The clonal and mutational evolution spectrum of primary triple-negative breast cancers

Voltage imaging reveals the dynamic electrical signatures of human breast cancer cells

Cancer cells feature a resting membrane potential (Vm) that is depolarized compared to normal cells, and express active ionic conductances, which factor directly in their pathophysiological behavior. Despite similarities to ‘excitable’ tissues, relatively little is known about cancer cell Vm dynamics. Here high-throughput, cellular-resolution Vm imaging reveals that Vm fluctuates dynamically in several breast cancer cell lines compared to non-cancerous MCF-10A cells. We characterize Vm fluctuations of hundreds of human triple-negative breast cancer MDA-MB-231 cells. By quantifying their Dynamic Electrical Signatures (DESs) through an unsupervised machine-learning protocol, we identify four classes ranging from "noisy” to “blinking/waving“. The Vm of MDA-MB-231 cells exhibits spontaneous, transient hyperpolarizations inhibited by the voltage-gated sodium channel blocker tetrodotoxin, and by calcium-activated potassium channel inhibitors apamin and iberiotoxin. The Vm of MCF-10A cells is comparatively static, but fluctuations increase following treatment with transforming growth factor-β1, a canonical inducer of the epithelial-to-mesenchymal transition. These data suggest that the ability to generate Vm fluctuations may be a property of hybrid epithelial-mesenchymal cells or those originated from luminal progenitors.

Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor

Mutational signature analysis is commonly performed in cancer genomic studies. Here, we present SigProfilerExtractor, an automated tool for de novo extraction of mutational signatures, and benchmark it against another 13 bioinformatics tools by using 34 scenarios encompassing 2,500 simulated signatures found in 60,000 synthetic genomes and 20,000 synthetic exomes. For simulations with 5% noise, reflecting high-quality datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true-positive signatures while yielding 5-fold less false-positive signatures. Applying SigProfilerExtractor to 4,643 whole-genome- and 19,184 whole-exome-sequenced cancers reveals four novel signatures. Two of the signatures are confirmed in independent cohorts, and one of these signatures is associated with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting signatures, and several novel mutational signatures, including one putatively attributed to direct tobacco smoking mutagenesis in bladder tissues.

Identification of HLA class I-restricted immunogenic neoantigens in triple negative breast cancer

Immune checkpoint inhibitor (ICI)-based immunotherapy in triple negative breast cancer (TNBC) is achieving limited therapeutic results, requiring the development of more potent strategies. Combination of ICI with vaccination strategies would enhance antitumor immunity and response rates to ICI in patients having poorly infiltrated tumors. In heavily mutated tumors, neoantigens (neoAgs) resulting from tumor mutations have induced potent responses when used as vaccines. Thus, our aim was the identification of immunogenic neoAgs suitable as vaccines in TNBC patients. By using whole exome sequencing, RNAseq and HLA binding algorithms of tumor samples from a cohort of eight TNBC patients, we identified a median of 60 mutations/patient, which originated a putative median number of 98 HLA class I-restricted neoAgs. Considering a group of 27 predicted neoAgs presented by HLA-A*02:01 allele in two patients, peptide binding to HLA was experimentally confirmed in 63% of them, whereas 55% were immunogenic in vivo in HLA-A*02:01⁺ transgenic mice, inducing T-cells against the mutated but not the wild-type peptide sequence. Vaccination with peptide pools or DNA plasmids expressing these neoAgs induced polyepitopic T-cell responses, which recognized neoAg-expressing tumor cells. These results suggest that TNBC tumors harbor neoAgs potentially useful in therapeutic vaccines, opening the way for new combined immunotherapies.

Safety in Multi-Assembly via Paths Appearing in All Path Covers of a DAG

A multi-assembly problem asks to reconstruct multiple genomic sequences from mixed reads sequenced from all of them. Standard formulations of such problems model a solution as a path cover in a directed acyclic graph, namely a set of paths that together cover all vertices of the graph. Since multi-assembly problems admit multiple solutions in practice, we consider an approach commonly used in standard genome assembly: output only partial solutions (contigs, or safe paths), that appear in all path cover solutions. We study constrained path covers, a restriction on the path cover solution that incorporate practical constraints arising in multi-assembly problems. We give efficient algorithms finding all maximal safe paths for constrained path covers. We compute the safe paths of splicing graphs constructed from transcript annotations of different species. Our algorithms run in less than 15 seconds per species and report RNA contigs that are over 99% precise and are up to 8 times longer than unitigs. Moreover, RNA contigs cover over 70% of the transcripts and their coding sequences in most cases. With their increased length to unitigs, high precision, and fast construction time, maximal safe paths can provide a better base set of sequences for transcript assembly programs.

Efficient Minimum Flow Decomposition via Integer Linear Programming

Minimum flow decomposition (MFD) is an NP-hard problem asking to decompose a network flow into a minimum set of paths (together with associated weights). Variants of it are powerful models in multiassembly problems in Bioinformatics, such as RNA assembly. Owing to its hardness, practical multiassembly tools either use heuristics or solve simpler, polynomial time-solvable versions of the problem, which may yield solutions that are not minimal or do not perfectly decompose the flow. Here, we provide the first fast and exact solver for MFD on acyclic flow networks, based on Integer Linear Programming (ILP). Key to our approach is an encoding of all the exponentially many solution paths using only a quadratic number of variables. We also extend our ILP formulation to many practical variants, such as incorporating longer or paired-end reads, or minimizing flow errors. On both simulated and real-flow splicing graphs, our approach solves any instance in <13 seconds. We hope that our formulations can lie at the core of future practical RNA assembly tools. Our implementations are freely available on Github.

Identification of a five genes prognosis signature for triple-negative breast cancer using multi-omics methods and bioinformatics analysis

Triple-negative breast cancer (TNBC) has a high degree of malignancy, lack of effective diagnosis and treatment, and poor prognosis. Bioinformatics methods are used to screen the hub genes and signal pathways involved in the progress of TNBC to provide reliable biomarkers for the diagnosis and treatment of TNBC. Download the raw data of four TNBC-related datasets from the Gene Expression Omnibus (GEO) database and use them for bioinformatics analysis. GEO2R tool was used to analyze and identify differentially expressed (DE) mRNAs. DAVID database was used to carry out gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genome Pathways (KEGG) signal pathway enrichment analysis for DE mRNAs. STRING database and Cytoscape were used to build DE mRNAs protein-protein interaction (PPI) network diagram and visualize PPI network, respectively. Through cytoHubba, cBioPortal database, Kaplan-Meier mapper database, Gene Expression Profiling Interactive Analysis (GEPIA) Database, UALCAN Database, The Cancer Genome Atlas (TCGA) database, Tumor Immunity Estimation Resource identify hub genes. Perform qRT-PCR, Human Protein Atlas analysis, mutation analysis, survival analysis, clinical-pathological characteristics, and infiltrating immune cell analysis. 22 DE mRNAs were identified from the four datasets, including 16 upregulated DE mRNAs and six downregulated DE mRNAs. Enrichment analysis of the KEGG showed that DE mRNAs were principally enriched in pathways in cancer, mismatch repair, cell cycle, platinum drug resistance, breast cancer. Six hub genes were screened based on the PPI network diagram of DE mRNAs. Survival analysis found that TOP2A, CCNA2, PCNA, MSH2, CDK6 are related to the prognosis of TNBC. In addition, mutations, clinical indicators, and immune infiltration analysis show that these five hub genes play an important role in the progress of TNBC and immune monitoring. Compared with MCF-10A, MCF-7, and SKBR-3 cells, TOP2A, PCNA, MSH2, and CDK6 were significantly upregulated in MDA-MB-321 cells. Compared with normal, luminal, and Her-2 positive tissues, CCNA2, MSH2, and CDK6 were significantly upregulated in TNBC. Through comparative analysis of GEO datasets related to colorectal cancer and lung adenocarcinoma, it was determined that these five hub genes were unique differentially expressed genes of TNBC. At last, the hub genes related to the progression, prognosis, and immunity of TNBC have been successfully screened. They are indeed specific to TNBC as prognostic features. They can be used as potential markers for the prognosis of TNBC and provide potential therapeutic targets.

