Molecular analysis of TCGA breast cancer histologic types

Immune evasion impacts the landscape of driver genes during cancer evolution

BACKGROUND

Carcinogenesis is driven by interactions between genetic mutations and the local tumor microenvironment. Recent research has identified hundreds of cancer driver genes; however, these studies often include a mixture of different molecular subtypes and ecological niches and ignore the impact of the immune system.

RESULTS

In this study, we compare the landscape of driver genes in tumors that escaped the immune system (escape +) versus those that did not (escape -). We analyze 9896 primary tumors from The Cancer Genome Atlas using the ratio of non-synonymous to synonymous mutations (dN/dS) and find 85 driver genes, including 27 and 16 novel genes, in escape - and escape + tumors, respectively. The dN/dS of driver genes in immune escaped tumors is significantly lower and closer to neutrality than in non-escaped tumors, suggesting selection buffering in driver genes fueled by immune escape. Additionally, we find that immune evasion leads to more mutated sites, a diverse array of mutational signatures and is linked to tumor prognosis.

CONCLUSIONS

Our findings highlight the need for improved patient stratification to identify new therapeutic targets for cancer treatment.

Contemporary Approaches to Immunotherapy of Solid Tumors

In recent years, the arrival of the immunotherapy industry has introduced the possibility of providing transformative, durable, and potentially curative outcomes for various forms of malignancies. However, further research has shown that there are a number of issues that significantly reduce the effectiveness of immunotherapy, especially in solid tumors. First of all, these problems are related to the protective mechanisms of the tumor and its microenvironment. Currently, major efforts are focused on overcoming protective mechanisms by using different adoptive cell therapy variants and modifications of genetically engineered constructs. In addition, a complex workforce is required to develop and implement these treatments. To overcome these significant challenges, innovative strategies and approaches are necessary to engineer more powerful variations of immunotherapy with improved antitumor activity and decreased toxicity. In this review, we discuss recent innovations in immunotherapy aimed at improving clinical efficacy in solid tumors, as well as strategies to overcome the limitations of various immunotherapies.

The somatic mutation profile of estrogen receptor-positive HER2-negative metastatic breast cancer in Brazilian patients

Background

Breast cancer is the leading cause of cancer death among women worldwide. Studies about the genomic landscape of metastatic breast cancer (MBC) have predominantly originated from developed nations. There are still limited data on the molecular epidemiology of MBC in low- and middle-income countries. This study aims to evaluate the prevalence of mutations in the PI3K-AKT pathway and other actionable drivers in estrogen receptor (ER)+/HER2- MBC among Brazilian patients treated at a large institution representative of the nation's demographic diversity.

Methods

We conducted a retrospective observational study using laboratory data (OC Precision Medicine). Our study included tumor samples from patients with ER+/HER2- MBC who underwent routine tumor testing from 2020 to 2023 and originated from several Brazilian centers within the Oncoclinicas network. Two distinct next-generation sequencing (NGS) assays were used: GS Focus (23 genes, covering PIK3CA, AKT1, ESR1, ERBB2, BRCA1, BRCA2, PALB2, TP53, but not PTEN) or GS 180 (180 genes, including PTEN, tumor mutation burden [TMB] and microsatellite instability [MSI]).

Results

Evaluation of tumor samples from 328 patients was undertaken, mostly (75.6%) with GS Focus. Of these, 69% were primary tumors, while 31% were metastatic lesions. The prevalence of mutations in the PI3K-AKT pathway was 39.3% (95% confidence interval, 33% to 43%), distributed as 37.5% in PIK3CA and 1.8% in AKT1. Stratification by age revealed a higher incidence of mutations in this pathway among patients over 50 (44.5% vs 29.1%, p=0.01). Among the PIK3CA mutations, 78% were canonical (included in the alpelisib companion diagnostic non-NGS test), while the remaining 22% were characterized as non-canonical mutations (identifiable only by NGS test). ESR1 mutations were detected in 6.1%, exhibiting a higher frequency in metastatic samples (15.1% vs 1.3%, p=0.003). Additionally, mutations in BRCA1, BRCA2, or PALB2 were identified in 3.9% of cases, while mutations in ERBB2 were found in 2.1%. No PTEN mutations were detected, nor were TMB high or MSI cases.

Conclusion

We describe the genomic landscape of Brazilian patients with ER+/HER2- MBC, in which the somatic mutation profile is comparable to what is described in the literature globally. These data are important for developing precision medicine strategies in this scenario, as well as for health systems management and research initiatives.

The VEGF-Hypoxia Signature Is Upregulated in Basal-like Breast Tumors from Women of African Ancestry and Associated with Poor Outcomes in Breast Cancer

PURPOSE

Black women experience the highest breast cancer mortality rate compared with women of other racial/ethnic groups. To gain a deeper understanding of breast cancer heterogeneity across diverse populations, we examined a VEGF-hypoxia gene expression signature in breast tumors from women of diverse ancestry.

EXPERIMENTAL DESIGN

We developed a NanoString nCounter gene expression panel and applied it to breast tumors from Nigeria (n = 182) and the University of Chicago (Chicago, IL; n = 161). We also analyzed RNA sequencing data from Nigeria (n = 84) and The Cancer Genome Atlas (TCGA) datasets (n = 863). Patient prognosis was analyzed using multiple datasets.

RESULTS

The VEGF-hypoxia signature was highest in the basal-like subtype compared with other subtypes, with greater expression in Black women compared with White women. In TCGA dataset, necrotic breast tumors had higher scores for the VEGF-hypoxia signature compared with non-necrosis tumors (P < 0.001), with the highest proportion in the basal-like subtype. Furthermore, necrotic breast tumors have higher scores for the proliferation signature, suggesting an interaction between the VEGF-hypoxia signature, proliferation, and necrosis. T-cell gene expression signatures also correlated with the VEGF-hypoxia signature when testing all tumors in TCGA dataset. Finally, we found a significant association of the VEGF-hypoxia profile with poor outcomes when using all patients in the METABRIC (P < 0.0001) and SCAN-B datasets (P = 0.002).

