Breast Cancer Recurrence Risk Predicted by Immune-Related Germline Variants
|
By LabMedica International staff writers Posted on 25 Nov 2019 |

Image: The Oncotype DX test is a genomic test that analyzes the activity of a group of genes that can affect how a cancer is likely to behave and respond to treatment (Photo courtesy of Michael Jarrett/Genomic Health)
A single normal cell randomly acquires a series of mutations that allows it to proliferate and to be transformed into a cancer cell (i.e., founding clone), which initiates tumor progression and recurrence.
Pre-existing germline variants provide a profound constraint on the evolution of tumor founding clones and subclones and therefore have a contingent effect on the genetic makeup of tumor and presumably patient outcomes. Family history remains one of the major risk factors that contribute to cancer.
Scientists from the University of Calgary (Calgary, AB, Canada) and their colleagues obtained whole-exome sequencing data of breast cancers from the National Cancer Institute-Genomic Data Commons (NCI GDC). They collected 755 ER+ breast cancer samples: a training set of 200 samples, a testing set of 60 samples, and two independent validation sets of 200 and 295 samples. To determine germline variants, they used variant allele frequencies (VAFs) between the tumor and healthy samples. The team used their so-called eTumorMetastasis algorithm to search for germline signatures of recurrence based on protein-coding sequences.
The scientists used the eTumorMetastasis tool, which combines variant, signaling, and network information, and initially tracked down 18 network operational gene (NOG) signatures, each containing variants involving dozens of genes. When they combined these signatures, the investigators came up with a unified germline gene set that appeared to coincide with recurrence in 60 more ER-positive breast tumors. In those validation cohorts, the authors reported, "germline variants are significantly correlated with tumor recurrence and support their hypothesis that the original germline genomic landscape of a cancer patient has a significant impact on clinical outcome."
The investigators noted that the germline variant-based prediction strategy compared favorably with prognostic insights that could be gleaned from the Oncotype DX test (Genomic Health, Redwood City, CA, USA) which considers expression levels for 21 genes in biopsied or surgically-removed tumor samples. They found that recurred patients possessed a higher rate of germline variants. In addition, the inherited germline variants from these gene signatures were predominately enriched in T cell function, antigen presentation, and cytokine interactions, likely impairing the adaptive and innate immune response thus favoring a pro-tumorigenic environment.
Edwin Wang, PhD, a professor and senior author of the study, said, “Prognostic prediction using a patient's germline genomic landscape opens up the possibility of assessing cancer patients' risk of recurrence, which allows for a better forecasting of cancer recurrence in a quick, convenient, and noninvasive manner.” The authors concluded that germline genomic information could be used for developing non-invasive genomic tests for predicting patients’ outcomes in breast cancer. The study was published on November 1, 2019 in the journal NPJ Precision Oncology.
Related Links:
University of Calgary
Genomic Health
Pre-existing germline variants provide a profound constraint on the evolution of tumor founding clones and subclones and therefore have a contingent effect on the genetic makeup of tumor and presumably patient outcomes. Family history remains one of the major risk factors that contribute to cancer.
Scientists from the University of Calgary (Calgary, AB, Canada) and their colleagues obtained whole-exome sequencing data of breast cancers from the National Cancer Institute-Genomic Data Commons (NCI GDC). They collected 755 ER+ breast cancer samples: a training set of 200 samples, a testing set of 60 samples, and two independent validation sets of 200 and 295 samples. To determine germline variants, they used variant allele frequencies (VAFs) between the tumor and healthy samples. The team used their so-called eTumorMetastasis algorithm to search for germline signatures of recurrence based on protein-coding sequences.
The scientists used the eTumorMetastasis tool, which combines variant, signaling, and network information, and initially tracked down 18 network operational gene (NOG) signatures, each containing variants involving dozens of genes. When they combined these signatures, the investigators came up with a unified germline gene set that appeared to coincide with recurrence in 60 more ER-positive breast tumors. In those validation cohorts, the authors reported, "germline variants are significantly correlated with tumor recurrence and support their hypothesis that the original germline genomic landscape of a cancer patient has a significant impact on clinical outcome."
The investigators noted that the germline variant-based prediction strategy compared favorably with prognostic insights that could be gleaned from the Oncotype DX test (Genomic Health, Redwood City, CA, USA) which considers expression levels for 21 genes in biopsied or surgically-removed tumor samples. They found that recurred patients possessed a higher rate of germline variants. In addition, the inherited germline variants from these gene signatures were predominately enriched in T cell function, antigen presentation, and cytokine interactions, likely impairing the adaptive and innate immune response thus favoring a pro-tumorigenic environment.
Edwin Wang, PhD, a professor and senior author of the study, said, “Prognostic prediction using a patient's germline genomic landscape opens up the possibility of assessing cancer patients' risk of recurrence, which allows for a better forecasting of cancer recurrence in a quick, convenient, and noninvasive manner.” The authors concluded that germline genomic information could be used for developing non-invasive genomic tests for predicting patients’ outcomes in breast cancer. The study was published on November 1, 2019 in the journal NPJ Precision Oncology.
