Genome-driven integrated classification of breast cancer validated in over 7,500 samples

Molecular Changes in Breast Cancer Induced by Radiation Therapy

PURPOSE

Breast cancer treatments are based on prognostic clinicopathologic features that form the basis for therapeutic guidelines. Although the utilization of these guidelines has decreased breast cancer-associated mortality rates over the past three decades, they are not adequate for individualized therapy. Radiation therapy (RT) is the backbone of breast cancer treatment. Although a highly successful therapeutic modality clinically, from a biological perspective, preclinical studies have shown RT to have the potential to alter tumor cell phenotype, immunogenicity, and the surrounding microenvironment, potentially changing the behavior of cancer cells and resulting in a significant variation in RT response. This review presents the recent advances in revealing the complex molecular changes induced by RT in the treatment of breast cancer and highlights the complexities of translating this information into clinically relevant tools for improved prognostic insights and the revelation of novel approaches for optimizing RT.

METHODS AND MATERIALS

Current literature was reviewed with a focus on recent advances made in the elucidation of tumor-associated radiation-induced molecular changes across molecular, genetic, and proteomic bases. This review was structured with the aim of providing an up-to-date overview over the very broad and complex subject matter of radiation-induced molecular changes and radioresistance, familiarizing the reader with the broader issue at hand.

RESULTS

The subject of radiation-induced molecular changes in breast cancer has been broached from various physiological focal points including that of the immune system, immunogenicity and the abscopal effect, tumor hypoxia, breast cancer classification and subtyping, molecular heterogeneity, and molecular plasticity. It is becoming increasingly apparent that breast cancer clinical subtyping alone does not adequately account for variation in RT response or radioresistance. Multiple components of the tumor microenvironment and immune system, delivered RT dose and fractionation schedules, radiation-induced bystander effects, and intrinsic tumor physiology and heterogeneity all contribute to the resultant RT outcome.

CONCLUSIONS

Despite recent advances and improvements in anticancer therapies, tumor resistance remains a significant challenge. As new analytical techniques and technologies continue to provide crucial insight into the complex molecular mechanisms of breast cancer and its treatment responses, it is becoming more evident that personalized anticancer treatment regimens may be vital in overcoming radioresistance.

Migratory Tumor Cells Cooperate with Cancer Associated Fibroblasts in Hormone Receptor-Positive and HER2-Negative Breast Cancer

Hormone receptor-positive and HER2-negative breast cancer (HR+/HER2-BC) is the most common type with a favorable prognosis under endocrine therapy. However, it still demonstrates unpredictable progression and recurrences influenced by high tumoral diversity and microenvironmental status. To address these heterogeneous molecular characteristics of HR+/HER2-BC, we aimed to simultaneously characterize its transcriptomic landscape and genetic architecture at the same resolution. Using advanced single-cell RNA and DNA sequencing techniques together, we defined four distinct tumor subtypes. Notably, the migratory tumor subtype was closely linked to genomic alterations of EGFR, related to the tumor-promoting behavior of IL6-positive inflammatory tumor-associated fibroblast, and contributing to poor prognosis. Our study comprehensively utilizes integrated analysis to uncover the complex dynamics of this breast cancer subtype, highlighting the pivotal role of the migratory tumor subtype in influencing surrounding cells. This sheds light on potential therapeutic targets by offering enhanced insights for HR+/HER2-BC treatment.

Development and validation of a reliable DNA copy-number-based machine learning algorithm (CopyClust) for breast cancer integrative cluster classification

The Integrative Cluster subtypes (IntClusts) provide a framework for the classification of breast cancer tumors into 10 distinct groups based on copy number and gene expression, each with unique biological drivers of disease and clinical prognoses. Gene expression data is often lacking, and accurate classification of samples into IntClusts with copy number data alone is essential. Current classification methods achieve low accuracy when gene expression data are absent, warranting the development of new approaches to IntClust classification. Copy number data from 1980 breast cancer samples from METABRIC was used to train multiclass XGBoost machine learning algorithms (CopyClust). A piecewise constant fit was applied to the average copy number profile of each IntClust and unique breakpoints across the 10 profiles were identified and converted into ~ 500 genomic regions used as features for CopyClust. These models consisted of two approaches: a 10-class model with the final IntClust label predicted by a single multiclass model and a 6-class model with binary reclassification in which four pairs of IntClusts were combined for initial multiclass classification. Performance was validated on the TCGA dataset, with copy number data generated from both SNP arrays and WES platforms. CopyClust achieved 81% and 79% overall accuracy with the TCGA SNP and WES datasets, respectively, a nine-percentage point or greater improvement in overall IntClust subtype classification accuracy. CopyClust achieves a significant improvement over current methods in classification accuracy of IntClust subtypes for samples without available gene expression data and is an easily implementable algorithm for IntClust classification of breast cancer samples with copy number data.

Efficient screening of pharmacological broad-spectrum anti-cancer peptides utilizing advanced bidirectional Encoder representation from Transformers strategy

In the vanguard of oncological advancement, this investigation delineates the integration of deep learning paradigms to refine the screening process for Anticancer Peptides (ACPs), epitomizing a new frontier in broad-spectrum oncolytic therapeutics renowned for their targeted antitumor efficacy and specificity. Conventional methodologies for ACP identification are marred by prohibitive time and financial exigencies, representing a formidable impediment to the evolution of precision oncology. In response, our research heralds the development of a groundbreaking screening apparatus that marries Natural Language Processing (NLP) with the Pseudo Amino Acid Composition (PseAAC) technique, thereby inaugurating a comprehensive ACP compendium for the extraction of quintessential primary and secondary structural attributes. This innovative methodological approach is augmented by an optimized BERT model, meticulously calibrated for ACP detection, which conspicuously surpasses existing BERT variants and traditional machine learning algorithms in both accuracy and selectivity. Subjected to rigorous validation via five-fold cross-validation and external assessment, our model exhibited exemplary performance, boasting an average Area Under the Curve (AUC) of 0.9726 and an F1 score of 0.9385, with external validation further affirming its prowess (AUC of 0.9848 and F1 of 0.9371). These findings vividly underscore the method's unparalleled efficacy and prospective utility in the precise identification and prognostication of ACPs, significantly ameliorating the financial and temporal burdens traditionally associated with ACP research and development. Ergo, this pioneering screening paradigm promises to catalyze the discovery and clinical application of ACPs, constituting a seminal stride towards the realization of more efficacious and economically viable precision oncology interventions.

Identifying and targeting key driver genes for collagen production within the 11q13/14 breast cancer amplicon

Genetic studies indicate that breast cancer can be divided into several basic molecular groups. One of these groups, termed IntClust-2, is characterized by amplification of a small portion of chromosome 11 and has a median survival of only five years. Several cancer-relevant genes occupy this portion of chromosome 11, and it is thought that overexpression of a combination of driver genes in this region is responsible for the poor outcome of women in this group. In this study we used a gene editing method to knock out, one by one, each of 198 genes that are located within the amplified region of chromosome 11 and determined how much each of these genes contributed to the survival of breast cancer cells. In addition to well-known drivers such as CCND1 and PAK1 , we identified two different genes ( SERPINH1 and P4HA3 ), that encode proteins involved in collagen synthesis and organization. Using both in vitro and in vivo functional analyses, we determined that P4HA3 and/or SERPINH1 provide a critical driver function on IntClust-2 basic processes, such as viability, proliferation, and migration. Inhibiting these enzymes via genetic or pharmacologic means reduced collagen synthesis and impeded oncogenic signaling transduction in cell culture models, and a small-molecule inhibitor of P4HA3 was effective in treating 11q13 tumor growth in an animal model. As collagen has a well-known association with tissue stiffness and aggressive forms of breast cancer, we believe that the two genes we identified provide an opportunity for a new therapeutic strategy in IntClust-2 breast cancers.

B-Cell-Mediated Immunity Predicts Survival of Patients With Estrogen Receptor-Positive Breast Cancer

PURPOSE

The estrogen receptor-positive (ER+) breast cancer (BC), which constitutes the majority of BC cases, exhibits highly heterogeneous clinical behavior. To aid precision treatments, we aimed to find molecular subtypes of ER+ BC representing the tumor microenvironment and prognosis.