CONCLUSIONS

These data provide further evidence for breast cancer heterogeneity across diverse populations and molecular subtypes. Interventions selectively targeting VEGF-hypoxia and the immune microenvironment have the potential to improve overall survival in aggressive breast cancers that disproportionately impact Black women in the African Diaspora.

Breast Cancer Histopathology in the Age of Molecular Oncology

For more than a century, microscopic histology has been the cornerstone for cancer diagnosis, and breast carcinoma is no exception. In recent years, clinical biomarkers, gene expression profiles, and other molecular tests have shown increasing utility for identifying the key biological features that guide prognosis and treatment of breast cancer. Indeed, the most common histologic pattern-invasive ductal carcinoma of no special type-provides relatively little guidance to management beyond triggering grading, biomarker testing, and clinical staging. However, many less common histologic patterns can be recognized by trained pathologists, which in many cases can be linked to characteristic biomarker and gene expression patterns, underlying mutations, prognosis, and therapy. Herein we describe more than a dozen such histomorphologic subtypes (including lobular, metaplastic, salivary analog, and several good prognosis special types of breast cancer) in the context of their molecular and clinical features.

Invasive cribriform carcinoma of the breast presenting as an erythematous papule on the nipple: A case report

Invasive cribriform carcinoma (ICC) is a rare form of invasive breast carcinoma with good prognosis. To date, case reports considering skin manifestations of ICC are scarce. We herein report a case of pure ICC presenting as an erythematous papule on the nipple with mammary Paget's disease in the epidermis. We aim to bring awareness to skin manifestation of ICC.

Germline-mediated immunoediting sculpts breast cancer subtypes and metastatic proclivity

Tumors with the same diagnosis can have different molecular profiles and response to treatment. It remains unclear when and why these differences arise. Somatic genomic aberrations occur within the context of a highly variable germline genome. Interrogating 5870 breast cancer lesions, we demonstrated that germline-derived epitopes in recurrently amplified genes influence somatic evolution by mediating immunoediting. Individuals with a high germline-epitope burden in human epidermal growth factor receptor 2 (HER2/ERBB2) are less likely to develop HER2-positive breast cancer compared with other subtypes. The same holds true for recurrent amplicons defining three aggressive estrogen receptor (ER)-positive subgroups. Tumors that overcome such immune-mediated negative selection are more aggressive and demonstrate an "immune cold" phenotype. These data show that the germline genome plays a role in dictating somatic evolution.

Single-cell analyses reveal evolution mimicry during the specification of breast cancer subtype

Background: The stem or progenitor antecedents confer developmental plasticity and unique cell identities to cancer cells via genetic and epigenetic programs. A comprehensive characterization and mapping of the cell-of-origin of breast cancer using novel technologies to unveil novel subtype-specific therapeutic targets is still absent. Methods: We integrated 195,144 high-quality cells from normal breast tissues and 406,501 high-quality cells from primary breast cancer samples to create a large-scale single-cell atlas of human normal and cancerous breasts. Potential heterogeneous origin of malignant cells was explored by contrasting cancer cells against reference normal epithelial cells. Multi-omics analyses and both in vitro and in vivo experiments were performed to screen and validate potential subtype-specific treatment targets. Novel biomarkers of identified immune and stromal cell subpopulations were validated by immunohistochemistry in our cohort. Results: Tumor stratification based on cancer cell-of-origin patterns correlated with clinical outcomes, genomic aberrations and diverse microenvironment constitutions. We found that the luminal progenitor (LP) subtype was robustly associated with poor prognosis, genomic instability and dysfunctional immune microenvironment. However, the LP subtype patients were sensitive to neoadjuvant chemotherapy (NAC), PARP inhibitors (PARPi) and immunotherapy. The LP subtype-specific target PLK1 was investigated by both in vitro and in vivo experiments. Besides, large-scale single-cell profiling of breast cancer inspired us to identify a range of clinically relevant immune and stromal cell subpopulations, including subsets of innate lymphoid cells (ILCs), macrophages and endothelial cells. Conclusion: The present single-cell study revealed the cellular repertoire and cell-of-origin patterns of breast cancer. Combining single-cell and bulk transcriptome data, we elucidated the evolution mimicry from normal to malignant subtypes and expounded the LP subtype with vital clinical implications. Novel immune and stromal cell subpopulations of breast cancer identified in our study could be potential therapeutic targets. Taken together, Our findings lay the foundation for the precise prognostic and therapeutic stratification of breast cancer.

DNA double-strand break-capturing nuclear envelope tubules drive DNA repair

Current models suggest that DNA double-strand breaks (DSBs) can move to the nuclear periphery for repair. It is unclear to what extent human DSBs display such repositioning. Here we show that the human nuclear envelope localizes to DSBs in a manner depending on DNA damage response (DDR) kinases and cytoplasmic microtubules acetylated by α-tubulin acetyltransferase-1 (ATAT1). These factors collaborate with the linker of nucleoskeleton and cytoskeleton complex (LINC), nuclear pore complex (NPC) protein NUP153, nuclear lamina and kinesins KIF5B and KIF13B to generate DSB-capturing nuclear envelope tubules (dsbNETs). dsbNETs are partly supported by nuclear actin filaments and the circadian factor PER1 and reversed by kinesin KIFC3. Although dsbNETs promote repair and survival, they are also co-opted during poly(ADP-ribose) polymerase (PARP) inhibition to restrain BRCA1-deficient breast cancer cells and are hyper-induced in cells expressing the aging-linked lamin A mutant progerin. In summary, our results advance understanding of nuclear structure-function relationships, uncover a nuclear-cytoplasmic DDR and identify dsbNETs as critical factors in genome organization and stability.