A Case Series Exploration of Multi-Regional Expression Heterogeneity in Triple-Negative Breast Cancer Patients

Extensive intratumoral heterogeneity (ITH) is believed to contribute to therapeutic failure and tumor recurrence, as treatment-resistant cell clones can survive and expand. However, little is known about ITH in triple-negative breast cancer (TNBC) because of the limited number of single-cell sequencing studies on TNBC. In this study, we explored ITH in TNBC by evaluating gene expression-derived and imaging-derived multi-region differences within the same tumor. We obtained tissue specimens from 10 TNBC patients and conducted RNA sequencing analysis of 2-4 regions per tumor. We developed a novel analysis framework to dissect and characterize different types of variability: between-patients (inter-tumoral heterogeneity), between-patients across regions (inter-tumoral and region heterogeneity), and within-patient, between-regions (regional intratumoral heterogeneity). We performed a Bayesian changepoint analysis to assess and classify regional variability as low (convergent) versus high (divergent) within each patient feature (TNBC and PAM50 subtypes, immune, stroma, tumor counts and tumor infiltrating lymphocytes). Gene expression signatures were categorized into three types of variability: between-patients (108 genes), between-patients across regions (183 genes), and within-patients, between-regions (778 genes). Based on the between-patient gene signature, we identified two distinct patient clusters that differed in menopausal status. Significant intratumoral divergence was observed for PAM50 classification, tumor cell counts, and tumor-infiltrating T cell abundance. Other features examined showed a representation of both divergent and convergent results. Lymph node stage was significantly associated with divergent tumors. Our results show extensive intertumoral heterogeneity and regional ITH in gene expression and image-derived features in TNBC. Our findings also raise concerns regarding gene expression based TNBC subtyping. Future studies are warranted to elucidate the role of regional heterogeneity in TNBC as a driver of treatment resistance.

The maximum entropy principle for compositional data

BACKGROUND

Compositional systems, represented as parts of some whole, are ubiquitous. They encompass the abundances of proteins in a cell, the distribution of organisms in nature, and the stoichiometry of the most basic chemical reactions. Thus, a central goal is to understand how such processes emerge from the behaviors of their components and their pairwise interactions. Such a study, however, is challenging for two key reasons. Firstly, such systems are complex and depend, often stochastically, on their constituent parts. Secondly, the data lie on a simplex which influences their correlations.

RESULTS

To resolve both of these issues, we provide a general and data-driven modeling tool for compositional systems called Compositional Maximum Entropy (CME). By integrating the prior geometric structure of compositions with sample-specific information, CME infers the underlying multivariate relationships between the constituent components. We provide two proofs of principle. First, we measure the relative abundances of different bacteria and infer how they interact. Second, we show that our method outperforms a common alternative for the extraction of gene-gene interactions in triple-negative breast cancer.

CONCLUSIONS

CME provides novel and biologically-intuitive insights and is promising as a comprehensive quantitative framework for compositional data.

Determining prognostic factors and optimal surgical intervention for early-onset triple-negative breast cancer

Background

Few studies have focused specifically on prognostic factors and optimal surgical intervention for early-onset triple-negative breast cancer (eTNBC), which is characterized by high malignancy and poor prognosis.

Methods

We performed a cohort study with a median follow-up of 31 months using Surveillance, Epidemiology, and End Results (SEER) data of patients diagnosed with stages I–III eTNBC between 2010 and 2016. In addition, we collected cases between 2006 and 2016 from our center as an external validation set. Clinical features, pathologic characteristics and oncologic outcomes were analyzed. Prognostic factors for overall survival (OS) and breast cancer-specific survival (BCSS) were determined by Cox proportional hazards analyses and were incorporated into the prognostic nomogram. Subgroup analysis based on propensity score matching method was conducted to explore the subset of patients that would benefit from breast-conserving therapy (BCT).

Results

Based on SEER dataset, patients with eTNBC were more likely to undergo mastectomy than BCT. On multivariable analysis, patients with better survival outcomes were those not married, uninsured, had higher T and N stage, and had histological type of mixed invasive ductal and lobular carcinoma. The prognostic nomogram based on these variables successfully predicted the 3- and 5-year BCSS (C-index in training cohort, 0.774; in validation cohort from SEER, 0.768; in validation cohort from our center, 0.723). Subgroup analysis illustrated that patients with T1N0M0 or T2-4N+M0 tumors who underwent BCT achieved longer overall survival than those who underwent mastectomy (for T1N0M0, P = 0.022; for T2-4N+M0, P = 0.003); however, the type of surgery did not influence OS among patients with T1N+M0 or T2-4N0M0 tumors (for T1N+M0, P = 0.305; for T2-4N0M0, P = 0.317).

Conclusions

The prognosis of patients with eTNBC is mainly affected by marital status, insurance status, T stage, N stage and histological type. The prognostic nomogram based on these factors is quite reliable. Subgroup analysis suggested that BCT may be a superior option for patients with eTNBC, especially those with T1N0M0 and T2-4N+M0 tumors.

Integrating Genomic Information with Tumor-Immune Microenvironment in Triple-Negative Breast Cancer

BACKGROUND

the development and progression of triple-negative breast cancer (TNBC) is driven by somatic driver mutations and the tumor-immune microenvironment. To date, data on somatic mutations has not been leveraged and integrated with information on the immune microenvironment to elucidate the possible oncogenic interactions and their potential effects on clinical outcomes. Here, we investigated possible oncogenic interactions between somatic mutations and the tumor-immune microenvironment, and their correlation with patient survival in TNBC.

METHODS

We performed analysis combining data on 7,875 somatic mutated genes with information on 1,751 immune-modulated genes, using gene-expression data as the intermediate phenotype, and correlated the resulting information with survival. We conducted functional analysis to identify immune-modulated molecular networks and signaling pathways enriched for somatic mutations likely to drive clinical outcomes.

RESULTS

We discovered differences in somatic mutation profiles between patients who died and those who survived, and a signature of somatic mutated immune-modulated genes transcriptionally associated with TNBC, predictive of survival. In addition, we discovered immune-modulated molecular networks and signaling pathways enriched for somatic mutations.

CONCLUSIONS

The investigation revealed possible oncogenic interactions between somatic mutations and the tumor-immune microenvironment in TNBC, likely to affect clinical outcomes.

RBCK1 is an endogenous inhibitor for triple negative breast cancer via hippo/YAP axis

BACKGROUND

Triple negative breast cancer (TNBC) is one of the most lethal breast cancer subtypes. Due to a lack of effective therapeutic targets, chemotherapy is still the main medical treatment for TNBC patients. Thus, it is important and necessary to find new therapeutic targets for TNBC. Recent genomic studies implicated the Hippo / Yap signal is over activated in TNBC, manifesting it plays a key role in TNBC carcinogenesis and cancer progression. RBCK1 was firstly identified as an important component for linear ubiquitin assembly complex (LUBAC) and facilitates NFKB signaling in immune response. Further studies showed RBCK1 also facilitated luminal type breast cancer growth and endocrine resistance via trans-activation estrogen receptor alpha.

METHODS

RBCK1 and YAP protein expression levels were measured by western blotting, while the mRNA levels of YAP target genes were measured by RT-PCR. RNA sequencing data were analyzed by Ingenuity Pathway Analysis. Identification of Hippo signaling activity was accomplished with luciferase assays, RT-PCR and western blotting. Protein stability assays and ubiquitin assays were used to detect YAP protein degradation. Ubiquitin-based immunoprecipitation assays were used to detect the specific ubiquitination modification on the YAP protein.

