bims-tumhet 2024-10-27 papers

Issue of 2024–10–27
five papers selected by
Sergio Marchini, Humanitas Research

Nat Commun. 2024 Oct 21. 15(1): 8801

Cancer treatment monitoring using cell-free DNA fragmentomes.

Circulating cell-free DNA (cfDNA) assays for monitoring individuals with cancer typically rely on prior identification of tumor-specific mutations. Here, we develop a tumor-independent and mutation-independent approach (DELFI-tumor fraction, DELFI-TF) using low-coverage whole genome sequencing to determine the cfDNA tumor fraction and validate the method in two independent cohorts of patients with colorectal or lung cancer. DELFI-TF scores strongly correlate with circulating tumor DNA levels (ctDNA) (r = 0.90, p < 0.0001, Pearson correlation) even in cases where mutations are undetectable. DELFI-TF scores prior to therapy initiation are associated with clinical response and are independent predictors of overall survival (HR = 9.84, 95% CI = 1.72-56.10, p < 0.0001). Patients with lower DELFI-TF scores during treatment have longer overall survival (62.8 vs 29.1 months, HR = 3.12, 95% CI 1.62-6.00, p < 0.001) and the approach predicts clinical outcomes more accurately than imaging. These results demonstrate the potential of using cfDNA fragmentomes to estimate tumor burden in cfDNA for treatment response monitoring and clinical outcome prediction.

DOI: https://doi.org/10.1038/s41467-024-53017-7

JNCI Cancer Spectr. 2024 Oct 21. pii: pkae101. [Epub ahead of print]

Impact of chemotherapy on patients with mismatch repair deficient advanced endometrial carcinomas-a meta-analysis.

Angelina Tjokrowidjaja, Peey-Sei Kok, Yoland C Antill, Clare L Scott, Linda R Mileshkin, Michael L Friedlander, Chee K Lee.

INTRODUCTION: Chemo-immunotherapy is standard of care for women with recurrent or advanced mismatch repair deficient (dMMR) endometrial carcinomas (EC). However, it is uncertain whether patients with dMMR advanced or recurrent EC derive less benefit from chemotherapy than those with mismatch repair proficient (pMMR) EC.
METHODS: We performed a meta-analysis of randomized controlled trials (RCTs) in advanced/recurrent EC to determine the difference in the benefit of chemotherapy in dMMR vs pMMR EC. Data on chemotherapy outcomes including objective response rate (ORR), progression-free survival (PFS) and overall survival (OS) were retrieved. We pooled these data using the inverse variance method and examined subgroup difference by MMR status. We also compared differences in PFS and OS outcomes by creating individual patient data from the Kaplan-Meier curves of trial publications for sensitivity analyses.
RESULTS: A total of five RCTs with 1137 participants (dMMR, 26%; pMMR, 74%) were included. All participants were treated with carboplatin-based chemotherapy. There was no difference between the dMMR and pMMR subgroups for ORR (66.5% vs 64.0%, P = .20 for subgroup difference), PFS (HR 0.93, 95% CI 0.77-1.12, P = .44; median PFS 7.6 vs 9.5 months) or OS (HR 1.03, 95% CI 0.73-1.44, P = .88; median OS not reached vs 28.6 months).
CONCLUSIONS: ORR, PFS and OS were similar among those with dMMR vs pMMR endometrial cancer treated with front-line, platinum-doublet chemotherapy in randomized clinical trials. These findings reinforce the importance of combining chemotherapy together with immune checkpoint inhibitors until the results of trials comparing immune checkpoint therapy alone with combination therapy are available.

Keywords: MMR; chemotherapy; endometrial cancer; immune checkpoint inhibition; mismatch repair

DOI: https://doi.org/10.1093/jncics/pkae101

Nat Rev Cancer. 2024 Oct 21.

Emerging strategies to investigate the biology of early cancer.

Ran Zhou, Xiwen Tang, Yuan Wang.

Early detection and intervention of cancer or precancerous lesions hold great promise to improve patient survival. However, the processes of cancer initiation and the normal-precancer-cancer progression within a non-cancerous tissue context remain poorly understood. This is, in part, due to the scarcity of early-stage clinical samples or suitable models to study early cancer. In this Review, we introduce clinical samples and model systems, such as autochthonous mice and organoid-derived or stem cell-derived models that allow longitudinal analysis of early cancer development. We also present the emerging techniques and computational tools that enhance our understanding of cancer initiation and early progression, including direct imaging, lineage tracing, single-cell and spatial multi-omics, and artificial intelligence models. Together, these models and techniques facilitate a more comprehensive understanding of the poorly characterized early malignant transformation cascade, holding great potential to unveil key drivers and early biomarkers for cancer development. Finally, we discuss how these new insights can potentially be translated into mechanism-based strategies for early cancer detection and prevention.

DOI: https://doi.org/10.1038/s41568-024-00754-y

Yi Chuan. 2024 Oct;46(10): 807-819

Processing pipelines and analytical methods for single-cell DNA methylation sequencing data.

Yan-Ni Wang, Jia Li.

Single-cell DNA methylation sequencing technology has seen rapid advancements in recent years, playing a crucial role in uncovering cellular heterogeneity and the mechanisms of epigenetic regulation. As sequencing technologies have progressed, the quality and quantity of single-cell methylation data have also increased, making standardized preprocessing workflows and appropriate analysis methods essential for ensuring data comparability and result reliability. However, a comprehensive data analysis pipeline to guide researchers in mining existing data has yet to be established. This review systematically summarizes the preprocessing steps and analysis methods for single-cell methylation data, introduces relevant algorithms and tools, and explores the application prospects of single-cell methylation technology in neuroscience, hematopoietic differentiation, and cancer research. The aim is to provide guidance for researchers in data analysis and to promote the development and application of single-cell methylation sequencing technology.

Keywords: data analysis; data preprocessing; epigenetics; single-cell methylation sequencing

DOI: https://doi.org/10.16288/j.yczz.24-154