METHODS

We analyzed RNA-seq data of 113 patients with BC and classified them according to the PAM50 intrinsic subtypes using gene expression profiles. Among them, we further focused on 44 patients with luminal-type (ER+) BC for subclassification. The Cancer Genome Atlas (TCGA) data of patients with BC were used as a validation data set to verify the new classification. We estimated the immune cell composition using CIBERSORT and further analyzed its association with clinical or molecular parameters.

RESULTS

Principal component analysis clearly divided the patients into two subgroups separately from the luminal A and B classification. The top differentially expressed genes between the subgroups were distinctly characterized by immunoglobulin and B-cell-related genes. We could also cluster a separate cohort of patients with luminal-type BC from TCGA into two subgroups on the basis of the expression of a B-cell-specific gene set, and patients who were predicted to have high B-cell immune activity had better prognoses than other patients.

CONCLUSION

Our transcriptomic approach emphasize a molecular phenotype of B-cell immunity in ER+ BC that may help to predict disease prognosis. Although further researches are required, B-cell immunity for patients with ER+ BC may be helpful for identifying patients who are good responders to chemotherapy or immunotherapy.

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.

Breast Cancer Immunity: It is TIME for the Next Chapter

Our ability to interrogate the tumor immune microenvironment (TIME) at an ever-increasing granularity has uncovered critical determinants of disease progression. Not only do we now have a better understanding of the immune response in breast cancer, but it is becoming possible to leverage key mechanisms to effectively combat this disease. Almost every component of the immune system plays a role in enabling or inhibiting breast tumor growth. Building on early seminal work showing the involvement of T cells and macrophages in controlling breast cancer progression and metastasis, single-cell genomics and spatial proteomics approaches have recently expanded our view of the TIME. In this article, we provide a detailed description of the immune response against breast cancer and examine its heterogeneity in disease subtypes. We discuss preclinical models that enable dissecting the mechanisms responsible for tumor clearance or immune evasion and draw parallels and distinctions between human disease and murine counterparts. Last, as the cancer immunology field is moving toward the analysis of the TIME at the cellular and spatial levels, we highlight key studies that revealed previously unappreciated complexity in breast cancer using these technologies. Taken together, this article summarizes what is known in breast cancer immunology through the lens of translational research and identifies future directions to improve clinical outcomes.

Breast cancer organoids derived from patients: A platform for tailored drug screening

Breast cancer stands as the most prevalent and heterogeneous malignancy affecting women globally, posing a substantial health concern. Enhanced comprehension of tumor pathology and the development of novel therapeutics are pivotal for advancing breast cancer treatment. Contemporary breast cancer investigation heavily leans on in vivo models and conventional cell culture techniques. Nonetheless, these approaches often encounter high failure rates in clinical trials due to species disparities and tissue structure variations. To address this, three-dimensional cultivation of organoids, resembling organ-like structures, has emerged as a promising alternative. Organoids represent innovative in vitro models that mirror in vivo tissue microenvironments. They retain the original tumor's diversity and facilitate the expansion of tumor samples from diverse origins, facilitating the representation of varying tumor stages. Optimized breast cancer organoid models, under precise culture conditions, offer benefits including convenient sample acquisition, abbreviated cultivation durations, and genetic stability. These attributes ensure a faithful replication of in vivo traits of breast cancer cells. As intricate cellular entities boasting spatial arrangements, breast cancer organoid models harbor substantial potential in precision medicine, organ transplantation, modeling intricate diseases, gene therapy, and drug innovation. This review delivers an overview of organoid culture techniques and outlines future prospects for organoid modeling.

Copy number aberrations in circulating tumor DNA enables prognosis prediction and molecular characterization of breast cancer

BACKGROUND

Low-pass whole-genome sequencing (LP-WGS)-based circulating tumor DNA (ctDNA) analysis is a versatile tool for somatic copy number aberration (CNA) detection, and this study aims to explore its clinical implication in breast cancer.

METHODS

We analyzed LP-WGS ctDNA data from 207 metastatic breast cancer (MBC) patients to explore prognostic value of ctDNA CNA burden and validated it in 465 stage II-III triple-negative breast cancer (TNBC) patients who received neoadjuvant chemotherapy in phase III PEARLY trial (NCT02441933). The clinical implication of locus level LP-WGS ctDNA profiling was further evaluated.

RESULTS

We found that a high baseline ctDNA CNA burden predicts poor overall survival and progression-free survival of MBC patients. The post hoc analysis of the PEARLY trial showed that a high baseline ctDNA CNA burden predicted poor disease-free survival independent from pathologic complete response (pCR), validating its robust prognostic significance. The 24-month disease-free survival rate was 96.9% and 55.9% in [pCR(+) and low I-score] and [non-pCR and high I-score] patients, respectively. The locus-level ctDNA CNA profile classified MBC patients into 5 molecular clusters and revealed targetable oncogenic CNAs. LP-WGS ctDNA and in vitro analysis identified the BCL6 amplification as a resistance factor for CDK4/6 inhibitors. We estimated ctDNA-based homologous recombination deficiency status of patients by shallowHRD algorithm, which was highest in the TNBC and correlated with platinum-based chemotherapy response.

CONCLUSIONS

These results demonstrate LP-WGS ctDNA CNA analysis as an essential tool for prognosis prediction and molecular profiling. Particularly, ctDNA CNA burden can serve as a useful determinant for escalating or de-escalating (neo)adjuvant strategy in TNBC patients.

Molecular features and clinical implications of the heterogeneity in Chinese patients with HER2-low breast cancer

The molecular heterogeneity and distinct features of HER2-low breast cancers, particularly in the Chinese population, are not well understood, limiting its precise management in the era of antibody‒drug conjugates. To address this issue, we established a cohort of 434 Chinese patients with HER2-low breast cancer (433 female and one male) and integrated genomic, transcriptomic, proteomic, and metabolomic profiling data. In this cohort, HER2-low tumors are more distinguished from HER2-0 tumors in the hormone receptor-negative subgroup. Within HER2-low tumors, significant interpatient heterogeneity also exists in the hormone receptor-negative subgroup: basal-like tumors resemble HER2-0 disease, and non-basal-like HER2-low tumors mimic HER2-positive disease. These non-basal-like HER2-low tumors are enriched in the HER2-enriched subtype and the luminal androgen receptor subtype and feature PIK3CA mutation, FGFR4/PTK6/ERBB4 overexpression and lipid metabolism activation. Among hormone receptor-positive tumors, HER2-low tumors show less loss/deletion in 17q peaks than HER2-0 tumors. In this work, we reveal the heterogeneity of HER2-low breast cancers and emphasize the need for more precise stratification regarding hormone receptor status and molecular subtype.

Omics-Based Investigations of Breast Cancer

Breast cancer (BC) is characterized by an extensive genotypic and phenotypic heterogeneity. In-depth investigations into the molecular bases of BC phenotypes, carcinogenesis, progression, and metastasis are necessary for accurate diagnoses, prognoses, and therapy assessments in predictive, precision, and personalized oncology. This review discusses both classic as well as several novel omics fields that are involved or should be used in modern BC investigations, which may be integrated as a holistic term, onco-breastomics. Rapid and recent advances in molecular profiling strategies and analytical techniques based on high-throughput sequencing and mass spectrometry (MS) development have generated large-scale multi-omics datasets, mainly emerging from the three "big omics", based on the central dogma of molecular biology: genomics, transcriptomics, and proteomics. Metabolomics-based approaches also reflect the dynamic response of BC cells to genetic modifications. Interactomics promotes a holistic view in BC research by constructing and characterizing protein-protein interaction (PPI) networks that provide a novel hypothesis for the pathophysiological processes involved in BC progression and subtyping. The emergence of new omics- and epiomics-based multidimensional approaches provide opportunities to gain insights into BC heterogeneity and its underlying mechanisms. The three main epiomics fields (epigenomics, epitranscriptomics, and epiproteomics) are focused on the epigenetic DNA changes, RNAs modifications, and posttranslational modifications (PTMs) affecting protein functions for an in-depth understanding of cancer cell proliferation, migration, and invasion. Novel omics fields, such as epichaperomics or epimetabolomics, could investigate the modifications in the interactome induced by stressors and provide PPI changes, as well as in metabolites, as drivers of BC-causing phenotypes. Over the last years, several proteomics-derived omics, such as matrisomics, exosomics, secretomics, kinomics, phosphoproteomics, or immunomics, provided valuable data for a deep understanding of dysregulated pathways in BC cells and their tumor microenvironment (TME) or tumor immune microenvironment (TIMW). Most of these omics datasets are still assessed individually using distinct approches and do not generate the desired and expected global-integrative knowledge with applications in clinical diagnostics. However, several hyphenated omics approaches, such as proteo-genomics, proteo-transcriptomics, and phosphoproteomics-exosomics are useful for the identification of putative BC biomarkers and therapeutic targets. To develop non-invasive diagnostic tests and to discover new biomarkers for BC, classic and novel omics-based strategies allow for significant advances in blood/plasma-based omics. Salivaomics, urinomics, and milkomics appear as integrative omics that may develop a high potential for early and non-invasive diagnoses in BC. Thus, the analysis of the tumor circulome is considered a novel frontier in liquid biopsy. Omics-based investigations have applications in BC modeling, as well as accurate BC classification and subtype characterization. The future in omics-based investigations of BC may be also focused on multi-omics single-cell analyses.

