Gene-expression signatures in breast cancer

Nucleolar casein kinase 2 alpha as a prognostic factor in patients with surgically resected early‑stage lung adenocarcinoma

Lung cancer remains a leading cause of global cancer‑related deaths, therefore the identification of prognostic factors for lung cancer is critical. Casein kinase 2 alpha (CK2α) is one of the driver kinases in various cancers, and it was previously demonstrated that CK2α localization was associated with a poor prognosis in invasive breast cancer. In the present study, the importance of CK2α in the nucleolus was explored as a potential prognostic marker for surgically resected early‑stage lung adenocarcinoma. The present study included 118 patients who underwent pulmonary lobectomy between 2014 and 2018 in Fukushima Medical University Hospital (Fukushima, Japan), and in whom CK2α localization in tumor samples was assessed by immunohistochemistry. Patient and tumor characteristics, including pathological stage, histological type and histological grade, were analyzed. Recurrence‑free survival (RFS) and overall survival were evaluated in relation to nucleolar CK2α staining. CK2α staining in the nucleoli was observed in 50.8% of lung adenocarcinoma tumors. Positive nucleolar CK2α staining was independent of pathological stage, histological type and histological grade. Patients with positive nucleolar CK2α staining exhibited significantly worse RFS compared with patients with negative staining. Multivariate analysis identified nucleolar CK2α staining and lymph node metastasis as independent poor prognostic factors. The results of the present study suggested that nucleolar CK2α staining is a novel and independent prognostic factor in surgically resected early‑stage lung adenocarcinoma. These findings indicated the potential of nucleolar CK2α as a predictive biomarker for future recurrence, and a guide to treatment decisions. Further research is required, particularly in understanding the molecular mechanisms linking nucleolar CK2α to recurrence.

Detection of Circulating Tumor Cells in the Prognostic Significance of Patients With Breast Cancer: A Retrospective Study

BACKGROUND

Breast cancer (BC) is an aggressive tumor. Circulating tumor cells (CTCs) are a potential biomarker for the prognosis of cancer patients. This study aimed to explore the prognostic significance of CTCs in patients with BC.

METHODS

Retrospectively, 108 BC patients were collected between January 2011 and December 2019, while 10 patients with benign nodules were included as controls. CTCs with different phenotypes of patients were isolated using CanPatrol and tricolor RNA in situ hybridization (RNA-ISH) methods. Estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER-2) levels were measured by immunohistochemistry (IHC). The progression-free survival (PFS) was calculated using the Kaplan-Meier method. Independent risk factors for BC recurrence were determined by Cox proportional risk regression analysis.

RESULTS

The higher the cancer stage (p = 0.00), the higher the ki-67 expression level (p < 0.01), and the lower the histologic grade (p < 0.01), the higher the number of CTCs. The PFS of patients with high CTCs was shorter than that of patients with low CTCs (p < 0.05). Total CTCs (≥ 6) and positive mesenchymal CTCs (MCTCs) were also associated with recurrence and metastasis.

CONCLUSIONS

Total CTCs in BC patients have an independent influence on PFS reduction. Higher total CTCs and MCTCs in peripheral blood are biomarkers for predicting the prognosis of BC patients. HER-2 high expression is also associated with the prognosis of the disease.

Single-Sample Networks Reveal Intra-Cytoband Co-Expression Hotspots in Breast Cancer Subtypes

Breast cancer is a heterogeneous disease comprising various subtypes with distinct molecular characteristics, clinical outcomes, and therapeutic responses. This heterogeneity evidences significant challenges for diagnosis, prognosis, and treatment. Traditional genomic co-expression network analyses often overlook individual-specific interactions critical for personalized medicine. In this study, we employed single-sample gene co-expression network analysis to investigate the structural and functional genomic alterations across breast cancer subtypes (Luminal A, Luminal B, Her2-enriched, and Basal-like) and compared them with normal breast tissue. We utilized RNA-Seq gene expression data to infer gene co-expression networks. The LIONESS algorithm allowed us to construct individual networks for each patient, capturing unique co-expression patterns. We focused on the top 10,000 gene interactions to ensure consistency and robustness in our analysis. Network metrics were calculated to characterize the topological properties of both aggregated and single-sample networks. Our findings reveal significant fragmentation in the co-expression networks of breast cancer subtypes, marked by a change from interchromosomal (TRANS) to intrachromosomal (CIS) interactions. This transition indicates disrupted long-range genomic communication, leading to localized genomic regulation and increased genomic instability. Single-sample analyses confirmed that these patterns are consistent at the individual level, highlighting the molecular heterogeneity of breast cancer. Despite these pronounced alterations, the proportion of CIS interactions did not significantly correlate with patient survival outcomes across subtypes, suggesting limited prognostic value. Furthermore, we identified high-degree genes and critical cytobands specific to each subtype, providing insights into subtype-specific regulatory networks and potential therapeutic targets. These genes play pivotal roles in oncogenic processes and may represent important keys for targeted interventions. The application of single-sample co-expression network analysis proves to be a powerful tool for uncovering individual-specific genomic interactions.

NSUN2-mediated RNA methylation: Molecular mechanisms and clinical relevance in cancer

Cancer remains a leading cause of morbidity and mortality worldwide, necessitating the ongoing investigation of molecular targets for improved diagnosis, prognosis, and therapy. Among these targets, RNA modifications, particularly N5-methylcytosine (m5C) in RNA, have emerged as critical regulators of gene expression and cellular functions. NOP2/Sun RNA methyltransferase family member 2 (NSUN2) is a key enzyme in m5C modification, significantly influencing various biological processes and tumorigenesis. NSUN2 methylates multiple RNA species, including transfer RNAs (tRNAs), messenger RNAs (mRNAs), and non-coding RNAs, impacting RNA stability, translation efficiency, and cellular stress responses. These modifications, in turn, affect cell proliferation, differentiation, and survival. In cancer, NSUN2 is frequently upregulated, associated with aggressive tumor phenotypes, poor prognosis, and therapy resistance. Its role in oncogenic signaling pathways further underscores its importance in cancer biology. This review offers a comprehensive overview of NSUN2's role in cancer, focusing on its involvement in RNA methylation and its implications for tumor initiation and progression. Additionally, we explore the potential of NSUN2 as a biomarker for cancer diagnosis and prognosis, and its promise as a therapeutic target.

Prognostic value of the 21-Gene Breast Recurrence Score® assay for hormone receptor-positive/human epidermal growth factor 2-negative advanced breast cancer: subanalysis from Japan Breast Cancer Research Group-M07 (FUTURE trial)

PURPOSE

This study aimed to determine whether the 21-Gene Breast Recurrence Score® assay from primary breast tissue predicts the prognosis of patients with hormone receptor-positive and human epidermal growth factor 2-negative advanced breast cancers (ABCs) treated with fulvestrant monotherapy (Group A) and the addition of palbociclib combined with fulvestrant (Group B), which included those who had progression in Group A from the Japan Breast Cancer Research Group-M07 (FUTURE trial).

METHODS

Progression-free survival (PFS) and overall survival (OS) were compared using the log-rank test and Cox regression analysis based on original recurrence score (RS) categories (Low: 0-17, Intermediate: 18-30, High: 31-100) by treatment groups (A and B) and types of ABCs (recurrence and de novo stage IV).

RESULTS

In total, 102 patients [Low: n = 44 (43.1%), Intermediate: n = 38 (37.5%), High: n = 20 (19.6%)] in Group A, and 45 in Group B, who had progression in Group A were analyzed. The median follow-up time was 23.8 months for Group A and 8.9 months for Group B. Multivariate analysis in Group A showed that low-risk [hazard ratio (HR) 0.15, 95% confidence interval (CI) 0.04-0.53, P = 0.003] and intermediate-risk (HR 0.22, 95% CI 0.06-0.78) with de novo stage IV breast cancer were significantly associated with better prognosis compared to high-risk. However, no significant difference was observed among patients with recurrence. No prognostic significance was observed in Group B.

CONCLUSION

We found a distinct prognostic value of the 21-Gene Breast Recurrence Score® assay by the types of ABCs and a poor prognostic value of the high RS for patients with de novo stage IV BC treated with fulvestrant monotherapy. Further validations of these findings are required.

SNHG15 Mediates MTSS1 Gene Expression via Interacting with the Gene Promoter and Regulating Transcription Pausing

Metastasis suppressor 1 (MTSS1) has been reported to play important roles in suppressing cancer progression. In this study, we investigated the underlying mechanism that regulates MTSS1 expression. We showed that in breast cancer cells, lncRNA-SNHG15-induced cell invasion and proliferation was accompanied with the decreased expression of MTSS1 mRNA. Further study revealed that SNHG15 mediated MTSS1 repression through blocking its promoter activity. Mechanistically, SNHG15 complexes with DDX5 and RTF1 and interacts with the core promoter of the MTSS1 gene to interfere with RNA-Pol-II-directed transcriptional initiation. Association with DDX5 stabilizes SNHG15 while binding to RTF1 allows SNHG15 to carry RTF1 to the core promoter, where RTF1 forms a complex with PNA pl II to enhance transcriptional pausing. Our findings revealed a molecular mechanism by which SNHG15 serves as a regulator to suppresses MTSS1 transcription via interaction with the gene core promoter.

