Cancer-derived small extracellular vesicles (sEVs) are capable of modifying the tumor microenvironment and promoting tumor progression. Ovarian cancer (OvCa) is a lethal malignancy that ...preferentially spreads through the abdominal cavity. Thus, the secretion of such vesicles into the peritoneal fluid could be a determinant factor in the dissemination and behavior of this disease. We designed a prospective observational study to assess the impact of peritoneal fluid-derived sEVs (PFD-sEVs) in OvCa clinical outcome. For this purpose, 2 patient cohorts were enrolled: patients with OvCa who underwent a diagnostic or cytoreductive surgery and nononcological patients, who underwent abdominal surgery for benign gynecological conditions and acted as the control group. Systematic extraction of PFD-sEVs from surgical samples enabled us to observe significant quantitative and qualitative differences associated with cancer diagnosis, disease stage, and platinum chemosensitivity. Proteomic profiling of PFD-sEVs led to the identification of molecular pathways and proteins of interest and to the biological validation of S100A4 and STX5. In addition, unsupervised analysis of PFD-sEV proteomic profiles in high-grade serous ovarian carcinomas (HGSOCs) revealed 2 clusters with different outcomes in terms of overall survival. In conclusion, comprehensive characterization of PFD-sEV content provided a prognostic value with potential implications in HGSOC clinical management.
Purpose
The identification of subpopulations harboring druggable targets has become a major step forward in the subclassification of solid tumors into small groups suitable for specific therapies.
...BRAF
fusions represent a paradigm of uncommon and targetable oncogenic events and have been widely correlated to the development of specific malignancies. However, they are only present in a limited frequency across most common tumor types. At this regard, we performed a genomic screening aimed to identifying rare variants associated to advanced prostate cancer development.
Methods
Tumoral tissue genomic screening of 41 patients developing advanced prostate cancer was performed at our center as part of the GETHI XX study. The project, sponsored by the Spanish Collaborative Group in Rare Cancers (GETHI), aims to analyze the molecular background of rare tumors and to discover unfrequent molecular variants in common tumors.
Results
Here we present the clinical outcome and an in-deep molecular analysis performed in a case harboring a
SND1-BRAF
fusion gene. The identification of such rearrangement in a patient refractory to standard therapies led to the administration of trametinib (MEK inhibitor). Despite unsensitive to standard therapies, the patient achieved a dramatic response to trametinib. A comprehensive study of the tumor demonstrated this event to be a trunk alteration with higher expression of MEK in areas of tumor invasion.
Conclusions
Our study describes the patient-driven discovery of the first
BRAF
fusion-driven prostate cancer effectively treated with trametinib. Consequently, MAPK pathway activation could define a new subtype of prostate cancer susceptible to a tailored management.
Although certain genetic alterations have been defined as predictive and prognostic biomarkers in the context of ovarian cancer (OC), data science methods represent alternative approaches to identify ...novel correlations and define relevant markers in these gynecological tumors. Considering this potential, our work focused both on clinical and genomic data information collected from patients with OC to identify relationships between clinical and genetic factors and disease progression-related variables. For this aim, we proposed two analyses: (1) a nonlinear exploration of an OC dataset using autoencoders, a type of neural network that can be used as a feature extraction tool to represent a dataset in 3-dimensional latent space, so that we could assess whether there are intrinsic or natural nonlinear separability patterns between disease progression groups (in our case, platinum-sensitive and platinum-resistant groups); and (2) the identification of relevant variable relationships by means of an adaptation of the informative variable identifier (IVI), a feature selection method that labels each input feature as informative or noisy with respect to the task at hand, identifies the relationships among features, and builds a ranking of features, allowing us to study which input features and relationships may be most informative for the OC disease progression classification to define new biomarkers involved in disease progression. Our interest has been in clinical and genetic factors and in the combination of clinical features and genetic profile. Results with autoencoders suggest a pattern of separability between disease progression groups in the clinical part and for the combination of genes and clinical features of the OC dataset, that is increased via supervised fine tuning. In the genetic part, this pattern of separability is not observed, but it is more defined when a supervised fine tuning is performed. Results of the IVI-mediated feature selection method show significance for relevant clinical variables (such as type of surgery and neoadjuvant chemotherapy), some mutation genes, and low-risk genetic features. These results highlight the efficacy of the considered approaches to better understand the clinical course of OC.
