Somatic mutations in benign breast disease tissue and risk of subsequent invasive breast cancer

Cellular interactions in tumor microenvironment during breast cancer progression: new frontiers and implications for novel therapeutics

The breast cancer tumor microenvironment (TME) is dynamic, with various immune and non-immune cells interacting to regulate tumor progression and anti-tumor immunity. It is now evident that the cells within the TME significantly contribute to breast cancer progression and resistance to various conventional and newly developed anti-tumor therapies. Both immune and non-immune cells in the TME play critical roles in tumor onset, uncontrolled proliferation, metastasis, immune evasion, and resistance to anti-tumor therapies. Consequently, molecular and cellular components of breast TME have emerged as promising therapeutic targets for developing novel treatments. The breast TME primarily comprises cancer cells, stromal cells, vasculature, and infiltrating immune cells. Currently, numerous clinical trials targeting specific TME components of breast cancer are underway. However, the complexity of the TME and its impact on the evasion of anti-tumor immunity necessitate further research to develop novel and improved breast cancer therapies. The multifaceted nature of breast TME cells arises from their phenotypic and functional plasticity, which endows them with both pro and anti-tumor roles during tumor progression. In this review, we discuss current understanding and recent advances in the pro and anti-tumoral functions of TME cells and their implications for developing safe and effective therapies to control breast cancer progress.

Commonalities and differences in the mutational signature and somatic driver mutation landscape across solid and hollow viscus organs

Advances in sequencing have revealed a highly variegated landscape of mutational signatures and somatic driver mutations in a range of normal tissues. Normal tissues accumulate mutations at varying rates ranging from 11 per cell per year in the liver, to 1879 per cell per year in the bladder. In addition, some normal tissues are also comprised of a large proportion of cells which possess driver mutations while appearing phenotypically normal, as in the oesophagus where a majority of cells harbour driver mutations. Individual tissue proliferation and mutation rate, unique mutagenic stimuli, and local tissue architecture contribute to this highly variegated landscape which confounds the functional characterization of driver mutations found in normal tissue. In particular, our understanding of the relationship between normal tissue somatic mutations and tumour initiation or future cancer risk remains poor. Here, we describe the mutational signatures and somatic driver mutations in solid and hollow viscus organs, highlighting unique characteristics in a tissue-specific manner, while simultaneously seeking to describe commonalities which can bring forward a basic unified theory on the role of these driver mutations in tumour initiation. We discuss novel findings which can be used to inform future research in this field.

Somatic mutations in benign breast disease tissues and association with breast cancer risk

BACKGROUND

Benign breast disease (BBD) is a risk factor for breast cancer (BC); however, little is known about the genetic alterations present at the time of BBD diagnosis and how these relate to risk of incident BC.

METHODS

A subset of a long-term BBD cohort was selected to examine DNA variation across three BBD groups (42 future estrogen receptor-positive (ER+) BC, 36 future estrogen receptor-negative (ER-) BC, and 42 controls cancer-free for at least 16 years post-BBD). DNA extracted from archival formalin fixed, paraffin-embedded (FFPE) tissue blocks was analyzed for presence of DNA alterations using a targeted panel of 93 BC-associated genes. To address artifacts frequently observed in FFPE tissues (e.g., C>T changes), we applied three filtering strategies based on alternative allele frequencies and nucleotide substitution context. Gene-level associations were performed using two types of burden tests and adjusted for clinical and technical covariates.

RESULTS

After filtering, the variant frequency of SNPs in our sample was highly consistent with population allele frequencies reported in 1 KG/ExAC (0.986, p < 1e-16). The top ten genes found to be nominally associated with later cancer status by four of 12 association methods(p < 0.05) were MED12, MSH2, BRIP1, PMS1, GATA3, MUC16, FAM175A, EXT2, MLH1 and TGFB1, although these were not statistically significant in permutation testing. However, all 10 gene-level associations had OR < 1 with lower mutation burden in controls compared to cases, which was marginally statistically significant in permutation testing (p = 0.04). Comparing between the three case groups, BBD ER+ cases were closer to controls in mutation profile, while BBD ER- cases were distinct. Notably, the variant burden was significantly higher in controls than in either ER+ or ER- cases. CD45 expression was associated with mutational burden (p < 0.001).

CONCLUSIONS

Somatic mutations were more frequent in benign breast tissue from women who did not develop cancer, opening questions of clonal diversity or immune-mediated restraint on future cancer development. CD45 expression was positively associated with mutational burden, most strongly in controls. Further studies in both normal and premalignant tissues are needed to better understand the role of somatic gene mutations and their contribution to future cancer development.

Somatic genetic aberrations in benign breast disease and the risk of subsequent breast cancer

It is largely unknown how the development of breast cancer (BC) is transduced by somatic genetic alterations in the benign breast. Since benign breast disease is an established risk factor for BC, we established a case-control study of women with a history of benign breast biopsy (BBB). Cases developed BC at least one year after BBB and controls did not develop BC over an average of 17 years following BBB. 135 cases were matched to 69 controls by age and type of benign change: non-proliferative or proliferation without atypia (PDWA). Whole-exome sequencing (WES) was performed for the BBB. Germline DNA (available from n = 26 participants) was utilized to develop a mutation-calling pipeline, to allow differentiation of somatic from germline variants. Among the 204 subjects, two known mutational signatures were identified, along with a currently uncatalogued signature that was significantly associated with triple negative BC (TNBC) (p = 0.007). The uncatalogued mutational signature was validated in 109 TNBCs from TCGA (p = 0.001). Compared to non-proliferative samples, PDWA harbors more abundant mutations at PIK3CA pH1047R (p < 0.001). Among the 26 BBB whose somatic copy number variation could be assessed, deletion of MLH3 is significantly associated with the mismatch repair mutational signature (p < 0.001). Matched BBB-cancer pairs were available for ten cases; several mutations were shared between BBB and cancers. This initial study of WES of BBB shows its potential for the identification of genetic alterations that portend breast oncogenesis. In future larger studies, robust personalized breast cancer risk indicators leading to novel interception paradigms can be assessed.