Contrast-Enhanced Ultrasound-Based Radiomics for the Prediction of Axillary Lymph Nodes Status in Breast Cancer

BACKGROUND

Breast cancer is the leading cause of cancer-related deaths in the female population. Axillary lymph nodes (ALN) are a group of the most common metastatic sites of breast cancer. Timely assessment of ALN status is of paramount clinical importance for medical decision making.

AIMS

To utilize contrast-enhanced ultrasound (CEUS)-based radiomics models for noninvasive pretreatment prediction of ALN status.

METHODS AND RESULTS

Clinical data and pretreatment CEUS images of primary breast tumors were retrospectively studied to build radiomics signatures for pretreatment prediction of nodal status between May 2015 and July 2021. The cases were divided into the training cohorts and test cohorts in a 9:1 ratio. The mRMR approach and stepwise forward logistic regression technique were used for feature selection, followed by the multivariate logistic regression technique for building radiomics signatures in the training cohort. The confusion matrix and receiver operating characteristic (ROC) analysis were used for accessing the prediction efficacy of the radiomics models. The radiomics models, which consist of six features, achieved predictive accuracy with the area under the ROC curve (AUC) of 0.713 in the test set for predicting lymph node metastasis.

CONCLUSION

The CEUS-based radiomics is promising to be developed as a reliable noninvasive tool for predicting ALN status.

Weakly-supervised deep learning models enable HER2-low prediction from H &E stained slides

BACKGROUND

Human epidermal growth factor receptor 2 (HER2)-low breast cancer has emerged as a new subtype of tumor, for which novel antibody-drug conjugates have shown beneficial effects. Assessment of HER2 requires several immunohistochemistry tests with an additional in situ hybridization test if a case is classified as HER2 2+. Therefore, novel cost-effective methods to speed up the HER2 assessment are highly desirable.

METHODS

We used a self-supervised attention-based weakly supervised method to predict HER2-low directly from 1437 histopathological images from 1351 breast cancer patients. We built six distinct models to explore the ability of classifiers to distinguish between the HER2-negative, HER2-low, and HER2-high classes in different scenarios. The attention-based model was used to comprehend the decision-making process aimed at relevant tissue regions.

RESULTS

Our results indicate that the effectiveness of classification models hinges on the consistency and dependability of assay-based tests for HER2, as the outcomes from these tests are utilized as the baseline truth for training our models. Through the use of explainable AI, we reveal histologic patterns associated with the HER2 subtypes.

CONCLUSION

Our findings offer a demonstration of how deep learning technologies can be applied to identify HER2 subgroup statuses, potentially enriching the toolkit available for clinical decision-making in oncology.

Impact of Prosigna test on adjuvant treatment decision in lymph node-negative early breast cancer-a prospective national multicentre study (EMIT-1)

BACKGROUND

EMIT-1 is a national, observational, single-arm trial designed to assess the value of the Prosigna, Prediction Analysis of Microarray using the 50 gene classifier (PAM50)/Risk of Recurrence (ROR), test as a routine diagnostic tool, examining its impact on adjuvant treatment decisions, clinical outcomes, side-effects and cost-effectiveness. Here we present the impact on treatment decisions.

PATIENTS AND METHODS

Patients with hormone receptor-positive, human epidermal growth factor receptor 2-negative pT1-pT2 lymph node-negative early breast cancer (EBC) were included. The Prosigna test and standard histopathology assessments were carried out. Clinicians' treatment decisions were recorded before (pre-Prosigna) and after (post-Prosigna) the Prosigna test results were disclosed.

RESULTS

Of 2217 patients included, 2178 had conclusive Prosigna results. The pre-Prosigna treatment decisions were: no systemic treatment (NT) in 27% of patients, endocrine treatment alone (ET) in 38% and chemotherapy (CT) followed by ET (CT + ET) in 35%. Post-Prosigna treatment decisions were 25% NT, 51% ET and 24% CT + ET, respectively. Adjuvant treatment changed in 28% of patients, including 21% change in CT use. Among patients assigned to CT + ET pre-Prosigna, 45% were de-escalated to ET post-Prosigna. Of patients assigned to ET, 12% were escalated to CT + ET and 8% were de-escalated to NT; of those assigned to NT, 18% were escalated to ET/CT + ET. CT was more frequently recommended for patients aged ≤50 years. In the subgroup with pT1c-pT2 G2 and intermediate Ki67 (0.5-1.5× local laboratory median Ki67 score), the pre-Prosigna CT treatment decision varied widely across hospitals (3%-51%). Post-Prosigna, the variability of CT use was markedly reduced (8%-24%). The correlation between Ki67 and ROR score within this subgroup was poor (r = 0.25-0.39). The median ROR score increased by increasing histological grade, but the ROR score ranges were wide (for G1 0-79, G2 0-90, G3 16-94).

CONCLUSION

The Prosigna test result changed adjuvant treatment decisions in all EBC clinical risk groups, markedly decreased the CT use for patients categorized as higher clinical risk pre-Prosigna and reduced treatment decision discrepancies between hospitals.

Smart Biosensor for Breast Cancer Survival Prediction Based on Multi-View Multi-Way Graph Learning

Biosensors play a crucial role in detecting cancer signals by orchestrating a series of intricate biological and physical transduction processes. Among various cancers, breast cancer stands out due to its genetic underpinnings, which trigger uncontrolled cell proliferation, predominantly impacting women, and resulting in significant mortality rates. The utilization of biosensors in predicting survival time becomes paramount in formulating an optimal treatment strategy. However, conventional biosensors employing traditional machine learning methods encounter challenges in preprocessing features for the learning task. Despite the potential of deep learning techniques to automatically extract useful features, they often struggle to effectively leverage the intricate relationships between features and instances. To address this challenge, our study proposes a novel smart biosensor architecture that integrates a multi-view multi-way graph learning (MVMWGL) approach for predicting breast cancer survival time. This innovative approach enables the assimilation of insights from gene interactions and biosensor similarities. By leveraging real-world data, we conducted comprehensive evaluations, and our experimental results unequivocally demonstrate the superiority of the MVMWGL approach over existing methods.

lncRNA MIAT promotes luminal B breast cancer cell proliferation, migration, and invasion in vitro

Long noncoding RNAs (lncRNAs) play a role in the emergence and progression of several human tumors, including luminal B breast cancer (BC). The biological functions and potential mechanisms of lncRNA myocardial infarction-associated transcripts (MIAT) in luminal B BC, on the contrary, are unknown. In this work, we used UALCAN database analysis to find high expression of lncRNA MIAT in luminal BC tissues and also confirmed high levels of lncRNA MIAT expression in luminal B BC tissues and cells. In vitro knockdown of MIAT inhibited the proliferation, migration, and invasion of BT474 cells. In addition, we found that miR-150-5p levels were significantly reduced in luminal B BC specimens and cells, and miR-150-5p levels were significantly increased when MIAT was knocked down. And TIMER database analysis showed that MIAT was positively associated with PDL1. Through bioinformatic tools and in vitro experiments, lncRNA MIAT could function as a competitive endogenous RNA (CeRNA) to further regulate programmed cell death ligand 1 (PDL1) expression by directly sponging miR-150-5p. In conclusion, our data suggest that MIAT, an oncogene, may sponge miR-150-5p to regulate PDL1 expression and affect proliferation, migration, and invasion in luminal B BC in vitro.

Real-world use of multigene signatures in early breast cancer: differences to clinical trials

PURPOSE

In Italy, Lombardy was the first region to reimburse multigene assays (MGAs) for patients otherwise candidates for chemotherapy. This is a real-world experience of MGAs usage in six referral cancer centers in Lombardy.

METHODS

Among MGAs, Oncotype DX (RS) was used in 97% of cases. Consecutive patients tested with Oncotype DX from July 2020 to July 2022 were selected. The distribution of clinicopathologic features by RS groups (low RS: 0-25, high RS: 26-100) was assessed using chi-square and compared with those of the TAILORx and RxPONDER trials.

RESULTS

Out of 1,098 patients identified, 73% had low RS. Grade and Ki67 were associated with RS (p < 0.001). In patients with both G3 and Ki67 > 30%, 39% had low RS, while in patients with both G1 and Ki67 < 20%, 7% had high RS. The proportion of low RS in node-positive patients was similar to that in RxPONDER (82% vs 83%), while node-negative patients with low RS were significantly less than in TAILORx (66% vs 86%, p < 0.001). The distribution of Grade was different from registration trials, with more G3 and fewer G1 (38% and 3%) than in TAILORx (18% and 27%) and RxPONDER (10% and 24%) (p < 0.001). Patients ≤ 50 years were overrepresented in this series (41%) than in TAILORx and RxPONDER (31% and 24%, respectively) (p < 0.001) and, among them, 42% were node positive.

