Keynote Speakers

Oliva Alberti, CEO Diatech Pharmacogenetics srlIMG_0510

She joined the company in 1998 and was appointed to CEO in 2004. She was actively supporting the creation of more than 7 sister and controlled companies helping to better structure and position Diatech into the molecular diagnostic market.
Oliva Alberti has played a critical role in support the company’s growth in the field of molecular diagnostics.
From 1998 to 2014 she operated like project manager into developing the projects financed to the company by the Italian Ministry for University and Research as well as the Industrial once, she was also supporting the filing of the JEV project that the company was presenting to EU in 2002 and the Horizon 2020 project that has been approved by EU in 2015.
She has over 15 years of experience in the biotechnology industry.

Summary. From life science to molecular diagnostics: a tailored approach
Diagnostic laboratories face a major challenge in being able to provide rapid, sensitive and state of the art molecular tests. We developed a novel mass spectrometry multiplexed genotyping solution named Myriapod®  to concurrently assess single nucleotide polymorphisms in most clinically relevant cancer gene.
The CE IVD system has been developed taking into consideration the diagnostic needs of the laboratories, the usability inside a routine diagnostic environment and it is a complete solution that includes instruments, accessories, software and reagents.
To evaluate and validate the systems, the research and development activities have been performed systematically addressing sensitivity, specificity, and reproducibility of our platform.
Our Myriapod® solution is a high throughput and robust tool, allowing genotyping for targeted therapy selection with a practical turnaround time of 8 working hours.
The system can provide an immediate, accurate and cost effective multiplex approach for clinically relevant gene mutation analysis using the state of the art technology in mutation analysis in multiplexing: Mass Spectrometry.


Charles Cantor, PhD. Agena Biosciences, Sequenom, Retrotope and Boston University, USA charles-cantor_812

Dr. Charles Cantor is a co-founder, and retired Chief Scientific Officer at SEQUENOM, Inc., the leading provider of noninvasive prenatal diagnostic testing. He consults for a number of biotech companies including SEQUENOM, AgenaBiosciences, Strand Life Sciences, Trovagene, Ann Jema, ProdermIQ, In Silico Biology, and he is executive director of Retrotope. (which he also co-founded) Dr. Cantor is professor emeritus of Biomedical Engineering and of Pharmacology and was the director of the Center for Advanced Biotechnology at Boston University. He is currently adjunct professor of Bioengineering at UC San Diego, adjunct professor of Molecular Biology at the Scripps Institute for Research, distinguished adjunct professor of Physiology and Biophysics at UC Irvine and adjunct professor at the Moscow institute of Physics and Technology. Prior to this, Dr. Cantor held positions in Chemistry and then in Genetics and Development at Columbia University and in Molecular Biology at the University of California at Berkeley. Cantor was educated in chemistry at Columbia College (AB) and at the University of California Berkeley (PhD). Dr. Cantor has been granted more than 60 US patents and, with Paul Schimmel, wrote a three-volume textbook on biophysical chemistry. He also co-authored the first textbook on Genomics titled ‘The Science and Technology of the Human Genome Project’. In addition, he has published more than 450 peer-reviewed articles, and is a member of the U.S. National Academy of Sciences. and The National Academy of Inventors. His major scientific accomplishments include the development of pulsed field electrophoresis, immuno-PCR, affinity capture electrophoresis, the earliest uses of FRET to characterize distances in protein complexes and nucleic acids, the standard methods for assaying and purifying microtubule protein, various applications of nucleic acid mass spectrometry, and methods for noninvasive prenatal diagnostics. He is also considered to be one of the founders of the new field of synthetic biology.

Title: sensitive detection of low levels of cancer- specific DNA sequence differences

Brief summary: For various reasons mutations that determine the properties and drug responsiveness of tumors are often present in only trace amounts in clinical samples such as fine needle biopsies, plasma or urine. A number of sensitive methods exist to detect these low levels of specific sequences, but even these are compromised when only a few molecules of the desired analytes are present, because experiments are then subject to stochastic noise. New strategies that attempt to bypass stochastic noise are under development, and these may increase detection sensitivity enough to allow,some day, pre symptomatic detection of cancer.


Giulio Caravagna, PhD. School of Informatics, Universiry of Edinburgh, UKgiulio

Giulio Caravagna is a Research Associate in the Laboratory of Machine Learning for Computational Biology and Bioinformatics run by Guido Sanguinetti at the University of Edinburgh, UK.
He is a member of the Institute of Adaptive and Neural Computation, located within the School of Informatics.
Before, he was a Postdoctoral Research Fellow at Milano-Bicocca Bioinformatics and at the National Research Council, Italy.
He has a PhD in Computer Science from the University of Pisa.

Summary.

Computational approaches are becoming key to automatically identify explanatory models of how (epi)genomic events are choreographed in cancer initiation and development.
Such models shed new lights on the evolutionary nature of cancer, possibly allowing to understand the dramatic heterogeneity and temporality of the disease.
Presently, the increasing availability of next generation sequencing data are creating the ground for successful applications of such techniques, both at the individual and the population level.

