Panoptic View of Prognostic Models for Personalized Breast Cancer Management

A multi-omics method for breast cancer diagnosis based on metabolites in exhaled breath, ultrasound imaging, and basic clinical information

Background and aims

Through a nested cohort study, we evaluated the diagnostic performance of breath-omics in differentiating between benign and malignant breast lesions, and assessed the diagnostic performance of a multi-omics approach that combines breath-omics, ultrasound radiomics, and clinic-omics in distinguishing between benign and malignant breast lesions.

Materials and methods

We recruited 1,723 consecutive patients who underwent an automated breast volume scanner (ABVS) examination. Breath samples were collected and analyzed by high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOF-MS) to obtain breath-omics features. 238 of 1,723 enrolled participants have received pathological confirmation of breast nodules finally. The breast lesions of the 238 participants were contoured manually based on ABVS images for ultrasound radiomics feature calculation. Then, single- and multi-omics models were constructed and evaluated for breast nodules diagnosis via five-fold cross-validation.

Results

The area under the curve (AUC) of the breath-omics model was 0.855. In comparison, the multi-omics model demonstrated superior diagnostic performance for breast cancer, with sensitivity, specificity, and AUC of 84.1 %, 89.9 %, and 0.946, respectively. The multi-omics performance was comparable to that of the Breast Imaging Reporting and Data System (BI-RADS) classification via senior ultrasound physician evaluation.

Conclusion

The multi-omics approach combining metabolites in exhaled breath, ultrasound imaging, and basic clinical information exhibits superior diagnostic performance and promises to be a non-invasive and reliable tool for breast cancer diagnosis.

Deciphering the role of apoptosis signature on the immune dynamics and therapeutic prognosis in breast cancer: Implication for immunotherapy

Background: In breast cancer oncogenesis, the precise role of cell apoptosis holds untapped potential for prognostic and therapeutic insights. Thus, it is important to develop a model predicated for breast cancer patients' prognosis and immunotherapy response based on apoptosis-related signature. Methods: Our approach involved leveraging a training dataset from The Cancer Genome Atlas (TCGA) to construct an apoptosis-related gene prognostic model. The model's validity was then tested across several cohorts, including METABRIC, Sun Yat-sen Memorial Hospital Sun Yat-sen University (SYSMH), and IMvigor210, to ensure its applicability and robustness across different patient demographics and treatment scenarios. Furthermore, we utilized Quantitative Polymerase Chain Reaction (qPCR) analysis to explore the expression patterns of these model genes in breast cancer cell lines compared to immortalized mammary epithelial cell lines, aiming to confirm their differential expression and underline their significance in the context of breast cancer. Results: Through the development and validation of our prognostic model based on seven apoptosis-related genes, we have demonstrated its substantial predictive power for the survival outcomes of breast cancer patients. The model effectively stratified patients into high and low-risk categories, with high-risk patients showing significantly poorer overall survival in the training cohort and across all validation cohorts. Importantly, qPCR analysis confirmed that the genes constituting our model indeed exhibit differential expression in breast cancer cell lines when contrasted with immortalized mammary epithelial cell lines. Conclusion: Our study establishes a groundbreaking prognostic model using apoptosis-related genes to enhance the precision of breast cancer prognosis and treatment, particularly in predicting immunotherapy response.

Construction of a fatty acid metabolism-related gene signature for predicting prognosis and immune response in breast cancer