Generative Adversarial Networks Accurately Reconstruct Pan-Cancer Histology from Pathologic, Genomic, and Radiographic Latent Features

Artificial intelligence models have been increasingly used in the analysis of tumor histology to perform tasks ranging from routine classification to identification of novel molecular features. These approaches distill cancer histologic images into high-level features which are used in predictions, but understanding the biologic meaning of such features remains challenging. We present and validate a custom generative adversarial network - HistoXGAN - capable of reconstructing representative histology using feature vectors produced by common feature extractors. We evaluate HistoXGAN across 29 cancer subtypes and demonstrate that reconstructed images retain information regarding tumor grade, histologic subtype, and gene expression patterns. We leverage HistoXGAN to illustrate the underlying histologic features for deep learning models for actionable mutations, identify model reliance on histologic batch effect in predictions, and demonstrate accurate reconstruction of tumor histology from radiographic imaging for a 'virtual biopsy'.

Locus-level L1 DNA methylation profiling reveals the epigenetic and transcriptional interplay between L1s and their integration sites

Long interspersed element 1 (L1) retrotransposons are implicated in human disease and evolution. Their global activity is repressed by DNA methylation, but deciphering the regulation of individual copies has been challenging. Here, we combine short- and long-read sequencing to unveil L1 methylation heterogeneity across cell types, families, and individual loci and elucidate key principles involved. We find that the youngest primate L1 families are specifically hypomethylated in pluripotent stem cells and the placenta but not in most tumors. Locally, intronic L1 methylation is intimately associated with gene transcription. Conversely, the L1 methylation state can propagate to the proximal region up to 300 bp. This phenomenon is accompanied by the binding of specific transcription factors, which drive the expression of L1 and chimeric transcripts. Finally, L1 hypomethylation alone is typically insufficient to trigger L1 expression due to redundant silencing pathways. Our results illuminate the epigenetic and transcriptional interplay between retrotransposons and their host genome.

Digital profiling of cancer transcriptomes from histology images with grouped vision attention

Cancer is a heterogeneous disease that demands precise molecular profiling for better understanding and management. Recently, deep learning has demonstrated potentials for cost-efficient prediction of molecular alterations from histology images. While transformer-based deep learning architectures have enabled significant progress in non-medical domains, their application to histology images remains limited due to small dataset sizes coupled with the explosion of trainable parameters. Here, we develop SEQUOIA, a transformer model to predict cancer transcriptomes from whole-slide histology images. To enable the full potential of transformers, we first pre-train the model using data from 1,802 normal tissues. Then, we fine-tune and evaluate the model in 4,331 tumor samples across nine cancer types. The prediction performance is assessed at individual gene levels and pathway levels through Pearson correlation analysis and root mean square error. The generalization capacity is validated across two independent cohorts comprising 1,305 tumors. In predicting the expression levels of 25,749 genes, the highest performance is observed in cancers from breast, kidney and lung, where SEQUOIA accurately predicts the expression of 11,069, 10,086 and 8,759 genes, respectively. The accurately predicted genes are associated with the regulation of inflammatory response, cell cycles and metabolisms. While the model is trained at the tissue level, we showcase its potential in predicting spatial gene expression patterns using spatial transcriptomics datasets. Leveraging the prediction performance, we develop a digital gene expression signature that predicts the risk of recurrence in breast cancer. SEQUOIA deciphers clinically relevant gene expression patterns from histology images, opening avenues for improved cancer management and personalized therapies.

Metabolomic Rewiring Promotes Endocrine Therapy Resistance in Breast Cancer

Approximately one-third of endocrine-treated women with estrogen receptor alpha-positive (ER+) breast cancers are at risk of recurrence due to intrinsic or acquired resistance. Thus, it is vital to understand the mechanisms underlying endocrine therapy resistance in ER+ breast cancer to improve patient treatment. Mitochondrial fatty acid β-oxidation (FAO) has been shown to be a major metabolic pathway in triple-negative breast cancer (TNBC) that can activate Src signaling. Here, we found metabolic reprogramming that increases FAO in ER+ breast cancer as a mechanism of resistance to endocrine therapy. A metabolically relevant, integrated gene signature was derived from transcriptomic, metabolomic, and lipidomic analyses in TNBC cells following inhibition of the FAO rate-limiting enzyme carnitine palmitoyl transferase 1 (CPT1), and this TNBC-derived signature was significantly associated with endocrine resistance in patients with ER+ breast cancer. Molecular, genetic, and metabolomic experiments identified activation of AMPK-FAO-oxidative phosphorylation (OXPHOS) signaling in endocrine-resistant ER+ breast cancer. CPT1 knockdown or treatment with FAO inhibitors in vitro and in vivo significantly enhanced the response of ER+ breast cancer cells to endocrine therapy. Consistent with the previous findings in TNBC, endocrine therapy-induced FAO activated the Src pathway in ER+ breast cancer. Src inhibitors suppressed the growth of endocrine-resistant tumors, and the efficacy could be further enhanced by metabolic priming with CPT1 inhibition. Collectively, this study developed and applied a TNBC-derived signature to reveal that metabolic reprogramming to FAO activates the Src pathway to drive endocrine resistance in ER+ breast cancer.

SIGNIFICANCE

Increased fatty acid oxidation induced by endocrine therapy activates Src signaling to promote endocrine resistance in breast cancer, which can be overcome using clinically approved therapies targeting FAO and Src.

