Meta-analysis of tumor- and T cell-intrinsic mechanisms of sensitization to checkpoint inhibition

Molecular and immunological features associated with long-term benefits in metastatic NSCLC patients undergoing immune checkpoint blockade

INTRODUCTION

Immunotherapy is firmly established as a treatment regimen in various solid tumors, driven by its exceptional benefits in a selected group of patients. Despite widespread adoption of immune checkpoint blockade (ICB) across diverse solid tumors, the quest for a clinically informative biomarker for long-term benefit remains unmet.

METHODS

A total of 49 patients with metastatic NSCLC treated with ICB were included. Long-term (LTR) and short-term responders (STR) were defined as those with a response to ICB lasting more than 24 months or less than 6 months, respectively. Longitudinal blood specimens were collected before ICB treatment initiation and early-on treatment. Plasma ctDNA next-generation sequencing panel (NGS) and serum proteomics were performed. GeoMx DSP on baseline tumor tissue was performed in a subset of patients.

RESULTS

Our analysis revealed specific characteristics of LTR compared with STR, namely higher PD-L1 in tumor cells (p = 0.005) and higher incidence of irAEs (p = 0.001). Genomic features associated with lack of benefit from ICB included co-occurring mutations in KRAS/STK11 and TP53/KMT2D (p < 0.05). At a baseline, LTR patients exhibited higher serum levels of proteins related with apoptosis (CASP8, PRKRA), chemotaxis, immune proteasome, processing of MHC class I (S100A4, PSMD9, RNF41) and immune homeostasis (HAVCR1, ARG1) (p < 0.05). Protein spatial profiling of tumor samples showed higher levels of proteins linked with the presence of immune cells (CD45), T cells (CD8), antigen presentation (HLA-DR) and immune regulation proteins (PD-L1, IDO1) within the tumor and tumor stroma component (p < 0.05) in LTR patients. Serum longitudinal analysis identified a set of proteins that presented distinct dynamics in LTR compared to STR, making them interesting candidates to evaluate as early predictors of treatment efficacy.

CONCLUSIONS

Our multimodal analysis of patients with metastatic NSCLC treated with ICB identified clinicopathological and immunological features associated with long-term benefits. The presence of preexisting antitumor immunity emerged as a strong predictor of long-term benefits, providing insights for potential biomarkers and therapeutic strategies for enhancing ICB outcomes in metastatic NSCLC.

Spatio-temporal T cell tracking for personalized TCR-T designs in childhood cancer

BACKGROUND

Immune checkpoint inhibition (ICI) has revolutionized oncology, offering extended survival and long-term remission in previously incurable cancers. While highly effective in tumors with high mutational burden, lowly mutated cancers, including pediatric malignancies, present low response rate and limited predictive biomarkers.

PATIENTS AND METHODS

We present a framework for the identification and validation of tumor-reactive T cells as a biomarker to quantify ICI efficacy and as candidates for a personalized TCR-T cell therapy. Therefore, we profiled a pediatric malignant rhabdoid tumor patient with complete remission after ICI therapy using deep single-cell T cell receptor (TCR) repertoire sequencing of the tumor microenvironment (TME) and the peripheral blood.

RESULTS

Tracking T cell dynamics longitudinally from the tumor to cells in circulation over a time course of 12 months revealed a systemic response and durable clonal expansion of tumor-resident and ICI-induced TCR clonotypes. We functionally validated tumor reactivity of TCRs identified from the TME and the blood by co-culturing patient-derived tumor cells with TCR-engineered autologous T cells. Here, we observed unexpectedly high frequencies of tumor-reactive TCR clonotypes in the TME and confirmed T cell dynamics in the blood post-ICI to predict tumor-reactivity.

CONCLUSION

These findings strongly support spatio-temporal tracking of T cell activity in response to ICI to inform therapy efficacy and to serve as a source of tumor-reactive TCRs for personalized TCR-T designs.

The application of compressed sensing on tumor mutation burden calculation from overlapped pooling sequencing data

BACKGROUND

Tumor Mutation Burden (TMB) is commonly characterized as the number of non-synonymous somatic SNVs per megabase within the gene region identified through whole exon sequencing or targeted sequencing in a tumor sample. It has been statistically demonstrated that TMB was related to the ability of neoantigen production and used to predict the efficacy of immunotherapy for various types of cancers. However, screening for TMB in patients poses challenges due to the extensive labor and financial resources required for the preparation of large quantities of parallel sequencing libraries.

