Archival single-cell genomics reveals persistent subclones during DCIS progression

Abstract

Ductal carcinoma in situ (DCIS) is a common precursor of invasive breast cancer. Our understanding of its genomic progression to recurrent disease remains poor, partly due to challenges associated with the genomic profiling of formalin-fixed paraffin-embedded (FFPE) materials. Here, we developed Arc-well, a high-throughput single-cell DNA-sequencing method that is compatible with FFPE materials. We validated our method by profiling 40,330 single cells from cell lines, a frozen tissue, and 27 FFPE samples from breast, lung, and prostate tumors stored for 3-31 years. Analysis of 10 patients with matched DCIS and cancers that recurred 2-16 years later show that many primary DCIS had already undergone whole-genome doubling and clonal diversification and that they shared genomic lineages with persistent subclones in the recurrences. Evolutionary analysis suggests that most DCIS cases in our cohort underwent an evolutionary bottleneck, and further identified chromosome aberrations in the persistent subclones that were associated with recurrence.

Keywords: Arc-well; FFPE material; archival samples; breast cancer; ductal carcinoma in situ recurrence; premalignancies; single-cell DNA sequencing; tumor evolution.

Figure 1.. Arc-well workflow and study overview.

(A) Overview of the Arc-well method, in which FFPE blocks are sectioned and deparaffinized to generate single nucleus suspensions that are used for FACS sorting (steps 1–4). Next, sorted nuclei are dispensed into nanowell chips followed by imaging of the nanowells for single cell selection. A five-step equal volume dispensing protocol is then used to dispense Arc-well chemistry into nanowell chips to perform cell barcoding (step 5 and inset panel). Finally, the amplified single cell libraries are pooled together for sequencing and data analysis (steps 6–7). (B) Schematic overview of the study design, in which 20 human breast FFPE samples from 10 patients with primary DCIS and matched recurrent DCIS/IDC were used to profile single cell copy number using Arc-well. The resulting Arc-well data was used to resolve clonal substructure and infer subclone lineages. (C) Clinical metadata of the 10 patients with matched DCIS and recurrent samples. See also Figure S1 and Table S1–S2.

Figure 2.. Technical performance of the Arc-well method.

(A) Comparison of overdispersion metrics for the genomic bin counts and (B) breadth of coverage metrics for six different scDNA-seq methods using cell line or frozen tissue data. Coverage was calculated from 80 randomly sampled cells per methods and using 500K reads per cell as input. The methods using cell lines included Arc-well (315A, diploid; MDA231, aneuploid), ACT (MDA231, aneuploid) and DLP+(GM18507, diploid), while the methods using frozen tissues included 10X CNA, DLP, DOP-PCR. (C) Overdispersion and breadth of coverage (500K reads per cell) computed from non-fixed and formalin fixed diploid 315A cell line, aneuploid MDA231 cell line and from frozen and FFPE tissue from the same human IDC sample. (D) Overdispersion of bin counts computed using Arc-well data from 22 breast cancer, 2 lung cancer and 2 prostate cancer samples. (E) Correlations between FFPE block age and QC metrics for the mean overdispersion, the mean PCR duplicate rates, and correlations between DNA integrity number (DIN) and overdispersion metrics. (F) Two examples of copy number profiles with ratio values (dots) and segmentation values (lines) of two different single cells from patient P6 (cell 1: ArcN759-ArcS519) and patient P10 (cell 2: ArcN741-ArcS541, table S6). BA: block age of FFPE samples. (G) UMAP plot of single cell copy number profiles from FFPE tissue of P1, where each color represents a subclone. (H) Clustered heatmap of single cell copy number profiles for P1 (top panel) and bottom panel shown the consensus integer CNA profiles of each subclone with selected breast cancer genes annotated below. See also Figure S2–S3 and Table S3–S4, S6.

Figure 3.. Overview of genomic diversity in DCIS with matched recurrent disease.

