Identifying ultrasound and clinical features of breast cancer molecular subtypes by ensemble decision.
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ABSTRACT:
SUBMITTER: Zhang L
PROVIDER: S-EPMC4457139 | biostudies-literature | 2015
REPOSITORIES: biostudies-literature
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Project description:BackgroundFeatures in preoperative ultrasound could predict the prognosis of triple-negative breast cancer (TNBC), while its prognostic value in other molecular subtypes of breast cancer (BC) was unknown. The study aimed to assess the prognostic value of preoperative sonographic features, including orientations, on long-term outcomes in BC and its association with different molecular subtypes.MethodsWomen diagnosed with invasive BC > 5 mm who underwent surgery were retrospectively reviewed. Clinical, pathological, and sonographic profiles were collected and recurrence-free survival (RFS) and breast cancer-specific survival (BCSS) were reported. Interactions between clinicopathological features and tumor orientations in predicting RFS and BCSS were analyzed. Competing risk model was performed to estimate prognostic values of sonographic features for RFS and BCSS.ResultsA total of 2812 patients were included. With a median follow-up of 60.0 months, 268 (9.5%) patients suffered from recurrences and 104 (3.7%) died of BC. The prognostic values of vertical orientation in predicting RFS (P = .001) and BCSS (P = .001) were strongly associated with molecular subtypes. Non-TNBC tumors with vertical orientation had less recurrence events compared with parallel orientation (6.3% vs 8.7%, P = .035), whereas failed to predict disease outcomes in multivariate analysis (P > .05). Oppositely, in TNBC, vertical orientation was associated with worse RFS (HR = 3.50; 95% confidence interval [CI] 1.69-7.24; P < .001) and BCSS (HR = 6.36; 95% CI 2.86-14.14; P < .001) in multivariate analysis with a 5-year RFS and BCSS of 73.4% and 74.6%. Meanwhile, vertical orientation was related with smaller tumor size (P < .001), human epidermal growth factor receptor 2 nonamplification (P < .001), and lower Ki-67 expression (P = .001) among non-TNBC population, whereas TNBC tumors with vertical orientation had a higher burden of axillary lymph node metastases (2.8 ± 1.0 vs 1.4 ± 0.2, P = .001).ConclusionsPrognostic values of sonographic orientation in predicting BC disease outcomes were associated with molecular subtypes. Vertical orientation in preoperative sonogram may serve as a prognostic biomarker for TNBC patients.
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Project description:PurposeThis work aims to identify differential metabolites and predicting molecular subtypes of breast cancer (BC).MethodsPlasma samples were collected from 96 BC patients and 79 normal participants. Metabolic profiles were determined by liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry based on multivariate statistical data analysis.ResultsWe observed 64 differential metabolites between BC and normal group. Compared to human epidermal growth factor receptor 2 (HER2)-negative patients, HER2-positive group showed elevated aerobic glycolysis, gluconeogenesis, and increased fatty acid biosynthesis with reduced Krebs cycle. Compared with estrogen receptor (ER)-negative group, ER-positive patients showed elevated alanine, aspartate and glutamate metabolism, decreased glycerolipid catabolism, and enhanced purine metabolism. A panel of 8 differential metabolites, including carnitine, lysophosphatidylcholine (20:4), proline, alanine, lysophosphatidylcholine (16:1), glycochenodeoxycholic acid, valine, and 2-octenedioic acid, was identified for the classification of BC subtypes. These markers showed potential diagnostic value with average area under the curve at 0.925 (95% CI 0.867-0.983) for the training set (n=51) and 0.893 (95% CI 0.847-0.939) for the test set (n=45).ConclusionHuman plasma metabolomics is useful in identifying differential metabolites and predicting breast cancer subtypes.
