Comprehensive identification and characterization of somatic copy number alterations in triple‑negative breast cancer

Tailored therapies for triple-negative breast cancer: current landscape and future perceptions

Triple-negative breast cancer (TNBC) has become one of the most challenging cancers to date due to its great variability in biological features, high growth rate, and rare options for treatment. This review examines several innovative strategies for tailored treatment of TNBC, focusing mainly on the most recent developments and potential directions. The molecular landscape of TNBC is covered in the first section, which keeps the focus on transcriptome and genomic profiling while highlighting key molecular targets like mutations in the BRCA1/2, PIK3CA, androgen receptors (AR), epidermal growth factor receptors (EGFR), and immunological checkpoint molecules. This review also covers novel therapies that aim to block well-defined pathways, including immune checkpoint inhibitors (ICI), EGFR inhibitors, drugs that target AR, poly ADP ribose polymerase (PARP) inhibitors, and drugs that disrupt the PI3K/AKT/mTOR pathway. Additionally, it covers novel strategies focusing on combination therapy, targeting the DNA damage response pathway, and epigenetic modulators. Conclusively, it emphasizes perspectives and directions on topics such as personalized medicine, artificial intelligence (AI), predictive biomarkers, and treatment planning with the inclusion of machine learning (ML).

Improving efficacy of TNBC immunotherapy: based on analysis and subtyping of immune microenvironment

Triple-negative breast cancer (TNBC) is a highly aggressive type of breast cancer that encompasses several distinct subtypes. Recent advances in immunotherapy offer a promising future for the treatment of these highly heterogeneous and readily metastatic tumors. Despite advancements, the efficacy of immunotherapy remains limited as shown by unimproved efficacy of PD-L1 biomarker and limited patient benefit. To enhance the effectiveness of TNBC immunotherapy, we conducted investigation on the microenvironment, and corresponding therapeutic interventions of TNBC and recommended further investigation into the identification of additional biomarkers that can facilitate the subtyping of TNBC for more targeted therapeutic approaches. TNBC is a highly aggressive subtype with dismal long-term survival due to the lack of opportunities for traditional endocrine and targeted therapies. Recent advances in immunotherapy have shown promise, but response rates can be limited due to the heterogeneous tumor microenvironments and developed therapy resistance, especially in metastatic cases. In this review, we will investigate the tumor microenvironment of TNBC and corresponding therapeutic interventions. We will summarize current subtyping strategies and available biomarkers for TNBC immunotherapy, with a particular emphasis on the need for further research to identify additional prognostic markers and refine tailored therapies for specific TNBC subtypes. These efforts aim to improve treatment sensitivity and ultimately enhance survival outcomes for advanced-stage TNBC patients.

Low-pass whole genome sequencing of circulating tumor cells to evaluate chromosomal instability in triple-negative breast cancer

Chromosomal Instability (CIN) is a common and evolving feature in breast cancer. Large-scale Transitions (LSTs), defined as chromosomal breakages leading to gains or losses of at least 10 Mb, have recently emerged as a metric of CIN due to their standardized definition across platforms. Herein, we report the feasibility of using low-pass Whole Genome Sequencing to assess LSTs, copy number alterations (CNAs) and their relationship in individual circulating tumor cells (CTCs) of triple-negative breast cancer (TNBC) patients. Initial assessment of LSTs in breast cancer cell lines consistently showed wide-ranging values (median 22, range 4-33, mean 21), indicating heterogeneous CIN. Subsequent analysis of CTCs revealed LST values (median 3, range 0-18, mean 5), particularly low during treatment, suggesting temporal changes in CIN levels. CNAs averaged 30 (range 5-49), with loss being predominant. As expected, CTCs with higher LSTs values exhibited increased CNAs. A CNA-based classifier of individual patient-derived CTCs, developed using machine learning, identified genes associated with both DNA proliferation and repair, such as RB1, MYC, and EXO1, as significant predictors of CIN. The model demonstrated a high predictive accuracy with an Area Under the Curve (AUC) of 0.89. Overall, these findings suggest that sequencing CTCs holds the potential to facilitate CIN evaluation and provide insights into its dynamic nature over time, with potential implications for monitoring TNBC progression through iterative assessments.

SCONCE2: jointly inferring single cell copy number profiles and tumor evolutionary distances

Background

Single cell whole genome tumor sequencing can yield novel insights into the evolutionary history of somatic copy number alterations. Existing single cell copy number calling methods do not explicitly model the shared evolutionary process of multiple cells, and generally analyze cells independently. Additionally, existing methods for estimating tumor cell phylogenies using copy number profiles are sensitive to profile estimation errors.

Results

We present SCONCE2, a method for jointly calling copy number alterations and estimating pairwise distances for single cell sequencing data. Using simulations, we show that SCONCE2 has higher accuracy in copy number calling and phylogeny estimation than competing methods. We apply SCONCE2 to previously published single cell sequencing data to illustrate the utility of the method.

Conclusions

SCONCE2 jointly estimates copy number profiles and a distance metric for inferring tumor phylogenies in single cell whole genome tumor sequencing across multiple cells, enabling deeper understandings of tumor evolution.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04890-w.

