Simulation and modelling of the aerodynamic impact of sensor setups on autonomous road vehicles

This study aims to simulate and model the aerodynamic impact of sensor setups on autonomous road vehicles. To achieve that, it focuses on the analysis of the ∆Cd contribution of sensor setups in comparison to a reference self-designed generic shuttle geometry, employing the CFD simulation software Pacefish®. In Chapter 2 the fundamentals of aerodynamics are explained, with a focus on external vehicle aerodynamics. They collect the fluid field concepts required to interpret the simulation post- processings. Additionally, this chapter gathers literature on aerodynamic data of similar vehicle shapes and add-on elements. However, due to the topic’s novelty, no previous studies on autonomous shuttle aerodynamics are stated. Finally, this chapter is concluded with the technical data collection of current autonomous shuttles. Chapter 3 describes the adopted methodology. It includes the validation of the simulation soft- ware, followed by the derivation and design of a generic shuttle, which defines the aerodynamic reference. Based on that geometry, this study assesses the impact of the modification of design features and the incorporation of single sensor positions and multi-sensor setups. The drag influence of such geometrical alterations is measured in ∆Cd , which aligns with the validation methodology of Pacefish®. Major results in Chapter 4 indicate the huge impact of the edge rounding (∆Cd ∈ [-0.099, 0.198]) design variable compared to the single sensor (∆Cd ∈ [-0.035, 0.047]) and multi-sensor setup (∆Cd ∈ [-0.054, 0.016]) influence. Differently from literature data, add-on geometries located in specific positions reduce the drag of the generic shuttle due to the appearance of flow detachment in such areas. However, the magnitude of the sensor impact is similar to the one observed in specific previous studies. Furthermore, this chapter includes the mathematical model to estimate the ∆Cd of sensor setups based on the simulation results. Finally, Chapter 5 discusses the thesis’ results and suggests possible future studies that could help to corroborate the validity of the proposed mathematical model and expand the understand- ing of autonomous shuttle aerodynamics. This study aims to shed some light on the barely studied topic of autonomous shuttle aerody- namics and the impact of sensor setups, which inevitably will gain relevance in the future of road transportation

Deconstructing the molecular portraits of breast cancer

Breast cancer is a heterogeneous disease in terms of histology, therapeutic response, dissemination patterns to distant sites, and patient outcomes. Global gene expression analyses using high-throughput technologies have helped to explain much of this heterogeneity and provided important new classifications of cancer patients. In the last decade, genomic studies have established five breast cancer intrinsic subtypes (Luminal A, Luminal B, HER2-enriched, Claudin-low, Basal-like) and a Normal Breast-like group. In this review, we dissect the most recent data on this genomic classification of breast cancer with a special focus on the Claudin-low subtype, which appears enriched for mesenchymal and stem cell features. In addition, we discuss how the combination of standard clinical-pathological markers with the information provided by these genomic entities might help further understand the biological complexity of this disease, increase the efficacy of current and novel therapies, and ultimately improve outcomes for breast cancer patients

Clinical implications of the intrinsic molecular subtypes in hormone receptor-positive and HER2-negative metastatic breast cancer

Traditionally, the classification of breast cancer relies on the expression of immunohistochemical (IHC) biomarkers readily available in clinical practice. Using highly standardized and reproducible assays across patient cohorts, intrinsic molecular subtypes of breast cancer - also called 'intrinsic subtypes' (IS) - have been identified based on the expression of 50 genes. Although IHC-based subgroups and IS moderately correlate to each other, they are not superimposable. In fact, non-luminal biology has been detected in a substantial proportion (5-20%) of hormone receptor-positive (HoR+) tumors, has prognostic value, and identifies reduced and increased sensitivity to endocrine therapy and chemotherapy, respectively. During tumor progression, a shift toward a non-luminal estrogen-independent and more aggressive phenotype has been demonstrated. Intrinsic genomic instability and cell plasticity, alone or combined with external constraints deriving from treatment selective pressure or interplay with the tumor microenvironment, may represent the determinants of such biological diversity between primary and metastatic disease, and during metastatic tumor evolution. In this review, we describe the distribution and the clinical behavior of IS as the disease progresses, focusing on HoR+/HER2-negative advanced breast cancer. In addition, we provide an overview of the ongoing clinical trials aiming to validate the predictive and prognostic value of IS towards their incorporation into routine care

Practical implications of gene-expression-based assays for breast oncologists

Gene-expression profiling has had a considerable impact on our understanding of breast cancer biology, and more recently on clinical care. Two statistical approaches underlie these advancements. Supervised analyses have led to the development of gene-expression signatures designed to predict survival and/or treatment response, which has resulted in the development of new clinical assays. Unsupervised analyses have identified numerous biological signatures including signatures of cell type of origin, signaling pathways, and of cellular proliferation. Included within these biological signatures are the molecular subtypes known as the ‘intrinsic’ subtypes of breast cancer. This classification has expanded our appreciation of the heterogeneity of breast cancer and has provided a way to sub-classify the disease in a manner that might have clinical utility. In this Review, we discuss the clinical utility of gene-expression-based assays and their technical potential as clinical tools vis-a-vis the performance of breast cancer biomarkers that are the current standard of care

