The landscape of cancer genes and mutational processes in breast cancer

K-RAS Associated Gene-Mutation-Based Algorithm for Prediction of Treatment Response of Patients with Subtypes of Breast Cancer and Especially Triple-Negative Cancer

Purpose: There is an urgent need for developing new biomarker tools to accurately predict treatment response of breast cancer, especially the deadly triple-negative breast cancer. We aimed to develop gene-mutation-based machine learning (ML) algorithms as biomarker classifiers to predict treatment response of first-line chemotherapy with high precision. Methods: Random Forest ML was applied to screen the algorithms of various combinations of gene mutation profiles of primary tumors at diagnosis using a TCGA Cohort (n = 399) with up to 150 months follow-up as a training set and validated in a MSK Cohort (n = 807) with up to 220 months follow-up. Subtypes of breast cancer including triple-negative and luminal A (ER+, PR+ and HER2−) were also assessed. The predictive performance of the candidate algorithms as classifiers was further assessed using logistic regression, Kaplan−Meier progression-free survival (PFS) plot, and univariate/multivariate Cox proportional hazard regression analyses. Results: A novel algorithm termed the 12-Gene Algorithm based on mutation profiles of KRAS, PIK3CA, MAP3K1, MAP2K4, PTEN, TP53, CDH1, GATA3, KMT2C, ARID1A, RunX1, and ESR1, was identified. The performance of this algorithm to distinguish non-progressed (responder) vs. progressed (non-responder) to treatment in the TCGA Cohort as determined using AUC was 0.96 (95% CI 0.94−0.98). It predicted progression-free survival (PFS) with hazard ratio (HR) of 21.6 (95% CI 11.3−41.5) (p < 0.0001) in all patients. The algorithm predicted PFS in the triple-negative subgroup with HR of 19.3 (95% CI 3.7−101.3) (n = 42, p = 0.000). The 12-Gene Algorithm was validated in the MSK Cohort with a similar AUC of 0.97 (95% CI 0.96−0.98) to distinguish responder vs. non-responder patients, and had a HR of 18.6 (95% CI 4.4−79.2) to predict PFS in the triple-negative subgroup (n = 75, p < 0.0001). Conclusions: The novel 12-Gene algorithm based on multitude gene-mutation profiles identified through ML has a potential to predict breast cancer treatment response to therapies, especially in triple-negative subgroups patients, which may assist personalized therapies and reduce mortality.

Interpretation of the role of germline and somatic non-coding mutations in cancer: expression and chromatin conformation informed analysis

BACKGROUND

There has been extensive scrutiny of cancer driving mutations within the exome (especially amino acid altering mutations) as these are more likely to have a clear impact on protein functions, and thus on cell biology. However, this has come at the neglect of systematic identification of regulatory (non-coding) variants, which have recently been identified as putative somatic drivers and key germline risk factors for cancer development. Comprehensive understanding of non-coding mutations requires understanding their role in the disruption of regulatory elements, which then disrupt key biological functions such as gene expression.

MAIN BODY

We describe how advancements in sequencing technologies have led to the identification of a large number of non-coding mutations with uncharacterized biological significance. We summarize the strategies that have been developed to interpret and prioritize the biological mechanisms impacted by non-coding mutations, focusing on recent annotation of cancer non-coding variants utilizing chromatin states, eQTLs, and chromatin conformation data.

CONCLUSION

We believe that a better understanding of how to apply different regulatory data types into the study of non-coding mutations will enhance the discovery of novel mechanisms driving cancer.

Immune landscape and risk prediction based on pyroptosis-related molecular subtypes in triple-negative breast cancer

The survival outcome of triple-negative breast cancer (TNBC) remains poor, with difficulties still existing in prognosis assessment and patient stratification. Pyroptosis, a newly discovered form of programmed cell death, is involved in cancer pathogenesis and progression. The role of pyroptosis in the tumor microenvironment (TME) of TNBC has not been fully elucidated. In this study, we disclosed global alterations in 58 pyroptosis-related genes at somatic mutation and transcriptional levels in TNBC samples collected from The Cancer Genome Atlas and Gene Expression Omnibus databases. Based on the expression patterns of genes related to pyroptosis, we identified two molecular subtypes that harbored different TME characteristics and survival outcomes. Then, based on differentially expressed genes between two subtypes, we established a 12-gene score with robust efficacy in predicting short- and long-term overall survival of TNBC. Patients at low risk exhibited a significantly better prognosis, more antitumor immune cell infiltration, and higher expression of immune checkpoints including PD-1, PD-L1, CTLA-4, and LAG3. The comprehensive analysis of the immune landscape in TNBC indicated that alterations in pyroptosis-related genes were closely related to the formation of the immune microenvironment and the intensity of the anticancer response. The 12-gene score provided new information on the risk stratification and immunotherapy strategy for highly heterogeneous patients with TNBC.

Combination of AURKA inhibitor and HSP90 inhibitor to treat breast cancer with AURKA overexpression and TP53 mutations

Breast cancer is the most common cancer among women worldwide. Researches show that Aurora kinase A (AURKA) is highly expressed in approximately 73% of breast cancer patients, which induces drug resistance in breast cancer patients and decreases the median survival time. AURKA regulates spindle assembly, centrosome maturation, and chromosome alignment. AURKA overexpression affects the occurrence and development of breast cancer. Besides AURKA overexpression, heat shock protein 90 (HSP90) maintains the survival and proliferation of tumor cells by stabilizing the structure of oncoproteins, including P53 mutants (mtP53). TP53 mutations accounted for approximately 13%, 40%, 80%, 33%, 71%, and 82% of luminal A, Luminal B, Luminal C, normal basal-like, HER2-amplified, and basal-like breast cancers, respectively. TP53 mutation can aggravate cell genome instability and enhance the invasion, migration, and resistance of cancer cell. This review describes the research status of AURKA and HSP90 in breast cancer, summarizes the structure, function, and the chaperone cycle of HSP90, elaborates the interrelation between HSP90, mtP53, P53, and AURKA, and proposes the combination of HSP90 inhibitor and AURKA inhibitor to treat breast cancer. Targeting AURKA and HSP90 to treat cancer with AURKA overexpression and TP53 mutations will help improve the specificity and efficiency of breast cancer treatment and solve the problem of drug resistance.

Comprehensive analysis of the prognostic value and immune infiltration of FGFR family members in gastric cancer

Background

Fibroblast growth factor receptors (FGFRs) modulate numerous cellular processes in tumor cells and tumor microenvironment. However, the effect of FGFRs on tumor prognosis and tumor-infiltrating lymphocytes in gastric cancer (GC) remains controversial.

Methods

The expression of four different types of FGFRs was analyzed via GEPIA, TCGA-STAD, and GTEX databases and our 27 pairs of GC tumor samples and the adjacent normal tissue. Furthermore, the Kaplan–Meier plot and the TCGA database were utilized to assess the association of FGFRs with clinical prognosis. The R software was used to evaluate FGFRs co-expression genes with GO/KEGG Pathway Enrichment Analysis. In vitro and in vivo functional analyses and immunoblotting were performed to verify FGFR4 overexpression consequence. Moreover, the correlation between FGFRs and cancer immune infiltrates was analyzed by TIMER and TCGA databases. And the efficacy of anti-PD-1 mAb treatment was examined in NOG mouse models with overexpressed FGFR1 or FGFR4.

Results

The expression of FGFRs was considerably elevated in STAD than in the normal gastric tissues and was significantly correlated with poor OS and PFS. ROC curve showed the accuracy of the FGFRs in tumor diagnosis, among which FGFR4 had the highest ROC value. Besides, univariate and multivariate analysis revealed that FGFR4 was an independent prognostic factor for GC patients. According to a GO/KEGG analysis, the FGFRs were implicated in the ERK/MAPK, PI3K-AKT and extracellular matrix (ECM) receptor signaling pathways. In vivo and in vitro studies revealed that overexpression of FGFR4 stimulated GC cell proliferation, invasion, and migration. In addition, FGFR1 expression was positively correlated with infiltrating levels of CD8+ T-cells, CD4+ T-cells, macrophages, and dendritic cells in STAD. In contrast, FGFR4 expression was negatively correlated with tumor-infiltrating lymphocytes. Interestingly, overexpression of FGFR1 in the NOG mouse model improved the immunotherapeutic impact of GC, while overexpression of FGFR4 impaired the effect. When combined with an FGFR4 inhibitor, the anti-tumor effect of anti-PD-1 treatment increased significantly in a GC xenograft mouse model with overexpressed FGFR4.

