Mesenchymal traits at the convergence of tumor-intrinsic and -extrinsic mechanisms of resistance to immune checkpoint blockers

Tumoral and stromal hMENA isoforms impact tertiary lymphoid structure localization in lung cancer and predict immune checkpoint blockade response in patients with cancer

BACKGROUND

Tertiary Lymphoid Structures (TLS) correlate with positive outcomes in patients with NSCLC and the efficacy of immune checkpoint blockade (ICB) in cancer. The actin regulatory protein hMENA undergoes tissue-specific splicing, producing the epithelial hMENA11a linked to favorable prognosis in early NSCLC, and the mesenchymal hMENAΔv6 found in invasive cancer cells and pro-tumoral cancer-associated fibroblasts (CAFs). This study investigates how hMENA isoforms in tumor cells and CAFs relate to TLS presence, localization and impact on patient outcomes and ICB response.

METHODS

Methods involved RNA-SEQ on NSCLC cells with depleted hMENA isoforms. A retrospective observational study assessed tissues from surgically treated N0 patients with NSCLC, using immunohistochemistry for tumoral and stromal hMENA isoforms, fibronectin, and TLS presence. ICB-treated patient tumors were analyzed using Nanostring nCounter and GeoMx spatial transcriptomics. Multiparametric flow cytometry characterized B cells and tissue-resident memory T cells (TRM). Survival and ICB response were estimated in the cohort and validated using bioinformatics pipelines in different datasets.

FINDINGS

Findings indicate that hMENA11a in NSCLC cells upregulates the TLS regulator LTβR, decreases fibronectin, and favors CXCL13 production by TRM. Conversely, hMENAΔv6 in CAFs inhibits LTβR-related NF-kB pathway, reduces CXCL13 secretion, and promotes fibronectin production. These patterns are validated in N0 NSCLC tumors, where hMENA11ahigh expression, CAF hMENAΔv6low, and stromal fibronectinlow are associated with intratumoral TLS, linked to memory B cells and predictive of longer survival. The hMENA isoform pattern, fibronectin, and LTβR expression broadly predict ICB response in tumors where TLS indicates an anti-tumor immune response.

INTERPRETATION

This study uncovers hMENA alternative splicing as an unexplored contributor to TLS-related Tumor Immune Microenvironment (TIME) and a promising biomarker for clinical outcomes and likely ICB responsiveness in N0 patients with NSCLC.

FUNDING

This work is supported by AIRC (IG 19822), ACC (RCR-2019-23669120), CAL.HUB.RIA Ministero Salute PNRR-POS T4, "Ricerca Corrente" granted by the Italian Ministry of Health.

Transcription factors in fibroblast plasticity and CAF heterogeneity

In recent years, research focused on the multifaceted landscape and functions of cancer-associated fibroblasts (CAFs) aimed to reveal their heterogeneity and identify commonalities across diverse tumors for more effective therapeutic targeting of pro-tumoral stromal microenvironment. However, a unified functional categorization of CAF subsets remains elusive, posing challenges for the development of targeted CAF therapies in clinical settings.The CAF phenotype arises from a complex interplay of signals within the tumor microenvironment, where transcription factors serve as central mediators of various cellular pathways. Recent advances in single-cell RNA sequencing technology have emphasized the role of transcription factors in the conversion of normal fibroblasts to distinct CAF subtypes across various cancer types.This review provides a comprehensive overview of the specific roles of transcription factor networks in shaping CAF heterogeneity, plasticity, and functionality. Beginning with their influence on fibroblast homeostasis and reprogramming during wound healing and fibrosis, it delves into the emerging insights into transcription factor regulatory networks. Understanding these mechanisms not only enables a more precise characterization of CAF subsets but also sheds light on the early regulatory processes governing CAF heterogeneity and functionality. Ultimately, this knowledge may unveil novel therapeutic targets for cancer treatment, addressing the existing challenges of stromal-targeted therapies.

Relevance of immune cell and tumor microenvironment imaging in the new era of immunotherapy

Tumor-infiltrating immune cells play a key role against cancer. However, malignant cells are able to evade the immune response and establish a very complex balance in which different immune subtypes may drive tumor progression, metastatization and resistance to therapy. New immunotherapeutic approaches aim at restoring the natural balance and increase immune response against cancer by different mechanisms. The complexity of these interactions and the heterogeneity of immune cell subpopulations are a real challenge when trying to develop new immunotherapeutics and evaluate or predict their efficacy in vivo. To this purpose, molecular imaging can offer non-invasive diagnostic tools like radiopharmaceuticals, contrast agents or fluorescent dyes. These agents can be useful for preclinical and clinical purposes and can overcome [18F]FDG limitations in discriminating between true-progression and pseudo-progression. This review provides a comprehensive overview of immune cells involved in microenvironment, available immunotherapies and imaging agents to highlight the importance of new therapeutic biomarkers and their in vivo evaluation to improve the management of cancer patients.

Combinations of immuno-checkpoint inhibitors predictive biomarkers only marginally improve their individual accuracy

BACKGROUND

There are no accepted universal biomarkers capable to accurately predict response to immuno-checkpoint inhibitors (ICI). Although recent literature has been flooded with studies on ICI predictive biomarkers, available data show that currently approved companion diagnostics either leave out many possible responders, as in the case of PD-L1 testing for first-line metastatic lung cancer, or apply to a small subset of patients, such as the recently approved treatment for microsatellite instability-high or mismatch repair deficiency tumors. In this study, we conducted a survey of the available data on ICI trials with matched genomic or transcriptomic datasets in order to cross-validate the proposed biomarkers, to assess whether their prediction power was confirmed and, mainly, to investigate if their combination was able to generate a better predictive tool.

METHODS

We extracted clinical information and sequencing data details from publicly available datasets, along with a list of possible biomarkers obtained from the recent literature. After an operation of data harmonization, we validated the performance of all the biomarkers taken individually. Furthermore, we tested two strategies to combine the best performing biomarkers in order to improve their predictive value.

RESULTS

When considered individually, some of the biomarkers, such as the ImmunoPhenoScore, and the IFN-γ signature, did not confirm their originally proposed predictive power. The best absolute scoring biomarkers are TIDE, one of the ICB resistance signatures and CTLA4 with a mean AUC > 0.66. Among the combinations tested, generalized linear models showed the best performance with an AUC of 0.78.

CONCLUSIONS

We confirmed that the available biomarkers, taken individually, fail to provide a satisfactory predictive value. Unfortunately, also combination of some of them only provides marginal improvements. Hence, in order to generate a more robust way to predict ICI efficacy it is necessary to analyze and combine additional biomarkers and interrogate a wider set of clinical data.