CONCLUSIONS

In this real-world series, Oncotype DX was the test almost exclusively used. Despite reimbursement being linked to pre-test chemotherapy recommendation, almost 3/4 patients resulted in the low-RS group. The significant proportion of node-positive patients ≤ 50 years tested indicates that oncologists considered Oncotype DX informative also in this population.

Evolution of chromosome-arm aberrations in breast cancer through genetic network rewiring

The basal breast cancer subtype is enriched for triple-negative breast cancer (TNBC) and displays consistent large chromosomal deletions. Here, we characterize evolution and maintenance of chromosome 4p (chr4p) loss in basal breast cancer. Analysis of The Cancer Genome Atlas data shows recurrent deletion of chr4p in basal breast cancer. Phylogenetic analysis of a panel of 23 primary tumor/patient-derived xenograft basal breast cancers reveals early evolution of chr4p deletion. Mechanistically we show that chr4p loss is associated with enhanced proliferation. Gene function studies identify an unknown gene, C4orf19, within chr4p, which suppresses proliferation when overexpressed-a member of the PDCD10-GCKIII kinase module we name PGCKA1. Genome-wide pooled overexpression screens using a barcoded library of human open reading frames identify chromosomal regions, including chr4p, that suppress proliferation when overexpressed in a context-dependent manner, implicating network interactions. Together, these results shed light on the early emergence of complex aneuploid karyotypes involving chr4p and adaptive landscapes shaping breast cancer genomes.

Breast cancer therapy: from the perspective of glucose metabolism and glycosylation

Breast cancer is a leading cause of mortality and the most prevalent form of malignant tumor among women worldwide. Breast cancer cells exhibit an elevated glycolysis and altered glucose metabolism. Moreover, these cells display abnormal glycosylation patterns, influencing invasion, proliferation, metastasis, and drug resistance. Consequently, targeting glycolysis and mitigating abnormal glycosylation represent key therapeutic strategies for breast cancer. This review underscores the importance of protein glycosylation and glucose metabolism alterations in breast cancer. The current research efforts in developing effective interventions targeting glycolysis and glycosylation are further discussed.

DNA-based molecular classifiers for the profiling of gene expression signatures

Although gene expression signatures offer tremendous potential in diseases diagnostic and prognostic, but massive gene expression signatures caused challenges for experimental detection and computational analysis in clinical setting. Here, we introduce a universal DNA-based molecular classifier for profiling gene expression signatures and generating immediate diagnostic outcomes. The molecular classifier begins with feature transformation, a modular and programmable strategy was used to capture relative relationships of low-concentration RNAs and convert them to general coding inputs. Then, competitive inhibition of the DNA catalytic reaction enables strict weight assignment for different inputs according to their importance, followed by summation, annihilation and reporting to accurately implement the mathematical model of the classifier. We validated the entire workflow by utilizing miRNA expression levels for the diagnosis of hepatocellular carcinoma (HCC) in clinical samples with an accuracy 85.7%. The results demonstrate the molecular classifier provides a universal solution to explore the correlation between gene expression patterns and disease diagnostics, monitoring, and prognosis, and supports personalized healthcare in primary care.

Magnetic Resonance Imaging in Breast Cancer Tissue In Vitro after PDT Therapy

Photodynamic therapy (PDT) is increasingly used in modern medicine. It has found application in the treatment of breast cancer. The most common cancer among women is breast cancer. We collected cancer cells from the breast from the material received after surgery. We focused on tumors that were larger than 10 mm in size. Breast cancer tissues for this quantitative non-contrast magnetic resonance imaging (MRI) study could be seen macroscopically. The current study aimed to present findings on quantitative non-contrast MRI of breast cancer cells post-PDT through the evaluation of relaxation times. The aim of this work was to use and optimize a 1.5 T MRI system. MRI tests were performed using a clinical scanner, namely the OPTIMA MR360 manufactured by General Electric HealthCare. The work included analysis of T1 and T2 relaxation times. This analysis was performed using the MATLAB package (produced by MathWorks). The created application is based on medical MRI images saved in the DICOM3.0 standard. T1 and T2 measurements were subjected to the Shapiro-Wilk test, which showed that both samples belonged to a normal distribution, so a parametric t-test for dependent samples was used to test for between-sample variability. The study included 30 sections tested in 2 stages, with consistent technical parameters. For T1 measurements, 12 scans were performed with varying repetition times (TR) and a constant echo time (TE) of 3 ms. For T2 measurements, 12 scans were performed with a fixed repetition time of 10,000 ms and varying echo times. After treating samples with PpIX disodium salt and bubbling with pure oxygen, PDT irradiation was applied. The cell relaxation time after therapy was significantly shorter than the cell relaxation time before PDT. The cells were exposed to PpIX disodium salt as the administered pharmacological substance. The study showed that the therapy significantly affected tumor cells, which was confirmed by a significant reduction in tumor cell relaxation time on the MRI results.

Clinical Relevance of Tumour-Infiltrating Immune Cells in HER2-Negative Breast Cancer Treated with Neoadjuvant Therapy

Currently, therapy response cannot be accurately predicted in HER2-negative breast cancer (BC). Measuring stromal tumour-infiltrating lymphocytes (sTILs) and mediators of the tumour microenvironment and characterizing tumour-infiltrating immune cells (TIICs) may improve treatment response in the neoadjuvant setting. Tumour tissue and peripheral blood samples were retrospectively collected from 118 patients, and sTILs were evaluated. Circulating exosomes and myeloid-derived suppressor cells were determined by flow cytometry. TIICs markers (CD4, CD8, CD20, CD1a, and CD68) were assessed immunohistochemically. High sTILs were significantly associated with pathological complete response (pCR; p = 0.048) and event-free survival (EFS; p = 0.027). High-CD68 cells were significantly associated with pCR in triple-negative (TN, p = 0.027) and high-CD1a cells with EFS in luminal-B (p = 0.012) BC. Cluster analyses of TIICs revealed two groups of tumours (C1 and C2) that had different immune patterns and clinical outcomes. An immunoscore based on clinicopathological variables was developed to identify high risk (C1) or low-risk (C2) patients. Additionally, cluster analyses revealed two groups of tumours for both luminal-B and TNBC. Our findings support the association of sTILs with pCR and show an immunological component in a subset of patients with HER2-negative BC. Our immunoscore may be useful for future escalation or de-escalation treatments.

A journey from omics to clinicomics in solid cancers: Success stories and challenges

The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease. Yet, there is a gap between basic research discoveries and their translation into clinically meaningful therapies for improving patient care. To bridge this gap, there is a need to analyse the vast amounts of high dimensional datasets from multi-omics platforms. The integration of multi-omics data with clinical information like patient history, histological examination and imaging has led to the novel concept of clinicomics and may expedite the bench-to-bedside transition in cancer. The journey from omics to clinicomics has gained momentum with development of radiomics which involves extracting quantitative features from medical imaging data with the help of deep learning and artificial intelligence (AI) tools. These features capture detailed information about the tumor's shape, texture, intensity, and spatial distribution. Together, the related fields of multiomics, translational bioinformatics, radiomics and clinicomics may provide evidence-based recommendations tailored to the individual cancer patient's molecular profile and clinical characteristics. In this chapter, we summarize multiomics studies in solid cancers with a specific focus on breast cancer. We also review machine learning and AI based algorithms and their use in cancer diagnosis, subtyping, prognosis and predicting treatment resistance and relapse.

Decoding the basis of histological variation in human cancer

Molecular abnormalities that shape human neoplasms dissociate their phenotypic landscape from that of the healthy counterpart. Through the lens of a microscope, tumour pathology optically captures such aberrations projected onto a tissue slide and has categorized human epithelial neoplasms into distinct histological subtypes based on the diverse morphogenetic and molecular programmes that they manifest. Tumour histology often reflects tumour aggressiveness, patient prognosis and therapeutic vulnerability, and thus has been used as a de facto diagnostic tool and for making clinical decisions. However, it remains elusive how the diverse histological subtypes arise and translate into pleiotropic biological phenotypes. Molecular analysis of clinical tumour tissues and their culture, including patient-derived organoids, and add-back genetic reconstruction of tumorigenic pathways using gene engineering in culture models and rodents further elucidated molecular mechanisms that underlie morphological variations. Such mechanisms include genetic mutations and epigenetic alterations in cellular identity codes that erode hard-wired morphological programmes and histologically digress tumours from the native tissues. Interestingly, tumours acquire the ability to grow independently of the niche-driven stem cell ecosystem along with these morphological alterations, providing a biological rationale for histological diversification during tumorigenesis. This Review comprehensively summarizes our current understanding of such plasticity in the histological and lineage commitment fostered cooperatively by molecular alterations and the tumour environment, and describes basic and clinical implications for future cancer therapy.

