Spectrum of MAP3K1 mutations in breast cancer is luminal subtype-predominant and related to prognosis

The Genomic and Biologic Landscapes of Breast Cancer and Racial Differences

Breast cancer is a significant health challenge worldwide and is the most frequently diagnosed cancer among women globally. This review provides a comprehensive overview of breast cancer biology, genomics, and microbial dysbiosis, focusing on its various subtypes and racial differences. Breast cancer is primarily classified into carcinomas and sarcomas, with carcinomas constituting most cases. Epidemiology and breast cancer risk factors are important for public health intervention. Staging and grading, based on the TNM and Nottingham grading systems, respectively, are crucial to determining the clinical outcome and treatment decisions. Histopathological subtypes include in situ and invasive carcinomas, such as invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC). The review explores molecular subtypes, including Luminal A, Luminal B, Basal-like (Triple Negative), and HER2-enriched, and delves into breast cancer's histological and molecular progression patterns. Recent research findings related to nuclear and mitochondrial genetic alterations, epigenetic reprogramming, and the role of microbiome dysbiosis in breast cancer and racial differences are also reported. The review also provides an update on breast cancer's current diagnostics and treatment modalities.

ALLSTAR: Inference of ReliAble CausaL RuLes between Somatic MuTAtions and CanceR Phenotypes

MOTIVATION

Recent advances in DNA sequencing technologies have allowed the detailed characterization of genomes in large cohorts of tumors, highlighting their extreme heterogeneity, with no two tumors sharing the same complement of somatic mutations. Such heterogeneity hinders our ability to identify somatic mutations important for the disease, including mutations that determine clinically relevant phenotypes (e.g., cancer subtypes). Several tools have been developed to identify somatic mutations related to cancer phenotypes. However, such tools identify correlations between somatic mutations and cancer phenotypes, with no guarantee of highlighting causal relations.

RESULTS

We describe ALLSTAR, a novel tool to infer reliable causal relations between somatic mutations and cancer phenotypes. ALLSTAR identifies reliable causal rules highlighting combinations of somatic mutations with the highest impact in terms of average effect on the phenotype. While we prove that the underlying computational problem is NP-hard, we develop a branch-and-bound approach that employs protein-protein interaction networks and novel bounds for pruning the search space, while properly correcting for multiple hypothesis testing. Our extensive experimental evaluation on synthetic data shows that our tool is able to identify reliable causal relations in large cancer cohorts. Moreover, the reliable causal rules identified by our tool in cancer data show that our approach identifies several somatic mutations known to be relevant for cancer phenotypes as well as novel biologically meaningful relations.

AVAILABILITY AND IMPLEMENTATION

Code, data, and scripts to reproduce the experiments available at https://github.com/VandinLab/ALLSTAR.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Association of Germline Variation in Driver Genes with Breast Cancer Risk in Chilean Population

Cancer is a genomic disease, with driver mutations contributing to tumorigenesis. These potentially heritable variants influence risk and underlie familial breast cancer (BC). This study evaluated associations between BC risk and 13 SNPs in driver genes MAP3K1, SF3B1, SMAD4, ARID2, ATR, KMT2C, MAP3K13, NCOR1, and TBX3, in BRCA1/2-negative Chilean families. SNPs were genotyped using TaqMan Assay in 492 cases and 1285 controls. There were no associations between rs75704921:C>T (ARID2); rs2229032:A>C (ATR); rs3735156:C>G (KMT2C); rs2276738:G>C, rs2293906:C>T, rs4075943T:>A, rs13091808:C>T (MAP3K13); rs178831:G>A (NCOR1); or rs3759173:C>A (TBX3) and risk. The MAP3K1 rs832583 A allele (C/A+A/A) showed a protective effect in families with moderate BC history (OR = 0.7 [95% CI 0.5-0.9] p = 0.01). SF3B1 rs16865677-T (G/T+T/T) increased risk in sporadic early-onset BC (OR = 1.4 [95% CI 1.0-2.0] p = 0.01). SMAD4 rs3819122-C (A/C+C/C) increased risk in cases with moderate family history (OR = 2.0 [95% CI 1.3-2.9] p ≤ 0.0001) and sporadic cases diagnosed ≤50 years (OR = 1.6 [95% CI 1.1-2.2] p = 0.006). SMAD4 rs12456284:A>G increased BC risk in G-allele carriers (A/G + G/G) in cases with ≥2 BC/OC cases and early-onset cases (OR = 1.2 [95% CI 1.0-1.6] p = 0.04 and OR = 1.4 [95% CI 1.0-1.9] p = 0.03, respectively). Our study suggests that specific germline variants in driver genes MAP3K1, SF3B1, and SMAD4 contribute to BC risk in Chilean population.