Conclusions

FGFRs has critical function in GC and associated with immune cell infiltration, which might be a potential prognosis biomarker and predictor of response to immunotherapy in GC.

Biological Random Walks: multi-omics integration for disease gene prioritization

MOTIVATION

Over the past decade, network-based approaches have proven useful in identifying disease modules within the human interactome, often providing insights into key mechanisms and guiding the quest for therapeutic targets. This is all the more important, since experimental investigation of potential gene candidates is an expensive task, thus not always a feasible option. On the other hand, many sources of biological information exist beyond the interactome and an important research direction is the design of effective techniques for their integration.

RESULTS

In this work, we introduce the Biological Random Walks (BRW) approach for disease gene prioritization in the human interactome. The proposed framework leverages multiple biological sources within an integrated framework. We perform an extensive, comparative study of BRW's performance against well-established baselines.

AVAILABILITY AND IMPLEMENTATION

All codes are publicly available and can be downloaded at https://github.com/LeoM93/BiologicalRandomWalks. We used publicly available datasets, details on their retrieval and preprocessing are provided in the Supplementary Material.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Clinical utility of whole-genome sequencing in precision oncology

Precision diagnostics is one of the two pillars of precision medicine. Sequencing efforts in the past decade have firmly established cancer as a primarily genetically driven disease. This concept is supported by therapeutic successes aimed at particular pathways that are perturbed by specific driver mutations in protein-coding domains and reflected in three recent FDA tissue agnostic cancer drug approvals. In addition, there is increasing evidence from studies that interrogate the entire genome by whole-genome sequencing that acquired global and complex genomic aberrations including those in non-coding regions of the genome might also reflect clinical outcome. After addressing technical, logistical, financial and ethical challenges, national initiatives now aim to introduce clinical whole-genome sequencing into real-world diagnostics as a rational and potentially cost-effective tool for response prediction in cancer and to identify patients who would benefit most from 'expensive' targeted therapies and recruitment into clinical trials. However, so far, this has not been accompanied by a systematic and prospective evaluation of the clinical utility of whole-genome sequencing within clinical trials of uniformly treated patients of defined clinical outcome. This approach would also greatly facilitate novel predictive biomarker discovery and validation, ultimately reducing size and duration of clinical trials and cost of drug development. This manuscript is the third in a series of three to review and critically appraise the potential and challenges of clinical whole-genome sequencing in solid tumors and hematological malignancies.

Harnessing gene fusion-derived neoantigens for 'cold' breast and prostate tumor immunotherapy

Breast and prostate cancers are generally considered immunologically 'cold' tumors due to multiple mechanisms rendering them unresponsive to immune checkpoint blockade therapies. With little success in garnering positive outcomes in modern immunotherapeutic clinical trials, it is prudent to re-examine the role of immunogenic neoantigens in these cold tumors. Gene fusions are driver mutations in hormone-driven cancers that can result in alternative mutation-specific neoantigens to promote immunotherapy sensitivity. This review focuses on 1) gene fusion formation mechanisms in neoantigen generation; 2) gene fusion neoantigens in cancer immunotherapeutic strategies and associated clinical trials; and 3) challenges and opportunities in computational and liquid biopsy technologies. This review is anticipated to initiate further research into gene fusion neoantigens of cold tumors for further experimental validation.

ABL kinases regulate translation in HER2+ cells through Y-box-binding protein 1 to facilitate colonization of the brain

Patients with human epidermal growth factor receptor 2-positive (HER2+/ERBB2) breast cancer often present with brain metastasis. HER2-targeted therapies have not been successful to treat brain metastases in part due to poor blood-brain barrier (BBB) penetrance and emergence of resistance. Here, we report that Abelson (ABL) kinase allosteric inhibitors improve overall survival and impair HER2+ brain metastatic outgrowth in vivo. Mechanistically, ABL kinases phosphorylate the RNA-binding protein Y-box-binding protein 1 (YB-1). ABL kinase inhibition disrupts binding of YB-1 to the ERBB2 mRNA and impairs translation, leading to a profound decrease in HER2 protein levels. ABL-dependent tyrosine phosphorylation of YB-1 promotes HER2 translation. Notably, loss of YB-1 inhibits brain metastatic outgrowth and impairs expression of a subset of ABL-dependent brain metastatic targets. These data support a role for ABL kinases in the translational regulation of brain metastatic targets through YB-1 and offer a therapeutic target for HER2+ brain metastasis patients.

The Multi-Omic Landscape of Primary Breast Tumors and Their Metastases: Expanding the Efficacy of Actionable Therapeutic Targets

Breast cancer (BC) mortality is almost exclusively due to metastasis, which is the least understood aspect of cancer biology and represents a significant clinical challenge. Although we have witnessed tremendous advancements in the treatment for metastatic breast cancer (mBC), treatment resistance inevitably occurs in most patients. Recently, efforts in characterizing mBC revealed distinctive genomic, epigenomic and transcriptomic (multi-omic) landscapes to that of the primary tumor. Understanding of the molecular underpinnings of mBC is key to understanding resistance to therapy and the development of novel treatment options. This review summarizes the differential molecular landscapes of BC and mBC, provides insights into the genomic heterogeneity of mBC and highlights the therapeutically relevant, multi-omic features that may serve as novel therapeutic targets for mBC patients.

Prevalence of Estrogen Receptor Alpha (ESR1) Somatic Mutations in Breast Cancer

Estrogen receptor–positive breast tumors, which initially respond effectively to endocrine therapy, progress due to acquired endocrine therapy resistance, including genomic alterations in estrogen receptor alpha (ESR1). A recent study has suggested that there is a sufficient number of preexisting ESR1 mutations acting as an intrinsic resistance mechanism to warrant primary screening. We determined the incidence of de novo ESR1 mutations in hormone-positive treatment-naïve primary breast tumors using 12 publicly available international datasets in the cBioPortal. The prevalence of mutation was statistically significantly lower in treatment-naïve primary tumors (n = 6 of 3682, 0.16%) than in metastatic (n = 156 of 1089, 14.3%, 2-sided P < .001) or previously treated primary tumors (n = 11 of 92, 12.0%, 2-sided P < .001). Pathogenic ESR1 mutations are a common mechanism of acquired but not intrinsic resistance to endocrine therapy and may not warrant universal testing of primary breast cancer populations.

Onepot-Seq: capturing single-cell transcriptomes simultaneously in a continuous medium via transient localization of mRNA

The development of single-cell RNA-seq has broadened the spectrum for biological research by providing a high-resolution analysis of cellular heterogeneity. However, the requirement for sophisticated devices for the compartmentalization of cells has limited its widespread applicability. Here, we develop Onepot-Seq, a device-free method, that harnesses the transient localization of mRNA after lysis to capture single-cell transcriptomes simultaneously in a continuous fluid medium. In mixed-species experiments, we obtained high-quality single-cell profiles. Further, cell type-specific poly(A)-conjugated antibodies allow Onepot-Seq to effectively capture target cells in complex populations. Chemical perturbations to cells can be profiled by Onepot-Seq at single-cell resolution. Onepot-Seq should allow routine transcriptional profiling at single-cell resolution, accelerating clinical and scientific discoveries in many fields of science.