RESULTS

In this study, we employed compressed sensing (CS) to calculate TMB from overlapped pooling sequencing data, aiming to reduce the sequencing cost by minimizing the number of library builds. Over 90% SNPs could still be detected without a significant loss of mutation information even when the data is pooled from ten different samples. Based on this, the orthogonal matching pursuit (OMP) algorithm and the basic pursuit (BP) algorithm were used to reconstruct TMB from pooling sequencing data. The performance of these two algorithms was evaluated. The BP algorithm consistently performed well across all cases, albeit necessitating extended computational time. The OMP algorithm has been proved to be suitable for scenarios where the original matrix was sparse but it showed low overall performance. Based on an accurate calculation of TMB, we determined that the number of sequencing runs could be reduced to 0.6 times the total number of samples, resulting in a 40% reduction in sequencing cost.

CONCLUSIONS

In conclusion, we calculated TMB from overlapped pooling sequencing data utilizing compressed sensing strategy to reduce sequencing cost. Our findings confirm that the SNP calling from ten samples' pooling sequencing data is feasible. Additionally, we performed an assessment of the reconstruction efficiency of both the BP model and the OMP model.

Next-generation immunotherapeutic approaches for blood cancers: Exploring the efficacy of CAR-T and cancer vaccines

Recent advancements in immunotherapy, particularly Chimeric antigen receptor (CAR)-T cell therapy and cancer vaccines, have significantly transformed the treatment landscape for leukemia. CAR-T cell therapy, initially promising in hematologic cancers, faces notable obstacles in solid tumors due to the complex and immunosuppressive tumor microenvironment. Challenges include the heterogeneous immune profiles of tumors, variability in antigen expression, difficulties in therapeutic delivery, T cell exhaustion, and reduced cytotoxic activity at the tumor site. Additionally, the physical barriers within tumors and the immunological camouflage used by cancer cells further complicate treatment efficacy. To overcome these hurdles, ongoing research explores the synergistic potential of combining CAR-T cell therapy with cancer vaccines and other therapeutic strategies such as checkpoint inhibitors and cytokine therapy. This review describes the various immunotherapeutic approaches targeting leukemia, emphasizing the roles and interplay of cancer vaccines and CAR-T cell therapy. In addition, by discussing how these therapies individually and collectively contribute to tumor regression, this article aims to highlight innovative treatment paradigms that could enhance clinical outcomes for leukemia patients. This integrative approach promises to pave the way for more effective and durable treatment strategies in the oncology field. These combined immunotherapeutic strategies hold great promise for achieving more complete and lasting remissions in leukemia patients. Future research should prioritize optimizing treatment sequencing, personalizing therapeutic combinations based on individual patient and tumor characteristics, and developing novel strategies to enhance T cell persistence and function within the tumor microenvironment. Ultimately, these efforts will advance the development of more effective and less toxic immunotherapeutic interventions, offering new hope for patients battling this challenging disease.

Mutational Landscape of Recurrent/Metastatic Head and Neck Squamous Cell Carcinoma and Association with Immune Checkpoint Inhibitor Outcomes

PURPOSE

Understanding the mutational landscape of recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC) is important in identifying biomarkers to determine which patients may benefit from immune checkpoint inhibitors (ICI).

EXPERIMENTAL DESIGN

The HAWK (NCT02207530), CONDOR (NCT02319044), and EAGLE (NCT02369874) studies evaluated R/M HNSCC treatment with durvalumab or durvalumab-tremelimumab. Tumor tissue samples pooled from HAWK/CONDOR (n = 153) and plasma cell-free DNA samples from EAGLE (n = 285) were analyzed to identify somatic alterations and association with survival.