Landmark Series: The Cancer Genome Atlas and the Study of Breast Cancer Disparities

Race-related variation in breast cancer incidence and mortality are well-documented in the United States. The effect of genetic ancestry on disparities in tumor genomics, risk factors, treatment, and outcomes of breast cancer is less understood. The Cancer Genome Atlas (TCGA) is a publicly available resource that has allowed for the recent emergence of genome analysis research seeking to characterize tumor DNA and protein expression by ancestry as well as the social construction of race and ethnicity. Results from TCGA based studies support previous clinical evidence that demonstrates that American women with African ancestry are more likely to be afflicted with breast cancers featuring aggressive biology and poorer outcomes compared with women with other backgrounds. Data from TCGA based studies suggest that Asian women have tumors with favorable immune microenvironments and may experience better disease-free survival compared with white Americans. TCGA contains limited data on Hispanic/Latinx patients due to small sample size. Overall, TCGA provides important opportunities to define the molecular, biologic, and germline genetic factors that contribute to breast cancer disparities.

Precise subtyping reveals immune heterogeneity for hormone receptor-positive breast cancer

A significant proportion of breast cancer cases are characterized by hormone receptor positivity (HR+). Clinically, the heterogeneity of HR+ breast cancer leads to different therapeutic effects on endocrine. Therefore, definition of subgroups in HR+ breast cancer is important for effective treatment. Here, we have developed a CMBR method utilizing computational functional networks based on DNA methylation to identify conserved subgroups in HR+ breast cancer. Calculated by CMBR, HR+ breast cancer was divided into five subgroups, of which HR+/negative epidermal growth factor receptor-2 (Her2-) was divided into two subgroups, and HR+/positive epidermal growth factor receptor-2 (Her2+) was divided into three subgroups. These subgroups had heterogeneity in the immune microenvironment, tumor infiltrating lymphocyte patterns, somatic mutation patterns and drug sensitivity. Specifically, CMBR identified two subgroups with the "Hot" tumor phenotype. In addition, these conserved subgroups were broadly validated on external validation datasets. CMBR identified the molecular signature of HR+ breast cancer subgroups, providing valuable insights into personalized treatment strategies and management options.

Ultrasonic Features and Molecular Subtype Predict Somatic Mutations in TP53 and PIK3CA Genes in Breast Cancer

RATIONALE AND OBJECTIVES

To predict mutations in TP53 and PIK3CA genes in breast cancer using ultrasound (US) signatures and clinicopathology.

MATERIALS AND METHODS

In this study, we developed and trained a model in 386 breast cancer patients to predict TP53 and PIK3CA mutations. The clinicopathological and US characteristics (including two-dimensional and color Doppler US) were investigated. Statistically significant variables were used to build predictive models, then a combined model was developed using the multivariate logistic regression analysis.

RESULTS

Univariate and multivariate analyses revealed that calcifications on US was an independent predictor of TP53 mutation (p < 0.05), whereas diameter on US and US type were independent predictors of PIK3CA mutation in breast cancer (all p < 0.05). Meanwhile, Luminal B/Human epidermal growth factor receptor two-positive (HER2+), HER2+/estrogen receptor-negative (ER-), and triple-negative breast cancer (TNBC) subtypes were strong predictors of TP53 mutation (odds ratio [OR] = 3.13, 3.18, 3.44, respectively, all p < 0.05). HER2+/ER- and TNBC subtypes were negative predictors of PIK3CA mutation (OR = 0.223, 0.241, respectively, all p < 0.05). The areas under curves (AUCs) for PIK3CA mutation in the training set increased from 0.553-0.610 to 0.741 in the multivariate model that combined US features and molecular subtype, with a sensitivity and specificity of 80.6% and 58.7%, respectively. The application of the multivariate model in the validation set achieved acceptable discrimination (AUC = 0.715). For TP53 mutation, the AUC was 0.653.

CONCLUSION

US is a non-invasive modality to recognize the presence of TP53 and PIK3CA mutation. The models combined with US features and molecular subtype have implications for the practical application of predicting gene mutation for individual decision-making regarding treatment planning.