RESULTS

In our current study, our data revealed an opposite function for RBCK1 in TNBC progression. RBCK1 over-expression inhibited TNBC cell progression in vitro and in vivo, while RBCK1 depletion promoted TNBC cell invasion. The whole genomic expression profiling showed that RBCK1 depletion activated Hippo/YAP axis. RBCK1 depletion increased YAP protein level and Hippo target gene expression in TNBC. The molecular biology studies confirmed that RBCK1 could bind to YAP protein and enhance the stability of YAP protein by promoting YAP K48-linked poly-ubiquitination at several YAP lysine sites (K76, K204 and K321).

CONCLUSION

Our study revealed the multi-faced RBCK1 function in different subtypes of breast cancer patients and a promising therapeutic target for TNBC treatment. Video abstract.

Elevated NRAS expression during DCIS is a potential driver for progression to basal-like properties and local invasiveness

BACKGROUND

Ductal carcinoma in situ (DCIS) is the most common type of in situ premalignant breast cancers. What drives DCIS to invasive breast cancer is unclear. Basal-like invasive breast cancers are aggressive. We have previously shown that NRAS is highly expressed selectively in basal-like subtypes of invasive breast cancers and can promote their growth and progression. In this study, we investigated whether NRAS expression at the DCIS stage can control transition from luminal DCIS to basal-like invasive breast cancers.

METHODS

Wilcoxon rank-sum test was performed to assess expression of NRAS in DCIS compared to invasive breast tumors in patients. NRAS mRNA levels were also determined by fluorescence in situ hybridization in patient tumor microarrays (TMAs) with concurrent normal, DCIS, and invasive breast cancer, and association of NRAS mRNA levels with DCIS and invasive breast cancer was assessed by paired Wilcoxon signed-rank test. Pearson's correlation was calculated between NRAS mRNA levels and basal biomarkers in the TMAs, as well as in patient datasets. RNA-seq data were generated in cell lines, and unsupervised hierarchical clustering was performed after combining with RNA-seq data from a previously published patient cohort.

RESULTS

Invasive breast cancers showed higher NRAS mRNA levels compared to DCIS samples. These NRAShigh lesions were also enriched with basal-like features, such as basal gene expression signatures, lower ER, and higher p53 protein and Ki67 levels. We have shown previously that NRAS drives aggressive features in DCIS-like and basal-like SUM102PT cells. Here, we found that NRAS-silencing induced a shift to a luminal gene expression pattern. Conversely, NRAS overexpression in the luminal DCIS SUM225 cells induced a basal-like gene expression pattern, as well as an epithelial-to-mesenchymal transition signature. Furthermore, these cells formed disorganized mammospheres containing cell masses with an apparent reduction in adhesion.

CONCLUSIONS

These data suggest that elevated NRAS levels in DCIS are not only a marker but can also control the emergence of basal-like features leading to more aggressive tumor activity, thus supporting the therapeutic hypothesis that targeting NRAS and/or downstream pathways may block disease progression for a subset of DCIS patients with high NRAS.

Identification of cuproptosis-related patterns and construction of a scoring system for predicting prognosis, tumor microenvironment-infiltration characteristics, and immunotherapy efficacy in breast cancer

Background

Cuproptosis, a recently discovered refreshing form of cell death, is distinct from other known mechanisms. As copper participates in cell death, the induction of cancer cell death with copper ionophores may emerge as a new avenue for cancer treatment. However, the role of cuproptosis in tumor microenvironment (TME) cell infiltration remains unknown.

Methods

We systematically evaluated the cuproptosis patterns in The Cancer Genome Atlas (TCGA) database in breast cancer (BRCA) samples based on 10 cuproptosis-related genes (CRGs), and correlated these patterns with the prognosis and characteristics of TME cell infiltration. A principal component analysis algorithm was used to construct a cuproptosis score to quantify the cuproptosis pattern in individual tumors. Further, the relationships between the cuproptosis score and transcription background, clinical features, characteristics of TME cell infiltration, drug response, and efficacy of immunotherapy were assessed.

Results

Two distinct cuproptosis patterns with distinct prognoses were identified; their TME characteristics were found to be consistent with the immune-excluded and immune-inflamed phenotypes, respectively. The cuproptosis patterns in individual patients were evaluated using the cuproptosis score based on the cuproptosis phenotype-related genes, contributing to distinguishing biological processes, clinical outcome, immune cell infiltration, genetic variation, and drug response. Univariate and multivariate Cox regression analyses verified this score as an independent prognostic predictor in BRCA. A high cuproptosis score, characterized by immune activation, suggests an inflamed tumor and immune-inflamed phenotype with poor survival and a low cuproptosis score, characterized by immune suppression, indicates a non-inflamed tumor and immune-excluded phenotype with better survival. Significant differences were observed in the IC50 between the high and low cuproptosis score groups receiving chemotherapy and targeted therapy drugs. In the two immunotherapy cohorts, patients with a higher cuproptosis score experienced considerable therapeutic advantages and clinical benefits.

Conclusions

This study is the first to elucidate the prominent role of cuproptosis in the clinical outcome and the formation of TME diversity and complexity in BRCA. Estimating cuproptosis patterns in tumors could help predict the prognosis and characteristics of TME cell infiltration and guide more effective chemotherapeutic and immunotherapeutic strategies.

Non-coding RNAs, another side of immune regulation during triple-negative breast cancer

Triple-negative breast cancer (TNBC) is considered about 12-24 % of all breast cancer cases. Patients experience poor overall survival, high recurrence rate, and distant metastasis compared to other breast cancer subtypes. Numerous studies have highlighted the crucial roles of non-coding RNAs (ncRNAs) in carcinogenesis and proliferation, migration, and metastasis of tumor cells in TNBC. Recent research has demonstrated that long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) play a role in the regulation of the immune system by affecting the tumor microenvironment, the epithelial-mesenchymal transition, the regulation of dendritic cells and myeloid-derived stem cells, and T and B cell activation and differentiation. Immune-related miRNAs and lncRNAs, which have been established as predictive markers for various cancers, are strongly linked to immune cell infiltration and could be a viable therapeutic target for TNBC. In the current review, we discuss the recent updates of ncRNAs, including miRNAs and lncRNAs in TNBC, including their biogenesis, target genes, and biological function of their targets, which are mostly involved in the immune response.

Impact of TP53 mutations in Triple Negative Breast Cancer

Identifying triple negative breast cancer (TNBC) patients expected to have poor outcomes provides an opportunity to enhance clinical management. We applied an Evolutionary Action Score to functionally characterize TP53 mutations (EAp53) in 96 TNBC patients and observed that EAp53 stratification may identify TP53 mutations associated with worse outcomes. These findings merit further exploration in larger TNBC cohorts and in patients treated with neoadjuvant chemotherapy regimens.

Targeting the Heterogeneous Genomic Landscape in Triple-Negative Breast Cancer through Inhibitors of the Transcriptional Machinery

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer defined by lack of the estrogen, progesterone and human epidermal growth factor receptor 2. Although TNBC tumors contain a wide variety of oncogenic mutations and copy number alterations, the direct targeting of these alterations has failed to substantially improve therapeutic efficacy. This efficacy is strongly limited by interpatient and intratumor heterogeneity, and thereby a lack in uniformity of targetable drivers. Most of these genetic abnormalities eventually drive specific transcriptional programs, which may be a general underlying vulnerability. Currently, there are multiple selective inhibitors, which target the transcriptional machinery through transcriptional cyclin-dependent kinases (CDKs) 7, 8, 9, 12 and 13 and bromodomain extra-terminal motif (BET) proteins, including BRD4. In this review, we discuss how inhibitors of the transcriptional machinery can effectively target genetic abnormalities in TNBC, and how these abnormalities can influence sensitivity to these inhibitors. These inhibitors target the genomic landscape in TNBC by specifically suppressing MYC-driven transcription, inducing further DNA damage, improving anti-cancer immunity, and preventing drug resistance against MAPK and PI3K-targeted therapies. Because the transcriptional machinery enables transcription and propagation of multiple cancer drivers, it may be a promising target for (combination) treatment, especially of heterogeneous malignancies, including TNBC.

