ESR1 genetic alterations and their association with clinicopathologic characteristics in advanced breast cancer: a single academic institution experience

Genetic analysis of PALB2 gene WD40 domain in canine mammary tumour patients

BACKGROUND

DNA repair mechanisms are essential for tumorigenesis and disruption of HR mechanism is an important predisposing factor of human breast cancers (BC). PALB2 is an important part of the HR. There are similarities between canine mammary tumours (CMT) and BCs. As its human counterpart, PALB2 mutations could be a predisposing factor of CMT.

OBJECTIVES

In this study, we aimed to investigate the impacts of PALB2 variants on tumorigenesis and canine mammary tumor (CMT) malignancy.

METHODS

We performed Sanger sequencing to detect germline mutations in the WD40 domain of the canine PALB2 gene in CMT patients. We conducted in silico analysis to investigate the variants, and compared the germline PALB2 mutations in humans that cause breast cancer (BC) with the variants detected in dogs with CMT.

RESULTS

We identified an intronic (c.3096+8C>G) variant, two exonic (p.A1050V and p.R1354R) variants, and a 3' UTR variant (c.4071T>C). Of these, p.R1354R and c.4071T>C novel variants were identified for the first time in this study. We found that the p.A1050V mutation had a significant effect. However, we could not determine sufficient similarity due to the differences in nucleotide/amino acid sequences between two species. Nonetheless, possible variants of human sequences in the exact location as their dog counterparts are associated with several cancer types, implying that the variants could be crucial for tumorigenesis in dogs. Our results did not show any effect of the variants on tumor malignancy.

CONCLUSIONS

The current project is the first study investigating the relationship between the PALB2 gene WD40 domain and CMTs. Our findings will contribute to a better understanding of the pathogenic mechanism of the PALB2 gene in CMTs. In humans, variant positions in canines have been linked to cancer-related phenotypes such as familial BC, endometrial tumor, and hereditary cancer predisposition syndrome. The results of bioinformatics analyses should be investigated through functional tests or case-control studies.

The Treatment Landscape of Elderly Patients with Hormone Receptor-Positive Her2 Negative Advanced Breast Cancer: Current Perspectives and Future Directions

Breast cancer (BC) in elderly women is an increasing health issue due to demographic changes. BC tends to present later and may receive less than standard treatment options. More often, BC in elderly patients is endocrine-positive (HR+). The treatment of elderly patients with metastatic BC (mBC) represents a therapeutic challenge. In recent years, the treatment landscape of patients that are HR+/Her2-negative has changed due to the introduction in clinical practice of new targeted drugs, which have improved patient outcomes. Elderly patients are a small percentage of all patients enrolled in clinical trials and, to date, there are no standardized guidelines that define the best treatment option for this patient population. This can lead to undertreatment or overtreatment, impacting patient morbidity and mortality. Geriatric Assessment tools to tailor the treatment in elderly patients are underused because they are long and difficult to apply in a busy routine clinical practice. For all these reasons, there is an urgent need to produce data about the best treatment for elderly patients with HR+ mBC. Herein, we report data from randomized clinical trials and real-world evidence on the therapeutic options for HR+ Her2-negative mBC elderly patients and explore future treatment directions.

Update on prognostic and predictive biomarkers of breast cancer

Breast cancer represents a heterogeneous group of human cancer at both histological and molecular levels. Estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) are the most commonly used biomarkers in clinical practice for making treatment plans for breast cancer patients by oncologists. Recently, PD-L1 testing plays an important role for immunotherapy for triple-negative breast cancer. With the increased understanding of the molecular characterization of breast cancer and the emergence of novel targeted therapies, more potential biomarkers are needed for the development of more personalized treatments. In this review, we summarized several main prognostic and predictive biomarkers in breast cancer at genomic, transcriptomic and proteomic levels, including hormone receptors, HER2, Ki67, multiple gene expression assays, PD-L1 testing, mismatch repair deficiency/microsatellite instability, tumor mutational burden, PIK3CA, ESR1 andNTRK and briefly introduced the roles of digital imaging analysis in breast biomarker evaluation.

Reducing variability of breast cancer subtype predictors by grounding deep learning models in prior knowledge

Deep learning neural networks have improved performance in many cancer informatics problems, including breast cancer subtype classification. However, many networks experience underspecificationwheremultiplecombinationsofparametersachievesimilarperformance, bothin training and validation. Additionally, certain parameter combinations may perform poorly when the test distribution differs from the training distribution. Embedding prior knowledge from the literature may address this issue by boosting predictive models that provide crucial, in-depth information about a given disease. Breast cancer research provides a wealth of such knowledge, particularly in the form of subtype biomarkers and genetic signatures. In this study, we draw on past research on breast cancer subtype biomarkers, label propagation, and neural graph machines to present a novel methodology for embedding knowledge into machine learning systems. We embed prior knowledge into the loss function in the form of inter-subject distances derived from a well-known published breast cancer signature. Our results show that this methodology reduces predictor variability on state-of-the-art deep learning architectures and increases predictor consistency leading to improved interpretation. We find that pathway enrichment analysis is more consistent after embedding knowledge. This novel method applies to a broad range of existing studies and predictive models. Our method moves the traditional synthesis of predictive models from an arbitrary assignment of weights to genes toward a more biologically meaningful approach of incorporating knowledge.

Prognostic value of androgen receptor expression and molecular alterations in metastatic triple-negative or low hormone receptor breast carcinomas

Metastatic breast carcinomas (BCs) with phenotype of triple-negative (TNBC) or low hormonal receptor levels [estrogen receptor (ER)/progesterone receptor (PR) < 10% and HER2-] are mainly treated with cytotoxic chemotherapy. Targeting androgen receptor (AR) pathway may represent a potential new therapeutic strategy in such group of BCs. We evaluated AR expression by immunohistochemistry and genetic alterations by next-generation sequencing. Among 114 metastatic BCs, 37 (32.5%) cases showed AR expression and 77 (67.5%) lacked AR expression. Statistical analysis revealed that AR expression is associated with older age, lobular carcinoma, positive ER and positive PR in primary tumors, and lymph node metastasis. Patients with AR-positive tumors had significantly longer metastatic intervals and overall survivals. In addition, AR-positive tumors had significantly higher rate of PI3CA mutation. Our results demonstrated that AR expression has prognostic value in this subgroup of metastatic BCs and tumors with AR expression had different molecular alterations compared with those without AR expression.