Search

Background: Non-Small Cell Lung Cancer (NSCLC) is the leading cause of cancer death worldwide. Although immune checkpoint inhibitors (ICIs) have shown remarkable clinical efficacy, they can also induce a paradoxical cancer acceleration, known as hyperprogressive disease (HPD), whose causative mechanisms are still unclear., Methods: This study investigated the mechanisms of ICI resistance in an HPD-NSCLC model. Two primary cell cultures were established from samples of a NSCLC patient, before ICI initiation ("baseline", NSCLC-B) and during HPD ("hyperprogression", NSCLC-H). The cell lines were phenotypically and molecularly characterized through immunofluorescence, Western Blotting and RNA-Seq analysis. To assess cell plasticity and aggressiveness, cellular growth patterns were evaluated both in vitro and in vivo through 2D and 3D cell growth assays and patient-derived xenografts establishment. In vitro investigations, including the evaluation of cell sensitivity to interferon-gamma (IFN-γ) and cell response to PD-L1 modulation, were conducted to explore the influence of these factors on cell plasticity regulation., Results: NSCLC-H exhibited increased expression of specific CD44 isoforms and a more aggressive phenotype, including organoid formation ability, compared to NSCLC-B. Plastic changes in NSCLC-H were well described by a deep transcriptome shift, that also affected IFN-γ-related genes, including PD-L1. IFN-γ-mediated cell growth inhibition was compromised in both 2D-cultured NSCLC-B and NSCLC-H cells. Further, the cytokine induced a partial activation of both type I and type II IFN-pathway mediators, together with a striking increase in NSCLC-B growth in 3D cell culture systems. Finally, low IFN-γ doses and PD-L1 modulation both promoted plastic changes in NSCLC-B, increasing CD44 expression and its ability to produce spheres., Conclusions: Our findings identified plasticity as a relevant hallmark of ICI-mediated HPD by demonstrating that ICIs can modulate the IFN-γ and PD-L1 pathways, driving tumor cell plasticity and fueling HPD development., Competing Interests: Declarations. Ethics approval and consent to participate: This study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). Human samples were collected after the patient gave her informed consent. The protocol was approved by the Ethics Committee Center Emilia-Romagna Region, Italy (GR-2018-12368031). Human samples and metadata including relevant clinical data were de-identified before being shared between laboratories involved in this study. All animal procedures were performed in accordance with European directive 2010/63/UE and Italian Law (No. DL26/2014). Experimental protocols were reviewed and approved by the institutional animal care and use committee of the University of Bologna and by the Italian Ministry of Health with letter 32/2020-PR. Consent for publication: Not applicable. Competing interests: Prof. Andrea Ardizzoni received research grants from Celgene, Bristol-Myers Squibb, Ipsen, and Roche; and honoraria for advisory roles from Bristol-Myers Squibb, Merck Sharp & Dohme, ROCHE, AstraZeneca, and Eli Lilly outside of the submitted work. Dr. Francesco Gelsomino received honoraria or personal fees for the advisory role or consulting from Eli Lilly, Novartis, AstraZeneca, Pfizer, Regeneron and Bristol-Myers Squibb outside the submitted work., (© 2025. The Author(s).)

Background/objectives: To evaluate T&N-staging diagnostic performance of [68Ga]Ga-FAPI-46 PET/CT (FAPI) in a suspected/confirmed lung cancer surgical cohort., Methods: Patients were enrolled in a prospective monocentric trial (EudraCT: 2021-006570-23) to perform FAPI, in addition to conventional-staging-flow-chart (including [18F]F-FDG PET/CT-FDG). For the current purpose, only surgical patients were included. PET-semiquantitative parameters were measured for T&N: SUVmax, target-to-background-ratios (using mediastinal blood pool-MBP, liver-L and pulmonary-parenchyma-P). Visual and semiquantitative T&N PET/CT performances were analysed per patient and per region for both tracers, with surgical histopathology as standard-of-truth., Results: 63 FAPI scans were performed in 64 patients enrolled (26 May 2022-30 November 2023). A total of 50/63 patients underwent surgery and were included. Agreement (%) with histopathological-T&N-StagingAJCC8thEdition was slightly in favour of FAPI (T-66% vs. 58%, N-78% vs. 70%), increasing when T&N dichotomised (T-92% vs. 80%, N-78% vs. 72%). The performance of Visual-Criteria for T-per patient (n = 50) resulted higher FAPI than FDG. For N-per patient (n = 46), sensitivity and NPV were slightly lower with FAPI. Among 59 T-regions surgically examined, malignancy was excluded in 6/59 (10%). FAPI showed (vs. FDG): sensitivity 85% (vs. 72%), specificity 67% (vs. 50%), PPV 96% (vs. 93%), NPV 33% (vs. 17%), accuracy 83% (vs. 69%). Among 217 N-stations surgically assessed (overall 746 ln removed), only 15/217 (7%) resulted malignant; FAPI showed (vs. FDG): sensitivity 53% (vs. 60%), PPV 53% (vs. 26%), NPV 97% (vs. 97%), and significantly higher specificity (97% vs. 88%, p = 0.001) and accuracy (94% vs. 86%, p = 0.018). Semiquantitative-PET parameters performed similarly, better for N ( p < 0.001) than for T, slightly in favour (although not significantly) of FAPI over FDG., Conclusions: In a suspected/confirmed lung cancer surgical cohort, PET/CT performances for preoperative T&Nstaging were slightly in favour of FAPI than FDG (except for suboptimal N-sensitivity), significantly better only for N (region-based) specificity and accuracy using visual assessment. The trial's conventional follow-up is still ongoing; future analyses are pending, including non-surgical findings and theoretical impact on patient management.