Evaluation of predictive and prognostic value of androgen receptor expression in breast cancer subtypes treated with neoadjuvant chemotherapy

BACKGROUND

Neoadjuvant chemotherapy is the standard treatment for local advanced breast cancer administered to shrink tumors and destroy undetected metastatic cells, thereby facilitating subsequent surgery. Previous studies have shown that AR may be used as a prognostic predictor in breast cancers, but its role in neoadjuvant therapy and the relationship with prognosis of different molecular subtypes of breast cancer need to be further explored.

METHODS

We retrospectively evaluated 1231 breast cancer patients with complete medical records at Tianjin Medical University Cancer Institute and Hospital who were treated with neoadjuvant chemotherapy between January 2018 to December 2021. All the patients were selected for prognostic analysis. The follow-up time ranged from 12 to 60 months. We first analyzed the AR expression in different subtypes of breast cancer and its correlation with clinicopathological features. Meanwhile, the association of AR expression and pCR of different breast cancer subtypes was investigated. Finally, the effect of AR status on the prognosis of different subtypes of breast cancer after neoadjuvant therapy was analyzed.

RESULTS

The positive rates of AR expression in HR + /HER2-, HR + /HER2 +, HR-/HER2 + and TNBC subtypes were 82.5%, 86.9%, 72.2% and 34.6%, respectively. Histological grade III (P = 0.014, OR = 1.862, 95% CI 1.137 to 2.562), ER positive expression (P = 0.002, OR = 0.381, 95% CI 0.102 to 0.754) and HER2 positive expression (P = 0.006, OR = 0.542, 95% CI 0.227 to 0.836) were independent related factors for AR positive expression. AR expression status was associated with pCR rate after neoadjuvant therapy only in subtype of TNBC. AR positive expression was independent protective factor for recurrence and metastasis in HR + /HER2- (P = 0.033, HR = 0.653, 95% CI 0.237 to 0.986) and HR + /HER2 + breast cancer (P = 0.012, HR = 0.803, 95% CI 0.167 to 0.959), but was independent risk factors for recurrence and metastasis in TNBC (P = 0.015, HR = 4.551, 95% CI 2.668 to 8.063). AR positive expression is not an independent predictor of HR-/HER2 + breast cancer.

CONCLUSIONS

AR expressed the lowest in TNBC, but it could be a potential marker for the prediction of pCR in neoadjuvant therapy. AR negative patients had a higher pCR rate. AR positive expression was an independent risk factor for pCR in TNBC after neoadjuvant therapy (P = 0.017, OR = 2.758, 95% CI 1.564 to 4.013). In HR + /HER2- subtype and in HR + /HER2 + subtype, the DFS rate in AR positive patients and AR negative patients was 96.2% vs 89.0% (P = 0.001, HR = 0.330, 95% CI 0.106 to 1.034) and was 96.0% vs 85.7% (P = 0.002, HR = 0.278, 95% CI 0.082 to 0.940), respectively. However, in HR-/HER2 + and TNBC subtypes, the DFS rate in AR positive patients and AR negative patients was 89.0% vs 95.9% (P = 0.102, HR = 3.211, 95% CI 1.117 to 9.224) and 75.0% vs 93.4% (P < 0.001, HR = 3.706, 95% CI 1.681 to 8.171), respectively. In HR + /HER2- and HR + /HER2 + breast cancer, AR positive patients had a better prognosis, however in TNBC, AR-positive patients have a poor prognosis.

The Tumorigenic Effect of the High Expression of Ladinin-1 in Lung Adenocarcinoma and Its Potential as a Therapeutic Target

The oncogenic role of Ladinin-1 (LAD1), an anchoring filament protein, is largely unknown. In this study, we conducted a series of studies on the oncogenic role of LAD1 in lung adenocarcinoma (LUAD). Firstly, we analyzed the aberrant expression of LAD1 in LUAD and its correlation with patient survival, tumor immune infiltration, and the activation of cancer signaling pathways. Furthermore, the relationship between LAD1 expression and K-Ras and EGF signaling activation, tumor cell proliferation, migration, and colony formation was studied by gene knockout/knockout methods. We found that LAD1 was frequently overexpressed in LUAD, and high LAD1 expression predicts a poor prognosis. LAD1 exhibits promoter hypomethylation in LUAD, which may contribute to its mRNA upregulation. Single-sample gene set enrichment analysis (ssGSEA) showed that acquired immunity was negatively correlated with LAD1 expression, which was verified by the downregulated GO terms of "Immunoglobulin receptor binding" and "Immunoglobulin complex circulating" in the LAD1 high-expression group through Gene Set Variation Analysis (GSVA). Notably, the Ras-dependent signature was the most activated signaling in the LAD1 high-expression group, and the phosphorylation of downstream effectors, such as ERK and c-jun, was strongly inhibited by LAD1 deficiency. Moreover, we demonstrated that LAD1 depletion significantly inhibited the proliferation, migration, and cell-cycle progression of LUAD cells and promoted sensitivity to Gefitinib, K-Ras inhibitor, and paclitaxel treatments. We also confirmed that LAD1 deficiency remarkably retarded tumor growth in the xenograft model. Conclusively, LAD1 is a critical prognostic biomarker for LUAD and has potential as an intervention target.

Molecular Classification of Breast Cancer: Relevance and Challenges

CONTEXT.—

Appropriate patient management requires precise and meaningful tumor classification. Breast cancer classification continues to evolve from traditional morphologic evaluation to more sophisticated systems with the integration of new knowledge from research being translated into practice. Breast cancer is heterogeneous at the molecular level, with diversified patterns of gene expression, which is presumably responsible for the difference in tumor behavior and prognosis. Since the beginning of this century, new molecular technology has been gradually applied to breast cancer research on issues pertinent to prognosis (prognostic signature) and therapeutic prediction (predictive signature), and much progress has been made.

OBJECTIVE.—

To summarize the current state and the prospective future of molecular classification of breast cancer.

DATA SOURCES.—

Sources include recent medical literature on molecular classification of breast cancer.

CONCLUSIONS.—

Identification of intrinsic tumor subtypes has set a foundation for refining the breast cancer molecular classification. Studies have explored the genetic features within the intrinsic cancer subtypes and have identified novel molecular targets that led to the innovation of clinical assays to predict a patient's prognosis and to provide specific guidelines for therapeutic decisions. With the development and implication of these molecular tools, we have remarkably advanced our knowledge and enhanced our power to provide optimal management to patients. However, challenges still exist. Besides accurate prediction of prognosis, we are still in urgent need of more molecular predictors for tumor response to therapeutic regimes. Further exploration along this path will be critical for improving a patient's prognosis.

EIF4G1 Is a Potential Prognostic Biomarker of Breast Cancer

BACKGROUND

Breast cancer (BRCA) is one of the most common cancers in women worldwide and a leading cause of death from malignancy. This study was designed to identify a novel biomarker for prognosticating the survival of BRCA patients.

METHODS

The prognostic potential of eukaryotic translation initiation factor 4 gamma 1 (EIF4G1) was assessed using RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) as training cohort and validation set, respectively. The functional enrichment analysis of differentially expressed genes (DEGs) was performed. The relationship between EIF4G1 and tumor microenvironment (TME) was analyzed. Immunotherapy responses were explored by the immunophenoscores (IPS) and tumor immune dysfunction and exclusion (TIDE) score. The Connectivity Map (CMap) was used to discover potentially effective therapeutic molecules against BRCA. Immunohistochemistry (IHC) was applied to compare the protein levels of EIF4G1 in normal and cancer tissues and to verify the prognostic value of EIF4G1.

