precisionlife markers BRCA2 2018-05-11T15:56:36+00:00

an innovative platform to study complex diseases, stratify patients,
inform next generation combinatorial diagnostics and therapies
and hence, enable precision medicine

Introducing precisionlife MARKERS

AI enabled precision medicine

an innovative platform to study complex diseases, stratify patients,
inform next generation combinatorial diagnostics and therapies
and hence, enable precision medicine

Introducing precisionlife MARKERS

AI enabled precision medicine

an innovative platform to study complex diseases,
stratify patients, inform next generation combinatorial
diagnostics and therapies and hence,
enable precision medicine

Introducing precisionlife MARKERS

AI enabled precision medicine

Complex Disease Modeling

A NEW LENS FOR THE STUDY OF COMPLEX DISEASES:

  • analyze up to 20 disease factors in combination
  • analyze genotypic, phenotypic, clinical and lifestyle disease factors in combination
  • runs quickly on affordable hardware

Complex Disease Modeling

A NEW LENS FOR THE STUDY OF COMPLEX DISEASES:

  • analyze up to 20 disease factors in combination
  • analyze genotypic, phenotypic, clinical and lifestyle disease factors in combination
  • runs quickly on affordable hardware

Complex Disease Modeling

A NEW LENS FOR THE STUDY OF COMPLEX DISEASES:

  • analyze up to 20 disease factors in combination
  • analyze genotypic, phenotypic, clinical and lifestyle disease factors in combination
  • runs quickly on affordable hardware

Enabling New Powerful Applications

Enabling New Powerful Applications

Enabling New Powerful Applications

BRCA2 White Paper Now Available

Detection and Validation of Clinically Relevant High Order Epistatic Interactions in a BRCA2 Positive Breast Cancer Population

Each quarter, we will be releasing a new case study highlighting insights
collected via combinatorial analysis of disease factors.

BRCA2 White Paper is now available

Detection and Validation of Clinically Relevant High Order Epistatic Interactions in a BRCA2 Positive Breast Cancer Population

Each quarter, we will be releasing a new case study highlighting insights collected via combinatorial analysis of disease factors.

BRCA2 White Paper is now available

Detection and Validation of Clinically Relevant High Order Epistatic Interactions in a BRCA2 Positive Breast Cancer Population

Each quarter, we will be releasing a new case study highlighting insights collected via combinatorial analysis of disease factors.

BRCA and Breast Cancer

BRCA1 & 2 are genes involved in our cells’ DNA repair system. Mutations in BRCA genes prevent proper DNA repair and leave cells open to becoming cancerous.

Mutations (SNPs) in both genes are known to be associated with higher risk of developing a range of cancers including breast, ovarian and prostate. About 3,000 such mutations or variants in the BRCA genes have been identified.

DNA Helix

BRCA and Breast Cancer

BRCA1 & 2 are genes involved in our cells’ DNA repair system. Mutations in BRCA genes prevent proper DNA repair and leave cells open to becoming cancerous.

Mutations (SNPs) in both genes are known to be associated with higher risk of developing a range of cancers including breast, ovarian and prostate. About 3,000 such mutations or variants in the BRCA genes have been identified.

DNA Helix

BRCA and Breast Cancer

BRCA1 & 2 are genes involved in our cells’ DNA repair system. Mutations in BRCA genes prevent proper DNA repair and leave cells open to becoming cancerous.

Mutations (SNPs) in both genes are known to be associated with higher risk of developing a range of cancers including breast, ovarian and prostate. About 3,000 such mutations or variants in the BRCA genes have been identified.