•Data science methods are suitable for identifying biomarkers in OC.•Feature selection methods show predictive roles of variables in an OC dataset.•Features extraction methods reveal some patterns of separability in an OC dataset.
Ovarian cancer (OC) is the second most common gynecological malignancy and the gynecological tumor with the worst prognosis. To try to improve this situation, Data Science technologies could be a ...useful tool to help clinicians to know more about the disease. In our case, we are interested in exploring OC data to discover relationships between clinical and genetic factors and the disease progression. For it, we propose an analysis framework for simple and univariate statistical descriptions of features of different types, based on bootstrap resampling. Foremost, we define the framework for metric, categorical, and dates variables and determine what are the advantages and disadvantages of using different bootstrap resampling strategies, based on their statistical basis. Then, we use it to perform a univariate analysis over an OC dataset that allows to explore how is the disease progression, having platinum-free interval as indicator, in relation to clinical and genetic features of different types. Also, it provides a first set of variables possibly relevant for survival prediction. Results obtained show that some features have led to individual differences between both platinum resistant (<6 months) and platinum sensitive(>6 months) groups. It can be concluded that this could be an indicator that the database could be discriminatory for the hypotheses studied, though it is convenient to make multivariate analyses to check how relationships among features are influenced.
Developments of richer integrative analysis methods for oncological studies are needed for efficiently leveraging the amount of clinical and genetic data available to provide the clinicians with ...better information. However, analyses of this nature often require mixing data of different types, which are not immediate to address jointly with classical methods. In this work, our aim is to find relationships between clinical and genetic features of different types (metric, categorical, and text) and the ovarian cancer (OC) disease progression. To this end, we first propose a univariate statistical method for text type applying bootstrap resampling to Bag of Words and Latent Dirichlet Allocation in order to include as features the free-text fields of the health recordings. Secondly, we extend bootstrap resampling for metric and categorical feature extraction with Principal Component Analysis (PCA) and Multiple Correspondence Analysis (MCA), respectively. We subsequently formulate a novel and integrative method for jointly considering metric, categorical, and text features. Results obtained in text analysis indicate individual differences in some words between two OC patients groups categorised according to their sensitivity to platinum drugs. These results indicate separability between both groups for text features. Also, regarding the multivariate analysis, clinical data results showed separability patterns for the three methods analysed according to the platinum-sensitivity degree. The use of these analytical tools in our OC cohort has allowed us to demonstrate their strengths by confirming the predictive and prognostic role of widely-known clinical and genetic variables (BRCA status, value of adjuvant therapy and optimal resection, or family history) and demonstrating significant associations in other variables whose role in OC development has been studied to a lesser extent (such as PMS1, GPC3, and SLX4 genes). These results highlight the value of implementing these approaches for the identification of novel biomarkers in the context of OC.