RESULTS

The mutational landscape was similar in tissue and plasma. Compared with the wild type, TP53 mutations were associated with significantly shorter overall survival (OS; HR; 95% confidence interval) with standard of care (SoC; EAGLE: 2.12; 1.20-3.78) and ICIs (HAWK/CONDOR: 1.49; 1.05-2.12 and EAGLE: 1.44; 0.99-2.10). In EAGLE, patients with TP53 mutations had significantly longer OS with durvalumab-tremelimumab versus SoC (P = 0.045). KMT2D mutations were associated with a trend toward longer OS (HR; 95% confidence interval) versus the wild type in HAWK/CONDOR (0.81; 0.56-1.19) and a trend toward longer OS with ICIs versus SoC in EAGLE. For both mutations, a European Cooperative Oncology Group performance status of 1 was associated with worsened OS, and PD-L1 positivity was associated with improved OS.

CONCLUSIONS

This is the first large-scale study to show the mutational landscape of R/M HNSCC and its association with clinical outcomes in patients treated with ICIs or SoC. The TP53 mutation was a negative prognostic marker; however, treatment with durvalumab-tremelimumab significantly improved survival over SoC. Further investigation of KMT2D as a predictive biomarker for immunotherapy in R/M HNSCC is warranted.

Single-cell meta-analysis of T cells reveals clonal dynamics of response to checkpoint immunotherapy

Despite the crucial role of T cell clones in anti-tumor activity, their characterization and association with clinical outcomes following immune checkpoint inhibitors are lacking. Here, we analyzed paired single-cell RNA sequencing/T cell receptor sequencing of 767,606 T cells across 460 samples spanning 6 cancer types. We found a robust signature of response based on expanded CD8+ clones that differentiates responders from non-responders. Analysis of persistent clones showed transcriptional changes that are differentially induced by therapy in the different response groups, suggesting an improved reinvigoration capacity in responding patients. Moreover, a gene trajectory analysis revealed changes in the pseudo-temporal state of de novo clones that are associated with clinical outcomes. Lastly, we found that clones shared between tumor and blood are more abundant in non-responders and execute distinct transcriptional programs. Overall, our results highlight differences in clonal transcriptional states that are linked to patient response, offering valuable insights into the mechanisms driving effective anti-tumor immunity.

Personalized syringes offer hope for pancreatic cancer patients

Pancreatic cancer has traditionally been considered refractory to immunotherapy. In a follow-up to the first clinical trial of personalized mRNA vaccines against this deadly cancer, published in Nature, Sethna et al. report compelling evidence of long-lasting immunity.

AI-driven predictive biomarker discovery with contrastive learning to improve clinical trial outcomes

Modern clinical trials can capture tens of thousands of clinicogenomic measurements per individual. Discovering predictive biomarkers, as opposed to prognostic markers, remains challenging. To address this, we present a neural network framework based on contrastive learning-the Predictive Biomarker Modeling Framework (PBMF)-that explores potential predictive biomarkers in an automated, systematic, and unbiased manner. Applied retrospectively to real clinicogenomic datasets, particularly for immuno-oncology (IO) trials, our algorithm identifies biomarkers of IO-treated individuals who survive longer than those treated with other therapies. We demonstrate how our framework retrospectively contributes to a phase 3 clinical trial by uncovering a predictive, interpretable biomarker based solely on early study data. Patients identified with this predictive biomarker show a 15% improvement in survival risk compared to those in the original trial. The PBMF offers a general-purpose, rapid, and robust approach to inform biomarker strategy, providing actionable outcomes for clinical decision-making.

A phase 2 trial of pembrolizumab for recurrent Lynch-like versus sporadic endometrial cancers with microsatellite instability (NCT02899793): Updated survival and response analyses

OBJECTIVE

Microsatellite instability-high (MSI-H)/mismatch repair deficiency (dMMR) is a biomarker for response to immune checkpoint inhibitors. We report updated results including objective response rate, progression free survival, and overall survival data with 5-year follow-up in recurrent platinum-resistant, MSI-H, endometrial cancer (EC) patients fully sequenced using whole exome sequencing (WES) and treated within a prospective phase II study with pembrolizumab (NCT02899793).

METHODS

Tumors from patients with measurable MSI-H/dMMR endometrial cancer confirmed by immunohistochemistry, polymerase chain reaction, and MLH-1 methylation assays were sequenced using whole exome sequencing and the FoundationOne platform for the identification of Lynch, Lynch-like, and MLH-1 methylated characteristics before receiving pembrolizumab 200 mg every 3 weeks for up to 24 months. The primary endpoint was objective response rate (ORR), and secondary endpoints were progression free survival (PFS), and overall survival (OS).

