Western University hopes to use artificial intelligence to improve breast cancer patient outcomes.
Western University researchers are working on a way to use artificial intelligence to predict a patient’s response to two common chemotherapy medications used to treat breast cancer – paclitaxel and gemcitabine.
Peter Rogan, PhD, and a team of researchers, including Stephanie Dorman, PhD, and Katherina Baranova, BMSc, at Western’s Schulich School of Medicine & Dentistry, are hoping to one day remove the guesswork from breast cancer treatment with this technique.
Based on personal genetic analysis of their tumours, patients with the same type of cancer can have different responses to the same medication. While some patients will respond well and go into remission, others will develop a resistance to the medication.
Identifying the genetic factors which lead to resistance or remission can help develop better targeted, individualized treatment regimens with better patient outcomes.
“Treating patients with therapies that are the most likely to be successful can help reduce unnecessary toxicity and improve overall outcomes,” said Dorman.
Rogan and Joan Knoll, PhD, professor, Schulich Medicine & Dentistry, began by defining a stable set of genes in 90 per cent of breast cancer tumours in 2012.
Beginning with 40 genes including several stable genes, the team then used artificial intelligence combined with data from cell lines and tumour tissue from cancer patients who had treatment with at least one of the medications to narrow down and identify the genetic signatures most important for determining resistance and remission for each medication. Their study has recently been published in the journal, Molecular Oncology.
Using the data, the researchers were able to identify the 84 per cent of women with breast cancer who would go into remission in response to the drug paclitaxel. The genetic signature identified for the drug gemcitabine was able to predict remission using preserved tumour tissue with 62 to 71 per cent accuracy.
Now, with this data in hand, the researchers are working to further refine the genetic signatures and improve the predictions further.
“Artificial intelligence is a powerful tool for predicting drug outcomes because it looks at the sum of all the interacting genes,” said Rogan, professor in the departments of Biochemistry, Oncology and Computer Science, Canada Research Chair in Genome Bioinformatics and president, Cytognomix Inc. “If we can use this technology to improve our knowledge of which medications to use, it could improve patient outcomes. The earlier we treat a patient with the most effective medication, the more likely we can effectively treat or possibly even cure that patient.”
Reference: Dorman SN, Baranova K, Knoll JH, Urquhart BL, Mariani G, Carcangiu ML, Rogan PK. Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning. Mol Oncol. 2015 Aug 22. pii: S1574-7891(15)00146-5. doi: 10.1016/j.molonc.2015.07.006. [Epub ahead of print] http://www.moloncol.org/article/S1574-7891%2815%2900146-5/fulltext
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We have published a video synopsis of :
Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning. Stephanie N. Dorman, Katherina Baranova, Joan H.M. Knoll, Brad L. Urquhart, Gabriella Mariani, Maria Luisa Carcangiu, Peter K. Rogan. Molecular Oncology, in press. DOI: http://dx.doi.org/10.1016/j.molonc.2015.07.006
The uncorrected proofs of our new paper:
Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning. Dorman et al. Mol. Oncol. 2015
Reversing chromatin accessibility differences that distinguish homologous mitotic metaphase chromosomes. Wahab Khan, Peter Rogan, Joan Knoll. Molecular Cytogenetics 2015, 8:65
was published on August 13th. In less than two weeks, it has become the most viewed article in this journal for the past month, averaging 55 per day.
Update: as of Sept. 6, the article is still the top viewed paper with 887 views.
Reversing chromatin accessibility differences that distinguish homologous mitotic metaphase chromosomes. Khan et. al. Molecular Cytogenetics 2015, 8:65
Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning
Authors: Stephanie N. Dormana, Katherina Baranovaa, Joan H.M. Knollb,c,d, Brad L. Urquharte, Gabriella Marianif, Maria Luisa Carcangiuf, Peter K. Rogana,d,g,h*
aDepartment of Biochemistry, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada, bDepartment of Pathology and Laboratory Medicine, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada, cMolecular Diagnostics Division, Laboratory Medicine Program, London Health Sciences Centre, ON, Canada, dCytognomix Inc., London ON, Canada, eDepartment of Physiology and Pharmacology, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada, fDepartment of Diagnostic and Laboratory Pathology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy, gDepartment of Computer Science, University of Western Ontario, London, ON, Canada, hDepartment of Oncology, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada
will be published in a forthcoming issue of the journal “Molecular Oncology”.