Fibroblast growth factor receptor (FGFR) genomic alterations (GAs) represent an actionable target, key to the pathogenesis of some urothelial cancers (UCs). Though FGFR GAs are common in noninvasive ...UC, little is known about their role in the metastatic(m) setting and response to therapy. This study aimed to assess the impact of FGFR alterations on sensitivity to systemic treatments and survival and to validate Bajorin’s and Bellmunt’s prognostic scores in mUC patients according to their FGFR status. We retrospectively analyzed data from 98 patients with tumor-sequenced UC who received treatment between January 2010 and December 2020. Up to 77 developed metastatic disease and were deemed the study population. Twenty-six showed FGFR GAs. A trend toward a better response to cisplatin and checkpoint inhibitors was suggested favoring FGFR GA tumors. FGFR GA patients who received an FGFR inhibitor as first-line had poorer responses compared with other options (20% vs. 68.4%, p = 0.0065). Median PFS was 6 vs. 5 months in the FGFR GA vs. FGFR WT cohort (p = 0.71). Median OS was significantly worse in the FGFR GA vs. FGFR WT cohort (16.2 vs. 31.9 months, p = 0.045). Multivariate analyses deemed FGFR GAs as a factor independently associated with the outcome (HR 2.59 (95% CI 1.21–5.55)). Bajorin’s model correctly predicted clinical outcomes in the whole study population but not in FGFR GA cases. FGFR GAs are a relevant biomarker in mUC that could condition the response to systemic therapy. New prognostic models, including this molecular determination, should be designed and validated.
Ovarian cancer (OC) is a deadly disease that affects a large number of women worldwide. Machine Learning (ML) models can help in the early detection of this disease, however, the use of these models ...may be limited by their lack of interpretability and the difficulty to evaluate their performance. In this work, five types of datasets were used, employing clinical features, different types of coding genomic features, and combining both. The use of interpretable ML (IML) models (one linear and one nonlinear model) provided us with better interpretability of the five feature sets. Following this study, nine binary classification models were compared, and the Accuracy, Recall, and Area Under the Curve were analyzed. The results showed that ML models employed the combination of clinical features and genomes with the coding of the position of genes in patients significantly improves the prediction. We demonstrated that the inclusion of different preprocessed patient data and especially through the information provided by IML models, can help clinicians to understand the disease better and make informed treatment decisions.
Tubo-ovarian high-grade serous carcinomas (HGSC) are highly proliferative neoplasms that generally respond well to platinum/taxane chemotherapy. We recently identified minichromosome maintenance ...complex component 3 (MCM3), which is involved in the initiation of DNA replication and proliferation, as a favorable prognostic marker in HGSC. Our objective was to further validate whether MCM3 mRNA expression and possibly MCM3 protein levels are associated with survival in patients with HGSC. MCM3 mRNA expression was measured using NanoString expression profiling on formalin-fixed and paraffin-embedded tissue (
N
= 2355 HGSC) and MCM3 protein expression was assessed by immunohistochemistry (
N
= 522 HGSC) and compared with Ki-67. Kaplan–Meier curves and the Cox proportional hazards model were used to estimate associations with survival. Among chemotherapy-naïve HGSC, higher MCM3 mRNA expression (one standard deviation increase in the score) was associated with longer overall survival (HR = 0.87, 95% CI 0.81–0.92,
p
< 0.0001,
N
= 1840) in multivariable analysis. MCM3 mRNA expression was highest in the HGSC C5.PRO molecular subtype, although no interaction was observed between MCM3, survival and molecular subtypes. MCM3 and Ki-67 protein levels were significantly lower after exposure to neoadjuvant chemotherapy compared to chemotherapy-naïve tumors: 37.0% versus 46.4% and 22.9% versus 34.2%, respectively. Among chemotherapy-naïve HGSC, high MCM3 protein levels were also associated with significantly longer disease-specific survival (HR = 0.52, 95% CI 0.36–0.74,
p
= 0.0003,
N
= 392) compared to cases with low MCM3 protein levels in multivariable analysis. MCM3 immunohistochemistry is a promising surrogate marker of proliferation in HGSC.
Abstract
Introduction
The Fibroblast Growth Factor Receptor (FGFR) has become a key target in urothelial cancer. The FGFR inhibitor (FGFRi) erdafitinib has been approved for clinical use and many ...others are in development. Unfortunately, responses have shown to last short. Thus, it is urgently required to identify the underlying mechanisms of resistance and establish strategies to overcome it.
