bims-tumhet 2025-01-19 papers

bims-tumhet

Biomed News

on Tumor Heterogeneity

Issue of 2025–01–19
eight papers selected by
Sergio Marchini, Humanitas Research

Visualizing Cancer Heterogeneity at the Molecular and Cellular Levels: Lessons from Rosetta.
Ovarian tumor microenvironment contributes to tumor progression and chemoresistance.
Probing the physical hallmarks of cancer.
Nanopore-based consensus sequencing enables accurate multimodal tumor cell-free DNA profiling.
Deep learning in integrating spatial transcriptomics with other modalities.
Detecting copy-number alterations from single-cell chromatin sequencing data by AtaCNA.
Biological and clinical significance of tumour-infiltrating lymphocytes in the era of immunotherapy: a multidimensional approach.
DriverDetector: An R package providing multiple statistical methods for cancer driver genes detection and tools for downstream analysis.

Cancer Discov. 2025 Jan 13. 15(1): 34-38

Visualizing Cancer Heterogeneity at the Molecular and Cellular Levels: Lessons from Rosetta.

Cancer Grand Challenges Rosetta Consortium

Understanding tumor heterogeneity is a major challenge that was recognized as one of the first Cancer Grand Challenges, with a call to provide solutions to visualize tumor heterogeneity. The Rosetta team took on this challenge, exploiting advances in spatial-omics approaches centered around mass spectrometry imaging to map tumor heterogeneity at the cellular and molecular scales with different levels of resolution. See related article by Bressan et al., p. 16 See related article by Stratton et al., p. 22 See related article by Bhattacharjee et al., p. 28.

DOI: https://doi.org/10.1158/2159-8290.CD-24-0016
Cancer Drug Resist. 2024 ;7 53

Ovarian tumor microenvironment contributes to tumor progression and chemoresistance.

Adriana Ponton-Almodovar, Samuel Sanderson, Ramandeep Rattan, Jamie J Bernard, Sachi Horibata.

  Ovarian cancer is one of the deadliest gynecologic cancers affecting the female reproductive tract. This is largely attributed to frequent recurrence and development of resistance to the platinum-based drugs cisplatin and carboplatin. One of the major contributing factors to increased cancer progression and resistance to chemotherapy is the tumor microenvironment (TME). Extracellular signaling from cells within the microenvironment heavily influences progression and drug resistance in ovarian cancer. This is frequently done through metabolic reprogramming, the process where cancer cells switch between biochemical pathways to increase their chances of survival and proliferation. Here, we focus on how crosstalk between components of the TME and the tumor promotes resistance to platinum-based chemotherapy. We highlight the role of cancer-associated fibroblasts, immune cells, adipocytes, and endothelial cells in ovarian tumor progression, invasion, metastasis, and chemoresistance. We also highlight recent advancements in targeting components of the TME as a novel therapeutic avenue to combat chemoresistance in ovarian cancer.

Keywords:  Ovarian cancer; chemoresistance; tumor microenvironment

DOI:  https://doi.org/10.20517/cdr.2024.111
Nat Methods. 2025 Jan 15.

Probing the physical hallmarks of cancer.

Hadi T Nia, Lance L Munn, Rakesh K Jain.

The physical microenvironment plays a crucial role in tumor development, progression, metastasis and treatment. Recently, we proposed four physical hallmarks of cancer, with distinct origins and consequences, to characterize abnormalities in the physical tumor microenvironment: (1) elevated compressive-tensile solid stresses, (2) elevated interstitial fluid pressure and the resulting interstitial fluid flow, (3) altered material properties (for example, increased tissue stiffness) and (4) altered physical micro-architecture. As this emerging field of physical oncology is being advanced by tumor biologists, cell and developmental biologists, engineers, physicists and oncologists, there is a critical need for model systems and measurement tools to mechanistically probe these physical hallmarks. Here, after briefly defining these physical hallmarks, we discuss the tools and model systems available for probing each hallmark in vitro, ex vivo, in vivo and in clinical settings. We finally review the unmet needs for mechanistic probing of the physical hallmarks of tumors and discuss the challenges and unanswered questions associated with each hallmark.

DOI: https://doi.org/10.1038/s41592-024-02564-4
Genome Res. 2025 Jan 13. pii: gr.279144.124. [Epub ahead of print]

Nanopore-based consensus sequencing enables accurate multimodal tumor cell-free DNA profiling.

