Breast cancer: The translation of big genomic data to cancer precision medicine

Capturing artificial intelligence applications' value proposition in healthcare - a qualitative research study

Artificial intelligence (AI) applications pave the way for innovations in the healthcare (HC) industry. However, their adoption in HC organizations is still nascent as organizations often face a fragmented and incomplete picture of how they can capture the value of AI applications on a managerial level. To overcome adoption hurdles, HC organizations would benefit from understanding how they can capture AI applications' potential.We conduct a comprehensive systematic literature review and 11 semi-structured expert interviews to identify, systematize, and describe 15 business objectives that translate into six value propositions of AI applications in HC.Our results demonstrate that AI applications can have several business objectives converging into risk-reduced patient care, advanced patient care, self-management, process acceleration, resource optimization, and knowledge discovery.We contribute to the literature by extending research on value creation mechanisms of AI to the HC context and guiding HC organizations in evaluating their AI applications or those of the competition on a managerial level, to assess AI investment decisions, and to align their AI application portfolio towards an overarching strategy.

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.

Exploring Biomarkers in Breast Cancer: Hallmarks of Diagnosis, Treatment, and Follow-Up in Clinical Practice

Breast cancer is a prevalent malignancy in the present day, particularly affecting women as one of the most common forms of cancer. A significant portion of patients initially present with localized disease, for which curative treatments are pursued. Conversely, another substantial segment is diagnosed with metastatic disease, which has a worse prognosis. Recent years have witnessed a profound transformation in the prognosis for this latter group, primarily due to the discovery of various biomarkers and the emergence of targeted therapies. These biomarkers, encompassing serological, histological, and genetic indicators, have demonstrated their value across multiple aspects of breast cancer management. They play crucial roles in initial diagnosis, aiding in the detection of relapses during follow-up, guiding the application of targeted treatments, and offering valuable insights for prognostic stratification, especially for highly aggressive tumor types. Molecular markers have now become the keystone of metastatic breast cancer diagnosis, given the diverse array of chemotherapy options and treatment modalities available. These markers signify a transformative shift in the arsenal of therapeutic options against breast cancer. Their diagnostic precision enables the categorization of tumors with elevated risks of recurrence, increased aggressiveness, and heightened mortality. Furthermore, the existence of therapies tailored to target specific molecular anomalies triggers a cascade of changes in tumor behavior. Therefore, the primary objective of this article is to offer a comprehensive review of the clinical, diagnostic, prognostic, and therapeutic utility of the principal biomarkers currently in use, as well as of their clinical impact on metastatic breast cancer. In doing so, our goal is to contribute to a more profound comprehension of this complex disease and, ultimately, to enhance patient outcomes through more precise and effective treatment strategies.

Genomics in Clinical trials for Breast Cancer

Breast cancer (B.C.) still has increasing incidences and mortality rates globally. It is known that B.C. and other cancers have a very high rate of genetic heterogeneity and genomic mutations. Traditional oncology approaches have not been able to provide a lasting solution. Targeted therapeutics have been instrumental in handling the complexity and resistance associated with B.C. However, the progress of genomic technology has transformed our understanding of the genetic landscape of breast cancer, opening new avenues for improved anti-cancer therapeutics. Genomics is critical in developing tailored therapeutics and identifying patients most benefit from these treatments. The next generation of breast cancer clinical trials has incorporated next-generation sequencing technologies into the process, and we have seen benefits. These innovations have led to the approval of better-targeted therapies for patients with breast cancer. Genomics has a role to play in clinical trials, including genomic tests that have been approved, patient selection and prediction of therapeutic response. Multiple clinical trials in breast cancer have been done and are still ongoing, which have applied genomics technology. Precision medicine can be achieved in breast cancer therapy with increased efforts and advanced genomic studies in this domain. Genomics studies assist with patient outcomes improvement and oncology advancement by providing a deeper understanding of the biology behind breast cancer. This article will examine the present state of genomics in breast cancer clinical trials.

Multiomics insights on the onset, progression, and metastatic evolution of breast cancer

Breast cancer is the most common malignant neoplasm in women. Despite progress to date, 700,000 women worldwide died of this disease in 2020. Apparently, the prognostic markers currently used in the clinic are not sufficient to determine the most appropriate treatment. For this reason, great efforts have been made in recent years to identify new molecular biomarkers that will allow more precise and personalized therapeutic decisions in both primary and recurrent breast cancers. These molecular biomarkers include genetic and post-transcriptional alterations, changes in protein expression, as well as metabolic, immunological or microbial changes identified by multiple omics technologies (e.g., genomics, epigenomics, transcriptomics, proteomics, glycomics, metabolomics, lipidomics, immunomics and microbiomics). This review summarizes studies based on omics analysis that have identified new biomarkers for diagnosis, patient stratification, differentiation between stages of tumor development (initiation, progression, and metastasis/recurrence), and their relevance for treatment selection. Furthermore, this review highlights the importance of clinical trials based on multiomics studies and the need to advance in this direction in order to establish personalized therapies and prolong disease-free survival of these patients in the future.

Exploring the Synergistic Mechanism of AP2A2 Transcription Factor Inhibition via Molecular Modeling and Simulations as a Novel Computational Approach for Combating Breast Cancer: In Silico Interpretations

Studies have shown that transcription factor AP2A2 (activator protein-2 alpha-2) is involved in the expression of DLEC1, a tumor suppressor gene, which, when mutated, will cause breast cancer and is thus an excellent target for breast cancer studies. Therefore, in the present research, a synergistic approach toward combating breast cancer is proposed by blocking AP2A2 factor, and allowing the cancer cells to be sensitive to anti-cancer drugs. The effect of AP2A2 on breast cancer was first understood via gene analysis from cBioPortal. AP2A2 was then modeled using RaptorX and its structure was validated from Ramachandran plots. Using all ligands from MolPort database, molecular docking was performed against AP2A2, from which the top three best docked ligands were studied for toxicity in humans using Protox-II. Once the ligands passed these tests, the best complexes were simulated at 200ns in Desmond Maestro, to comprehend their stabilities, followed by the computations of free energies of binding via Molecular mechanics- Generalized Born Solvent Accessibility method (MM-GBSA). The results showed that molecules MolPort-005-945-556 (sachharolipids), MolPort-001-741-124 (flavonoids), and MolPort-005-944-667 (lignan glycosides) with AP2A2 passed toxicity evaluation and belonged to toxicity classes 6, 5, and 5, respectively, with good docking energies. 200 ns simulations revealed stable complexes with slight conformational changes. Stability of ligands was confirmed via snapshots at every 20 ns of the trajectory. Radial distribution of these molecules against the protein revealed very slight deviation from binding pocket. Additionally, the free binding energies for these complexes were found to be - 54.93 ± 12.982 kcal/mol, - 44.39 ± 14.393 kcal/mol, and - 66.51 ± 13.522 kcal/mol, respectively. A preliminary computational validation of the inability of AP2A2 to bind to DLEC1 in the presence of ligands offers beneficial insights into the potential of these ligands. Therefore, this study sheds light on the potential natural molecules that could stably block AP2A2 with least deviation and act in synergy to aid anti-cancer drugs work on breast cancer cells.

A pan-cancer analysis of pituitary tumor-transforming 3, pseudogene

BACKGROUND

Although evidence regarding pituitary tumor-transforming 3, pseudogene (PTTG3P) involvement in human cancers has been acquired via human and animal model-based molecular studies, there is a lack of pan-cancer analysis of this gene in human tumors.