Unified classification and risk-stratification in Acute Myeloid Leukemia

Clinical recommendations for Acute Myeloid Leukemia (AML) classification and risk-stratification remain heavily reliant on cytogenetic findings at diagnosis, which are present in <50% of patients. Using comprehensive molecular profiling data from 3,653 patients we characterize and validate 16 molecular classes describing 100% of AML patients. Each class represents diverse biological AML subgroups, and is associated with distinct clinical presentation, likelihood of response to induction chemotherapy, risk of relapse and death over time. Secondary AML-2, emerges as the second largest class (24%), associates with high-risk disease, poor prognosis irrespective of flow Minimal Residual Disease (MRD) negativity, and derives significant benefit from transplantation. Guided by class membership we derive a 3-tier risk-stratification score that re-stratifies 26% of patients as compared to standard of care. This results in a unified framework for disease classification and risk-stratification in AML that relies on information from cytogenetics and 32 genes. Last, we develop an open-access patient-tailored clinical decision support tool.

The spatio-temporal evolution of multiple myeloma from baseline to relapse-refractory states

Deciphering Multiple Myeloma evolution in the whole bone marrow is key to inform curative strategies. Here, we perform spatial-longitudinal whole-exome sequencing, including 140 samples collected from 24 Multiple Myeloma patients during up to 14 years. Applying imaging-guided sampling we observe three evolutionary patterns, including relapse driven by a single-cell expansion, competing/co-existing sub-clones, and unique sub-clones at distinct locations. While we do not find the unique relapse sub-clone in the baseline focal lesion(s), we show a close phylogenetic relationship between baseline focal lesions and relapse disease, highlighting focal lesions as hotspots of tumor evolution. In patients with ≥3 focal lesions on positron-emission-tomography at diagnosis, relapse is driven by multiple distinct sub-clones, whereas in other patients, a single-cell expansion is typically seen (p < 0.01). Notably, we observe resistant sub-clones that can be hidden over years, suggesting that a prerequisite for curative therapies would be to overcome not only tumor heterogeneity but also dormancy.

Association of Pathway Mutations With Survival in Taiwanese Breast Cancers

Breast cancer is the most common invasive cancer in women worldwide. Next-generation sequencing (NGS) provides a high-resolution profile of cancer genome. Our study ultimately gives the insight for genetic screening to identify the minority of patients with breast cancer with a poor prognosis, who might benefit from the most intensive possible treatment. The detection of mutations can polish the traditional method to detect high-risk patients who experience poor prognosis, recurrence and death early. In total, 147 breast cancer tumors were sequenced with targeted sequencing using a RainDance Cancer Hotspot Panel. The average age of all 147 breast cancer patients in the study was 51.7 years, with a range of 21-77 years. The average sequencing depth was 5,222x (range 2,900x-8,633x), and the coverage was approximately 100%. A total of 235 variants in 43 genes were detected in 147 patients by high-depth Illumina sequencing. A total of 219 single nucleotide variations were found in 42 genes from 147 patients, and 16 indel mutations were found in 13 genes from 84 patients. After filtering with the 1000 Genomes database and for synonymous SNPs, we focused on 54 somatic functional point mutations. The functional point mutations contained 54 missense mutations in 22 genes. Additionally, mutation of genes within the RET, PTEN, CDH1, MAP2K4, NF1, ERBB2, RUNX1, PIK3CA, FGFR3, KIT, KDR, APC, SMO, NOTCH1, and FBXW7 in breast cancer patients were with poor prognosis. Moreover, TP53 and APC mutations were enriched in triple-negative breast cancer. APC mutations were associated with a poor prognosis in human breast cancer (log-rank P<0.001). Our study identified tumor mutation hotspot profiles in Taiwanese breast cancer patients, revealing new targetable gene mutations in Asian breast cancer patients.

Machine learning-based tissue of origin classification for cancer of unknown primary diagnostics using genome-wide mutation features

Cancers of unknown primary (CUP) origin account for ∼3% of all cancer diagnoses, whereby the tumor tissue of origin (TOO) cannot be determined. Using a uniformly processed dataset encompassing 6756 whole-genome sequenced primary and metastatic tumors, we develop Cancer of Unknown Primary Location Resolver (CUPLR), a random forest TOO classifier that employs 511 features based on simple and complex somatic driver and passenger mutations. CUPLR distinguishes 35 cancer (sub)types with ∼90% recall and ∼90% precision based on cross-validation and test set predictions. We find that structural variant derived features increase the performance and utility for classifying specific cancer types. With CUPLR, we could determine the TOO for 82/141 (58%) of CUP patients. Although CUPLR is based on machine learning, it provides a human interpretable graphical report with detailed feature explanations. The comprehensive output of CUPLR complements existing histopathological procedures and can enable improved diagnostics for CUP patients.

Multiomics technologies: role in disease biomarker discoveries and therapeutics

 

Medical research has been revolutionized after the publication of the full human genome. This was the major landmark that paved the way for understanding the biological functions of different macro and micro molecules. With the advent of different high-throughput technologies, biomedical research was further revolutionized. These technologies constitute genomics, transcriptomics, proteomics, metabolomics, etc. Collectively, these high-throughputs are referred to as multi-omics technologies. In the biomedical field, these omics technologies act as efficient and effective tools for disease diagnosis, management, monitoring, treatment and discovery of certain novel disease biomarkers. Genotyping arrays and other transcriptomic studies have helped us to elucidate the gene expression patterns in different biological states, i.e. healthy and diseased states. Further omics technologies such as proteomics and metabolomics have an important role in predicting the role of different biological molecules in an organism. It is because of these high throughput omics technologies that we have been able to fully understand the role of different genes, proteins, metabolites and biological pathways in a diseased condition. To understand a complex biological process, it is important to apply an integrative approach that analyses the multi-omics data in order to highlight the possible interrelationships of the involved biomolecules and their functions. Furthermore, these omics technologies offer an important opportunity to understand the information that underlies disease. In the current review, we will discuss the importance of omics technologies as promising tools to understand the role of different biomolecules in diseases such as cancer, cardiovascular diseases, neurodegenerative diseases and diabetes.

SUMMARY POINTS

The crucial role of epigenetic regulation in breast cancer anti-estrogen resistance: Current findings and future perspectives

Breast cancer (BC) cell de-sensitization to Tamoxifen (TAM) or other selective estrogen receptor (ER) modulators (SERM) is a complex process associated with BC heterogeneity and the transformation of ER signalling. The most influential resistance-related mechanisms include modifications in ER expression and gene regulation patterns. During TAM/SERM treatment, epigenetic mechanisms can effectively silence ER expression and facilitate the development of endocrine resistance. ER status is efficiently regulated by specific epigenetic tools including hypermethylation of CpG islands within ER promoters, increased histone deacetylase activity in the ER promoter, and/or translational repression by miRNAs. Over-methylation of the ER α gene (ESR1) promoter by DNA methyltransferases was associated with poor prognosis and indicated the development of resistance. Moreover, BC progression and spreading were marked by transformed chromatin remodelling, post-translational histone modifications, and expression of specific miRNAs and/or long non-coding RNAs. Therefore, targeted inhibition of histone acetyltransferases (e.g. MYST3), deacetylases (e.g. HDAC1), and/or demethylases (e.g. lysine-specific demethylase LSD1) was shown to recover and increase BC sensitivity to anti-estrogens. Indicated as a powerful molecular instrument, the administration of epigenetic drugs can regain ER expression along with the activation of tumour suppressor genes, which can in turn prevent selection of resistant cells and cancer stem cell survival. This review examines recent advances in the epigenetic regulation of endocrine drug resistance and evaluates novel anti-resistance strategies. Underlying molecular mechanisms of epigenetic regulation will be discussed, emphasising the utilization of epigenetic enzymes and their inhibitors to re-program irresponsive BCs.