In this talk, I will present a general overview of approaches to extract such models from data. Thus I will focus on techniques inspired by recent works relating causality and probability theory.
I will present a versatile and modular pipeline to extract ensemble-level progression models from cross-sectional sequenced cancer genomes, and discuss its application to colorectal cancer.
I will briefly discuss various proposed techniques to solve the same problem from multiple biopsy and single-cell data as well.


Francesca DemichelisUniversità degli Studi di Trento, Trento, Italyfrancesca-demichelis

Francesca Demichelis, PhD, is expert in the area of Cancer Genomics. Dr. Demichelis’ research group at the Centre for Integrative Biology at the University of Trento, Italy, focuses on the characterization of tumor evolution and progression through the study of intra- and inter-tumor heterogeneity. Using single base level information from tissue biopsies or circulating DNA (plasma), tumor dynamics and evolution maps are charted to inform on patient’s status and treatment response. Dr. Demichelis also studies the impact of inherited polymorphisms, including structural variants, within transcriptionally active regulatory regions of the genome on the initiation of hormone regulated cancer phenotypes. The research group is involved in consortia studies including The Cancer Genome Atlas (TCGA) and Stand Up 2 Cancer International/ PCF Dream Team. Dr. Demichelis has received support from the European Research Committee (ERC), Department of Defense (USA), the National Cancer Institute (NCI), and the Prostate Cancer Foundation. She trained at the University of Trento (Physics Department; Information and Communication Technology PhD School), did her post-doc at Brigham and Women’s Hospital/Harvard Medical School in Boston, and led her own laboratory at Weill Cornell Medicine (NY) between 2007-2011 before moving to Centre for Integrative Biology at the University of Trento.

Summary

Understanding treatment resistance is emerging as a critical hurdle for precision medicine in cancer care. We exploit single base resolution data and allele-specific analysis to reconstruct tumor evolution charts and to quantify intra- and inter-tumor molecular heterogeneity. These approaches proved useful in identifying potential mechanisms of resistance to AR (androgen receptor) directed therapies in prostate cancer. Specifically, we found evidence of the emergence of an alternative ‘AR-indifferent’ cell state through divergent clonal evolution as a mechanism of treatment resistance in advanced disease. Translating this new information to a biomarker assay readily applicable in the clinical setting requires the implementation of high performing non-invasive tests. We will discuss technical aspects related to the detection of somatic structural variants in the circulation of cancer patients’ during treatment.


Pietro LiòUniversity of Cambridge, Cambridge, UKlio

Title of the lectures:

  • Inflammatory events and cancer: a statistical Bioinformatics perspectives.
  • Combining Bioinformatics and cancer survival analysis.

Antonina MitrofanovaDepartment of Health Informatics, Rutgers University, Newark (NJ), USA antonina_mitrofanova_bibm

Antonina Mitrofanova is an Assistant Professor and Director of Biomedical Informatics Research in the Department of Health Informatics at Rutgers University. Antonina received her PhD in Computer Science from New York University, with the Best Dissertation Award and the Henning Biermann Prize for outstanding contribution to Education at NYU. She completed her PostDoctoral training in Computational Systems Biology at Columbia University, where she was a recipient of Computing Innovation Fellowship from the National Science Foundation and Young Investigator Award from the Prostate Cancer Foundation. At Rutgers, Antonina’s lab develops and applies computational algorithms to elucidate transcriptional and epigenetic mechanisms of cancer progression and to identify optimal therapeutic strategies to target specific cancer malignancies.

Summary. Computational Approaches to Investigate Mechanisms of Progression and Drug Response in Human Cancer.

Complexity of human cancer is driven by the coordinated activation and inactivation of multiple genes, which makes the identification of causal drivers of cancer progression a daunting challenge. Although animal models are often used to study mechanisms of cancer progression and evaluate new cancer therapies, the accurate extrapolation of animal studies to human cancer has been difficult. I will present novel cross-species systems biology algorithms that identify conserved regulatory programs between human and mouse cancer models and inform on therapeutic strategies for human patients with the most aggressive disease. These algorithms identify causal gene “drivers” of aggressive cancer, which may also serve as biomarkers to categorize patients with poor prognosis. We have generated complementary human and mouse prostate cancer gene regulatory networks (interactomes) assembled from molecular profiles of human tumors and genetically engineered mouse models. Our computational systems biology network-based approaches and subsequent experimental validation have elucidated a synergistic interaction of two genes, FOXM1 and CENPF, that drives prostate cancer aggressiveness and is a robust prognostic indicator of cancer outcome. I will demonstrate that these identified drivers are excellent candidates for targeted therapeutics, especially for patients with aggressive prostate cancer. Furthermore, I will describe an innovative computational algorithm to identify drugs and drug combinations that inhibit the transcriptional activity of these molecular drivers. Experimental validation confirms high efficacy of the top predicted drug combination for inhibiting tumorigenesis in mouse and human prostate cancer models. Although these approaches have been specifically applied to prostate cancer, they also address issues of broad general relevance for the prognosis, diagnosis, and treatment of human disease.


