bims-ovdlit 2025-03-23 papers

Issue of 2025–03–23
six papers selected by
Lara Paracchini, Humanitas Research

Br J Cancer. 2025 Mar 17.

Overall survival with maintenance olaparib in platinum-sensitive relapsed ovarian cancer by somatic or germline BRCA and homologous recombination repair mutation status.

BACKGROUND: The open-label, single-arm, multicentre ORZORA trial (NCT02476968) evaluated maintenance olaparib in patients with platinum-sensitive relapsed ovarian cancer (PSR OC) with a germline (g) or somatic (s) BRCA1 and/or BRCA2 mutation (BRCAm) or a non-BRCA homologous recombination repair mutation (non-BRCA HRRm).
METHODS: Patients were in response to platinum-based chemotherapy after ≥2 prior lines of treatment and underwent prospective central screening for tumour BRCA status, then central gBRCAm testing to determine sBRCAm or gBRCAm status. An exploratory cohort evaluated non-BRCA HRRm in 13 predefined genes. Patients received olaparib 400 mg (capsules) twice daily until investigator-assessed disease progression. Secondary endpoints included overall survival (OS) and safety.
RESULTS: 177 patients received olaparib. At the final data cutoff (25 June 2021), median OS from study enrolment was 46.8 (95% confidence interval [CI] 37.9-54.4), 43.2 (31.7-NC [not calculated]), 47.4 (37.9-NC) and 44.9 (28.9-NC) months in the BRCAm, sBRCAm, gBRCAm and non-BRCA HRRm cohorts, respectively. No new safety signals were identified.
CONCLUSION: Maintenance olaparib showed consistent clinical activity in the BRCAm and sBRCAm cohorts; exploratory analysis suggested similar activity in the non-BRCA HRRm cohort. These findings highlight that patients with PSR OC, beyond those with gBRCAm, may benefit from maintenance olaparib.

DOI: https://doi.org/10.1038/s41416-025-02966-x

Front Oncol. 2025 ;15 1520869

Evaluation of an innovative multi-cancer early detection test: high sensitivity and specificity in differentiating cancer, inflammatory conditions, and healthy individuals.

Nike Walter, Jörg Groth, Berthold von Und Zu Zwerger.

Background: Cancer is a leading cause of death worldwide, with early detection crucial for effective treatment. Traditional diagnostic methods, such as imaging and biopsies, are often limited by invasiveness, cost, and sensitivity. Blood-based multi-cancer early detection (MCED) tests offer a less invasive and potentially more comprehensive approach. Recently, a novel screening tool, the Carcimun® test was reported, detecting conformational changes in plasma proteins through optical extinction measurements. This study evaluates the Carcimun® test's performance, including participants with inflammatory conditions.
Methods: This prospective, single-blinded study included 172 participants: 80 healthy volunteers, 64 cancer patients (various types), and 28 individuals with inflammatory conditions (fibrosis, sarcoidosis, pneumonia) or benign tumors. Plasma samples were analyzed using the Carcimun® test. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated.
Results: Mean extinction values were significantly higher in cancer patients (315.1) compared to healthy individuals (23.9) and those with inflammatory conditions (62.7) (p<0.001). The Carcimun® test distinguished these groups with high accuracy (95.4%), sensitivity (90.6%), and specificity (98.2%). Significant differences were found between healthy participants and cancer patients (p<0.001), and between cancer patients and those with inflammation (p<0.001).
Conclusion: The Carcimun® test achieved high accuracy, sensitivity, and specificity, effectively identifying cancer patients while minimizing false positives and negatives. By including participants with inflammatory conditions, we addressed a significant limitation of previous studies, demonstrating the test's robustness in real-world clinical scenarios. These findings underscore the potential of the Carcimun® test as a valuable tool for early cancer detection and screening.

Keywords: cancer screening; diagnostic; liquid biopsy; multi-cancer early detection; plasma protein test

DOI: https://doi.org/10.3389/fonc.2025.1520869

Genome Res. 2025 Mar 19.

De novo detection of somatic variants in high-quality long-read single-cell RNA sequencing data.

Tumor Profiler Consortium

In cancer, genetic and transcriptomic variations generate clonal heterogeneity, leading to treatment resistance. Long-read single-cell RNA sequencing (LR scRNA-seq) has the potential to detect genetic and transcriptomic variations simultaneously. Here, we present LongSom, a computational workflow leveraging high-quality LR scRNA-seq data to call de novo somatic single-nucleotide variants (SNVs), including in mitochondria (mtSNVs), copy number alterations (CNAs), and gene fusions, to reconstruct the tumor clonal heterogeneity. Before somatic variant calling, LongSom reannotates marker gene-based cell types using cell mutational profiles. LongSom distinguishes somatic SNVs from noise and germline polymorphisms by applying an extensive set of hard filters and statistical tests. Applying LongSom to human ovarian cancer samples, we detected clinically relevant somatic SNVs that were validated against matched DNA samples. Leveraging somatic SNVs and fusions, LongSom found subclones with different predicted treatment outcomes. In summary, LongSom enables de novo variant detection without the need for normal samples, facilitating the study of cancer evolution, clonal heterogeneity, and treatment resistance.

DOI: https://doi.org/10.1101/gr.279281.124

Nat Genet. 2025 Mar 18.

Quantitative characterization of cell niches in spatially resolved omics data.

Sebastian Birk, Irene Bonafonte-Pardàs, Adib Miraki Feriz, Adam Boxall, Eneritz Agirre, Fani Memi, Anna Maguza, Anamika Yadav, Erick Armingol, Rong Fan, Gonçalo Castelo-Branco, Fabian J Theis, Omer Ali Bayraktar, Carlos Talavera-López, Mohammad Lotfollahi.

Spatial omics enable the characterization of colocalized cell communities that coordinate specific functions within tissues. These communities, or niches, are shaped by interactions between neighboring cells, yet existing computational methods rarely leverage such interactions for their identification and characterization. To address this gap, here we introduce NicheCompass, a graph deep-learning method that models cellular communication to learn interpretable cell embeddings that encode signaling events, enabling the identification of niches and their underlying processes. Unlike existing methods, NicheCompass quantitatively characterizes niches based on communication pathways and consistently outperforms alternatives. We show its versatility by mapping tissue architecture during mouse embryonic development and delineating tumor niches in human cancers, including a spatial reference mapping application. Finally, we extend its capabilities to spatial multi-omics, demonstrate cross-technology integration with datasets from different sequencing platforms and construct a whole mouse brain spatial atlas comprising 8.4 million cells, highlighting NicheCompass' scalability. Overall, NicheCompass provides a scalable framework for identifying and analyzing niches through signaling events.

DOI: https://doi.org/10.1038/s41588-025-02120-6