RESULTS

After almost 97 person-years of follow-up, the Lynch-like subgroup (n = 6) of MSI-H/dMMR patients continues to exhibit better ORR than the methylated (n = 18) subgroup (100 % versus 44 %, Fisher's exact P = 0.024), as well as improved PFS (unreached for Lynch-like versus 14.6 months, Log-Rank P = 0.005) and improved OS (unreached for Lynch-like versus 32.6 months, Log-Rank P = 0.058). Toxicity was manageable in both groups of MSI-H patients.

CONCLUSION

Mature follow-up results continue to suggest the prognostic significance of Lynch-like versus methylated MSI-H/dMMR features in endometrial cancer patients treated with pembrolizumab in terms of ORR, PFS, and OS. Stratification for these translational aspects may be warranted in future clinical trials with immune checkpoint inhibitors in MSI-H/dMMR endometrial cancer patients.

Why combine and why neoadjuvant? Tumor immunological perspectives on chemoimmunotherapy in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by limited targeted therapies and high recurrence rates. While immune checkpoint inhibitors (ICIs) have shown promise, their efficacy as monotherapy is limited. Clinically, ICIs demonstrate significant benefit primarily when combined with chemotherapy, particularly in the neoadjuvant setting for early-stage TNBC, which yields superior outcomes compared to adjuvant therapy. This review elucidates the tumor immunological principles underlying these observations. We discussed how the suppressive tumor microenvironment (TME), progressive T cell exhaustion, and associated epigenetic scarring constrain ICI monotherapy effectiveness. Crucially, we highlight the immunological advantages of the neoadjuvant approach: the presence of the primary tumor provides abundant antigens, and intact tumor-draining lymph nodes (TDLNs) act as critical sites for ICI-mediated priming and expansion of naïve and precursor exhausted T cells. This robust activation within TDLNs enhances systemic anti-tumor immunity and expands the T cell repertoire, a process less effectively achieved in the adjuvant setting after tumor resection. These mechanisms provide a strong rationale for the improved pathological complete response (pCR) rates and event-free survival observed with neoadjuvant chemoimmunotherapy, as demonstrated in trials like KEYNOTE-522. We further explore the implications for adjuvant therapy decisions based on treatment response, the challenges of ICI resistance, the need for predictive biomarkers, management of immune-related adverse events (irAEs), and future therapeutic directions. Understanding the dynamic interplay between chemotherapy, ICIs, T cells, and the TME, particularly the role of TDLNs in the neoadjuvant context, is essential for optimizing immunotherapy strategies and improving outcomes for patients with TNBC.

PathNetDRP: a novel biomarker discovery framework using pathway and protein-protein interaction networks for immune checkpoint inhibitor response prediction

BACKGROUND

Predicting immune checkpoint inhibitor (ICI) response remains a significant challenge in cancer immunotherapy. Many existing approaches rely on differential gene expression analysis or predefined immune signatures, which may fail to capture the complex regulatory mechanisms underlying immune response. Network-based models attempt to integrate biological interactions, but they often lack a quantitative framework to assess how individual genes contribute within pathways, limiting the specificity and interpretability of biomarkers. Given these limitations, we developed PathNetDRP, a framework that integrates biological pathways, protein-protein interaction networks, and machine learning to identify functionally relevant biomarkers for ICI response prediction.

RESULTS

We introduce PathNetDRP, a novel biomarker discovery approach that applies the PageRank algorithm to prioritize ICI-associated genes, maps them to relevant biological pathways, and calculates PathNetGene scores to quantify their contribution to immune response. Unlike conventional methods that focus solely on gene expression differences, PathNetDRP systematically incorporates biological context to improve biomarker selection. Validation across multiple independent cancer cohorts showed that PathNetDRP achieved strong predictive performance, with cross-validation the area under the receiver operating characteristic curves increasing from 0.780 to 0.940. Interestingly, PathNetDRP did not merely improve predictive accuracy; it also provided insights into key immune-related pathways, reinforcing its potential for identifying clinically relevant biomarkers.

CONCLUSION

The biomarkers identified by PathNetDRP demonstrated robust predictive performance across cross-validation and independent validation datasets, suggesting their potential utility in clinical applications. Furthermore, enrichment analysis highlighted key immune-related pathways, providing a deeper understanding of their role in ICI response regulation. While these findings underscore the promise of PathNetDRP, future work will explore the integration of additional predictive features, such as tumor mutational burden and microsatellite instability, to further refine its applicability.