METHODS

Tumor-causing effects of PTTG3P in 24 human tumors were explored using The Cancer Genome Atlas (TCGA) datasets from different bioinformatics databases and applying in silico tools such as The University of ALabama at Birmingham CANcer (UALCAN), Human Protein Atlas (HPA), Kaplan Meier (KM) plotter, cBioPortal, Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), Cytoscape, Database for Annotation, Visualization, and Integrated Discovery (DAVID), Tumor IMmune Estimation Resource (TIMER), and Comparative Toxicogenomics Database (CTD). Then, via in vitro experiments, including RNA sequencing (RNA-seq) and targeted bisulfite sequencing (bisulfite-seq), expression and promoter methylation levels of PTTG3P were verified in cell lines.

RESULTS

The PTTG3P expression was overexpressed across 23 malignancies and its overexpression was further found significantly effecting the overall survival (OS) durations of the esophageal carcinoma (ESCA) and head and neck cancer (HNSC) patients. This important information helps us to understand that PTTG3P plays a significant role in the development and progression of ESCA and HNSC. As for PTTG3P functional mechanisms, this gene along with its other binding partners was significantly concentrated in "Oocyte meiosis", "Cell cycle", "Ubiquitin mediated proteolysis", and "Progesterone-mediated oocyte maturation". Moreover, ESCA and HNSC tissues having the higher expression of PTTG3P were found to have lower promoter methylation levels of PTTG3P and higher CD8+ T immune cells level. Additionally, PTTG3P expression-regulatory drugs were also explored in the current manuscript for designing appropriate treatment strategies for ESCA and HNSC with respect to PTTG3P expression.

CONCLUSION

Our pan-cancer based findings provided a comprehensive account of the oncogenic role and utilization of PTTG3P as a novel molecular biomarker of ESCA and HNSC.

Challenges and Opportunities for Data Science in Women's Health

The intersection of women's health and data science is a field of research that has historically trailed other fields, but more recently it has gained momentum. This growth is being driven not only by new investigators who are moving into this area but also by the significant opportunities that have emerged in new methodologies, resources, and technologies in data science. Here, we describe some of the resources and methods being used by women's health researchers today to meet challenges in biomedical data science. We also describe the opportunities and limitations of applying these approaches to advance women's health outcomes and the future of the field, with emphasis on repurposing existing methodologies for women's health.

MRI-based breast cancer radiogenomics using RNA profiling: association with subtypes in a single-center prospective study

BACKGROUND

There are few prospective studies on the correlations between MRI features and whole RNA-sequencing data in breast cancer according to molecular subtypes. The purpose of our study was to explore the association between genetic profiles and MRI phenotypes of breast cancer and to identify imaging markers that influences the prognosis and treatment according to subtypes.

METHODS

From June 2017 to August 2018, MRIs of 95 women with invasive breast cancer were prospectively analyzed, using the breast imaging-reporting and data system and texture analysis. Whole RNA obtained from surgical specimens was analyzed using next-generation sequencing. The association between MRI features and gene expression profiles was analyzed in the entire tumor and subtypes. Gene networks, enriched functions, and canonical pathways were analyzed using Ingenuity Pathway Analysis. The P value for differential expression was obtained using a parametric F test comparing nested linear models and adjusted for multiple testing by reporting Q value.

RESULTS

In 95 participants (mean age, 53 years ± 11 [standard deviation]), mass lesion type was associated with upregulation of CCL3L1 (sevenfold) and irregular mass shape was associated with downregulation of MIR421 (sixfold). In estrogen receptor-positive cancer with mass lesion type, CCL3L1 (21-fold), SNHG12 (11-fold), and MIR206 (sevenfold) were upregulated, and MIR597 (265-fold), MIR126 (12-fold), and SOX17 (fivefold) were downregulated. In triple-negative breast cancer with increased standard deviation of texture analysis on precontrast T1-weighted imaging, CLEC3A (23-fold), SRGN (13-fold), HSPG2 (sevenfold), KMT2D (fivefold), and VMP1 (fivefold) were upregulated, and IGLC2 (73-fold) and PRDX4 (sevenfold) were downregulated (all, P < 0.05 and Q < 0.1). Gene network and functional analysis showed that mass type estrogen receptor-positive cancers were associated with cell growth, anti-estrogen resistance, and poor survival.

CONCLUSION

MRI characteristics are associated with the different expressions of genes related to metastasis, anti-drug resistance, and prognosis, depending on the molecular subtypes of breast cancer.

Deep Learning Techniques with Genomic Data in Cancer Prognosis: A Comprehensive Review of the 2021-2023 Literature

Deep learning has brought about a significant transformation in machine learning, leading to an array of novel methodologies and consequently broadening its influence. The application of deep learning in various sectors, especially biomedical data analysis, has initiated a period filled with noteworthy scientific developments. This trend has majorly influenced cancer prognosis, where the interpretation of genomic data for survival analysis has become a central research focus. The capacity of deep learning to decode intricate patterns embedded within high-dimensional genomic data has provoked a paradigm shift in our understanding of cancer survival. Given the swift progression in this field, there is an urgent need for a comprehensive review that focuses on the most influential studies from 2021 to 2023. This review, through its careful selection and thorough exploration of dominant trends and methodologies, strives to fulfill this need. The paper aims to enhance our existing understanding of applications of deep learning in cancer survival analysis, while also highlighting promising directions for future research. This paper undertakes aims to enrich our existing grasp of the application of deep learning in cancer survival analysis, while concurrently shedding light on promising directions for future research in this vibrant and rapidly proliferating field.

A novel risk score model based on gamma-aminobutyric acid signature predicts the survival prognosis of patients with breast cancer

Background

Gamma-aminobutyric acid (GABA) participates in the migration, differentiation, and proliferation of tumor cells. However, the GABA-related risk signature has never been investigated. Hence, we aimed to develop a reliable gene signature based on GABA pathways-related genes (GRGs) to predict the survival prognosis of breast cancer patients.

Methods

GABA-related gene sets were acquired from the MSigDB database, while mRNA gene expression profiles and corresponding clinical data of breast cancer patients were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Univariate Cox regression analysis was used to identify prognostic-associated GRGs. Subsequently, LASSO analysis was applied to establish a risk score model. We also constructed a clinical nomogram to perform the survival evaluation. Besides, ESTIMATE and ssGSEA algorithms were used to assess the immune cell infiltration among the risk score subgroups.

Results

A GRGs-related risk score model was constructed in the TCGA cohort, and validated in the GSE21653 cohort. The risk score was significantly related to the overall survival of breast cancer patients, which could predict the survival prognosis of breast cancer patients independently of other clinical features. Breast cancer patients in the low-risk score group exhibited higher immune cell infiltration levels.

Conclusion

A novel prognostic model containing five GRGs could accurately predict the survival prognosis and immune infiltration of breast cancer patients. Our findings provided a novel insight into investigating the immunoregulation roles of GRGs.

miRNA-874-3p inhibits the migration, invasion and proliferation of breast cancer cells by targeting VDAC1

Breast cancer is an important cause of crisis for women's life and health. Voltage-dependent anion channel 1 (VDAC1) is mainly localized in the outer mitochondrial membrane of all eukaryotes, and it plays a crucial role in the cell as the main interface between mitochondria and cellular metabolism. Through bioinformatics, we found that VDAC1 is abnormally highly expressed in breast cancer, and the prognosis of breast cancer patients with high VDAC1 expression is poor. Through in vivo and in vitro experiments, we found that VDAC1 can promote the proliferation, migration and invasion of breast cancer cells. Further research we found that VDAC1 can activate the wnt signaling pathway. Through analysis, we found that miR-874-3p can regulate the expression of VDAC1, and the expression of miR-874-3p is decreased in breast cancer, resulting in the increase of VDAC1 expression. Our findings will provide new targets and ideas for the prevention and treatment of breast cancer.

