/OAK cohort), extracting clinical and survival data for survival analyses. Results: 100 different BRAF non-V600 alterations were identified through the systematic review. In the cBioPortal cohort (n = 139), patients harboring class 2 and 3 alterations were more frequently smokers and had higher tumor mutational burden compared to those carrying class 1 alterations. The spectrum of most frequently co-altered genes was significantly different between BRAF alterations classes, including SETD2, STK11, POM121L12, MUC16, KEAP1, TERT, TP53 and other genes. In the POPLAR/OAK cohort, patients carrying non-V600 BRAF alterations were characterized by poor prognosis compared to BRAF wild-type patients. Conclusions: Different classes of BRAF alterations confer distinctive clinical features, biomolecular signature and disease behavior to NSCLC patients. Non-V600 alterations are characterized by poor prognosis, but key gene co-alterations involved in cancer cell survival and immune pathways may suggest their potential sensitivity to tailored treatments.
Luisa Di Sciascio, Francesca Ambrosi, Tania Franceschini, Francesca Giunchi, Eugenia Franchini, Francesco Massari, Federico Mineo Bianchi, Maurizio Colecchia, Michelangelo Fiorentino, and Costantino Ricci
Elsevier BV
Francesca Ambrosi, Francesca Giunchi, Elisa Capizzi, Alessandra Cancellieri, Rocco Trisolini, Andrea Ardizzoni, Michelangelo Fiorentino, and Costantino Ricci
Elsevier BV