Breast Cancer Patient

PRECISION MEDICINE REQUIRES IMPROVED PATIENT STRATIFICATION TO ASSESS INDIVIDUAL RISK:

  • BRCA1 & BRCA2 carriers have a lifetime overall group risk of 50-85% of developing breast cancer
  • most cancers present with early onset (<40 yrs old) & have a 40-60% risk of recurrence
  • risk of ovarian cancer is 40-60% for BRCA1 & 15-20% for BRCA2 carriers
  • the precisionlife™ MARKERS platform links specific combinations of disease factors to risk which more accurately stratifies patients

Breast Cancer Patient

PRECISION MEDICINE REQUIRES IMPROVED PATIENT STRATIFICATION TO ASSESS INDIVIDUAL RISK:

  • BRCA1 & BRCA2 carriers have a lifetime overall group risk of 50-85% of developing breast cancer
  • most cancers present with early onset (<40 yrs old) & have a 40-60% risk of recurrence
  • risk of ovarian cancer is 40-60% for BRCA1 & 15-20% for BRCA2 carriers
  • the precisionlife™ MARKERS platform links specific combinations of disease factors to risk which more accurately stratifies patients

PRECISION MEDICINE REQUIRES IMPROVED PATIENT STRATIFICATION TO ASSESS INDIVIDUAL RISK:

  • BRCA1 & BRCA2 carriers have a lifetime overall group risk of 50-85% of developing breast cancer
  • most cancers present with early onset (<40 yrs old) & have a 40-60% risk of recurrence
  • risk of ovarian cancer is 40-60% for BRCA1 & 15-20% for BRCA2 carriers
  • the precisionlife™ MARKERS platform links specific combinations of disease factors to risk which more accurately stratifies patients

BRCA2 Disease Population

Data Source:
CIMBA Consortium, an international study of BRCA1/2 mutation carriers
http://cimba.ccge.medschl.cam.ac.uk/

BRCA2 Disease Population

Data Source:
CIMBA Consortium, an international study of BRCA1/2 mutation carriers
http://cimba.ccge.medschl.cam.ac.uk/

BRCA2 Disease Population

Data Source:
CIMBA Consortium, an international study of BRCA1/2 mutation carriers
http://cimba.ccge.medschl.cam.ac.uk/

Method & Analysis

OUR PROCESS

five step process

Method & Analysis

OUR PROCESS

five step process

Method & Analysis

OUR PROCESS

five step process

BRCA2 Study Results

GWAS

Standard GWAS - Manhattan Plot

GWAS only provides a simplistic view of the disease associated SNPs and is limited to identifying single locus variants. It is poor at identifying multi-locus, combined variant effects on disease. The Manhattan plot shows p-values for over-representation of single variants in cases with PLINK 1.9. Fischer’s test for single-locus associations show only FGFR2 satisfying the criterion for genome-wide significance (p-values <10-8). Variants with p-values <10-5 are marked with the corresponding names of adjacent genes.

Mining and Analysis

Graph: 3,045 novel disease associated networks with 5-13 SNPs in combination.

Table: at layers (order) 5 and 7-13 using a False Discovery Rate (FDR) of 5% that were found to differentiate breast cancer susceptibility. The penetrance in the cohort varies with FDR.

Disease risk table

precisionlife MARKERS Network Graph

precisionlife Markers network graph

The network graph above shows 10 genetically-distinct, non-overlapping cohorts, comprising 841 SNPs which correspond to 744 independent haplotypes (SNPs in approx. linkage equilibrium). Causal variants involved in the disease process and co-occuring with specific disease factors were identified, opening new research avenues for the discovery of novel druggable targets and improved disease risk profiling.

BRCA2 Study Results

GWAS

Standard GWAS - Manhattan Plot

GWAS only provides a simplistic view of the disease associated SNPs and is limited to identifying single locus variants. It is poor at identifying multi-locus, combined variant effects on disease. The Manhattan plot shows p-values for over-representation of single variants in cases with PLINK 1.9. Fischer’s test for single-locus associations show only FGFR2 satisfying the criterion for genome-wide significance (p-values <10-8). Variants with p-values <10-5 are marked with the corresponding names of adjacent genes.

Mining and Analysis

Graph: 3,045 novel disease associated networks with 5-13 SNPs in combination.