Applications of artificial intelligence multiomics in precision oncology

Cancer is the second leading worldwide disease that depends on oncogenic mutations and non-mutated genes for survival. Recent advancements in next-generation sequencing (NGS) have transformed the health care sector with big data and machine learning (ML) approaches. NGS data are able to detect the abnormalities and mutations in the oncogenes. These multi-omics analyses are used for risk prediction, early diagnosis, accurate prognosis, and identification of biomarkers in cancer patients. The availability of these cancer data and their analysis may provide insights into the biology of the disease, which can be used for the personalized treatment of cancer patients. Bioinformatics tools are delivering this promise by managing, integrating, and analyzing these complex datasets. The clinical outcomes of cancer patients are improved by the use of various innovative methods implicated particularly for diagnosis and therapeutics. ML-based artificial intelligence (AI) applications are solving these issues to a great extent. AI techniques are used to update the patients on a personalized basis about their treatment procedures, progress, recovery, therapies used, dietary changes in lifestyles patterns along with the survival summary of previously recovered cancer patients. In this way, the patients are becoming more aware of their diseases and the entire clinical treatment procedures. Though the technology has its own advantages and disadvantages, we hope that the day is not so far when AI techniques will provide personalized treatment to cancer patients tailored to their needs in much quicker ways.

Comprehensive analysis of nicotinamide metabolism-related signature for predicting prognosis and immunotherapy response in breast cancer

Background

Breast cancer (BC) is the most common malignancy among women. Nicotinamide (NAM) metabolism regulates the development of multiple tumors. Herein, we sought to develop a NAM metabolism-related signature (NMRS) to make predictions of survival, tumor microenvironment (TME) and treatment efficacy in BC patients.

Methods

Transcriptional profiles and clinical data from The Cancer Genome Atlas (TCGA) were analyzed. NAM metabolism-related genes (NMRGs) were retrieved from the Molecular Signatures Database. Consensus clustering was performed on the NMRGs and the differentially expressed genes between different clusters were identified. Univariate Cox, Lasso, and multivariate Cox regression analyses were sequentially conducted to develop the NAM metabolism-related signature (NMRS), which was then validated in the International Cancer Genome Consortium (ICGC) database and Gene Expression Omnibus (GEO) single-cell RNA-seq data. Further studies, such as gene set enrichment analysis (GSEA), ESTIMATE, CIBERSORT, SubMap, and Immunophenoscore (IPS) algorithm, cancer-immunity cycle (CIC), tumor mutation burden (TMB), and drug sensitivity were performed to assess the TME and treatment response.

Results

We identified a 6-gene NMRS that was significantly associated with BC prognosis as an independent indicator. We performed risk stratification according to the NMRS and the low-risk group showed preferable clinical outcomes (P < 0.001). A comprehensive nomogram was developed and showed excellent predictive value for prognosis. GSEA demonstrated that the low-risk group was predominantly enriched in immune-associated pathways, whereas the high-risk group was enriched in cancer-related pathways. The ESTIMATE and CIBERSORT algorithms revealed that the low-risk group had a higher abundance of anti-tumor immunocyte infiltration (P < 0.05). Results of Submap, IPS, CIC, TMB, and external immunotherapy cohort (iMvigor210) analyses showed that the low-risk group were indicative of better immunotherapy response (P < 0.05).

Conclusions

The novel signature offers a promising way to evaluate the prognosis and treatment efficacy in BC patients, which may facilitate clinical practice and management.

A Combined Nomogram Model to Preoperatively Predict Positive Sentinel Lymph Biopsy for Breast Cancer In Iranian Population

Background

Axillary dissection in breast cancer provides useful information on the degree of axillary nodule involvement, which serves as a reliable indicator for the prognosis and staging of breast cancer in patients. The aim of this study was to develop and validate the nomogram model by combining prognostic factors and clinical features to predict the node status of preoperative breast guard positive node cancer.

Materials and Methods

Subjects consisted of patients referring to hospitals with the diagnosis of breast cancer. Patients were allowed to substitute molecular subtypes with data on breast cancer diagnosis and prognosis as well as sentinel node status. The bootstrap review was used for internal validation. The predicted performance was evaluated based on the area under the receiver operating characteristic curve. According to the logistic regression analysis, the nomograms reported material strength between predictors and final status reliability.

Results

1172 patients participated in the study, of whom only 539 patients had axillary lymph node involvement. The subtype, family history, calcification, and necrosis were not significantly related to axillary lymph node involvement. Tumor size, histological type, and lymphovascular invasion in multivariate logistic regression were significantly and directly correlated with axillary lymph node involvement.

Conclusion

Nomograms, depending on the population, help make decisions to prevent axillary surgery. It seems that the prediction model presented in this study, based on the results of the neuromography, can help surgeons make a more informed decision on underarm surgery. Moreover, in some cases, their surgical program will be informed by accurate medical care and preclusion of major surgeries such as ALND.

Novel Markers for Liquid Biopsies in Cancer Management: Circulating Platelets and Extracellular Vesicles

Although radiologic imaging and histologic assessment of tumor tissues are classic approaches for diagnosis and monitoring of treatment response, they have many limitations. These include challenges in distinguishing benign from malignant masses, difficult access to the tumor, high cost of the procedures, and tumor heterogeneity. In this setting, liquid biopsy has emerged as a potential alternative for both diagnostic and monitoring purposes. The approaches to liquid biopsy include cell-free DNA/circulating tumor DNA, long and micro noncoding RNAs, proteins/peptides, carbohydrates/lectins, lipids, and metabolites. Other approaches include detection and analysis of circulating tumor cells, extracellular vesicles, and tumor-activated platelets. Ultimately, reliable use of liquid biopsies requires bioinformatics and statistical integration of multiple datasets to achieve approval in a Clinical Laboratory Improvement Amendments setting. This review provides a balanced and critical assessment of recent discoveries regarding tumor-derived biomarkers in liquid biopsies along with the potential and pitfalls for cancer detection and longitudinal monitoring.

Breast Cancer Genomics: Primary and Most Common Metastases

Specific genomic alterations have been found in primary breast cancer involving driver mutations that result in tumorigenesis. Metastatic breast cancer, which is uncommon at the time of disease onset, variably impacts patients throughout the course of their disease. Both the molecular profiles and diverse genomic pathways vary in the development and progression of metastatic breast cancer. From the most common metastatic site (bone), to the rare sites such as orbital, gynecologic, or pancreatic metastases, different levels of gene expression indicate the potential involvement of numerous genes in the development and spread of breast cancer. Knowledge of these alterations can, not only help predict future disease, but also lead to advancement in breast cancer treatments. This review discusses the somatic landscape of breast primary and metastatic tumors.

A Signature of Three Apoptosis-Related Genes Predicts Overall Survival in Breast Cancer

Background

The commonest malignancy in women is known as breast cancer (BC). Numerous studies demonstrated that apoptosis appears to be critical to the management and clinical outcome of BC patients. The purpose of this study is to explore the potential connection between apoptosis and BC and establish the apoptosis-associated gene signature in BC.