Background: Breast cancer has the highest incidence among malignant tumors in women, and its prevalence ranks first in global cancer morbidity. Aim: This study aimed to explore the feasibility of a prognostic model for patients with breast cancer based on the differential expression of genes related to fatty acid metabolism. Methods: The mRNA expression matrix of breast cancer and paracancer tissues was downloaded from The Cancer Genome Atlas database. The differentially expressed genes related to fatty acid metabolism were screened in R language. The TRRUST database was used to predict transcriptional regulators related to hub genes and construct an mRNA-transcription factor interaction network. A consensus clustering approach was used to identify different fatty acid regulatory patterns. In combination with patient survival data, Lasso and multivariate Cox proportional risk regression models were used to establish polygenic prognostic models based on fatty acid metabolism. The median risk score was used to categorize patients into high- and low-risk groups. Kaplan-Meier survival curves were used to analyze the survival differences between both groups. The Cox regression analysis included risk score and clinicopathological factors to determine whether risk score was an independent risk factor. Models based on genes associated with fatty acid metabolism were evaluated using receiver operating characteristic curves. A comparison was made between risk score levels and the fatty acid metabolism-associated genes in different subtypes of breast cancer. The differential gene sets of the Kyoto Encyclopedia of Genes and Genomes for screening high- and low-risk populations were compared using a gene set enrichment analysis. Furthermore, we utilized CIBERSORT to examine the abundance of immune cells in breast cancer in different clustering models. Results: High expression levels of ALDH1A1 and UBE2L6 prevented breast cancer, whereas high RDH16 expression levels increased its risk. Our comprehensive assessment of the association between prognostic risk scoring models and tumor microenvironment characteristics showed significant differences in the abundance of various immune cells between high- and low-risk breast cancer patients. Conclusions: By assessing fatty acid metabolism patterns, we gained a better understanding of the infiltration characteristics of the tumor microenvironment. Our findings are valuable for prognosis prediction and treatment of patients with breast cancer based on their clinicopathological characteristics.

Ultrasound radiomics in personalized breast management: Current status and future prospects

Breast cancer is the most common cancer in women worldwide. Providing accurate and efficient diagnosis, risk stratification and timely adjustment of treatment strategies are essential steps in achieving precision medicine before, during and after treatment. Radiomics provides image information that cannot be recognized by the naked eye through deep mining of medical images. Several studies have shown that radiomics, as a second reader of medical images, can assist physicians not only in the detection and diagnosis of breast lesions but also in the assessment of risk stratification and prediction of treatment response. Recently, more and more studies have focused on the application of ultrasound radiomics in breast management. We summarized recent research advances in ultrasound radiomics for the diagnosis of benign and malignant breast lesions, prediction of molecular subtype, assessment of lymph node status, prediction of neoadjuvant chemotherapy response, and prediction of survival. In addition, we discuss the current challenges and future prospects of ultrasound radiomics.

Biomarker discovery studies for patient stratification using machine learning analysis of omics data: a scoping review

OBJECTIVE

To review biomarker discovery studies using omics data for patient stratification which led to clinically validated FDA-cleared tests or laboratory developed tests, in order to identify common characteristics and derive recommendations for future biomarker projects.

DESIGN

Scoping review.

METHODS

We searched PubMed, EMBASE and Web of Science to obtain a comprehensive list of articles from the biomedical literature published between January 2000 and July 2021, describing clinically validated biomarker signatures for patient stratification, derived using statistical learning approaches. All documents were screened to retain only peer-reviewed research articles, review articles or opinion articles, covering supervised and unsupervised machine learning applications for omics-based patient stratification. Two reviewers independently confirmed the eligibility. Disagreements were solved by consensus. We focused the final analysis on omics-based biomarkers which achieved the highest level of validation, that is, clinical approval of the developed molecular signature as a laboratory developed test or FDA approved tests.

RESULTS

Overall, 352 articles fulfilled the eligibility criteria. The analysis of validated biomarker signatures identified multiple common methodological and practical features that may explain the successful test development and guide future biomarker projects. These include study design choices to ensure sufficient statistical power for model building and external testing, suitable combinations of non-targeted and targeted measurement technologies, the integration of prior biological knowledge, strict filtering and inclusion/exclusion criteria, and the adequacy of statistical and machine learning methods for discovery and validation.

CONCLUSIONS

While most clinically validated biomarker models derived from omics data have been developed for personalised oncology, first applications for non-cancer diseases show the potential of multivariate omics biomarker design for other complex disorders. Distinctive characteristics of prior success stories, such as early filtering and robust discovery approaches, continuous improvements in assay design and experimental measurement technology, and rigorous multicohort validation approaches, enable the derivation of specific recommendations for future studies.