RESULTS

BRCA patients with increased expression of EIF4G1 had a shorter overall survival (OS) in all cohorts and results from IHC. EIF4G1-related genes were mainly involved in DNA replication, BRCA metastasis, and the MAPK signaling pathway. Infiltration levels of CD4⁺-activated memory T cells, macrophages M0, macrophages M1, and neutrophils were higher in the EIF4G1 high-expression group than those in the EIF4G1 low-expression group. EIF4G1 was positively correlated with T cell exhaustion. Lower IPS was revealed in high EIF4G1 expression patients. Five potential groups of drugs against BRCA were identified.

CONCLUSION

EIF4G1 might regulate the TME and affect BRCA metastasis, and it is a potential prognostic biomarker and therapeutic target for BRCA.

Lidocaine promotes apoptosis in breast cancer cells by affecting VDAC1 expression

OBJECTIVE

To investigate the effect of lidocaine on the expression of voltage-dependent anion channel 1 (VDAC1) in breast invasive carcinoma (BRCA) and its impact on the apoptosis of breast cancer cells.

METHODS

We collected clinical data from patients with invasive breast cancer from 2010 to 2020 in the First affiliated hospital of Nanchang University, evaluated the prognostic value of VDAC1 gene expression in breast cancer, and detected the expression of VDAC1 protein in breast cancer tissues and paracancerous tissues by immunohistochemical staining of paraffin sections. Also, we cultured breast cancer cells (MCF-7) to observe the effect of lidocaine on the apoptosis of MCF-7 cells.

RESULTS

Analysis of clinical data and gene expression data of BRCA patients showed VDAC1 was a differentially expressed gene in BRCA, VDAC1 may be of great significance for the diagnosis and prognosis of BRCA patients. Administration of lidocaine 3 mM significantly decreased VDAC1 expression, the expression of protein Bcl-2 was significantly decreased (p < 0.05), and the expression of p53 increased significantly (p < 0.05). Lidocaine inhibited the proliferation of MCF-7 breast cancer cells, increased the percentage of G2 / M phase cells and apoptosis.

CONCLUSION

Lidocaine may inhibit the activity of breast cancer cells by inhibiting the expression of VDAC1, increasing the apoptosis in breast cancer cells.

Deep learning-based transcription factor activity for stratification of breast cancer patients

Transcription factors directly bind to DNA and regulate the expression of the gene, causing epigenetic modification of the DNA. They often mediate epigenetic parameters of transcriptional and posttranscriptional mechanisms, and their expression activities can be used to characterize genomic aberrations in cancer cell. In this study, the activity profile of transcription factors inferred by VIPER algorithm. The autoencoder model was applied for compressing the transcription factor activity profile for obtaining more useful transformed features for stratifying patients into two different breast cancer subtypes. The deep learning-based subtypes exhibited superior prognostic value and yielded better risk-stratification than the transcription factor activity-based method. Importantly, according to transformed features, a deep neural network was constructed to predict the subtypes, and achieved the accuracy of 94.98% and area under the ROC curve of 0.9663, respectively. The proposed subtypes were found to be significantly associated with immune infiltration, tumor immunogenicity and so on. Furthermore, the ceRNA network was constructed for the breast cancer subtypes. Besides, 11 master regulators were found to be associated with patients in cluster 1. Given the robustness performance of our deep learning model over multiple breast cancer cohorts, we expected this model may be useful in the area of prognosis prediction and lead some possibility for personalized medicine in breast cancer patients.

Chemokines in Triple-Negative Breast Cancer Heterogeneity: New Challenges for Clinical Implications

Tumor heterogeneity is a hallmark of cancer and one of the primary causes of resistance to therapies. Triple-negative breast cancer (TNBC), which accounts for 15% to 20% of all breast cancers and is the most aggressive subtype, is very diverse, connected to metastatic potential and response to therapy. It is a very diverse disease at the molecular, pathologic, and clinical levels. TNBC is substantially more likely to recur and has a worse overall survival rate following diagnosis than other breast cancer subtypes. Chemokines, low molecular weight proteins that stimulate chemotaxis, have been shown to control the cues responsible for TNBC heterogeneity. In this review, we have focused on tumor heterogeneity and the role of chemokines in modulating tumor heterogeneity, since this is the most critical issue in treating TNBC. Additionally, we examined numerous cues mediated by chemokine networks that contribute to the heterogeneity of TNBC. Recent developments in our knowledge of the chemokine networks that regulate TNBC heterogeneity may pave the door for developing difficult-to-treat TNBC treatment options.

Heterogeneity and function of macrophages in the breast during homeostasis and cancer

Macrophages are diverse immune cells populating all tissues and adopting a unique tissue-specific identity. Breast macrophages play an essential role in the development and function of the mammary gland over one's lifetime. In the recent years, with the development of fate-mapping, imaging and scRNA-seq technologies we grew a better understanding of the origin, heterogeneity and function of mammary macrophages in homeostasis but also during breast cancer development. Here, we aim to provide a comprehensive review of the latest improvements in studying the macrophage heterogeneity in healthy mammary tissues and breast cancer.

Multi-omic machine learning predictor of breast cancer therapy response

Breast cancers are complex ecosystems of malignant cells and the tumour microenvironment1. The composition of these tumour ecosystems and interactions within them contribute to responses to cytotoxic therapy2. Efforts to build response predictors have not incorporated this knowledge. We collected clinical, digital pathology, genomic and transcriptomic profiles of pre-treatment biopsies of breast tumours from 168 patients treated with chemotherapy with or without HER2 (encoded by ERBB2)-targeted therapy before surgery. Pathology end points (complete response or residual disease) at surgery3 were then correlated with multi-omic features in these diagnostic biopsies. Here we show that response to treatment is modulated by the pre-treated tumour ecosystem, and its multi-omics landscape can be integrated in predictive models using machine learning. The degree of residual disease following therapy is monotonically associated with pre-therapy features, including tumour mutational and copy number landscapes, tumour proliferation, immune infiltration and T cell dysfunction and exclusion. Combining these features into a multi-omic machine learning model predicted a pathological complete response in an external validation cohort (75 patients) with an area under the curve of 0.87. In conclusion, response to therapy is determined by the baseline characteristics of the totality of the tumour ecosystem captured through data integration and machine learning. This approach could be used to develop predictors for other cancers.

Terminal differentiation and anti-tumorigenic effects of prolactin in breast cancer

Breast cancer is a major disease affecting women worldwide. A woman has 1 in 8 lifetime risk of developing breast cancer, and morbidity and mortality due to this disease are expected to continue to rise globally. Breast cancer remains a challenging disease due to its heterogeneity, propensity for recurrence and metastasis to distant vital organs including bones, lungs, liver and brain ultimately leading to patient death. Despite the development of various therapeutic strategies to treat breast cancer, still there are no effective treatments once metastasis has occurred. Loss of differentiation and increased cellular plasticity and stemness are being recognized molecularly and clinically as major derivers of heterogeneity, tumor evolution, relapse, metastasis, and therapeutic failure. In solid tumors, breast cancer is one of the leading cancer types in which tumor differentiation state has long been known to influence cancer behavior. Reprograming and/or restoring differentiation of cancer cells has been proposed to provide a viable approach to reverse the cancer through differentiation and terminal maturation. The hormone prolactin (PRL) is known to play a critical role in mammary gland lobuloalveolar development/remodeling and the terminal differentiation of the mammary epithelial cells promoting milk proteins gene expression and lactation. Here, we will highlight recent discoveries supporting an anti-tumorigenic role for PRL in breast cancer as a "pro/forward-differentiation" pathway restricting plasticity, stemness and tumorigenesis.

Validation of HER2 Status in Whole Genome Sequencing Data of Breast Cancers with the Ploidy-Corrected Copy Number Approach

BACKGROUND AND OBJECTIVE

Human epidermal growth factor receptor 2 (HER2) protein overexpression is one of the most significant biomarkers for breast cancer diagnostics, treatment prediction, and prognostics. The high accessibility of HER2 inhibitors in routine clinical practice directly translates into the diagnostic need for precise and robust marker identification. Even though multigene next-generation sequencing methodologies have slowly taken over the field of single-biomarker molecular tests, the copy number alterations such as amplification of the HER2-coding ERBB2 gene are hard to validate on next-generation sequencing platforms as they are characterized by chromosomal structural heterogeneity, polysomy, and genomic context of ploidy. In our study, we tested the approach of using whole genome sequencing instead of next-generation sequencing panels to determine HER2 status in the clinical set-up.