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.

Heterogeneity of triple negative breast cancer: Current advances in subtyping and treatment implications

As the field of translational ‘omics has progressed, refined classifiers at both genomic and proteomic levels have emerged to decipher the heterogeneity of breast cancer in a clinically-applicable way. The integration of ‘omics knowledge at the DNA, RNA and protein levels is further expanding biologic understanding of breast cancer and opportunities for customized treatment, a particularly pressing need in clinically triple negative tumors. For this group of aggressive breast cancers, work from multiple groups has now validated at least four major biologically and clinically distinct omics-based subtypes. While to date most clinical trial designs have considered triple negative breast cancers as a single group, with an expanding arsenal of targeted therapies applicable to distinct biological pathways, survival benefits may be best realized by designing and analyzing clinical trials in the context of major molecular subtypes. While RNA-based classifiers are the most developed, proteomic classifiers proposed for triple negative breast cancer based on new technologies have the potential to more directly identify the most clinically-relevant biomarkers and therapeutic targets. Phospho-proteomic data further identify targetable signalling pathways in a unique subtype-specific manner. Single cell profiling of the tumor microenvironment represents a promising way to allow a better characterization of the heterogeneity of triple negative breast cancer which could be integrated in a spatially resolved context to build an ecosystem-based patient classification. Multi-omic data further allows in silico analysis of genetic and pharmacologic screens to map therapeutic vulnerabilities in a subtype-specific context. This review describes current knowledge about molecular subtyping of triple negative breast cancer, recent advances in omics-based genomics and proteomics diagnostics addressing the diversity of this disease, key advances made through single cell analysis approaches, and developments in treatments including targeted therapeutics being tested in major clinical trials.

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.

Recent advances in therapeutic strategies for triple-negative breast cancer

Triple-negative breast cancer (TNBC) is the most malignant subtype of breast cancer (BC) with a poor prognosis. Current treatment options are limited to surgery, adjuvant chemotherapy and radiotherapy; however, a proportion of patients have missed the surgical window at the time of diagnosis. TNBC is a highly heterogeneous cancer with specific mutations and aberrant activation of signaling pathways. Hence, targeted therapies, such as those targeting DNA repair pathways, androgen receptor signaling pathways, and kinases, represent promising treatment options against TNBC. In addition, immunotherapy has also been demonstrated to improve overall survival and response in TNBC. In this review, we summarize recent key advances in therapeutic strategies based on molecular subtypes in TNBC.

Identification of necroptosis-related subtypes and prognosis model in triple negative breast cancer

Background

Necroptosis is considered to be a new form of programmed necrotic cell death, which is associated with metastasis, progression and prognosis of various types of tumors. However, the potential role of necroptosis-related genes (NRGs) in the triple negative breast cancer (TNBC) is unclear.

Methods

We extracted the gene expression and relevant clinicopathological data of TNBC from The Cancer Genome Atlas (TCGA) databases and the Gene Expression Omnibus (GEO) databases. We analyzed the expression, somatic mutation, and copy number variation (CNV) of 67 NRGs in TNBC, and then observed their interaction, biological functions, and prognosis value. By performing Lasso and COX regression analysis, a NRGs-related risk model for predicting overall survival (OS) was constructed and its predictive capabilities were verified. Finally, the relationship between risk_score and immune cell infiltration, tumor microenvironment (TME), immune checkpoint, and tumor mutation burden (TMB), cancer stem cell (CSC) index, and drug sensitivity were analyzed.

Results

A total 67 NRGs were identified in our analysis. A small number of genes (23.81%) detected somatic mutation, most genes appeared to have a high frequency of CNV, and there was a close interaction between them. These genes were remarkably enriched in immune-related process. A seven-gene risk_score was generated, containing TPSG1, KRT6A, GPR19, EIF4EBP1, TLE1, SLC4A7, ESPN. The low-risk group has a better OS, higher immune score, TMB and CSC index, and lower IC50 value of common therapeutic agents in TNBC. To improve clinical practicability, we added age, stage_T and stage_N to the risk_score and construct a more comprehensive nomogram for predicting OS. It was verified that nomogram had good predictive capability, the AUC values for 1-, 3-, and 5-year OS were 0.847, 0.908, and 0.942.

Conclusion

Our research identified the significant impact of NRGs on immunity and prognosis in TNBC. These findings were expected to provide a new strategy for personalize the treatment of TNBC and improve its clinical benefit.

Comprehensive characterization of clonality of driver genes revealing their clinical relevance in colorectal cancer

Background

Genomic studies of colorectal cancer have revealed the complex genomic heterogeneity of the tumor. The acquisition and selection of genomic alterations may be critical to understanding the initiation and progression of this disease.

Methods

In this study, we have systematically characterized the clonal architecture of 97 driver genes in 536 colorectal cancer patients from TCGA.

Results

A high proportion of clonal mutations in 93 driver genes were observed. 40 genes showed significant associations between their clonality and multiple clinicopathologic factors. Kaplan–Meier analysis suggested that the mutation clonality of ANK1, CASP8, SMAD2, and ARID1A had a significant impact on the CRC patients' outcomes. Multivariable analysis revealed that subclonal ANK1 mutations, clonal CASP8 mutations, and clonal SMAD2 mutations independently predicted for shorter overall survival after adjusting for clinicopathological factors. The poor outcome of the subclonal ANK1 mutation may be caused by upregulation of IL4I1, IDO1, IFNG and MAPK12 which showed potential roles in tumor immune evasion through accumulation of immunosuppressive cells such as regulatory T cells and myeloid derived suppressor cells.

Conclusion

These results suggested that the clonality of driver genes could act as prognostic markers and potential therapeutic targets in human colorectal cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03529-x.

Precision Breast Cancer Medicine: Early Stage Triple Negative Breast Cancer-A Review of Molecular Characterisation, Therapeutic Targets and Future Trends

Personalised approaches to the management of all solid tumours are increasing rapidly, along with wider accessibility for clinicians. Advances in tumour characterisation and targeted therapies have placed triple-negative breast cancers (TNBC) at the forefront of this approach. TNBC is a highly heterogeneous disease with various histopathological features and is driven by distinct molecular alterations. The ability to tailor individualised and effective treatments for each patient is of particular importance in this group due to the high risk of distant recurrence and death. The mainstay of treatment across all subtypes of TNBC has historically been cytotoxic chemotherapy, which is often associated with off-target tissue toxicity and drug resistance. Neoadjuvant chemotherapy is commonly used as it allows close monitoring of early treatment response and provides valuable prognostic information. Patients who achieve a complete pathological response after neoadjuvant chemotherapy are known to have significantly improved long-term outcomes. Conversely, poor responders face a higher risk of relapse and death. The identification of those subgroups that are more likely to benefit from breakthroughs in the personalised approach is a challenge of the current era where several targeted therapies are available. This review presents an overview of contemporary practice, and promising future trends in the management of early TNBC. Platinum chemotherapy, DNA damage response (DDR) inhibitors, immune checkpoint inhibitors, inhibitors of the PI3K-AKT-mTOR, and androgen receptor (AR) pathways are some of the increasingly studied therapies which will be reviewed. We will also discuss the growing evidence for less-developed agents and predictive biomarkers that are likely to contribute to the forthcoming advances in this field. Finally, we will propose a framework for the personalised management of TNBC based upon the integration of clinico-pathological and molecular features to ensure that long-term outcomes are optimised.

Mapping Phenotypic Plasticity upon the Cancer Cell State Landscape Using Manifold Learning

ABSTRACT

Phenotypic plasticity describes the ability of cancer cells to undergo dynamic, nongenetic cell state changes that amplify cancer heterogeneity to promote metastasis and therapy evasion. Thus, cancer cells occupy a continuous spectrum of phenotypic states connected by trajectories defining dynamic transitions upon a cancer cell state landscape. With technologies proliferating to systematically record molecular mechanisms at single-cell resolution, we illuminate manifold learning techniques as emerging computational tools to effectively model cell state dynamics in a way that mimics our understanding of the cell state landscape. We anticipate that "state-gating" therapies targeting phenotypic plasticity will limit cancer heterogeneity, metastasis, and therapy resistance.

