Publications by Elisa Ficarra

The brain comprises a complex system of neurons interconnected by an intricate network of anatomical links. While recent studies demonstrated the correlation between anatomical connectivity patterns and gene expression of neurons, using transcriptomic information to automatically predict such patterns is still an open challenge. In this work, we present a completely data-driven approach relying on machine learning (i.e., neural networks) to learn the anatomical connection directly from a training set of gene expression data. To do so, we combined gene expression and connectivity data from the Allen Mouse Brain Atlas to generate thousands of gene expression profile pairs from different brain regions. To each pair, we assigned a label describing the physical connection between the corresponding brain regions. Then, we exploited these data to train neural networks, designed to predict brain area connectivity. We assessed our solution on two prediction problems (with three and two connectivity class categories) involving cortical and cerebellum regions. As demonstrated by our results, we distinguish between connected and unconnected regions with 85% prediction accuracy and good balance of precision and recall. In our future work we may extend the analysis to more complex brain structures and consider RNA-Seq data as additional input to our model.

The early diagnosis of colorectal cancer (CRC) traditionally leverages upon the microscopic examination of histological slides by experienced pathologists, which is very time-consuming and rises many issues about the reliability of the results. In this paper we propose using Convolutional Neural Networks (CNNs), a class of deep networks that are successfully used in many contexts of pattern recognition, to automatically distinguish the cancerous tissues from either healthy or benign lesions. For this purpose, we designed and compared different CNN-based classification frameworks, involving either training CNNs from scratch on three classes of colorectal images, or transfer learning from a different classification problem. While a CNN trained from scratch obtained very good (about 90%) classification accuracy in our tests, the same CNN model pre-trained on the ImageNet dataset obtained even better accuracy (around 96%) on the same testing samples, requiring much lesser computational resources.

Approximately 18% of acute myeloid leukemia (AML) cases express a fusion transcript. However, few fusions are recurrent across AML and the identification of these rare chimeras is of interest to characterize AML patients. Here, we studied the transcriptome of 8 adult AML patients with poorly described chromosomal translocation(s), with the aim of identifying novel and rare fusion transcripts. We integrated RNA-sequencing data with multiple approaches including computational analysis, Sanger sequencing, fluorescence in situ hybridization and in vitro studies to assess the oncogenic potential of the ZEB2-BCL11B chimera. We detected 7 different fusions with partner genes involving transcription factors (OAZ-MAFK, ZEB2-BCL11B), tumor suppressors (SAV1-GYPB, PUF60-TYW1, CNOT2-WT1) and rearrangements associated with the loss of NF1 (CPD-PXT1, UTP6-CRLF3). Notably, ZEB2-BCL11B rearrangements co-occurred with FLT3 mutations and were associated with a poorly differentiated or mixed phenotype leukemia. Although the fusion alone did not transform murine c-Kit+ bone marrow cells, 45.4% of 14q32 non-rearranged AML cases were also BCL11B-positive, suggesting a more general and complex mechanism of leukemogenesis associated with BCL11B expression. Overall, by combining different approaches, we described rare fusion events contributing to the complexity of AML and we linked the expression of some chimeras to genomic alterations hitting known genes in AML.

Nowadays, single-cell DNA (sc-DNA) sequencing is showing up to be a valuable instrument to investigate intra and inter-tumor heterogeneity and infer its evolutionary dynamics, by using the high-resolution data it produces. That is why the demand for analytical tools to manage this kind of data is increasing. Here we propose a pipeline capable of producing multi-sample copy-number variation (CNV) analysis on large-scale single-cell DNA sequencing data and investigate spatial and temporal tumor heterogeneity.

In order to help cancer researchers in understanding tumor heterogeneity and its evolutionary dynamics, we propose a software pipeline to explore intra-tumor heterogeneity by means of scDNA sequencing data.

The analysis of histological samples is of paramount importance for the early diagnosis of colorectal cancer (CRC). The traditional visual assessment is time-consuming and highly unreliable because of the subjectivity of the evaluation. On the other hand, automated analysis is extremely challenging due to the variability of the architectural and colouring characteristics of the histological images. In this work, we propose a deep learning technique based on Convolutional Neural Networks (CNNs) to differentiate adenocarcinomas from healthy tissues and benign lesions. Fully training the CNN on a large set of annotated CRC samples provides good classification accuracy (around 90% in our tests), but on the other hand has the drawback of a very computationally intensive training procedure. Hence, in our work we also investigate the use of transfer learning approaches, based on CNN models pre-trained on a completely different dataset (i.e. the ImageNet). In our results, transfer learning considerably outperforms the CNN fully trained on CRC samples, obtaining an accuracy of about 96% on the same test dataset.

Changes in pupil size are governed by the autonomic nervous system but may also be systematically driven by voluntary shifting the gaze in depth. Thus, the pupil accommodative response (PAR) that accompanies voluntary gaze shifts from a far (3 m distance) to a near (30 cm) visual target might be exploited as a simple human-computer interface (HCI), bypassing the somato-motor system.