High prevalence of somatic PIK3CA and TP53 pathogenic variants in the normal mammary gland tissue of sporadic breast cancer patients revealed by duplex sequencing

The mammary gland undergoes hormonally stimulated cycles of proliferation, lactation, and involution. We hypothesized that these factors increase the mutational burden in glandular tissue and may explain high cancer incidence rate in the general population, and recurrent disease. Hence, we investigated the DNA sequence variants in the normal mammary gland, tumor, and peripheral blood from 52 reportedly sporadic breast cancer patients. Targeted resequencing of 542 cancer-associated genes revealed subclonal somatic pathogenic variants of: PIK3CA, TP53, AKT1, MAP3K1, CDH1, RB1, NCOR1, MED12, CBFB, TBX3, and TSHR in the normal mammary gland at considerable allelic frequencies (9 × 10-2- 5.2 × 10-1), indicating clonal expansion. Further evaluation of the frequently damaged PIK3CA and TP53 genes by ultra-sensitive duplex sequencing demonstrated a diversified picture of multiple low-level subclonal (in 10-2-10-4 alleles) hotspot pathogenic variants. Our results raise a question about the oncogenic potential in non-tumorous mammary gland tissue of breast-conserving surgery patients.

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.

Immunology and immunotherapy in breast cancer

Immuno-oncology is a rapidly developing field in medicine. Drug combination therapies have already been studied in many clinical trials on various tumor types. In recent years, a checkpoint inhibition therapy with monoclonal antibodies targeting PD-1 and its ligand PD-L1 has been developed. Breast cancer had been examined in the field of immune-oncology relatively recently. This review focuses on clinical evidence regarding immune checkpoint inhibition for curative treatment of various breast cancer subtypes. In addition, we present the results of studies demonstrating the prognostic and predictive value of levels of tumorinfiltrating lymphocytes (CD4 and CD8), their quantitative ratios, and their correlation with regulatory genes (PD-1, PD-L1, and FOX-P3).

Hormonal and Genetic Regulatory Events in Breast Cancer and Its Therapeutics: Importance of the Steroidogenic Acute Regulatory Protein

Estrogen promotes the development and survival of the majority of breast cancers (BCs). Aromatase is the rate-limiting enzyme in estrogen biosynthesis, and it is immensely expressed in both cancerous and non-cancerous breast tissues. Endocrine therapy based on estrogen blockade, by aromatase inhibitors, has been the mainstay of BC treatment in post-menopausal women; however, resistance to hormone therapy is the leading cause of cancer death. An improved understanding of the molecular underpinnings is the key to develop therapeutic strategies for countering the most prevalent hormone receptor positive BCs. Of note, cholesterol is the precursor of all steroid hormones that are synthesized in a variety of tissues and play crucial roles in diverse processes, ranging from organogenesis to homeostasis to carcinogenesis. The rate-limiting step in steroid biosynthesis is the transport of cholesterol from the outer to the inner mitochondrial membrane, a process that is primarily mediated by the steroidogenic acute regulatory (StAR) protein. Advances in genomic and proteomic technologies have revealed a dynamic link between histone deacetylases (HDACs) and StAR, aromatase, and estrogen regulation. We were the first to report that StAR is abundantly expressed, along with large amounts of 17β-estradiol (E2), in hormone-dependent, but not hormone-independent, BCs, in which StAR was also identified as a novel acetylated protein. Our in-silico analyses of The Cancer Genome Atlas (TCGA) datasets, for StAR and steroidogenic enzyme genes, revealed an inverse correlation between the amplification of the StAR gene and the poor survival of BC patients. Additionally, we reported that a number of HDAC inhibitors, by altering StAR acetylation patterns, repress E2 synthesis in hormone-sensitive BC cells. This review highlights the current understanding of molecular pathogenesis of BCs, especially for luminal subtypes, and their therapeutics, underlining that StAR could serve not only as a prognostic marker, but also as a therapeutic candidate, in the prevention and treatment of this life-threatening disease.

Panel Informativity Optimizer: An R Package to Improve Cancer Next-Generation Sequencing Panel Informativity

Mutation detection by next-generation sequencing is routinely used for cancer diagnosis. Selecting an optimal set of genes for a given cancer is not trivial as it has to optimize informativity (ie, the number of patients with at least one mutation in the panel), while minimizing panel length to reduce sequencing costs and increase sensitivity. We propose herein Panel Informativity Optimizer (PIO), an open-source software developed as an R package with a user-friendly graphical interface to help optimize cancer next-generation sequencing panel informativity. Using patient-level mutational data from either private data sets or preloaded data set of 91 independent cohorts from 31 different cancer types, PIO selects an optimal set of genomic intervals to maximize informativity and panel size in a given cancer type. Different options are offered, such as the definition of genomic intervals at the gene or exon level and the use of optimization strategy at the patient or patient per kilobase level. PIO can also propose an optimal set of genomic intervals to increase informativity of custom panels. A panel tester function is also available for panel benchmarking. Using public databases, as well as data from real-life settings, we demonstrate that PIO allows panel size reduction of up to 1000 kb, and accurately predicts the performance of custom or commercial panels.

ROS1 altered breast cancers - a distinctive molecular subtype of PR- metastatic breast cancers: Expanding the scope of targeted therapeutics

BACKGROUND

Breast cancer, one of the leading causes of cancer-related mortality in women worldwide, exhibits wide-ranging histo-morphologic, clinical and molecular diversity.

OBJECTIVE

This study compares the genetic alterations of breast tumors with the histo-morphological, hormone receptor status and metastatic "organotropism".

MATERIALS AND METHODS

Twenty-two cases of primary invasive breast carcinoma with local/distant metastasis were retrieved from the pathology archives. The status of estrogen and progesterone receptors by immunohistochemistry was recorded along with other pertinent case data. Next generation sequencing was performed on formalin-fixed paraffin embedded blocks of tumor.

RESULTS

The mean age of the study subjects was 57.9 ± 13.3 years. TP53 mutation was the most common gene alteration in this study and was seen in 40.9% cases. ROS1 gene was mutated in 44.4% PR negative breast cancers while being wild type in the twelve PR positive tumors. (p = 0.021).STRING interaction network constructed with ROS1 and PR revealed a significantly higher number of interactions in this network than expected (p-value 0.000973).

CONCLUSION

This study highlights the significantly higher incidence of ROS1 gene alterations in metastatic PR- breast cancers, with STRING network analysis revealing higher nodal interaction in the nodal network comprised of PR and ROS1 exclusive of ER.

Heritable genomic diversity in breast cancer driver genes and associations with risk in a Chilean population

BACKGROUND

Driver mutations are the genetic components responsible for tumor initiation and progression. These variants, which may be inherited, influence cancer risk and therefore underlie many familial cancers. The present study examines the potential association between SNPs in driver genes SF3B1 (rs4685), TBX3 (rs12366395, rs8853, and rs1061651) and MAP3K1 (rs72758040) and BC in BRCA1/2-negative Chilean families.

METHODS

The SNPs were genotyped in 486 BC cases and 1258 controls by TaqMan Assay.

RESULTS

Our data do not support an association between rs4685:C > T, rs8853:T > C, or rs1061651:T > C and BC risk. However, the rs12366395-G allele (A/G + G/G) was associated with risk in families with a strong history of BC (OR = 1.2 [95% CI 1.0-1.6] p = 0.02 and OR = 1.5 [95% CI 1.0-2.2] p = 0.02, respectively). Moreover, rs72758040-C was associated with increased risk in cases with a moderate-to-strong family history of BC (OR = 1.3 [95% CI 1.0-1.7] p = 0.02 and OR = 1.3 [95% CI 1.0-1.8] p = 0.03 respectively). Finally, risk was significantly higher in homozygous C/C cases from families with a moderate-to-strong BC history (OR = 1.8 [95% CI 1.0-3.1] p = 0.03 and OR = 1.9 [95% CI 1.1-3.4] p = 0.01, respectively). We also evaluated the combined impact of rs12366395-G and rs72758040-C. Familial BC risk increased in a dose-dependent manner with risk allele count, reflecting an additive effect (p-trend = 0.0002).

CONCLUSIONS

Our study suggests that germline variants in driver genes TBX3 (rs12366395) and MAP3K1 (rs72758040) may influence BC risk in BRCA1/2-negative Chilean families. Moreover, the presence of rs12366395-G and rs72758040-C could increase BC risk in a Chilean population.

