Divergent Resistance Mechanisms to Immunotherapy Explain Responses in Different Skin Cancers

Advanced insights on tumor-associated macrophages revealed by single-cell RNA sequencing: The intratumor heterogeneity, functional phenotypes, and cellular interactions

Single-cell RNA sequencing (scRNA-seq) is an emerging technology used for cellular transcriptome analysis. The application of scRNA-seq has led to profoundly advanced oncology research, continuously optimizing novel therapeutic strategies. Intratumor heterogeneity extensively consists of all tumor components, contributing to different tumor behaviors and treatment responses. Tumor-associated macrophages (TAMs), the core immune cells linking innate and adaptive immunity, play significant roles in tumor progression and resistance to therapies. Moreover, dynamic changes occur in TAM phenotypes and functions subject to the regulation of the tumor microenvironment. The heterogeneity of TAMs corresponding to the state of the tumor microenvironment has been comprehensively recognized using scRNA-seq. Herein, we reviewed recent research and summarized variations in TAM phenotypes and functions from a developmental perspective to better understand the significance of TAMs in the tumor microenvironment.

Skin basal cell carcinomas assemble a pro-tumorigenic spatially organized and self-propagating Trem2+ myeloid niche

Cancer immunotherapies have revolutionized treatment but have shown limited success as single-agent therapies highlighting the need to understand the origin, assembly, and dynamics of heterogeneous tumor immune niches. Here, we use single-cell and imaging-based spatial analysis to elucidate three microenvironmental neighborhoods surrounding the heterogeneous basal cell carcinoma tumor epithelia. Within the highly proliferative neighborhood, we find that TREM2+ skin cancer-associated macrophages (SCAMs) support the proliferation of a distinct tumor epithelial population through an immunosuppression-independent manner via oncostatin-M/JAK-STAT3 signaling. SCAMs represent a unique tumor-specific TREM2+ population defined by VCAM1 surface expression that is not found in normal homeostatic skin or during wound healing. Furthermore, SCAMs actively proliferate and self-propagate through multiple serial tumor passages, indicating long-term potential. The tumor rapidly drives SCAM differentiation, with intratumoral injections sufficient to instruct naive bone marrow-derived monocytes to polarize within days. This work provides mechanistic insights into direct tumor-immune niche dynamics independent of immunosuppression, providing the basis for potential combination tumor therapies.

Machine Learning of Single Cell Transcriptomic Data From anti-PD-1 Responders and Non-responders Reveals Distinct Resistance Mechanisms in Skin Cancers and PDAC

Immune checkpoint therapies such as PD-1 blockade have vastly improved the treatment of numerous cancers, including basal cell carcinoma (BCC). However, patients afflicted with pancreatic ductal carcinoma (PDAC), one of the deadliest malignancies, overwhelmingly exhibit negative responses to checkpoint therapy. We sought to combine data analysis and machine learning to differentiate the putative mechanisms of BCC and PDAC non-response. We discover that increased MHC-I expression in malignant cells and suppression of MHC and PD-1/PD-L expression in CD8⁺ T cells is associated with nonresponse to treatment. Furthermore, we leverage machine learning to predict response to PD-1 blockade on a cellular level. We confirm divergent resistance mechanisms between BCC, PDAC, and melanoma and highlight the potential for rapid and affordable testing of gene expression in BCC patients to accurately predict response to checkpoint therapies. Our findings present an optimistic outlook for the use of quantitative cross-cancer analyses in characterizing immune responses and predicting immunotherapy outcomes.

Immune checkpoint inhibitors for advanced or metastatic basal cell carcinoma: how much evidence do we need?

Basal cell carcinoma (BCC) is one of the most frequent and most curable tumors at its early stages. BCC rarely metastasizes and its treatment in this setting is still challenging. Hedgehog inhibitors showed an activity in advanced or metastatic disease. However, there is an unmet need for new agents. Immune checkpoint inhibitors have been assessed in melanoma and other cutaneous tumors, and very recently an anti-PD1 was approved for advanced BCC. In this paper, available data are reviewed on experimental and preclinical studies evaluating immunotherapy in BCC, as well as on the clinical evidence supporting the efficacy and safety of immune checkpoint inhibitors for advanced or metastatic BCC based on case reports, case series and clinical trials.

Unifying deterministic and stochastic ecological dynamics via a landscape-flux approach

The frequency distributions can characterize the population-potential landscape related to the stability of ecological states. We illustrate the practical utility of this approach by analyzing a forest-savanna model. Savanna and forest states coexist under certain conditions, consistent with past theoretical work and empirical observations. However, a grassland state, unseen in the corresponding deterministic model, emerges as an alternative quasi-stable state under fluctuations, providing a theoretical basis for the appearance of widespread grasslands in some empirical analyses. The ecological dynamics are determined by both the population-potential landscape gradient and the steady-state probability flux. The flux quantifies the net input/output to the ecological system and therefore the degree of nonequilibriumness. Landscape and flux together determine the transitions between stable states characterized by dominant paths and switching rates. The intrinsic potential landscape admits a Lyapunov function, which provides a quantitative measure of global stability. We find that the average flux, entropy production rate, and free energy have significant changes near bifurcations under both finite and zero fluctuation. These may provide both dynamical and thermodynamic origins of the bifurcations. We identified the variances in observed frequency time traces, fluctuations, and time irreversibility as kinematic measures for bifurcations. This framework opens the way to characterize ecological systems globally, to uncover how they change among states, and to quantify the emergence of quasi-stable states under stochastic fluctuations.