Novel insights linking BRCA1-IRIS role in mammary gland development to formation of aggressive PABCs: the case for longer breastfeeding

Pregnancy-associated breast cancer (PABC) is diagnosed during or shortly after pregnancy. Although rare, PABC is a serious occurrence often of the triple negative (TNBC) subtype. Here we show progesterone, prolactin, and RANKL upregulate BRCA1-IRIS (IRIS) in separate and overlapping subpopulations of human mammary epithelial cell lines, which exacerbates the proliferation, survival, and the TNBC-like phenotype in them. Conversely, vitamin D₃ reduces IRIS expression in TNBC cell lines, which attenuates growth, survival, and the TNBC-like phenotype in them. In the mouse, Brca1-Iris (Iris, mouse IRIS homolog) is expressed at low-level in nulliparous mice, increases ~10-fold in pregnant/lactating mice, to completely disappear in involuting mice, and reappears at low-level in regressed glands. Mice underwent 3 constitutive pregnancies followed by a forced involution (after 5 days of lactation) contained ~10-fold higher Iris in their mammary glands compared to those underwent physiological involution (after 21 days of lactation). While protein extracts from lactating glands promote proliferation in IRIS^low and IRIS overexpressing (IRISOE) cells, extracts from involuting glands promote apoptosis in IRIS^low, and aneuploidy in IRISOE cells. In a cohort of breast cancer patients, lack of breastfeeding was associated with formation of chemotherapy resistant, metastatic IRISOE breast cancers. We propose that terminal differentiation triggered by long-term breastfeeding reduces IRIS expression in mammary cells allowing their elimination by the inflammatory microenvironment during physiological involution. No/short-term breastfeeding retains in the mammary gland IRISOE cells that thrive in the inflammatory microenvironment during forced involution to become precursors for aggressive breast cancers shortly after pregnancy.

QNBC Is Associated with High Genomic Instability Characterized by Copy Number Alterations and miRNA Deregulation

Triple-negative breast cancer (TNBC) can be further classified into androgen receptor (AR)-positive TNBC and AR-negative TNBC or quadruple-negative breast cancer (QNBC). Here, we investigated genomic instability in 53 clinical cases by array-CGH and miRNA expression profiling. Immunohistochemical analysis revealed that 64% of TNBC samples lacked AR expression. This group of tumors exhibited a higher level of copy number alterations (CNAs) and a higher frequency of cases affected by CNAs than TNBCs. CNAs in genes of the chromosome instability 25 (CIN25) and centrosome amplification (CA) signatures were more frequent in the QNBCs and were similar between the groups, respectively. However, expression levels of CIN25 and CA20 genes were higher in QNBCs. miRNA profiling revealed 184 differentially expressed miRNAs between the groups. Fifteen of these miRNAs were mapped at cytobands with CNAs, of which eight (miR-1204, miR-1265, miR-1267, miR-23c, miR-548ai, miR-567, miR-613, and miR-943), and presented concordance of expression and copy number levels. Pathway enrichment analysis of these miRNAs/mRNAs pairings showed association with genomic instability, cell cycle, and DNA damage response. Furthermore, the combined expression of these eight miRNAs robustly discriminated TNBCs from QNBCs (AUC = 0.946). Altogether, our results suggest a significant loss of AR in TNBC and a profound impact in genomic instability characterized by CNAs and deregulation of miRNA expression.

Survival outcomes are associated with genomic instability in luminal breast cancers

Breast cancer is the leading cause of cancer related death among women. Breast cancers are generally diagnosed and treated based on clinical and histopathological features, along with subtype classification determined by the Prosigna Breast Cancer Prognostic Gene Signature Assay (also known as PAM50). Currently the copy number alteration (CNA) landscape of the tumour is not considered. We set out to examine the role of genomic instability (GI) in breast cancer survival since CNAs reflect GI and correlate with survival in other cancers. We focused on the 70% of breast cancers classified as luminal and carried out a comprehensive survival and association analysis using Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) data to determine whether CNA Score Quartiles derived from absolute CNA counts are associated with survival. Analysis revealed that patients diagnosed with luminal A breast cancer have a CNA landscape associated with disease specific survival, suggesting that CNA Score can provide a statistically robust prognostic factor. Furthermore, stratification of patients into subtypes based on gene expression has shown that luminal A and B cases overlap, and it is in this region we largely observe luminal A cases with reduced survival outlook. Therefore, luminal A breast cancer patients with quantitatively elevated CNA counts may benefit from more aggressive therapy. This demonstrates how individual genomic landscapes can facilitate personalisation of therapeutic interventions to optimise survival outcomes.

Triple-Negative Breast Cancer Comparison With Canine Mammary Tumors From Light Microscopy to Molecular Pathology

Many similar characteristics in human and dog cancers including, spontaneous development, clinical presentation, tumor heterogeneity, disease progression, and response to standard therapies have promoted the approval of this comparative model as an alternative to mice. Breast cancer represents the second most frequent neoplasm in humans after lung cancer. Triple-negative breast cancers (TNBC) constitute around 15% of all cases of breast cancer and do not express estrogen receptor (ER), progesterone receptor (PR), and do not overexpress human epidermal growth factor receptor 2 (HER2). As a result, they do not benefit from hormonal or trastuzumab-based therapy. Patients with TNBC have worse overall survival than patients with non-TNBC. Lehmann and collaborators described six different molecular subtypes of TNBC which further demonstrated its transcriptional heterogeneity. This six TNBC subtype classification has therapeutic implications. Breast cancer is the second most frequent neoplasm in sexually intact female dogs after skin cancer. Canine mammary tumors are a naturally occurring heterogeneous group of cancers that have several features in common with human breast cancer (HBC). These similarities include etiology, signaling pathway activation, and histological classification. Molecularly CMTs are more like TNBCs, and therefore dogs are powerful spontaneous models of cancer to test new therapeutic approaches, particularly for human TNBCs. More malignant tumors of the breast are more often ER and PR negative in both humans and dogs. Promising breast cancer biomarkers in both humans and canines are cancer-associated stroma (CAS), circulating tumor cells and tumor DNA (ctDNA), exosomes and miRNAs, and metabolites.