Clinical importance of serum miRNA levels in breast cancer patients

There is limited data on the relationship of miRNAs with parameters that may affect surgical management or reflect tumour prognosis. It was aimed to evaluate serum miRNA levels in breast carcinoma cases and reveal the relationship between these levels and prognosis-related factors such as the histological type of the tumour, estrogen receptor, progesterone receptor, Ki-67 index, HER-2neu, E-cadherin, tumour size, CK5/6, CA15.3 levels, number of tumour foci, number of metastatic lymph nodes, and status of receiving neoadjuvant therapy. Thirty-five patients with a histopathologically confirmed breast carcinoma diagnosis in the case group and 35 healthy individuals in the control group were examined. miR-206, miR-17-5p, miR-125a, miR-125b, miR-200a, Let-7a, miR-34a, miR-31, miR-21, miR-155, miR-10b, miR-373, miR-520c, miR-210, miR-145, miR-139-5p, miR-195, miR-99a, miR-497 and miR-205 expression levels in the serum of participants were determined using the Polymerase Chain Reaction method. While serum miR-125b and Let-7a expression levels were significantly higher in breast cancer patients, miR-17-5p, miR-125a, miR-200a, miR-34a, miR-21, miR-99a and miR-497 levels were significantly lower in them. The Let-7a expression level had a statistically significant relationship with breast cancer histological type and HER-2neu parameters, miR-17-5p, miR-125b, Let-7a, miR-34a, miR-21 and miR-99a levels with E-cadherin, miR-34a, miR-99a and miR-497 with CA15.3, miR-125b, miR-200a and miR-34a with the number of metastatic lymph nodes, miR-125a with the number of tumour foci and miR-200a with the status of having the neoadjuvant therapy. Serum miR-17-5p, miR-125a, miR-125b, miR-200a, Let-7a, miR-34a, miR-21, miR-99a and miR-497 expression levels were determined to have predictive and prognostic importance in breast cancer.

Copper Chaperone for Superoxide Dismutase Subtypes as a Prognostic Marker in Luminal B Breast Cancer

Background

Copper chaperone for superoxide dismutase (CCS) is an essential component of the oxidation-reduction system. In breast cancer cells, CCS expression is highly up-regulated, which contributes to cellular proliferation and migration. Breast cancer is a multifaceted disease with different tumor prognoses and responses to clinical treatments, which may be associated with multiple molecular subtypes of CCS.

Methods

The CCS expression patterns in breast cancer were investigated by TNMplot, cBioPortal, and HPA network database. The correlation of CCS expression with clinicopathological parameters was analyzed using the UALCAN database. The Cancer Genome Atlas (TCGA) data set was used to analyze the Clinical characteristics of CCS in luminal B patients. The bc-GenExMiner database was used to analyze the effects of BReast-CAncer susceptibility gene (BRCA)1/2, TP53 mutation status, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor (HER) expression on CCS expression. The survival curves and prognostic value of CCS in luminal B breast cancer were performed through Kaplan-Meier curves, univariate and multivariate Cox regression using the PrognoScan, bc-GenExMiner, and Clinical bioinformatics analysis platform.

Results

We found that CCS expression was associated with patient age, race, ER, and PR status. We also discovered that BRCA1/2 mutations had an effect on CCS expression. The luminal B subtype had the highest CCS expression, which was linked to poor survival compared with other subtypes. In addition, Kaplan-Meier curve analysis showed that luminal B patients with high CCS mRNA expression showed a poor survival and the CCS gene is an independent predictor of outcome in patients with luminal B breast cancer by univariate and multivariate Cox regression.

Conclusions

Our findings emphasize the significant expression of CCS in luminal B breast cancer and its potential as an autonomous prognostic determinant for this specific molecular subtype. These findings suggest that CCS holds promise as a prospective marker for the treatment of luminal B breast cancer.

Gene-Expression Profiling to Decipher Breast Cancer Inter- and Intratumor Heterogeneity

Breast cancer is heterogeneous and differs substantially across different tumors (intertumor heterogeneity) and even within an individual tumor (intratumor heterogeneity). Gene-expression profiling has considerably impacted our understanding of breast cancer biology. Four main "intrinsic subtypes" of breast cancer (i.e., luminal A, luminal B, HER2-enriched, and basal-like) have been consistently identified by gene expression, showing significant prognostic and predictive value in multiple clinical scenarios. Thanks to the molecular profiling of breast tumors, breast cancer is a paradigm of treatment personalization. Several standardized prognostic gene-expression assays are presently being used in the clinic to guide treatment decisions. Moreover, the development of single-cell-level resolution molecular profiling has allowed us to appreciate that breast cancer is also heterogeneous within a single tumor. There is an evident functional heterogeneity within the neoplastic and tumor microenvironment cells. Finally, emerging insights from these studies suggest a substantial cellular organization of neoplastic and tumor microenvironment cells, thus defining breast cancer ecosystems and highlighting the importance of spatial localizations.

Identification of MicroRNAs Associated with Histological Grade in Early-Stage Invasive Breast Cancer

This study aimed to identify microRNAs associated with histological grade using comprehensive microRNA analysis data obtained by next-generation sequencing from early-stage invasive breast cancer. RNA-seq data from normal breast and breast cancer samples were compared to identify candidate microRNAs with differential expression using bioinformatics. A total of 108 microRNAs were significantly differentially expressed in normal breast and breast cancer tissues. Using clinicopathological information and microRNA sequencing data of 430 patients with breast cancer from The Cancer Genome Atlas (TCGA), the differences in candidate microRNAs between low- and high-grade tumors were identified. Comparing the expression of the 108 microRNAs between low- and high-grade cases, 25 and 18 microRNAs were significantly upregulated and downregulated, respectively, in high-grade cases. Clustering analysis of the TCGA cohort using these 43 microRNAs identified two groups strongly predictive of histological grade. miR-3677 is a microRNA upregulated in high-grade breast cancer. The outcome analysis revealed that patients with high miR-3677 expression had significantly worse prognosis than those with low miR-3677 expression. This study shows that microRNAs are associated with histological grade in early-stage invasive breast cancer. These findings contribute to the elucidation of a new mechanism of breast cancer growth regulated by specific microRNAs.

Molecular diagnostic approaches for SARS-CoV-2 detection and pathophysiological consequences

SARS-CoV-2, a novel coronavirus within the Coronaviridae family, is the causative agent behind the respiratory ailment referred to as COVID-19. Operating on a global scale, COVID-19 has led to a substantial number of fatalities, exerting profound effects on both public health and the global economy. The most frequently reported symptoms encompass fever, cough, muscle or body aches, loss of taste or smell, headaches, and fatigue. Furthermore, a subset of individuals may manifest more severe symptoms, including those consistent with viral pneumonitis, which can be so profound as to result in fatalities. Consequently, this situation has spurred the rapid advancement of disease diagnostic technologies worldwide. Predominantly employed in diagnosing COVID-19, the real-time quantitative reverse transcription PCR has been the foremost diagnostic method, effectively detecting SARS-CoV-2 viral RNA. As the pandemic has evolved, antigen and serological tests have emerged as valuable diagnostic tools. Antigen tests pinpoint specific viral proteins of SARS-CoV-2, offering swift results, while serological tests identify the presence of antibodies in blood samples. Additionally, there have been notable strides in sample collection methods, notably with the introduction of saliva-based tests, presenting a non-invasive substitute to nasopharyngeal swabs. Given the ongoing mutations in SARS-CoV-2, there has been a continuous need for genomic surveillance, encompassing full genome sequencing and the identification of new variants through Illumina technology and, more recently, nanopore metagenomic sequencing (SMTN). Consequently, while diagnostic testing methods for COVID-19 have experienced remarkable progress, no test is flawless, and there exist limitations with each technique, including sensitivity, specificity, sample collection, and the minimum viral load necessary for accurate detection. These aspects are comprehensively addressed within this current review.

Young Women with Breast Cancer: The Current Role of Precision Oncology

Young adults aged 40 years and younger with breast cancer represent less than 5% of all breast cancer cases, yet it is the leading cause of death among young women with cancer worldwide. Breast cancer that develops at a young age is more aggressive and has biological features that carry an increased risk of relapse and death. Young adults are more likely to have a genetic predisposition and key biomarkers, including endocrine receptors, the HER2 receptor, and proliferation biomarkers, that appear different compared to older adults. Despite being more aggressive, management strategies are largely the same irrespective of age. Given the higher rates of genetic predisposition, fast access to genetic counselling and testing is a necessity. In this review, the biological differences in young adult breast cancer and the current role precision medicine holds in the treatment of young adults with breast cancer are explored. Given the relatively high risk of relapse, developing novel genomic tools to refine the treatment options beyond the current standard is critical. Existing predictive genomic tests require careful interpretation with consideration of the patient's clinical and pathological features in the young patient cohort. Careful evaluation is also required when considering extended endocrine therapy options. Improved characterization of mutations occurring in tumors using next-generation sequencing could identify important driver mutations that arise in young women. Applying the advances of precision medicine equitably to patients in resource-rich and low- and middle-income countries will be critical to impacting the survival of young adults with breast cancer worldwide.