SIGNIFICANCE

Nongenetic mechanisms underlying phenotypic plasticity have emerged as significant drivers of tumor heterogeneity, metastasis, and therapy resistance. Herein, we discuss new experimental and computational techniques to define phenotypic plasticity as a scaffold to guide accelerated progress in uncovering new vulnerabilities for therapeutic exploitation.

Accurate determination of CRISPR-mediated gene fitness in transplantable tumours

Assessing tumour gene fitness in physiologically-relevant model systems is challenging due to biological features of in vivo tumour regeneration, including extreme variations in single cell lineage progeny. Here we develop a reproducible, quantitative approach to pooled genetic perturbation in patient-derived xenografts (PDXs), by encoding single cell output from transplanted CRISPR-transduced cells in combination with a Bayesian hierarchical model. We apply this to 181 PDX transplants from 21 breast cancer patients. We show that uncertainty in fitness estimates depends critically on the number of transplant cell clones and the variability in clone sizes. We use a pathway-directed allelic series to characterize Notch signaling, and quantify TP53 / MDM2 drug-gene conditional fitness in outlier patients. We show that fitness outlier identification can be mirrored by pharmacological perturbation. Overall, we demonstrate that the gene fitness landscape in breast PDXs is dominated by inter-patient differences.

Ordered and deterministic cancer genome evolution after p53 loss

Although p53 inactivation promotes genomic instability1 and presents a route to malignancy for more than half of all human cancers2,3, the patterns through which heterogenous TP53 (encoding human p53) mutant genomes emerge and influence tumorigenesis remain poorly understood. Here, in a mouse model of pancreatic ductal adenocarcinoma that reports sporadic p53 loss of heterozygosity before cancer onset, we find that malignant properties enabled by p53 inactivation are acquired through a predictable pattern of genome evolution. Single-cell sequencing and in situ genotyping of cells from the point of p53 inactivation through progression to frank cancer reveal that this deterministic behaviour involves four sequential phases-Trp53 (encoding mouse p53) loss of heterozygosity, accumulation of deletions, genome doubling, and the emergence of gains and amplifications-each associated with specific histological stages across the premalignant and malignant spectrum. Despite rampant heterogeneity, the deletion events that follow p53 inactivation target functionally relevant pathways that can shape genomic evolution and remain fixed as homogenous events in diverse malignant populations. Thus, loss of p53-the 'guardian of the genome'-is not merely a gateway to genetic chaos but, rather, can enable deterministic patterns of genome evolution that may point to new strategies for the treatment of TP53-mutant tumours.

Functional screening reveals HORMAD1-driven gene dependencies associated with translesion synthesis and replication stress tolerance

HORMAD1 expression is usually restricted to germline cells, but it becomes mis-expressed in epithelial cells in ~60% of triple-negative breast cancers (TNBCs), where it is associated with elevated genomic instability (1). HORMAD1 expression in TNBC is bimodal with HORMAD1-positive TNBC representing a biologically distinct disease group. Identification of HORMAD1-driven genetic dependencies may uncover novel therapies for this disease group. To study HORMAD1-driven genetic dependencies, we generated a SUM159 cell line model with doxycycline-inducible HORMAD1 that replicated genomic instability phenotypes seen in HORMAD1-positive TNBC (1). Using small interfering RNA screens, we identified candidate genes whose depletion selectively inhibited the cellular growth of HORMAD1-expressing cells. We validated five genes (ATR, BRIP1, POLH, TDP1 and XRCC1), depletion of which led to reduced cellular growth or clonogenic survival in cells expressing HORMAD1. In addition to the translesion synthesis (TLS) polymerase POLH, we identified a HORMAD1-driven dependency upon additional TLS polymerases, namely POLK, REV1, REV3L and REV7. Our data confirms that out-of-context somatic expression of HORMAD1 can lead to genomic instability and reveals that HORMAD1 expression induces dependencies upon replication stress tolerance pathways, such as translesion synthesis. Our data also suggest that HORMAD1 expression could be a patient selection biomarker for agents targeting replication stress.

Research progress on immunotherapy in triple‑negative breast cancer (Review)

Triple‑negative breast cancer (TNBC) is a highly heterogeneous and aggressive malignancy. Due to the absence of estrogen receptors and progesterone receptors and the lack of overexpression of human epidermal growth factor receptor 2, TNBC responds poorly to endocrine and targeted therapies. As a neoadjuvant therapy, chemotherapy is usually the only option for TNBC; however, chemotherapy may induce tumor resistance. The emergence of immunotherapy as an adjuvant therapy is expected to make up for the deficiency of chemotherapy. Most of the research on immunotherapies has been performed on advanced metastatic TNBC, which has provided significant clinical benefits. In the present review, possible immunotherapy targets and ongoing immunotherapy strategies were discussed. In addition, progress in research on immune checkpoint inhibitors in early TNBC was outlined.

NPC1 Confers Metabolic Flexibility in Triple Negative Breast Cancer

Triple-negative breast cancer (TNBC) often undergoes at least partial epithelial-to-mesenchymal transition (EMT) to facilitate metastasis. Identifying EMT-associated characteristics can reveal novel dependencies that may serve as therapeutic vulnerabilities in this aggressive breast cancer subtype. We found that NPC1, which encodes the lysosomal cholesterol transporter Niemann-Pick type C1 is highly expressed in TNBC as compared to estrogen receptor-positive (ER+) breast cancer, and is significantly elevated in high-grade disease. We demonstrated that NPC1 is directly targeted by microRNA-200c (miR-200c), a potent suppressor of EMT, providing a mechanism for its differential expression in breast cancer subtypes. The silencing of NPC1 in TNBC causes an accumulation of cholesterol-filled lysosomes, and drives decreased growth in soft agar and invasive capacity. Conversely, overexpression of NPC1 in an ER+ cell line increases invasion and growth in soft agar. We further identified TNBC cell lines as cholesterol auxotrophs, however, they do not solely depend on NPC1 for adequate cholesterol supply. The silencing of NPC1 in TNBC cell lines led to altered mitochondrial function and morphology, suppression of mTOR signaling, and accumulation of autophagosomes. A small molecule inhibitor of NPC1, U18666A, decreased TNBC proliferation and synergized with the chemotherapeutic drug, paclitaxel. This work suggests that NPC1 promotes aggressive characteristics in TNBC, and identifies NPC1 as a potential therapeutic target.

The Roles of ATP13A2 Gene Mutations Leading to Abnormal Aggregation of α-Synuclein in Parkinson’s Disease

Parkinson’s disease (PD) is the second most common neurodegenerative disease. PARK9 (also known as ATP13A2) is recognized as one of the key genes that cause PD, and a mutation in this gene was first discovered in a rare case of PD in an adolescent. Lewy bodies (LBs) formed by abnormal aggregation of α-synuclein, which is encoded by the SNCA gene, are one of the pathological diagnostic criteria for PD. LBs are also recognized as one of the most important features of PD pathogenesis. In this article, we first summarize the types of mutations in the ATP13A2 gene and their effects on ATP13A2 mRNA and protein structure; then, we discuss lysosomal autophagy inhibition and the molecular mechanism of abnormal α-synuclein accumulation caused by decreased levels and dysfunction of the ATP13A2 protein in lysosomes. Finally, this article provides a new direction for future research on the pathogenesis and therapeutic targets for ATP13A2 gene-related PD from the perspective of ATP13A2 gene mutations and abnormal aggregation of α-synuclein.