METHODS

We used a large dataset of 876 patients with breast cancer whole genomes with curated clinical data and an additional set of 551 patients' external genomic data. We used the decision-tree-based algorithm for optimization of the diagnostic tool for HER2 status assessment by whole genome sequencing.

RESULTS

The most efficient approach to assess HER2 status in whole genome sequencing data was the ploidy-corrected copy number, utilizing ERBB2 copy number and mean tumor ploidy. The classifier achieved sensitivity of 91.18% and specificity of 98.69% on the internal validation dataset and 89.86% and 96.06% on the external data, which is similar to other next-generation sequencing methods, currently tested in the clinic.

CONCLUSIONS

We provide evidence that the HER2 status may be reliably determined by whole genome sequencing and is applicable across different laboratory protocols and pipelines. We suggest using the ploidy-corrected copy number for diagnostic purposes.

Quantitative single-cell analysis of immunofluorescence protein multiplex images illustrates biomarker spatial heterogeneity within breast cancer subtypes

BACKGROUND

The extent of cellular heterogeneity in breast cancer could have potential impact on diagnosis and long-term outcome. However, pathology evaluation is limited to biomarker immunohistochemical staining and morphology of the bulk cancer. Inter-cellular heterogeneity of biomarkers is not usually assessed. As an initial evaluation of the extent of breast cancer cellular heterogeneity, we conducted quantitative and spatial imaging of Estrogen Receptor (ER), Progesterone Receptor (PR), Epidermal Growth Factor Receptor-2 (HER2), Ki67, TP53, CDKN1A (P21/WAF1), CDKN2A (P16INK4A), CD8 and CD20 of a tissue microarray (TMA) representing subtypes defined by St. Gallen surrogate classification.

METHODS

Quantitative, single cell-based imaging was conducted using an Immunofluorescence protein multiplexing platform (MxIF) to study protein co-expression signatures and their spatial localization patterns. The range of MxIF intensity values of each protein marker was compared to the respective IHC score for the TMA core. Extent of heterogeneity in spatial neighborhoods was analyzed using co-occurrence matrix and Diversity Index measures.

RESULTS

On the 101 cores from 59 cases studied, diverse expression levels and distributions were observed in MxIF measures of ER and PR among the hormonal receptor-positive tumor cores. As expected, Luminal A-like cancers exhibit higher proportions of cell groups that co-express ER and PR, while Luminal B-like (HER2-negative) cancers were composed of ER+, PR- groups. Proliferating cells defined by Ki67 positivity were mainly found in groups with PR-negative cells. Triple-Negative Breast Cancer (TNBC) exhibited the highest proliferative fraction and incidence of abnormal P53 and P16 expression. Among the tumors exhibiting P53 overexpression by immunohistochemistry, a group of TNBC was found with much higher MxIF-measured P53 signal intensity compared to HER2+, Luminal B-like and other TNBC cases. Densities of CD8 and CD20 cells were highest in HER2+ cancers. Spatial analysis demonstrated variability in heterogeneity in cellular neighborhoods in the cancer and the tumor microenvironment.

CONCLUSIONS

Protein marker multiplexing and quantitative image analysis demonstrated marked heterogeneity in protein co-expression signatures and cellular arrangement within each breast cancer subtype. These refined descriptors of biomarker expressions and spatial patterns could be valuable in the development of more informative tools to guide diagnosis and treatment.

Can Systems Biology Advance Clinical Precision Oncology?

Precision oncology is perceived as a way forward to treat individual cancer patients. However, knowing particular cancer mutations is not enough for optimal therapeutic treatment, because cancer genotype-phenotype relationships are nonlinear and dynamic. Systems biology studies the biological processes at the systems' level, using an array of techniques, ranging from statistical methods to network reconstruction and analysis, to mathematical modeling. Its goal is to reconstruct the complex and often counterintuitive dynamic behavior of biological systems and quantitatively predict their responses to environmental perturbations. In this paper, we review the impact of systems biology on precision oncology. We show examples of how the analysis of signal transduction networks allows to dissect resistance to targeted therapies and inform the choice of combinations of targeted drugs based on tumor molecular alterations. Patient-specific biomarkers based on dynamical models of signaling networks can have a greater prognostic value than conventional biomarkers. These examples support systems biology models as valuable tools to advance clinical and translational oncological research.

Prognostic and predictive value of a metabolic risk score model in breast cancer: an immunogenomic landscape analysis

Breast cancer is a kind of malignant tumor that occurs in breast tissue, which is the most common cancer in women. Cellular metabolism is a critical determinant of the viability and function of cancer cells in tumor microenvironment. In this study, based on the gene expression profile of metabolism-related genes, the prognostic value of 20 metabolic pathways in patients with breast cancer was identified. A universal risk stratification signature that relies on 20 metabolic pathways was established and validated in training cohort, two testing cohorts and The Cancer Genome Atlas pan cancer cohort. Then, the relationship between metabolic risk score subtype, prognosis, immune infiltration level, cancer genotypes and their impact on therapeutic benefit were characterized. Results demonstrated that the patients with the low metabolic risk score subtype displayed good prognosis, high level of immune infiltration and exhibited a favorable response to neoadjuvant chemotherapy and immunotherapy. Taken together, the work presented in this study may deepen the understanding of metabolic hallmarks of breast cancer, and may provide some valuable information for personalized therapies in patients with breast cancer.

Tumor Microenvironment Characterization in Breast Cancer Identifies Prognostic and Neoadjuvant Chemotherapy Relevant Signatures

Immune response which involves distinct immune cells is associated with prognosis of breast cancer. Nonetheless, less study have determined the associations of different types of immune cells with patient survival and treatment response. In this study, A total of 1,502 estrogen receptor(ER)-negative breast cancers from public databases were used to infer the proportions of 22 subsets of immune cells. Another 320 ER-negative breast cancer patients from Guangdong Provincial People's Hospital were also included and divided into the testing and validation cohorts. CD8⁺ T cells, CD4⁺ T cells, B cells, and M1 macrophages were associated with favourable outcome (all p <0.01), whereas Treg cells were strongly associated with poor outcome (p = 0.005). Using the LASSO model, we classified patients into the stromal immunotype A and B subgroups according to immunoscores. The 10 years OS and DFS rates were significantly higher in the immunotype A subgroup than immunotype B subgroup. Stromal immunotype was identified as an independent prognostic indicator in multivariate analysis in all cohorts and was also related to pathological complete response(pCR) after neoadjuvant chemotherapy. The nomogram that integrated the immunotype and clinicopathologic features showed good predictive accuracy for pCR and discriminatory power. The stromal immunotype A subgroup had higher expression levels of immune checkpoint molecules (PD-L1, PD-1, and CTLA-4) and cytokines (IL-2, INF-γ, and TGF-β). In addition, patients with immunotype A and B diseases had distinct mutation signatures. Therefore, The stromal immunotypes could predict survival and responses of ER-negative breast cancer patients to neoadjuvant chemotherapy.

Breast cancer heterogeneity through the lens of single-cell analysis and spatial pathologies

Breast cancer ecosystems are composed of complex cell types, including tumor, stromal and immune cells, each of which can assume diverse phenotypes. Both the heterogeneous composition and spatially distinct tumor microenvironment impact breast cancer progression, treatment response and therapeutic resistance. Thus, a deeper understanding of breast cancer heterogeneity may help facilitate the development of novel therapies and improve outcomes for patients. The advent of paradigm shifting single-cell analysis and spatial pathologies allows for a comprehensive analysis of the tumor ecosystem as well as the interactions between its components at unprecedented resolution. In this review, we discuss the insights gained through single-cell analysis and spatial pathologies on breast cancer heterogeneity.

Systematic dissection of transcriptional regulatory networks by genome-scale and single-cell CRISPR screens

Millions of putative transcriptional regulatory elements (TREs) have been cataloged in the human genome, yet their functional relevance in specific pathophysiological settings remains to be determined. This is critical to understand how oncogenic transcription factors (TFs) engage specific TREs to impose transcriptional programs underlying malignant phenotypes. Here, we combine cutting edge CRISPR screens and epigenomic profiling to functionally survey ≈15,000 TREs engaged by estrogen receptor (ER). We show that ER exerts its oncogenic role in breast cancer by engaging TREs enriched in GATA3, TFAP2C, and H3K27Ac signal. These TREs control critical downstream TFs, among which TFAP2C plays an essential role in ER-driven cell proliferation. Together, our work reveals novel insights into a critical oncogenic transcription program and provides a framework to map regulatory networks, enabling to dissect the function of the noncoding genome of cancer cells.