Bud MishraCourant Institute of Mathematical Sciences and Tandon School of Engineering, NYU, New York, USAbud-mishra2-289x235

Bhubaneswar Mishra (or Bud Mishra) is an Indian American computer scientist and professor at the Courant Institute of Mathematical Sciences of New York University. He is known for his applied contributions to bioinformatics, cybersecurity, and computational finance.


Andrea SottorivaCentre for Evolution and Cancer, The Institute of Cancer Research, London, UKandrea-sottoriva

Andrea Sottoriva trained in computer science (BSc University of Bologna) and computational physics (MSc University of Amsterdam and National Institute for Nuclear and High-Energy
Physics – NIKHEF), before switching to computational biology and bioinformatics (PhD University of Cambridge, postdoc University of Southern California).
He now leads the Evolutionary Genomics and Modelling team within the Centre for Evolution and Cancer at The Institute of Cancer Research in London.
His research focuses on using multi-disciplinary approaches based on high-throughput genomics and mathematical modelling to understand cancer as a complex system
driven by evolutionary principles. The goal of his team is to identify those patient-specific rules that regulate tumour evolution in individual patients, in order to predict
the future course of the disease.

Talk: Functional versus non-functional intra-tumour heterogeneity

Despite extraordinary efforts to profile cancer genomes, interpreting the vast amount of genomic data in the light of cancer evolution remains challenging. In particular, although genomic intra-tumour heterogeneity (ITH) has become a hot topic in cancer, determining how much of it is actually functional and clinically relevant remains an open question. Here we will present a null model of genomic ITH that can be applied to next-generation sequencing data from human malignancies. This mathematical framework is based on neutral evolution and allows identifying which tumours are characterized by complex evolutionary dynamics, such as clonal selection and cooperation, and which ones do not. Importantly, reanalyzing cancer genomic data within the neutral framework allowed the measurement, in each individual patient, of both the in vivo mutation rate and the timing of mutations. This result provides a new way to interpret existing cancer genomic data and to discriminate between functional and non-functional ITH.


Rory StarkPrincipal Bioinformatics Analyst – Cancer Research UK, Cambridge Institute (Univ. of Cambridge)1c542311ad825324513ae7be0bcb585d

Dr. Rory Stark is the Principal Bioinformatics Analyst at the University
of Cambridge, where he leads the core analysis team at Cancer Research
UK’s Cambridge Institute. He has been working extensively with
next-generation, high-throughput sequencing technologies since 2007, when
he helped establish the CRUK sequencing facility and associated core
bioinformatics group. He is most interested in the analysis of
transcriptional regulatory elements such as transcription factor binding
and epigenetic marks. Besides supervising and performing analyses of
experimental datasets for breast, prostate, and colon cancer, he has
developed a number of computational tools including DiffBind, a popular
Bioconductor package for differential binding analysis of ChIP-seq data.
Dr. Stark’s history in the commercial sector includes founding several
Silicon Valley start-ups, joining Microsoft after they acquired NetCarta,
an Internet software company he started in 1994. As a Group Manager at
Microsoft he was involved in technology transfer between research and
development, ultimately overseeing the development of speech recognition
platforms and products. Dr. Stark’s academic degrees cover a range of
computational sciences, including a BA in Computer and Information Science
(UCSC), MSc in Cognitive Science (Edinburgh), PhD in Artificial
Intelligence (Edinburgh/Sussex), and an MPhil from the Cambridge
Computational Biology Institute, where he now also lectures.

Talks: Demo: Overview of Bioconductor for analysis of high-throughput cancer experiments (60 minutes)
Lecture: Analysis of regulatory protein binding dynamics in cancer: (60 minutes)
Practical: differential binding analysis (45 minutes)


Giovanni Tonon, MD PhD, Head, Functional Genomics of Cancer Unit, San Raffaele Scientific Institute, Milan Italytonon

Dr. Giovanni Tonon is the Director of the Center for Translational Genomics and Bioinformatics and of the Functional Genomics of Cancer Unit at the San Raffaele Scientific Institute. He has a long-standing interest in the identification of cancer genes and pathways through cytogenetic and bioinformatic approaches, the elucidation of their oncogenic mechanism and the translation of these results in novel therapies. He has contributed to the identification of cancer genes in multiple myeloma, lung cancer and colon cancer, and of tumor-related pathways such as MYC in glioma and mitochondria in telomere dysfunction.

Summary. Synthetic Lethal Approaches Exploiting DNA Damage DNA damage in cancer fosters heterogeneity, increasing the evolutionary diversity of cancer cells. Based on recent findings, we argue that subset of patients, and potentially of cells within a tumor, present genomically unstable cells that are particularly apt to enhance the clonal pool and as such should be preferentially targeted. We have identified synthetic lethality approaches exploiting DNA damage thus inducing apoptosis in these particularly aggressive yet frail cells.