SUMMARY Tumor ecosystems evolve in response to chemotherapy, but how treatment reshapes clonal architecture and the transcriptional programs of distinct cell states remains unclear. To investigate the response to chemotherapy by individual single cell-derived clones, we used a high-complexity lentiviral barcoding strategy by genetically labelling with unique, heritable and expressible barcode sequences individual cells from four human breast cancer patient-derived tumor xenograft models. We tracked 3,248 single cell-derived clones across 39 xenografts and profiled 676,292 cells with single cell RNA sequencing. The most striking changes following chemotherapy occurred in non-responsive xenografts, attributed to the emergence of previously minor cell clones that are chemotherapy resistant. In a triple negative model, epithelial and mesenchymal cell states showed distinct sensitivities to chemotherapy. ER+/HER2-models activated stress adaptive programs in response to carboplatin, highlighting DUSP1 and KLF4 as markers of platinum tolerance and a slow cycling, persister-like state. Together, these findings reveal that chemotherapy triggers an immediate and substantial reorganization of the cellular clonal landscape, driven by the selective engagement of stress response programs in cell clones that survive treatment.

Breast cancer is the second most common cancer globally, with rising incidence and poor prognosis following recurrence. Genomic analysis of primary breast tumours has identified subtypes with widely varying risk of relapse, highlighting the importance of tumour genomics in understanding metastasis. However, the genomic alterations associated with metastatic transformation—and how they differ between genomic subtypes—remain unclear due to limited sample sizes, lack of primary tumour baselines, and limited genomic coverage by panel sequencing. To address this gap, we analysed nearly 1300 whole-genome sequenced unmatched primary tumours and metastases using a unified computational pipeline. Somatic copy number profiles were classified into genomic subtypes, called the Integrative Clusters, with an improved classifier. By employing various genome-wide approaches, we identify candidate genes in regions with copy number alterations enriched or depleted in metastases, and nominate biological pathways that may contribute to metastatic disease in each genomic subtype. These subtype-specific candidates provide a framework for prioritising therapeutic hypotheses and future functional studies in metastatic breast cancer.

Clonal fitness and plasticity drive cancer heterogeneity. We used expressed lentiviral-based cellular barcodes combined with single-cell RNA sequencing to associate single-cell profiles with in vivo clonal growth. This generated a significant resource of growth measurements from over 20,000 single-cell-derived clones in 110 xenografts from 26 patient-derived breast cancer xenograft models. 167,375 single-cell RNA profiles were obtained from 5 models and revealed that rare propagating clones display a highly conserved model-specific differentiation program with reproducible regeneration of the entire transcriptomic landscape of the original xenograft. In 2 models of basal breast cancer, propagating clones demonstrated remarkable transcriptional plasticity at single-cell resolution. Dichotomous cell populations with different clonal growth properties, signaling pathways, and metabolic programs were characterized. By directly linking clonal growth with single-cell transcriptomes, these findings provide a profound understanding of clonal fitness and plasticity with implications for cancer biology and therapy.

Monitoring levels of circulating tumour‐derived DNA (ctDNA) provides both a noninvasive snapshot of tumour burden and also potentially clonal evolution. Here, we describe how applying a novel statistical model to serial ctDNA measurements from shallow whole genome sequencing (sWGS) in metastatic breast cancer patients produces a rapid and inexpensive predictive assessment of treatment response and progression‐free survival. A cohort of 149 patients had DNA extracted from serial plasma samples (total 1013, mean samples per patient = 6.80). Plasma DNA was assessed using sWGS and the tumour fraction in total cell‐free DNA estimated using ichorCNA. This approach was compared with ctDNA targeted sequencing and serial CA15‐3 measurements. We identified a transition point of 7% estimated tumour fraction to stratify patients into different categories of progression risk using ichorCNA estimates and a time‐dependent Cox Proportional Hazards model and validated it across different breast cancer subtypes and treatments, outperforming the alternative methods. We used the longitudinal ichorCNA values to develop a Bayesian learning model to predict subsequent treatment response with a sensitivity of 0.75 and a specificity of 0.66. In patients with metastatic breast cancer, a strategy of sWGS of ctDNA with longitudinal tracking of tumour fraction provides real‐time information on treatment response. These results encourage a prospective large‐scale clinical trial to evaluate the clinical benefit of early treatment changes based on ctDNA levels.

Homologous recombination deficiency (HRD) leads to genomic instability, and patients with HRD can benefit from HRD-targeting therapies. Previous studies have primarily focused on identifying HRD biomarkers using data from a single technology. Here we integrated features from different genomic data types, including total copy number (CN), allele-specific copy number (ASCN) and single nucleotide variants (SNV). Using a semi-supervised method, we developed HRD classifiers from 1404 breast tumours across two datasets based on their BRCA1/2 status, demonstrating improved HRD identification when aggregating different data types. Notably, HRD-positive tumours in ER-negative disease showed improved survival post-adjuvant chemotherapy, while HRD status strongly correlated with neoadjuvant treatment response. Furthermore, our analysis of cell lines highlighted a sensitivity to PARP inhibitors, particularly rucaparib, among predicted HRD-positive lines. Exploring somatic mutations outside BRCA1/2, we confirmed variants in several genes associated with HRD. Our method for HRD classification can adapt to different data types or resolutions and can be used in various scenarios to help refine patient selection for HRD-targeting therapies that might lead to better clinical outcomes.