AXL – a new player in resistance to HER2 blockade

Cancer; HER2 disease; ResistanceCàncer; Malaltia HER2; ResistènciaCáncer; Enfermedad HER2; ResistenciaHER2 is a driver in solid tumors, mainly breast, oesophageal and gastric cancer, through activation of oncogenic signaling pathways such as PI3K or MAPK. HER2 overexpression associates with aggressive disease and poor prognosis. Despite targeted anti-HER2 therapy has improved outcomes and is the current standard of care, resistance emerge in some patients, requiring additional therapeutic strategies. Several mechanisms, including the upregulation of receptors tyrosine kinases such as AXL, are involved in resistance. AXL signaling leads to cancer cell proliferation, survival, migration, invasion and angiogenesis and correlates with poor prognosis. In addition, AXL overexpression accompanied by a mesenchymal phenotype result in resistance to chemotherapy and targeted therapies. Preclinical studies show that AXL drives anti-HER2 resistance and metastasis through dimerization with HER2 and activation of downstream pathways in breast cancer. Moreover, AXL inhibition restores response to HER2 blockade in vitro and in vivo. Limited data in gastric and oesophageal cancer also support these evidences. Furthermore, AXL shows a strong value as a prognostic and predictive biomarker in HER2+ breast cancer patients, adding a remarkable translational relevance. Therefore, current studies enforce the potential of co-targeting AXL and HER2 to overcome resistance and supports the use of AXL inhibitors in the clinic

Phospho-kinase profile of triple negative breast cancer and androgen receptor signaling

BACKGROUND: The androgen receptor (AR) plays a central role in the oncogenesis of different tumors, as is the case in prostate cancer. In triple negative breast cancer (TNBC) a gene expression classification has described different subgroups including a luminal androgen subtype. The AR can be controlled by several mechanisms like the activation of membrane tyrosine kinases and downstream signaling pathways. However little is known in TNBC about how the AR is modulated by these mechanisms and the potential therapeutic strategists to inhibit its expression. METHODS: We used human samples to evaluate the expression of AR by western-blot and phospho-proteomic kinase arrays that recognize membrane tyrosine kinase receptors and downstream mediators. Western-blots in human cell lines were carried out to analyze the expression and activation of individual proteins. Drugs against these kinases in different conditions were used to measure the expression of the androgen receptor. PCR experiments were performed to assess changes in the AR gene after therapeutic modulation of these pathways. RESULTS: AR is present in a subset of TNBC and its expression correlates with activated membrane receptor kinases-EGFR and PDGFRβ in human samples and cell lines. Inhibition of the PI3K/mTOR pathway in TNBC cell lines decreased notably the expression of the AR. Concomitant administration of the anti-androgen bicalutamide with the EGFR, PDGFRβ and Erk1/2 inhibitors, decreased the amount of AR compared to each agent given alone, and had an additive anti-proliferative effect. Administration of dihydrotestosterone augmented the expression of AR that was not modified by the inhibition of the PI3K/mTOR or Erk1/2 pathways. AR expression was posttranscriptionally regulated by PI3K or Erk1/2 inhibition. CONCLUSION: Our results describe the expression of the AR in TNBC as a druggable target and further suggest the combination of bicalutamide with inhibitors of EGFR, PDGFRβ or Erk1/2 for future development

Development and validation of the new HER2DX assay for predicting pathological response and survival outcome in early-stage HER2-positive breast cancer

Gene expression; Breast cancer; PrognosisExpressió gènica; Càncer de mama; PronòsticExpresión génica; Cáncer de mama; PronósticoBackground: Both clinical and genomic data independently predict survival and treatment response in early-stage HER2-positive breast cancer. Here we present the development and validation of a new HER2DX risk score, and a new HER2DX pathological complete response (pCR) score, both based on a 27-gene expression plus clinical feature-based classifier. Methods: HER2DX is a supervised learning algorithm incorporating tumour size, nodal staging, and 4 gene expression signatures tracking immune infiltration, tumour cell proliferation, luminal differentiation, and the expression of the HER2 amplicon, into a single score. 434 HER2-positive tumours from the Short-HER trial were used to train a prognostic risk model; 268 cases from an independent cohort were used to verify the accuracy of the HER2DX risk score. In addition, 116 cases treated with neoadjuvant anti-HER2-based chemotherapy were used to train a predictive model of pathological complete response (pCR); two independent cohorts of 91 and 67 cases were used to verify the accuracy of the HER2DX pCR likelihood score. Five publicly available independent datasets with >1,000 patients with early-stage HER2-positive disease were also analysed. Findings: In Short-HER, HER2DX variables were associated with good risk outcomes (i.e., immune, and luminal) and poor risk outcomes (i.e., proliferation, and tumour and nodal staging). In an independent cohort, continuous HER2DX risk score was significantly associated with disease-free survival (DFS) (p=0·002); the 5-year DFS in the low-risk group was 97·4% (94·4-100·0%). For the neoadjuvant pCR predictor training cohort, HER2DX variables were associated with pCR (i.e., immune, proliferation and HER2 amplicon) and non-pCR (i.e., luminal, and tumour and nodal staging). In both independent test set cohorts, continuous HER2DX pCR likelihood score was significantly associated with pCR (p<0·0001). A weak negative correlation was found between the HER2DX risk score versus the pCR score (correlation coefficient -0·19). Interpretation: The two HER2DX tests provide accurate estimates of the risk of recurrence, and the likelihood to achieve a pCR, in early-stage HER2-positive breast cancer.This study received funding from Reveal Genomics, IDIBAPS and the University of Padova