TSHZ2 is an EGF-regulated tumor suppressor that binds to the cytokinesis regulator PRC1 and inhibits metastasis

Unlike early transcriptional responses to mitogens, later events are less well-characterized. Here, we identified delayed down-regulated genes (DDGs) in mammary cells after prolonged treatment with epidermal growth factor (EGF). The expression of these DDGs was low in mammary tumors and correlated with prognosis. The proteins encoded by several DDGs directly bind to and inactivate oncoproteins and might therefore act as tumor suppressors. The transcription factor teashirt zinc finger homeobox 2 (TSHZ2) is encoded by a DDG, and we found that overexpression of TSHZ2 inhibited tumor growth and metastasis and accelerated mammary gland development in mice. Although the gene TSHZ2 localizes to a locus (20q13.2) that is frequently amplified in breast cancer, we found that hypermethylation of its promoter correlated with down-regulation of TSHZ2 expression in patients. Yeast two-hybrid screens and protein-fragment complementation assays in mammalian cells indicated that TSHZ2 nucleated a multiprotein complex containing PRC1/Ase1, cyclin B1, and additional proteins that regulate cytokinesis. TSHZ2 increased the inhibitory phosphorylation of PRC1, a key driver of mitosis, mediated by cyclin-dependent kinases. Furthermore, similar to the tumor suppressive transcription factor p53, TSHZ2 inhibited transcription from the PRC1 promoter. By recognizing DDGs as a distinct group in the transcriptional response to EGF, our findings uncover a group of tumor suppressors and reveal a role for TSHZ2 in cell cycle regulation.

Immune checkpoint inhibitors for triple-negative breast cancer: From immunological mechanisms to clinical evidence

Breast cancer is the most common cancer type in women worldwide. Triple-negative breast cancer (TNBC), which is characterized by the absence of estrogen receptor/progesterone receptor (ER/PR) and human epidermal growth factor receptor 2 (Her2) expressions, has a poorer prognosis compared with non-TNBC breast tumors. Until recently systemic treatment for TNBC was confined to chemotherapy owing to the lack of actionable targets. Immune checkpoint molecules are expressed on malignant cells or tumor-infiltrating immune cells and can inhibit anti-cancer immune responses. Immune checkpoint inhibitors (ICI), including anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), anti-programmed cell death protein 1 (PD-1), and anti-programmed cell death 1 ligand 1 (PD-L1), induce immune responses in different types of neoplasms. They have recently gained attention for their possible role in TNBC treatment. Several clinical trials have been conducted on the role of immune checkpoint blockade in different settings for TNBC treatment. Available evidence justifies the application of ICI and chemotherapy combination in the management of metastatic TNBC and early-stage TNBC in neoadjuvant setting. This study aims to provide information on the mechanisms of action of ICIs, review the efficacy results of clinical trials using ICIs for TNBC treatment, and assess the side effects of such drugs.

ProTICS reveals prognostic impact of tumor infiltrating immune cells in different molecular subtypes

Different subtypes of the same cancer often show distinct genomic signatures and require targeted treatments. The differences at the cellular and molecular levels of tumor microenvironment in different cancer subtypes have significant effects on tumor pathogenesis and prognostic outcomes. Although there have been significant researches on the prognostic association of tumor infiltrating lymphocytes in selected histological subtypes, few investigations have systemically reported the prognostic impacts of immune cells in molecular subtypes, as quantified by machine learning approaches on multi-omics datasets. This paper describes a new computational framework, ProTICS, to quantify the differences in the proportion of immune cells in tumor microenvironment and estimate their prognostic effects in different subtypes. First, we stratified patients into molecular subtypes based on gene expression and methylation profiles by applying nonnegative tensor factorization technique. Then we quantified the proportion of cell types in each specimen using an mRNA-based deconvolution method. For tumors in each subtype, we estimated the prognostic effects of immune cell types by applying Cox proportional hazard regression. At the molecular level, we also predicted the prognosis of signature genes for each subtype. Finally, we benchmarked the performance of ProTICS on three TCGA datasets and another independent METABRIC dataset. ProTICS successfully stratified tumors into different molecular subtypes manifested by distinct overall survival. Furthermore, the different immune cell types showed distinct prognostic patterns with respect to molecular subtypes. This study provides new insights into the prognostic association between immune cells and molecular subtypes, showing the utility of immune cells as potential prognostic markers. Availability: R code is available at https://github.com/liu-shuhui/ProTICS.

Proteogenomic Workflow Reveals Molecular Phenotypes Related to Breast Cancer Mammographic Appearance

Proteogenomic approaches have enabled the generat̲ion of novel information levels when compared to single omics studies although burdened by extensive experimental efforts. Here, we improved a data-independent acquisition mass spectrometry proteogenomic workflow to reveal distinct molecular features related to mammographic appearances in breast cancer. Our results reveal splicing processes detectable at the protein level and highlight quantitation and pathway complementarity between RNA and protein data. Furthermore, we confirm previously detected enrichments of molecular pathways associated with estrogen receptor-dependent activity and provide novel evidence of epithelial-to-mesenchymal activity in mammography-detected spiculated tumors. Several transcript-protein pairs displayed radically different abundances depending on the overall clinical properties of the tumor. These results demonstrate that there are differentially regulated protein networks in clinically relevant tumor subgroups, which in turn alter both cancer biology and the abundance of biomarker candidates and drug targets.

Landscapes of cellular phenotypic diversity in breast cancer xenografts and their impact on drug response

The heterogeneity of breast cancer plays a major role in drug response and resistance and has been extensively characterized at the genomic level. Here, a single-cell breast cancer mass cytometry (BCMC) panel is optimized to identify cell phenotypes and their oncogenic signalling states in a biobank of patient-derived tumour xenograft (PDTX) models representing the diversity of human breast cancer. The BCMC panel identifies 13 cellular phenotypes (11 human and 2 murine), associated with both breast cancer subtypes and specific genomic features. Pre-treatment cellular phenotypic composition is a determinant of response to anticancer therapies. Single-cell profiling also reveals drug-induced cellular phenotypic dynamics, unravelling previously unnoticed intra-tumour response diversity. The comprehensive view of the landscapes of cellular phenotypic heterogeneity in PDTXs uncovered by the BCMC panel, which is mirrored in primary human tumours, has profound implications for understanding and predicting therapy response and resistance.

Aberrations of Chromosomes 1 and 16 in Breast Cancer: A Framework for Cooperation of Transcriptionally Dysregulated Genes

Derivative chromosome der(1;16), isochromosome 1q, and deleted 16q-producing arm-level 1q-gain and/or 16q-loss-are recurrent cytogenetic abnormalities in breast cancer, but their exact role in determining the malignant phenotype is still largely unknown. We exploited The Cancer Genome Atlas (TCGA) data to generate and analyze groups of breast invasive carcinomas, called 1,16-chromogroups, that are characterized by a pattern of arm-level somatic copy number aberrations congruent with known cytogenetic aberrations of chromosome 1 and 16. Substantial differences were found among 1,16-chromogroups in terms of other chromosomal aberrations, aneuploidy scores, transcriptomic data, single-point mutations, histotypes, and molecular subtypes. Breast cancers with a co-occurrence of 1q-gain and 16q-loss can be distinguished in a "low aneuploidy score" group, congruent to der(1;16), and a "high aneuploidy score" group, congruent to the co-occurrence of isochromosome 1q and deleted 16q. Another three groups are formed by cancers showing separately 1q-gain or 16q-loss or no aberrations of 1q and 16q. Transcriptome comparisons among the 1,16-chromogroups, integrated with functional pathway analysis, suggested the cooperation of overexpressed 1q genes and underexpressed 16q genes in the genesis of both ductal and lobular carcinomas, thus highlighting the putative role of genes encoding gamma-secretase subunits (APH1A, PSEN2, and NCSTN) and Wnt enhanceosome components (BCL9 and PYGO2) in 1q, and the glycoprotein E-cadherin (CDH1), the E3 ubiquitin-protein ligase WWP2, the deubiquitinating enzyme CYLD, and the transcription factor CBFB in 16q. The analysis of 1,16-chromogroups is a strategy with far-reaching implications for the selection of cancer cell models and novel experimental therapies.

