Test Determines If Cancer Patients Require Chemotherapy
By LabMedica International staff writers Posted on 09 Jul 2019 |
Image: A histological preparation from a patient with invasive lobular carcinoma, demonstrating a predominantly lobular growth pattern (Photo courtesy of KGH).
Invasive lobular carcinoma (ILC) is the most common special type of breast cancer, and is characterized by functional loss of E-cadherin, resulting in cellular adhesion defects. ILC typically present as estrogen receptor positive, grade 2 breast cancers, with a good short-term prognosis.
Many of the clinical challenges associated with diagnosing and managing patients with ILC are directly related to this behavior, including the difficulty in imaging by mammography and obtaining clear surgical margins. Subsequently, more patients present late, with larger tumors, more frequently involved axillary lymph nodes and requiring higher frequency of mastectomies compared to patients diagnosed with invasive carcinoma of no special type (IC-NST).
A large team of medical scientists led by The University of Queensland (Herston, QLD, Australia) accessed fresh frozen tumors and matching blood samples and used integrative analysis of gene expression and DNA copy number to identify novel drivers and prognostic biomarkers, using 25 in-house, 125 METABRIC and 146 TCGA samples.
DNA and RNA were extracted from frozen tissue sections by either collecting frozen sections directly into extraction tubes or following needle dissection to enrich for tumor cellularity, which was estimated by a pathologist from adjacent-stained frozen sections. QIAgen extraction kits were used. Quantification and quality assessment of nucleic acids were performed using the Qubit dsDNA BR and RNA BR assays and Bioanalyzer RNA 6000 Nano assay. Gene expression profiling of UQCCR samples was performed using the Whole-Genome Gene Expression Direct Hybridization Assay.
The team used in silico integrative analyses, and derived a 194-gene set that was highly prognostic in ILC, that they named this metagene ‘LobSig’. Assessing a 10-year follow-up period, LobSig outperformed other similar commercial analyses. LobSig status predicted outcome with 94.6% accuracy amongst cases classified as ‘moderate-risk’. Network analysis identified few candidate pathways, though genesets related to proliferation were identified, and a LobSig-high phenotype was associated with the TCGA proliferative subtype. ILC with a poor outcome as predicted by LobSig were enriched with mutations in ERBB2, ERBB3, TP53, AKT1 and ROS1.
Amy E. McCart Reed, PhD, a clinical research scientists and first author of the study said, “In this study, we pulled together a set of 194 genes that, when working together, act as a signature to help clarify which patients are likely to have a positive outcome with their breast cancer. If they have a low-risk signature score, it means we might relieve them of the burden of chemotherapy. If they have a high-risk signature score, we could continue to recommend chemotherapy as the course of treatment.”
The authors concluded that the molecular signature, LobSig, which captures the peculiar genomic landscape of ILC tumors, and together with clinico-pathology information, provides a robust mechanism for prognostication in ILC. The study was published on June 27, 2019, in the journal npj Breast Cancer.
Related Links:
The University of Queensland
Many of the clinical challenges associated with diagnosing and managing patients with ILC are directly related to this behavior, including the difficulty in imaging by mammography and obtaining clear surgical margins. Subsequently, more patients present late, with larger tumors, more frequently involved axillary lymph nodes and requiring higher frequency of mastectomies compared to patients diagnosed with invasive carcinoma of no special type (IC-NST).
A large team of medical scientists led by The University of Queensland (Herston, QLD, Australia) accessed fresh frozen tumors and matching blood samples and used integrative analysis of gene expression and DNA copy number to identify novel drivers and prognostic biomarkers, using 25 in-house, 125 METABRIC and 146 TCGA samples.
DNA and RNA were extracted from frozen tissue sections by either collecting frozen sections directly into extraction tubes or following needle dissection to enrich for tumor cellularity, which was estimated by a pathologist from adjacent-stained frozen sections. QIAgen extraction kits were used. Quantification and quality assessment of nucleic acids were performed using the Qubit dsDNA BR and RNA BR assays and Bioanalyzer RNA 6000 Nano assay. Gene expression profiling of UQCCR samples was performed using the Whole-Genome Gene Expression Direct Hybridization Assay.
The team used in silico integrative analyses, and derived a 194-gene set that was highly prognostic in ILC, that they named this metagene ‘LobSig’. Assessing a 10-year follow-up period, LobSig outperformed other similar commercial analyses. LobSig status predicted outcome with 94.6% accuracy amongst cases classified as ‘moderate-risk’. Network analysis identified few candidate pathways, though genesets related to proliferation were identified, and a LobSig-high phenotype was associated with the TCGA proliferative subtype. ILC with a poor outcome as predicted by LobSig were enriched with mutations in ERBB2, ERBB3, TP53, AKT1 and ROS1.
Amy E. McCart Reed, PhD, a clinical research scientists and first author of the study said, “In this study, we pulled together a set of 194 genes that, when working together, act as a signature to help clarify which patients are likely to have a positive outcome with their breast cancer. If they have a low-risk signature score, it means we might relieve them of the burden of chemotherapy. If they have a high-risk signature score, we could continue to recommend chemotherapy as the course of treatment.”
The authors concluded that the molecular signature, LobSig, which captures the peculiar genomic landscape of ILC tumors, and together with clinico-pathology information, provides a robust mechanism for prognostication in ILC. The study was published on June 27, 2019, in the journal npj Breast Cancer.
Related Links:
The University of Queensland
Latest Molecular Diagnostics News
- New Genetic Testing Procedure Combined With Ultrasound Detects High Cardiovascular Risk
- Blood Samples Enhance B-Cell Lymphoma Diagnostics and Prognosis
- Blood Test Predicts Knee Osteoarthritis Eight Years Before Signs Appears On X-Rays
- Blood Test Accurately Predicts Lung Cancer Risk and Reduces Need for Scans
- Unique Autoantibody Signature to Help Diagnose Multiple Sclerosis Years before Symptom Onset
- Blood Test Could Detect HPV-Associated Cancers 10 Years before Clinical Diagnosis
- Low-Cost Point-Of-Care Diagnostic to Expand Access to STI Testing
- 18-Gene Urine Test for Prostate Cancer to Help Avoid Unnecessary Biopsies
- Urine-Based Test Detects Head and Neck Cancer
- Blood-Based Test Detects and Monitors Aggressive Small Cell Lung Cancer
- Blood-Based Machine Learning Assay Noninvasively Detects Ovarian Cancer
- Simple PCR Assay Accurately Differentiates Between Small Cell Lung Cancer Subtypes
- Revolutionary T-Cell Analysis Approach Enables Cancer Early Detection
- Single Genetic Test to Accelerate Diagnoses for Rare Developmental Disorders
- Upgraded Syndromic Testing Analyzer Enables Remote Test Results Access
- Respiratory and Throat Infection PCR Test Detects Multiple Pathogens with Overlapping Symptoms