Li-Ting Chen, Myrthe Jager, Dàmi Rebergen, Geertruid J Brink, Tom van den Ende, Willem Vanderlinden, Pauline Kolbeck, Marc Pagès-Gallego, Ymke van der Pol, Nicolle Besselink, Norbert Moldovan, Nizar Hami, Wigard P Kloosterman, Hanneke van Laarhoven, Florent Mouliere, Ronald Zweemer, Jan Lipfert, Sarah Derks, Alessio Marcozzi, Jeroen de Ridder.

Shallow genome-wide cell-free DNA (cfDNA) sequencing holds great promise for non-invasive cancer monitoring by providing reliable copy number alteration (CNA) and fragmentomic profiles. Single nucleotide variations (SNVs) are, however, much harder to identify with low sequencing depth due to sequencing errors. Here we present Nanopore Rolling Circle Amplification (RCA)-enhanced Consensus Sequencing (NanoRCS), which leverages RCA and consensus calling based on genome-wide long-read nanopore sequencing to enable simultaneous multimodal tumor fraction estimation through SNVs, CNAs, and fragmentomics. Efficacy of NanoRCS is tested on 18 cancer patient samples and seven healthy controls, demonstrating its ability to reliably detect tumor fractions as low as 0.24%. In vitro experiments confirm that SNV measurements are essential for detecting tumor fractions below 3%. NanoRCS provides the opportunity for cost-effective and rapid processing, which aligns well with clinical needs, particularly in settings where quick and accurate cancer monitoring is essential for personalized treatment strategies.

DOI: https://doi.org/10.1101/gr.279144.124
Brief Bioinform. 2024 Nov 22. pii: bbae719. [Epub ahead of print]26(1):

Deep learning in integrating spatial transcriptomics with other modalities.

Jiajian Luo, Jiye Fu, Zuhong Lu, Jing Tu.

  Spatial transcriptomics technologies have been extensively applied in biological research, enabling the study of transcriptome while preserving the spatial context of tissues. Paired with spatial transcriptomics data, platforms often provide histology and (or) chromatin images, which capture cellular morphology and chromatin organization. Additionally, single-cell RNA sequencing (scRNA-seq) data from matching tissues often accompany spatial data, offering a transcriptome-wide gene expression profile of individual cells. Integrating such additional data from other modalities can effectively enhance spatial transcriptomics data, and, conversely, spatial transcriptomics data can supplement scRNA-seq with spatial information. Moreover, the rapid development of spatial multi-omics technology has spurred the demand for the integration of spatial multi-omics data to present a more detailed molecular landscape within tissues. Numerous deep learning (DL) methods have been developed for integrating spatial transcriptomics with other modalities. However, a comprehensive review of DL approaches for integrating spatial transcriptomics data with other modalities remains absent. In this study, we systematically review the applications of DL in integrating spatial transcriptomics data with other modalities. We first delineate the DL techniques applied in this integration and the key tasks involved. Next, we detail these methods and categorize them based on integrated modality and key task. Furthermore, we summarize the integration strategies of these integration methods. Finally, we discuss the challenges and future directions in integrating spatial transcriptomics with other modalities, aiming to facilitate the development of robust computational methods that more comprehensively exploit multimodal information.

Keywords:  deep learning; image; integration; multi-omics; scRNA-seq; spatial transcriptomics

DOI:  https://doi.org/10.1093/bib/bbae719
Cell Rep Methods. 2025 Jan 08. pii: S2667-2375(24)00329-1. [Epub ahead of print] 100939

Detecting copy-number alterations from single-cell chromatin sequencing data by AtaCNA.

Xiaochen Wang, Zijie Jin, Yang Shi, Ruibin Xi.