Random Forest Modelling of High-Dimensional Mixed-Type Data for Breast Cancer Classification

Advances in high-throughput technologies encourage the generation of large amounts of multiomics data to investigate complex diseases, including breast cancer. Given that the aetiologies of such diseases extend beyond a single biological entity, and that essential biological information can be carried by all data regardless of data type, integrative analyses are needed to identify clinically relevant patterns. To facilitate such analyses, we present a permutation-based framework for random forest methods which simultaneously allows the unbiased integration of mixed-type data and assessment of relative feature importance. Through simulation studies and machine learning datasets, the performance of the approach was evaluated. The results showed minimal multicollinearity and limited overfitting. To further assess the performance, the permutation-based framework was applied to high-dimensional mixed-type data from two independent breast cancer cohorts. Reproducibility and robustness of our approach was demonstrated by the concordance in relative feature importance between the cohorts, along with consistencies in clustering profiles. One of the identified clusters was shown to be prognostic for clinical outcome after standard-of-care adjuvant chemotherapy and outperformed current intrinsic molecular breast cancer classifications.

Low Expression of miR-375 and miR-190b Differentiates Grade 3 Patients with Endometrial Cancer

Endometrial cancer (EC) is treated according to the stage and prognostic risk factors. Most EC patients are in the early stages and they are treated surgically. However some of them, including those with high grade (grade 3) are in the intermediate and high intermediate prognostic risk groups and may require adjuvant therapy. The goal of the study was to find differences between grades based on an miRNA gene expression profile. Tumor samples from 24 patients with grade 1 (n = 10), 2 (n = 7), and 3 (n = 7) EC were subjected to miRNA profiling using next generation sequencing. The results obtained were validated using the miRNA profile of 407 EC tumors from the external Cancer Genome Atlas (TCGA) cohort. We obtained sets of differentially expressed (DE) miRNAs with the largest amount between G2 to G1 (50 transcripts) and G3 to G1 (40 transcripts) patients. Validation of our results with external data (TCGA) gave us a reasonable gene overlap of which we selected two miRNAs (miR-375 and miR190b) that distinguish the high grade best from the low grade EC. Unsupervised clustering showed a high degree of heterogeneity within grade 2 samples. MiR-375 as well as 190b might be useful to create grading verification test for high grade EC. One of the possible mechanisms that is responsible for the high grade is modulation by virus of host morphology or physiology.

Molecular analyses of triple-negative breast cancer in the young and elderly

Background

Breast cancer in young adults has been implicated with a worse outcome. Analyses of genomic traits associated with age have been heterogenous, likely because of an incomplete accounting for underlying molecular subtypes. We aimed to resolve whether triple-negative breast cancer (TNBC) in younger versus older patients represent similar or different molecular diseases in the context of genetic and transcriptional subtypes and immune cell infiltration.

Patients and methods

In total, 237 patients from a reported population-based south Swedish TNBC cohort profiled by RNA sequencing and whole-genome sequencing (WGS) were included. Patients were binned in 10-year intervals. Complimentary PD-L1 and CD20 immunohistochemistry and estimation of tumor-infiltrating lymphocytes (TILs) were performed. Cases were analyzed for differences in patient outcome, genomic, transcriptional, and immune landscape features versus age at diagnosis. Additionally, 560 public WGS breast cancer profiles were used for validation.

Results

Median age at diagnosis was 62 years (range 26–91). Age was not associated with invasive disease-free survival or overall survival after adjuvant chemotherapy. Among the BRCA1-deficient cases (82/237), 90% were diagnosed before the age of 70 and were predominantly of the basal-like subtype. In the full TNBC cohort, reported associations of patient age with changes in Ki67 expression, PIK3CA mutations, and a luminal androgen receptor subtype were confirmed. Within DNA repair deficiency or gene expression defined molecular subgroups, age-related alterations in, e.g., overall gene expression, immune cell marker gene expression, genetic mutational and rearrangement signatures, amount of copy number alterations, and tumor mutational burden did, however, not appear distinct. Similar non-significant associations for genetic alterations with age were obtained for other breast cancer subgroups in public WGS data. Consistent with age-related immunosenescence, TIL counts decreased linearly with patient age across different genetic TNBC subtypes.

Conclusions

Age-related alterations in TNBC, as well as breast cancer in general, need to be viewed in the context of underlying genomic phenotypes. Based on this notion, age at diagnosis alone does not appear to provide an additional layer of biological complexity above that of proposed genetic and transcriptional phenotypes of TNBC. Consequently, treatment decisions should be less influenced by age and more driven by tumor biology.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13058-021-01392-0.

Random Forest of Perfect Trees: Concept, Performance, Applications, and Perspectives

Motivation

The principle of Breiman's random forest (RF) is to build and assemble complementary classification trees in a way that maximizes their variability. We propose a new type of random forest that disobeys Breiman’s principles and involves building trees with no classification errors in very large quantities. We used a new type of decision tree that uses a neuron at each node as well as an in-innovative half Christmas tree structure. With these new RFs, we developed a score, based on a family of ten new statistical information criteria, called Nguyen information criteria (NICs), to evaluate the predictive qualities of features in three dimensions.

Results

The first NIC allowed the Akaike information criterion to be minimized more quickly than data obtained with the Gini index when the features were introduced in a logistic regression model. The selected features based on the NICScore showed a slight advantage compared to the support vector machines—recursive feature elimination (SVM-RFE) method. We demonstrate that the inclusion of artificial neurons in tree nodes allows a large number of classifiers in the same node to be taken into account simultaneously and results in perfect trees without classification errors.

Availability and implementation

The methods used to build the perfect trees in this article were implemented in the ‘ROP’ R package, archived at https://cran.r-project.org/web/packages/ROP/index.html.

Supplementary information

Supplementary data are available at Bioinformatics online.

Autophagy is associated with a robust specific transcriptional signature in breast cancer subtypes

Previous works have described that autophagy could be associated to both pro- and anti-cancer properties according to numerous factors, such as the gene considered, the step of autophagy involved or the cancer model used. These data might be explained by the fact that some autophagy-related genes may be involved in other cellular processes and therefore differently regulated according to the type or the grade of the tumor. Indeed, using different approaches of transcriptome analysis in breast cancers, and further confirmation using digital PCR, we identified a specific signature of autophagy gene expression associated to Luminal A or Triple Negative Breast Cancers (TNBC). Moreover, we confirmed that ATG5, an autophagy gene specifically expressed in TNBC, favored cell migration, whereas BECN1, an autophagy gene specifically associated with ER-positive breast cancers, induced opposite effects. We also showed that overall inhibition of autophagy promoted cell migration suggesting that the role of individual ATG genes in cancer phenotypes was not strictly dependent of their function during autophagy. Finally, our work led to the identification of TXNIP1 as a potential biomarker associated to autophagy induction in breast cancers. This gene could become an essential tool to quantify autophagy levels in fixed biopsies, sort tumors according to their autophagy levels and determine the best therapeutic treatment.

Molecular intrinsic versus clinical subtyping in breast cancer: A comprehensive review

Breast cancer is a heterogeneous disease manifesting diversity at the molecular, histological and clinical level. The development of breast cancer classification was centered on informing clinical decisions. The current approach to the classification of breast cancer, which categorizes this disease into clinical subtypes based on the detection of estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and proliferation marker Ki67, is not ideal. This is manifested as a heterogeneity of therapeutic responses and outcomes within the clinical subtypes. The newer classification model, based on gene expression profiling (intrinsic subtyping) informs about transcriptional responses downstream from IHC single markers, revealing deeper appreciation for the disease heterogeneity and capturing tumor biology in a more comprehensive way than an expression of a single protein or gene alone. While accumulating evidences suggest that intrinsic subtypes provide clinically relevant information beyond clinical surrogates, it is imperative to establish whether the current conventional immunohistochemistry-based clinical subtyping approach could be improved by gene expression profiling and if this approach has a potential to translate into clinical practice.