“Discovery of Primary, Cofactor, and Novel Transcription Factor Binding Site Motifs by Recursive, Thresholded Entropy Minimization”
by Ruipeng Lu, Eliseos Mucaki, and Peter Rogan
at the Regulatory Genomics Special Interest Group meeting in Dublin, Ireland: Link to abstract
Khan WA, Rogan PK, Knoll JH. Reversing chromatin accessibility differences that distinguish homologous mitotic metaphase chromosomes. Molecular Cytogenetics, in press.
Stay tuned for posts providing details and links to the manuscript once it is available online at the journal website.
FANCM c.5791C>T nonsense mutation (rs144567652) induces exon skipping, affects DNA repair activity, and is a familial breast cancer risk factor.
Peterlongo et al. Hum Mol Genet. 2015 Jun 30. pii: ddv251.
In this paper, we use information theory to demonstrate a new mechanism for disease mutations. It turns out that this a fairly common type of mutation in our unpublished studies of other data sources.
Natasha Caminsky presented:
A Unified Framework for the Identification and Prioritization of Coding and Non-Coding Variants in Heritable Breast and Ovarian Cancer (HBOC).
which introduces Cytognomix’s approach for analysis of a wide range of regulatory mutations in complete human gene and genome data. The other authors of this study were Mucaki EJ, Lu R, Perri AM, and Rogan PK.
This is an annual event for scientists, clinicians, graduate students and Post-Doctoral fellows at the University of Western Ontario and affiliated hospitals to share cancer research discoveries and promote cancer research collaboration and training. She was awarded Best Oral Presentation for this paper.
The Breast Cancer Society of Canada blogged the results of the paper competition: link
Today, Cytognomix Inc. and Illumina signed a distribution agreement to make MutationForecaster software available through the BaseSpace ecosystem. Work is underway to enable Illumina users to analyze data processed in BaseSpace to be interpreted with Cytognomix’s software. The BaseSpace environment enables MiSeq users to carry out sequence analyses with requiring an onsite computing infrastructure, with scalable cloud data storage, to manage all of your data analyses, to securely collaborate, and to share and access Illumina and community-developed applications. MutationForecaster will import variant control format files, validate them with RNASeq Bam files from the same sample directly from BaseSpace and download results from Cytognomix back to BaseSpace.
Dr. Peter Rogan’s laboratory at the University of Western Ontario will present:
Discovery of Primary, Cofactor, and Novel Transcription Factor Binding Site Motifs by Recursive, Thresholded Entropy Minimization
by Ruipeng Lu 1, Eliseos Mucaki 2, and Peter Rogan 1,2,3. Departments of (1) Computer Science and (2)Biochemistry, University of Western Ontario, and (3)Cytognomix Inc., London ON
at Compute Ontario (abstract), Conestoga College, Cambridge Ontario, Canada. The presentation is in Room A2107 at 13:55 at the Conestoga College Institute of Technology and Advanced Learning.
Dr. Joan Knoll, Chief Scientific Officer of Cytognomix, will present:
Localized, Structural Differences in Condensation of Homologous Metaphase Chromosomes and the Underlying Mechanism
at the 53rd Annual Great Lakes Chromosome Conference at the University of Toronto, Ontario, Canada.
Cytognomix has sold an annual subscription to MutationForecaster, our comprehensive solution for next generation sequencing based mutation interpretation, to a hospital in Toronto, Ontario Canada. This customer was a previous subscriber to the Automated Site and Exon Definition Server, which is embedded in MutationForecaster and no longer available as standalone software.
Cytognomix’s standalone version of the Shannon mRNA splicing mutation analysis pipeline is distributed through Qiagen CLC bio as a plug-in for their Genomics Workbench and Server software.This product was recently purchased by Dr. Hidetaka Eguchi at the Research Center for Genomic Medicine, Saitama Medical University, Japan.
Collaborative effort led by Greg Zaric at the University of Western Ontario:
Identification and survival outcomes of a cohort of patients with cancer of unknown primary in Ontario, Canada. Kim CS1, Hannouf MB, Sarma S, Rodrigues GB, Rogan PK, Mahmud SM, Winquist E, Brackstone M, Zaric GS. Acta Oncol. 2015 Mar 31:1-7. (link)