MANIFEST: Multiomic Platform for Cancer Immunotherapy

Immunotherapy has revolutionized survival outcomes for many patients diagnosed with cancer. However, biomarkers that can reliably distinguish treatment responders from nonresponders, predict potential life-threatening and life-changing drug-induced toxicities, or rationalize treatment choices are still lacking. In response to this unmet clinical need, we introduce Multiomic ANalysis of Immunotherapy Features Evidencing Success and Toxicity, a tumor type-agnostic platform to provide deep profiling of patients receiving immunotherapy that will enable integrative identification of biomarkers and discovery of novel targets using artificial intelligence and machine learning.

Cancer immune evasion, immunoediting and intratumour heterogeneity

Cancers can avoid immune-mediated elimination by acquiring traits that disrupt antitumour immunity. These mechanisms of immune evasion are selected and reinforced during tumour evolution under immune pressure. Some immunogenic subclones are effectively eliminated by antitumour T cell responses (a process known as immunoediting), which results in a clonally selected tumour. Other cancer cells arise to resist immunoediting, which leads to a tumour that includes several distinct cancer cell populations (referred to as intratumour heterogeneity (ITH)). Tumours with high ITH are associated with poor patient outcomes and a lack of responsiveness to immune checkpoint blockade therapy. In this Review, we discuss the different ways that cancer cells evade the immune system and how these mechanisms impact immunoediting and tumour evolution. We also describe how subclonal antigen presentation in tumours with high ITH can result in immune evasion.

CXCL9 and CXCL13 shape endometrial cancer immune-activated microenvironment via tertiary lymphoid structure formation

Immune checkpoint inhibitor (ICI) therapy has been successfully applied to various cancers; however, not all patients respond to ICI therapy. Tumors with an immune-activated environment are highly responsive to ICIs. To identify the cells and molecules essential to the formation of an immune-activated cancer microenvironment, we focused on the tertiary lymphoid structure (TLS) and performed histological and genomic analyses using endometrial cancer material. In the high immunogenic group, numerous TLSs were observed, and CXCL9 and CXCL13 expression was markedly increased. CXCL9-positive antigen-presenting and CXCL13-positive follicular dendritic cells were distributed in the T- and B-cell zones of TLSs, respectively. A group of molecules whose expression was upregulated along with CXCL9 and CXCL13 expression was strongly associated with cellular immunity. These results suggest that CXCL9-expressing antigen-presenting cells and CXCL13-expressing follicular dendritic cells coordinately shape the immune-activated microenvironment through TLS formation. The current findings will contribute to a better understanding of the mechanisms underlying the activated cancer immune microenvironment, thereby advancing the field of precision cancer medicine.

Resistance to PD-1/PD-L1 immune checkpoint blockade in advanced non-small cell lung cancer

Lung cancer is one of the most common malignant tumors, of which non-small cell lung cancer (NSCLC) accounts for about 85 %. Although immune checkpoint inhibitors (ICIs), particularly PD-1/PD-L1 inhibitors, have significantly improved the prognosis of patients with NSCLC. There are still many patients do not benefit from ICIs. Primary resistance remains a major challenge in advanced NSCLC. The cancer-immunity cycle describes the process from antigen release to T cell recognition and killing of the tumor, which provides a framework for understanding anti-tumor immunity. The classical cycle consists of seven steps, and alterations at each stage can result in resistance. This review examines the current status of PD-1/PD-L1 blockade in the treatment of advanced NSCLC and explores potential mechanisms of resistance. We summarize the latest clinical trials of PD-1/PD-L1 inhibitors combined with other therapies and explore potential targets for overcoming primary resistance to PD-1/PD-L1 inhibitors.