Cecal microbiota and mammary gland microRNA signatures are related and modifiable by dietary flaxseed with implications for breast cancer risk

IMPORTANCE

Breast cancer is a leading cause of cancer mortality worldwide. There is a growing interest in using dietary approaches, including flaxseed (FS) and its oil and lignan components, to mitigate breast cancer risk. Importantly, there is recognition that pubertal processes and lifestyle, including diet, are important for breast health throughout life. Mechanisms remain incompletely understood. Our research uncovers a link between mammary gland miRNA expression and the gut microbiota in young female mice. We found that this relationship is modifiable via a dietary intervention. Using data from The Cancer Genome Atlas, we also show that the expression of miRNAs involved in these relationships is altered in breast cancer in humans. These findings highlight a role for the gut microbiome as a modulator, and thus a target, of interventions aiming at reducing breast cancer risk. They also provide foundational knowledge to explore the effects of early life interventions and mechanisms programming breast health.

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

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

A population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer

Breast cancer is a heterogeneous disease with variable survival outcomes. Pathologists grade the microscopic appearance of breast tissue using the Nottingham criteria, which are qualitative and do not account for noncancerous elements within the tumor microenvironment. Here we present the Histomic Prognostic Signature (HiPS), a comprehensive, interpretable scoring of the survival risk incurred by breast tumor microenvironment morphology. HiPS uses deep learning to accurately map cellular and tissue structures to measure epithelial, stromal, immune, and spatial interaction features. It was developed using a population-level cohort from the Cancer Prevention Study-II and validated using data from three independent cohorts, including the Prostate, Lung, Colorectal, and Ovarian Cancer trial, Cancer Prevention Study-3, and The Cancer Genome Atlas. HiPS consistently outperformed pathologists in predicting survival outcomes, independent of tumor-node-metastasis stage and pertinent variables. This was largely driven by stromal and immune features. In conclusion, HiPS is a robustly validated biomarker to support pathologists and improve patient prognosis.

Are There More HER2 FISH in the Sea? An Institution's Experience in Identifying HER2 Positivity Using Fluorescent In Situ Hybridization in Patients with HER2 Negative Immunohistochemistry

BACKGROUND

Approximately 20% of breast cancers express HER2-positive receptors in the USA. HER2 receptor immunohistochemistry (IHC) staining with equivocal (2+) results commonly undergoes fluorescence in-situ hybridization (FISH) for further classification. Current guidelines do not recommend routine FISH testing in IHC-negative (0 or 1+) cases. This study investigates an institution that performs both IHC and FISH testing on all cases to identify the true HER2-positive rate.

PATIENTS AND METHODS

A retrospective chart review from 2015 to 2021 was conducted at an institution where both HER2 IHC and FISH testing were performed at the time of diagnosis for all invasive breast cancers. The rate of true HER2-positive patients was determined, and patient and tumor characteristics were further explored.

RESULTS

A total of 1835 invasive breast cancer cases were primarily treated at this institution. A total of 289 cases were HER2 positive on IHC and FISH testing (15.7%). An additional 38 cases were identified as HER2 negative on IHC, but reclassified as HER2 positive on reflex FISH testing. Total HER2 positive cases increased from 289 (15.7%) to 327 cases (17.8%) with reflex FISH testing.

CONCLUSIONS

The additional HER2-positive cases after completing FISH testing on IHC-negative tumors suggests there may be a role for routine FISH testing in addition to standard IHC staining to determine HER2 status for breast cancer. The ethical, prognostic and even  benefits of a correct diagnosis outweigh the added expense of FISH testing.

Identification of hub genes to determine drug-disease correlation in breast carcinomas

The exact molecular mechanism underlying the heterogeneous drug response against breast carcinoma remains to be fully understood. It is urgently required to identify key genes that are intricately associated with varied clinical response of standard anti-cancer drugs, clinically used to treat breast cancer patients. In the present study, the utility of transcriptomic data of breast cancer patients in discerning the clinical drug response using machine learning-based approaches were evaluated. Here, a computational framework has been developed which can be used to identify key genes that can be linked with clinical drug response and progression of cancer, offering an immense opportunity to predict potential prognostic biomarkers and therapeutic targets. The framework concerned utilizes DeSeq2, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Cytoscape, and machine learning techniques to find these crucial genes. Total RNA extraction and qRT-PCR were performed to quantify relative expression of few hub genes selected from the networks. In our study, we have experimentally checked the expression of few key hub genes like APOA2, DLX5, APOC3, CAMK2B, and PAK6 that were predicted to play an immense role in breast cancer tumorigenesis and progression in response to anti-cancer drug Paclitaxel. However, further experimental validations will be required to get mechanistic insights of these genes in regulating the drug response and cancer progression which will likely to play pivotal role in cancer treatment and precision oncology.

The Role of Transcription Factors in the Loss of Inter-Chromosomal Co-Expression for Breast Cancer Subtypes

Breast cancer encompasses a diverse array of subtypes, each exhibiting distinct clinical characteristics and treatment responses. Unraveling the underlying regulatory mechanisms that govern gene expression patterns in these subtypes is essential for advancing our understanding of breast cancer biology. Gene co-expression networks (GCNs) help us identify groups of genes that work in coordination. Previous research has revealed a marked reduction in the interaction of genes located on different chromosomes within GCNs for breast cancer, as well as for lung, kidney, and hematopoietic cancers. However, the reasons behind why genes on the same chromosome often co-express remain unclear. In this study, we investigate the role of transcription factors in shaping gene co-expression networks within the four main breast cancer subtypes: Luminal A, Luminal B, HER2+, and Basal, along with normal breast tissue. We identify communities within each GCN and calculate the transcription factors that may regulate these communities, comparing the results across different phenotypes. Our findings indicate that, in general, regulatory behavior is to a large extent similar among breast cancer molecular subtypes and even in healthy networks. This suggests that transcription factor motif usage does not fully determine long-range co-expression patterns. Specific transcription factor motifs, such as CCGGAAG, appear frequently across all phenotypes, even involving multiple highly connected transcription factors. Additionally, certain transcription factors exhibit unique actions in specific subtypes but with limited influence. Our research demonstrates that the loss of inter-chromosomal co-expression is not solely attributable to transcription factor regulation. Although the exact mechanism responsible for this phenomenon remains elusive, this work contributes to a better understanding of gene expression regulatory programs in breast cancer.

