Aziz Khan

Immune checkpoint inhibition (ICI) benefits only a subset of patients with metastatic triple-negative breast cancer and determinants of response remain unclear. We assembled a longitudinal cohort of 103 female patients from the phase 2 TONIC trial, with samples spanning primary tumors, pretreatment metastases and on-treatment metastases during nivolumab therapy. We profiled 37 proteins in 270 tumors using highly multiplexed imaging and developed SpaceCat, an open-source pipeline that extracts more than 800 imaging features per sample, including cell density, diversity, spatial interactions and functional marker expression. Metastatic but not primary tumors contained features predictive of outcome. Spatial metrics such as immune diversity and T cell infiltration at tumor borders were most informative, while ratios of T cells to cancer cells and PDL1 on myeloid cells were also associated with response. Multivariate models stratified patients with the highest performance on treatment (area under the curve = 0.90). Bulk RNA-seq confirmed the predictive value of on-treatment samples. These findings highlight the value of longitudinal profiling to resolve evolving tumor microenvironment dynamics driving ICI response.

Extrachromosomal DNA (ecDNA) are circularized genomic elements that reside outside canonical chromosomes. ecDNA amplify oncogene copy number, enhance chromatin accessibility, and act as mobile enhancers through cis- and trans-regulatory interactions, collectively boosting oncogene expression. ecDNA has been implicated in tumor progression, intratumoral heterogeneity, and poor patient prognosis. Despite various lines of evidence that ecDNA promotes aggressive disease, the mechanisms and selective pressures leading to ecDNA formation and propagation remain poorly understood as are their structures. While several computational tools have been developed to infer ecDNA presence or absence from short read sequencing data, accurate identification of large or complex ecDNA structures remains challenging. Here we introduce ecDNAInspector, a novel computational framework to systematically assess the confidence of ecDNA predictions from existing inference tools. Leveraging abundant short-read whole genome sequencing (WGS) data from population-scale cohorts, we demonstrate that ecDNAInspector accurately identifies high-confidence ecDNA calls, improving interpretability and facilitating the association with clinical features. As an illustrative example, applied to a cohort of 250 breast cancers, ecDNAInspector identifies associations between ecDNA structure and molecular subgroups of disease. These findings are supported by orthogonal omic data and experimental characterization of ecDNA captured in representative cell lines. ecDNAInspector provides a scalable, data-driven approach to characterize ecDNA structure, enabling integrative studies of the clinical and biological impact of this non-mendelian mode of oncogene amplification and inheritance.

JASPAR (https://jaspar.elixir.no/) is an open-access database that has provided high-quality, manually curated, and non-redundant DNA binding profiles for transcription factors (TFs) as position frequency matrices (PFMs) for over 20 years. We expanded the CORE (306 new profiles, 12% increase) and UNVALIDATED (433, 60% increase) collections with new PFMs and updated 13 existing profiles. We updated the TF binding site predictions and genome tracks for eight species. TF binding profile clusters and familial TF binding sites were updated accordingly. We integrate the inMOTIFin software to easily simulate regulatory sequences using JASPAR PFMs. To enrich TFs’ annotations, we provide scientific literature-based human TF target information. Notably, this release features a deep learning (DL) collection, providing a paradigm shift in modeling and characterizing TF–DNA interactions with 1259 BPNet models trained on Homo sapiens ENCODE chromatin immunoprecipitation followed by sequencing (ChIP-seq) datasets from 240 TFs and interpreted to reveal predictive motif patterns for the models. The motifs associated with the same TF were clustered to provide a summary of the binding properties, resulting in 240 primary and 113 alternative motif patterns in the DL collection. The JASPAR 2026 collections lay a foundation for future endeavors in genomic research, serving the scientific community in uncovering the mechanisms of gene regulation.

Cancer is generally thought to be caused by expansion of a single mutant cell¹. However, analyses of early colorectal cancer lesions suggest that tumors may instead originate from multiple, genetically distinct cell populations^2,3. Detecting polyclonal tumor initiation is challenging in patients, as it requires profiling early-stage lesions before clonal sweeps obscure diversity. To investigate this, we analyzed normal colorectal mucosa, benign and dysplastic premalignant polyps, and malignant adenocarcinomas (123 samples) from six individuals with familial adenomatous polyposis (FAP). Individuals with FAP have a germline heterozygous APC mutation, predisposing them to colorectal cancer and numerous premalignant polyps by early adulthood⁴. Whole-genome and/or whole-exome sequencing revealed that many premalignant polyps—40% with benign histology and 28% with dysplasia—were composed of multiple genetic lineages that diverged early, consistent with polyclonal origins. This conclusion was reinforced by whole-genome sequencing of single crypts from multiple polyps in additional patients which showed limited sharing of mutations among crypts within the same lesion. In some cases, multiple distinct APC mutations co-existed in different lineages of a single polyp, consistent with polyclonality. These findings reshape our understanding of early neoplastic events, demonstrating that tumor initiation can arise from the convergence of diverse mutant clones. They also suggest that cell-intrinsic growth advantages alone may not fully explain tumor initiation, highlighting the importance of microenvironmental and tissue-level factors in early cancer evolution.