Inhibition of MBTPS1 enhances antitumor immunity and potentiates anti-PD-1 immunotherapy

Despite advances in cancer immunotherapy, colorectal cancer patients exhibit limited therapeutic responses. Therefore, the exploration of strategies combining immunotherapy with adjuvant approaches to enhance adaptive immune responses is in demand. Here, we perform a customized in vivo CRISPR-Cas9 screen to target genes encoding membrane and secreted proteins in CRC mouse models with different immune characteristics. We observe that loss of membrane-bound transcription factor site-1 protease (MBTPS1) in tumor cells enhances antitumor immunity and potentiates anti-PD-1 therapy. Mechanistic studies reveal that tumor cell-intrinsic MBTPS1 competes with USP13 for binding to STAT1, thereby disrupting the USP13-dependent deubiquitination-mediated STAT1 stabilization. The upregulated STAT1-transcribed chemokines including CXCL9, CXCL10, and CXCL11, promote CXCR3+CD8+ T cell infiltration. Notably, the regulatory role of MBTPS1 in antitumor immunity operates independently of its classic function in cleaving membrane-bound transcription factors. Collectively, our results provide a theoretical basis for MBTPS1 as a potential immunotherapy target.

Deciphering immune predictors of immunotherapy response: A multiomics approach at the pan-cancer level

Immune checkpoint blockade (ICB) therapy has transformed cancer treatment, yet many patients fail to respond. Employing single-cell multiomics, we unveil T cell dynamics influencing ICB response across 480 pan-cancer and 27 normal tissue samples. We identify four immunotherapy response-associated T cells (IRATs) linked to responsiveness or resistance and analyze their pseudotemporal patterns, regulatory mechanisms, and T cell receptor clonal expansion profiles specific to each response. Notably, transforming growth factor β1 (TGF-β1)+ CD4+ and Temra CD8+ T cells negatively correlate with therapy response, in stark contrast to the positive response associated with CXCL13+ CD4+ and CD8+ T cells. Validation with a cohort of 23 colorectal cancer (CRC) samples confirms the significant impact of TGF-β1+ CD4+ and CXCL13+ CD4+ and CD8+ T cells on ICB efficacy. Our study highlights the effectiveness of single-cell multiomics in pinpointing immune markers predictive of immunotherapy outcomes, providing an important resource for crafting targeted immunotherapies for successful ICB treatment across cancers.

Inflammation and cancer cell survival: TRAF2 as a key player

TNF receptor-associated factor 2 (TRAF2) plays a crucial role in both physiological and pathological processes. It takes part in the regulation of cell survival and death, tissue regeneration, development, endoplasmic reticulum stress response, autophagy, homeostasis of the epithelial barrier and regulation of adaptive and innate immunity. Initially identified for its interaction with TNF receptor 2 (TNFR2), TRAF2 contains a TRAF domain that enables homo- and hetero-oligomerization, allowing it to interact with multiple receptors and signaling molecules. While best known for mediating TNFR1 and TNFR2 signaling, TRAF2 also modulates other receptor pathways, including MAPK, NF-κB, and Wnt/β-catenin cascades. By regulating NF-κB-inducing kinase (NIK), TRAF2 is a key activator of the alternative NF-κB pathway, linking it to inflammatory diseases, immune dysfunction, and tumorigenesis. In the innate immune system, TRAF2 influences macrophage differentiation, activation, and survival and stimulates natural killer cell cytotoxicity. In the adaptive immune system, it represses effector B- and T-cell activity while sustaining regulatory T-cell function, thus promoting immune suppression. The lack of fine-tuning of TRAF2 activity leads to excessive NF-kB activation, driving chronic inflammation and autoimmunity. Although TRAF2 can act as a tumor suppressor, it is predominantly described as a tumor promoter, as its expression has been correlated with increased metastatic potential and poorer prognosis in several types of cancer. Targeting TRAF2 or TRAF2-dependent signaling pathways might represent a promising anti-cancer therapeutic strategy.

Navigating established and emerging biomarkers for immune checkpoint inhibitor therapy

Immune checkpoint inhibitors (ICIs) have improved outcomes of patients with many different cancers. These antibodies target molecules such as programmed cell death 1 (PD-1) or cytotoxic T lymphocyte associated protein 4 (CTLA-4) which normally function to limit immune activity. Treatment with ICIs reactivates T cells to destroy tumor cells in a highly specific manner, which in some patients, results in dramatic remissions and durable disease control. Over the last decade, much effort has been directed at characterizing factors that drive efficacy and resistance to ICI therapy. Food and Drug Administration (FDA)-approved biomarkers for ICI therapy have facilitated more judicious treatment of cancer patients and transformed the field of precision oncology. Yet, adaptive immunity against cancers is complex, and newer data have revealed the potential utility of other biomarkers. In this review, we discuss the utility of currently approved biomarkers and highlight how emerging biomarkers can further improve the identification of patients who benefit from ICIs.

