scholarly journals Targeted mutation detection in breast cancer using MammaSeq™

2018 ◽  
Author(s):  
Nicholas G. Smith ◽  
Rekha Gyanchandani ◽  
Grzegorz Gurda ◽  
Peter C. Lucas ◽  
Ryan J. Hartmaier ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Solid Tumors ◽  
Solid Tumor ◽  
Copy Number ◽  
Clinical Decision Making ◽  
Mutation Detection ◽  
Metastatic Breast ◽  
Clinical Decision ◽  
Precision Oncology ◽  
Actionable Variants

AbstractBackgroundBreast cancer is the most common invasive cancer among women worldwide. Next-generation sequencing (NGS) has revolutionized the study of cancer across research labs around the globe, however genomic testing in clinical settings remain limited. Advances in sequencing reliability, pipeline analysis, accumulation of relevant data, and the reduction of costs are rapidly increasing the feasibility of NGS-based clinical decision making.MethodsWe report the development of MammaSeq, a breast cancer specific NGS panel, targeting 79 genes and 1369 mutations, optimized for use in primary and metastatic breast cancer. To validate the panel, 46 solid tumor and 14 plasma circulating-free cfDNA samples were sequenced to a mean depth of 2311X and 1820 X respectively. Variants were called using Ion Torrent Suite 4.0 and annotated with cravat CHASM. CNVKit was used to call copy number variants in the solid tumor cohort. The oncoKB Precision Oncology Database was used to identify clinically actionable variants. ddPCR was used to validate select cfDNA mutations.ResultsIn cohorts of 46 solid tumors and 14 cfDNA samples from patients with advanced breast cancer we identified 592 and 43 protein coding mutations. Mutations per sample in the solid tumor cohort ranged from 1 to 128 (median 3) and the cfDNA cohort ranged from 0 to 26 (median 2.5). Copy number analysis in the solid tumor cohort identified 46 amplifications and 35 deletions. We identified 26 clinically actionable variants (levels 1-3) annotated by OncoKB, distributed across 20 out of 46 cases (40%), in the solid tumor cohort. Allele frequencies of ESR1 and FOXA1 mutations correlated with CA.27.29 levels in patient matched blood draws.ConclusionsIn solid tumors biopsies and cfDNA, MammaSeq detects clinicaly actionable mutations (oncoKB levels 1-3) in 22/46 (48%) solid tumors and in 4/14 (29%) of cfDNA samples. MammaSeq is a targeted panel suitable for clinically actionable mutation detection in breast cancer.

scholarly journals Precision Oncology via NMR-Based Metabolomics: A Review on Breast Cancer

2021 ◽  
Vol 22 (9) ◽  
pp. 4687
Author(s):  
Alessia Vignoli ◽  
Emanuela Risi ◽  
Amelia McCartney ◽  
Ilenia Migliaccio ◽  
Erica Moretti ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Clinical Decision Making ◽  
Biochemical Characterization ◽  
Clinical Decision ◽  
Precision Oncology ◽  
Breast Cancer Patients ◽  
The Status ◽  
Individual Health Status ◽  
Optimal Approach ◽  
The Right

Precision oncology is an emerging approach in cancer care. It aims at selecting the optimal therapy for the right patient by considering each patient’s unique disease and individual health status. In the last years, it has become evident that breast cancer is an extremely heterogeneous disease, and therefore, patients need to be appropriately stratified to maximize survival and quality of life. Gene-expression tools have already positively assisted clinical decision making by estimating the risk of recurrence and the potential benefit from adjuvant chemotherapy. However, these approaches need refinement to further reduce the proportion of patients potentially exposed to unnecessary chemotherapy. Nuclear magnetic resonance (NMR) metabolomics has demonstrated to be an optimal approach for cancer research and has provided significant results in BC, in particular for prognostic and stratification purposes. In this review, we give an update on the status of NMR-based metabolomic studies for the biochemical characterization and stratification of breast cancer patients using different biospecimens (breast tissue, blood serum/plasma, and urine).

scholarly journals Systematic discovery of gene fusions in pediatric cancer by integrating RNA-seq and WGS

2021 ◽  
Author(s):  
Ianthe A.E.M. van Belzen ◽  
Casey Cai ◽  
Marc van Tuil ◽  
Shashi Badloe ◽  
Eric Strengman ◽  
...  
Keyword(s):  
Decision Making ◽  
Pediatric Cancer ◽  
Copy Number ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Specific Gene ◽  
Precision Oncology ◽  
Gene Fusions ◽  
Rna Seq ◽  
High Confidence