Methods

The data of BC patient transcripts and related clinical information comes from the Cancer Genome Atlas Database (TCGA), and the genes related to apoptosis come from the Molecular Characterization Database (MSigDB). We identified the abnormally expressed apoptosis-related genes in BC samples. The optimal apoptosis-related genes screened by Cox regression analysis were designed to construct a prognostic model for predicting BC patients. Using the Nom Chart to Predict 1-Year, 3-Year, and 5-Year overall survival for BC patients. The gene signature-related functional pathways were explored by gene set enrichment analysis (GSEA).

Results

Three genes [alpha subunit of the interleukin 3 receptor (IL3RA), apoptosis-inducing factor mitochondrial-associated 1 (AIFM1), and phosphatidylinositol-3 kinase catalytic alpha (PIK3CA)] correlated with apoptosis were shown to be strongly linked to the overall survival of BC. Survival analysis shows that the risk score is directly proportional to the poor prognosis of BC patients. Risk assessment based on three genetic characteristics (age, pathological stage N, and pathological stage M) can independently predict the prognosis of patients with BC. The Nom chart is most suitable for assessing the long-term survival rate of BC patients. The results of GSEA demonstrated that numerous cell cycle-related pathways were abundant in the high-risk group.

Conclusion

We constructed an apoptosis-associated gene signature in BC, which had a potential clinical application prospect for BC patients.

A Proposed TUSC7/miR-211/Nurr1 ceRNET Might Potentially be Disturbed by a cer-SNP rs2615499 in Breast Cancer

Evidence and in silico analyses showed that TUSC7, miR-211, and Nurr1 may be involved in BC pathogenesis by ceRNET signaling axis. This study aimed to investigate the potential role of TUSC7/miR-211/Nurr1 ceRNET and rs2615499 variant as a novel cer-SNP in BC subjects. The expression assays were conducted by qPCR on tumor tissues (n = 50), tumor-adjacent normal tissues (TANTs) (n = 50), and clinically healthy control tissues (n = 50). The expression of TUSC7 and Nurr1 significantly decreased, but the level of miR-211 significantly increased in tumor tissues compared to TANTs and healthy normal tissues. Altered expression of TUSC7 and miR-211 was associated with poor prognosis of patients. The Nurr1 exhibited a double-edged sword-like activity in BC. In addition, TUSC7, Nurr1, and miR-211 expressions were significantly related to a novel BC-associated rs2615499 (A > C) located in the miR-211 binding site on Nurr1 3'-UTR. In the second part of the study, a case-control study was performed on BC patients (n = 100) and matched healthy controls (n = 100). The genomic DNA was isolated and genotyping was performed using Tetra-Primer ARMS PCR. The CC and AC genotypes were associated with higher expression levels of Nurr1 and worse outcomes of the disease. Our findings revealed that TUSC7 functions as a tumor suppressor in BC potentially via miR-211/Nurr1, which might be disturbed by the cer-SNP rs2615499. However, functional studies are needed to validate these results.

Monoplex and multiplex immunoassays: approval, advancements, and alternatives

Immunoassays are a powerful diagnostic tool and are widely used for the quantification of proteins and biomolecules in medical diagnosis and research. Enzyme-linked immunosorbent assay (ELISA) is the most commonly used immunoassay format and allows the detection of biomarkers at a very low concentration. The diagnostic platforms such as enzyme immunoassay (EIA), chemiluminescence (CL) assay, polymerase chain reaction (PCR), flow cytometry (FC), and mass spectrometry (MS) have been used to identify molecular biomarkers. However, these diagnostic tools requiring expensive equipment, long testing time, and qualified personnel that is not always available in small local hospitals with limited resources. The lateral flow immunoassay (LFIA) platform was developed for rapidly obtaining laboratory results and to make urgent decisions in emergency medicine, as well as the recently introduced concept of testing at the site of care (point-of-care, POC). The simultaneous measurement of different substances from a single sample called multiplex assays have become increasingly significant for in vitro quantification of multiple analytes in a single sample, thereby minimising cost, time, and volume. In multiplex immunoassays, the ligands are immobilized either in planar format (flat surface) or on microspheres in suspension that binds to target analytes in sample. The multiplex technology has established itself in proteomic networks and pathways, validation of genomic discoveries, and in the development of clinical biomarkers. In the present review article, various types of monoplex/simplex and complex/multiplex immunoassays have been analysed that are increasingly being applied in laboratory medicine. Also, some advantages and disadvantages of these multiplex assays have also been included such as experimental animals, in vitro tests using cell lines and tissue samples, 3-dimensional modelling and bioprinting, in silico tests, organ-on-chip, and computer modelling.

The Single-Cell Sequencing: A Dazzling Light Shining on the Dark Corner of Cancer

Tumorigenesis refers to the process of clonal dysplasia that occurs due to the collapse of normal growth regulation in cells caused by the action of various carcinogenic factors. These “successful” tumor cells pass on the genetic templates to their generations in evolutionary terms, but they also constantly adapt to ever-changing host environments. A unique peculiarity known as intratumor heterogeneity (ITH) is extensively involved in tumor development, metastasis, chemoresistance, and immune escape. An understanding of ITH is urgently required to identify the diversity and complexity of the tumor microenvironment (TME), but achieving this understanding has been a challenge. Single-cell sequencing (SCS) is a powerful tool that can gauge the distribution of genomic sequences in a single cell and the genetic variability among tumor cells, which can improve the understanding of ITH. SCS provides fundamental ideas about existing diversity in specific TMEs, thus improving cancer diagnosis and prognosis prediction, as well as improving the monitoring of therapeutic response. Herein, we will discuss advances in SCS and review SCS application in tumors based on current evidence.

Clinical utility of liquid biopsy in breast cancer: A systematic review

Advancements in genetic sequencing techniques along with the identification of specific mutations and structural changes in multiple cancer genes, make it possible to identify circulating tumor cells and cell free nucleic acids as blood-based biomarkers, serving as a liquid biopsy (LB) with great utility for the diagnosis, treatment and follow-up of patients with neoplasms. This systematic review focuses on the clinical utility of LB in patients with breast cancer (BC). Articles published between 1990 and 2021 were included. Databases searched: Trip Database, WoS, EMBASE, PubMed, SCOPUS, and Clinical Keys. Variables studied: Publication year, country, number of cases, primary study design, LB detection methods, genes found, overall survival, disease-free survival, stage, response to treatment, clinical utility, BC molecular type, systemic treatment and methodological quality of primary studies. Of 2619 articles, 74 were retained representing 12 658 patients, mainly cohort studies (66.2%), the majority were from China (15%) and Japan (12.2%). All primary studies described clinical stage and type of systemic treatment used. Most used biomarker detection method: DNA (52.7%) and type of analysis: quantification of total cfDNA (35.1%). PIK3CA mutation was most frequent (62.9%). Evidence suggests clinically useful applications of BC. Though heterogeneous, publications suggest that LB will constitute part of the standard diagnostic-therapeutic process of BC.

