Molecular characteristics of breast tumors in patients screened for germline predisposition from a population-based observational study

Abstract

Background: Pathogenic germline variants (PGVs) in certain genes are linked to higher lifetime risk of developing breast cancer and can influence preventive surgery decisions and therapy choices. Public health programs offer genetic screening based on criteria designed to assess personal risk and identify individuals more likely to carry PGVs, dividing patients into screened and non-screened groups. How tumor biology and clinicopathological characteristics differ between these groups is understudied and could guide refinement of screening criteria.

Methods: Six thousand six hundred sixty breast cancer patients diagnosed in South Sweden during 2010-2018 were included with available clinicopathological and RNA sequencing data, 900 (13.5%) of which had genes screened for PGVs through routine clinical screening programs. We compared characteristics of screened patients and tumors to non-screened patients, as well as between screened patients with (n = 124) and without (n = 776) PGVs.

Results: Broadly, breast tumors in screened patients showed features of a more aggressive disease. However, few differences related to tumor biology or patient outcome remained significant after stratification by clinical subgroups or PAM50 subtypes. Triple-negative breast cancer (TNBC), the subgroup most enriched for PGVs, showed the most differences between screening subpopulations (e.g., higher tumor proliferation in screened cases). Significant differences in PGV prevalence were found between clinical subgroups/molecular subtypes, e.g., TNBC cases were enriched for BRCA1 PGVs. In general, clinicopathological differences between screened and non-screened patients mimicked those between patients with and without PGVs, e.g., younger age at diagnosis for positive cases. However, differences in tumor biology/microenvironment such as immune cell composition were additionally seen within PGV carriers/non-carriers in ER + /HER2 - cases, but not between screening subpopulations in this subgroup.

Conclusions: Characterization of molecular tumor features in patients clinically screened and not screened for PGVs represents a relevant read-out of guideline criteria. The general lack of molecular differences between screened/non-screened patients after stratification by relevant breast cancer subsets questions the ability to improve the identification of screening candidates based on currently used patient and tumor characteristics, pointing us towards universal screening. Nevertheless, while that is not attained, molecular differences identified between PGV carriers/non-carriers suggest the possibility of further refining patient selection within certain patient subsets using RNA-seq through, e.g., gene signatures.

Trial registration: The Sweden Cancerome Analysis Network - Breast (SCAN-B) was prospectively registered at ClinicalTrials.gov under the identifier NCT02306096.

Keywords: Clinical screening; Gene expression; Gene variants; Hereditary breast cancer; Molecular subtypes.

Conflict of interest statement

The authors declare that they have no competing interests.

© 2023. The Author(s).

Figures

Fig. 1

Project outline. Cohort division into clinically relevant breast cancer subgroups and PAM50 molecular subtypes including which information was used to compare the screened and non-screened subpopulations. Patients without ER/HER2 information are not shown in the PAM50 division. Subsets surrounded by dotted lines were not included in the analyses (see Additional file 1: Supplementary Methods)

Fig. 2

Patient outcome. a Kaplan–Meier curves contrasting the two screening subpopulations (S: screened, N-S: non-screened) including all patients in the study and including only patients that belong to the three main clinical subgroups using overall survival as endpoint. b Univariate OS and DRFi hazard ratio with 95% confidence interval for screened patients in clinical subgroups/molecular subtypes when using non-screened patients as reference (ref). c Multivariate OS hazard ratio with confidence interval for patients in three clinical subgroups. Categories used as reference for each variable are marked in gray. Asterisks in b and c indicate failure of fulfilling the proportional hazards assumption for Cox regression (p < 0.05)

Fig. 3

Differences between screening subpopulations within clinical subgroups/PAM50 molecular subtypes found through gene expression data. a Distribution of a tumor proliferation measure calculated in silico per sample by screening status of patients and by relevant clinical subgroups or PAM50 molecular subtypes. b Fold change distribution and number of differentially expressed genes in screened patients when compared to non-screened patients. c, d Enrichment scores of two immune cell types by screening status and by age at diagnosis (in years) within all TNBC cases. R = Spearman’s correlation coefficient

Fig. 4

Expressed somatic variants patterns in the cohort. a Proportion of SCAN-B patients with expressed somatic variants in the 10 genes with highest number of somatic variants in the cohort. b, c Distribution of filtered somatic variants found per tumor sample stratified by b screening status or by c clinical subgroup, with median variant number annotated. d Proportion of patients with expressed somatic variants by screening status of patients and by clinical subgroups. Only the 10 genes with the most somatic variants in each subgroup are shown

Fig. 5

Overview of pathogenic germline variants found in 900 screened patients. a Referring clinicians requested different genes be investigated for germline variants in different patients, thus genes were screened at different rates in SCAN-B. b Outer horizontal barplots show the distribution of clinical subgroups (left) and molecular subtypes (right) of those with PGVs for each gene of interest that showed at least one patient with PGV in SCAN-B. Vertical barplots show the number of patients with PGVs among those screened specifically for a gene divided by clinical subgroups (left) and molecular subtypes (right). Waterfall plot in the middle shows the predicted molecular consequence of PGVs found in the 124 patients. c Lollipop plots showing predicted protein impact of PGVs (above) and variants of uncertain significance (VUS, below) found in BRCA1, BRCA2, and CHEK2 colored by clinical subgroup (left) and PAM50 molecular subtype (right). Splicing variants are not shown. Each circle represents one patient with that variant. T367M amino acid modification in CHEK2 = c.1100delC variant

Fig. 6

Outline of the comparison within screened patients. Division of screened patients into clinical subgroups and PAM50 molecular subtypes and which information was used to compare those with and without PGVs. Patients without information or belonging to different groups than the ones included are not shown

Fig. 7

Comparison between screened patients with and without pathogenic germline variants. a Distribution of clinicopathological variables in 560 screened patients from the ER + /HER2 − clinical subgroup. b Fold change of differentially expressed genes (FDR ≤ 0.01) in patients with PGVs when compared to those without PGVs in clinical subgroups/molecular subtypes that had more than 10 patients with PGVs. c Distribution of an immune response measure calculated in silico per sample stratified by patient germline variant status and clinical subgroups/PAM50 molecular subtypes. p = corrected Mann–Whitney test p-values, 6 tests. d Cell fraction of two immune cell types with statistically significant differences between PGV groups in ER + /HER2 − cases. e Distribution of a gene expression immune response score in ER + /HER2 − cases stratified by whether patients had PGVs in specific genes. f Proportion of patients with expressed somatic variants in 10 genes stratified by patient germline variant status for BRCA1, BRCA2, and CHEK2. Only genes with a statistically significant difference between groups through Fisher’s exact test are identified