  Single-cell assay of transposase-accessible chromatin sequencing (scATAC-seq) unbiasedly profiles genome-wide chromatin accessibility in single cells. In single-cell tumor studies, identification of normal cells or tumor clonal structures often relies on copy-number alterations (CNAs). However, CNA detection from scATAC-seq is difficult due to the high noise, sparsity, and confounding factors. Here, we describe AtaCNA, a computational algorithm that accurately detects high-resolution CNAs from scATAC-seq data. We benchmark AtaCNA using simulation and real data and find AtaCNA's superior performance. Analyses of 10 scATAC-seq datasets show that AtaCNA could effectively distinguish malignant from non-malignant cells. In glioblastoma, endometrial, and ovarian cancer samples, AtaCNA identifies subclones at distinct cellular states, suggesting an important interplay between genetic and epigenetic plasticity. Some tumor subclones only differ in small-scale (10-20 Mb) CNAs, demonstrating the importance of high-resolution CNA detection. These data show that AtaCNA can aid in integrative analysis to understand the complex heterogeneity in cancer.

Keywords:  Bayesian model; CP: Cancer biology; CP: Genetics; change points; copy-number alterations; high resolution; normalization; single-cell chromatin sequencing; tumor cell detection; tumor heterogeneity

DOI:  https://doi.org/10.1016/j.crmeth.2024.100939
Nat Rev Clin Oncol. 2025 Jan 16.

Biological and clinical significance of tumour-infiltrating lymphocytes in the era of immunotherapy: a multidimensional approach.

Miguel Lopez de Rodas, Maria Villalba-Esparza, Miguel F Sanmamed, Lieping Chen, David L Rimm, Kurt A Schalper.

Immune-checkpoint inhibitors (ICIs) have improved clinical outcomes across several solid tumour types. Prominent efforts have focused on understanding the anticancer mechanisms of these agents, identifying biomarkers of response and uncovering resistance mechanisms to develop new immunotherapeutic approaches. This research has underscored the crucial roles of the tumour microenvironment and, particularly, tumour-infiltrating lymphocytes (TILs) in immune-mediated tumour elimination. Numerous studies have evaluated the prognostic and predictive value of TILs and the mechanisms that govern T cell dysfunction, fuelled by technical developments in single-cell transcriptomics, proteomics, high-dimensional spatial platforms and advanced computational models. However, questions remain regarding the definition of TILs, optimal strategies to study them, specific roles of different TIL subpopulations and their clinical implications in different treatment contexts. Additionally, most studies have focused on the abundance of major TIL subpopulations but have not developed standardized quantification strategies or analysed other crucial aspects such as their functional profile, spatial distribution and/or arrangement, tumour antigen-reactivity, clonal diversity and heterogeneity. In this Review, we discuss a conceptual framework for the systematic study of TILs and summarize the evidence regarding their biological properties and biomarker potential for ICI therapy. We also highlight opportunities, challenges and strategies to support future developments in this field.

DOI: https://doi.org/10.1038/s41571-024-00984-x
Heliyon. 2024 Jul 30. 10(14): e33582

DriverDetector: An R package providing multiple statistical methods for cancer driver genes detection and tools for downstream analysis.

Zeyuan Wang, Hong Gu, Pan Qin, Jia Wang.

  Identifying driver genes in cancer is a difficult task because of the heterogeneity of cancer as well as the complex interactions among genes. As sequencing data become more readily available, there is a growing need for detecting cancer driver genes based on statistical and mathematical modeling methods. Currently, plenty of driver gene identification algorithms have been published, but they fail to achieve consistent results. In order to obtain gene sets with high confidence, we present DriverDetector, an R package providing a convenient workflow for cancer driver genes detection and downstream analysis. We develop the background mutation rate calculating module based on the distance between genes in covariate space and binomial test, followed by the driver gene selection module which integrates 11 methods, including two already recognized approaches, a de novo method, and five variants of Fisher's method which are applied to driver gene identification for the first time. Through verification on 12 TCGA datasets, each method is able to identify a set of confirmed driver genes while the number of resulting genes vary significantly across different methods. For robust driver genes detection, a voting strategy based on 10 of the statistical methods is further applied. Results show that the collective prediction based on the voting strategy demonstrates superiority in achieving the consistency of prediction while ensuring a reasonable number of predicted genes and confirmed drivers. By comparing the results of each cancer dataset, we also find that sample size has a huge impact on the number of predicted genes. For downstream analysis, DriverDetector automatically generates plenty of plots and tables to elaborate the results. We propose DriverDetector as a user-friendly tool promoting early diagnosis of cancer and the development of targeted drugs.

Keywords:  Background mutation rate; Cancer driver genes; Genome analysis software

DOI:  https://doi.org/10.1016/j.heliyon.2024.e33582