MarchionniLab

MarchionniLabMarchionniLabMarchionniLab

MarchionniLab

MarchionniLabMarchionniLabMarchionniLab

Welcome to MarchionniLab

Welcome to MarchionniLabWelcome to MarchionniLabWelcome to MarchionniLab

Welcome to MarchionniLab

Welcome to MarchionniLabWelcome to MarchionniLabWelcome to MarchionniLab

I am an Associate Professor of Pathology and Precision Medicine at the Weill Cornell Medical College in New York, USA. I am a doctor and a computational biologist working on cancer. I apply quantitative approaches to interpret high-dimensional genomic data, understand cancer cell biology, and develop useful clinical tools to improve cancer patients' outcomes. 

My lab aims to improve human health, disease outcomes, and treatments through the development and application of innovative methods to analyze molecular and imaging data.

I am also the Vice-Chair for Computational and System Pathology. 

You can look at my CV here.

Follow my lab on Twitter 

Running the 2022 NYC Marathon to Fundraise for Men’s Health

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Research

Computation Biology

Bioinformatics can be regarded as the field of intersection between biology and computer science. It uses the power of computation to store, analyze, integrate, and visualize complex biological data to answer biological questions. This field is concerned with organizing biomolecular databases, managing the quality of data input, getting useful information out of such databases, and integrating information from disparate sources. Genomics, systems biology, and high-throughput biological research are based on such sophisticated computer-based tools and advances in these fields would not have been impossible without the parallel development of Bioinformatics.

Cancer Genomics

Millions of cells are dividing every minute in our human body. Normally, this process is well coordinated and cell divisions are followed by ordered patterning and cell specialization, allowing the maintenance of tissues and organs internal equilibrium (homeostasis). Cancer can be regarded as a deviation from this balanced behavior: a single cell, that has undergone mutations in its DNA, instead of maturing and dying normally reproduces without restraint. This is usually accomplished by incessant rather than faster dividing and it gives rise to a progeny that usually fails to mature. In this perspective it is clear that cancer is a very complex disease, involving not only cancerous cells, but also surrounding and distant normal cells, which engage an infinite number of complex interactions. Thus the effort of unraveling such a complexity by using genomic approaches is the focus of my laboratory. 

Digital Pathology

Technological advances over the past decades have enabled the development of tools, systems, and infrastructure for the massive and parallel digitization of pathology slides with the associated meta‐data, their storage, review, and analysis. At the same time, advances in algorithmics, statistics, mathematics, and computer science have provided the tools for the extraction and analysis of quantifiable information from these images. This has created unprecedented opportunities for the systematic and quantitative analysis of images routinely generated in pathology departments around the world. The adoption of Artificial Intelligence (AI), Machine Learning (ML) – including more data and computation intensive approaches like Deep Learning (DL) – holds the promise to project pathology in the next millennium.

Digital Pathology

Medicine is transitioning from treating the “average patient” to seeking to treat each individual in a tailored way. At present, this approach to medicine, however, can only be delivered at lead academic institutions, hence the opportunity to deliver state of the art clinical care on a large scale and in the community is still missed. By integrating big molecular data, standard clinical laboratory measurements, and digital pathology images, my laboratory strive to bridge this gap. The goal here is to develop robust, parsimonious models that can forecast patients molecular make-up (e.g., their mutational profiles) and outcome (e.g., their response to targeted treatments). While the development of these approaches can only be achieved in leading academic institutions, deploying and disseminating such methods in community and rural hospitals nationwide is of paramount importance. Ultimately, through research in this domain, my lab fosters the democratization of precision and personalized medicine. 

Selected Publications

Selected Publications

Selected Publications

  • Building Tools for Machine Learning and Artificial Intelligence in Cancer Research: Best Practices and a Case Study with the PathML Toolkit for Computational Pathology. Rosenthal J, Carelli R, Omar M, Brundage D, Halbert E, Nyman J, Hari SN, Van Allen EM, Marchionni L, Umeton R, Loda M. Mol Cancer Res. 2022 Feb;20(2):202-206. doi: 10.1158/1541-7786.MCR-21-0665. Epub 2021 Dec 8. 
  • A robust and interpretable gene signature for predicting the lymph node status of primary T1/T2 oral cavity squamous cell carcinoma. Ghantous Y, Omar M, Broner EC, Agrawal N, Pearson AT, Rosenberg AJ, Mishra V, Singh A, El-Naaj IA, Savage PA, Sidransky D, Marchionni L, Izumchenko E. Int J Cancer. 2022 Feb 1;150(3):450-460. doi: 10.1002/ijc.33828. Epub 2021 Oct 14. PMID: 34569064 
  • Transcriptional landscape of PTEN loss in primary prostate cancer. Imada EL, Sanchez DF, Dinalankara W, Vidotto T, Ebot EM, Tyekucheva S, Franco GR, Mucci LA, Loda M, Schaeffer EM, Lotan T, Marchionni L. BMC Cancer. 2021 Jul 26;21(1):856. doi: 10.1186/s12885-021-08593-y. 


For the complete list of publications see

my google scholar or my my NCBI publication list

Software

Selected Publications

Selected Publications

  • The seventyGeneData:  an R-Bioconductor package containing gene expression data for the van't Veer and Van de Vijver breast cancer cohorts.
  • The mammaPrintData: an R-Bioconductor package containing gene expression data for the Glas and Buyse breast cancer cohorts. 
  • The switchBox: an R-Bioconductor package for K-Top-Scoring-Pair classifier development and more.
  • The matchBox: an R-Bioconductor package for Correspondence-At-the_Top curve analysis and more.
  • The RTopper: an R-Bioconductor package designed to perform Gene Set Analysis across multiple genomic platforms (and more).
  • The divergence: an R-Bioconductor package to perform divergence analysis.
  • Additional software projects available on the lab's gitHub repository

People

Education

Education

Eddie Luidy Imada 

Mohammed Omar 

Wikum Dinalankara 

Claudio Zanettini 

Karen Zhuoran Xu 

Lucio Queiroz 

Education

Education

Education

Classe, workshops, lectures, and resources are listed below

Marchionni Lab

Luigi Marchionni, M.D., Ph.D.

Associate Professor of Pathology and Laboratory Medicine
Vice Chair for Computational and Systems Pathology
Weill-Cornell Medicine


413 East 69th Street, 
The Belfer Research Building, Suite 1524
New York, NY, 10021, USA
Tel: (001) 646-962-8767
Fax: (001) 646-962-0437

e-mail: lum4003 at med.cornell.edu

Affiliations

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