The MLL3/GRHL2 complex regulates malignant transformation and anti-tumor immunity in squamous cancer

Upper aerodigestive squamous cell carcinoma (UASCC) presents significant challenges in clinical management due to its aggressive nature. Here, we elucidate the role of MLL3 mutations as early, clonal genomic events in UASCC tumorigenesis, highlighting their role as foundational drivers of cancer development. Utilizing CRISPR-edited, cross-species organoid modeling, we demonstrate that loss of MLL3 contributes to early squamous neoplastic evolution. Furthermore, we identify an MLL3/GRHL2 protein complex that regulates the UASCC epigenome, particularly impacting immune response pathways. Notably, a novel MLL3/GRHL2-IRF1 axis promotes the expression of Th1 chemokines, enhancing anti-tumor immunity by facilitating T cell infiltration into the tumor microenvironment. Consequently, MLL3 regulates the in vivo efficacy of immune checkpoint blockade (ICB) therapy, corroborated by the strong association between MLL3 expression and human patients' clinical response to ICB therapy. Our work underscores the significance of MLL3 in UASCC pathogenesis and highlights the interplay between MLL3/GRHL2 and immune response pathways as potential therapeutic targets for UASCC treatment.

Case Report: Lasting complete response to pembrolizumab in mismatch repair-deficient cardiac sarcoma: a genomic characterization

Sarcomas are traditionally considered "cold" tumors with poor response to immunotherapy. However, evidence accumulating over the last years shows that immune checkpoint inhibitors (ICIs) may have a role in selected sarcoma patients according to predictive markers. Here, we report the case of a woman diagnosed with a primary cardiac undifferentiated sarcoma. Following failure of standard first line chemotherapy, high-throughput sequencing (HTS) revealed a high tumor mutational burden (TMB), pathogenic mutations in FAT1 and NOTCH2 and a microsatellite instability (MSI)-associated signature. Immunohistochemistry confirmed mismatch repair-deficiency (MMRd) and abundant CD8+ tumor-infiltrating lymphocytes (TILs), in the absence of tertiary lymphoid structures. The patient was, therefore, treated with the ICI pembrolizumab, reaching a complete response that continues to persist at last follow-up, more than seven years from initial diagnosis and nearly six years from initiation of ICI treatment. This case illustrates the importance of performing HTS in rare sarcomas given the availability of efficient therapies, such as those for tumors displaying high TMB or MMRd/MSI. In agreement with other reports, it supports the contention that MMRd/MSI status and high numbers of TILs are valuable predictive markers of response to immunotherapy in sarcomas.

Recent Advances in Immune Checkpoint Inhibitors for Triple-Negative Breast Cancer

While immunotherapy has transformed treatment across various cancers, its impact on breast cancer is relatively limited. Recent advances have established immunotherapy as an effective approach for triple-negative breast cancer (TNBC), an aggressive subtype with limited therapeutic targets and poor prognosis. Specifically, pembrolizumab, an immune checkpoint inhibitor (ICI), is now approved for both first-line metastatic and early-stage TNBC. In metastatic TNBC, combining ICIs with chemotherapy, particularly pembrolizumab, has demonstrated survival benefits in patients with PD-L1-positive disease. However, extending these benefits to broader populations has proven challenging, highlighting the need for better patient selection and novel strategies. Emerging approaches include combining ICIs with antibody-drug conjugates, PARP inhibitors, dual ICIs, and bispecific antibodies targeting angiogenesis and immune checkpoints. These strategies aim to overcome resistance and expand immunotherapy's efficacy beyond the PD-1/PD-L1 pathway. In early-stage disease, pembrolizumab combined with chemotherapy in the neoadjuvant setting has significantly improved pathologic complete response, event-free survival, and overall survival, establishing a new standard of care. Ongoing research aims to determine the optimal timing for ICI administration, explore less toxic chemotherapy backbones, utilize biomarkers for personalized treatment, and assess whether adding complementary treatments, such as radiation therapy for high-risk cases, can improve outcomes. This review examines the successes and setbacks of ICI use in TNBC, offering a comprehensive overview of current practices and future directions. It emphasizes optimizing ICI timing, leveraging biomarkers, and integrating novel agents to refine treatment approaches for both metastatic and early-stage TNBC. As immunotherapy continues to evolve, future research must address the unmet needs of this challenging breast cancer subtype, offering hope for improved outcomes.