Gene of the month: GATA3

GATA binding protein 3 (GATA3) is a zinc-finger pioneer transcription factor involved in diverse processes. GATA3 regulates gene expression through binding nucleosomal DNA and facilitating chromatin remodelling. Post-translational modifications modulate its activity. During development, GATA3 plays a key role in cell differentiation. Mutations in GATA3 are linked to breast and bladder cancer. GATA3 expression is a feature of the luminal subtype of bladder cancer and has implications for immune status and therapeutic response. It also has clinical relevance in squamous cell carcinomas and soft tissue sarcomas. This paper reviews the structure and function of GATA3, its role in cancer and its use and pitfalls as an immunohistochemical marker.

Structural deep clustering network for stratification of breast cancer patients through integration of somatic mutation profiles

BACKGROUND AND OBJECTIVE

Breast cancer is among of the most malignant tumor that occurs in women and is one of the leading causes of death from gynecologic malignancy worldwide. The high degree of heterogeneity that characterizes breast cancer makes it challenging to devise effective therapeutic strategies. Accumulating evidence highlights the crucial role of stratifying breast cancer patients into clinically significant subtypes to achieve better prognoses and treatments. The structural deep clustering network is a graph convolutional network-based clustering algorithm that integrates structural information and has achieved state-of-the-art performance in various applications.

METHODS

In this study, we employed structural deep clustering network to integrate somatic mutation profiles for stratifying 2526 breast cancer patients from the Memorial Sloan Kettering Cancer Center into two clinically differentiable subtypes.

RESULTS

Breast cancer patients in cluster 1 exhibited better prognosis than breast cancer patients in cluster 2, and the difference between them was statistically significant. The immunogenomic landscape further demonstrated that cluster 1 was associated with remarkable infiltration of the tumor infiltrating lymphocytes. The clustering subtype could be used to evaluate the therapeutic benefit of immunotherapy and chemotherapy in breast cancer patients. Furthermore, our approach effectively classified patients from eight different cancer types, demonstrating its generalizability.

CONCLUSIONS

Our study represents a step towards a generic methodology for classifying cancer patients using only somatic mutation data and structural deep clustering network approaches. Employing structural deep clustering network to identify breast cancer subtypes is promising and can inform the development of more accurate and personalized therapies.

Pan-cancer landscape of epigenetic factor expression predicts tumor outcome

Oncogenic pathways that drive cancer progression reflect both genetic changes and epigenetic regulation. Here we stratified primary tumors from each of 24 TCGA adult cancer types based on the gene expression patterns of epigenetic factors (epifactors). The tumors for five cancer types (ACC, KIRC, LGG, LIHC, and LUAD) separated into two robust clusters that were better than grade or epithelial-to-mesenchymal transition in predicting clinical outcomes. The majority of epifactors that drove the clustering were also individually prognostic. A pan-cancer machine learning model deploying epifactor expression data for these five cancer types successfully separated the patients into poor and better outcome groups. Single-cell analysis of adult and pediatric tumors revealed that expression patterns associated with poor or worse outcomes were present in individual cells within tumors. Our study provides an epigenetic map of cancer types and lays a foundation for discovering pan-cancer targetable epifactors.

N-of-one differential gene expression without control samples using a deep generative model

Differential analysis of bulk RNA-seq data often suffers from lack of good controls. Here, we present a generative model that replaces controls, trained solely on healthy tissues. The unsupervised model learns a low-dimensional representation and can identify the closest normal representation for a given disease sample. This enables control-free, single-sample differential expression analysis. In breast cancer, we demonstrate how our approach selects marker genes and outperforms a state-of-the-art method. Furthermore, significant genes identified by the model are enriched in driver genes across cancers. Our results show that the in silico closest normal provides a more favorable comparison than control samples.

Understanding the need for digital twins' data in patient advocacy and forecasting oncology

Digital twins are made of a real-world component where data is measured and a virtual component where those measurements are used to parameterize computational models. There is growing interest in applying digital twins-based approaches to optimize personalized treatment plans and improve health outcomes. The integration of artificial intelligence is critical in this process, as it enables the development of sophisticated disease models that can accurately predict patient response to therapeutic interventions. There is a unique and equally important application of AI to the real-world component of a digital twin when it is applied to medical interventions. The patient can only be treated once, and therefore, we must turn to the experience and outcomes of previously treated patients for validation and optimization of the computational predictions. The physical component of a digital twins instead must utilize a compilation of available data from previously treated cancer patients whose characteristics (genetics, tumor type, lifestyle, etc.) closely parallel those of a newly diagnosed cancer patient for the purpose of predicting outcomes, stratifying treatment options, predicting responses to treatment and/or adverse events. These tasks include the development of robust data collection methods, ensuring data availability, creating precise and dependable models, and establishing ethical guidelines for the use and sharing of data. To successfully implement digital twin technology in clinical care, it is crucial to gather data that accurately reflects the variety of diseases and the diversity of the population.