Disease risk table

Table: At layers (order) 5 and 7-13 using a False Discovery Rate (FDR) of 5% that were found to differentiate breast cancer susceptibility. The penetrance in the cohort varies with FDR.

precisionlife MARKERS Network Graph

precisionlife Markers network graph

The network graph above shows 10 genetically-distinct, non-overlapping cohorts, comprising 841 SNPs which correspond to 744 independent haplotypes (SNPs in approx. linkage equilibrium). Causal variants involved in the disease process and co-occuring with specific disease factors were identified, opening new research avenues for the discovery of novel druggable targets and improved disease risk profiling.

BRCA2 Study Results

GWAS

Standard GWAS - Manhattan Plot

GWAS only provides a simplistic view of the disease associated SNPs and is limited to identifying single locus variants. It is poor at identifying multi-locus, combined variant effects on disease. The Manhattan plot shows p-values for over-representation of single variants in cases with PLINK 1.9. Fischer’s test for single-locus associations show only FGFR2 satisfying the criterion for genome-wide significance (p-values <10-8). Variants with p-values <10-5 are marked with the corresponding names of adjacent genes.

Mining and Analysis

Graph: 3,045 novel disease associated networks with 5-13 SNPs in combination.

Disease risk table

Table: At layers (order) 5 and 7-13 using a False Discovery Rate (FDR) of 5% that were found to differentiate breast cancer susceptibility. The penetrance in the cohort varies with FDR.

precisionlife MARKERS Network Graph

precisionlife Markers network graph

The network graph above shows 10 genetically-distinct, non-overlapping cohorts, comprising 841 SNPs which correspond to 744 independent haplotypes (SNPs in approx. linkage equilibrium). Causal variants involved in the disease process and co-occuring with specific disease factors were identified, opening new research avenues for the discovery of novel druggable targets and improved disease risk profiling.

Novel Targets

Drug Repurposing

Hierarchical cluster analysis of the 16 most commonly occurring genes associated with nonzero phenotypes (i.e. containing minor alleles). These 16 genes are clustered into 6 groups that correlate with 10 known and 6 previously unreported mechanisms.

Cluster analysis of 16 genes associated with non zero genotypes

Analysis of a single cluster (#4) revealed 3 strong potential drug targets, the strongest belongs to the displayed 8 SNP cluster. The cluster occurs in 4 simple networks present in 53 cases and 0 controls. The single variant is located within the OFR of a gene associated with breast cancer metastasis potential. Targets implicated includes known druggable targets cancer and non-cancer drugs.

Novel Targets

Drug Repurposing

Hierarchical cluster analysis of the 16 most commonly occurring genes associated with nonzero phenotypes (i.e. containing minor alleles). These 16 genes are clustered into 6 groups that correlate with 10 known and 6 previously unreported mechanisms.

Cluster analysis of 16 genes associated with non zero genotypes

Analysis of a single cluster (#4) revealed 3 strong potential drug targets, the strongest belongs to the displayed 8 SNP cluster. The cluster occurs in 4 simple networks present in 53 cases and 0 controls. The single variant is located within the OFR of a gene associated with breast cancer metastasis potential. Targets implicated includes known druggable targets cancer and non-cancer drugs.

Novel Targets

Cluster analysis of 16 genes associated with non zero genotypes

Hierarchical cluster analysis of the 16 most commonly occurring genes associated with nonzero phenotypes (i.e. containing minor alleles). These 16 genes are clustered into 6 groups that correlate with 10 known and 6 previously unreported mechanisms.

Drug Repurposing

Analysis of a single cluster (#4) revealed 3 strong potential drug targets, the strongest belongs to the displayed 8 SNP cluster. The cluster occurs in 4 simple networks present in 53 cases and 0 controls. The single variant is located within the OFR of a gene associated with breast cancer metastasis potential. Targets implicated includes known druggable targets cancer and non-cancer drugs.

Chart: BRCA2 population further segregated according to onset age.

Table: Protective effect factors identified in 451 out of 1,458 cases of BRCA1 & 2 carriers without cancer (30.9% penetrance). This is the first reported discovery of factors eliciting a protective effect.