Triple-Negative Breast Cancer Analysis Based on Metabolic Gene Classification and Immunotherapy

Triple negative breast cancer (TNBC) has negative expression of ER, PR and HER-2. TNBC shows high histological grade and positive rate of lymph node metastasis, easy recurrence and distant metastasis. Molecular typing based on metabolic genes can reflect deeper characteristics of breast cancer and provide support for prognostic evaluation and individualized treatment. Metabolic subtypes of TNBC samples based on metabolic genes were determined by consensus clustering. CIBERSORT method was applied to evaluate the score distribution and differential expression of 22 immune cells in the TNBC samples. Linear discriminant analysis (LDA) established a subtype classification feature index. Kaplan-Meier (KM) and receiver operating characteristic (ROC) curves were generated to validate the performance of prognostic metabolic subtypes in different datasets. Finally, we used weighted correlation network analysis (WGCNA) to cluster the TCGA expression profile dataset and screen the co-expression modules of metabolic genes. Consensus clustering of the TCGA cohort/dataset obtained three metabolic subtypes (MC1, MC2, and MC3). The ROC analysis showed a high prognostic performance of the three clusters in different datasets. Specifically, MC1 had the optimal prognosis, MC3 had a poor prognosis, and the three metabolic subtypes had different prognosis. Consistently, the immune characteristic index established based on metabolic subtypes demonstrated that compared with the other two subtypes, MC1 had a higher IFNγ score, T cell lytic activity and lower angiogenesis score, T cell dysfunction and rejection score. TIDE analysis showed that MC1 patients were more likely to benefit from immunotherapy. MC1 patients were more sensitive to immune checkpoint inhibitors and traditional chemotherapy drugs Cisplatin, Paclitaxel, Embelin, and Sorafenib. Multiclass AUC based on RNASeq and GSE datasets were 0.85 and 0.85, respectively. Finally, based on co-expression network analysis, we screened 7 potential gene markers related to metabolic characteristic index, of which CLCA2, REEP6, SPDEF, and CRAT can be used to indicate breast cancer prognosis. Molecular classification related to TNBC metabolism was of great significance for comprehensive understanding of the molecular pathological characteristics of TNBC, contributing to the exploration of reliable markers for early diagnosis of TNBC and predicting metastasis and recurrence, improvement of the TNBC staging system, guiding individualized treatment.

Anticancer Activity of Bee Venom Components against Breast Cancer

While the survival rate has increased due to treatments for breast cancer, the quality of life has decreased because of the side effects of chemotherapy. Various toxins are being developed as alternative breast cancer treatments, and bee venom is drawing attention as one of them. We analyzed the effect of bee venom and its components on breast cancer cells and reviewed the mechanism underlying the anticancer effects of bee venom. Data up to March 2022 were searched from PubMed, EMBASE, OASIS, KISS, and Science Direct online databases, and studies that met the inclusion criteria were reviewed. Among 612 studies, 11 were selected for this research. Diverse drugs were administered, including crude bee venom, melittin, phospholipase A2, and their complexes. All drugs reduced the number of breast cancer cells in proportion to the dose and time. The mechanisms of anticancer effects included cytotoxicity, apoptosis, cell targeting, gene expression regulation, and cell lysis. Summarily, bee venom and its components exert anticancer effects on human breast cancer cells. Depending on the mechanisms of anticancer effects, side effects are expected to be reduced by using various vehicles. Bee venom and its components have the potential to prevent and treat breast cancer in the future.

Aberrant calcium signalling downstream of mutations in TP53 and the PI3K/AKT pathway genes promotes disease progression and therapy resistance in triple negative breast cancer

Triple-negative breast cancer (TNBC) is characterized as an aggressive form of breast cancer (BC) associated with poor patient outcomes. For the majority of patients, there is a lack of approved targeted therapies. Therefore, chemotherapy remains a key treatment option for these patients, but significant issues around acquired resistance limit its efficacy. Thus, TNBC has an unmet need for new targeted personalized medicine approaches. Calcium (Ca²⁺) is a ubiquitous second messenger that is known to control a range of key cellular processes by mediating signalling transduction and gene transcription. Changes in Ca²⁺ through altered calcium channel expression or activity are known to promote tumorigenesis and treatment resistance in a range of cancers including BC. Emerging evidence shows that this is mediated by Ca²⁺ modulation, supporting the function of tumour suppressor genes (TSGs) and oncogenes. This review provides insight into the underlying alterations in calcium signalling and how it plays a key role in promoting disease progression and therapy resistance in TNBC which harbours mutations in tumour protein p53 (TP53) and the PI3K/AKT pathway.

Development and Verification of a Prognostic Ferroptosis-Related Gene Model in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is the subtype with the worst prognosis of breast cancer. Ferroptosis, a novel iron-dependent programmed cell death, has an increasingly important role in tumorigenesis and development. However, there is still a lack of research on the relationship between ferroptosis-related genes and the prognosis of TNBC. In this study, we obtained the gene expression profile of TNBC patients and matched clinical data from The Cancer Genome Atlas (TCGA) database. Univariate Cox analysis was used to screen out ferroptosis-related genes associated with TNBC prognosis. Then, the least absolute shrinkage and selection operator (LASSO) regression analysis was employed to establish a prognostic prediction model. A 15-ferroptosis-related gene prognostic prediction model was developed, which classified patients into low-risk (LR) or high-risk (HR) groups. Kaplan-Meier analysis results showed that the prognosis of the LR group was better. The receiver operating characteristic (ROC) curve also confirmed the satisfactory predictive ability of this model. Evaluation of the immune microenvironment of TNBC patients in the HR and LR group suggested these 15 ferroptosis-related genes might affect the prognosis of TNBC by regulating the tumor microenvironment. Our prognostic model can provide a theoretical basis for accurate prognosis prediction of TNBC in clinical practice.

A Multiomics Signature Highlights Alterations Underlying Homologous Recombination Deficiency in Triple-Negative Breast Cancer

BACKGROUND

Homologous recombination (HR) is a key pathway in DNA double-strand damage repair. HR deficiency (HRD) occurs more commonly in triple-negative breast cancers (TNBCs) than in other breast cancer subtypes. Several clinical trials have demonstrated the value of HRD in stratifying breast cancer patients into distinct groups based on their responses to poly(ADP ribose) polymerase inhibitors and chemotherapy.

METHODS

We retrospectively collected TNBC samples to establish a multiomics cohort (n = 343) and explored the biological and phenotypic mechanisms underlying the better prognosis of patients with high HRD scores. Gene set enrichment analysis was conducted to elucidate the underlying pathways in patients with low HRD scores, and a radiomics model was established to predict the HRD score via a noninvasive method.

RESULTS

Multivariable Cox analysis revealed the independent prognostic value of a low HRD score (hazard ratio 2.20, 95% confidence interval 1.05-4.59; p = 0.04). Furthermore, amino acid and lipid metabolism pathways were highly enriched in tumors from patients with low HRD scores, which was also demonstrated by differential abundant metabolite analysis. A noninvasive radiomics method was developed to predict the HRD status and it performed well in the independent validation cohort (support vector machine model: area under the curve [AUC] 0.739, sensitivity 0.571, and specificity 0.824; logistic regression model: AUC 0.695, sensitivity 0.571, and specificity 0.882).

CONCLUSIONS

We revealed the prognostic value of the HRD score, predicted the HRD status with noninvasive radiomics features, and preliminarily explored druggable targets for TNBC patients with low HRD scores.

Antiprogestins reduce epigenetic field cancerization in breast tissue of young healthy women

Background

Breast cancer is a leading cause of death in premenopausal women. Progesterone drives expansion of luminal progenitor cells, leading to the development of poor-prognostic breast cancers. However, it is not known if antagonising progesterone can prevent breast cancers in humans. We suggest that targeting progesterone signalling could be a means of reducing features which are known to promote breast cancer formation.

