Browsing articles by " Peter Rogan"

November 21, 2015. Literature based filtering in the MutationForecaster system

Nov 21, 2015   //   by Peter Rogan   //   News  //  Comments Off

In next generation sequencing, exomes in particular, the challenge is to find relevant pathogenic gene variants among a sea of superfluous sequence changes. But the track record for filtering the most likely causative changes is dismal (20-25%). Most filtering methods remove common variants but do little else. Cytognomix has developed CytoVA, software that relates variants to patient peer-reviewed phenotypes in real time. We are adding this to our MutationForecaster system. Check it out!

Upcoming Presentation at University of Windsor, Ontario, Canada.

Nov 11, 2015   //   by Peter Rogan   //   News  //  Comments Off

Peter Rogan will present:

“Genomic analysis of metastasis and tumor chemotherapy response based on information theory and machine learning”

Department of Computer Science

University of Windsor

Date:  Friday, November 13th, 2015
Time: 11:00 am
Location: Chrysler Hall – G100

 Abstract: The integrated analyses of cancer phenotypes with complex genomic datasets has resulted in many new insights into diagnosis and prognosis. However, there is no single correct way to analyze these data, and the data themselves can vary significantly  in content and interpretation between different studies of the same tumor type.   We have used mutation, expression and copy number data to study breast cancer genes and genomes (hereditary and somatic). A major challenge in inherited breast cancer is the missing heritability; pathogenic mutations are not detected despite strong family historie. Our approach has been to prioritize functionally significant variants using information theory-based models of DNA and RNA binding protein binding sites.  These same approaches – when applied to breast tumour exome sequences – have revealed numerous missed mRNA splicing mutations, and identified mutated pathways, validated by RNA sequencing, that are overrepresented in these tumour genomes. Application of biochemically-inspired machine learning to these integrated genomic data from cell lines produces gene signatures that robustly predict therapeutic response that we have validated with patient tumor data. Machine learning is a promising general approach that can be used for other drugs and tumor types with good recall.

Presentation. 2015 Canadian Cancer Research Conference

Nov 5, 2015   //   by Peter Rogan   //   News  //  Comments Off

Peter Rogan will be presenting:

Seeking the “Missing Heritability” in High-Risk Hereditary Breast and Ovarian Cancer (HBOC) Patients By Prioritizing Coding and Non-Coding Variants in 21 Genes.  Natasha Caminsky G, Eliseos Mucaki J,  Amelia Perri M, Ruipeng Lu, Matthew Halvorsen, Alain Laederach, Joan Knoll HM, Peter Rogan K

on Tuesday, November 10 from 12-2 PM in the poster session: Genomics, Proteomics, and Bioinformatics

in Montréal – Hôtel Bonaventure.

Scientific Program: link


Current BRCA1 and BRCA2 genetic testing for hereditary breast and ovarian cancer (HBOC) is often uninformative. The “missing heritability” may be due to variants in uninvestigated regions of these genes or variants in other genes. We have applied a unified framework based on information theory (IT) to predict and prioritize non-coding variants of uncertain significance. We captured complete gene sequences of 21 diseaserelevant genes in HBOC patients with uninformative hereditary predisposition testing (N=336) by hybridization enrichment using ab initio single copy probes that comprehensively span non-coding regions and flanking sequences of ATM, ATP8B1, BARD1, BRCA1, BRCA2, CDH1, CHEK2, EPCAM, MLH1, MRE11A, MSH2, MSH6, MUTYH, NBN, PALB2, PMS2, PTEN, RAD51B, STK11, TP53, and XRCC2. We identified 38,538 unique variants. Eight were likely pathogenic BRCA1/2 mutations previously undetected by clinical testing. Eight proteintruncating mutations were identified in non-BRCA genes, the majority of which were in PALB2 (N=5), and 148 missense variants were flagged. Information weight matrices were derived for transcription factor (TFBS), splicing regulatory (SRBS), and RNA-binding (RBBS) protein binding sites from high-throughput sequencing data. IT analysis prioritized 12 variants affecting splicing (6 natural, 6 cryptic), 71 TFBS, 218 SRBS, and 29 RBBS. Co-segregation analysis found the relative risk of breast cancer for likely pathogenic BRCA variants torange from 1.55 to 75.78. According to clinically accepted guidelines, twenty-three were possibly pathogenic (13 confirmed by Sanger sequencing to date), 472 were of uncertain significance, and all remaining were likely not pathogenic. Complete gene analysis of BRCA1/2 and other genes is a successful strategy for identifying probable mutations in previously uninformative HBOC patients.

October 22, 2015. Presentation at the 2015 Toronto NGS Symposium

Oct 21, 2015   //   by Peter Rogan   //   News  //  Comments Off

Ben Shirley, Chief software architect at Cytognomix, will be presenting:

Interpreting variants in complete gene and genome sequences with MutationForecaster®

at 11:50 AM at the Toronto NGS Symposium (Ben Sadowski Auditorium, 18th Floor, Mt Sinai Hospital, University Ave.).

Presentation schedule

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

Oct 19, 2015   //   by Peter Rogan   //   News  //  Comments Off

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 .

September 30, 2015. Illumina announces MutationForecaster on their BaseSpace blog

Oct 1, 2015   //   by Peter Rogan   //   News  //  Comments Off

Please read our colorful blog entry describing the MutationForecaster app, now available on the Illumina® BaseSpace website (link).  Short term subscriptions are available immediately from Illumina.


September 27, 2015. Interview about breast cancer signature on CTV News, London

Sep 27, 2015   //   by Peter Rogan   //   News  //  Comments Off

Peter Rogan and Katherina Baranova were interviewed by Jan Sims about the use of artificial intelligence in predicting response to breast cancer treatment on CTV on Friday September 25 , 2015: Link

September 24, 2015. MutationForecaster® now available on BaseSpace ecosystem!

Sep 25, 2015   //   by Peter Rogan   //   News  //  Comments Off


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

Sep 18, 2015   //   by Peter Rogan   //   News  //  Comments Off

Western University hopes 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]

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


Western delivers an academic experience second to none. Since 1878, The Western Experience has combined academic excellence with life-long opportunities for intellectual, social and cultural growth in order to better serve our communities. Our research excellence expands knowledge and drives discovery with real-world application. Western attracts individuals with a broad worldview, seeking to study, influence and lead in the international community.


The Schulich School of Medicine & Dentistry at Western University is one of Canada’s preeminent medical and dental schools. Established in 1881, it was one of the founding schools of Western University and is known for being the birthplace of family medicine in Canada. For more than 130 years, the School has demonstrated a commitment to academic excellence and a passion for scientific discovery.

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Links to story:


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

Sep 12, 2015   //   by Peter Rogan   //   News  //  Comments Off


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

Sep 7, 2015   //   by Peter Rogan   //   News  //  Comments Off

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:

August 28, 2015. Article on chemotherapy gene signature published

Aug 25, 2015   //   by Peter Rogan   //   News  //  Comments Off

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

DOI: 10.1016/j.molonc.2015.07.006

are now available online at this link  (

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

Aug 25, 2015   //   by Peter Rogan   //   News  //  Comments Off

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.


August 13, 2015. New paper on metaphase epigenetics published

Aug 13, 2015   //   by Peter Rogan   //   News  //  Comments Off
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

Jul 31, 2015   //   by Peter Rogan   //   News  //  Comments Off

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”.