Disease protective effects

Disease protective effects table

Disease Protective Effects

precisionlife MARKERS allows reversal of case:controls to study features associated with disease protective effects rather than just the disease risk. In our study, we successfully identified a number of protective effects that may reduce an individuals’s lifetime risk of developing breast cancer. It should be highlighted that known gene associations include druggable candidate targets. Further study of the genes implicated may lead to novel druggable R&D opportun

Disease Risk Scoring

Protective combination of genetic factors may also be used to improve the accuracy and specificity of current disease risk scoring models and genetic tests. With further understanding of the downstream impacts of these protective and indicative risk factors, when combined with the all the possible combinations contained within an individual’s genome, it may be possible to develop a truly personalized disease risk scoring model.

Chart: BRCA2 population further segregated according to onset age.

Table: Protective effect factors identified in 451 out of 1,458 cases of BRCA1 & 2 carriers without cancer (30.9% penetrance). This is the first reported discovery of factors eliciting a protective effect.

Disease protective effects

Disease protective effects table

Disease Protective Effects

precisionlife MARKERS allows reversal of case:controls to study features associated with disease protective effects rather than just the disease risk. In our study, we successfully identified a number of protective effects that may reduce an individuals’s lifetime risk of developing breast cancer. It should be highlighted that known gene associations include druggable candidate targets. Further study of the genes implicated may lead to novel druggable R&D opportunities.

Disease Risk Scoring

Protective combination of genetic factors may also be used to improve the accuracy and specificity of current disease risk scoring models and genetic tests. With further understanding of the downstream impacts of these protective and indicative risk factors, when combined with the all the possible combinations contained within an individual’s genome, it may be possible to develop a truly personalized disease risk scoring model.

Disease Protective Effects

precisionlife MARKERS allows reversal of case:controls to study features associated with disease protective effects rather than just the disease risk. In our study, we successfully identified a number of protective effects that may reduce an individuals’s lifetime risk of developing breast cancer. It should be highlighted that known gene associations include druggable candidate targets. Further study of the genes implicated may lead to novel druggable R&D opportunities.

Disease Risk Scoring

Protective combination of genetic factors may also be used to improve the accuracy and specificity of current disease risk scoring models and genetic tests. With further understanding of the downstream impacts of these protective and indicative risk factors, when combined with the all the possible combinations contained within an individual’s genome, it may be possible to develop a truly personalized disease risk scoring model.

Chart: BRCA2 population further segregated according to onset age.

Disease protective effects table

Table: Protective effect factors identified in 451 out of 1,458 cases of BRCA1 & 2 carriers without cancer (30.9% penetrance). This is the first reported discovery of factors eliciting a protective effect.

GOT DATA?

precisionlife MARKERS Generates New Insights for all Genomics Datasets

Our platform makes it easy to quickly identify, validate and understand the complex and multi-dimensional relationships between disease, risk and treatment response

We are looking for partners and offer a number of pricing packages:

  • Single Study Projects
  • Study Series – 3 or more projects
  • Therapeutic Area Studies
  • Open Collaboration Studies
Contact Us

Our Engagement Process

Download BRCA2 Whitepaper Now!

GOT DATA?

precisionlife MARKERS Generates New Insights for all Genomics Datasets

Our platform makes it easy to quickly identify, validate and understand the complex and multi-dimensional relationships between disease, risk and treatment response

We are looking for partners and offer a number of pricing packages:

  • Single Study Projects
  • Study Series – 3 or more projects
  • Therapeutic Area Studies
  • Open Collaboration Studies
Contact Us

Our Engagement Process

Download BRCA2 Whitepaper Now!

GOT DATA?

precisionlife MARKERS Generates New Insights for all Genomics Datasets

Our platform makes it easy to quickly identify, validate and understand the complex and multi-dimensional relationships between disease, risk and treatment response

Download BRCA2 Whitepaper Now!

We are looking for partners and offer a number of pricing packages:

  • Single Study Projects
  • Study Series – 3 or more projects
  • Therapeutic Area Studies
  • Open Collaboration Studies
Contact Us

Our Engagement Process