Kindlin-2 promotes Src-mediated tyrosine phosphorylation of androgen receptor and contributes to breast cancer progression

Androgen receptor (AR) signaling plays important roles in breast cancer progression. We show here that Kindlin-2, a focal adhesion protein, is critically involved in the promotion of AR signaling and breast cancer progression. Kindlin-2 physically associates with AR and Src through its two neighboring domains, namely F1 and F0 domains, resulting in formation of a Kindlin-2-AR-Src supramolecular complex and consequently facilitating Src-mediated AR Tyr-534 phosphorylation and signaling. Depletion of Kindlin-2 was sufficient to suppress Src-mediated AR Tyr-534 phosphorylation and signaling, resulting in diminished breast cancer cell proliferation and migration. Re-expression of wild-type Kindlin-2, but not AR-binding-defective or Src-binding-defective mutant forms of Kindlin-2, in Kindlin-2-deficient cells restored AR Tyr-534 phosphorylation, signaling, breast cancer cell proliferation and migration. Furthermore, re-introduction of phosphor-mimic mutant AR-Y534D, but not wild-type AR reversed Kindlin-2 deficiency-induced inhibition of AR signaling and breast cancer progression. Finally, using a genetic knockout strategy, we show that ablation of Kindlin-2 from mammary tumors in mouse significantly reduced AR Tyr-534 phosphorylation, breast tumor progression and metastasis in vivo. Our results suggest a critical role of Kindlin-2 in promoting breast cancer progression and shed light on the molecular mechanism through which it functions in this process.

Frequent 4EBP1 Amplification Induces Synthetic Dependence on FGFR Signaling in Cancer

The eIF4E translation initiation factor has oncogenic properties and concordantly, the inhibitory eIF4E-binding protein (4EBP1) is considered a tumor suppressor. The exact molecular effects of 4EBP1 activation in cancer are still unknown. Surprisingly, 4EBP1 is a target of genomic copy number gains (Chr. 8p11) in breast and lung cancer. We noticed that 4EBP1 gains are genetically linked to gains in neighboring genes, including WHSC1L1 and FGFR1. Our results show that FGFR1 gains act to attenuate the function of 4EBP1 via PI3K-mediated phosphorylation at Thr37/46, Ser65, and Thr70 sites. This implies that not 4EBP1 but instead FGFR1 is the genetic target of Chr. 8p11 gains in breast and lung cancer. Accordingly, these tumors show increased sensitivity to FGFR1 and PI3K inhibition, and this is a therapeutic vulnerability through restoring the tumor-suppressive function of 4EBP1. Ribosome profiling reveals genes involved in insulin signaling, glucose metabolism, and the inositol pathway to be the relevant translational targets of 4EBP1. These mRNAs are among the top 200 translation targets and are highly enriched for structure and sequence motifs in their 5'UTR, which depends on the 4EBP1-EIF4E activity. In summary, we identified the translational targets of 4EBP1-EIF4E that facilitate the tumor suppressor function of 4EBP1 in cancer.

Computational Screening of Anti-Cancer Drugs Identifies a New BRCA Independent Gene Expression Signature to Predict Breast Cancer Sensitivity to Cisplatin

Simple Summary

Using a collection of publicly available drug screening resources, we identified different partners of genes associated with either sensitivity or resistance to 90 anti-cancer therapies. When subsequently applying these signatures to multiple datasets, we found that these predictive models could predict a large range of drug responses in patient samples. In particular, we discovered a new gene signature to identify breast cancer tumors that are likely to respond to cisplatin in the absence of BRCA1 mutations. This work constitutes an important advance to accelerate the application of platinum-based therapies in patient groups that are not routinely treated with these drugs. In the future, this approach may help to guide the choice of drugs based on the molecular profile of the tumors.

Abstract

The development of therapies that target specific disease subtypes has dramatically improved outcomes for patients with breast cancer. However, survival gains have not been uniform across patients, even within a given molecular subtype. Large collections of publicly available drug screening data matched with transcriptomic measurements have facilitated the development of computational models that predict response to therapy. Here, we generated a series of predictive gene signatures to estimate the sensitivity of breast cancer samples to 90 drugs, comprising FDA-approved drugs or compounds in early development. To achieve this, we used a cell line-based drug screen with matched transcriptomic data to derive in silico models that we validated in large independent datasets obtained from cell lines and patient-derived xenograft (PDX) models. Robust computational signatures were obtained for 28 drugs and used to predict drug efficacy in a set of PDX models. We found that our signature for cisplatin can be used to identify tumors that are likely to respond to this drug, even in absence of the BRCA-1 mutation routinely used to select patients for platinum-based therapies. This clinically relevant observation was confirmed in multiple PDXs. Our study foreshadows an effective delivery approach for precision medicine.

Serial Tumor Molecular Profiling of Newly Diagnosed HER2-Negative Breast Cancers During Chemotherapy in Combination with Angiogenesis Inhibitors

Background

Breast cancers are heterogeneous with variable clinical courses and treatment responses.

Objective

We sought to evaluate dynamic changes in the molecular landscape of HER2-negative tumors treated with chemotherapy and anti-angiogenic agents.

Patients and Methods

Newly diagnosed HER2-negative breast cancer patients received low-dose sunitinib or bevacizumab prior to four 2-weekly cycles of dose-dense doxorubicin and cyclophosphamide. Tumor biopsies were obtained at baseline, after 2 weeks and after 8 weeks of chemotherapy. Next-generation sequencing was performed to assess for single nucleotide variants (SNVs) and copy number alterations (CNAs) of 440 cancer-related genes (ACTOnco^®). Observed genomic changes were correlated with the Miller-Payne histological response to treatment.

Results

Thirty-four patients received sunitinib and 18 received bevacizumab. In total, 77% were hormone receptor positive (HER2−/HR+) and 23% were triple negative breast cancers (TNBC). New therapy-induced mutations were infrequent, occurring only in 13%, and appeared early after a single cycle of treatment. Seventy-two percent developed changes in the variant allele frequency (VAF) of pathogenic SNVs; the majority (51%) of these changes occurred early at 2 weeks and were sustained for 8 weeks. Changes in VAF of SNVs were most commonly seen in the PI3K/mTOR/AKT pathway; 13% developed changes in pathogenic mutations, which potentially confer sensitivity to PIK3CA inhibitors. Tumors with poor Miller-Payne response to treatment were less likely to experience changes in VAF of SNVs compared with those with good response (50% [7/14] vs 15% [4/24] had no changes observed at any timepoint, p = 0.029).

Conclusions

Serial molecular profiling identifies early therapy-induced genomic alterations, which may guide future selection of targeted therapies in breast cancer patients who progress after standard chemotherapy.

Clinical trial registration

ClinicalTrials.gov: NCT02790580 (first posted June 6, 2016).

Supplementary Information

The online version contains supplementary material available at 10.1007/s11523-022-00886-x.

Mechanistic Effects of Estrogens on Breast Cancer

PURPOSE

Current concepts regarding estrogen and its mechanistic effects on breast cancer in women are evolving. This article reviews studies that address estrogen-mediated breast cancer development, the prevalence of occult tumors at autopsy, and the natural history of breast cancer as predicted by a newly developed tumor kinetic model.

METHODS

This article reviews previously published studies from the authors and articles pertinent to the data presented.

RESULTS

We discuss the concepts of adaptive hypersensitivity that develops in response to long-term deprivation of estrogen and results in both increased cell proliferation and apoptosis. The effects of menopausal hormonal therapy on breast cancer in postmenopausal women are interpreted based on the tumor kinetic model. Studies of the administration of a tissue selective estrogen complex in vitro, in vivo, and in patients are described. We review the various clinical studies of breast cancer prevention with selective estrogen receptor modulators and aromatase inhibitors. Finally, the effects of the underlying risk of breast cancer on the effects of menopausal hormone therapy are outlined.

DISCUSSION

The overall intent of this review is to present data supporting recent concepts, discuss pertinent literature, and critically examine areas of controversy.