Bioinformatics and DNA-extraction strategies to reliably detect genetic variants from FFPE breast tissue samples

Background

Archived formalin fixed paraffin embedded (FFPE) samples are valuable clinical resources to examine clinically relevant morphology features and also to study genetic changes. However, DNA quality and quantity of FFPE samples are often sub-optimal, and resulting NGS-based genetics variant detections are prone to false positives. Evaluations of wet-lab and bioinformatics approaches are needed to optimize variant detection from FFPE samples.

Results

As a pilot study, we designed within-subject triplicate samples of DNA derived from paired FFPE and fresh frozen breast tissues to highlight FFPE-specific artifacts. For FFPE samples, we tested two FFPE DNA extraction methods to determine impact of wet-lab procedures on variant calling: QIAGEN QIAamp DNA Mini Kit (“QA”), and QIAGEN GeneRead DNA FFPE Kit (“QGR”). We also used negative-control (NA12891) and positive control samples (Horizon Discovery Reference Standard FFPE). All DNA sample libraries were prepared for NGS according to the QIAseq Human Breast Cancer Targeted DNA Panel protocol and sequenced on the HiSeq 4000. Variant calling and filtering were performed using QIAGEN Gene Globe Data Portal. Detailed variant concordance comparisons and mutational signature analysis were performed to investigate effects of FFPE samples compared to paired fresh frozen samples, along with different DNA extraction methods.

In this study, we found that five times or more variants were called with FFPE samples, compared to their paired fresh-frozen tissue samples even after applying molecular barcoding error-correction and default bioinformatics filtering recommended by the vendor. We also found that QGR as an optimized FFPE-DNA extraction approach leads to much fewer discordant variants between paired fresh frozen and FFPE samples. Approximately 92% of the uniquely called FFPE variants were of low allelic frequency range (< 5%), and collectively shared a “C > T|G > A” mutational signature known to be representative of FFPE artifacts resulting from cytosine deamination. Based on control samples and FFPE-frozen replicates, we derived an effective filtering strategy with associated empirical false-discovery estimates.

Conclusions

Through this study, we demonstrated feasibility of calling and filtering genetic variants from FFPE tissue samples using a combined strategy with molecular barcodes, optimized DNA extraction, and bioinformatics methods incorporating genomics context such as mutational signature and variant allelic frequency.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-6056-8) contains supplementary material, which is available to authorized users.

Genetic Alterations in Benign Breast Biopsies of Subsequent Breast Cancer Patients

Background: Fibrocystic changes are associated with an increased risk of breast cancer. Genetic alterations have been found in fibrocystic changes with or without epithelial changes, suggesting that critical oncogenic events are occurring at an early stage.

Methods: We investigated a unique collective of 17 breast cancer patients who, prior to the diagnosis of invasive breast cancer, underwent open surgical biopsy showing fibrocystic changes of the breast. The time span between biopsy for fibrocystic changes and invasive carcinoma ranged from 1 to 11 years (average 5.3 years). Ten (58.8%) of the patients had an ipsilateral invasive carcinoma, and 7 (41.2%) of the patients developed an invasive carcinoma of the contralateral breast. Massive parallel sequencing targeting genes frequently mutated in breast cancer was performed on the fibrocystic breast tissue as well as the ensuing cancer tissue.

Results: In 9 cases, somatic mutations were found in the tumor tissue, the most prevalent being PIK3CA mutations (n = 4), followed by TP53 mutations (n = 2). None of these mutations were present in the previously removed mastopathy tissue. In one of the cases, an ERBB3 E928G mutation was present in the mastopathy as well as in the tumor tissue, with the variant allele frequency in the mastopathy being <0.1%. In two patients, we found two mutations (MAP3K1 L380fs and PIK3CA I391M, respectively) present in the mastopathy as well as in the subsequent breast cancer. These two mutations, however, could also be due to fixation artifacts.

Conclusion: Since no significant somatic mutations in the fibrocystic breast tissue, and only doubtful shared mutations between benign and associated cancer tissue were detected, it remains unclear why women with fibrocystic breast disease have a statistically significant increased risk of breast cancer. Further analyses, maybe on the level of gene expression, could help to clarify the role of these benign alterations in the development of breast cancer and help to identify women at greater risk of developing subsequent invasive cancer.

A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data

Cancer genomic analysis requires accurate identification of somatic variants in sequencing data. Manual review to refine somatic variant calls is required as a final step after automated processing. However, manual variant refinement is time-consuming, costly, poorly standardized, and non-reproducible. Here, we systematized and standardized somatic variant refinement using a machine learning approach. The final model incorporates 41,000 variants from 440 sequencing cases. This model accurately recapitulated manual refinement labels for three independent testing sets (13,579 variants) and accurately predicted somatic variants confirmed by orthogonal validation sequencing data (212,158 variants). The model improves on manual somatic refinement by reducing bias on calls otherwise subject to high inter-reviewer variability.