Radiogenomic Analysis of Breast Cancer by Using B-Mode and Vascular US and RNA Sequencing

IMAGGS: a radiogenomic framework for identifying multi-way associations in breast cancer subtypes

Investigating correlations between radiomic and genomic profiling in breast cancer (BC) molecular subtypes is crucial for understanding disease mechanisms and providing personalized treatment. We present a well-designed radiogenomic framework image-gene-gene set (IMAGGS), which detects multi-way associations in BC subtypes by integrating radiomic and genomic features. Our dataset consists of 721 patients, each of whom has 12 ultrasound (US) images captured from different angles and gene mutation data. To better characterize tumor traits, 12 multi-angle US images are fused using two distinct strategies. Then, we analyze complex many-to-many associations between phenotypic and genotypic features using a machine learning algorithm, deviating from the prevalent one-to-one relationship pattern observed in previous studies. Key radiomic and genomic features are screened using these associations. In addition, gene set enrichment analysis is performed to investigate the joint effects of gene sets and delve deeper into the biological functions of BC subtypes. We further validate the feasibility of IMAGGS in a glioblastoma multiforme dataset to demonstrate the scalability of IMAGGS across different modalities and diseases. Taken together, IMAGGS provides a comprehensive characterization for diseases by associating imaging, genes, and gene sets, paving the way for biological interpretation of radiomics and development of targeted therapy.

Preclinical multi-physiologic monitoring of immediate-early responses to diverse treatment strategies in breast cancer by optoacoustic imaging

Optoacoustic imaging enables the measurement of tissue oxygen saturation (sO2) and blood perfusion while being utilized for detecting tumor microenvironments. Our aim was to employ multispectral optoacoustic tomography (MSOT) to assess immediate-early changes of hemoglobin level and sO2 within breast tumors during diverse treatments. Mouse breast cancer models were allocated into four groups: control, everolimus (EVE), paclitaxel (PTX), and photodynamic therapy (PDT). Hemoglobin was quantified daily, as well as sO2 and blood perfusion were verified by immunohistochemical (IHC) staining. MSOT showed a temporal window of enhanced oxygenation and improved perfusion in EVE and PTX groups, while sO2 consistently remained below baseline in PDT. The same results were obtained for the IHC. Therefore, MSOT can monitor tumor hypoxia and indirectly reflect blood perfusion in a non-invasive and non-labeled way, which has the potential to monitor breast cancer progression early and enable individualized treatment in clinical practice.

Unraveling the Pivotal Network of Ultrasound and Somatic Mutations in Triple-Negative and Non-Triple-Negative Breast Cancer

Purpose

The emergence of genomic targeted therapy has improved the prospects of treatment for breast cancer (BC). However, genetic testing relies on invasive and sophisticated procedures.

Patients and Methods

Here, we performed ultrasound (US) and target sequencing to unravel the possible association between US radiomics features and somatic mutations in TNBC (n=83) and non-TNBC (n=83) patients. Least absolute shrinkage and selection operator (Lasso) were utilized to perform radiomic feature selection. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was utilized to identify the signaling pathways associated with radiomic features.

Results

Thirteen differently represented radiomic features were identified in TNBC and non-TNBC, including tumor shape, textual, and intensity features. The US radiomic-gene pairs were differently exhibited between TNBC and non-TNBC. Further investigation with KEGG verified radiomic-pathway (ie, JAK-STAT, MAPK, Ras, Wnt, microRNAs in cancer, PI3K-Akt) associations in TNBC and non-TNBC.

Conclusion

The pivotal network provided the connections of US radiogenomic signature and target sequencing for non-invasive genetic assessment of precise BC treatment.

Role of qualitative contrast-enhanced ultrasound in the diagnosis of malignant breast lesions

Background

Carcinoma breast is the commonest cancer among women. Various authors have studied breast cancer with Contrast-Enhanced Ultrasound (CEUS) with promising results. Despite promising results, the additional cost of post-processing software limits its availability. In this study, we evaluated the utility of CEUS in differentiating malignant from benign breast lesions on regular ultrasound equipment without the use of dedicated software.

Methods

We performed CEUS in 121 women with 121 breast lesions. CEUS was done by creating a custom preset on existing ultrasound equipment with the help of an application specialist authorized by the vendor. Lesions were evaluated qualitatively without the use of any commercial software. The pattern of enhancement i.e. homogenous, heterogeneous, peripheral, or no enhancement, and the number of penetrating vessels i.e., few or multiple were recorded. Results were compared with histopathological diagnosis.

