October 6, 2015. Presentations at the 2015 International EPR Biodose meeting

Drs. Joan Knoll and Peter Rogan gave platform presentations about the underlying algorithms and application of the Automated Dicentric Chromosome Identifier and Radiation Dose Estimator:

Radiation dose estimation by automated chromosome biodosimetry”  and

Automated Discrimination of Dicentric and Monocentric Chromosomes by Machine Learning-based Image Processing

at the EPRBiodose meeting at Dartmouth College, organized by the International Association of Biological and EPR Radiation Dosimetry .

Sept. 18, 2015. Press release about chemotherapy resistance paper

Western University hopes to use artificial intelligence to improve breast cancer patient outcomes.

(http://mediarelations.uwo.ca/2015/09/18/researchers-at-western-university-hope-to-use-artificial-intelligence-to-improve-breast-cancer-patient-outcomes/, other links at end of post)

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

MEDIA CONTACT: Tristan Joseph, Media Relations Officer, Schulich School of Medicine & Dentistry, Western University, 519-661-2111 ext. 80387, c: 519-777-1573, tristan.joseph@schulich.uwo.ca

ABOUT WESTERN

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ABOUT THE SCHULICH SCHOOL OF MEDICINE & DENTISTRY

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Links to story:

(http://www.eurekalert.org/pub_releases/2015-09/uowo-wuh091815.php,

http://www.1069thex.com/2015/09/18/western-researches-use-artificial-intelligence-to-predict/

http://medicalxpress.com/news/2015-09-artificial-intelligence-breast-cancer-patient.html,

http://u-s.news/researchers-hope-to-use-artificial-intelligence-to-improve-breast-cancer-patient-outcomes,

http://linkis.com/eurekalert.org/Western_University_h.html,

http://jerseytribune.com/2015/09/18/western-university-hopes-to-use-artificial-intelligence-to-improve-breast-cancer-patient-outcomes/,

http://www.dotmed.com/news/story/27121,

http://www.sciencenewsline.com/summary/2015091817530026.html,

http://www.demanjo.com/news/health/5084082/western-university-hopes-to-use-artificial-intelligence-to-improve-breast-cancer-patient-outcomes.html,

http://danhartshorn.com/2015/09/researchers-hope-to-use-artificial-intelligence-to-improve-breast-cancer-patient-outcomes/,

http://www.bioportfolio.com/news/article/2463671/Western-University-hopes-to-use-artificial-intelligence-to-improve-breast-cancer-patient.html,

http://scifeeds.com/news/western-university-hopes-to-use-artificial-intelligence-to-improve-breast-cancer-patient-outcomes/,

http://www.biospace.com/news_story.aspx?StoryID=391867&full=1,

http://breastcancer.einnews.com/article__detail/287108153-western-university-hopes-to-use-artificial-intelligence-to-improve-breast-cancer-patient-outcomes?vcode=XIbw,

https://twitter.com/cambridgepharma,

http://www.newslocker.com/en-uk/news/science/western-university-hopes-to-use-artificial-intelligence-to-improve-breast-cancer-patient-outcomes/,

http://gadgets.ndtv.com/science/news/artificial-intelligence-could-improve-breast-cancer-treatment-742015,

http://www.news-medical.net/news/20150919/Artificial-intelligence-may-improve-outcomes-in-breast-cancer-patients.aspx,

http://odishasamaya.com/news/artificial-intelligence-may-be-the-antidote-to-breast-cancer/55895,

http://focusnews.com/lifestyle/artificial-intelligence-could-improve-breast-cancer-treatment/127603/,

http://www.thehansindia.com/posts/index/2015-09-20/Artificial-intelligence-could-improve-breast-cancer-treatment-176708,

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http://www.thehealthsite.com/news/a-new-way-to-gauge-breast-cancer-drug-efficacy-on-the-cards/,

http://www.dailypioneer.com/health-and-fitness/artificial-intelligence-could-improve-breast-cancer-treatment.html,

http://timesofindia.indiatimes.com/life-style/health-fitness/health-news/Artificial-intelligence-could-improve-breast-cancer-treatment/articleshow/49024103.cms,

http://zeenews.india.com/news/health/health-news/artificial-intelligence-could-improve-breast-cancer-treatment_1799356.html,

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http://www.newkerala.com/news/2015/fullnews-122242.html,

http://www.healthcanal.com/cancers/breast-cancer/67107-researchers-at-western-university-hope-to-use-artificial-intelligence-to-improve-breast-cancer-patient-outcomes.html,

http://www.exchangemagazine.com/morningpost/2015/week38/Monday/15092106.htm,

https://www.facebook.com/breastcancersocietyofcanada?fref=nf,

http://www.medicaldaily.com/artificial-intelligence-may-be-able-predict-remission-or-resistance-certain-drugs-353628,

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http://www.dqindia.com/can-artificial-intelligence-be-used-to-improve-breast-cancer-treatment/,

http://www.science20.com/news_articles/artificial_intelligence_to_improve_breast_cancer_patient_outcomes-157221,

http://geekview.baijia.baidu.com/article/179752,

http://md.tech-ex.com/2015/news/oversea/41383.html,

http://www.cnbeta.com/articles/432439.htm)

September 11, 2015. Final version of paclitaxel and gemcitabine chemotherapy signature paper now published

Molonccover

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
Received: July 20, 2015; Accepted: July 31, 2015; Published Online: August 21, 2015
Publication stage: In Press, Corrected Proof

September 7, 2015. Video presentation of Molecular Oncology article on chemotherapy response

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. KnollBrad L. UrquhartGabriella MarianiMaria Luisa Carcangiu, Peter K. Rogan. Molecular Oncology, in press. DOI: http://dx.doi.org/10.1016/j.molonc.2015.07.006

Aug. 25, 2015. Top viewed article in Molecular Cytogenetics

Our article:

Reversing chromatin accessibility differences that distinguish homologous mitotic metaphase chromosomesWahab 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.

WeareNo1_8252015

August 13, 2015. New paper on metaphase epigenetics published

The next exciting installment of the “story” about differential accessibility of metaphase chromosomes has been published.  Cytognomix’s single copy FISH technology was key to making these observations. 
 
Reversing chromatin accessibility differences that distinguish homologous mitotic metaphase chromosomes.  Khan et. al. Molecular Cytogenetics 2015, 8:65 

July 31, 2015. Chemotherapy resistance in breast cancer manuscript accepted for publication

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”.

July 3, 2015. New publication on breast cancer gene mutation

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.

 

June 27, 2015. Best oral presentation at the 12th Annual London Oncology Research & Education Conference

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

April 18, 2015. New software distribution agreement for MutationForecaster

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.

May 21, 2015. Platform presentation at Compute Ontario Research Day

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.