A Coagulation-Related Gene-Based Prognostic Model for Invasive Ductal Carcinoma

Background: Invasive ductal carcinoma (IDC) is the most common type of metastatic breast cancer. Due to the lack of valuable molecular biomarkers, the diagnosis and prognosis of IDC remain a challenge. A large number of studies have confirmed that coagulation is positively correlated with angiogenesis-related factors in metastatic breast cancer. Therefore, the purpose of this study was to construct a COAGULATION-related genes signature for IDC using the bioinformatics approaches. Methods: The 50 hallmark gene sets were obtained from the molecular signature database (MsigDB) to conduct Gene Set Variation Analysis (GSVA). Gene Set Enrichment Analysis (GSEA) was applied to analyze the enrichment of HALLMARK_COAGULATION. The COAGULATION-related genes were extracted from the gene set. Then, Limma Package was used to identify the differentially expressed COAGULATION-related genes (DECGs) between ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) samples in GSE26340 data set. A total of 740 IDC samples from The Cancer Genome Atlas (TCGA) database were divided into a training set and a validation set (7:3). The univariate and multivariate Cox regression analyses were performed to construct a risk signature, which divided the IDC samples into the high- and low-risk groups. The overall survival (OS) curve and receiver operating characteristic (ROC) curve were drawn in both training set and validation set. Finally, a nomogram was constructed to predict the 1-, 2-, 3-, 4-, and 5-year survival rates of IDC patients. Quantitative real-time fluorescence PCR (qRT-PCR) was performed to verify the expression levels of the prognostic genes. Results: The "HALLMARK_COAGULATION" was significantly activated in IDC. There was a significant difference in the clinicopathological parameters between the DCIS and IDC patients. Twenty-four DECGs were identified, of which five genes (SERPINA1, CAPN2, HMGCS2, MMP7, and PLAT) were screened to construct the prognostic model. The high-risk group showed a significantly lower survival rate than the low-risk group both in the training set and validation set (p=3.5943e-06 and p=0.014243). The risk score was demonstrated to be an independent predictor of IDC prognosis. A nomogram including risk score, pathological_stage, and pathological_N provided a quantitative method to predict the survival probability of 1-, 2-, 3-, 4-, and 5-year in IDC patients. The results of decision curve analysis (DCA) further demonstrated that the nomogram had a high potential for clinical utility. Conclusion: This study established a COAGULATION-related gene signature and showed its prognostic value in IDC through a comprehensive bioinformatics analysis, which may provide a potential new prognostic mean for patients with IDC.

Chimeric antigen receptor-T cells immunotherapy for targeting breast cancer

Redirected chimeric antigen receptor (CAR) T-cells can recognize and eradicate cancer cells in a major histocompatibility complex independent manner. Genetic engineering of T cells through CAR expression has yielded great results in the treatment of hematological malignancies compared with solid tumors. There has been a constant effort to enhance the effectiveness of these living drugs, due to their limited success in targeting solid tumors. Poor T cell trafficking, tumor-specific antigen selection, and the immunosuppressive tumor microenvironment are considered as the main barriers in targeting solid tumors by CAR T-cells. Here, we reviewed the current state of CAR T-cell therapy in breast cancer, as the second cancer-related death in women worldwide, as well as some strategies adopted to keep the main limitations of CAR T-cells under control. Also, we summarized various approaches that have been developed to enhance the therapeutic outcomes of this treatment in solid tumors targeting.

Prospects and challenges of cancer systems medicine: from genes to disease networks

It is becoming evident that holistic perspectives toward cancer are crucial in deciphering the overwhelming complexity of tumors. Single-layer analysis of genome-wide data has greatly contributed to our understanding of cellular systems and their perturbations. However, fundamental gaps in our knowledge persist and hamper the design of effective interventions. It is becoming more apparent than ever, that cancer should not only be viewed as a disease of the genome but as a disease of the cellular system. Integrative multilayer approaches are emerging as vigorous assets in our endeavors to achieve systemic views on cancer biology. Herein, we provide a comprehensive review of the approaches, methods and technologies that can serve to achieve systemic perspectives of cancer. We start with genome-wide single-layer approaches of omics analyses of cellular systems and move on to multilayer integrative approaches in which in-depth descriptions of proteogenomics and network-based data analysis are provided. Proteogenomics is a remarkable example of how the integration of multiple levels of information can reduce our blind spots and increase the accuracy and reliability of our interpretations and network-based data analysis is a major approach for data interpretation and a robust scaffold for data integration and modeling. Overall, this review aims to increase cross-field awareness of the approaches and challenges regarding the omics-based study of cancer and to facilitate the necessary shift toward holistic approaches.

Altona Prognostic Index: A New Prognostic Index for ER-Positive and Her2-Negative Breast Cancer of No Special Type

Simple Summary

Breast cancer is the most common tumor-related cause of death in women in Europe and worldwide. The aim of our retrospective study, including 6654 women, was on the one hand to verify the validity of the worldwide known Nottingham prognostic index (NPI), and on the other hand to create a new model with even more prognostic validity. Our newly developed Altona prognostic index (API) shows significantly superior outcome in calculating progression free survival. In contrast to the NPI, the API considers characteristics such as subtypes of breast cancer, as this disease is heterogenous involving different entities, and patient’s age. Evaluating progression free survival in different subgroups, our study shows that both these prognostic indices should only be applied on a patient collective that is ≤70 years old with first primary, unifocal, unilateral breast cancer that is of no special type (NST), estrogen receptor-positive and Her2-negative to get valid prediction data.

Abstract

Breast cancer is a heterogeneous disease representing a number of different histopathologic and molecular types which should be taken into consideration if prognostic or predictive models are to be developed. The aim of the present study was to demonstrate the validity of the long-known Nottingham prognostic index (NPI) in a large retrospective study (n = 6654 women with a first primary unilateral and unifocal invasive breast cancer diagnosed and treated between April 1996 and October 2018; median follow-up time of breast cancer cases was 15.5 years [14.9–16.8]) from a single pathological institution. Furthermore, it was intended to develop an even superior risk stratification model considering an additional variable, namely the patient’s age at the time of diagnosis. Heterogeneity of these cases was addressed by focusing on estrogen receptor-positive as well as Her2-negative cases and taking the WHO-defined different tumor types into account. Calculating progression free survival Cox-regression and CART-analysis revealed significantly superior iAUC as well as concordance values in comparison to the NPI based stratification, leading to an alternative, namely the Altona prognostic index (API). The importance of the histopathological tumor type was corroborated by the fact that when calculated separately and in contrast to the most frequent so-called “No Special Type” (NST) carcinomas, neither NPI nor API could show valid prognostic stratification.

Precision Medicine and Triple-Negative Breast Cancer: Current Landscape and Future Directions

Simple Summary

The implementation of precision medicine will revolutionize cancer treatment paradigms. Notably, this goal is not far from reality: genetically similar cancers can be treated similarly. The heterogeneous nature of triple-negative breast cancer (TNBC) made it a suitable candidate to practice precision medicine. Using TNBC molecular subtyping and genomic profiling, a precision medicine-based clinical trial is ongoing. This review summarizes the current landscape and future directions of precision medicine and TNBC.

Abstract

Triple-negative breast cancer (TNBC) is an aggressive and heterogeneous subtype of breast cancer associated with a high recurrence and metastasis rate that affects African-American women disproportionately. The recent approval of targeted therapies for small subgroups of TNBC patients by the US ‘Food and Drug Administration’ is a promising development. The advancement of next-generation sequencing, particularly somatic exome panels, has raised hopes for more individualized treatment plans. However, the use of precision medicine for TNBC is a work in progress. This review will discuss the potential benefits and challenges of precision medicine for TNBC. A recent clinical trial designed to target TNBC patients based on their subtype-specific classification shows promise. Yet, tumor heterogeneity and sub-clonal evolution in primary and metastatic TNBC remain a challenge for oncologists to design adaptive precision medicine-based treatment plans.

A panel of 8-lncRNA predicts prognosis of breast cancer patients and migration of breast cancer cells

BACKGROUND

Breast cancer (BCa) is the most commonly diagnosed cancer and the leading cause of cancer death among females around the world. Recent studies have indicated that long non-coding RNAs (lncRNAs) can serve as an independent biomarker for diagnosis and prognosis in many types of cancer, including pancreatic adenocarcinoma, gastric cancer, liver cancer, and lung cancer. Previous studies have shown that many lncRNAs are associated with the occurrence and development of BCa. However, few studies have combined multiple lncRNAs to predict the prognosis of early-stage BCa patients.