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Project description:The clinical impact of the biological heterogeneity within HER2-positive (HER2+) breast cancer is not fully understood. Here, we evaluated the molecular features and survival outcomes of the intrinsic subtypes within HER2+ breast cancer. We interrogated The Cancer Genome Atlas (n = 495) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets (n = 1730) of primary breast cancers for molecular data derived from DNA, RNA and protein, and determined intrinsic subtype. Clinical HER2 status was defined according to American Society of Clinical Oncology (ASCO)/College of American Pathologists (CAP) guidelines or DNA copy-number aberration by single nucleotide polymorphism arrays. Cox models tested the prognostic significance of each variable in patients not treated with trastuzumab (n = 1711). Compared with clinically HER2 (cHER2)-negative breast cancer, cHER2+ breast cancer had a higher frequency of the HER2-enriched (HER2E) subtype (47.0% vs 7.1%) and a lower frequency of Luminal A (10.7% vs 39.0%) and Basal-like (14.1% vs 23.4%) subtypes. The likelihood of cHER2-positivity in HER2E, Luminal B, Basal-like and Luminal A subtypes was 64.6%, 20.0%, 14.4% and 7.3%, respectively. Within each subtype, only 0.3% to 3.9% of genes were found differentially expressed between cHER2+ and cHER2-negative tumors. Within cHER2+ tumors, HER2 gene and protein expression was statistically significantly higher in the HER2E and Basal-like subtypes than either luminal subtype. Neither cHER2 status nor the new 10-subtype copy number-based classification system (IntClust) added independent prognostic value to intrinsic subtype. When the intrinsic subtypes are taken into account, cHER2-positivity does not translate into large changes in the expression of downstream signaling pathways, nor does it affect patient survival in the absence of HER2 targeting.
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Project description:Objective: Cervical cancer poses a remarkable health burden to females globally. Despite major advances in early detection and treatment modalities, some patients still relapse. The present study proposed a novel immune molecular classification that reflected distinct recurrent risk and therapeutic responses in cervical cancer. Methods: We retrospectively collected two cervical cancer cohorts: TCGA and GSE44001. Consensus clustering approach was conducted based on expression profiling of recurrence- and immune-related genes. The abundance of immune cells was inferred via five algorithms. Immune functions and signatures were quantified through ssGSEA. Genetic mutations were analyzed by maftools package. Immunotherapeutic response was inferred via tumor mutation burden (TMB), Tumor Immune Dysfunction and Exclusion (TIDE), and Submap methods. Finally, we developed a LASSO model for recurrence prediction. Results: Cervical cancer samples were categorized into two immune subtypes (IC1, and IC2). IC2 exhibited better disease free survival (DFS), increased immune cell infiltration within the immune microenvironment, higher expression of immune checkpoints, higher activity of immune-relevant pathways (APC co-inhibition and co-stimulation, inflammation-promoting, MHC class I, IFN response, leukocyte and stromal fractions, macrophage regulation, and TCR Shannon), and higher frequencies of genetic mutations. This molecular classification exhibited a remarkable difference with existing immune subtypes, with diverse PANoptosis (pyroptosis, apoptosis and necroptosis) features. Patients in IC2 were more likely to respond to immunotherapy and targeted, and chemotherapeutic agents. The immune subtype-relevant signature was quantified to predict patients' recurrence risk. Conclusion: Altogether, we developed an immune molecular classification, which can be utilized in clinical practice to aid decision-making on recurrence management.
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Project description:BackgroundBreast cancer is a common malignancy with varying clinical behaviors and for the more aggressive subtypes, novel and more efficient therapeutic approaches are needed. Qualities of the tumor microenvironment as well as cancer cell secretion have independently been associated with malignant clinical behaviors and a better understanding of the interplay between these two features could potentially reveal novel targetable key events linked to cancer progression.MethodsA newly developed human derived in vivo-like growth system, consisting of decellularized patient-derived scaffolds (PDSs) recellularized with standardized breast cancer cell lines (MCF7 and MDA-MB-231), were used to analyze how 63 individual patient specific microenvironments influenced secretion determined by proximity extension assays including 184 proteins and how these relate to clinical outcome.ResultsThe secretome from cancer cells in PDS cultures varied distinctly from cells grown as standard monolayers and besides a general increase in secretion from PDS cultures, several secreted proteins were only detectable in PDSs. Monolayer cells treated with conditioned media from PDS cultures, further showed increased mammosphere formation demonstrating a cancer stem cell activating function of the PDS culture induced secretion. The detailed secretomic profiles from MCF7s growing on 57 individual PDSs differed markedly but unsupervised clustering generated three separate groups having similar secretion profiles that significantly correlated to different clinical behaviors. The secretomic profile that associated with cancer relapse and high grade breast cancer showed induced secretion of the proteins IL-6, CCL2 and PAI-1, all linked to cancer stem cell activation, metastasis and priming of the pre-metastatic niche. Cancer promoting pathways such as "Suppress tumor immunity" and "Vascular and tissue remodeling" was also linked to this more malignant secretion cluster.ConclusionPDSs repopulated with cancer cells can be used to assess how cancer secretion is effected by specific and varying microenvironments. More malignant secretion patterns induced by specific patient based cancer microenvironments could further be identified pinpointing novel therapeutic opportunities targeting micro environmentally induced cancer progression via secretion of potent cytokines. Video abstract.
