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

Homologous recombination deficiency reflects the heterogeneity and monitoring treatment response for patients with breast cancer

BACKGROUND

In breast cancer (BC), homologous recombination defect (HRD) is a common carcinogenic mechanism. It is meaningful to classify BC according to HRD biomarkers and to develop a platform for identifying BC molecular features, pathological features and therapeutic responses.

METHODS

In total, 109 HRD genes were collected and screened by univariate Cox regression analysis to determine the prognostic genes, which were used to construct a consensus matrix to identify BC subtype. Differentially expressed genes (DEGs) were filtered by the Limma package and screened by random forest analysis to build a model to analyze the immunotherapy response and sensitivity and prognosis of patients suffering from BC to different drugs.

RESULTS

Thirteen out of 109 HRD genes were prognostic genes of BC, and BC was classified into two subgroups based on their expression. Cluster 1 had a significantly backward survival outcome and a significantly higher adaptive immunity score relative to cluster 2. Six genes were identified by random forest analysis as factors for developing the model. The model provided a prediction called risk score, which showed a significant stratification effect on BC prognosis, immunotherapy response and IC50 values of 62 drugs.

CONCLUSIONS

In the present study, two HRD subtypes of BC were successfully identified, for which mutation and immunological features were determined. A model based on differential genes of HRD subtypes was established, which was a potential predictor of prognosis, immunotherapy response and drug sensitivity of BC.

Axial Skeleton Radiomics of 18F-FDG PET/CT: Impact on Event-Free Survival Prediction in High-Risk Pediatric Neuroblastoma

OBJECTIVES

To construct and validate a combined model based on axial skeleton radiomics of 18F-FDG PET/CT for predicting event-free survival in high-risk pediatric neuroblastoma patients.

MATERIALS AND METHODS

Eighty-seven high-risk neuroblastoma patients were retrospectively enrolled in this study and randomized in a 7:3 ratio to the training and validation cohorts. The radiomics model was constructed using radiomics features that were extracted from the axial skeleton. A univariate Cox regression analysis was then performed to screen clinical risk factors associated with event-free survival for building clinical model. Radiomics features and clinical risk factors were incorporated to construct the combined model for predicting the event-free survival in high-risk neuroblastoma patients. The performance of the models was evaluated by the C-index.

RESULTS

Eighteen radiomics features were selected to build the radiomics model. The radiomics model achieved better event-free survival prediction than the clinical model in the training cohort (C-index: 0.846 vs. 0.612) and validation cohort (C-index: 0.754 vs. 0.579). The combined model achieved the best prognostic prediction performance with a C-index of 0.863 and 0.799 in the training and validation cohorts, respectively.

CONCLUSION

The combined model integrating radiomics features and clinical risk factors showed more accurate predictive performance for event-free survival in high-risk pediatric neuroblastoma patients, which helps to design individualized treatment strategies and regular follow-ups.

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.

An omics-to-omics joint knowledge association subtensor model for radiogenomics cross-modal modules from genomics and ultrasonic images of breast cancers

The radiogenomics analysis can provide the connections between genomics and radiomics, which can infer the genomic features of tumors from their radiogenomic associations through the low-cost and non-invasiveness screening ultrasonic images. Although there are a number of pioneer approaches exploring the connections between genomic aberrations and ultrasonic features, these studies mainly focus on the relationship between ultrasonic features and only the most popular cancer genes, confronting two difficulties: missing many-to-many relationships as omics-to-omics view, and confounding group-specific associations with whole sample associations. To overcome the difficulty of omics-to-omics view and the issue of tumor heterogeneity, we propose an omics-to-omics joint knowledge association subtensor model. Specifically, the subtensor factorization framework can successfully discover the joint cross-modal module via an omics-to-omics view, while the sparse weight sample indication strategy can mine sample subgroups from the multi-omic data with tumor heterogeneity. The experimental evaluation result shows the jointness of the discovered modules across omics, their association with tumorigenesis contribution, and their relation for cancer related functions. In summary, our proposed omics-to-omics joint knowledge association subtensor model can serve as an efficient tool for radiogenomic knowledge associations, promoting the cross-modal knowledge graph construction of in explainable artificial intelligence cancer diagnosis.