Circulating Glycan Monosaccharide Composite-Based Biomarker Diagnoses Colorectal Cancer at Early Stages and Predicts Prognosis

Introduction

Early diagnosis could lead to a cure of colorectal cancer (CRC). Since CRC is related to aging and lifestyles, we tested if the environmental information-enriched monosaccharide composite (MC) of circulating glycans could serve as an early diagnostic biomarker for CRC. Meanwhile, we evaluated its role in predicting prognosis.

Methods

HPAEC-PAD was used to quantify glycan monosaccharide compositions from a total of 467 serum samples including CRC patients, colorectal adenoma (CRA) patients and healthy individuals. Two diagnostic model was constructed by logistic regression analysis. The diagnostic performance of the two models was verified in the retrospective validation group and the prospective validation group. The prognostic performance of the model was assessed by survival analysis.

Results

The concentrations of monosaccharides in serum were significantly higher in CRA and CRC patients than in healthy individuals. Two diagnostic models were constructed: MC1 was used to distinguish between healthy individuals and CRC; MC2 was used to distinguish between healthy individuals and CRA. Area under receptor operating characteristic curve (AUC) of MC2 and MC1 was 0.8025 and 0.9403 respectively. However, the AUC of CEA between healthy individuals and CRC was 0.7384. Moreover, in early stage of CRC (without lymph node metastasis), the positive rates of CEA and MC1 were 28% and 80%, respectively. The follow-up data showed that the increased MC1 value was associated with poor survival in patients with CRC (p=0.0010, HR=5.30).

Discussion

The MC1 model is superior to CEA in the diagnosis of CRC, especially in the early diagnosis. MC1 can be used for predicting prognosis of CRC patients, and elevated MC1 values indicate poor survival.

‘Breast Cancer Resistance Likelihood and Personalized Treatment Through Integrated Multiomics’

In recent times, enormous progress has been made in improving the diagnosis and therapeutic strategies for breast carcinoma, yet it remains the most prevalent cancer and second highest contributor to cancer-related deaths in women. Breast cancer (BC) affects one in eight females globally. In 2018 alone, 1.4 million cases were identified worldwide in postmenopausal women and 645,000 cases in premenopausal females, and this burden is constantly increasing. This shows that still a lot of efforts are required to discover therapeutic remedies for this disease. One of the major clinical complications associated with the treatment of breast carcinoma is the development of therapeutic resistance. Multidrug resistance (MDR) and consequent relapse on therapy are prevalent issues related to breast carcinoma; it is due to our incomplete understanding of the molecular mechanisms of breast carcinoma disease. Therefore, elucidating the molecular mechanisms involved in drug resistance is critical. For management of breast carcinoma, the treatment decision not only depends on the assessment of prognosis factors but also on the evaluation of pathological and clinical factors. Integrated data assessments of these multiple factors of breast carcinoma through multiomics can provide significant insight and hope for making therapeutic decisions. This omics approach is particularly helpful since it identifies the biomarkers of disease progression and treatment progress by collective characterization and quantification of pools of biological molecules within and among the cancerous cells. The scrupulous understanding of cancer and its treatment at the molecular level led to the concept of a personalized approach, which is one of the most significant advancements in modern oncology. Likewise, there are certain genetic and non-genetic tests available for BC which can help in personalized therapy. Genetically inherited risks can be screened for personal predisposition to BC, and genetic changes or variations (mutations) can also be identified to decide on the best treatment. Ultimately, further understanding of BC at the molecular level (multiomics) will define more precise choices in personalized medicine. In this review, we have summarized therapeutic resistance associated with BC and the techniques used for its management.

Response of Leptomeningeal Metastasis of Breast Cancer With a HER2/neu Activating Variant to Tucatinib: A Case Report

Metastatic breast cancer demonstrates HER2/neu amplification approximately 15% of the time. However, HER2 mutations, which often stimulate tumor growth, occur in only 3% to 5% of patients, and are seen more frequently in metastatic versus primary tumors. They are more frequent in lobular carcinoma, including triple-negative lobular cancer. Many of these variants are resistant to trastuzumab and lapatinib. However, neratinib can be efficacious, and recent data suggest that antibody-drug conjugates (ADCs) such as ado-trastuzumab emtansine (T-DM1) and trastuzumab deruxtecan may also be helpful. Laboratory and clinical data raise the possibility that simultaneous treatment with ADCs plus neratinib may be even more efficacious. Tucatinib, which has demonstrated significant activity in the central nervous system, has also been shown in vitro to be active against a number of these HER2 variants. This report describes a patient with metastatic estrogen receptor-positive, HER2-nonamplified breast cancer with an activating HER2 mutation whose tumor became resistant to neratinib as well as capecitabine, but whose subsequent leptomeningeal disease had a dramatically successful response to tucatinib plus capecitabine. As the frequency of HER2 mutations increases during the evolution of metastatic breast cancer, it is important to obtain genomic evaluation on these tumors with either repeat tissue or liquid biopsy as they progress over time.

A Comprehensive Survey of Genomic Mutations in Breast Cancer Reveals Recurrent Neoantigens as Potential Therapeutic Targets

Neoantigens are mutated antigens specifically generated by cancer cells but absent in normal cells. With high specificity and immunogenicity, neoantigens are considered as an ideal target for immunotherapy. This study was aimed to investigate the signature of neoantigens in breast cancer. Somatic mutations, including SNVs and indels, were obtained from cBioPortal of 5991 breast cancer patients. 738 non-silent somatic variants present in at least 3 patients for neoantigen prediction were selected. PIK3CA (38%), the highly mutated gene in breast cancer, could produce the highest number of neoantigens per gene. Some pan-cancer hotspot mutations, such as PIK3CA E545K (6.93%), could be recognized by at least one HLA molecule. Since there are more SNVs than indels in breast cancer, SNVs are the major source of neoantigens. Patients with hormone receptor-positive or HER2 negative are more competent to produce neoantigens. Age, but not the clinical stage, is a significant contributory factor of neoantigen production. We believe a detailed description of breast cancer neoantigen signatures could contribute to neoantigen-based immunotherapy development.

Retinoblastoma protein regulates carcinogen susceptibility at heterochromatic cancer driver loci

Carcinogenic insult, such as UV light exposure, creates DNA lesions that evolve into mutations if left unrepaired. These resulting mutations can contribute to carcinogenesis and drive malignant phenotypes. Susceptibility to carcinogens (i.e., the propensity to form a carcinogen-induced DNA lesion) is regulated by both genetic and epigenetic factors. Importantly, carcinogen susceptibility is a critical contributor to cancer mutagenesis. It is known that mutations can be prevented by tumor suppressor regulation of DNA damage response pathways; however, their roles carcinogen susceptibility have not yet been reported. In this study, we reveal that the retinoblastoma (RB1) tumor suppressor regulates UV susceptibility across broad regions of the genome. In particular, centromere and telomere-proximal regions exhibit significant increases in UV lesion susceptibility when RB1 is deleted. Several cancer-related genes are located within genomic regions of increased susceptibility, including telomerase reverse transcriptase, TERT, thereby accelerating mutagenic potential in cancers with RB1 pathway alterations. These findings reveal novel genome stability mechanisms of a tumor suppressor and uncover new pathways to accumulate mutations during cancer evolution.

MicroRNA-367 Inhibits Breast Cancer and Promotes Apoptosis by Targeting AT-Rich Interactive Domain-Containing Protein 1B

Introduction: Breast cancer (BC) developed in the glandular epithelial tissue of breast. microRNA (miR)-367 is an important player in cancer progression, but has never been studied in BC. This experiment tries to probe the mechanism of miR-367 in BC treatment with downstream target gene. Materials and Methods: Human BC cell lines and healthy breast epithelium cells were applied in this study. After the transfection of miR-367 inhibitor or mimic into BC cells, functional assays were conducted to measure cell growth. Afterwards, flow cytometry was employed in apoptosis verification. Then, target relation between miR-367 and ARID1B was certified. Furthermore, ARID1B level was also measured. Results: miR-367 was underexpressed in human BC cells (p < 0.05). Besides, overexpressed miR-367 inhibited BC cell proliferation and encouraged apoptosis, while underexpressed miR-367 led to an opposite outcome (p < 0.05). This experiment then implied that miR-367 dramatically suppressed the activity of cell transfected with ARID1B-wild type. miR-367 overexpression quenched ARID1B level in BC cells; while silencing miR-367 upregulated ARID1B expression (p < 0.05). Conclusion: Our experiment discovered that miR-367 quenched BC cell growth and promoted apoptosis by targeting ARID1B. This investigation may provide novel insights in BC treatment.