Cancer cells exhibit metabolic plasticity to meet oncogene-driven dependencies while coping with nutrient availability. A better understanding of how systemic metabolism impacts the accumulation of metabolites that reprogram the tumor microenvironment (TME) and drive cancer could facilitate development of precision nutrition approaches. Using the Hi-MYC prostate cancer mouse model, we demonstrated that an obesogenic high-fat diet (HFD) rich in saturated fats accelerates the development of c-MYC-driven invasive prostate cancer through metabolic rewiring. Although c-MYC modulated key metabolic pathways, interaction with an obesogenic HFD was necessary to induce glycolysis and lactate accumulation in tumors. These metabolic changes were associated with augmented infiltration of CD206+ and PD-L1+ tumor-associated macrophages (TAM) and FOXP3+ regulatory T cells, as well as with the activation of transcriptional programs linked to disease progression and therapy resistance. Lactate itself also stimulated neoangiogenesis and prostate cancer cell migration, which were significantly reduced following treatment with the lactate dehydrogenase inhibitor FX11. In patients with prostate cancer, high saturated fat intake and increased body mass index were associated with tumor glycolytic features that promote the infiltration of M2-like TAMs. Finally, upregulation of lactate dehydrogenase, indicative of a lactagenic phenotype, was associated with a shorter time to biochemical recurrence in independent clinical cohorts. This work identifies cooperation between genetic drivers and systemic metabolism to hijack the TME and promote prostate cancer progression through oncometabolite accumulation. This sets the stage for the assessment of lactate as a prognostic biomarker and supports strategies of dietary intervention and direct lactagenesis blockade in treating advanced prostate cancer., Significance: Lactate accumulation driven by high-fat diet and MYC reprograms the tumor microenvironment and promotes prostate cancer progression, supporting the potential of lactate as a biomarker and therapeutic target in prostate cancer. See related commentary by Frigo, p. 1742., (©2024 The Authors; Published by the American Association for Cancer Research.)

Introduction: Prostate-specific membrane antigen (PSMA) is a transmembrane protein expressed by normal prostatic tissue. Therefore, molecular imaging targeting PSMA (PSMA-PET) has gained particular interest and diffusion for PCa staging and restaging. Several factors may affect PSMA-PET results, and many tools have been proposed to improve patient selection. Furthermore, PSMA expression is not homogeneous among different tissues and within the prostate itself. The aims of this study were to evaluate immunohistochemistry (IHC) features of prostate biopsy samples and to assess their correlation with whole-mount specimens and PSMA-PET parameters., Methods: We included consecutive high-risk PCa patients who underwent PSMA-PET for staging proposal at our institution from January 2022 to December 2022. The PET parameters selected were SUVmax, total volume (TV), and total lesion activity (TL). Each patient underwent multiparametric MRI (mpMRI) and fusion-targeted prostate biopsy prior to surgery. IHC analyses were performed on the index lesion cores. IHC visual score (VS) (1, 2, 3) and visual pattern (VP) (membranous, cytoplasmic, and combined) and the percentage of PSMA-negative tumor areas (PSMA%neg) within biopsy cores were evaluated., Results: Forty-three patients who underwent robotic radical prostatectomy after PSMA-PET were available for analyses. Concordance between VS and VP at biopsy and final pathology showed a Cohen's kappa coefficient of 0.39 and 0.38, respectively. Patients with PSMA%neg <20% had a higher concordance in VS and VP (Cohen's kappa 0.49 and 0.4, respectively). No difference emerged in terms of median PSMA-TV ( p = 0.3) and PSMA-TL ( p = 0.9) according to VS at biopsy, while median SUVmax was higher in patients with VS 3 ( p = 0.04). Higher SUVmax was associated with membranous and combined VP expression ( p = 0.008). No difference emerged between patients with PSMA%neg <20% or PSMA%neg >20% on biopsy cores in terms of SUVmax, PSMA-TL, and PSMA-TV ( p = 0.5, p = 0.5, and p = 0.9 respectively)., Conclusions: We found a correlation between IHC VS and VP on targeted biopsy cores and SUVmax at PSMA-PET. However, the correlation between the IHC parameters of biopsy cores and final pathology was not as high as expected. Nevertheless, the presence of PSMA%neg <20% seems to have a better concordance in terms of visual score., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Droghetti, Bianchi, Presutti, Vetrone, Farolfi, Mei, Giunchi, Degiovanni, Mottaran, Piazza, Cangemi, Castellucci, D’Errico, Schiavina, Brunocilla and Fanti.)