(A) Bar plots of the shared and unique subclones (top panel) and number of cells (bottom panel) across all 10 patients. (B) UMAP plots of single cell copy number profiles from each patient colored by subclones/clusters and timepoints. (C) Line plots showing the change in the number of copy number events between the matched primary DCIS and recurrences (p = 0.28, PSWT). (D) Diversity index (MPD) of subclonal frequencies in the 10 patients between the matched primary DCIS and recurrences (p = 0.56, PSWT). (E) The correlation between MPD diversity index and the number of cells included in the calculation for all 10 patients with paired samples. (F) Correlations between the diversity index (MPD), number of CNAs events and patient clinical features (ER, PR, HER2, histology and disease grade). All tests were performed by Wilcoxon test. * Indicates the tests show a significant difference (p< 0.05) between the groups. (G) FACS DNA ploidies from each primary DCIS and recurrent sample for each patient. See also Figure S4 and Table S4.

Figure 4.. Clonal substructure of matched primary DCIS and recurrences Clustered heatmaps of single cell copy number profiles showing subclones in matched DCIS and recurrent DCIS/IDC in 4 patients (P3, P5, P6, P7).

(A) to (D). Upper panels show the histopathological H&E images (scale bar: 50 μm) and clinical timelines of primary diagnosis and recurrences for each patient. Bottom panels show single cell copy number clustered heatmaps, with left header columns indicating time points and subclone groups. Bottom annotation panels indicate the clonal and subclonal classification of CNAs and selected cancer gene annotations. See also Figure S5–S6.

Figure 5.. Evolutionary lineages of matched DCIS and recurrences.

(A) to (D). Left panels show the event-based evolutionary trees of subclonal consensus integer copy number profiles that are rooted by a diploid profile and annotated for WGD events based on the FACS DNA ploidy data for four patients (P3, P5, P6 and P7). The blue dots on the tree represent the most recent common ancestor (MRCA), while the purple dots represent the primary common ancestor (PCA), and green dots represent the recurrence common ancestor (RCA). The top-right panels show the heatmap of subclonal consensus integer copy number profiles with the right annotation bar representing the cell fractions at each time point. The bottom-right panels show the heatmap of the inferred PCA and RCA (or subclone) profiles with selected gene annotations, in which the orange bars represent the regions that have different copy number states between the PCA and RCA. Selected cancer genes are annotated below the heatmaps. See also Figure S6–S7.

Figure 6.. CNA events associated with recurrence across different patients.

(A) Number of accumulated CNA events in the inferred PCA and RCA after the divergence of the MRCA in patients with an evolutionary bottleneck model of progression. (B) The top panel shows a heatmap of copy number profiles calculated from the difference between the PCA and RCA across the 7 patients with evolutionary bottleneck lineages (excluding P11 in which the recurrence shared its genotype with the primary DCIS). Profiles represent CNA events that were acquired or lost in the recurrences after the primary DCIS. The bottom panel shows the frequency of recurrence-associated CNA events (losses, gains or neutral events) as a histogram of variable bins from the 7 patients with selected cancer genes annotated below. The y-axis represents the number of patients that had the loss, gain or neutral CNA events.

Figure 7.. Evolutionary models of primary DCIS to recurrent disease progression.

(A) In the Evolutionary Bottleneck model, subclones diverge and expand in the ducts after which a single clone is selected and persists until it expands to form the recurrent disease many years to decades later. (B) In the multiclonal evolution model, subclones diverge and expand in the DCIS and then multiple subclones persist in the tissue until they co-invade and expand to form the recurrent tumor many years later. (C) In the independent evolution model, subclones in primary and recurrence originate from different normal epithelial cells and have no shared copy number events. (A-C) In the lower panels, the expected phylogenies are shown that are consistent with each model, in which the evolutionary bottleneck has distinct lineages for subclones in the primary and recurrent disease, while the multiclonal evolution model has intermixing of many subclonal genotypes across the two time points in the clonal lineages, and the independent evolution model have no common MRCA that is shared across the two different time points.