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!
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.
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.
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.).
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:
at the EPRBiodose meeting at Dartmouth College, organized by the International Association of Biological and EPR Radiation Dosimetry .
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
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
MEDIA CONTACT: Tristan Joseph, Media Relations Officer, Schulich School of Medicine & Dentistry, Western University, 519-661-2111 ext. 80387, c: 519-777-1573, email@example.com
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Links to story:
September 11, 2015. Final version of paclitaxel and gemcitabine chemotherapy signature paper now published
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”.