Background Gene fusions are important cancer drivers in pediatric cancer and their accurate detection is essential for diagnosis and treatment. Clinical decision-making requires high confidence and precision of detection. Recent developments show RNA sequencing (RNA-seq) is promising for genome-wide detection of fusion products, but hindered by many false positives that require extensive manual curation and impede discovery of pathogenic fusions. Results We developed Fusion-sq to detect tumor-specific gene fusions by integrating and 'fusing' evidence from RNA-seq and whole genome sequencing (WGS) using intron-exon gene structure. In a pediatric pan-cancer cohort of 130 patients, we identified 165 high confidence tumor-specific gene fusions and their underlying structural variants (SVs). This includes all clinically relevant fusions known to be present in this cohort (30 patients). Fusion-sq distinguishes healthy-occurring from tumor-specific fusions, and resolves fusions in amplified regions and copy number unstable genomes. A high gene fusion burden is associated with copy number instability. We identified 27 potentially pathogenic fusions involving oncogenes or tumor-suppressor genes characterised by underlying SVs or expression changes indicative of activating or disruptive effects. Conclusions Our results indicate how clinically relevant and potentially pathogenic gene fusions can be identified and their functional effects investigated by combining WGS and RNA-seq. Integrating RNA fusion predictions with underlying SVs advances fusion detection beyond extensive manual filtering. Taken together, we developed a method for identifying candidate fusions that is suitable for precision oncology applications. Our method provides multi-omics evidence for assessing the pathogenicity of tumor-specific fusions for future clinical decision making.

B2 Prognostic Score: External Validation of a Clinical Decision-making Tool for Metastatic Breast Cancer

2019 ◽  
Vol 19 (5) ◽  
pp. 333-339
Author(s):  
Tanja Nadine Stueber ◽  
Manfred Wischnewsky ◽  
Elena Leinert ◽  
Joachim Diessner ◽  
Catharina Bartmann ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Decision Making ◽  
Metastatic Breast Cancer ◽  
Clinical Decision Making ◽  
Prognostic Score ◽  
External Validation ◽  
Metastatic Breast ◽  
Clinical Decision ◽  
Decision Making Tool

scholarly journals VEGF-Related Germinal Polymorphisms May Identify a Subgroup of Breast Cancer Patients with Favorable Outcome under Bevacizumab-Based Therapy—A Message from COMET, a French Unicancer Multicentric Study

2020 ◽  
Vol 13 (11) ◽  
pp. 414
Author(s):  
Jocelyn Gal ◽  
Gérard Milano ◽  
Patrick Brest ◽  
Nathalie Ebran ◽  
Julia Gilhodes ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Clinical Decision Making ◽  
Metastatic Breast ◽  
Clinical Decision ◽  
Risk Scores ◽  
Nucleotide Polymorphisms ◽  
Breast Cancer Patients ◽  
Survival Prognosis ◽  
Multicentric Study

The prospective multicenter COMET trial followed a cohort of 306 consecutive metastatic breast cancer patients receiving bevacizumab and paclitaxel as first-line chemotherapy. This study was intended to identify and validate reliable biomarkers to better predict bevacizumab treatment outcomes and allow for a more personalized use of this antiangiogenic agent. To that end, we aimed to establish risk scores for survival prognosis dichotomization based on classic clinico-pathological criteria combined or not with single nucleotide polymorphisms (SNPs). The genomic DNA of 306 patients was extracted and a panel of 13 SNPs, covering seven genes previously documented to be potentially involved in drug response, were analyzed by means of high-throughput genotyping. In receiver operating characteristic (ROC) analyses, the hazard model based on a triple-negative cancer phenotype variable, combined with specific SNPs in VEGFA (rs833061), VEGFR1 (rs9582036) and VEGFR2 (rs1870377), had the highest predictive value. The overall survival hazard ratio of patients assigned to the poor prognosis group based on this model was 3.21 (95% CI (2.33–4.42); p < 0.001). We propose that combining this pharmacogenetic approach with classical clinico-pathological characteristics could markedly improve clinical decision-making for breast cancer patients receiving bevacizumab-based therapy.

scholarly journals FDG-PET/CT for Response Monitoring in Metastatic Breast Cancer: The Feasibility and Benefits of Applying PERCIST

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 723
Author(s):  
Marianne Vogsen ◽  
Jakob Lykke Bülow ◽  
Lasse Ljungstrøm ◽  
Hjalte Rasmus Oltmann ◽  
Tural Asgharzadeh Alamdari ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Metastatic Breast Cancer ◽  
Fdg Pet ◽  
Clinical Decision Making ◽  
Metastatic Breast ◽  
Clinical Decision ◽  
Response Monitoring ◽  
Metastatic Lesions ◽  
Pet Ct ◽  
Fdg Pet Ct