Success Factors and Barriers in Combining Personalized Medicine and Patient Centered Care in Breast Cancer. Results from a Systematic Review and Proposal of Conceptual Framework

Breast Cancer (BC) is the leading cause of death due to cancer in women. Ensuring equitable, quality-assured and effective care has increased the complexity of BC management. This systematic review reports on the state-of-the art of available literature investigating the enactment of personalized treatment and patient-centered care models in BC clinical practice, building a framework for the delivery of personalized BC care within a Patient-Centered model. Databases were searched for articles (from the inception to December 2020) reporting on Patient-Centered or Personalized Medicine BC management models, assessing success factors or limits. Out of 1885 records, 25 studies were included in our analysis. The main success factors include clearly defined roles and responsibilities within a multi-professional collaboration, appropriate training programs and adequate communication strategies and adopting a universal genomic language to improve patients' involvement in the decision-making process. Among detected barriers, delays in the use of genetic testing were linked to the lack of public reimbursement schemes and of clear indications in timing and appropriateness. Overall, both care approaches are complementary and necessary to effectively improve BC patient management. Our framework attempts to bridge the gap in assigning a central role played by shared decision-making, still scarcely investigated in literature.

New Biomarkers in Cancers

In this Special Issue of Cancers, the latest insights on biomarkers in cancers are presented in 33 up-to-the-minute research papers and reviews summing up the tremendous progress in this interesting and important field of research. The recent development of new therapeutic approaches has provided clinicians with more efficient tools than ever before for the treatment of cancerous diseases. However, choosing the right option requires to [...].

Prognostic and clinical significance of focal adhesion kinase expression in breast cancer: A systematic review and meta-analysis

Background

The prognostic significance of focal adhesion kinase (FAK) in breast cancer remains controversial. Here, we conducted a meta-analysis to explore the prognostic value of FAK expression in breast cancer.

Materials and methods

Possible prognostic significance of protein or mRNA expression of FAK in breast cancer was investigated with searches of electronic databases for relevant publications. Pooled hazard ratios (HRs) and odds ratios (ORs) with 95% confidence intervals (CIs) were extracted from eligible studies.

Results

A total of eight eligible studies which included 2604 participants were analyzed in this meta-analysis. Increased expression of FAK protein was found to significantly correlate with shorter overall survival (OS) (HR = 1.43, 95% CI: 1.12–1.83; P = 0.004), and not with disease-free survival (HR = 1.31, 95% CI: 0.92–1.85; P = 0.14). Elevated FAK protein expression was also associated with negative estrogen receptor (ER) expression (OR, 1.34; 95% CI, 1.06–1.68; P = 0.01), negative progesterone receptor (PR) expression (OR, 1.54; 95% CI, 1.22–1.93; P < 0.001), positive human epidermal growth factor receptor 2 (HER2) expression (OR, 1.64; 95% CI, 1.28–2.09; P < 0.001), triple-negative breast cancer (TNBC) (OR, 1.57; 95% CI, 1.14–2.17; P = 0.006), high nuclear grade (OR, 1.70; 95% CI, 1.05–2.78; P = 0.03), high Ki-67 expression level (OR, 2.87; 95% CI, 1.94–4.24; P < 0.001), and positive p53 status (OR, 2.28; 95% CI, 1.58–3.29; P < 0.001).

Conclusion

Our meta-analysis identifies an association between increased FAK protein expression and worse OS among breast cancer patients. Moreover, enhanced FAK expression is associated with negative ER expression, negative PR expression, positive HER2 expression, TNBC, high nuclear grade, high Ki-67 expression level, and positive p53 status in breast carcinoma.

Development and validation of a hypoxia-related prognostic signature for breast cancer

Hypoxia, an important component of the tumor microenvironment, plays a crucial role in the occurrence and progression of cancer. However, to the best of our knowledge, a systematic analysis of a hypoxia-related prognostic signature for breast cancer is lacking and is urgently required. Therefore, in the present study, RNA-seq data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and served as a discovery cohort. Cox proportional hazards regression analysis was performed to construct a 14-gene prognostic signature (PFKL, P4HA2, GRHPR, SDC3, PPP1R15A, SIAH2, NDRG1, BTG1, TPD52, MAFF, ISG20, LALBA, ERRFI1 and VHL). The hypoxia-related signature successfully predicted survival outcomes of the discovery cohort (P<0.001 for the TCGA dataset). Three independent Gene Expression Omnibus databases (GSE10886, GSE20685 and GSE96058) were used as validation cohorts to verify the value of the predictive signature (P=0.007 for GSE10886, P=0.021 for GSE20685, P<0.001 for GSE96058). In the present study, a robust predictive signature was developed for patients with breast cancer, and the findings revealed that the 14-gene hypoxia-related signature could serve as a potential prognostic biomarker for breast cancer.