Clinical Implications of Transcriptomic Changes After Neoadjuvant Chemotherapy in Patients with Triple-Negative Breast Cancer

Role of machine learning in molecular pathology for breast cancer: A review on gene expression profiling and RNA sequencing application

INTRODUCTION

Breast cancer is the most prevalent cancer among women, with growing incidence and mortality rates. Regardless of remarkable progress in cancer research, breast cancer remains a major concern due to its complex nature. These factors underscore the necessity of innovative research and diagnostic tools. Attention to gene signatures and biotechnology methods have shown significant performance in the diagnosis and management of breast cancer. Currently, artificial intelligence (AI) is known as a revolutionary tool to analyze data, identify biomarkers, and enrich diagnostic and prognostic accuracy. Therefore, the integration of breast cancer datasets with artificial intelligence can play a crucial role in the control of breast cancer. This review explores advanced machine learning techniques to analyze transcriptomic data while focusing on breast cancer subtype classification and its potential impact and limitations.

METHOD

A comprehensive literature search was performed in PubMed, Scopus, WoS, Embase, and IEEE Xplore. Duplicates were removed, two reviewers screened articles, and two additional reviewers resolved conflicts. Data extraction included details on molecular methods, AI techniques, clinical targets, study populations, and data analysis methods which were used to categorize relevant studies into RNA sequencing and gene expression profiling groups.

RESULT

In the initial stage, 7287 articles were identified, and 54 were retained following further screening, 24 in RNA sequencing and 30 in gene expression profiling. A review of these studies showed how artificial intelligence is advancing breast cancer research by using RNA sequencing and gene expression profiling. AI algorithms, including Random Forest, CNNs, SVMs, and LASSO, were the most applied techniques that showed significant potential to identify biomarkers, prognostic survival, and optimize drug responses to manage breast cancer.

CONCLUSION

The methods of artificial intelligence hold very great potential for change in the field of breast cancer. This promising progress can be seen in every aspect including diagnosis, prognosis, and treatment. However, it is important to note that we are still in the early stages of progress, and larger-scale studies and interdisciplinary collaborations in this field are needed.

Transcriptome Meta-Analysis of Triple-Negative Breast Cancer Response to Neoadjuvant Chemotherapy

Triple-negative breast cancer (TNBC) is a heterogeneous disease with varying responses to neoadjuvant chemotherapy (NAC). The identification of biomarkers to predict NAC response and inform personalized treatment strategies is essential. In this study, we conducted large-scale gene expression meta-analyses to identify genes associated with NAC response and survival outcomes. The results showed that immune, cell cycle/mitotic, and RNA splicing-related pathways were significantly associated with favorable clinical outcomes. Furthermore, we integrated and divided the gene association results from NAC response and survival outcomes into four quadrants, which provided more insights into potential NAC response mechanisms and biomarker discovery.

Triple-Negative Breast Cancer and Predictive Markers of Response to Neoadjuvant Chemotherapy: A Systematic Review

Around 40-50% of all triple-negative breast cancer (TNBC) patients achieve a pathological complete response (pCR) after treatment with neoadjuvant chemotherapy (NAC). The identification of biomarkers predicting the response to NAC could be helpful for personalized treatment. This systematic review provides an overview of putative biomarkers at baseline that are predictive for a pCR following NAC. Embase, Medline and Web of Science were searched for articles published between January 2010 and August 2022. The articles had to meet the following criteria: patients with primary invasive TNBC without distant metastases and patients must have received NAC. In total, 2045 articles were screened by two reviewers resulting in the inclusion of 92 articles. Overall, the most frequently reported biomarkers associated with a pCR were a high expression of Ki-67, an expression of PD-L1 and the abundance of tumor-infiltrating lymphocytes, particularly CD8+ T cells, and corresponding immune gene signatures. In addition, our review reveals proteomic, genomic and transcriptomic markers that relate to cancer cells, the tumor microenvironment and the peripheral blood, which also affect chemo-sensitivity. We conclude that a prediction model based on a combination of tumor and immune markers is likely to better stratify TNBC patients with respect to NAC response.

Research perspectives-Pipelines to human tendon transcriptomics

Tendon transcriptomics is a rapidly growing field in musculoskeletal biology. The ultimate aim of many current tendon transcriptomic studies is characterization of in vitro, ex vivo, or in vivo, healthy, and diseased tendon microenvironments to identify the underlying pathways driving human tendon pathology. The transcriptome interfaces between genomic, proteomic, and metabolomic signatures of the tendon cellular niche and the response of this niche to stimuli. Some of the greatest bottlenecks in tendon transcriptomics relate to the availability and quality of human tendon tissue, hence animal tissues are frequently used even though human tissue is most translationally relevant. Here, we review the variability associated with human donor and procurement factors, such as whether the tendon is cadaveric or a clinical remnant, and how these variables affect the quality and relevance of the transcriptomes obtained. Moreover, age, sex, and health demographic variables impact the human tendon transcriptome. Tendons present tissue-specific challenges for cell, nuclei, and RNA extraction that include a dense extracellular matrix, low cellularity, and therefore low RNA yield of variable quality. Consideration of these factors is particularly important for single-cell and single-nuclei resolution transcriptomics due to the necessity for unbiased and representative cell or nuclei populations. Different cell, nuclei, and RNA extraction methods, library preparation, and quality control methods are used by the tendon research community and attention should be paid to these when designing and reporting studies. We discuss the different components and challenges of human tendon transcriptomics, and propose pipelines, quality control, and reporting guidelines for future work in the field.

Is the 21-Gene Recurrence Score on Core Needle Biopsy Equivalent to Surgical Specimen in Early-Stage Breast Cancer? A Comparison of Gene Expression Between Paired Core Needle Biopsy and Surgical Specimens

BACKGROUND

Molecular testing on surgical specimens predicts disease recurrence and benefit of adjuvant chemotherapy in hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) early-stage breast cancer (EBC). Testing on core biopsies has become common practice despite limited evidence of concordance between core/surgical samples. In this study, we compared the gene expression of the 21 genes and the recurrence score (RS) between paired core/surgical specimens.

METHODS

Eighty patients with HR+/HER2- EBC were evaluated from two publicly available gene expression datasets (GSE73235, GSE76728) with paired core/surgical specimens without neoadjuvant systemic therapy. The expression of the 21 genes was compared in paired samples. A microarray-based RS was calculated and a value ≥ 26 was defined as high-RS. The concordance rate and kappa statistic were used to evaluate the agreement between the RS of paired samples.

RESULTS

Overall, there was no significant difference and a high correlation in the gene expression levels of the 21 genes between paired samples. However, CD68 and RPLP0 in GSE73235, AURKA, BAG1, and TFRC in GSE76728, and MYLBL2 and ACTB in both datasets exhibited weak to moderate correlation (r < 0.5). There was a high correlation of the microarray-based RS between paired samples in GSE76728 (r = 0.91, 95% confidence interval [CI] 0.81-0.96) and GSE73235 (r = 0.82, 95% CI 0.71-0.89). There were no changes in RS category in GSE76728, whereas 82% of patients remained in the same RS category in GSE73235 (κ = 0.64).

CONCLUSIONS

Gene expression levels of the 21-gene RS showed a high correlation between paired specimens. Potential sampling and biological variability on a set of genes need to be considered to better estimate the RS from core needle biopsy.