Systems immunology spanning tumors, lymph nodes, and periphery

Spatial genomics reveals cholesterol metabolism as a key factor in colorectal cancer immunotherapy resistance

Immune checkpoint inhibitors (ICIs) have transformed the treatment landscape across multiple cancer types achieving durable responses for a significant number of patients. Despite their success, many patients still fail to respond to ICIs or develop resistance soon after treatment. We sought to identify early treatment features associated with ICI outcome. We leveraged the MC38 syngeneic tumor model because it has variable response to ICI therapy driven by tumor intrinsic heterogeneity. ICI response was assessed based on the level of immune cell infiltration into the tumor - a well-established clinical hallmark of ICI response. We generated a spatial atlas of 48,636 transcriptome-wide spots across 16 tumors using spatial transcriptomics; given the tumors were difficult to profile, we developed an enhanced transcriptome capture protocol yielding high quality spatial data. In total, we identified 8 tumor cell subsets (e.g., proliferative, inflamed, and vascularized) and 4 stroma subsets (e.g., immune and fibroblast). Each tumor had orthogonal histology and bulk-RNA sequencing data, which served to validate and benchmark observations from the spatial data. Our spatial atlas revealed that increased tumor cell cholesterol regulation, synthesis, and transport were associated with a lack of ICI response. Conversely, inflammation and T cell infiltration were associated with response. We further leveraged spatially aware gene expression analysis, to demonstrate that high cholesterol synthesis by tumor cells was associated with cytotoxic CD8 T cell exclusion. Finally, we demonstrate that bulk RNA-sequencing was able to detect immune correlates of response but lacked the sensitivity to detect cholesterol synthesis as a feature of resistance.

Tumor Microenvironment Drives the Cross-Talk Between Co-Stimulatory and Inhibitory Molecules in Tumor-Infiltrating Lymphocytes: Implications for Optimizing Immunotherapy Outcomes

This review explores some of the complex mechanisms underlying antitumor T-cell response, with a specific focus on the balance and cross-talk between selected co-stimulatory and inhibitory pathways. The tumor microenvironment (TME) fosters both T-cell activation and exhaustion, a dual role influenced by the local presence of inhibitory immune checkpoints (ICs), which are exploited by cancer cells to evade immune surveillance. Recent advancements in IC blockade (ICB) therapies have transformed cancer treatment. However, only a fraction of patients respond favorably, highlighting the need for predictive biomarkers and combination therapies to overcome ICB resistance. A crucial aspect is represented by the complexity of the TME, which encompasses diverse cell types that either enhance or suppress immune responses. This review underscores the importance of identifying the most critical cross-talk between inhibitory and co-stimulatory molecules for developing approaches tailored to patient-specific molecular and immune profiles to maximize the therapeutic efficacy of IC inhibitors and enhance clinical outcomes.

Virtual patient analysis identifies strategies to improve the performance of predictive biomarkers for PD-1 blockade

Patients with metastatic triple-negative breast cancer (TNBC) show variable responses to PD-1 inhibition. Efficient patient selection by predictive biomarkers would be desirable but is hindered by the limited performance of existing biomarkers. Here, we leveraged in silico patient cohorts generated using a quantitative systems pharmacology model of metastatic TNBC, informed by transcriptomic and clinical data, to explore potential ways to improve patient selection. We evaluated and quantified the performance of 90 biomarker candidates, including various cellular and molecular species, at different cutoffs by a cutoff-based biomarker testing algorithm combined with machine learning-based feature selection. Combinations of pretreatment biomarkers improved the specificity compared to single biomarkers at the cost of reduced sensitivity. On the other hand, early on-treatment biomarkers, such as the relative change in tumor diameter from baseline measured at two weeks after treatment initiation, achieved remarkably higher sensitivity and specificity. Further, blood-based biomarkers had a comparable ability to tumor- or lymph node-based biomarkers in identifying a subset of responders, potentially suggesting a less invasive way for patient selection.

Virtual patient analysis identifies strategies to improve the performance of predictive biomarkers for PD-1 blockade

Patients with metastatic triple-negative breast cancer (TNBC) show variable responses to PD-1 inhibition. Efficient patient selection by predictive biomarkers would be desirable, but is hindered by the limited performance of existing biomarkers. Here, we leveraged in-silico patient cohorts generated using a quantitative systems pharmacology model of metastatic TNBC, informed by transcriptomic and clinical data, to explore potential ways to improve patient selection. We tested 90 biomarker candidates, including various cellular and molecular species, by a cutoff-based biomarker testing algorithm combined with machine learning-based feature selection. Combinations of pre-treatment biomarkers improved the specificity compared to single biomarkers at the cost of reduced sensitivity. On the other hand, early on-treatment biomarkers, such as the relative change in tumor diameter from baseline measured at two weeks after treatment initiation, achieved remarkably higher sensitivity and specificity. Further, blood-based biomarkers had a comparable ability to tumor- or lymph node-based biomarkers in identifying a subset of responders, potentially suggesting a less invasive way for patient selection.

Immunological Mechanisms behind Anti-PD-1/PD-L1 Immune Checkpoint Blockade: Intratumoral Reinvigoration or Systemic Induction?

Anti-PD-1/PD-L1 immune checkpoint blockade (ICB) has been widely used to treat many types of cancer. It is well established that PD-L1 expressing cancer cells could directly inhibit the cytotoxicity of PD-1+ T cells via PD-L1-PD-1 interaction. However, histological quantification of intratumoral PD-L1 expression provides limited predictive value and PD-L1 negative patients could still benefit from ICB treatment. Therefore, the current major clinical challenges are low objective response rate and unclear immunological mechanisms behind responding vs. non-responding patients. Here, we review recent studies highlighting the importance of longitudinal pre- and post-ICB treatment on patients with various types of solid tumor to elucidate the mechanisms behind ICB treatment. On one hand, ICB induces changes in the tumor microenvironment by reinvigorating intratumoral PD-1+ exhausted T cells ("releasing the brakes"). On the other hand, ICB can also affect systemic antitumor immunity in the tumor-draining lymph node to induce priming/activation of cancer specific T cells, which is evident by T cell clonal expansion/replacement in peripheral blood. These studies reveal that ICB treatment not only acts on the tumor microenvironment ("battlefield") but also acts on immune organs ("training camp") of patients with solid tumors. A deeper understanding of the immunological mechanisms behind ICB treatment will pave the way for further improvements in clinical response.