METHODS

Systematic and comprehensive analysis of data from The Cancer Genome Atlas (TCGA) was conducted to identify lncRNA signatures with prognostic value in BCa. Additionally, the relative expression levels of the 8 lncRNA of several BCa cell lines were detected by quantitative real-time PCR (qPCR) and the results were substituted into a risk score formula. Finally, migration assays were used to verify the result from prognostic analysis according to the risk scores among cell lines with different risk scores.

RESULTS

Our study included 808 BCa patients with complete clinical data. A panel of 8 lncRNAs was identified using Wilcox tests as different between normal and tumor tissue of the BCa patients. This panel was used to analyze the survival of BCa patients. Patients with low risk scores had greater overall survival (OS) than those with high risk scores. Multivariate Cox regression analyses demonstrated that the lncRNA signature was an independent prognostic factor. Gene Set Enrichment Analysis (GSEA) suggested that the lncRNAs might be involved in several molecular signaling pathways implicated in BCa such as the DNA replication pathway, the cell cycle pathway, and the pentose phosphate pathway. Validation experiments in breast cancer cells to test cell migration by using wound-healing assays supported the results of the model.

CONCLUSION

Our study demonstrated that a panel of 8 lncRNAs has the potential to be used as an independent prognostic biomarker of BCa.

Making Sense of Genetic Information: The Promising Evolution of Clinical Stratification and Precision Oncology Using Machine Learning

Precision medicine is a medical approach to administer patients with a tailored dose of treatment by taking into consideration a person's variability in genes, environment, and lifestyles. The accumulation of omics big sequence data led to the development of various genetic databases on which clinical stratification of high-risk populations may be conducted. In addition, because cancers are generally caused by tumor-specific mutations, large-scale systematic identification of single nucleotide polymorphisms (SNPs) in various tumors has propelled significant progress of tailored treatments of tumors (i.e., precision oncology). Machine learning (ML), a subfield of artificial intelligence in which computers learn through experience, has a great potential to be used in precision oncology chiefly to help physicians make diagnostic decisions based on tumor images. A promising venue of ML in precision oncology is the integration of all available data from images to multi-omics big data for the holistic care of patients and high-risk healthy subjects. In this review, we provide a focused overview of precision oncology and ML with attention to breast cancer and glioma as well as the Bayesian networks that have the flexibility and the ability to work with incomplete information. We also introduce some state-of-the-art attempts to use and incorporate ML and genetic information in precision oncology.

WW45 inhibits breast cancer cell proliferation by the Hedgehog signaling pathway

WW45 is a recently-discovered tumor suppressor gene. Overexpression of WW45 was found to significantly weaken proliferation and colony formation in a human breast cancer cell line, but the molecular mechanism of WW45's inhibitiory effect on proliferation was uncertain. It is a key transcription factor of the Hedgehog signaling pathway. In particular, the mechanism of Gli1's upstream proteins in regulating Gli1's nuclear import was not clear.We collected different breast cancer cell lines and detected WW45 and Gli1 expression by western blot. Gli1 expression was detected after WW45 was overexpressed in breast cancer cells. Gli1 and WW45 were transfected into breast cancer cells, and co-immunoprecipitation was used to detect whether the two proteins had physical interaction. We confirmed Gli1 blocks WW45-induced growth inhibition and colony formation in ZR-75-30 cells through cell functional experiments. Expression of WW45 negatively correlated with Gli1 expression in breast cancer cells. WW45 affected Gli1 intracellular localization though ww-PPxY/PsP interaction. Gli1 blocked WW45-induced growth inhibition and colony formation in ZR-75-30 cells. Our results strongly suggest that expression of WW45 negatively correlates with Gli1 expression in breast cancer cells. direct physical interaction occurred between WW45 and Gli1, and WW45 affected Gli1 intracellular localization though WW-PPxY/PsP interaction. Furthermore, Gli1 blocked WW45-induced breast cancer cell growth inhibition.

Integrative analysis of DNA methylation and gene expression profiles identified potential breast cancer-specific diagnostic markers

Breast cancer is a common malignant tumor among women whose prognosis is largely determined by the period and accuracy of diagnosis. We here propose to identify a robust DNA methylation-based breast cancer-specific diagnostic signature. Genome-wide DNA methylation and gene expression profiles of breast cancer patients along with their adjacent normal tissues from the Cancer Genome Atlas (TCGA) were obtained as the training set. CpGs that with significantly elevated methylation level in breast cancer than not only their adjacent normal tissues and the other ten common cancers from TCGA but also the healthy breast tissues from the Gene Expression Omnibus (GEO) were finally remained for logistic regression analysis. Another independent breast cancer DNA methylation dataset from GEO was used as the testing set. Lots of CpGs were hyper-methylated in breast cancer samples compared with adjacent normal tissues, which tend to be negatively correlated with gene expressions. Eight CpGs located at RIIAD1, ENPP2, ESPN, and ETS1, were finally retained. The diagnostic model was reliable in separating BRCA from normal samples. Besides, chromatin accessibility status of RIIAD1, ENPP2, ESPN and ETS1 showed great differences between MCF-7 and MDA-MB-231 cell lines. In conclusion, the present study should be helpful for breast cancer early and accurate diagnosis.

Is It Possible to Personalize the Diagnosis and Treatment of Breast Cancer during Pregnancy?

The main goal of precision medicine in patients with breast cancer is to tailor the treatment according to the particular genetic makeup and the genetic changes in the cancer cells. Breast cancer occurring during pregnancy (BCP) is a complex and difficult clinical problem. Although it is not very common, both maternal and fetal outcome must be always considered when planning treatment. Pregnancy represents a significant barrier to the implementation of personalized treatment for breast cancer. Tailoring therapy mainly takes into account the stage of pregnancy, the subtype of cancer, the stage of cancer, and the patient’s preference. Results of the treatment of breast cancer in pregnancy are as yet not very satisfactory because of often delayed diagnosis, and it usually has an unfavorable outcome. Treatment of patients with pregnancy-associated breast cancer should be centralized. Centralization may result in increased experience in diagnosis and treatment and accumulated data may help us to optimize the treatment approaches, modify general treatment recommendations, and improve the survival and quality of life of the patients.

Identification of dual therapeutic targets assisted by in situ automatous DNA assembly for combined therapy in breast cancer

Breast cancer is the most common malignant disease among women worldwide. Nowadays, combined therapy against several therapeutic targets is becoming a promising treatment to enhance the survival rate of the patients with some lethal subtypes, and also proposes high demand on the discrimination of the co-existing targets in breast cancer. In this work, we designed in situ automatous DNA assembly reaction and applied it for the simultaneous identification of dual therapeutic targets using electrochemical techniques. Taking triple-negative breast cancer cell MDA-MB-231 as a model, chained strand displacement reactions were initiated after the capture probes recognized the surface biomarkers, epidermal growth factor receptor and intercellular adhesion molecule-1, respectively. Then, an increased electrochemical signaling was created to reveal the co-expression of the two targets using quantum dots as electrochemical labeling. Electrochemical results demonstrated high sensitivity and specificity of our method toward the identification of the coexisted therapeutic targets even in the serum samples, which also allowed to monitor the enhanced efficiency of combined therapy. Therefore, our method suggested a potential use in the accurate identification of therapeutic targets in breast cancer that might provide more information to facilitate the combined therapy in clinic.