Results

There were a total of 121 breast lesions. The study showed sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 86.67 %, 54.10 %, 65 %, and 80.49% respectively for differentiating benign vs malignant lesions on the basis of the pattern of contrast enhancement. Using penetrating vessels for differentiating malignant lesions from benign lesions, the sensitivity, specificity, PPV, and NPV were found to be 64%, 67.86%, 78.05%, and 51.35% respectively.

Conclusion

CEUS is useful in differentiating malignant from benign breast lesions. It can be easily performed by creating a custom preset on standard ultrasound equipment without the use of expensive software.

Expanding Horizons: The Realities of CAD, the Promise of Artificial Intelligence, and Machine Learning's Role in Breast Imaging beyond Screening Mammography

Artificial intelligence (AI) applications in mammography have gained significant popular attention; however, AI has the potential to revolutionize other aspects of breast imaging beyond simple lesion detection. AI has the potential to enhance risk assessment by combining conventional factors with imaging and improve lesion detection through a comparison with prior studies and considerations of symmetry. It also holds promise in ultrasound analysis and automated whole breast ultrasound, areas marked by unique challenges. AI's potential utility also extends to administrative tasks such as MQSA compliance, scheduling, and protocoling, which can reduce the radiologists' workload. However, adoption in breast imaging faces limitations in terms of data quality and standardization, generalizability, benchmarking performance, and integration into clinical workflows. Developing methods for radiologists to interpret AI decisions, and understanding patient perspectives to build trust in AI results, will be key future endeavors, with the ultimate aim of fostering more efficient radiology practices and better patient care.

Association between vascular ultrasound features and DNA sequencing in breast cancer: a preliminary study

There are few radiogenomic studies to correlate ultrasound features of breast cancer with genomic changes. We investigated whether vascular ultrasound phenotypes are associated with breast cancer gene profiles for predicting angiogenesis and prognosis. We prospectively correlated quantitative and qualitative features of microvascular ultrasound (vascular index, vessel morphology, distribution, and penetrating vessel) and contrast-enhanced ultrasound (time-intensity curve parameters and enhancement pattern) with genomic characteristics in 31 breast cancers. DNA obtained from breast tumors and normal tissues were analyzed using targeted next-generation sequencing of 105 genes. The single-variant association test was used to identify correlations between vascular ultrasound features and genomic profiles. Chi-square analysis was used to detect single nucleotide polymorphisms (SNPs) associated with ultrasound features by estimating p values and odds ratios (ORs). Eight ultrasound features were significantly associated with 9 SNPs (p < 0.05). Among them, four ultrasound features were positively associated with 5 SNPs: high vascular index with rs1136201 in ERBB2 (p = 0.04, OR = 7.75); large area under the curve on contrast-enhanced ultrasound with rs35597368 in PDGFRA (p = 0.04, OR = 4.07); high peak intensity with rs35597368 in PDGFRA (p = 0.049, OR = 4.05) and rs2305948 in KDR (p = 0.04, OR = 5.10); and long mean transit time with rs2275237 in ARNT (p = 0.02, OR = 10.25) and rs755793 in FGFR2 (p = 0.02, OR = 10.25). We identified 198 non-silent SNPs in 71 various cancer-related genes. Vascular ultrasound features can reflect genomic changes associated with angiogenesis and prognosis in breast cancer.

A Focus on the Synergy of Radiomics and RNA Sequencing in Breast Cancer

Radiological imaging is currently employed as the most effective technique for screening, diagnosis, and follow up of patients with breast cancer (BC), the most common type of tumor in women worldwide. However, the introduction of the omics sciences such as metabolomics, proteomics, and molecular genomics, have optimized the therapeutic path for patients and implementing novel information parallel to the mutational asset targetable by specific clinical treatments. Parallel to the "omics" clusters, radiological imaging has been gradually employed to generate a specific omics cluster termed "radiomics". Radiomics is a novel advanced approach to imaging, extracting quantitative, and ideally, reproducible data from radiological images using sophisticated mathematical analysis, including disease-specific patterns, that could not be detected by the human eye. Along with radiomics, radiogenomics, defined as the integration of "radiology" and "genomics", is an emerging field exploring the relationship between specific features extracted from radiological images and genetic or molecular traits of a particular disease to construct adequate predictive models. Accordingly, radiological characteristics of the tissue are supposed to mimic a defined genotype and phenotype and to better explore the heterogeneity and the dynamic evolution of the tumor over the time. Despite such improvements, we are still far from achieving approved and standardized protocols in clinical practice. Nevertheless, what can we learn by this emerging multidisciplinary clinical approach? This minireview provides a focused overview on the significance of radiomics integrated by RNA sequencing in BC. We will also discuss advances and future challenges of such radiomics-based approach.