Related Links:
University of Calgary
Genomic Health
Latest Pathology News
- Uncertainty-Aware AI Platform Supports Automated HER2 Assessment in Breast Cancer
- AI Tool Speeds Brain Tumor Classification from Routine Histology Slides
- IHC Companion Diagnostic Standardizes Mismatch Repair Testing for Cancer Immunotherapy
- AI Pathology Tool Predicts Meningioma Recurrence from Routine Slides
- 3D Spatial Multi-Omics Maps Intra-Tumor Diversity in Colorectal Cancer
- Blood-Based Method Tracks Gene Activity in the Living Brain
- FDA Approval Expands Automated PD-L1 Testing Across Solid Tumors
- AI-Powered Atlas Maps Immune Structures Linked to Cancer Outcomes
- AI Tool Extracts Immune Signals from Biopsy to Inform Myeloma Therapy
- Rapid AI Tool Predicts Cancer Spatial Gene Expression from Pathology Images
- AI Pathology Test Receives FDA Breakthrough for Bladder Cancer Risk Stratification
- FDA Clears AI Digital Pathology Tool for Breast Cancer Risk Stratification
- New AI Tool Reveals Hidden Genetic Signals in Routine H&E Slides
- AI System Analyzes Routine Pathology Slides to Predict Cancer Outcomes
- New Tissue Mapping Approach Identifies High-Risk Form of Diabetic Kidney Disease
- Multimodal AI Tool Predicts Genetic Alterations to Guide Breast Cancer Treatment
Channels
Clinical Chemistry
view channel
Simple Blood-Based Cholesterol Efflux Assay Identifies High-Risk Coronary Plaque Features
Unstable coronary plaques are difficult to identify before they trigger acute cardiovascular events. Standard high-density lipoprotein (HDL) measurements do not always capture how well HDL particles function... Read more
Plasma Vitamin C Levels Associated with Brain Structure and Connectivity in Aging
Previous studies have linked vitamin C–rich diets with lower risk of cognitive impairment in older adults. However, few investigations have directly examined blood plasma vitamin C in relation to brain... Read more
Mass Spectrometry Detects Tumor Metabolites for Cancer Monitoring
Cancer’s altered metabolism complicates how clinicians detect and monitor tumors, because nutrient use can shift with context and time. Measuring small-molecule metabolites that distinguish malignant from... Read more
Urinary Biomarker Assay Predicts Kidney Disease Progression Beyond Standard Measures
Many patients with type 2 diabetes and chronic kidney disease continue to experience progressive renal decline, yet conventional markers such as albuminuria and estimated glomerular filtration rate (eGFR)... Read moreHematology
view channel
Next-Generation Hematology Platform Streamlines High-Complexity Lab Workflows
Sysmex America (Chicago, IL, USA) has introduced the next generation XR-Series, centered on the XR-10 Automated Hematology Module for high-complexity laboratories. The platform builds on the widely used... Read more
Blood Eosinophil Count May Predict Cancer Immunotherapy Response and Toxicity
Immune checkpoint inhibitors have improved outcomes across many cancers, yet only a subset of patients derive durable benefit and biomarkers to guide treatment remain limited. Eosinophils, best known for... Read moreImmunology
view channelAptamer-Based Biosensor Enables Mutation-Resilient SARS-CoV-2 Detection
Rapid evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can undermine existing molecular diagnostics, especially when assays target small viral components. Double-antibody sandwich... Read more
Study Points to Autoimmune Pathway Behind Long COVID Symptoms
Long COVID leaves many SARS-CoV-2 survivors with persistent fatigue, cognitive issues, palpitations, and musculoskeletal pain for months or years. Estimates cited in new research suggest 4%–20% of infected... Read more
Metabolic Biomarker Distinguishes Latent from Active Tuberculosis and Tracks Treatment Response
Tuberculosis (TB) remains the world’s leading infectious killer, with 10.8 million cases and 1.25 million deaths recorded globally in 2023. Yet many infected individuals never develop active disease, underscoring... Read moreMicrobiology
view channel
TORCH Infection Trends Point to Need for Tailored Screening in Pregnancy
Congenital TORCH infections can be asymptomatic during pregnancy yet cause stillbirth, birth defects, and lifelong disability in infants. Many regions still lack robust surveillance to guide testing and... Read more
New Culture Medium Speeds C. difficile Resistance Detection and Reduces Costs
Clostridioides difficile infections remain a persistent threat in hospitals and communities, affecting about 500,000 people in the United States each year. Severe cases can be fatal within 30 days of diagnosis,... Read morePathology
view channel
Uncertainty-Aware AI Platform Supports Automated HER2 Assessment in Breast Cancer
Accurate assessment of human epidermal growth factor receptor 2 (HER2) is critical for breast cancer diagnosis and treatment selection, yet scoring variability and infrastructure requirements can complicate... Read more
AI Tool Speeds Brain Tumor Classification from Routine Histology Slides
Accurate classification of brain and spinal cord tumors increasingly depends on molecular profiling alongside histology, but access to such testing remains limited and results can take about two weeks.... Read more
IHC Companion Diagnostic Standardizes Mismatch Repair Testing for Cancer Immunotherapy
Deficient DNA mismatch repair is an established predictive biomarker for response to immune checkpoint inhibitors, yet access to standardized assessment has varied across tumor types. Cancer remains the... Read moreTechnology
view channel
AI Platform Links Biomarker Results to Cancer Clinical Trials and Guidelines
Oncology teams must manage growing volumes of genomic data, rapidly evolving clinical trial options, and frequently updated care guidelines, all within tight clinic schedules. Translating complex tumor... Read more
Agentic AI Platform Supports Genomic Decision-Making in Oncology
Oncology care teams increasingly face the challenge of managing complex molecular diagnostics, evolving treatment options, and extensive electronic health record documentation. Translating multimodal data... Read moreIndustry
view channel
Open-Source Consortium Aims to Standardize Digital Pathology Workflows
Digital pathology is expanding rapidly as laboratories adopt whole-slide imaging and computational tools to meet growing diagnostic and biomarker-testing demand. However, fragmented software infrastructure... Read more