The Integrative Cluster subtypes (IntClusts) provide a framework for the classification of breast cancer tumors into 10 distinct groups based on copy number and gene expression, each with unique biological drivers of disease and clinical prognoses. Gene expression data is often lacking, and accurate classification of samples into IntClusts with copy number data alone is essential. Current classification methods achieve low accuracy when gene expression data are absent, warranting the development of new approaches to IntClust classification. Copy number data from 1980 breast cancer samples from METABRIC was used to train multiclass XGBoost machine learning algorithms (CopyClust). A piecewise constant fit was applied to the average copy number profile of each IntClust and unique breakpoints across the 10 profiles were identified and converted into ~ 500 genomic regions used as features for CopyClust. These models consisted of two approaches: a 10-class model with the final IntClust label predicted by a single multiclass model and a 6-class model with binary reclassification in which four pairs of IntClusts were combined for initial multiclass classification. Performance was validated on the TCGA dataset, with copy number data generated from both SNP arrays and WES platforms. CopyClust achieved 81% and 79% overall accuracy with the TCGA SNP and WES datasets, respectively, a nine-percentage point or greater improvement in overall IntClust subtype classification accuracy. CopyClust achieves a significant improvement over current methods in classification accuracy of IntClust subtypes for samples without available gene expression data and is an easily implementable algorithm for IntClust classification of breast cancer samples with copy number data.

e12593 Background: Breast cancer (BC) is the most diagnosed cancer globally with over 2.2 million new cases and 680,000 deaths annually. Radiotherapy (RT) is essential for curative BC treatment; however, 20% of BC cases are radio-resistant, with the underlying mechanisms remaining poorly understood. Previous studies in prostate cancer (PCa) have shown that androgen receptor (AR) promotes radio-resistance (RT-resistance) through transcriptional regulation and increased DNA repair capacity, including through upregulation of DNA repair proteins DNA-PK and PARP1. Although AR is expressed in 60% of BCs, the implications of AR in BC RT-resistance are not yet fully characterized. We hypothesized that AR may drive RT-resistance in BC through a similar AR-DNA repair loop. Methods: To characterize the repertoire of cofactors that cooperate with AR on the TF complex, we exposed a BC cell line to RT and performed rapid immunoprecipitation mass spectrometry of endogenous proteins (RIME) proteomics. We identified several candidate proteins that interact with AR to further investigate as targets for RT-sensitization. Survival analyses in BC and PCa cell lines were performed to identify RT-sensitizing agents. Molecular mechanisms of RT-sensitization were assessed using ATAC-seq and RNA-seq analyses. Results: Using RIME, we identified several DNA repair proteins including DNA-PK, PARP-1, and XRCC5 as differentially recruited to chromatin-bound AR. This suggests a cooperative role and provides potential druggable targets to improve RT-sensitivity. Based on these results, we also performed a survival analysis in AR-driven BC and PCa cell lines and showed that inhibition of either AR or DNA-PK led to RT-sensitization, and dual inhibition provided an additive effect. We sought to characterize the molecular mechanisms driving the RT-sensitizing effects of AR and DNA-PK inhibition and performed ATAC-seq and RNA-seq on treated and untreated cells. Combining enzalutamide with a DNA-PK inhibitor altered expression of several pathways implicated in RT-sensitization, although these pathways were largely distinct in BC and PCa. Further, we identified distinct genes that may be responsible for this phenotype in AR-driven BC. Additionally, we found a larger effect in chromatin rearrangement after treatment in PCa than BC. Conclusions: Identifying mechanisms of RT-resistance in hormone-driven cancers is essential to develop therapeutic strategies and improve patient outcomes. This study provides key evidence for AR-driven RT-resistance in BC and PCa, and identifies DNA-PK inhibition as a potential drug target. Distinct mechanisms appear to be driving this phenotype across BC and PCa, in our models. Understanding the cooperation of DNA repair and hormone signaling pathways will help support the use of DNA-damaging agents for hormone-driven cancers, and potentially inform rational design of novel drug-RT combinations.

Breast cancer exhibits significant heterogeneity, manifesting in various subtypes that are critical in guiding treatment decisions. This study aimed to investigate the existence of distinct subtypes of breast cancer within the Asian population, by analysing the transcriptomic profiles of 934 breast cancer patients from a Malaysian cohort. Our findings reveal that the HR + /HER2− breast cancer samples display a distinct clustering pattern based on immune phenotypes, rather than conforming to the conventional luminal A-luminal B paradigm previously reported in breast cancers from women of European descent. This suggests that the activation of the immune system may play a more important role in Asian HR + /HER2− breast cancer than has been previously recognized. Analysis of somatic mutations by whole exome sequencing showed that counter-intuitively, the cluster of HR + /HER2− samples exhibiting higher immune scores was associated with lower tumour mutational burden, lower homologous recombination deficiency scores, and fewer copy number aberrations, implicating the involvement of non-canonical tumour immune pathways. Further investigations are warranted to determine the underlying mechanisms of these pathways, with the potential to develop innovative immunotherapeutic approaches tailored to this specific patient population.