Ultrasonic Features and Molecular Subtype Predict Somatic Mutations in TP53 and PIK3CA Genes in Breast Cancer

RATIONALE AND OBJECTIVES

To predict mutations in TP53 and PIK3CA genes in breast cancer using ultrasound (US) signatures and clinicopathology.

MATERIALS AND METHODS

In this study, we developed and trained a model in 386 breast cancer patients to predict TP53 and PIK3CA mutations. The clinicopathological and US characteristics (including two-dimensional and color Doppler US) were investigated. Statistically significant variables were used to build predictive models, then a combined model was developed using the multivariate logistic regression analysis.

RESULTS

Univariate and multivariate analyses revealed that calcifications on US was an independent predictor of TP53 mutation (p < 0.05), whereas diameter on US and US type were independent predictors of PIK3CA mutation in breast cancer (all p < 0.05). Meanwhile, Luminal B/Human epidermal growth factor receptor two-positive (HER2+), HER2+/estrogen receptor-negative (ER-), and triple-negative breast cancer (TNBC) subtypes were strong predictors of TP53 mutation (odds ratio [OR] = 3.13, 3.18, 3.44, respectively, all p < 0.05). HER2+/ER- and TNBC subtypes were negative predictors of PIK3CA mutation (OR = 0.223, 0.241, respectively, all p < 0.05). The areas under curves (AUCs) for PIK3CA mutation in the training set increased from 0.553-0.610 to 0.741 in the multivariate model that combined US features and molecular subtype, with a sensitivity and specificity of 80.6% and 58.7%, respectively. The application of the multivariate model in the validation set achieved acceptable discrimination (AUC = 0.715). For TP53 mutation, the AUC was 0.653.

CONCLUSION

US is a non-invasive modality to recognize the presence of TP53 and PIK3CA mutation. The models combined with US features and molecular subtype have implications for the practical application of predicting gene mutation for individual decision-making regarding treatment planning.

Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer

Background

To investigate reliable associations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and gene expression characteristics in breast cancer (BC) and to develop and validate classifiers for predicting PAM50 subtypes and prognosis from DCE-MRI non-invasively.

Methods

Two radiogenomics cohorts with paired DCE-MRI and RNA-sequencing (RNA-seq) data were collected from local and public databases and divided into discovery (n = 174) and validation cohorts (n = 72). Six external datasets (n = 1,443) were used for prognostic validation. Spatial–temporal features of DCE-MRI were extracted, normalized properly, and associated with gene expression to identify the imaging features that can indicate subtypes and prognosis.

Results

Expression of genes including RBP4, MYBL2, and LINC00993 correlated significantly with DCE-MRI features (q-value < 0.05). Importantly, genes in the cell cycle pathway exhibited a significant association with imaging features (p-value < 0.001). With eight imaging-associated genes (CHEK1, TTK, CDC45, BUB1B, PLK1, E2F1, CDC20, and CDC25A), we developed a radiogenomics prognostic signature that can distinguish BC outcomes in multiple datasets well. High expression of the signature indicated a poor prognosis (p-values < 0.01). Based on DCE-MRI features, we established classifiers to predict BC clinical receptors, PAM50 subtypes, and prognostic gene sets. The imaging-based machine learning classifiers performed well in the independent dataset (areas under the receiver operating characteristic curve (AUCs) of 0.8361, 0.809, 0.7742, and 0.7277 for estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2)-enriched, basal-like, and obtained radiogenomics signature). Furthermore, we developed a prognostic model directly using DCE-MRI features (p-value < 0.0001).

Conclusions

Our results identified the DCE-MRI features that are robust and associated with the gene expression in BC and displayed the possibility of using the features to predict clinical receptors and PAM50 subtypes and to indicate BC prognosis.

