GTx-Onco™
Genetic Data Analysis and Actionable Recommendations for Oncology !
Genetic mutations in cancer cells can be identified in tumour biopsies or liquid biopsies through DNA sequencing and bioinformatics. Some mutations generate impactful insights on the cancer’s weaknesses and strenghts. GTx-Onco crosses information from a person’s specific tumour mutation’s and a knowledgebase of recommendations.
What is GTx-Onco™?
GTx Onco™ is a computational tool for the interpretation of cancer genome data (large gene panels, WES and/or WGS).
GTx Onco™ provides actionable recommendations, linked to therapeutic and nutritional aspects, based on the genetic profile of the tumor.
GTx-Onco™ analyzes tumor-specific genetic mutations to generate evidence-based treatment recommendations for cancer patients.
Key Features:
Genetic Data Interpretation
Variants Analysis: Comprehensive analysis of genetic variants, including single nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).
Integration of genomic data available [single nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs), mitocondrial variants, fusion gene events]
Annotation and Prioritization: Annotation of variants to identify potential disease-causing variants and prioritize their significance based on various proprietary databases and algorithms.
Cross-referencing tumor mutations with curated recommendation databases for actionable insights
Gene-Phenotype Associations: Assessment of gene-phenotype relationships to understand the impact of genetic variations on disease risk and clinical outcomes.
Clinical decision support, off-label treatment suggestions, drug recommendation stratification, metabolic-based dietary recommendations.
Disease Risk Assessment
Disease Profiling: Identification of genetic factors associated with different types of cancer.
Risk Stratification: Stratification of patients into different risk categories based on genetic data, allowing for targeted interventions and preventive measures.
Pharmacogenomics:
Drug-Gene Interactions: Identification of genetic variations that influence drug response, metabolism, and efficacy.
Personalized Medication Recommendations: Tailored recommendations for medication selection, dosage adjustment, and potential adverse reactions based on an individual's genetic profile.
Drug-Gene Interaction Database: Integration of databases containing information on drug-gene interactions and pharmacogenomic guidelines.
Clinical Decision Support
Evidence-Based Recommendations: Provision of evidence-based recommendations backed by scientific literature and clinical trials.
Treatment Options: Suggesting appropriate treatment options based on genetic profiles and associated disease risks.
Therapeutic Monitoring: Guidance on monitoring disease progression, treatment response, and potential adverse events based on genetic data.
Dietary recommendations: Metabolic reprogramming based on somatic genetic changes in the tumor.
User-Friendly Reporting:
Actionable Reports: Generation of clear and concise reports summarizing genetic findings, recommendations, and supporting evidence.
Interactive Visualizations: Visual representation of genetic data and associated risks to facilitate understanding and communication with patients.
Applications
Preventive Medicine: Assessing genetic predispositions for cancer and recommending lifestyle modifications and screening strategies.
Pharmacogenomics: Optimizing drug selection, dosage, and reducing adverse drug reactions based on individual genetic variations.
Rare Diseases: Aiding in the diagnosis of rare genetic disorders and providing guidance on management and treatment options.
Family Planning: Assessing genetic risks for inherited conditions and providing genetic counseling for reproductive decision-making.
Clinical decision support with evidence-based suggestions
Off-label recommendations for challenging cases
Stratification of drug options based on level of evidence
Tailoring of diets based on tumor metabolic vulnerabilies
Time and cost savings through assertive treatment
Technical Information
Input Requirements: Raw sample (to be sequenced) or sequenced exome or large panel
Variants Identified: SNVs, CNVs, InDels, Mitochondrial, Fusion events
Standard Analysis: Quality control, variant calling, annotation and crossing with recommendation database
Workflow
Collection
DNA extraction
Sequencing
Report
Data-Driven Prescriptions
Benefits
Precision Medicine: Enable personalized healthcare by leveraging genetic information to guide treatment decisions and interventions.
Improved Patient Outcomes: Empower health professionals to make informed decisions and provide targeted interventions for better patient outcomes.