We designed a comprehensive in vitro study in FGFR3-altered urothelial cancer cell lines after acquiring resistance to FGFRis. In parallel, we monitored the clinical and molecular evolution (through tumor biopsies and ctDNA) of three patients treated with FGFRi at our institution.
Experimental Procedures
Drug sensitivity to FGFRi (Erdafitinib and AZD4547) was evaluated in bladder cancer cell lines harboring FGFR3 point mutations (pS249C) or rearrangements (FGFR3/BAIAP2L1 or TACC3). After performing 7-days proliferation assays, cell lines showing nM-range sensitivity were long-term treated with high concentrations of both compounds to induce therapeutic resistance.
Cell lysates were collected and protein arrays (Human Phospho-RTK and Kinases Array, R&D Systems) were used for the identification of proteins involved in the desensitization to FGFRi. Later, cell lines were treated with selective inhibitors of such kinases.
Regarding patients, after providing written consent, tumor tissue was collected retrospectively from the initial diagnosis and prospectively in any new tumor resection performed in daily practice. ctDNA from peripheral blood was also periodically extracted.
Results & Conclusions
Protein arrays showed an overactivation of phosphorylated forms of proteins involved in PI3K (EGFR and AKT, 4 and 9-fold increase) and MAPK pathways or proteins related to signal transduction (PLCγ, 80-fold increase, among others). When treated with trametinib, IGF1Ri, ipatasertib, everolimus or erlotinib, only trametinib and IGF1Ri demonstrated significant activity in cell cultures.
Three patients were included in the clinical cohort of our study (2 males, 1 female). All harbored the mutation p.S249C and initially achieved a partial response. Multiple biopsies (baseline and at tumor progression) were analyzed in case 1, showing a secondary mutation in FGFR3 and molecular alterations in EGFR and IGFR pathways as potential mechanisms of resistance, matching the results previously observed in in vitro models. Studies of the other 2 cases are ongoing and will be presented at the meeting.
Combination of FGFRi with additional TKIs could improve the efficacy of these drugs. Further studies and validation in clinical trials are required.
Citation Format: Sergio Ruiz-Llorente, Elena Fernández-Sevillano, Paloma Navarro, Juan Francisco Rodríguez-Moreno, Arantzazu Barquín-García, Sandra Amarilla-Quintana, Mónica Yagüe-Fernández, Juan María Roldan-Romero, Raquel Martín, Cristina Rodriguez-Antona, Jesús García-Donas. Mechanisms of resistance to FGFR inhibitors in urothelial cancer cell lines and patients harboring FGFR3 alterations and strategies to overcome it abstract. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5288.
There is a pressing clinical need to develop cell-based bone therapies due to a lack of viable, autologous bone grafts and a growing demand for bone grafts in musculoskeletal surgery. Such therapies ...can be tissue engineered and cellular, such as osteoblasts, combined with a material scaffold. Because mesenchymal stem cells (MSCs) are both available and fast growing compared to mature osteoblasts, therapies that utilize these progenitor cells are particularly promising. We have developed a nanovibrational bioreactor that can convert MSCs into bone-forming osteoblasts in two- and three-dimensional, but the mechanisms involved in this osteoinduction process remain unclear. Here, to elucidate this mechanism, we use increasing vibrational amplitude, from 30 nm (N30) to 90 nm (N90) amplitudes at 1000 Hz and assess MSC metabolite, gene, and protein changes. These approaches reveal that dose-dependent changes occur in MSCs’ responses to increased vibrational amplitude, particularly in adhesion and mechanosensitive ion channel expression and that energetic metabolic pathways are activated, leading to low-level reactive oxygen species (ROS) production and to low-level inflammation as well as to ROS- and inflammation-balancing pathways. These events are analogous to those that occur in the natural bone-healing processes. We have also developed a tissue engineered MSC-laden scaffold designed using cells’ mechanical memory, driven by the stronger N90 stimulation. These mechanistic insights and cell-scaffold design are underpinned by a process that is free of inductive chemicals.
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