Background: We aimed to examine the feasibility and potential benefit of applying PET Response Criteria in Solid Tumors (PERCIST) for response monitoring in metastatic breast cancer (MBC). Further, we introduced the nadir scan as a reference. Methods: Response monitoring FDG-PET/CT scans in 37 women with MBC were retrospectively screened for PERCIST standardization and measurability criteria. One-lesion PERCIST based on changes in SULpeak measurements of the hottest metastatic lesion was used for response categorization. The baseline (PERCISTbaseline) and the nadir scan (PERCISTnadir) were used as references for PERCIST analyses. Results: Metastatic lesions were measurable according to PERCIST in 35 of 37 (94.7%) patients. PERCIST was applied in 150 follow-up scans, with progression more frequently reported by PERCISTnadir (36%) than PERCISTbaseline (29.3%; p = 0.020). Reasons for progression were (a) more than 30% increase in SULpeak of the hottest lesion (n = 7, 15.9%), (b) detection of new metastatic lesions (n = 28, 63.6%), or both (a) and (b) (n = 9, 20.5%). Conclusions: PERCIST, with the introduction of PERCISTnadir, allows a graphical interpretation of disease fluctuation that may be beneficial in clinical decision-making regarding potential earlier termination of non-effective toxic treatment. PERCIST seems feasible for response monitoring in MBC but prospective studies are needed to come this closer.

scholarly journals Improved risk estimation of locoregional recurrence, secondary contralateral tumors and distant metastases in early breast cancer: the INFLUENCE 2.0 model

2021 ◽  
Author(s):  
Vinzenz Völkel ◽  
Tom A. Hueting ◽  
Teresa Draeger ◽  
Marissa C. van Maaren ◽  
Linda de Munck ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Locoregional Recurrence ◽  
Clinical Decision Making ◽  
Metastatic Breast ◽  
Distant Metastases ◽  
Time Dependent ◽  
Individual Risk ◽  
Primary Tumors ◽  
Statistical Approaches

Abstract Purpose To extend the functionality of the existing INFLUENCE nomogram for locoregional recurrence (LRR) of breast cancer toward the prediction of secondary primary tumors (SP) and distant metastases (DM) using updated follow-up data and the best suitable statistical approaches. Methods Data on women diagnosed with non-metastatic invasive breast cancer were derived from the Netherlands Cancer Registry (n = 13,494). To provide flexible time-dependent individual risk predictions for LRR, SP, and DM, three statistical approaches were assessed; a Cox proportional hazard approach (COX), a parametric spline approach (PAR), and a random survival forest (RSF). These approaches were evaluated on their discrimination using the Area Under the Curve (AUC) statistic and on calibration using the Integrated Calibration Index (ICI). To correct for optimism, the performance measures were assessed by drawing 200 bootstrap samples. Results Age, tumor grade, pT, pN, multifocality, type of surgery, hormonal receptor status, HER2-status, and adjuvant therapy were included as predictors. While all three approaches showed adequate calibration, the RSF approach offers the best optimism-corrected 5-year AUC for LRR (0.75, 95%CI: 0.74–0.76) and SP (0.67, 95%CI: 0.65–0.68). For the prediction of DM, all three approaches showed equivalent discrimination (5-year AUC: 0.77–0.78), while COX seems to have an advantage concerning calibration (ICI < 0.01). Finally, an online calculator of INFLUENCE 2.0 was created. Conclusions INFLUENCE 2.0 is a flexible model to predict time-dependent individual risks of LRR, SP and DM at a 5-year scale; it can support clinical decision-making regarding personalized follow-up strategies for curatively treated non-metastatic breast cancer patients.

scholarly journals Cancer Grade Model: a multi-gene machine learning-based risk classification for improving prognosis in breast cancer

2021 ◽  
Author(s):  
E. Amiri Souri ◽  
A. Chenoweth ◽  
A. Cheung ◽  
S. N. Karagiannis ◽  
S. Tsoka
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Clinical Decision Making ◽  
Histological Grade ◽  
Tumour Size ◽  
Clinical Decision ◽  
Gene Signature ◽  
Risk Classification ◽  
Breast Cancers ◽  
Grade 3

Abstract Background Prognostic stratification of breast cancers remains a challenge to improve clinical decision making. We employ machine learning on breast cancer transcriptomics from multiple studies to link the expression of specific genes to histological grade and classify tumours into a more or less aggressive prognostic type. Materials and methods Microarray data of 5031 untreated breast tumours spanning 33 published datasets and corresponding clinical data were integrated. A machine learning model based on gradient boosted trees was trained on histological grade-1 and grade-3 samples. The resulting predictive model (Cancer Grade Model, CGM) was applied on samples of grade-2 and unknown-grade (3029) for prognostic risk classification. Results A 70-gene signature for assessing clinical risk was identified and was shown to be 90% accurate when tested on known histological-grade samples. The predictive framework was validated through survival analysis and showed robust prognostic performance. CGM was cross-referenced with existing genomic tests and demonstrated the competitive predictive power of tumour risk. Conclusions CGM is able to classify tumours into better-defined prognostic categories without employing information on tumour size, stage, or subgroups. The model offers means to improve prognosis and support the clinical decision and precision treatments, thereby potentially contributing to preventing underdiagnosis of high-risk tumours and minimising over-treatment of low-risk disease.

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