The role of radiogenomics in the diagnosis of breast cancer: a systematic review

Background

One of the most common cancers diagnosed worldwide is breast cancer (BC), which is the leading cause of cancer death among women. The radiogenomics method is more accurate for managing and inhibiting this disease, which takes individual diagnosis on genes, environments, and lifestyles of each person. The present study aims to highlight the current state-of-the-art, the current role and limitations, and future directions of radiogenomics in breast cancer.

Method

This systematic review article was searched from databases such as Embase, PubMed, Web of Science, Google Scholar, Scopus, and Cochrane Library without any date or language limitations of databases. Searches were performed using Boolean OR and AND operators between the main terms and keywords of particular topic of the subject under investigation. All retrospective, prospective, cohort, and pilot studies were included, which were provided with more details about the topic. Articles such as letter to the editor, review, and short communications were excluded because of lack of information, discussions, or use of radiogenomics method on other cancers. For quality assessment of articles, STROBE checklist was used.

Result

For the systematic review, 18 articles were approved after assessing the full text of selected articles. In this review, 3614 patients with BC of selected articles were evaluated, and all radiogenomics were associated with more power in classification, differential diagnosis, and prognosis of BC. Among the various modalities to predict genomic indicators and molecular subtypes, DCE-MRI has the higher performance and finally the highest amount of AUC value (0.956) belonged to PI3K gene.

Conclusion

This review shows that radiogenomics can help with the diagnosis and treatment of breast cancer in patients. It has shown that recognizing and specifying radiogenomic phenotypes in the genomic signatures can be helpful in treatment and diagnosis of disease. The molecular methods used in these articles are limited to miRNAs expression, gene expression, Ki67 proliferation index, next-generation RNA sequencing, whole RNA sequencing, and molecular histopathology that can be completed in future studies by other methods such as exosomal miRNAs, specific proteins expression, DNA repair capacity, and other biomarkers that have prognostic and predictive value for cancer treatment response. Studies with control group and large sample size for evaluation of radiogenomics in diagnosis and treatment recommended.

Correlation between contrast-enhanced cone-beam breast computed tomography features and prognostic staging in breast cancer

OBJECTIVE

To evaluate whether contrast-enhanced cone-beam breast CT (CE-CBBCT) features can risk-stratify prognostic stage in breast cancer.

METHODS

Overall, 168 biopsy-proven breast cancer patients were analysed: 115 patients in the training set underwent scanning using v. 1.5 CE-CBBCT between August 2019 and December 2019, whereas 53 patients in the test set underwent scanning using v. 1.0 CE-CBBCT between May 2012 and August 2014. All patients were restaged according to the American Joint Committee on Cancer eighth edition prognostic staging system. Following the combination of CE-CBBCT imaging parameters and clinicopathological factors, predictors that were correlated with stratification of prognostic stage via logistic regression were analysed. Predictive performance was assessed according to the area under the receiver operating characteristic curve (AUC). Goodness-of-fit of the models was assessed using the Hosmer-Lemeshow test.

RESULTS

As regards differentiation between prognostic stage (PS) I and II/III, increased tumour-to-breast volume ratio (TBR), rim enhancement pattern, and the presence of penetrating vessels were significant predictors for PS II/III disease (p < 0.05). The AUCs in the training and test sets were 0.967 [95% confidence interval (CI) 0.938-0.996; p < 0.001] and 0.896 (95% CI, 0.809-0.983; p = 0.001), respectively. Two features were selected in the training set of PS II vs III, including tumour volume [odds ratio (OR)=1.817, p = 0.019] and calcification (OR = 4.600, p = 0.040), achieving an AUC of 0.790 (95% CI, 0.636-0.944, p = 0.001). However, there was no significant difference in the test set of PS II vs III (P＞0.05).

CONCLUSION

CE-CBBCT imaging biomarkers may provide a large amount of anatomical and radiobiological information for the pre-operative distinction of prognostic stage.

ADVANCES IN KNOWLEDGE

CE-CBBCT features have distinctive promise for stratification of prognostic stage in breast cancer.

Expression and prognostic impact of FZDs in pancreatic adenocarcinoma

Background

Despite the high number of researches on pancreatic adenocarcinoma (PAAD) over past decades, little progress had been made due to lack of effective treatment regimens. We aimed to investigate the expression level, mutation, and clinical significance of the Frizzled (FZD) family in PAAD so as to establish a sufficient scientific evidence for clinical decisions and risk management.