Methods

In healthy premenopausal women with and without a BRCA mutation we studied (i) estrogen and progesterone levels in saliva over an entire menstrual cycle (n = 20); (ii) cancer-free normal breast-tissue from a control population who had no family or personal history of breast cancer and equivalently from BRCA1/2 mutation carriers (n = 28); triple negative breast cancer (TNBC) biopsies and healthy breast tissue taken from sites surrounding the TNBC in the same individuals (n = 14); and biopsies of ER+ve/PR+ve stage T1–T2 cancers and healthy breast tissue taken from sites surrounding the cancer in the same individuals (n = 31); and (iii) DNA methylation and DNA mutations in normal breast tissue (before and after treatment) from clinical trials that assessed the potential preventative effects of vitamins and antiprogestins (mifepristone and ulipristal acetate; n = 44).

Results

Daily levels of progesterone were higher throughout the menstrual cycle of BRCA1/2 mutation carriers, raising the prospect of targeting progesterone signalling as a means of cancer risk reduction in this population. Furthermore, breast field cancerization DNA methylation signatures reflective of (i) the mitotic age of normal breast epithelium and (ii) the proportion of luminal progenitor cells were increased in breast cancers, indicating that luminal progenitor cells with elevated replicative age are more prone to malignant transformation. The progesterone receptor antagonist mifepristone reduced both the mitotic age and the proportion of luminal progenitor cells in normal breast tissue of all control women and in 64% of BRCA1/2 mutation carriers. These findings were validated by an alternate progesterone receptor antagonist, ulipristal acetate, which yielded similar results. Importantly, mifepristone reduced both the TP53 mutation frequency as well as the number of TP53 mutations in mitotic-age-responders.

Conclusions

These data support the potential usage of antiprogestins for primary prevention of poor-prognostic breast cancers.

Trial registration

Clinical trial 1 Mifepristone treatment prior to insertion of a levonorgestrel releasing intrauterine system for improved bleeding control – a randomized controlled trial, clinicaltrialsregister.eu, 2009-009014-40; registered on 20 July 2009.

Clinical trial 2 The effect of a progesterone receptor modulator on breast tissue in women with BRCA1 and 2 mutations, clinicaltrials.gov, NCT01898312; registered on 07 May 2013.

Clinical trial 3 A pilot prevention study of the effects of the anti- progestin Ulipristal Acetate (UA) on surrogate markers of breast cancer risk, clinicaltrialsregister.eu, 2015-001587-19; registered on 15 July 2015.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01063-5.

Classification of triple negative breast cancer by epithelial mesenchymal transition and the tumor immune microenvironment

Triple-negative breast cancer (TNBC) accounts for about 15–20% of all breast cancers and differs from other invasive breast cancer types because it grows and spreads rapidly, it has limited treatment options and typically worse prognosis. Since TNBC does not express estrogen or progesterone receptors and little or no human epidermal growth factor receptor (HER2) proteins are present, hormone therapy and drugs targeting HER2 are not helpful, leaving chemotherapy only as the main systemic treatment option. In this context, it would be important to find molecular signatures able to stratify patients into high and low risk groups. This would allow oncologists to suggest the best therapeutic strategy in a personalized way, avoiding unnecessary toxicity and reducing the high costs of treatment. Here we compare two independent patient stratification strategies for TNBC based on gene expression data: The first is focusing on the epithelial mesenchymal transition (EMT) and the second on the tumor immune microenvironment. Our results show that the two stratification strategies are not directly related, suggesting that the aggressiveness of the tumor can be due to a multitude of unrelated factors. In particular, the EMT stratification is able to identify a high-risk population with high immune markers that is, however, not properly classified by the tumor immune microenvironment based strategy.

Heterogeneity of triple-negative breast cancer: understanding the Daedalian labyrinth and how it could reveal new drug targets

INTRODUCTION

Triple-negative breast cancer (TNBC) is considered the most aggressive breast cancer subtype with the least favorable outcomes. However, recent research efforts have generated an enhanced knowledge of the biology of the disease and have provided a new, more comprehensive understanding of the multifaceted ecosystem that underpins TNBC.

AREAS COVERED

In this review, the authors illustrate the principal biological characteristics of TNBC, the molecular driver alterations, targetable genes, and the biomarkers of immune engagement that have been identified across the subgroups of TNBC. Accordingly, the authors summarize the landscape of the innovative and investigative biomarker-driven therapeutic options in TNBC that emerge from the unique biological basis of the disease.

EXPERT OPINION

The therapeutic setting of TNBC is rapidly evolving. An enriched understanding of the tumor spatial and temporal heterogeneity and the surrounding microenvironment of this complex disease can effectively support the development of novel and tailored opportunities of treatment.

Focal Adhesion Kinase (FAK)-Hippo/YAP transduction signaling mediates the stimulatory effects exerted by S100A8/A9-RAGE system in triple-negative breast cancer (TNBC)

BACKGROUND

Understanding the intricate signaling network involved in triple-negative breast cancer (TNBC) represents a challenge for developing novel therapeutic approaches. Here, we aim to provide novel mechanistic insights on the function of the S100A8/A9-RAGE system in TNBC.

METHODS

TNM plot analyzer, Kaplan-Meier plotter, Meta-analysis, GEPIA2 and GOBO publicly available datasets were used to evaluate the clinical significance of S100A8/A9 and expression levels of S100A8/A9, RAGE and Filamin family members in breast cancer (BC) subtypes. METABRIC database and Cox proportional hazard model defined the clinical impact of high RAGE expression in BC patients. Multiple bioinformatics programs identified the main enriched pathways within high RAGE expression BC cohorts. By lentiviral system, TNBC cells were engineered to overexpress RAGE. Western blotting, immunofluorescence, nucleus/cytoplasm fractionation, qRT-PCR, gene silencing and luciferase experiments were performed to identify signal transduction mediators engaged by RAGE upon stimulation with S100A8/A9 in TNBC cells. Proliferation, colony formation and transwell migration assays were carried out to evaluate the growth and migratory capacity of TNBC cells. Statistical analysis was performed by ANOVA and independent t-tests.

RESULTS

We found a remarkable high expression of S100A8 and S100A9 in BC, particularly in HER2-positive and TNBC, with the latter associated to worst clinical outcomes. In addition, high RAGE expression correlated with a poor overall survival in BC. Next, we determined that the S100A8/A9-RAGE system triggers FAK activation by engaging a cytoskeleton mechanosensing complex in TNBC cells. Through bioinformatics analysis, we identified the Hippo pathway as the most enriched in BC patients expressing high RAGE levels. In accordance with these data, we demonstrated the involvement of S100A8/A9-RAGE-FAK signaling in the control of Hippo/YAP activities, and we established the crucial contribution of RAGE-FAK-YAP circuitry in the growth and migratory effects initiated by S100A8/A9 in TNBC cells.

CONCLUSIONS

The present study provides novel mechanistic insights on RAGE actions in TNBC. Moreover, our findings suggest that RAGE-FAK-YAP transduction pathway could be exploited as a druggable system halting the aggressive TNBC subtype.

Beyond Genetics: Metastasis as an Adaptive Response in Breast Cancer

Metastatic disease represents the primary cause of breast cancer (BC) mortality, yet it is still one of the most enigmatic processes in the biology of this tumor. Metastatic progression includes distinct phases: invasion, intravasation, hematogenous dissemination, extravasation and seeding at distant sites, micro-metastasis formation and metastatic outgrowth. Whole-genome sequencing analyses of primary BC and metastases revealed that BC metastatization is a non-genetically selected trait, rather the result of transcriptional and metabolic adaptation to the unfavorable microenvironmental conditions which cancer cells are exposed to (e.g., hypoxia, low nutrients, endoplasmic reticulum stress and chemotherapy administration). In this regard, the latest multi-omics analyses unveiled intra-tumor phenotypic heterogeneity, which determines the polyclonal nature of breast tumors and constitutes a challenge for clinicians, correlating with patient poor prognosis. The present work reviews BC classification and epidemiology, focusing on the impact of metastatic disease on patient prognosis and survival, while describing general principles and current in vitro/in vivo models of the BC metastatic cascade. The authors address here both genetic and phenotypic intrinsic heterogeneity of breast tumors, reporting the latest studies that support the role of the latter in metastatic spreading. Finally, the review illustrates the mechanisms underlying adaptive stress responses during BC metastatic progression.