Chemical carcinogen-induced rat mammary carcinogenesis is a potential model of p21-activated kinase positive female breast cancer

The p21-activated kinase 1 (PAK1) gene encodes a serine/threonine kinase that is overexpressed in a subset of human breast carcinomas with poor prognosis. The laboratory rat (Rattus norvegicus) orthologous gene is located at Mammary carcinoma susceptibility 3 (Mcs3) QTL on rat chromosome 1. We used quantitative PCR to determine effects of Mcs3 genotype and 7,12-dimethylbenz(a)anthracene (DMBA) exposure on Pak1 expression. There was no effect of Mcs3 genotype; however, there was a 3.5-fold higher Pak1 level in DMBA-exposed mammary glands (MGs) than in unexposed glands (P < 0.05). Sequence variants in Pak1 exons did not alter amino acid sequence between Mcs3-susceptible and -resistant strains. Protein expression of PAK1/Pak1 in human breast carcinomas and DMBA-exposed rat mammary glands was detected using immunohistochemistry (IHC). Rat mammary glands from 12-wk-old females unexposed to DMBA were negative for Pak1, whereas 24% of carcinogen-exposed mammary glands from age-matched females stained positive for Pak1. The positive mammary glands exposed to carcinogen had no pathological signs of disease. Human breast carcinomas, used as comparative controls, had a 22% positivity rats. This was consistent with other human breast cancer studies of PAK1 expression. Similar frequencies of human/rat PAK1/Pak1 expression in female breast carcinomas and carcinogen-induced rat mammary glands, showing no visible pathogenesis of disease, suggests aberrant PAK1 expression is an early event in development of some breast cancers. Laboratory rats will be a useful experimental organism for comparative studies of Pak1-mediated mechanisms of breast carcinogenesis. Future studies of PAK1 as a diagnostic marker of early breast disease are warranted.

Classifying Breast Cancer Molecular Subtypes by Using Deep Clustering Approach

Cancer is a complex disease with a high rate of mortality. The characteristics of tumor masses are very heterogeneous; thus, the appropriate classification of tumors is a critical point in the effective treatment. A high level of heterogeneity has also been observed in breast cancer. Therefore, detecting the molecular subtypes of this disease is an essential issue for medicine that could be facilitated using bioinformatics. This study aims to discover the molecular subtypes of breast cancer using somatic mutation profiles of tumors. Nonetheless, the somatic mutation profiles are very sparse. Therefore, a network propagation method is used in the gene interaction network to make the mutation profiles dense. Afterward, the deep embedded clustering (DEC) method is used to classify the breast tumors into four subtypes. In the next step, gene signature of each subtype is obtained using Fisher's exact test. Besides the enrichment of gene signatures in numerous biological databases, clinical and molecular analyses verify that the proposed method using mutation profiles can efficiently detect the molecular subtypes of breast cancer. Finally, a supervised classifier is trained based on the discovered subtypes to predict the molecular subtype of a new patient. The code and material of the method are available at: https://github.com/nrohani/MolecularSubtypes.

A case-control study to identify molecular risk factors for local recurrence in young breast cancer patients

PURPOSE

To investigate risk factors for local recurrence (LR) after breast conserving therapy in young breast cancer patients randomized in the "Young Boost Trial".

MATERIAL & METHODS

In the "Young Boost Trial" 2421 patients ≤50 years old were randomized between a 16 Gy and 26 Gy boost after breast conserving surgery and whole breast radiation (50 Gy). We performed a case-control study comparing patients who developed a LR (cases) and for each of them three control patients free of recurrence (controls). Clinicopathological factors, copy number- and gene expression profiles of primary tumors were compared between cases and controls, and between primary tumors and local recurrences.

RESULTS

The cumulative 5-year LR rate was 1.07% (95% CI 0.72-1.59%) and 10-year LR rate 2.56% (1.81-3.62%). Analysis of a subset of primary tumors and local recurrences showed similar histopathological characteristics (n = 15), copy number (n = 13) and gene expression profiles (n = 14). Basal subtype was strongly associated with LR in univariable and multivariable analysis. Gains of CCND1 were identified more frequently among controls, while more frequent gains of FGFR1 and IGF1R were observed among cases. Upregulation of genes involved in the p53-pathway was observed in recurring tumors compared to non-recurring tumors. We could not identify a genomic classifier for LR.

CONCLUSIONS

All investigated local recurrences were true genomic recurrences. Although differences in copy number variation and gene expression pathways were observed in recurring tumors compared to non-recurring tumors, no genomic classifier for LR could be identified.

Pathologic and molecular responses to neoadjuvant trastuzumab and/or lapatinib from a phase II randomized trial in HER2-positive breast cancer (TRIO-US B07)

In this multicenter, open-label, randomized phase II investigator-sponsored neoadjuvant trial with funding provided by Sanofi and GlaxoSmithKline (TRIO-US B07, Clinical Trials NCT00769470), participants with early-stage HER2-positive breast cancer (N = 128) were recruited from 13 United States oncology centers throughout the Translational Research in Oncology network. Participants were randomized to receive trastuzumab (T; N = 34), lapatinib (L; N = 36), or both (TL; N = 58) as HER2-targeted therapy, with each participant given one cycle of this designated anti-HER2 therapy alone followed by six cycles of standard combination chemotherapy with the same anti-HER2 therapy. The primary objective was to estimate the rate of pathologic complete response (pCR) at the time of surgery in each of the three arms. In the intent-to-treat population, we observed similar pCR rates between T (47%, 95% confidence interval [CI] 30-65%) and TL (52%, 95% CI 38-65%), and a lower pCR rate with L (25%, 95% CI 13-43%). In the T arm, 100% of participants completed all protocol-specified treatment prior to surgery, as compared to 69% in the L arm and 74% in the TL arm. Tumor or tumor bed tissue was collected whenever possible pre-treatment (N = 110), after one cycle of HER2-targeted therapy alone (N = 89), and at time of surgery (N = 59). Higher-level amplification of HER2 and hormone receptor (HR)-negative status were associated with a higher pCR rate. Large shifts in the tumor, immune, and stromal gene expression occurred after one cycle of HER2-targeted therapy. In contrast to pCR rates, the L-containing arms exhibited greater proliferation reduction than T at this timepoint. Immune expression signatures increased in all arms after one cycle of HER2-targeted therapy, decreasing again by the time of surgery. Our results inform approaches to early assessment of sensitivity to anti-HER2 therapy and shed light on the role of the immune microenvironment in response to HER2-targeted agents.

Proteogenomic examination of esophageal squamous cell carcinoma (ESCC): new lines of inquiry

Introduction: Esophageal squamous cell carcinoma (ESCC), a histopathologic subtype of esophageal cancer is a major cause of cancer-related morbidity and mortality worldwide. This is primarily because patients are diagnosed at an advanced stage by the time symptoms appear. The genomics and mass spectrometry-based proteomics continue to provide important leads toward biomarker discovery for ESCC. However, such leads are yet to be translated into clinical utilities. Areas covered: We gathered information pertaining to proteomics and proteogenomics efforts in ESCC from the literature search until 2020. An overview of omics approaches to discover the candidate biomarkers for ESCC were highlighted. We present a summary of recent investigations of alterations in the level of gene and protein expression observed in biological samples including body fluids, tissue/biopsy and in vitro-based models. Expert opinion: A large number of protein-based biomarkers and therapeutic targets are being used in cancer therapy. Several candidates are being developed as diagnostics and prognostics for the management of cancers. High-resolution proteomic and proteogenomic approaches offer an efficient way to identify additional candidate biomarkers for diagnosis, monitoring of disease progression, prediction of response to chemo and radiotherapy. Some of these biomarkers can also be developed as therapeutic targets.

Hypercluster: a flexible tool for parallelized unsupervised clustering optimization

BACKGROUND

Unsupervised clustering is a common and exceptionally useful tool for large biological datasets. However, clustering requires upfront algorithm and hyperparameter selection, which can introduce bias into the final clustering labels. It is therefore advisable to obtain a range of clustering results from multiple models and hyperparameters, which can be cumbersome and slow.

RESULTS

We present hypercluster, a python package and SnakeMake pipeline for flexible and parallelized clustering evaluation and selection. Users can efficiently evaluate a huge range of clustering results from multiple models and hyperparameters to identify an optimal model.

CONCLUSIONS

Hypercluster improves ease of use, robustness and reproducibility for unsupervised clustering application for high throughput biology. Hypercluster is available on pip and bioconda; installation, documentation and example workflows can be found at: https://github.com/ruggleslab/hypercluster .