Advancing Therapies for Cancer—From Mustard Gas to CAR T

The development of targeted therapeutics for cancer continues to receive intense research attention as laboratories and pharmaceutical companies seek to develop drugs and technologies that improve treatment efficacy and mitigate harmful side effects. In the aftermath of World War I, it was discovered that mustard gas destroys rapidly dividing cells and could be used to treat cancer. Since then, chemotherapy has remained a predominant treatment for cancer; however, the destruction of dividing cells throughout the body yields devastating side effects including off-target damage of the digestive tract, bone marrow, skin, and reproductive tract. Furthermore, the high mutation rate of cancerous cells often renders chemotherapy ineffective long-term. Therapies with improved specificity, localization, and efficacy are redefining cancer treatment. Herein, we define and summarize the principal advancements in targeted cancer treatment and briefly comment on the march towards personalized medicine in the treatment of human cancer.

A novel DNA methylation 10-CpG prognostic signature of disease-free survival reveal that MYBL2 is associated with high risk in prostate cancer

BACKGROUND

Prostate cancer (PC) is the most common non-cutaneous malignancy among men in the western world. However, heterogeneity remains a pressing clinical problem.

RESEARCH DESIGN AND METHODS

The least absolute shrinkage and selection operator (LASSO) was used to screen the prognostic signature. Weighted correlation network analysis (WGCNA) was used to identify the target genes associated with high-risk characteristics. Gene set enrichment analysis was used to suggest the molecular mechanism of MYBL2 in PC. In addition, in vitro experiments were carried out to validate the role of MYBL2 in PC.

RESULTS

Ten DNA methylation sites were selected as the prognostic signature. A high expression of MYBL2 was associated with a poor prognosis in PC patients. The effect of MYBL2 in PC was related to KRAS, AKT, IL21, and ATM. MYBL2 facilitates the proliferation, migration, invasion, and metastasis of PC cells.

CONCLUSIONS

We developed a DNA methylation 10-CpG prognostic signature to predict the prognosis of PC patients. And the high expression of MYBL2 in PC may be related to poor prognosis.

Precision Medicine, AI, and the Future of Personalized Health Care

The convergence of artificial intelligence (AI) and precision medicine promises to revolutionize health care. Precision medicine methods identify phenotypes of patients with less‐common responses to treatment or unique healthcare needs. AI leverages sophisticated computation and inference to generate insights, enables the system to reason and learn, and empowers clinician decision making through augmented intelligence. Recent literature suggests that translational research exploring this convergence will help solve the most difficult challenges facing precision medicine, especially those in which nongenomic and genomic determinants, combined with information from patient symptoms, clinical history, and lifestyles, will facilitate personalized diagnosis and prognostication.

Big Data Solutions for Controversies in Breast Cancer Treatment

The digital world of data is expanding with an annual growth rate of 40%, and health care is among the fastest growing sector of the digital world with an annual growth rate of 48%. Rapid growth in technology has augmented data generation; for example, electronic health records produce huge amounts of patient-level data, whereas national registries capture information on numerous factors affecting health care delivery and patient outcomes. This big data can be utilized to improve health care outcomes. This review discusses relevant applications in breast cancer treatment.

Machine learning improves the prediction of febrile neutropenia in Korean inpatients undergoing chemotherapy for breast cancer

Febrile neutropenia (FN) is one of the most concerning complications of chemotherapy, and its prediction remains difficult. This study aimed to reveal the risk factors for and build the prediction models of FN using machine learning algorithms. Medical records of hospitalized patients who underwent chemotherapy after surgery for breast cancer between May 2002 and September 2018 were selectively reviewed for development of models. Demographic, clinical, pathological, and therapeutic data were analyzed to identify risk factors for FN. Using machine learning algorithms, prediction models were developed and evaluated for performance. Of 933 selected inpatients with a mean age of 51.8 ± 10.7 years, FN developed in 409 (43.8%) patients. There was a significant difference in FN incidence according to age, staging, taxane-based regimen, and blood count 5 days after chemotherapy. The area under the curve (AUC) built based on these findings was 0.870 on the basis of logistic regression. The AUC improved by machine learning was 0.908. Machine learning improves the prediction of FN in patients undergoing chemotherapy for breast cancer compared to the conventional statistical model. In these high-risk patients, primary prophylaxis with granulocyte colony-stimulating factor could be considered.

Advancing Therapies for Cancer—From Mustard Gas to CAR T

The development of targeted therapeutics for cancer continues to receive intense research attention as laboratories and pharmaceutical companies seek to develop drugs and technologies that improve treatment efficacy and mitigate harmful side effects. In the aftermath of World War I, it was discovered that mustard gas destroys rapidly dividing cells and could be used to treat cancer. Since then, chemotherapy has remained a predominant treatment for cancer; however, the destruction of dividing cells throughout the body yields devastating side effects including off-target damage of the digestive tract, bone marrow, skin, and reproductive tract. Furthermore, the high mutation rate of cancerous cells often renders chemotherapy ineffective long-term. Therapies with improved specificity, localization, and efficacy are redefining cancer treatment. Herein, we define and summarize the principal advancements in targeted cancer treatment and briefly comment on the march towards personalized medicine in the treatment of human cancer.

Advances in the Molecular Taxonomy of Breast Cancer

Breast cancers are heterogeneous with variable morphologic features, biologic behavior and response to therapy. Traditional histopathologic features such as size, grade, and lymph node status may be used to provide a general estimate of outcome, stratifying patients into broad prognostic groups with prescribed guidelines for therapy. With this approach however, up to 85% of breast cancer patients are overtreated, and at the other end of the spectrum, 20% of patients succumb to their disease despite receiving maximum therapy. The current routine testing for the Estrogen receptor (ER) and HER2 growth factor receptor (HER2) represents the earliest attempts to provide a targeted approach to breast cancer therapy, based on molecular drivers of the disease. The pioneering works by Perou and Sorlie et al using global gene expression profiling introduced a molecular taxonomy of breast cancer with associated prognostic implications. The Luminal, HER2-enriched, and Basal-like intrinsic subtypes are generally characterized by the presence or absence of ER and HER2. They have been further analyzed and refined using integration of genomic and transcriptomic data made possible by advancements in high throughput molecular techniques and bioinformatics. Indeed, an increased understanding of the genomic landscape of these subtypes, and the molecular basis of breast cancer growth regulation, holds the promise of a more personalized patient selection for specific targeted therapies and improved outcomes.

Discordance in 21-gene recurrence scores between paired breast cancer samples is inversely associated with patient age

Background

The Oncotype DX 21-gene Recurrence Score is a genomic-based algorithm that guides adjuvant chemotherapy treatment decisions for women with early-stage, oestrogen receptor (ER)-positive breast cancer. However, there are age-related differences in chemotherapy benefit for women with intermediate Oncotype DX Recurrence Scores that are not well understood. Menstrual cycling in younger women is associated with hormonal fluctuations that might affect the expression of genomic predictive biomarkers and alter Recurrence Scores. Here, we use paired human breast cancer samples to demonstrate that the clinically employed Oncotype DX algorithm is critically affected by patient age.

Methods

RNA was extracted from 25 pairs of formalin-fixed paraffin-embedded, invasive ER-positive breast cancer samples that had been collected approximately 2 weeks apart. A 21-gene signature analogous to the Oncotype DX platform was assessed through quantitative real-time PCR, and experimental recurrence scores were calculated using the Oncotype DX algorithm.