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Project description:BackgroundThis study aimed to explore whether there is an association between androgen receptor (AR) expression and ultrasound, clinicopathological features and prognosis of breast cancer.MethodsA total of 141 breast cancer patients were included in this retrospective study. AR expression was analyzed by immunohistochemistry. The images of B-mode, color Doppler and strain elastography from 104 patients were collected continuously, and the corresponding ultrasound characteristics were obtained. The differences in ultrasound and clinicopathological features in different AR status were analyzed. Progression-free survival (PFS) of patients was obtained through up to 90 months of follow-up; then, the effect of AR on PFS was analyzed. Subsequently, a nomogram was constructed to predict the AR status. The predictive accuracy was calculated using C-index.ResultsThe positive expression of AR (AR +) was associated with lower histological grade (p = 0.034) and lower Ki-67 level (p = 0.029). Triple-negative breast cancer (TNBC) had the lowest probability of AR + (p < 0.001). The AR + group mostly showed unsmooth margin (p < 0.001), posterior acoustic shadowing (p = 0.002) and higher elasticity score (p = 0.022) on ultrasound. The echo pattern of most tumors with AR + was heterogeneous (p = 0.024) in Luminal A subtype. AR + could be a sign of a better prognosis in overall breast cancer (p < 0.001), as well as in human epidermal growth factor receptor 2 (HER2) overexpression and Luminal B subtypes (p = 0.001 and 0.025). The nomogram showed relatively reliable performance with a C-index of 0.799.ConclusionOur research demonstrated that AR expression was closely related to ultrasound, clinicopathological features and prognosis of breast cancer.
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Project description:PurposeTo determine whether dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) characteristics of the breast tumor and background parenchyma can distinguish molecular subtypes (ie, luminal A/B or basal) of breast cancer.Materials and methodsIn all, 84 patients from one institution and 126 patients from The Cancer Genome Atlas (TCGA) were used for discovery and external validation, respectively. Thirty-five quantitative image features were extracted from DCE-MRI (1.5 or 3T) including morphology, texture, and volumetric features, which capture both tumor and background parenchymal enhancement (BPE) characteristics. Multiple testing was corrected using the Benjamini-Hochberg method to control the false-discovery rate (FDR). Sparse logistic regression models were built using the discovery cohort to distinguish each of the three studied molecular subtypes versus the rest, and the models were evaluated in the validation cohort.ResultsOn univariate analysis in discovery and validation cohorts, two features characterizing tumor and two characterizing BPE were statistically significant in separating luminal A versus nonluminal A cancers; two features characterizing tumor were statistically significant for separating luminal B; one feature characterizing tumor and one characterizing BPE reached statistical significance for distinguishing basal (Wilcoxon P < 0.05, FDR < 0.25). In discovery and validation cohorts, multivariate logistic regression models achieved an area under the receiver operator characteristic curve (AUC) of 0.71 and 0.73 for luminal A cancer, 0.67 and 0.69 for luminal B cancer, and 0.66 and 0.79 for basal cancer, respectively.ConclusionDCE-MRI characteristics of breast cancer and BPE may potentially be used to distinguish among molecular subtypes of breast cancer.Level of evidence3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017;46:1017-1027.
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