Biology subjects

Broeks, A.

Publications and source records attributed to Broeks, A..

3 recordsLinked to original sources

bioRxiv2024

Karyotype Evolution in Response to Chemoradiotherapy and Upon Recurrence of Esophageal Adenocarcinomas.

The genome of esophageal adenocarcinoma (EAC) is highly unstable and might evolve over time. Here, we track karyotype evolution in EACs in response to treatment and upon recurrence through multi-region and longitudinal analysis. To this end, we introduce L-PAC, a bio-informatics technique that allows inference of absolute copy number aberrations (CNA) of low-purity samples by leveraging information of high-purity samples from the same cancer. Quantitative analysis of matched absolute CNAs reveals that the amount of karyotype evolution induced by chemoradiotherapy (CRT) is predictive for early recurrence and depends on the initial level of karyotype intra-tumor heterogeneity. We observe that CNAs acquired in response to CRT are partially reversed back to the initial state upon recurrence. CRT hence alters the fitness landscape to which tumors can adjust by adapting their karyotype. Together, our results indicate that karyotype plasticity contributes to therapy resistance of EACs.

bioinformatics↗

bioRxiv2023

Spatial relationships in the urothelial and head and neck tumor microenvironment predict response to combination immune checkpoint inhibitors

Immune checkpoint inhibitors (ICI) currently achieve remarkable clinical results in urothelial cancer (UC). However, the relationship between the tumor microenvironment (TME), usually characterized by immune cell density, and response to ICI is unclear. We quantified the TME immune cell densities and spatial relationships (SRs) using the multiplex immunofluorescence data of 24 UC pre-treatment tumor resections. We described SRs by approximating the 1-NN distance distribution with a Weibull distribution and evaluated the association between TME metrics (spatial and density parameters) and ipilimumab+nivolumab response. Immune cell density did not discriminate between response groups. However, the Weibull SR metrics of CD8+ T-cells or macrophages to their closest cancer cell were positively associated with response. CD8+ T-cells close to B-cells were characteristic of non-response. The G- function, a threshold dependent alternative SR metric, yielded variable effect sizes and statistical power in association studies with response. We validated our SR response associations in a cohort of head and neck tumors with a comparable treatment design. Our data confirm that SRs, in contrast to density metrics, are strong biomarkers of response to ICIs, a finding with significant translational relevance.

cancer biology↗

bioRxiv2019

Genome-wide association study identifies 32 novel breast cancer susceptibility loci from overall and subtype-specific analyses

Breast cancer susceptibility variants frequently show heterogeneity in associations by tumor subtype. To identify novel loci, we performed a genome-wide association study (GWAS) including 133,384 breast cancer cases and 113,789 controls, plus 18,908 BRCA1 mutation carriers (9,414 with breast cancer) of European ancestry, using both standard and novel methodologies that account for underlying tumor heterogeneity by estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status and tumor grade. We identified 32 novel susceptibility loci (P<5.0x10-8), 15 of which showed evidence for associations with at least one tumor feature (false discovery rate <0.05). Five loci showed associations (P<0.05) in opposite directions between luminal- and non-luminal subtypes. In-silico analyses showed these five loci contained cell-specific enhancers that differed between normal luminal and basal mammary cells. The genetic correlations between five intrinsic-like subtypes ranged from 0.35 to 0.80. The proportion of genome-wide chip heritability explained by all known susceptibility loci was 37.6% for triple-negative and 54.2% for luminal A-like disease. These findings provide an improved understanding of genetic predisposition to breast cancer subtypes and will inform the development of subtype-specific polygenic risk scores.

genetics↗