Introduction: Prostate 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., Methods: The 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 31 st 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., Results: The 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., Discussion: This 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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Destefanis, Fiano, Milani, Vasapolli, Fiorentino, Giunchi, Lianas, Del Rio, Frexia, Pireddu, Molinaro, Cassoni, Papotti, Gontero, Calleris, Oderda, Ricardi, Iorio, Fariselli, Isaevska, Akre, Zelic, Pettersson, Zugna and Richiardi.)

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.

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Gaudiano, Renzetti, De Fino, Corcioni, Ciccarese, Bianchi, Schiavina, Droghetti, Giunchi, Brunocilla and Fiorentino.)

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.

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.

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.

Background: To 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., Methods: Sensitivity, 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., Results: Out of 120 PI-RADS 3 lesions, 54 (45.0%) were PCa with 34 (28.3%) csPCas. Median TransPA, TransCGA, TransPZA and TransPAI were 15.4cm 2 , 9.1cm 2 , 5.5cm 2 and 0.57, respectively. At multivariate analysis, location in the transition zone (OR=7.92, 95% CI: 2.70-23.29, P<0.001) and TransPA (OR=0.83, 95% CI: 0.76-0.92, P<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<0.031)., Conclusions: In PI-RADS 3 lesions, the TransPA could be useful in selecting patients requiring biopsy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Gaudiano, Braccischi, Taninokuchi Tomassoni, Paccapelo, Bianchi, Corcioni, Ciccarese, Schiavina, Droghetti, Giunchi, Fiorentino, Brunocilla and Golfieri.)

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 [ 68 Ga]Ga-PSMA-PET/CT, the primary Pca was detected by none of the diagnostic techniques (12 random sample biopsy, mpMRI, PSMA PET/CT). However, [ 68 Ga]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 [ 68 Ga]Ga-PSMA-PET/CT optimized patient's management., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Vetrone, Cuzzani, Mei, Zanoni, Bertaccini, Bianchi, Castellucci, Gaudiano, Cappelli, Giunchi and Fanti.)

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.

Introduction: The reactivation of telomerase represents a key moment in the carcinogenesis process. Mutations in the central promoter region of the telomerase reverse transcriptase (TERT) gene cause telomerase reactivation in approximately 90% of solid tumors. In some of these, its prognostic and predictive role in response to treatments has already been demonstrated, in others (such as tumors of the genitourinary tract like urothelial carcinoma) data are controversial and the research is still ongoing. In the future, TERT promoter mutations and telomerase activity could have diagnostic, prognostic, and therapeutic applications in many types of cancer., Areas Covered: We performed a review the literature with the aim of describing the current evidence on the prognostic and predictive role of TERT promoter mutations. In some tumor types, TERT promoter mutations have been associated with a worse prognosis and could have a potential value as biomarkers to guide therapeutic decisions. Mutations in TERT promoter seems to make the tumor particularly immunogenic and more responsive to immunotherapy, although data is controversial., Expert Opinion: We described the role of TERT promoter mutations in solid tumors with a particular focus in genitourinary cancers, considering their frequency in this tract.

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., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-22-163/coif). Andrea Ardizzoni received grants and personal fees from Bristol-Myers Squibb, Merck Sharp & Dohme, Eli Lilly, Boehringer Ingelheim and Pfizer, grants from Celgene and grants and personal fees from Roche, outside of the submitted work. FG received grants from Astrazeneca and honoraria for advisory board participation from Eli-Lilly. The other authors declare no conflicts of interest., (2022 Translational Lung Cancer Research. All rights reserved.)