Cancer cells exist within a complex spatially structured ecosystem composed of resources and different cell types. As the selective pressures imposed by this environment determine the fate of cancer cells, an improved understanding of how this ecosystem evolves will better elucidate how tumors grow and respond to therapy. State of the art imaging methods can now provide highly resolved descriptions of the microenvironment, yielding the data required for a thorough study of its role in tumor growth and treatment resistance. The field of landscape ecology has been studying such species-environment relationship for decades, and offers many tools and perspectives that cancer researchers could greatly benefit from. Here, we discuss one such tool, species distribution modeling (SDM), that has the potential to, among other things, identify critical environmental factors that drive tumor evolution and predict response to therapy. SDMs only scratch the surface of how ecological theory and methods can be applied to cancer, and we believe further integration will take cancer research in exciting new and productive directions. Here we describe how species distribution modeling can be used to quantitatively describe the complex relationship between tumor cells and their microenvironment. Such a description facilitates a deeper understanding of cancers eco-evolutionary dynamics, which in turn sheds light on the factors that drive tumor growth and response to treatment.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7710396 | PMC |
http://dx.doi.org/10.1177/1073274820946804 | DOI Listing |
Stem Cell Res Ther
January 2025
Shenzhen Key Laboratory of Epigenetics and Precision Medicine for Cancers, Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China.
Background: Patient-derived lung cancer organoids (PD-LCOs) demonstrate exceptional potential in preclinical testing and serve as a promising model for the multimodal management of lung cancer. However, certain lung cancer cells derived from patients exhibit limited capacity to generate organoids due to inter-tumor or intra-tumor variability. To overcome this limitation, we have created an in vitro system that employs mesenchymal stromal cells (MSCs) or fibroblasts to serve as a supportive scaffold for lung cancer cells that do not form organoids.
View Article and Find Full Text PDFBreast Cancer Res
January 2025
College of Pharmacy, Seoul National University, Seoul, 08826, South Korea.
Background: Patients with estrogen receptor (ER)-positive breast cancer (BC) can be treated with endocrine therapy targeting ER, however, metastatic recurrence occurs in 25% of the patients who have initially been treated. Secreted proteins from tumors play important roles in cancer metastasis but previous methods for isolating secretory proteins had limitations in identifying novel targets.
Methods: We applied an in situ secretory protein labeling technique using TurboID to analyze secretome from tamoxifen-resistant (TAMR) BC.
Biomark Res
January 2025
Department of Hematology and Medical Oncology, Emory University, 201 Dowman Dr, Atlanta, GA, 30322, USA.
Background: Oncolytic viruses (OVs) are increasingly recognized as promising tools for cancer therapy, as they selectively infect and destroy tumor cells while leaving healthy cells unharmed. Despite considerable progress, the limited therapeutic efficacy of OV-based virotherapy continues to be a significant challenge in cancer treatment.
Methods: The SMAC/DIABLO gene was inserted into the genome of vesicular stomatitis virus (VSV) to generate VSV-S.
Biol Direct
January 2025
School of Medicine, South China University of Technology, Guangzhou, 510006, China.
Background: Pancreatic cancer is characterized by a complex tumor microenvironment that hinders effective immunotherapy. Identifying key factors that regulate the immunosuppressive landscape is crucial for improving treatment strategies.
Methods: We constructed a prognostic and risk assessment model for pancreatic cancer using 101 machine learning algorithms, identifying OSBPL3 as a key gene associated with disease progression and prognosis.
Biomark Res
January 2025
Institute of Biochemistry and Molecular Biology, College of Life Sciences, China Medical University, Taichung, Taiwan.
Background: Up to 23% of breast cancer patients recurred within a decade after trastuzumab treatment. Conversely, one trial found that patients with low HER2 expression and metastatic breast cancer had a positive response to trastuzumab-deruxtecan (T-Dxd). This indicates that relying solely on HER2 as a single diagnostic marker to predict the efficacy of anti-HER2 drugs is insufficient.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!