Learning to distinguish progressive and non-progressive ductal carcinoma in situ

http://www.thebreastonline.com/article/S096097762200073X/pdf

Molecular subtyping reveals uniqueness of prognosis in breast ductal carcinoma in situ patients with lumpectomy

Background: Inherited variants have been shown to contribute to cancer risk, disease progression, and response to treatment. Such studies are, however, arduous to conduct, requiring large sample sizes, cohorts or families, and more importantly, a long follow-up to measure a relevant outcome such as disease onset or progression. Unless collected for a dedicated study, germline DNA from blood or saliva are typically not available retrospectively, in contrast to surgical tissue specimens which are systematically archived. Results: We evaluated the feasibility of using DNA extracted from low amounts of fixed-formalin paraffin-embedded (FFPE) tumor tissue to obtain accurate germline genetic profiles. Using matching blood and archival tissue DNA from 10 individuals, we benchmarked low-coverage whole-genome sequencing (lc-WGS) combined with genotype imputation and measured genome-wide concordance of genotypes, polygenic risk scores (PRS), and HLA haplotypes. Concordance between blood and tissue was high (r2>0.94) for common genome-wide single nucleotide polymorphisms (SNPs) and across 22 disease-related PRS (mean r=0.93). HLA haplotypes imputed from tissue DNA were 96.7% (Class I genes) and 82.5% (Class II genes) concordant with deep targeted sequencing of HLA from blood DNA. Using the validated methodology, we estimated breast cancer PRS in 36 patients diagnosed with breast ductal carcinoma in situ (11.7 years median follow-up time) including 22 who were diagnosed with breast cancer subsequent event (BSCE). PRS was significantly associated with BCSE (HR=2.5, 95%CI: 1.4-4.5) and the top decile patients were modeled to have a 24% chance of BCSE at 10 years, hence suggesting the addition of PRS could improve prognostic models which are currently inadequate. Conclusions: The abundance and broad availability of archival tissue specimens in oncology clinics, paired with the effectiveness of germline profiling using lc-WGS and imputation, represents an alternative cost and resource-effective alternative in the design of long-term disease progression studies.

Accurate genome-wide germline profiling from decade-old archival tissue DNA reveals the contribution of common variants to precancer disease outcome

Cancer represents a broad spectrum of molecularly and morphologically diverse diseases. Individuals with the same clinical diagnosis can have tumors with drastically different molecular profiles and clinical response to treatment. It remains unclear when these differences arise during disease course and why some tumors are addicted to one oncogenic pathway over another. Somatic genomic aberrations occur within the context of an individual’s germline genome, which can vary across millions of polymorphic sites. An open question is whether germline differences influence somatic tumor evolution. Interrogating 3,855 breast cancer lesions, spanning pre-invasive to metastatic disease, we demonstrate that germline variants in highly expressed and amplified genes influence somatic evolution by modulating immunoediting at early stages of tumor development. Specifically, we show that the burden of germline-derived epitopes in recurrently amplified genes selects against somatic gene amplification in breast cancer. For example, individuals with a high burden of germline-derived epitopes in ERBB2, encoding human epidermal growth factor receptor 2 (HER2), are significantly less likely to develop HER2-positive breast cancer compared to other subtypes. The same holds true for recurrent amplicons that define four subgroups of ER-positive breast cancers at high risk of distant relapse. High epitope burden in these recurrently amplified regions is associated with decreased likelihood of developing high risk ER-positive cancer. Tumors that overcome such immune-mediated negative selection are more aggressive and demonstrate an “immune cold” phenotype. These data show the germline genome plays a previously unappreciated role in dictating somatic evolution. Exploiting germline-mediated immunoediting may inform the development of biomarkers that refine risk stratification within breast cancer subtypes.

https://www.biorxiv.org/content/biorxiv/early/2023/03/16/2023.03.15.532870.full.pdf

Germline-mediated immunoediting sculpts breast cancer subtypes and metastatic proclivity

SUMMARY Ductal carcinoma in situ (DCIS) is the most common precursor of invasive breast cancer (IBC), with variable propensity for progression. We have performed the first multiscale, integrated profiling of DCIS with clinical outcomes by analyzing 677 DCIS samples from 481 patients with 7.1 years median follow-up from the Translational Breast Cancer Research Consortium (TBCRC) 038 study and the Resource of Archival Breast Tissue (RAHBT) cohorts. We made observations on DNA, RNA, and protein expression, and generated a de novo clustering scheme for DCIS that represents a fundamental transcriptomic organization at this early stage of breast neoplasia. Distinct stromal expression patterns and immune cell compositions were identified. We found RNA expression patterns that correlate with later events. Our multiscale approach employed in situ methods to generate a spatially resolved atlas of breast precancers, where complementary modalities can be directly compared and correlated with conventional pathology findings, disease states, and clinical outcome. HIGHLIGHTS New transcriptomic classification solution reveals 3 major subgroups in DCIS. Four stroma-specific signatures identified. utcome analysis identifies pathways involved in DCIS progression. CNAs characterize high risk of distant relapse IBC subtypes observed in DCIS.

/pdf/dcis-genomic-signatures-define-biology-and-correlate-with-34qifoarif.pdf

Shi Wei

Papers

DCIS genomic signatures define biology and correlate with clinical outcome: a Human Tumor Atlas Network (HTAN) analysis of TBCRC 038 and RAHBT cohorts