Enhancer regulatory networks globally connect non-coding breast cancer loci to cancer genes

Genetic studies have associated thousands of enhancers with breast cancer. However, the vast majority have not been functionally characterized. Thus, it remains unclear how variant-associated enhancers contribute to cancer. Here, we perform single-cell CRISPRi screens of 3,512 regulatory elements associated with breast cancer to measure the impact of these regions on transcriptional phenotypes. Analysis of >500,000 single-cell transcriptomes in two breast cancer cell lines shows that perturbation of variant-associated enhancers disrupts breast cancer gene programs. We observe variant-associated enhancers that directly or indirectly regulate the expression of cancer genes. We also find one-to-multiple and multiple-to-one network motifs where enhancers indirectly regulate cancer genes. Notably, multiple variant-associated enhancers indirectly regulate TP53. Comparative studies illustrate sub-type specific functions between enhancers in ER+ and ER- cells. Finally, we developed the pySpade package to facilitate analysis of single-cell enhancer screens. Overall, we demonstrate that enhancers form regulatory networks that link cancer genes in the genome, providing a more comprehensive understanding of the contribution of enhancers to breast cancer development.

Nomogram prediction of the 70-gene signature (MammaPrint) binary and quartile categorized risk using medical history, imaging features and clinicopathological data among Chinese breast cancer patients

BACKGROUND

The 70-gene signature (70-GS, MammaPrint) test has been recommended by the main guidelines to evaluate prognosis and chemotherapy benefit of hormonal receptor positive human epidermal receptor 2 negative (HR + /Her2-) early breast cancer (BC). However, this expensive assay is not always accessible and affordable worldwide. Based on our previous study, we established nomogram models to predict the binary and quartile categorized risk of 70-GS.

METHODS

We retrospectively analyzed a consecutive cohort of 150 female patients with HR + /Her2- BC and eligible 70-GS test. Comparison of 40 parameters including the patients' medical history risk factors, imaging features and clinicopathological characteristics was performed between patients with high risk (N = 62) and low risk (N = 88) of 70-GS test, whereas risk calculations from established models including Clinical Treatment Score Post-5 years (CTS5), Immunohistochemistry 3 (IHC3) and Nottingham Prognostic Index (NPI) were also compared between high vs low binary risk of 70-GS and among ultra-high (N = 12), high (N = 50), low (N = 65) and ultra-low (N = 23) quartile categorized risk of 70-GS. The data of 150 patients were randomly split by 4:1 ratio with training set of 120 patients and testing set 30 patients. Univariate analyses and multivariate logistic regression were performed to establish the two nomogram models to predict the the binary and quartile categorized risk of 70-GS.

RESULTS

Compared to 70-GS low-risk patients, the high-risk patients had significantly less cardiovascular co-morbidity (p = 0.034), more grade 3 BC (p = 0.006), lower progesterone receptor (PR) positive percentage (p = 0.007), more Ki67 high BC (≥ 20%, p < 0.001) and no significant differences in all the imaging parameters of ultrasound and mammogram. The IHC3 risk and the NPI calculated score significantly correlated with both the binary and quartile categorized 70-GS risk classifications (both p < 0.001). The area under curve (AUC) of receiver-operating curve (ROC) of nomogram for binary risk prediction were 0.826 (C-index 0.903, 0.799-1.000) for training and 0.737 (C-index 0.785, 0.700-0.870) for validation dataset respectively. The AUC of ROC of nomogram for quartile risk prediction was 0.870 (C-index 0.854, 0.746-0.962) for training and 0.592 (C-index 0.769, 0.703-0.835) for testing set. The prediction accuracy of the nomogram for quartile categorized risk groups were 55.0% (likelihood ratio tests, p < 0.001) and 53.3% (p = 0.04) for training and validation, which more than double the baseline probability of 25%.

CONCLUSIONS

To our knowledge, we are the first to establish easy-to-use nomograms to predict the individualized binary (high vs low) and the quartile categorized (ultra-high, high, low and ultra-low) risk classification of 70-GS test with fair performance, which might provide information for treatment choice for those who have no access to the 70-GS testing.

Application of metagenomics for diagnosis of broilers displaying neurological symptoms

BACKGROUND

Thirty-two-day-old broiler chickens at a farm located in northwestern South Korea displayed adverse neurological symptoms including limping, lying down, and head shaking. Approximately 2.1% of chickens died or were culled due to severe symptoms. Five carcasses were submitted to the Avian Disease Division of the Animal and Plant Quarantine Agency (APQA) for disease diagnosis.

RESULTS

Broilers displayed severe pericarditis and perihepatitis associated with gross lesions. Broilers also displayed microscopic lesions in the cerebrum and in the granular layer of the cerebellum, which were associated with multifocal perivascular cuffing and purulent necrosis in the cerebrum, and severe meningitis with heterophil and lymphocyte infiltration. Staphylococcus spp. were identified in the liver and heart using bacteriological culture. PCR/RT-PCR assays revealed that broilers were negative for avian Clostridium botulinum, Newcastle disease virus, and avian encephalomyelitis virus. Bacterial and viral metagenomic analysis of brain sample further revealed the presence of Pseudomonas spp. and Marek's disease virus, which are known etiological agents of chicken meningoencephalitis.

CONCLUSIONS

This study reports a diagnostic analysis of gross and histopathological lesions from 32-day-old broilers displaying unique neurological symptoms that revealed the presence of the several neurological diseases including meningoencephalitis. The causative agents associated with meningoencephalitis of broilers that had not been identified by routine diagnostic methods could be diagnosed by metagenomics, which proves the usefulness of metagenomics as a diagnostic tool for unknown neurological diseases in broilers.

Integrin β4 promotes DNA damage-related drug resistance in triple-negative breast cancer via TNFAIP2/IQGAP1/RAC1

Anti-tumor drug resistance is a challenge for human triple-negative breast cancer (TNBC) treatment. Our previous work demonstrated that TNFAIP2 activates RAC1 to promote TNBC cell proliferation and migration. However, the mechanism by which TNFAIP2 activates RAC1 is unknown. In this study, we found that TNFAIP2 interacts with IQGAP1 and Integrin β4. Integrin β4 activates RAC1 through TNFAIP2 and IQGAP1 and confers DNA damage-related drug resistance in TNBC. These results indicate that the Integrin β4/TNFAIP2/IQGAP1/RAC1 axis provides potential therapeutic targets to overcome DNA damage-related drug resistance in TNBC.

Survival Benefit of Adjuvant Chemotherapy in Node-Positive Breast Cancer With a 21-Gene Recurrence Score of 14 to 25: A Real-World Study Based on the Inverse Probability of Treatment Weighting Method

INTRODUCTION

The role of recurrence score in predicting the benefits of adjuvant chemotherapy for lymph-node-positive breast cancer remains uncertain. We studied chemotherapy usage in patients with 1 to 3 positive lymph nodes and a recurrence score (RS) of 25 or lower to assess changes in clinical practice based on the RxPONDER trial.

METHODS

A retrospective study using the SEER database identified female patients diagnosed with ER-positive, HER2-negative breast cancer, 1 to 3 positive lymph nodes, and an RS of 25 or lower between 2010 and 2015. Patients were divided into nonchemotherapy and chemotherapy groups, with propensity score weighting to balance clinicopathologic factors.

RESULTS

Among 7965 patients, 5774 (72.5%) were in the nonchemotherapy group, while 2191 (27.5%) were in the chemotherapy group. Median follow-up was 39 months. Breast cancer accounted for 67 deaths, while 128 deaths were due to other causes. The weighted 5-year overall survival (OS) rates were 95.7% for the nonchemotherapy group and 97.2% for the chemotherapy group. For high-risk patients, the weighted 5-year OS rates were 95.2% and 97.0% for the nonchemotherapy and chemotherapy groups, respectively, with a significant absolute difference of 1.8% (P = .014). Multivariate analysis showed a significant difference in weighted hazard ratios for OS between the nonchemotherapy and chemotherapy groups in high-risk patients (hazard ratio: 0.64; 95% CI: 0.48-0.86). However, there were no significant differences in weighted hazard ratios for lower-risk patients, and similar results were observed for breast cancer-specific survival (BCSS).

CONCLUSION

Patients with ER-positive, HER2-negative breast cancer and 1 to 3 positive lymph nodes, assessed by a 21-gene RS of 0 to 25, exhibited heterogeneous prognosis. Adjuvant chemotherapy provided a significant survival benefit, especially for patients with RS of 14 to 25, particularly those with invasive ductal carcinoma (IDC) and 2 to 3 positive lymph nodes.

Chromatin factors: Ready to roll as biomarkers in metastatic colorectal cancer?

Colorectal cancer (CRC) ranks as the third most prevalent cancer globally and stands as the fourth leading cause of cancer-related fatalities in 2020. Survival rates for metastatic disease have slightly improved in recent decades, with clinical trials showing median overall survival of approximately 24-30 months. This progress can be attributed to the integration of chemotherapeutic treatments alongside targeted therapies and immunotherapy. Despite these modest improvements, the primary obstacle to successful treatment for advanced CRC lies in the development of chemoresistance, whether inherent or acquired, which remains the major cause of treatment failure. Epigenetics has emerged as a hallmark of cancer, contributing to master transcription regulation and genome stability maintenance. As a result, epigenetic factors are starting to appear as potential clinical biomarkers for diagnosis, prognosis, and prediction of treatment response in CRC.In recent years, numerous studies have investigated the influence of DNA methylation, histone modifications, and chromatin remodelers on responses to chemotherapeutic treatments. While there is accumulating evidence indicating their significant involvement in various types of cancers, the exact relationship between chromatin landscapes and treatment modulation in CRC remains elusive. This review aims to provide a comprehensive summary of the most pertinent and extensively researched epigenetic-associated mechanisms described between 2015 and 2022 and their potential usefulness as predictive biomarkers in the metastatic disease.