Breast cancer is a highly heterogeneous disease whose prognosis and treatment as defined by the expression of three receptors—oestrogen receptor (ER), progesterone receptor and human epidermal growth factor receptor 2 (HER2; encoded by ERBB2)—is insufficient to capture the full spectrum of clinical outcomes and therapeutic vulnerabilities. Previously, we demonstrated that transcriptional and genomic profiles define eleven integrative subtypes with distinct clinical outcomes, including four ER⁺ subtypes with increased risk of relapse decades after diagnosis^1,2. Here, to determine whether these subtypes reflect distinct evolutionary histories, interactions with the immune system and pathway dependencies, we established a meta-cohort of 1,828 breast tumours spanning pre-invasive, primary invasive and metastatic disease with whole-genome and transcriptome sequencing. We demonstrate that breast tumours fall along a continuum constrained by three genomic archetypes. The ER⁺ high-risk integrative subgroup is characterized by complex focal amplifications, similar to HER2⁺ tumours, including cyclic extrachromosomal DNA amplifications induced by ER through R-loop formation and APOBEC3B-editing, which arise in pre-invasive lesions. By contrast, triple-negative tumours exhibit genome-wide instability and tandem duplications and are enriched for homologous repair deficiency-like signatures, whereas ER⁺ typical-risk tumours are largely genomically stable. These genomic archetypes, which replicate in an independent cohort of 2,659 primary tumours, are established early during tumorigenesis, sculpt the tumour microenvironment and are conserved in metastatic disease. These complex structural alterations contribute to replication stress and immune evasion, and persist throughout tumour evolution, unveiling potential vulnerabilities.

Familial adenomatous polyposis (FAP) is a genetic disease causing hundreds of premalignant polyps in affected persons and is an ideal model to study transitions of early precancer states to colorectal cancer (CRC). We performed deep multiomic profiling of 93 samples, including normal mucosa, benign polyps and dysplastic polyps, from six persons with FAP. Transcriptomic, proteomic, metabolomic and lipidomic analyses revealed a dynamic choreography of thousands of molecular and cellular events that occur during precancerous transitions toward cancer formation. These involve processes such as cell proliferation, immune response, metabolic alterations (including amino acids and lipids), hormones and extracellular matrix proteins. Interestingly, activation of the arachidonic acid pathway was found to occur early in hyperplasia; this pathway is targeted by aspirin and other nonsteroidal anti-inflammatory drugs, a preventative treatment under investigation in persons with FAP. Overall, our results reveal key genomic, cellular and molecular events during the earliest steps in CRC formation and potential mechanisms of pharmaceutical prophylaxis.

Progression from pre-cancers like ductal carcinoma in situ (DCIS) to invasive disease (cancer) is driven by somatic evolution and is altered by clinical interventions. We hypothesized that genetic and/or phenotypic intra-tumor heterogeneity would predict clinical outcomes for DCIS since it serves as the substrate for natural selection among cells. We profiled two samples from two geographically distinct foci from each DCIS in both cross-sectional (N = 119) and longitudinal cohorts (N = 224), with whole exome sequencing, low-pass whole genome sequencing, and a panel of immunohistochemical markers. In the longitudinal cohorts, the only statistically significant predictors of time to non-invasive DCIS recurrence were the combination of treatment (lumpectomy only vs mastectomy or lumpectomy with radiation, HR = 12.13, p = 0.003, Wald test with FDR correction), ER status (HR = 0.16 for ER+ compared to ER-, p = 0.0045), and divergence in SNVs between the two samples (HR = 1.33 per 10% divergence, p = 0.018). SNV divergence also distinguished between pure DCIS and DCIS synchronous with invasive disease in the cross-sectional cohort. In contrast, the only statistically significant predictors of time to progression to invasive disease were the combination of the width of the surgical margin (HR = 0.67 per mm, p = 0.043) and the number of mutations that were detectable at high allele frequencies (HR = 1.30 per 10 SNVs, p = 0.02). These results imply that recurrence with DCIS is a clinical and biological process different from invasive progression. Significance: Evolutionary measures of breast pre-cancers associate with local recurrence after surgery, as well as progression to cancer. Recurrence and progression are different biological processes impacted differently by clinical interventions.

Tumors with the same diagnosis can have different molecular profiles and response to treatment. It remains unclear when and why these differences arise. Somatic genomic aberrations occur within the context of a highly variable germline genome. Interrogating 5870 breast cancer lesions, we demonstrated that germline-derived epitopes in recurrently amplified genes influence somatic evolution by mediating immunoediting. Individuals with a high germline-epitope burden in human epidermal growth factor receptor 2 (HER2/ ERBB2 ) are less likely to develop HER2-positive breast cancer compared with other subtypes. The same holds true for recurrent amplicons defining three aggressive estrogen receptor (ER)–positive subgroups. Tumors that overcome such immune-mediated negative selection are more aggressive and demonstrate an “immune cold” phenotype. These data show that the germline genome plays a role in dictating somatic evolution.

JASPAR (https://jaspar.elixir.no/) is a widely-used open-access database presenting manually curated high-quality and non-redundant DNA-binding profiles for transcription factors (TFs) across taxa. In this 10th release and 20th-anniversary update, the CORE collection has expanded with 329 new profiles. We updated three existing profiles and provided orthogonal support for 72 profiles from the previous release's UNVALIDATED collection. Altogether, the JASPAR 2024 update provides a 20% increase in CORE profiles from the previous release. A trimming algorithm enhanced profiles by removing low information content flanking base pairs, which were likely uninformative (within the capacity of the PFM models) for TFBS predictions and modelling TF-DNA interactions. This release includes enhanced metadata, featuring a refined classification for plant TFs’ structural DNA-binding domains. The new JASPAR collections prompt updates to the genomic tracks of predicted TF binding sites (TFBSs) in 8 organisms, with human and mouse tracks available as native tracks in the UCSC Genome browser. All data are available through the JASPAR web interface and programmatically through its API and the updated Bioconductor and pyJASPAR packages. Finally, a new TFBS extraction tool enables users to retrieve predicted JASPAR TFBSs intersecting their genomic regions of interest.