Triple-negative breast cancers (TNBCs) are a subset of breast cancers that have remained difficult to treat. A proportion of TNBCs arising in non-carriers of BRCA pathogenic variants have genomic features that are similar to BRCA carriers and may also benefit from PARP inhibitor treatment. Using genomic data from 129 TNBC samples from the Malaysian Breast Cancer (MyBrCa) cohort, we developed a gene expression-based machine learning classifier for homologous recombination deficiency (HRD) in TNBCs. The classifier identified samples with HRD mutational signature at an AUROC of 0.93 in MyBrCa validation datasets and 0.84 in TCGA TNBCs. Additionally, the classifier strongly segregated HRD-associated genomic features in TNBCs from TCGA, METABRIC, and ICGC. Thus, our gene expression classifier may identify triple-negative breast cancer patients with homologous recombination deficiency, suggesting an alternative method to identify individuals who may benefit from treatment with PARP inhibitors or platinum chemotherapy.

B cells and T cells are important components of the adaptive immune system and mediate anticancer immunity. The T cell landscape in cancer is well characterized, but the contribution of B cells to anticancer immunosurveillance is less well explored. Here we show an integrative analysis of the B cell and T cell receptor repertoire from individuals with metastatic breast cancer and individuals with early breast cancer during neoadjuvant therapy. Using immune receptor, RNA and whole-exome sequencing, we show that both B cell and T cell responses seem to coevolve with the metastatic cancer genomes and mirror tumor mutational and neoantigen architecture. B cell clones associated with metastatic immunosurveillance and temporal persistence were more expanded and distinct from site-specific clones. B cell clonal immunosurveillance and temporal persistence are predictable from the clonal structure, with higher-centrality B cell antigen receptors more likely to be detected across multiple metastases or across time. This predictability was generalizable across other immune-mediated disorders. This work lays a foundation for prioritizing antibody sequences for therapeutic targeting in cancer. In this Resource paper, the authors integrate T cell antigen receptor, B cell antigen receptor and exome sequencing comparing early and metastatic breast cancer in humans, showing how the immune response and tumors coevolve.

PDF file - 29KB, Figure S6. Kaplan-Meier survival curves. A. OS of TP53 mutation status in whole cohort B. BCSS of TP53 mutation status in patients not treated with adjuvant chemotherapy. Numbers at risk are listed below each chart e = no. of all deaths (OS) or Breast Cancer specific deaths (BCSS).

PDF file - 22KB, Figure S4. Distribution of TP53 mutations in IC subtypes. A. METABRIC cohort. B. TCGA cohort.

Abstract Most studies of genomic rearrangements in common cancers have focused on regional gains and losses, but some rearrangements may break within specific genes. We previously reported that five breast cancer cell lines have chromosome translocations that break in the NRG1 gene and that could cause abnormal NRG1 expression. NRG1 encodes the Neuregulins 1 (formerly the Heregulins), ligands for members of the ErbB/epidermal growth factor-receptor family, which includes ErbB2/HER2. We have now screened for breaks at NRG1 in paraffin sections of breast tumors. Tissue microarrays were screened by fluorescence in situ hybridization, with hybridization probes proximal and distal to the expected breakpoints. This screen detects breaks but does not distinguish between translocation or deletion breakpoints. The screen was validated with array-comparative genomic hybridization on a custom 8p12 high-density genomic array to detect a lower copy number of the sequences that were lost distal to the breaks. We also precisely mapped the breaks in five tumors with different hybridization probes. Breaks in NRG1 were detected in 6% (19 of 323) of breast cancers and in some lung and ovarian cancers. In an unselected series of 213 cases with follow-up, breast cancers where the break was detected tended to be high-grade (65% grade III compared with 28% of negative cases). They were, like breast tumors in general, mainly ErbB2 low (11 of 13 were low) and estrogen receptor positive (11 of 13 positive).

Table S1. Patient data from the RATHER primary ILC cohort

Table S4: Multivariate analysis on RATHER RNA-seq data

Supplementary Figure Legends

Updated FigS3 to include wnt11 knockdown data

Table S3: Cox regression analysis on RATHER RNA-seq data

PDF file - 162KB, Figure S5. Boxplot diagram showing APOBEC3B mRNA expression levels as log intensity values in TP53 mutated and wildtype samples stratified by PAM50 subtypes. The bold black line represents the mean APOBEC3B expression value and the box represents 25th and 75th percentiles.

PDF file - 26KB, Figure S2. Distribution of TP53 mutation class in breast cancer subtypes. A. PAM50 and B. IC subtypes.