bims-tumhet 2026-03-22 papers

Issue of 2026–03–22
six papers selected by
Sergio Marchini, Humanitas Research

EMBO Mol Med. 2026 Mar 19.

ctDNA monitoring using tumor-informed copy number analysis.

Ze Zhou, Ros Cutts, Sarah Hrebien, Christina X Zhang, Isaac Garcia-Murillas, Woody Z Zhang, Alexander M Frankell, Wendy N Cooper, Amit Roshan, Nicholas C Turner, Tommy Kaplan, Nitzan Rosenfeld, Hui Zhao.

Methods to detect circulating tumor DNA (ctDNA) enable minimally invasive responsive monitoring of cancer dynamics. However, sensitive and cost-effective methods are still lacking. Current methods for detecting cancer signals in shallow whole-genome sequencing (sWGS) data from cell-free DNA (cfDNA) via copy number aberration (CNA) analysis typically have a limit of detection of approximately 3% tumor fraction (TF). We developed informCNA, a bioinformatics method that leverages CNA information from sWGS of tumor or pre-treatment plasma samples with high TF as references, enabling ctDNA detection down to 0.2% TF across multiple cancer types. In 177 serial plasma samples from 18 patients with ovarian cancer, informCNA showed high concordance with the standard serum protein marker CA-125 and identified recurrence a median of 3.7 months earlier than CA-125 test. These results demonstrate the potential of personalized CNA analysis through sWGS for estimating ctDNA burden, enabling precise and cost-effective disease monitoring and early detection of relapse.

Keywords: Copy Number Aberration (CNA); Liquid Biopsy; Tumor-informed; cfDNA; ctDNA

DOI: https://doi.org/10.1038/s44321-026-00399-4

Cell Rep. 2026 Mar 13. pii: S2211-1247(26)00176-2. [Epub ahead of print]45(3): 117098

Pan-cancer evolution signatures link clonal expansion to dynamic changes in the tumor immune microenvironment.

Xinyu Yang, Wei Liu, Geoff Macintyre, Peter Van Loo, Florian Markowetz, Peter Bailey, Ke Yuan.

Cancer is an evolutionary process characterized by profound intratumor heterogeneity (ITH), which can be quantified using in silico estimates of cancer cell fractions (CCFs) of tumor-specific somatic mutations. We demonstrate a data-driven approach based on CCF distributions to identify 4 robust pan-cancer evolutionary signatures from 4,146 tumors across 17 cancer types in The Cancer Genome Atlas (TCGA). These signatures define a continuum of cancer cell fractions reflecting neutral evolution, clonal expansion, and clonal fixation. Correlating evolutionary signatures with mutational and biological programs reveals that tumors enriched for clonal expansion and fixation are associated with immune evasion and distinct changes in the tumor immune microenvironment. Our analysis reveals a dynamic shift from adaptive to innate immune programs as tumors progress toward clonal fixation and escape immune surveillance, accompanied by the clonal expansion of driver genes modulating tumor-stroma interactions. These evolutionary dynamic subtypes are further associated with clinical outcomes and immunotherapy responses.

Keywords: CP: cancer; CP: genomics; cancer evolution; evolutionary dynamics; intratumor heterogeneity; machine learning; pan-cancer; tumor immune microenvironment

DOI: https://doi.org/10.1016/j.celrep.2026.117098

Cancer Med. 2026 Mar;15(3): e71728

The Emerging Role of Tertiary Lymphoid Structures as Predictive and Prognostic Biomarkers of Immunotherapy in Cancer.

Lidia B Medhin, Rodas Amanuel, Lydia Warburton, Leslie Calapre, Benhur Amanuel, Elin S Gray.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have improved outcomes across several cancers, yet many patients do not respond, highlighting the need for robust predictive biomarkers. Tertiary lymphoid structures (TLS), ectopic lymphoid aggregates that support local antigen presentation and adaptive immune activation, have emerged as potential indicators of favourable prognosis and immunotherapy responsiveness.
METHODS: This review summarises current clinical and translational evidence examining the prognostic and predictive value of TLS in solid malignancies. Studies assessing TLS presence, organisation, and biological function were identified through searches of major scientific databases and evaluated with respect to their association with patient outcomes and responses to ICIs.
RESULTS: Across multiple tumour types, TLS correlate with improved survival and enhanced anti‑tumour immune activity. TLS‑rich tumours typically show increased infiltration of effector immune cells and more inflamed tumour microenvironments. Several studies also indicate that TLS maturity, particularly the presence of germinal‑centre‑like features, strengthens their predictive value for ICI benefit. However, substantial variation exists in TLS assessment methods and definitions, limiting comparability and hindering translation into routine clinical use.
CONCLUSIONS: TLS represent a promising biomarker for prognosis and immunotherapy response. Standardised evaluation methods and prospective clinical validation are essential to enable their integration into personalised treatment strategies.