A computational framework for the inference of protein complex remodeling from whole-proteome measurements

Protein complexes are responsible for the enactment of most cellular functions. For the protein complex to form and function, its subunits often need to be present at defined quantitative ratios. Typically, global changes in protein complex composition are assessed with experimental approaches that tend to be time consuming. Here, we have developed a computational algorithm for the detection of altered protein complexes based on the systematic assessment of subunit ratios from quantitative proteomic measurements. We applied it to measurements from breast cancer cell lines and patient biopsies and were able to identify strong remodeling of HDAC2 epigenetic complexes in more aggressive forms of cancer. The presented algorithm is available as an R package and enables the inference of changes in protein complex states by extracting functionally relevant information from bottom-up proteomic datasets.

Matched analysis of circulating selenium with the breast cancer selenotranscriptome: a multicentre prospective study

INTRODUCTION

Low serum selenium and altered tumour RNA expression of certain selenoproteins are associated with a poor breast cancer prognosis. Selenoprotein expression stringently depends on selenium availability, hence circulating selenium may interact with tumour selenoprotein expression. However, there is no matched analysis to date.

METHODS

This study included 1453 patients with newly diagnosed breast cancer from the multicentric prospective Sweden Cancerome Analysis Network - Breast study. Total serum selenium, selenoprotein P and glutathione peroxidase 3 were analysed at time of diagnosis. Bulk RNA-sequencing was conducted in matched tumour tissues. Fully adjusted Cox regression models with an interaction term were employed to detect dose-dependent interactions of circulating selenium with the associations of tumour selenoprotein mRNA expression and mortality.

RESULTS

237 deaths were recorded within ~ 9 years follow-up. All three serum selenium biomarkers correlated positively (p < 0.001). All selenoproteins except for GPX6 were expressed in tumour tissues. Single cell RNA-sequencing revealed a heterogeneous expression pattern in the tumour microenvironment. Circulating selenium correlated positively with tumour SELENOW and SELENON expression (p < 0.001). In fully adjusted models, the associations of DIO1, DIO3 and SELENOM with mortality were dose-dependently modified by serum selenium (p < 0.001, p = 0.020, p = 0.038, respectively). With increasing selenium, DIO1 and SELENOM associated with lower, whereas DIO3 expression associated with higher mortality. Association of DIO1 with lower mortality was only apparent in patients with high selenium [above median (70.36 µg/L)], and the HR (95%CI) for one-unit increase in log(FPKM + 1) was 0.70 (0.50-0.98).

CONCLUSIONS

This first unbiased analysis of serum selenium with the breast cancer selenotranscriptome identified an effect-modification of selenium on the associations of DIO1, SELENOM, and DIO3 with prognosis. Selenium substitution in patients with DIO1-expressing tumours merits consideration to improve survival.

5,6-Dichloro-1-β-D-ribofuranosylbenzimidazole (DRB) induces apoptosis in breast cancer cells through inhibiting of Mcl-1 expression

The effective treatment of breast cancer remains a profound clinical challenge, especially due to drug resistance and metastasis which unfortunately arise in many patients. The transcription inhibitor 5,6-dichloro-1-beta-D-ribofuranosyl-benzimidazole (DRB), as a selective inhibitor of cyclin-dependent kinase 9, was shown to be effective in inducing apoptosis in various hematopoietic malignancies. However, the anticancer efficacy of DRB against breast cancer is still unclear. Herein, we demonstrated that administration of DRB to the breast cancer cell line led to the inhibition of cellular proliferation and induction of the typical signs of apoptotic cells, including the increases in Annexin V-positive cells, DNA fragmentation, and activation of caspase-7, caspase-9, and poly (ADP ribose) polymerase (PARP). Treatment of DRB resulted in a rapid decline in the myeloid cell leukemia 1 (Mcl-1) protein, whereas levels of other antiapoptotic proteins did not change. Overexpression of Mcl-1 decreased the DRB-induced PARP cleavage, whereas knockdown of Mcl-1 enhanced the effects of DRB on PARP activation, indicating that loss of Mcl-1 accounts for the DRB-mediated apoptosis in MCF-7 cells, but not in T-47D. Furthermore, we found that co-treatment of MCF-7 cells with an inhibitor of AKT (LY294002) or an inhibitor of the proteasome (MG-132) significantly augmented the DRB-induced apoptosis. These data suggested that DRB in combination with LY294002 or MG-132 may have a greater therapeutic potency against breast cancer cells.

"Diagnostic and Prognostic Biomarkers of Luminal Breast Cancer: Where are We Now?"

Luminal breast cancers are hormone receptor (estrogen and/or progesterone) positive that are further divided into HER2-negative luminal A and HER2-positive luminal B subtypes. According to currently accepted convention, they represent the most common subtypes of breast cancer, accounting for approximately 70% of cases. Biomarkers play a critical role in the functional characterization, prognostication, and therapeutic prediction, rendering them indispensable for the clinical management of invasive breast cancer. Traditional biomarkers include clinicopathological parameters, which are increasingly extended by genetic and other molecular markers, enabling the comprehensive characterization of patients with luminal breast cancer. Liquid biopsies capturing and analyzing circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) are emerging technologies that envision personalized management through precision oncology. This article reviews key biomarkers in luminal breast cancer and ongoing developments.

ZNF703 mRNA-Targeting Antisense Oligonucleotide Blocks Cell Proliferation and Induces Apoptosis in Breast Cancer Cell Lines

The luminal B molecular subtype of breast cancers (BC) accounts for more than a third of BCs and is associated with aggressive clinical behavior and poor prognosis. The use of endocrine therapy in BC treatment has significantly contributed to the decrease in the number of deaths in recent years. However, most BC patients with prolonged exposure to estrogen receptor (ER) selective modulators such as tamoxifen develop resistance and become non-responsive over time. Recent studies have implicated overexpression of the ZNF703 gene in BC resistance to endocrine drugs, thereby highlighting ZNF703 inhibition as an attractive modality in BC treatment, especially luminal B BCs. However, there is no known inhibitor of ZNF703 due to its nuclear association and non-enzymatic activity. Here, we have developed an antisense oligonucleotide (ASO) against ZNF703 mRNA and shown that it downregulates ZNF703 protein expression. ZNF703 inhibition decreased cell proliferation and induced apoptosis. Combined with cisplatin, the anti-cancer effects of ZNF703-ASO9 were improved. Moreover, our work shows that ASO technology may be used to increase the number of targetable cancer genes.

Systematic review of comparative transcriptomic studies of cellular resistance to genotoxic stress

The development of resistance by tumor cells to various types of therapy is a significant problem that decreases the effectiveness of oncology treatments. For more than two decades, comparative transcriptomic studies of tumor cells with different sensitivities to ionizing radiation and chemotherapeutic agents have been conducted in order to identify the causes and mechanisms underlying this phenomenon. However, the results of such studies have little in common and often contradict each other. We have assumed that a systematic analysis of a large number of such studies will provide new knowledge about the mechanisms of development of therapeutic resistance in tumor cells. Our comparison of 123 differentially expressed gene (DEG) lists published in 98 papers suggests a very low degree of consistency between the study results. Grouping the data by type of genotoxic agent and tumor type did not increase the similarity. The most frequently overexpressed genes were found to be those encoding the transport protein ABCB1 and the antiviral defense protein IFITM1. We put forward a hypothesis that the role played by the overexpression of the latter in the development of resistance may be associated not only with the stimulation of proliferation, but also with the limitation of exosomal communication and, as a result, with a decrease in the bystander effect. Among down regulated DEGs, BNIP3 was observed most frequently. The expression of BNIP3, together with BNIP3L, is often suppressed in cells resistant to non-platinum genotoxic chemotherapeutic agents, whereas it is increased in cells resistant to ionizing radiation. These observations are likely to be mediated by the binary effects of these gene products on survival, and regulation of apoptosis and autophagy. The combined data also show that even such obvious mechanisms as inhibition of apoptosis and increase of proliferation are not universal but show multidirectional changes.

Can chemotherapy be omitted for patients with N0 or N1 endocrine-sensitive breast cancer treated with gonadotropin-releasing hormone agonist and tamoxifen?

Purpose

Whether administering chemotherapy followed by tamoxifen plus a gonadotropin-releasing hormone (GnRH) agonist to treat patients with lower-risk hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancer provides a greater benefit than administering tamoxifen plus GnRH agonist alone remains unclear. This study aimed to compare the outcomes of propensity score-matched (PSM) patients who underwent these 2 types of treatment plans.