The Role of Tumor Stem Cell Exosomes in Cancer Invasion and Metastasis

Exosomes are lipid membrane bilayer-encapsulated vesicles secreted by cells into the extracellular space. They carry abundant inclusions (such as nucleic acids, proteins, and lipids) that play pivotal roles in intercellular communication. Tumor stem cells are capable of self-renewal and are crucial for survival, proliferation, drug resistance, metastasis, and recurrence of tumors. The miRNAs (microRNAs) in exosomes have various functions, such as participating in inflammatory response, cell migration, proliferation, apoptosis, autophagy, and epithelial-mesenchymal transition. Tumor stem cells secrete exosomes that act as important messengers involved in various tumor processes and several studies provide increasing evidence supporting the importance of these exosomes in tumor recurrence and metastasis. This review primarily focuses on the production and secretion of exosomes from tumors and tumor stem cells and their effects on cancer progression. Cancer stem cancer derived exosome play an important massager in the tumor microenvironment. It also emphasizes on the study of tumor stem cell exosomes in the light of cancer metastasis and recurrence aiming to provide valuable insights and novel perspectives, which could be beneficial for developing effective diagnostic and treatment strategies.

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.

Image Analysis of Circulating Tumor Cells and Leukocytes Predicts Survival and Metastatic Pattern in Breast Cancer Patients

Background

The purpose of the present work was to test whether quantitative image analysis of circulating cells can provide useful clinical information targeting bone metastasis (BM) and overall survival (OS >30 months) in metastatic breast cancer (MBC).

Methods

Starting from cell images of epithelial circulating tumor cells (eCTC) and leukocytes (CD45pos) obtained with DEPArray, we identified the most significant features and applied single-variable and multi-variable methods, screening all combinations of four machine-learning approaches (Naïve Bayes, Logistic regression, Decision Trees, Random Forest).

Results

Best predictive features were circularity (OS) and diameter (BM), in both eCTC and CD45pos. Median difference in OS was 15 vs. 43 (months), p = 0.03 for eCTC and 19 vs. 36, p = 0.16 for CD45pos. Prediction for BM showed low accuracy (64%, 53%) but strong positive predictive value PPV (79%, 91%) for eCTC and CD45, respectively. Best machine learning model was Naïve Bayes, showing 46 vs 11 (months), p <0.0001 for eCTC; 12.5 vs. 45, p = 0.0004 for CD45pos and 11 vs. 45, p = 0.0003 for eCTC + CD45pos. BM prediction reached 91% accuracy with eCTC, 84% with CD45pos and 91% with combined model.

Conclusions

Quantitative image analysis and machine learning models were effective methods to predict survival and metastatic pattern, with both eCTC and CD45pos containing significant and complementary information.

Cell-Free DNA Variables including Gene Mutations in CA15-3 Normal Breast Cancer Reflect Prognosis

Background

Cell-free DNA (cfDNA) has attracted considerable attention in precision medicine. However, few data are available regarding to the prognostic value of cfDNA variables in CA15-3 normal breast cancer (BC) patients. Here, we aimed at investigating the prognostic value of cfDNA variables including gene mutations in CA15-3 normal BC patients.

Methods

A total of 68 BC patients with normal CA15-3 levels were enrolled. cfDNA concentration and integrity were assessed based on qPCR. cfDNA gene mutations were conducted by using next gene sequencing (NGS). The association between cfDNA variables and the prognosis of patients was analyzed.

Results

cfDNA concentration was related to tumor stage (P = 0.002), metastases (P = 0.001), and distant metastases (P < 0.001). The elevated copy number variants (CNV) were found in distant metastasis patients compared with patients without distant metastases (P = 0.008). Nineteen mutant genes were validated in enrolled CA15-3 normal BC patients. Thirty-two patients (47.0%) had single nucleotide variants (SNV), and 13 (19.1%) patients had TP53 mutations (TP53^mut). SNV (P = 0.033) was related to tumor stage, and TP53^mut was related to metastases (P = 0.016) and distant metastases (P = 0.006). In multivariate logistic analysis, cfDNA concentration was associated with metastases (OR = 3.404, 95% CI: 1.074-10.788, P = 0.037) and distant metastases (OR = 13.750, 95% CI: 1.473-128.358, P = 0.021). Cases with high cfDNA levels (>15.6 ng/ml), SNV, and TP53^mut showed worse DFS compared with patients with low cfDNA levels (P < 0.001), without SNV (P = 0.002) and with TP53 wildtype (P < 0.001), respectively. In the multivariate Cox proportional hazard model, cfDNA concentration was an independent predictor of poor survival (HR = 5.786, 95% CI: 1.101-30.407, P = 0.038).

Conclusions

Assessment of cfDNA concentration, CNV, SNV, and TP53^mut could be useful in predicting prognosis for CA15-3 normal BC patients. The cfDNA concentration was an independent predictor prognostic factor in CA15-3 normal BC patients.

Proteomic analysis of archival breast cancer clinical specimens identifies biological subtypes with distinct survival outcomes

Despite advances in genomic classification of breast cancer, current clinical tests and treatment decisions are commonly based on protein level information. Formalin-fixed paraffin-embedded (FFPE) tissue specimens with extended clinical outcomes are widely available. Here, we perform comprehensive proteomic profiling of 300 FFPE breast cancer surgical specimens, 75 of each PAM50 subtype, from patients diagnosed in 2008-2013 (n = 178) and 1986-1992 (n = 122) with linked clinical outcomes. These two cohorts are analyzed separately, and we quantify 4214 proteins across all 300 samples. Within the aggressive PAM50-classified basal-like cases, proteomic profiling reveals two groups with one having characteristic immune hot expression features and highly favorable survival. Her2-Enriched cases separate into heterogeneous groups differing by extracellular matrix, lipid metabolism, and immune-response features. Within 88 triple-negative breast cancers, four proteomic clusters display features of basal-immune hot, basal-immune cold, mesenchymal, and luminal with disparate survival outcomes. Our proteomic analysis characterizes the heterogeneity of breast cancer in a clinically-applicable manner, identifies potential biomarkers and therapeutic targets, and provides a resource for clinical breast cancer classification.

In silico recognition of a prognostic signature in basal-like breast cancer patients

Background

Triple-negative breast cancers (TNBCs) display poor prognosis, have a high risk of tumour recurrence, and exhibit high resistance to drug treatments. Based on their gene expression profiles, the majority of TNBCs are classified as basal-like breast cancers. Currently, there are not available widely-accepted prognostic markers to predict outcomes in basal-like subtype, so the selection of new prognostic indicators for this BC phenotype represents an unmet clinical challenge.

Results

Here, we attempted to address this challenging issue by exploiting a bioinformatics pipeline able to integrate transcriptomic, genomic, epigenomic, and clinical data freely accessible from public repositories. This pipeline starts from the application of the well-established network-based SWIM methodology on the transcriptomic data to unveil important (switch) genes in relation with a complex disease of interest. Then, survival and linear regression analyses are performed to associate the gene expression profiles of the switch genes with both the patients’ clinical outcome and the disease aggressiveness. This allows us to identify a prognostic gene signature that in turn is fed to the last step of the pipeline consisting of an analysis at DNA level, to investigate whether variations in the expression of identified prognostic switch genes could be related to genetic (copy number variations) or epigenetic (DNA methylation differences) alterations in their gene loci, or to the activities of transcription factors binding to their promoter regions. Finally, changes in the protein expression levels corresponding to the so far identified prognostic switch genes are evaluated by immunohistochemical staining results taking advantage of the Human Protein Atlas.