Methods

PAAD samples were extracted from The Cancer Genome Atlas (TCGA). Oncomine, Gene expression profiling interactive analysis (GEPIA), human protein atlas (HPA), Kaplan–Meier Plotter, cBioPortal, LinkedOmics, DAVID database, and R software (× 64 3.6.2) were used to comprehensively analyze the roles of FZDs. p value below to 0.05 was considered as significant difference.

Results

In total, 179 PAAD tissues and 171 paracancerous tissues were included. The expression levels of FZD1, 2, 6, 7, and 8 were higher in PAAD tissues than those in normal pancreatic tissue. The higher the expression levels of FZD2 and FZD7, the higher the clinical stage. The overall survival (OS) time was significantly different between low FZD3, 4, 5, 6, and 9 expression group and high expression group. Multivariable analysis showed that FZD3 and FZD6 were independent prognostic factors. The recurrence free survival (RFS) time was significantly different between low FZD4 and FZD8 expression group and high expression group. The RFS difference between low FZD6 expression group and high expression group had not reached statistical significance (p = 0.067), which might be due to the small sample size. However, multivariable analysis showed that FZD6 was the only independent factor for RFS. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that FZDs played a critical role in the Wnt signaling pathway, which was further confirmation that FZDs were transmembrane receptors of Wnt signaling pathway.

Conclusions

Our results strongly indicated a crucial role of the FZD family in PAAD. FZD3 and FZD6 could be potential prognostic and predictive markers, and FZD6 might also function as a potential therapeutic target in PAAD by blocking Wnt/β-catenin pathway.

Breast Ultrasound Microvascular Imaging and Radiogenomics

Microvascular ultrasound (US) techniques are advanced Doppler techniques that provide high sensitivity and spatial resolution for detailed visualization of low-flow vessels. Microvascular US imaging can be applied to breast lesion evaluation with or without US contrast agents. Microvascular US imaging without a contrast agent uses a sophisticated wall filtering system to selectively obtain low-flow Doppler signals from overlapped artifacts. Microvascular US imaging with second-generation contrast agents amplifies flow signals and makes them last longer, which facilitates hemodynamic evaluation of breast lesions. In this review article, we will introduce various microvascular US techniques, explain their clinical applications in breast cancer diagnosis and radiologic-histopathologic correlation, and provide a summary of a recent radiogenomic study using microvascular US.

Construction and Analysis of a Diagnostic Model Based on Differential Expression Genes in Patients With Major Depressive Disorder

Background: Major depressive disorder (MDD) is a common and severe psychiatric disorder with a heavy burden on the individual and society. However, the prevalence varies significantly owing to the lack of auxiliary diagnostic biomarkers. To identify the shared differential expression genes (DEGs) with potential diagnostic value in both the hippocampus and whole blood, a systematic and integrated bioinformatics analysis was carried out. Methods: Two datasets from the Gene Expression Omnibus database (GSE53987 and GSE98793) were downloaded and analyzed separately. A weighted gene co-expression network analysis was performed to construct the co-expression gene network of DEGs from GSE53987, and the most disease-related module was extracted. The shared DEGs from the module and GSE98793 were identified using a Venn diagram. Functional pathway prediction was used to identify the most disease-related DEGs. Finally, several DEGs were chosen, and their potential diagnostic value was determined by receiver operating characteristic curve analysis. Results: After weighted gene co-expression network analysis, the most MDD-related module (MEgrey) was identified, and 623 DEGs were extracted from this module. The intersection between MEgrey and GSE98793 was calculated, and 163 common DEGs were identified. The co-expression network of 163 DEGs from these was then reconstructed. All hub genes were identified based on the connective degree of the reconstructed co-expression network. Based on the results of functional pathway enrichment, 17 candidate hub genes were identified. Finally, logistic regression and receiver operating characteristic curves showed that three candidate hub genes (CEP350, SMAD5, and HSPG2) had relatively high auxiliary value in the diagnosis of MDD. Conclusion: Our results showed that the combination of CEP350, SMAD5, and HSPG2 has a relatively high diagnostic value for MDD. Pathway enrichment analysis also showed that these genes may play an important role in the pathogenesis of MDD. These results suggest a potentially important role for this gene combination in clinical practice.