Molecular Classification of Breast Cancer

Breast carcinomas classified based on traditional morphologic assessment provide useful prognostic information. Although morphology is still the gold standard of classification, recent advances in molecular technologies have enabled the classification of these tumors into four distinct subtypes based on its intrinsic molecular profile that provide both predictive and prognostic information. This article describes the association between the different molecular subtypes with the histologic subtypes of breast cancer and illustrates how these subtypes may affect the appearance of tumors on imaging studies.

Stromal heterogeneity may explain increased incidence of metaplastic breast cancer in women of African descent

The biologic basis of genetic ancestry-dependent variability in disease incidence and outcome is just beginning to be explored. We recently reported enrichment of a population of ZEB1-expressing cells located adjacent to ductal epithelial cells in normal breasts of women of African ancestry compared to those of European ancestry. In this study, we demonstrate that these cells have properties of fibroadipogenic/mesenchymal stromal cells that express PROCR and PDGFRα and transdifferentiate into adipogenic and osteogenic lineages. PROCR + /ZEB1 + /PDGFRα+ (PZP) cells are enriched in normal breast tissues of women of African compared to European ancestry. PZP: epithelial cell communication results in luminal epithelial cells acquiring basal cell characteristics and IL-6-dependent increase in STAT3 phosphorylation. Furthermore, level of phospho-STAT3 is higher in normal and cancerous breast tissues of women of African ancestry. PZP cells transformed with HRasG12V ± SV40-T/t antigens generate metaplastic carcinoma suggesting that these cells are one of the cells-of-origin of metaplastic breast cancers.

Metaplastic breast cancer: an all-round multidisciplinary consensus

Metaplastic breast cancer (MpBC) is a rare and aggressive histologic subtype of breast cancer (BC) characterized by the presence of at least two cellular types, commonly epithelial and mesenchymal components. Despite growing evidence that MpBC is a unique entity, it has long been treated as a variant of nonspecial type (NST) BC. MpBC typically shows the phenotype of triple-negative breast cancer (TNBC), but compared to NST-TNBC, it is a relatively chemorefractory tumor associated with worse outcomes. Therefore, there is an urgent need to develop management guidelines specifically for MpBC to improve the prognosis of patients with early MpBC. This expert consensus aims to guide diagnosis and standardize clinical management of early MpBC among treating physicians. We provide guidance on the challenging radiological and pathological diagnosis of MpBC. Evidence on the involvement of genetic predisposition in the development of MpBC is also explored. We emphasize the importance of a multidisciplinary approach for the treatment of patients with early MpBC. The optimal surgery and radiotherapy approach is presented, as well as the opportunity offered by novel therapeutic approaches to increase treatment response in this chemoresistant subtype. Appropriate management of patients with MpBC is critical to reduce the high risk of local and distant recurrence that characterizes this disease.

Integration of clinical features and deep learning on pathology for the prediction of breast cancer recurrence assays and risk of recurrence

Gene expression-based recurrence assays are strongly recommended to guide the use of chemotherapy in hormone receptor-positive, HER2-negative breast cancer, but such testing is expensive, can contribute to delays in care, and may not be available in low-resource settings. Here, we describe the training and independent validation of a deep learning model that predicts recurrence assay result and risk of recurrence using both digital histology and clinical risk factors. We demonstrate that this approach outperforms an established clinical nomogram (area under the receiver operating characteristic curve of 0.83 versus 0.76 in an external validation cohort, p = 0.0005) and can identify a subset of patients with excellent prognoses who may not need further genomic testing.

Copy Number Variation in Inflammatory Breast Cancer

Identification of a unique genomic biomarker in de novo inflammatory breast cancer (IBC) may provide an insight into the biology of this aggressive disease. The goal of our study was to elucidate biomarkers associated with IBC. We examined breast biopsies collected from Dana–Farber Cancer Institute patients with IBC prior to initiating preoperative systemic treatment (30 samples were examined, of which 14 were eligible). Patients without available biopsies (n = 1), with insufficient tumor epithelial cells (n = 10), or insufficient DNA yield (n = 5) were excluded from the analysis. Molecular subtype and tumor grade were abstracted from a medical records’ review. Ten IBC tumors were estrogen-receptor-positive (ER+) and human epidermal growth factor receptor 2 (HER2)-negative (n = 10 out of 14). Sufficient RNA and DNA were simultaneously extracted from 14 biopsy specimens using the Qiagen AllPrep Kit. RNA was amplified using the Sensation kit and profiled using the Affymetrix Human Transcriptome Array 2.0. DNA was profiled for genome-wide copy number variation (CNV) using the Affymetrix OncoScan Array and analyzed using the Nexus Chromosome Analysis Suite. Among the 14 eligible samples, we first confirmed biological concordance and quality control metrics using replicates and gene expression data. Second, we examined CNVs and gene expression change by IBC subtype. We identified significant CNVs in IBC patients after adjusting for multiple comparisons. Next, to assess whether the CNVs were unique to IBC, we compared the IBC CNV data to fresh-frozen non-IBC CNV data from The Cancer Genome Atlas (n = 388). On chromosome 7p11.2, we identified significant CN gain located at position 58,019,983-58,025,423 in 8 ER+ IBC samples compared to 338 non-IBC ER+ samples (region length: 5440 bp gain and 69,039 bp, False Discovery Rate (FDR) p-value = 3.12 × 10−10) and at position 57,950,944–58,025,423 in 3 TN-IBC samples compared to 50 non-IBC TN samples (74,479 base pair, gain, FDR p-value = 4.27 × 10−5; near the EGFR gene). We also observed significant CN loss on chromosome 21, located at position 9,648,315–9,764,385 (p-value = 4.27 × 10−5). Secondarily, differential gene expression in IBC patients with 7p11.2 CN gain compared to SUM149 were explored after FDR correction for multiple testing (p-value = 0.0016), but the results should be interpreted with caution due to the small sample size. Finally, the data presented are hypothesis-generating. Validation of CNVs that contribute to the unique presentation and biological features associated with IBC in larger datasets may lead to the optimization of treatment strategies.