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.

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.

The translational challenges of precision oncology

The translational challenges in the field of precision oncology are in part related to the biological complexity and diversity of this disease. Technological advances in genomics have facilitated large sequencing efforts and discoveries that have further supported this notion. In this review, we reflect on the impact of these discoveries on our understanding of several concepts: cancer initiation, cancer prevention, early detection, adjuvant therapy and minimal residual disease monitoring, cancer drug resistance, and cancer evolution in metastasis. We discuss key areas of focus for improving cancer outcomes, from biological insights to clinical application, and suggest where the development of these technologies will lead us. Finally, we discuss practical challenges to the wider adoption of molecular profiling in the clinic and the need for robust translational infrastructure.

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.

Long noncoding RNA (lncRNA) metallothionein 1 J, pseudogene (MT1JP) is downregulated in triple-negative breast cancer and upregulates microRNA-138 (miR-138) to downregulate hypoxia-inducible factor-1α (HIF-1α)

Triple-negative breast cancer (TNBC) is a highly invasive subtype of breast cancer. This study explored the molecular mechanism and influences of metallothionein 1 J, pseudogene (MT1JP), microRNA-138 (miR-138), and hypoxia-inducible factor-1α (HIF-1α) on TNBC cell proliferation and migration. We confirmed TNBC cases by immunohistochemistry (IHC) staining. The expression of MT1JP in two types of tissue collected from 78 TNBC patients was detected by performing real-time quantitative fluorescence PCR (RT-qPCR). To further evaluate the relationship among MT1JP, miR-138 and HIF-1α, expression vectors of MT1JP and HIF-1α, as well as miR-138 mimic and inhibitor, were delivered into BT-549 cells. We observed that MT1JP was downregulated in TNBC. MT1JP was positively correlated with miR-138 but negatively correlated with HIF-1α in TNBC tissues. In TNBC cells, upregulation of miR-138 and downregulation of HIF-1α were observed after overexpression of MT1JP. In addition, overexpression of miR-138 resulted in downregulation of HIF-1α but did not affect the expression of MT1JP. Decreased proliferation rate of TNBC cells was observed after overexpression of MT1JP and miR-138. HIF-1α increased cell proliferation and migration. HIF-1α also suppressed the role of MT1JP and miR-138 in TNBC cell proliferation and migration. In conclusion, our findings demonstrated that MT1JP inhibited TNBC by regulating the miR-138/HIF-1α axis, indicating that MT1JP might serve as a biomarker or target for TNBC treatment.

Comparative characterization of 3D chromatin organization in triple-negative breast cancers

Triple-negative breast cancer (TNBC) is a malignant cancer subtype with a high risk of recurrence and an aggressive phenotype compared to other breast cancer subtypes. Although many breast cancer studies conducted to date have investigated genetic variations and differential target gene expression, how 3D chromatin architectures are reorganized in TNBC has been poorly elucidated. Here, using in situ Hi-C technology, we characterized the 3D chromatin organization in cells representing five distinct subtypes of breast cancer (including TNBC) compared to that in normal cells. We found that the global and local 3D architectures were severely disrupted in breast cancer. TNBC cell lines (especially BT549 cells) showed the most dramatic changes relative to normal cells. Importantly, we detected CTCF-dependent TNBC-susceptible losses/gains of 3D chromatin organization and found that these changes were strongly associated with perturbed chromatin accessibility and transcriptional dysregulation. In TNBC tissue, 3D chromatin disorganization was also observed relative to the 3D chromatin organization in normal tissues. We observed that the perturbed local 3D architectures found in TNBC cells were partially conserved in TNBC tissues. Finally, we discovered distinct tissue-specific chromatin loops by comparing normal and TNBC tissues. In this study, we elucidated the characteristics of the 3D chromatin organization in breast cancer relative to normal cells/tissues at multiple scales and identified associations between disrupted structures and various epigenetic features and transcriptomes. Collectively, our findings reveal important 3D chromatin structural features for future diagnostic and therapeutic studies of TNBC.

The 3D architecture of the genome is dramatically altered in an aggressive form of breast cancer, leading to changes in the regulation of gene expression that can fuel tumor growth. A team from South Korea, led by Hyeong-Gon Moon of Seoul National University College of Medicine and Daeyoup Lee of the Korea Advanced Institute of Science and Technology, Daejeon, detailed how chromosomes are positioned and folded within the nucleus of cell liness from five different subtypes of breast cancer. They found that triple-negative breast cancers displayed the most extreme reorganization of their genomes, a pattern also observed in biopsy tissues taken from patients with this subtype of cancer. Knowledge of these conformational changes could inform future efforts to develop therapies and diagnostics for patients with triple-negative breast tumors.

Tumour genotypes account for survival differences in right- and left-sided colon cancers

AIM

We sought to identify genetic differences between right- and left-sided colon cancers and using these differences explain lower survival in right-sided cancers.

METHOD

A retrospective review of patients diagnosed with colon cancer was performed using The Cancer Genome Atlas, a cancer genetics registry with patient and tumour data from 20 North American institutions. The primary outcome was 5-year overall survival. Predictors for survival were identified using directed acyclic graphs and Cox proportional hazards models.

RESULTS

A total of 206 right- and 214 left-sided colon cancer patients with 84 recorded deaths were identified. The frequency of mutated alleles differed significantly in 12 of 25 genes between right- and left-sided tumours. Right-sided tumours had worse survival with a hazard ratio of 1.71 (95% confidence interval 1.10-2.64, P = 0.017). The total effect of the genetic loci on survival showed five genes had a sizeable effect on survival: DNAH5, MUC16, NEB, SMAD4, and USH2A. Lasso-penalized Cox regression selected 13 variables for the highest-performing model, which included cancer stage, positive resection margin, and mutated alleles at nine genes: MUC16, USH2A, SMAD4, SYNE1, FLG, NEB, TTN, OBSCN, and DNAH5. Post-selection inference demonstrated that mutations in MUC16 (P = 0.01) and DNAH5 (P = 0.02) were particularly predictive of 5-year overall survival.

CONCLUSIONS

Our study showed that genetic mutations may explain survival differences between tumour sites. Further studies on larger patient populations may identify other genes, which could form the foundation for more precise prognostication and treatment decisions beyond current rudimentary TNM staging.

Paradoxical Behavior of Oncogenes Undermines the Somatic Mutation Theory

The currently accepted theory on the influence of DNA mutations on carcinogenesis (the Somatic Mutation Theory, SMT) is facing an increasing number of controversial results that undermine the explanatory power of mutated genes considered as "causative" factors. Intriguing results have demonstrated that several critical genes may act differently, as oncogenes or tumor suppressors, while phenotypic reversion of cancerous cells/tissues can be achieved by modifying the microenvironment, the mutations they are carrying notwithstanding. Furthermore, a high burden of mutations has been identified in many non-cancerous tissues without any apparent pathological consequence. All things considered, a relevant body of unexplained inconsistencies calls for an in depth rewiring of our theoretical models. Ignoring these paradoxes is no longer sustainable. By avoiding these conundrums, the scientific community will deprive itself of the opportunity to achieve real progress in this important biomedical field. To remedy this situation, we need to embrace new theoretical perspectives, taking the cell-microenvironment interplay as the privileged pathogenetic level of observation, and by assuming new explanatory models based on truly different premises. New theoretical frameworks dawned in the last two decades principally focus on the complex interaction between cells and their microenvironment, which is thought to be the critical level from which carcinogenesis arises. Indeed, both molecular and biophysical components of the stroma can dramatically drive cell fate commitment and cell outcome in opposite directions, even in the presence of the same stimulus. Therefore, such a novel approach can help in solving apparently inextricable paradoxes that are increasingly observed in cancer biology.