Clinical implications of the intrinsic molecular subtypes of breast cancer

AbstractGene-expression profiling has had a considerable impact on our understanding of breast cancer biology. During the last 15 years, 5 intrinsic molecular subtypes of breast cancer (Luminal A, Luminal B, HER2-enriched, Basal-like and Claudin-low) have been identified and intensively studied. In this review, we will focus on the current and future clinical implications of the intrinsic molecular subtypes beyond the current pathological-based classification endorsed by the 2013 St. Gallen Consensus Recommendations. Within hormone receptor-positive and HER2-negative early breast cancer, the Luminal A and B subtypes predict 10-year outcome regardless of systemic treatment administered as well as residual risk of distant recurrence after 5 years of endocrine therapy. Within clinically HER2-positive disease, the 4 main intrinsic subtypes can be identified and dominate the biological and clinical phenotype. From a clinical perspective, patients with HER2+/HER2-enriched disease seem to benefit the most from neoadjuvant trastuzumab, or dual HER2 blockade with trastuzumab/lapatinib, in combination with chemotherapy, and patients with HER2+/Luminal A disease seem to have a relative better outcome compared to the other subtypes. Finally, within triple-negative breast cancer (TNBC), the Basal-like disease predominates (70–80%) and, from a biological perspective, should be considered a cancer-type by itself. Importantly, the distinction between Basal-like versus non-Basal-like within TNBC might predict survival following (neo)adjvuvant multi-agent chemotherapy, bevacizumab benefit in the neoadjuvant setting (CALGB40603), and docetaxel vs. carboplatin benefit in first-line metastatic disease (TNT study). Overall, this data suggests that intrinsic molecular profiling provides clinically relevant information beyond current pathology-based classifications

Genomic analysis identifies unique signatures predictive of brain, lung, and liver relapse

The ability to predict metastatic potential could be of great clinical importance, however, it is uncertain if predicting metastasis to specific vital organs is feasible. As a first step in evaluating metastatic predictions, we analyzed multiple primary tumors and metastasis pairs and determined that >90% of 298 gene expression signatures were found to be similarly expressed between matched pairs of tumors and metastases; therefore, primary tumors may be a good predictor of metastatic propensity. Next, using a dataset of >1,000 human breast tumor gene expression microarrays we determined that HER2-enriched subtype tumors aggressively spread to the liver, while basal-like and claudin-low subtypes colonize the brain and lung. Correspondingly, brain and lung metastasis signatures, along with embryonic stem cell, tumor initiating cell, and hypoxia signatures, were also strongly expressed in the basal-like and claudin-low tumors. Interestingly, low “Differentiation Scores,” or high expression of the aforementioned signatures, further predicted for brain and lung metastases. In total, these data identify that depending upon the organ of relapse, a combination of gene expression signatures most accurately predicts metastatic behavior

Identification of cell surface targets for CAR-T cell therapies and antibody-drug conjugates in breast cancer

Background: Two promising therapeutic strategies in oncology are chimeric antigen receptor-T cell (CAR-T) therapies and antibody-drug conjugates (ADCs). To be effective and safe, these immunotherapies require surface antigens to be sufficiently expressed in tumors and less or not expressed in normal tissues. To identify new targets for ADCs and CAR-T specifically targeting breast cancer (BC) molecular and pathology-based subtypes, we propose a novel in silico strategy based on multiple publicly available datasets and provide a comprehensive explanation of the workflow for a further implementation. Methods: We carried out differential gene expression analyses on The Cancer Genome Atlas BC RNA-sequencing data to identify BC subtype-specific upregulated genes. To fully explain the proposed target-discovering methodology, as proof of concept, we selected the 200 most upregulated genes for each subtype and undertook a comprehensive analysis of their protein expression in BC and normal tissues through several publicly available databases to identify the potentially safest and viable targets. Results: We identified 36 potentially suitable and subtype-specific tumor surface antigens (TSAs), including fibroblast growth factor receptor-4 (FGFR4), carcinoembryonic antigen-related cell adhesion molecule 6 (CEACAM6), GDNF family receptor alpha 1 (GFRA1), integrin beta-6 (ITGB6) and ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1). We also identified 63 potential TSA pairs that might be appropriate for co-targeting strategies. Finally, we validated subtype specificity in a cohort of our patients, multiple BC cell lines and the METABRIC database. Conclusions: Overall, our in silico analysis provides a framework to identify novel and specific TSAs for the development of new CAR-T and antibody-based therapies in BC