Proteomics-Based Identification of Dysregulated Proteins and Biomarker Discovery in Invasive Ductal Carcinoma, the Most Common Breast Cancer Subtype

Invasive ductal carcinoma (IDC) is the most common histological subtype of malignant breast cancer (BC), and accounts for 70–80% of all invasive BCs. IDC demonstrates great heterogeneity in clinical and histopathological characteristics, prognoses, treatment strategies, gene expressions, and proteomic profiles. Significant proteomic determinants of the progression from intraductal pre-invasive malignant lesions of the breast, which characterize a ductal carcinoma in situ (DCIS), to IDC, are still poorly identified, validated, and clinically applied. In the era of “6P” medicine, it remains a great challenge to determine which patients should be over-treated versus which need to be actively monitored without aggressive treatment. The major difficulties for designating DCIS to IDC progression may be solved by understanding the integrated genomic, transcriptomic, and proteomic bases of invasion. In this review, we showed that multiple proteomics-based techniques, such as LC–MS/MS, MALDI-ToF MS, SELDI-ToF-MS, MALDI-ToF/ToF MS, MALDI-MSI or MasSpec Pen, applied to in-tissue, off-tissue, BC cell lines and liquid biopsies, improve the diagnosis of IDC, as well as its prognosis and treatment monitoring. Classic proteomics strategies that allow the identification of dysregulated protein expressions, biological processes, and interrelated pathway analyses based on aberrant protein–protein interaction (PPI) networks have been improved to perform non-invasive/minimally invasive biomarker detection of early-stage IDC. Thus, in modern surgical oncology, highly sensitive, rapid, and accurate MS-based detection has been coupled with “proteome point sampling” methods that allow for proteomic profiling by in vivo “proteome point characterization”, or by minimal tissue removal, for ex vivo accurate differentiation and delimitation of IDC. For the detection of low-molecular-weight proteins and protein fragments in bodily fluids, LC–MS/MS and MALDI-MS techniques may be coupled to enrich and capture methods which allow for the identification of early-stage IDC protein biomarkers that were previously invisible for MS-based techniques. Moreover, the detection and characterization of protein isoforms, including posttranslational modifications of proteins (PTMs), is also essential to emphasize specific molecular mechanisms, and to assure the early-stage detection of IDC of the breast.

Integration of multi-omics data reveals a novel hybrid breast cancer subtype and its biomarkers

Tumor heterogeneity in breast cancer hinders proper diagnosis and treatment, and the identification of molecular subtypes may help enhance the understanding of its heterogeneity. Therefore, we proposed a novel integrated multi-omics approach for breast cancer typing, which led to the identification of a hybrid subtype (Mix_Sub subtype) with a poor survival prognosis. This subtype is characterized by lower levels of the inflammatory response, lower tumor malignancy, lower immune cell infiltration, and higher T-cell dysfunction. Moreover, we found that cell-cell communication mediated by NCAM1-FGFR1 ligand-receptor interaction and cellular functional states, such as cell cycle, DNA damage, and DNA repair, were significantly altered and upregulated in patients with this subtype, and that such patients displayed greater sensitivity to targeted therapies. Subsequently, using differential genes among subtypes as biomarkers, we constructed prognostic risk models and subtype classifiers for the Mix_Sub subtype and validated their generalization ability in external datasets obtained from the GEO database, indicating their potential therapeutic and prognostic significance. These biomarkers also showed significant spatially variable expression in malignant tumor cells. Collectively, the identification of the Mix_Sub breast cancer subtype and its biomarkers, based on the driving relationship between omics, has deepened our understanding of breast cancer heterogeneity and facilitated the development of breast cancer precision therapy.

Prognostic impact of HER2-low expression in HER2-negative breast cancer under different hormone receptor status

BACKGROUND

HER2-low expression in breast cancer has received increasing attention as a target for novel antibody-drug conjugates (ADCs). The purpose of this study was to investigate the impact of HER2-low status on survival outcomes in patients with HER2-negative early breast cancer.

METHODS

Medical records of patients with HER2-negative non-metastatic breast cancer who were treated at our institution from January 2008 and June 2019 were retrospectively reviewed. The main outcome measurements of our study were overall survival (OS) and disease-free survival (DFS), which were compared between the HER2-low and HER2-0 groups stratified by hormone receptor (HR) status.

RESULTS

A total of 2605 HER2-negative cases were identified, of which 1418 (54.4%) had HER2-low and 1187 (45.6%) had HER2-0 disease. The proportion of HER2-low tumors was significantly higher in HR-positive tumors than in HR-negative tumors. No significant difference was observed in DFS and OS between the HER2-low and HER2-0 groups in univariate analyses, regardless of HR status. Multivariate analysis of the Cox proportional hazard regression model revealed that HER2-low was independently associated with improved OS in patients with HR-negative disease (HR 0.32, 95% CI 0.13-0.80, p = 0.015).

CONCLUSION

Our findings demonstrate that the prognostic impact of low HER2 expression varies according to HR status, with slightly favorable outcomes among HER2-low tumors in patients with HR-negative disease.