Keywords: biomarker; immunotherapy; tertiary lymphoid structures

DOI: https://doi.org/10.1002/cam4.71728

BMJ Oncol. 2026 ;5(1): e001002

Circulating-tumour DNA (ctDNA) in NSCLC: mechanisms, evidence and the future of MRD detection.

Beatriz Jimenez-Munarriz, Berenice Urtecho, Mary R Rabey, Natasha B Leighl.

Circulating tumour DNA (ctDNA) offers a minimally invasive approach for early detection of cancer, monitoring molecular residual disease, assessing molecular response and identifying treatment resistance in advanced stages. Additionally, ctDNA enhances clinical decision-making by enabling real-time tumour assessment. This review highlights the growing role of liquid biopsy as a predictive and prognostic tool in non-small cell lung cancer management.

Keywords: Lung cancer (non-small cell); Tumour biomarkers

DOI: https://doi.org/10.1136/bmjonc-2025-001002

Brief Bioinform. 2026 Mar 01. pii: bbag111. [Epub ahead of print]27(2):

Sensitive detection of copy number alterations in low-pass liquid biopsy sequencing data.

Lotta Eriksson, Eszter Lakatos.

Liquid biopsies, coupled with analysis of copy number alterations (CNAs), have emerged as a promising tool for non-invasive monitoring of cancer progression and tumor composition. However, methods utilizing CNA data from liquid biopsies are limited by the low signal in the samples, caused by a low percentage of cancer DNA in the blood, and inherent noise introduced in the sequencing. To address this challenge, we developed BayesCNA, a method designed to improve signal extraction from low-pass liquid biopsy sequencing data, by utilizing a Bayesian changepoint detection algorithm. We use information of the posterior changepoint probabilities to identify likely changepoints, where a changepoint indicates a shift in the copy number state. The signal is then reconstructed using the identified partition. We show the effectiveness of the method on synthetically generated datasets and compare the method with state-of-the-art bioinformatics tools under noisy conditions. Our results show that this novel approach increases sensitivity in detecting CNAs, particularly in low-quality cases.

Keywords: Bayesian changepoint detection; copy number alterations; liquid biopsies; low-pass sequencing

DOI: https://doi.org/10.1093/bib/bbag111

Cell Rep Med. 2026 Mar 17. pii: S2666-3791(26)00083-2. [Epub ahead of print]7(3): 102666

Spatial tumor evolution panorama of ovarian cancer.

Ovarian cancer is an aggressive disease characterized by intraperitoneal dissemination and a distinctive microenvironment. By generating metastatic cohorts encompassing approximately 60 pairs of whole-genome and RNA sequencing, 100 single-cell samples, and 2.5 million spatial transcriptomics (ST) spots, we delineate site-specific tumor-host colocalization patterns. Utilizing our STARLETS framework, we elucidate a Darwinian evolutionary trajectory in which hypoxia and immune pressures select for clones that eventually metastasize. High-resolution ST and ultimate dimensional imaging of solvent-cleared organs (uDISCO) imaging further identify a tripartite ensemble comprising MMP11+ myCAFs, epithelial cells, and SPP1+ macrophages in ascites and metastases, which can be modulated via SPP1-CD44 inhibition. SPP1+ macrophages predict therapeutic responses in clinical trials, including oncolytic virus and poly(ADP-ribose) polymerase inhibitor treatments. Collectively, our study advances insights into spatial dynamics that hold promise for therapeutic approaches in ovarian cancer.

Keywords: clonal evolution; metastasis; ovarian cancer; tumor microenvironment

DOI: https://doi.org/10.1016/j.xcrm.2026.102666