Results

There was a significant inverse association between patient age and discordance in the recurrence score. For every 1-year decrease in age, discordance in recurrence scores between paired samples increased by 0.08 units (95% CI − 0.14, − 0.01; p = 0.017). Discordance in recurrence scores for women under the age of 50 was driven primarily by proliferation- and HER2-associated genes.

Conclusion

The Oncotype DX 21-gene Recurrence Score algorithm is critically affected by patient age. These findings emphasise the need for the consideration of patient age, particularly for women younger than 50, in the development and application of genomic-based algorithms for breast cancer care.

Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology

The multitude of multi-omics data generated cost-effectively using advanced high-throughput technologies has imposed challenging domain for research in Artificial Intelligence (AI). Data curation poses a significant challenge as different parameters, instruments, and sample preparations approaches are employed for generating these big data sets. AI could reduce the fuzziness and randomness in data handling and build a platform for the data ecosystem, and thus serve as the primary choice for data mining and big data analysis to make informed decisions. However, AI implication remains intricate for researchers/clinicians lacking specific training in computational tools and informatics. Cancer is a major cause of death worldwide, accounting for an estimated 9.6 million deaths in 2018. Certain cancers, such as pancreatic and gastric cancers, are detected only after they have reached their advanced stages with frequent relapses. Cancer is one of the most complex diseases affecting a range of organs with diverse disease progression mechanisms and the effectors ranging from gene-epigenetics to a wide array of metabolites. Hence a comprehensive study, including genomics, epi-genomics, transcriptomics, proteomics, and metabolomics, along with the medical/mass-spectrometry imaging, patient clinical history, treatments provided, genetics, and disease endemicity, is essential. Cancer Moonshot℠ Research Initiatives by NIH National Cancer Institute aims to collect as much information as possible from different regions of the world and make a cancer data repository. AI could play an immense role in (a) analysis of complex and heterogeneous data sets (multi-omics and/or inter-omics), (b) data integration to provide a holistic disease molecular mechanism, (c) identification of diagnostic and prognostic markers, and (d) monitor patient's response to drugs/treatments and recovery. AI enables precision disease management well beyond the prevalent disease stratification patterns, such as differential expression and supervised classification. This review highlights critical advances and challenges in omics data analysis, dealing with data variability from lab-to-lab, and data integration. We also describe methods used in data mining and AI methods to obtain robust results for precision medicine from "big" data. In the future, AI could be expanded to achieve ground-breaking progress in disease management.

Advancing Therapies for Cancer—From Mustard Gas to CAR T

The development of targeted therapeutics for cancer continues to receive intense research attention as laboratories and pharmaceutical companies seek to develop drugs and technologies that improve treatment efficacy and mitigate harmful side effects. In the aftermath of World War I, it was discovered that mustard gas destroys rapidly dividing cells and could be used to treat cancer. Since then, chemotherapy has remained a predominant treatment for cancer; however, the destruction of dividing cells throughout the body yields devastating side effects including off-target damage of the digestive tract, bone marrow, skin, and reproductive tract. Furthermore, the high mutation rate of cancerous cells often renders chemotherapy ineffective long-term. Therapies with improved specificity, localization, and efficacy are redefining cancer treatment. Herein, we define and summarize the principal advancements in targeted cancer treatment and briefly comment on the march towards personalized medicine in the treatment of human cancer.

Applications of personalised signalling network models in precision oncology

As our ability to provide in-depth, patient-specific characterisation of the molecular alterations within tumours rapidly improves, it is becoming apparent that new approaches will be required to leverage the power of this data and derive the full benefit for each individual patient. Systems biology approaches are beginning to emerge within this field as a potential method of incorporating large volumes of network level data and distilling a coherent, clinically-relevant prediction of drug response. However, the initial promise of this developing field is yet to be realised. Here we argue that in order to develop these precise models of individual drug response and tailor treatment accordingly, we will need to develop mathematical models capable of capturing both the dynamic nature of drug-response signalling networks and key patient-specific information such as mutation status or expression profiles. We also review the modelling approaches commonly utilised within this field, and outline recent examples of their use in furthering the application of systems biology for a precision medicine approach to cancer treatment.

Genetics and Genomics of Breast Cancer: update and translational perspectives

In the recent years the rapid scientific innovation in the evaluation of the individual's genome have allowed the identification of variants associated with the onset, treatment and prognosis of various pathologies including cancer, and with a potential impact in the assessment of therapy responses. Despite the analysis and interpretation of genomic information is considered incomplete, in many cases the identification of specific genomic profile has allowed the stratification of subgroups of patients characterized by a better response to drug therapies. Individual genome analysis has changed profoundly the diagnostic and therapeutic approach of breast cancer in the last 15 years by identifying selective molecular lesions that drive the development of neoplasms, showing that each tumor has its own genomic signature, with some specific features and some features common to several sub-types. Several personalized therapies have been (and still are being) developed showing a remarkable efficacy in the treatment of breast cancer.

Acquisition of Invasiveness by Breast Adenocarcinoma Cells Engages Established Hallmarks and Novel Regulatory Mechanisms

BACKGROUND/AIM

Proteomics of invasiveness opens a window on the complexity of the metastasis-engaged mechanisms. The extend and types of this complexity require elucidation.

MATERIALS AND METHODS

Proteomics, immunohistochemistry, immunoblotting, network analysis and systems cancer biology were used to analyse acquisition of invasiveness by human breast adenocarcinoma cells.

RESULTS

We report here that invasiveness network highlighted the involvement of hallmarks such as cell proliferation, migration, cell death, genome stability, immune system regulation and metabolism. Identified involvement of cell-virus interaction and gene silencing are potentially novel cancer mechanisms. Identified 6,113 nodes with 11,055 edges affecting 1,085 biological processes show extensive re-arrangements in cell physiology. These high numbers are in line with a similar broadness of networks built with diagnostic signatures approved for clinical use.

CONCLUSION

Our data emphasize a broad systemic regulation of invasiveness, and describe the network of this regulation.

The pan-cancer landscape of prognostic germline variants in 10,582 patients

Background

While clinical factors such as age, grade, stage, and histological subtype provide physicians with information about patient prognosis, genomic data can further improve these predictions. Previous studies have shown that germline variants in known cancer driver genes are predictive of patient outcome, but no study has systematically analyzed multiple cancers in an unbiased way to identify genetic loci that can improve patient outcome predictions made using clinical factors.

Methods

We analyzed sequencing data from the over 10,000 cancer patients available through The Cancer Genome Atlas to identify germline variants associated with patient outcome using multivariate Cox regression models.

Results

We identified 79 prognostic germline variants in individual cancers and 112 prognostic germline variants in groups of cancers. The germline variants identified in individual cancers provide additional predictive power about patient outcomes beyond clinical information currently in use and may therefore augment clinical decisions based on expected tumor aggressiveness. Molecularly, at least 12 of the germline variants are likely associated with patient outcome through perturbation of protein structure and at least five through association with gene expression differences. Almost half of these germline variants are in previously reported tumor suppressors, oncogenes or cancer driver genes with the other half pointing to genomic loci that should be further investigated for their roles in cancers.

Conclusions

Germline variants are predictive of outcome in cancer patients and specific germline variants can improve patient outcome predictions beyond predictions made using clinical factors alone. The germline variants also implicate new means by which known oncogenes, tumor suppressor genes, and driver genes are perturbed in cancer and suggest roles in cancer for other genes that have not been extensively studied in oncology. Further studies in other cancer cohorts are necessary to confirm that germline variation is associated with outcome in cancer patients as this is a proof-of-principle study.

Electronic supplementary material

The online version of this article (10.1186/s13073-020-0718-7) contains supplementary material, which is available to authorized users.