Conclusion

The application of the proposed pipeline on the dataset of The Cancer Genome Atlas (TCGA)-Breast Invasive Carcinoma (BRCA) patients affected by basal-like subtype led to an in silico recognition of a basal-like specific gene signature composed of 11 potential prognostic biomarkers to be further investigated.

Large Animal Models of Breast Cancer

In this mini review the status, advantages, and disadvantages of large animal modeling of breast cancer (BC) will be discussed. While most older studies of large animal BC models utilized canine and feline subjects, more recently there has been interest in development of porcine BC models, with some early promising results for modeling human disease. Widely used rodent models of BC were briefly reviewed to give context to the work on the large animal BC models. Availability of large animal BC models could provide additional tools for BC research, including availability of human-sized subjects and BC models with greater biologic relevance.

Parental histone deposition on the replicated strands promotes error-free DNA damage tolerance and regulates drug resistance

In this study, Dolce et al. investigated connections between Ctf4-mediated processes involved in drug resistance, and conducted a suppressor screen of ctf4Δ sensitivity to the methylating agent MMS. Their findings demonstrate a chromatin-based drug resistance mechanism in which defects in parental histone transfer after replication fork passage impair error-free recombination bypass and lead to up-regulation of TLS-mediated mutagenesis and drug resistance.

Ctf4 is a conserved replisome component with multiple roles in DNA metabolism. To investigate connections between Ctf4-mediated processes involved in drug resistance, we conducted a suppressor screen of ctf4Δ sensitivity to the methylating agent MMS. We uncovered that mutations in Dpb3 and Dpb4 components of polymerase ε result in the development of drug resistance in ctf4Δ via their histone-binding function. Alleviated sensitivity to MMS of the double mutants was not associated with rescue of ctf4Δ defects in sister chromatid cohesion, replication fork architecture, or template switching, which ensures error-free replication in the presence of genotoxic stress. Strikingly, the improved viability depended on translesion synthesis (TLS) polymerase-mediated mutagenesis, which was drastically increased in ctf4 dpb3 double mutants. Importantly, mutations in Mcm2–Ctf4–Polα and Dpb3–Dpb4 axes of parental (H3–H4)₂ deposition on lagging and leading strands invariably resulted in reduced error-free DNA damage tolerance through gap filling by template switch recombination. Overall, we uncovered a chromatin-based drug resistance mechanism in which defects in parental histone transfer after replication fork passage impair error-free recombination bypass and lead to up-regulation of TLS-mediated mutagenesis and drug resistance.

RAS and beyond: the many faces of the neurofibromatosis type 1 protein

Neurofibromatosis type 1 is a rare neurogenetic syndrome, characterized by pigmentary abnormalities, learning and social deficits, and a predisposition for benign and malignant tumor formation caused by germline mutations in the NF1 gene. With the cloning of the NF1 gene and the recognition that the encoded protein, neurofibromin, largely functions as a negative regulator of RAS activity, attention has mainly focused on RAS and canonical RAS effector pathway signaling relevant to disease pathogenesis and treatment. However, as neurofibromin is a large cytoplasmic protein the RAS regulatory domain of which occupies only 10% of its entire coding sequence, both canonical and non-canonical RAS pathway modulation, as well as the existence of potential non-RAS functions, are becoming apparent. In this Special article, we discuss our current understanding of neurofibromin function.

Neurofibromin and suppression of tumorigenesis: beyond the GAP

Neurofibromatosis type 1 (NF1) is an autosomal dominant genetic disease and one of the most common inherited tumor predisposition syndromes, affecting 1 in 3000 individuals worldwide. The NF1 gene encodes neurofibromin, a large protein with RAS GTP-ase activating (RAS-GAP) activity, and loss of NF1 results in increased RAS signaling. Neurofibromin contains many other domains, and there is considerable evidence that these domains play a role in some manifestations of NF1. Investigating the role of these domains as well as the various signaling pathways that neurofibromin regulates and interacts with will provide a better understanding of how neurofibromin acts to suppress tumor development and potentially open new therapeutic avenues. In this review, we discuss what is known about the structure of neurofibromin, its interactions with other proteins and signaling pathways, its role in development and differentiation, and its function as a tumor suppressor. Finally, we discuss the latest research on potential therapeutics for neurofibromin-deficient neoplasms.

The Mutational Landscape of Early-Onset Breast Cancer: A Next-Generation Sequencing Analysis

BACKGROUND

Around 5%-7% of breast cancer cases are diagnosed in women younger than 40, making it the leading cause of female cancer in the 25- to 39-year-old age group. Unfortunately, young age at diagnosis is linked to a more aggressive tumor biology and a worse clinical outcome. The identification of the mutational landscape of breast cancer in this age group could optimize the management.

METHODS

We performed NGS analysis in paraffin blocks and blood samples of 32 young patients with breast cancer [<40 years] and 90 older patients during the period 2019 through 2021. All patients were treated in a single institution at the Oncology Department of "Alexandra" Hospital, Medical School, University of Athens, Greece.

RESULTS

Breast tumors were characterized more frequently by HER2 overexpression [25% vs 18.9%], higher ki67 levels [75% vs 61%] and lower differentiation [71.9% vs 60%] in the younger group. PIK3CA [6/20; 30%] and TP53 [6/20; 30%] were the most frequent pathogenic somatic mutations identified in young patients, while one case of BRCA2 somatic mutation [1/20; 5%] and one case of PTEN somatic mutation [1/20; 5%] were also identified. PIK3CA mutations [16/50; 32%] and TP53 mutations [20/50; 40%] were the most common somatic mutations identified in older patients, however other somatic mutations were also reported (ATM, AKT, CHEK2, NRAS, CDKN2A, PTEN, NF1, RB1, FGFR1, ERBB2). As for germline mutations, CHEK2 [3/25; 12%] was the most common pathogenic germline mutation in younger patients followed by BRCA1 [2/25; 8%]. Of note, CHEK2 germline mutations were identified less frequently in older patients [2/61; 3%] among others [BRCA1 (2/61; 3%), ATM (2/61; 3%), APC (1/61; 1,6%) and BRCA2 (1/61; 1,6%)].

CONCLUSION

We here report the mutational profile identified via NGS in patients with early-onset breast cancer compared to their older counterparts. Although the sample size is small and no statistically significant differences were detected, we highlight the need of genetic testing to most patients in this subgroup.

Computational Prediction of the Pathogenic Status of Cancer-Specific Somatic Variants

In-silico classification of the pathogenic status of somatic variants is shown to be promising in promoting the clinical utilization of genetic tests. Majority of the available classification tools are designed based on the characteristics of germline variants or the combination of germline and somatic variants. Significance of somatic variants in cancer initiation and progression urges for development of classifiers specialized for classifying pathogenic status of cancer somatic variants based on the model trained on cancer somatic variants. We established a gold standard exclusively for cancer somatic single nucleotide variants (SNVs) collected from the catalogue of somatic mutations in cancer. We developed two support vector machine (SVM) classifiers based on genomic features of cancer somatic SNVs located in coding and non-coding regions of the genome, respectively. The SVM classifiers achieved the area under the ROC curve of 0.94 and 0.89 regarding the classification of the pathogenic status of coding and non-coding cancer somatic SNVs, respectively. Our models outperform two well-known classification tools including FATHMM-FX and CScape in classifying both coding and non-coding cancer somatic variants. Furthermore, we applied our models to predict the pathogenic status of somatic variants identified in young breast cancer patients from METABRIC and TCGA-BRCA studies. The results indicated that using the classification threshold of 0.8 our "coding" model predicted 1853 positive SNVs (out of 6,910) from the TCGA-BRCA dataset, and 500 positive SNVs (out of 1882) from the METABRIC dataset. Interestingly, through comparative survival analysis of the positive predictions from our models, we identified a young-specific pathogenic somatic variant with potential for the prognosis of early onset of breast cancer in young women.