Methods

This retrospective study included patients treated at our institution between 2009 and 2019. Eligible patients had HR-positive, HER2-negative, invasive breast cancer who had undergone surgery. There were 579 patients with HR-positive, HER2-negative breast cancer who were treated with a GnRH agonist and tamoxifen; patients with pathologic N2 and those who received neoadjuvant chemotherapy were excluded. After 1:1 PSM of patients who underwent GnRH agonist treatment and tamoxifen with versus without chemotherapy, 122 patients from these 2 groups were analyzed. Survival rates were calculated using the Kaplan-Meier method and compared via the log-rank test.

Results

After PSM, there were no significant differences in several baseline characteristics between the 2 groups. After a median follow-up of 62.8 months, the patients in both groups demonstrated similar outcomes with no significant difference in disease-free survival (P = 0.596).

Conclusion

Patients derived no significant survival benefit from undergoing a chemotherapy regimen before receiving tamoxifen and GnRH agonist therapy compared to forgoing such chemotherapy.

Biomarkers in Breast Cancer: An Old Story with a New End

Breast cancer is the second most frequent cancer in the world. It is a heterogeneous disease and the leading cause of cancer mortality in women. Advances in molecular technologies allowed for the identification of new and more specifics biomarkers for breast cancer diagnosis, prognosis, and risk prediction, enabling personalized treatments, improving therapy, and preventing overtreatment, undertreatment, and incorrect treatment. Several breast cancer biomarkers have been identified and, along with traditional biomarkers, they can assist physicians throughout treatment plan and increase therapy success. Despite the need of more data to improve specificity and determine the real clinical utility of some biomarkers, others are already established and can be used as a guide to make treatment decisions. In this review, we summarize the available traditional, novel, and potential biomarkers while also including gene expression profiles, breast cancer single-cell and polyploid giant cancer cells. We hope to help physicians understand tumor specific characteristics and support decision-making in patient-personalized clinical management, consequently improving treatment outcome.

Identification of Novel Diagnostic and Prognostic Gene Signature Biomarkers for Breast Cancer Using Artificial Intelligence and Machine Learning Assisted Transcriptomics Analysis

BACKGROUND

Breast cancer (BC) is one of the most common female cancers. Clinical and histopathological information is collectively used for diagnosis, but is often not precise. We applied machine learning (ML) methods to identify the valuable gene signature model based on differentially expressed genes (DEGs) for BC diagnosis and prognosis.

METHODS

A cohort of 701 samples from 11 GEO BC microarray datasets was used for the identification of significant DEGs. Seven ML methods, including RFECV-LR, RFECV-SVM, LR-L1, SVC-L1, RF, and Extra-Trees were applied for gene reduction and the construction of a diagnostic model for cancer classification. Kaplan-Meier survival analysis was performed for prognostic signature construction. The potential biomarkers were confirmed via qRT-PCR and validated by another set of ML methods including GBDT, XGBoost, AdaBoost, KNN, and MLP.

RESULTS

We identified 355 DEGs and predicted BC-associated pathways, including kinetochore metaphase signaling, PTEN, senescence, and phagosome-formation pathways. A hub of 28 DEGs and a novel diagnostic nine-gene signature (COL10A, S100P, ADAMTS5, WISP1, COMP, CXCL10, LYVE1, COL11A1, and INHBA) were identified using stringent filter conditions. Similarly, a novel prognostic model consisting of eight-gene signatures (CCNE2, NUSAP1, TPX2, S100P, ITM2A, LIFR, TNXA, and ZBTB16) was also identified using disease-free survival and overall survival analysis. Gene signatures were validated by another set of ML methods. Finally, qRT-PCR results confirmed the expression of the identified gene signatures in BC.

CONCLUSION

The ML approach helped construct novel diagnostic and prognostic models based on the expression profiling of BC. The identified nine-gene signature and eight-gene signatures showed excellent potential in BC diagnosis and prognosis, respectively.

Oncotype DX results increase concordance in adjuvant chemotherapy recommendations for early-stage breast cancer

Adjuvant chemotherapy recommendations for ER+/HER2- early-stage breast cancers (eBC) involve integrating prognostic and predictive information which rely on physician judgment; this can lead to discordant recommendations. In this study we aim to evaluate whether Oncotype DX improves confidence and agreement among oncologists in adjuvant chemotherapy recommendations. We randomly select 30 patients with ER+/HER2- eBC and recurrence score (RS) available from an institutional database. We ask 16 breast oncologists with varying years of clinical practice in Italy and the US to provide recommendation for the addition of chemotherapy to endocrine therapy and their degree of confidence in the recommendation twice; first, based on clinicopathologic features only (pre-RS), and then with RS result (post-RS). Pre-RS, the average rate of chemotherapy recommendation is 50.8% and is higher among junior (62% vs 44%; p < 0.001), but similar by country. Oncologists are uncertain in 39% of cases and recommendations are discordant in 27% of cases (interobserver agreement K 0.47). Post-RS, 30% of physicians change recommendation, uncertainty in recommendation decreases to 5.6%, and discordance decreases to 7% (interobserver agreement K 0.85). Interpretation of clinicopathologic features alone to recommend adjuvant chemotherapy results in 1 out of 4 discordant recommendations and relatively high physician uncertainty. Oncotype DX results decrease discordancy to 1 out of 15, and reduce physician uncertainty. Genomic assay results reduce subjectivity in adjuvant chemotherapy recommendations for ER +/HER2- eBC.

Learning vector quantized representation for cancer subtypes identification

BACKGROUND AND OBJECTIVE

Defining and separating cancer subtypes is essential for facilitating personalized therapy modality and prognosis of patients. The definition of subtypes has been constantly recalibrated as a result of our deepened understanding. During this recalibration, researchers often rely on clustering of cancer data to provide an intuitive visual reference that could reveal the intrinsic characteristics of subtypes. The data being clustered are often omics data such as transcriptomics that have strong correlations to the underlying biological mechanism. However, while existing studies have shown promising results, they suffer from issues associated with omics data: sample scarcity and high dimensionality while they impose unrealistic assumptions to extract useful features from the data while avoiding overfitting to spurious correlations.

METHODS

This paper proposes to leverage a recent strong generative model, Vector-Quantized Variational AutoEncoder, to tackle the data issues and extract discrete representations that are crucial to the quality of subsequent clustering by retaining only information relevant to reconstructing the input.

RESULTS

Extensive experiments and medical analysis on multiple datasets comprising 10 distinct cancers demonstrate the proposed clustering results can significantly and robustly improve prognosis over prevalent subtyping systems.

CONCLUSION

Our proposal does not impose strict assumptions on data distribution; while, its latent features are better representations of the transcriptomic data in different cancer subtypes, capable of yielding superior clustering performance with any mainstream clustering method.

FOXD1 is associated with poor outcome and maintains tumor-promoting enhancer-gene programs in basal-like breast cancer

Breast cancer biology varies markedly among patients. Basal-like breast cancer is one of the most challenging subtypes to treat because it lacks effective therapeutic targets. Despite numerous studies on potential targetable molecules in this subtype, few targets have shown promise. However, the present study revealed that FOXD1, a transcription factor that functions in both normal development and malignancy, is associated with poor prognosis in basal-like breast cancer. We analyzed publicly available RNA sequencing data and conducted FOXD1-knockdown experiments, finding that FOXD1 maintains gene expression programs that contribute to tumor progression. We first conducted survival analysis of patients grouped via a Gaussian mixture model based on gene expression in basal-like tumors, finding that FOXD1 is a prognostic factor specific to this subtype. Then, our RNA sequencing and chromatin immunoprecipitation sequencing experiments using the basal-like breast cancer cell lines BT549 and Hs578T with FOXD1 knockdown revealed that FOXD1 regulates enhancer-gene programs related to tumor progression. These findings suggest that FOXD1 plays an important role in basal-like breast cancer progression and may represent a promising therapeutic target.

ALAN is a computational approach that interprets genomic findings in the context of tumor ecosystems

Gene behavior is governed by activity of other genes in an ecosystem as well as context-specific cues including cell type, microenvironment, and prior exposure to therapy. Here, we developed the Algorithm for Linking Activity Networks (ALAN) to compare gene behavior purely based on patient -omic data. The types of gene behaviors identifiable by ALAN include co-regulators of a signaling pathway, protein-protein interactions, or any set of genes that function similarly. ALAN identified direct protein-protein interactions in prostate cancer (AR, HOXB13, and FOXA1). We found differential and complex ALAN networks associated with the proto-oncogene MYC as prostate tumors develop and become metastatic, between different cancer types, and within cancer subtypes. We discovered that resistant genes in prostate cancer shared an ALAN ecosystem and activated similar oncogenic signaling pathways. Altogether, ALAN represents an informatics approach for developing gene signatures, identifying gene targets, and interpreting mechanisms of progression or therapy resistance.

