Combined Identification of Novel Markers for Diagnosis and Prognostic of Classic Hodgkin Lymphoma

Immune and stromal transcriptional patterns that influence the outcome of classic Hodgkin lymphoma

Classic Hodgkin lymphoma (cHL) is characterized by a rich immune microenvironment as the main tumor component. It involves a broad range of cell populations, which are largely unexplored, even though they are known to be essential for growth and survival of Hodgkin and Reed-Sternberg cells. We profiled the gene expression of 25 FFPE cHL samples using NanoString technology and resolved their microenvironment compositions using cell-deconvolution tools, thereby generating patient-specific signatures. The results confirm individual immune fingerprints and recognize multiple clusters enriched in refractory patients, highlighting the relevance of: (1) the composition of immune cells and their functional status, including myeloid cell populations (M1-like, M2-like, plasmacytoid dendritic cells, myeloid-derived suppressor cells, etc.), CD4-positive T cells (exhausted, regulatory, Th17, etc.), cytotoxic CD8 T and natural killer cells; (2) the balance between inflammatory signatures (such as IL6, TNF, IFN-γ/TGF-β) and MHC-I/MHC-II molecules; and (3) several cells, pathways and genes related to the stroma and extracellular matrix remodeling. A validation model combining relevant immune and stromal signatures identifies patients with unfavorable outcomes, producing the same results in an independent cHL series. Our results reveal the heterogeneity of immune responses among patients, confirm previous findings, and identify new functional phenotypes of prognostic and predictive utility.

Insights on hematopoietic cell kinase: An oncogenic player in human cancer

Hematopoietic cell kinase (Hck) is a member of the Src family and is expressed in hematopoietic cells. By regulating multiple signaling pathways, HCK can interact with multiple receptors to regulate signaling events involved in cell adhesion, proliferation, migration, invasion, apoptosis, and angiogenesis. However, aberrant expression of Hck in various hematopoietic cells and solid tumors plays a crucial role in tumor-related properties, including cell proliferation and epithelial-mesenchymal transition. In addition, Hck signaling regulates the function of immune cells such as macrophages, contributing to an immunosuppressive tumor microenvironment. The clinical success of various kinase inhibitors targeting the Src kinase family has validated the efficacy of targeting Src, and therapies with highly selective Hck kinase inhibitors are in clinical trials. This article reviews Hck inhibition as an emerging cancer treatment strategy, focusing on the expressions and functions of Hck in tumors and its impact on the tumor microenvironment. It also explores preclinical and clinical pharmacological strategies for Hck targeting to shed light on Hck-targeted tumor therapy.

Applications of machine learning in tumor-associated macrophages

Evaluation of tumor-host interaction and intratumoral heterogeneity in the tumor microenvironment (TME) is gaining increasing attention in modern cancer therapies because it can reveal unique information about the tumor status. As tumor-associated macrophages (TAMs) are the major immune cells infiltrating in TME, a better understanding of TAMs could help us further elucidate the cellular and molecular mechanisms responsible for cancer development. However, the high-dimensional and heterogeneous data in biology limit the extensive integrative analysis of cancer research. Machine learning algorithms are particularly suitable for oncology data analysis due to their flexibility and scalability to analyze diverse data types and strong computation power to learn underlying patterns from massive data sets. With the application of machine learning in analyzing TME, especially TAM’s traceable status, we could better understand the role of TAMs in tumor biology. Furthermore, we envision that the promotion of machine learning in this field could revolutionize tumor diagnosis, treatment stratification, and survival predictions in cancer research. In this article, we described key terms and concepts of machine learning, reviewed the applications of common methods in TAMs, and highlighted the challenges and future direction for TAMs in machine learning.