Multiomics in primary and metastatic breast tumors from the AURORA US network finds microenvironment and epigenetic drivers of metastasis

The AURORA US Metastasis Project was established with the goal to identify molecular features associated with metastasis. We assayed 55 females with metastatic breast cancer (51 primary cancers and 102 metastases) by RNA sequencing, tumor/germline DNA exome and low-pass whole-genome sequencing and global DNA methylation microarrays. Expression subtype changes were observed in ~30% of samples and were coincident with DNA clonality shifts, especially involving HER2. Downregulation of estrogen receptor (ER)-mediated cell-cell adhesion genes through DNA methylation mechanisms was observed in metastases. Microenvironment differences varied according to tumor subtype; the ER+/luminal subtype had lower fibroblast and endothelial content, while triple-negative breast cancer/basal metastases showed a decrease in B and T cells. In 17% of metastases, DNA hypermethylation and/or focal deletions were identified near HLA-A and were associated with reduced expression and lower immune cell infiltrates, especially in brain and liver metastases. These findings could have implications for treating individuals with metastatic breast cancer with immune- and HER2-targeting therapies.

Mucinous carcinoma of the breast: distinctive histopathologic and genetic characteristics

Mucinous carcinoma is a rare histologic type of breast cancer that, when classified with favorable histology, can be treated with different therapeutic options. This study reviews the histologic findings of mucinous carcinoma that support or exclude favorable histology and emphasizes the necessity of an appropriate gross examination with radiologic findings for an accurate diagnosis. In addition, unusual findings such as micropapillary arrangements and lobular differentiation in mucinous carcinoma and their implications for prognosis and treatment are reviewed. Mucinous carcinoma involves upregulation of MUC2, a mucus-associated gene common in mucinous carcinoma of the breast as well as various other organs. In mucinous carcinoma, the fraction of genome altered and tumor mutation burden are lower than those of invasive carcinoma of no special type, the most common histology of breast cancer. In addition, the genetic alterations found in mucinous carcinoma are diverse, unlike the pathognomonic genetic alterations observed in other histologic types of breast cancer. These genetic features support the importance of conventional microscopic evaluations for the pathologic differential diagnosis of mucinous carcinoma of the breast in routine practice. A variety of breast lesions, including mucinous cystadenocarcinoma and mucocele-like lesions, as well as mucinous carcinoma from other organs, can mimic mucinous carcinoma of the breast. In order to obtain an accurate pathologic diagnosis, careful evaluation of the overall histopathologic characteristics and ancillary testing are required to provide information on appropriate treatment and prognosis.

Breast cancer in the era of precision medicine

Breast cancer is a heterogeneous disorder with different molecular subtypes and biological characteristics for which there are diverse therapeutic approaches and clinical outcomes specific to any molecular subtype. It is a global health concern due to a lack of efficient therapy regimens that might be used for all disease subtypes. Therefore, treatment customization for each patient depending on molecular characteristics should be considered. Precision medicine for breast cancer is an approach to diagnosis, treatment, and prevention of the disease that takes into consideration the patient's genetic makeup. Precision medicine provides the promise of highly individualized treatment, in which each individual breast cancer patient receives the most appropriate diagnostics and targeted therapies based on the genetic profile of cancer. The knowledge about the molecular features and development of breast cancer treatment approaches has increased, which led to the development of new targeted therapeutics. Tumor genomic profiling is the standard of care for breast cancer that could contribute to taking steps to better management of malignancies. It holds great promise for accurate prognostication, prediction of response to common systemic therapies, and individualized monitoring of the disease. The emergence of targeted treatment has significantly enhanced the survival of patients with breast cancer and contributed to reducing the economic costs of the health system. In this review, we summarized the therapeutic approaches associated with the molecular classification of breast cancer to help the best treatment selection specific to the target patient.

Matrix Gla protein (MGP), GATA3, and TRPS1: a novel diagnostic panel to determine breast origin

Background

Metastatic breast carcinoma is commonly considered during differential diagnosis when metastatic disease is detected in females. In addition to the tumor morphology and documented clinical history, sensitive and specific immunohistochemical (IHC) markers such as GCDFP-15, mammaglobin, and GATA3 are helpful for determining breast origin. However, these markers are reported to show lower sensitivity in certain subtypes, such as triple-negative breast cancer (TNBC).

Materials and methods

Using bioinformatics analyses, we identified a potential diagnostic panel to determine breast origin: matrix Gla protein (MGP), transcriptional repressor GATA binding 1 (TRPS1), and GATA-binding protein 3 (GATA3). We compared MGP, TRPS1, and GATA3 expression in different subtypes of breast carcinoma of (n = 1201) using IHC. As a newly identified marker, MGP expression was also evaluated in solid tumors (n = 2384) and normal tissues (n = 1351) from different organs.

Results

MGP and TRPS1 had comparable positive expression in HER2-positive (91.2% vs. 92.0%, p = 0.79) and TNBC subtypes (87.3% vs. 91.2%, p = 0.18). GATA3 expression was lower than MGP (p < 0.001) or TRPS1 (p < 0.001), especially in HER2-positive (77.0%, p < 0.001) and TNBC (43.3%, p < 0.001) subtypes. TRPS1 had the highest positivity rate (97.9%) in metaplastic TNBCs, followed by MGP (88.6%), while only 47.1% of metaplastic TNBCs were positive for GATA3. When using MGP, GATA3, and TRPS1 as a novel IHC panel, 93.0% of breast carcinomas were positive for at least two markers, and only 9 cases were negative for all three markers. MGP was detected in 36 cases (3.0%) that were negative for both GATA3 and TRPS1. MGP showed mild-to-moderate positive expression in normal hepatocytes, renal tubules, as well as 31.1% (99/318) of hepatocellular carcinomas. Rare cases (0.6–5%) had focal MGP expression in renal, ovarian, lung, urothelial, and cholangiocarcinomas.

Conclusions

Our findings suggest that MGP is a newly identified sensitive IHC marker to support breast origin. MGP, TRPS1, and GATA3 could be applied as a reliable diagnostic panel to determine breast origin in clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13058-022-01569-1.