BCR-Net: A deep learning framework to predict breast cancer recurrence from histopathology images

Breast cancer is the most common malignancy in women, with over 40,000 deaths annually in the United States alone. Clinicians often rely on the breast cancer recurrence score, Oncotype DX (ODX), for risk stratification of breast cancer patients, by using ODX as a guide for personalized therapy. However, ODX and similar gene assays are expensive, time-consuming, and tissue destructive. Therefore, developing an AI-based ODX prediction model that identifies patients who will benefit from chemotherapy in the same way that ODX does would give a low-cost alternative to the genomic test. To overcome this problem, we developed a deep learning framework, Breast Cancer Recurrence Network (BCR-Net), which automatically predicts ODX recurrence risk from histopathology slides. Our proposed framework has two steps. First, it intelligently samples discriminative features from whole-slide histopathology images of breast cancer patients. Then, it automatically weights all features through a multiple instance learning model to predict the recurrence score at the slide level. On a dataset of H&E and Ki67 breast cancer resection whole slides images (WSIs) from 99 anonymized patients, the proposed framework achieved an overall AUC of 0.775 (68.9% and 71.1% accuracies for low and high risk) on H&E WSIs and overall AUC of 0.811 (80.8% and 79.2% accuracies for low and high risk) on Ki67 WSIs of breast cancer patients. Our findings provide strong evidence for automatically risk-stratify patients with a high degree of confidence. Our experiments reveal that the BCR-Net outperforms the state-of-the-art WSI classification models. Moreover, BCR-Net is highly efficient with low computational needs, making it practical to deploy in limited computational settings.

Evaluation of the Sensitivity to Endocrine Therapy Index and 21-Gene Breast Recurrence Score in the SWOG S8814 Trial

PURPOSE

Chemotherapy has not demonstrated benefit over adjuvant endocrine therapy alone for postmenopausal patients with node-positive breast cancer with a 21-gene breast recurrence score (RS) of 25 or below (RS ≤ 25). We tested whether combined results from RS and the sensitivity to endocrine therapy (SET2,3) index of endocrine-related transcription (SETER/PR) adjusted for baseline prognostic index (BPI) improve prognostic assessment, and whether SET2,3 predicted benefit from anthracycline-based chemotherapy.

METHODS

A blinded retrospective clinical validation of SET2,3 in two randomized treatment arms from the SWOG S8814 trial comparing adjuvant anthracycline-based chemotherapy followed by tamoxifen endocrine therapy for 5 years, versus tamoxifen alone. SET2,3 assay was calibrated and measured using whole-transcriptome RNA sequence of tumor samples already tested for RS. The primary end point was disease-free survival (DFS).

RESULTS

There were 106 events in 283 patients over a median follow-up of 8.99 years. Proportional hazards assumptions were met during the first 5 years only. SET2,3 index and RS were not correlated (r = -0.04) and were independently prognostic (SET2,3: hazard ratio [HR], 0.48 per unit; 95% CI, 0.34 to 0.68; P < .001; RS: HR, 1.28 per 10 units; 95% CI, 1.14 to 1.44; P < .001). SET2,3 index did not predict chemotherapy benefit (interaction P = .77). SET2,3 was high in 93/175 (53%) patients with RS ≤ 25 (concordant low-risk), with 5-year DFS 97%. SET2,3 was low in 55/108 (51%) patients with RS > 25 (concordant high-risk), with 5-year DFS 53%. Both components of SET2,3 index were prognostic after adjustment for RS: SETER/PR (HR, 0.65; 95% CI, 0.46 to 0.92) and BPI (HR, 0.45; 95% CI, 0.31 to 0.64).

CONCLUSION

SET2,3 index was not correlated with RS, demonstrated additive prognostic performance, and was not chemopredictive in this subset of patients from S8814. The SETER/PR and BPI components of SET2,3 each added prognostic information to RS.

A novel survival prediction signature outperforms PAM50 and artificial intelligence-based feature-selection methods

The robustness of a breast cancer gene signature, the super-proliferation set (SPS), is initially tested and investigated on breast cancer cell lines from the Cancer Cell Line Encyclopaedia (CCLE). Previously, SPS was derived via a meta-analysis of 47 independent breast cancer gene signatures, benchmarked on survival information from clinical data in the NKI dataset. Here, relying on the stability of cell line data and associative prior knowledge, we first demonstrate through Principal Component Analysis (PCA) that SPS prioritizes survival information over secondary subtype information, surpassing both PAM50 and Boruta, an artificial intelligence-based feature-selection algorithm, in this regard. We can also extract higher resolution 'progression' information using SPS, dividing survival outcomes into several clinically relevant stages ('good', 'intermediate', and 'bad) based on different quadrants of the PCA scatterplot. Furthermore, by transferring these 'progression' annotations onto independent clinical datasets, we demonstrate the generalisability of our method on actual patient data. Finally, via the characteristic genetic profiles of each quadrant/stage, we identified efficacious drugs using their gene reversal scores that can shift signatures across quadrants/stages, in a process known as gene signature reversal. This confirms the power of meta-analytical approaches for gene signature inference in breast cancer, as well as the clinical benefit in translating these inferences onto real-world patient data for more targeted therapies.

A Comprehensive Landscape of Imaging Feature-Associated RNA Expression Profiles in Human Breast Tissue

The expression abundance of transcripts in nondiseased breast tissue varies among individuals. The association study of genotypes and imaging phenotypes may help us to understand this individual variation. Since existing reports mainly focus on tumors or lesion areas, the heterogeneity of pathological image features and their correlations with RNA expression profiles for nondiseased tissue are not clear. The aim of this study is to discover the association between the nucleus features and the transcriptome-wide RNAs. We analyzed both microscopic histology images and RNA-sequencing data of 456 breast tissues from the Genotype-Tissue Expression (GTEx) project and constructed an automatic computational framework. We classified all samples into four clusters based on their nucleus morphological features and discovered feature-specific gene sets. The biological pathway analysis was performed on each gene set. The proposed framework evaluates the morphological characteristics of the cell nucleus quantitatively and identifies the associated genes. We found image features that capture population variation in breast tissue associated with RNA expressions, suggesting that the variation in expression pattern affects population variation in the morphological traits of breast tissue. This study provides a comprehensive transcriptome-wide view of imaging-feature-specific RNA expression for healthy breast tissue. Such a framework could also be used for understanding the connection between RNA expression and morphology in other tissues and organs. Pathway analysis indicated that the gene sets we identified were involved in specific biological processes, such as immune processes.

Predictive Biomarkers for Response to Immunotherapy in Triple Negative Breast Cancer: Promises and Challenges

Triple negative breast cancer (TNBC) is a highly heterogeneous disease with a poor prognosis and a paucity of therapeutic options. In recent years, immunotherapy has emerged as a new treatment option for patients with TNBC. However, this therapeutic evolution is paralleled by a growing need for biomarkers which allow for a better selection of patients who are most likely to benefit from this immune checkpoint inhibitor (ICI)-based regimen. These biomarkers will not only facilitate a better optimization of treatment strategies, but they will also avoid unnecessary side effects in non-responders, and limit the increasing financial toxicity linked to the use of these agents. Huge efforts have been deployed to identify predictive biomarkers for the ICI, but until now, the fruits of this labor remained largely unsatisfactory. Among clinically validated biomarkers, only programmed death-ligand 1 protein (PD-L1) expression has been prospectively assessed in TNBC trials. In addition to this, microsatellite instability and a high tumor mutational burden are approved as tumor agnostic biomarkers, but only a small percentage of TNBC fits this category. Furthermore, TNBC should no longer be approached as a single biological entity, but rather as a complex disease with different molecular, clinicopathological, and tumor microenvironment subgroups. This review provides an overview of the validated and evolving predictive biomarkers for a response to ICI in TNBC.

Survival advantage of locoregional and systemic therapy in oligometastatic breast cancer: an international retrospective cohort study (OLIGO-BC1)

BACKGROUND

An international retrospective cohort study was conducted to clarify the survival advantage of combination therapy with locoregional and systemic therapy (ST) in oligometastatic breast cancer (BC).

METHODS

Patients with oligometastatic BC diagnosed from 2007 to 2012 were enrolled in center hospitals in China, Korea and Japan. It was defined as a low-volume metastatic disease at up to five sites and not necessarily in the same organ. Cases with brain, pleural, peritoneal and pericardial metastases were excluded. The primary endpoint was overall survival (OS) from the initial diagnosis of oligometastases. OS was summarized using the Kaplan-Meier method. A multivariable Cox regression model was used to estimate the hazard ratio (HR) for clinicopathological factors.

RESULTS

Among 1,295 cases registered from February 2018 to May 2019, 932 remained for analysis after the exclusion of unavailable cases and locoregional recurrence. One metastatic site was found in 400 cases, 2 in 243, 3 in 130, 4 in 86 and 5 in 73. At the median follow-up of 4.5 years, 5-year OS was 54.7% and 39.7% for 321 cases in the combination therapy group and 611 cases in the ST group, respectively. An adjusted HR was 0.66 (95% confidence interval: 0.55, 0.79). Some types of ST without chemotherapy alone, younger age, ECOG performance status 0, early-stage BC, non-triple negative subtype, fewer metastatic sites and longer duration of surgery to relapse were significantly favorable prognostic factors.

CONCLUSION

Combination therapy may be considered for longer survival under some conditions in oligometastatic BC.