Time and Cost Savings: Streamline the analysis process and provide actionable recommendations, saving time and reducing unnecessary tests and treatments.
Evidence-Based Practice: Incorporation of up-to-date scientific literature and clinical guidelines to ensure recommendations are based on the best available evidence.
Enhanced Collaboration: Facilitate interdisciplinary collaboration among healthcare professionals by providing a standardized platform for genetic data analysis and interpretation.
GTx-Onco
Genetic mutations in cancer cells can be identified in tumour biopsies or liquid biopsies through DNA sequencing and bioinformatics. Some mutations generate impactful insights on the cancer’s weaknesses and strenghts. GTx-Onco crosses information from a person’s specific tumour mutation’s and a knowledgebase of recommendations.
What is GTx-Onco?
GTx Onco™ is a computational tool for the interpretation of cancer genome data (large gene panels, WES and/or WGS).
GTx Onco™ provides actionable recommendations, linked to therapeutic and nutritional aspects, based on the genetic profile of the tumor.
GTx-Onco™ analyzes tumor-specific genetic mutations to generate evidence-based treatment recommendations for cancer patients.
Key Features:
Genetic Data Interpretation
Integration of genomic data available [single nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs), mitocondrial variants, fusion gene events
Cross-referencing tumor mutations with curated recommendation databases for actionable insights
Clinical decision support, off-label treatment suggestions, drug recommendation stratification, metabolic-based dietary recommendations.
Disease Risk Assessment
Disease Profiling: Identification of genetic factors associated with different types of cancer.
Risk Stratification: Stratification of patients into different risk categories based on genetic data, allowing for targeted interventions and preventive measures.
Pharmacogenomics:
Drug-Gene Interactions: Identification of genetic variations that influence drug response, metabolism, and efficacy.
Personalized Medication Recommendations: Tailored recommendations for medication selection, dosage adjustment, and potential adverse reactions based on an individual's genetic profile.
Drug-Gene Interaction Database: Integration of databases containing information on drug-gene interactions and pharmacogenomic guidelines.
Clinical Decision Support
Evidence-Based Recommendations: Provision of evidence-based recommendations backed by scientific literature and clinical trials.
Treatment Options: Suggesting appropriate treatment options based on genetic profiles and associated disease risks.
Therapeutic Monitoring: Guidance on monitoring disease progression, treatment response, and potential adverse events based on genetic data.
Dietary recommendations: Metabolic reprogramming based on somatic genetic changes in the tumor.
User-Friendly Reporting:
Actionable Reports: Generation of clear and concise reports summarizing genetic findings, recommendations, and supporting evidence.
Interactive Visualizations: Visual representation of genetic data and associated risks to facilitate understanding and communication with patients.
Applications
Clinical decision support with evidence-based suggestions
Off-label recommendations for challenging cases
Stratification of drug options based on level of evidence
Tailoring of diets based on tumor metabolic vulnerabilies
Time and cost savings through assertive treatment
Technical Information
Input Requirements: Raw sample (to be sequenced) or sequenced exome or large panel
Variants Identified: SNVs, CNVs, InDels, Mitochondrial, Fusion events
Standard Analysis: Quality control, variant calling, annotation and crossing with recommendation database
WORKFLOW
Collection
DNA extraction
Sequencing
Report
Data-Driven Prescriptions
Benefits
Precision Medicine: Enable personalized healthcare by leveraging genetic information to guide treatment decisions and interventions.
Improved Patient Outcomes: Empower health professionals to make informed decisions and provide targeted interventions for better patient outcomes.
Time and Cost Savings: Streamline the analysis process and provide actionable recommendations, saving time and reducing unnecessary tests and treatments.
Evidence-Based Practice: Incorporation of up-to-date scientific literature and clinical guidelines to ensure recommendations are based on the best available evidence.
Enhanced Collaboration: Facilitate interdisciplinary collaboration among healthcare professionals by providing a standardized platform for genetic data analysis and interpretation.