Genetic immune escape in cancer: timing and implications for treatment

Genetic immune escape (GIE) alterations pose a significant challenge in cancer by enabling tumors to evade immune detection. These alterations, which can vary significantly across cancer types, may often arise early in clonal evolution and contribute to malignant transformation. As tumors evolve, GIE alterations are positively selected, allowing immune-resistant clones to proliferate. In addition to genetic changes, the tumor microenvironment (TME) and non-genetic factors such as inflammation, smoking, and environmental exposures play crucial roles in promoting immune evasion. Understanding the timing and mechanisms of GIE, alongside microenvironmental influences, is crucial for improving early detection and developing more effective therapeutic interventions. This review highlights the implications of GIE in cancer development and immunotherapy resistance, and emphasizes the need for integrative approaches.

Antigen-presenting cancer-associated fibroblasts in murine pancreatic tumors differentially control regulatory T cell phenotype and function via CXCL9 and CCL22

Pancreatic ductal adenocarcinoma (PDAC) is characterized by a complex tumor microenvironment (TME) including stromal cells that influence resistance to therapy. Recent studies have revealed that stromal cancer-associated fibroblasts (CAFs) are heterogeneous in origin, gene expression, and function. Antigen-presenting CAFs (apCAFs), are defined by major histocompatibility complex (MHC)-II expression and can activate effector CD4 + T cells that have the potential to contribute to the anti-cancer immune response, but also can induce regulatory T cell (Treg) differentiation. Whether apCAFs promote or restrain the antitumor response remains uncertain. Using tumor clones of the KPC murine PDAC model differing in sensitivity to immune checkpoint blockade (ICB), we found that immunosensitive (sKPC) tumors were characterized by higher immune cell and apCAF infiltration than resistant (rKPC) tumors. IMC analysis showed proximity of apCAFs and CD4 + T cells in both sKPC and rKPC tumors implicating interaction within the TME. apCAF-depleted sKPC tumor-bearing mice had diminished sensitivity to ICB. apCAFs from both sKPC and rKPC tumors activated tumor-infiltrating CD4 + T cells and induced Treg differentiation. However, transcriptomic analysis showed that Tregs induced by apCAFs were overexpressed for immunosuppressive genes in rKPCs relative to sKPCs, and that this is associated with differential chemokine signaling from apCAFs depending on tumor origin. Together these data implicate apCAFs as important mediators of the antitumor immune response, modulation of which could facilitate the development of more effective anti-tumor immune based approaches for PDAC patients.

Integrated single-cell and spatial analysis identifies context-dependent myeloid-T cell interactions in head and neck cancer immune checkpoint blockade response

Background

Approximately 15-20% of head and neck cancer squamous cell carcinoma (HNSCC) patients respond favorably to immune checkpoint blockade (ICB). Previous single-cell RNA-Seq (scRNA-Seq) studies identified immune features, including macrophage subset ratios and T-cell subtypes, in HNSCC ICB response. However, the spatial features of HNSCC-infiltrated immune cells in response to ICB treatment need to be better characterized.

Methods

Here, we perform a systematic evaluation of cell interactions between immune cell types within the tumor microenvironment using spatial omics data using complementary techniques from both 10X Visium spot-based spatial transcriptomics and Nanostring CosMx single-cell spatial omics with RNA gene panel including 435 ligands and receptors. In this study, we used integrated bioinformatics analyses to identify cellular neighborhoods of co-localizing cell types in single-cell spatial transcriptomics and proteomics data. In addition, we used both publicly available scRNA-Seq and in-house spatial RNA-Seq data to identify spatially constrained Ligand-Receptor interactions in Responder patients.

Results

With 522,399 single cells profiled with both RNA and protein from 26 patients, in addition to spot-resolved spatial RNA-Seq from 8 patients treated with ICB together with bioinformatics analysis of publicly available single-cell and bulk RNA-Seq, we have identified a spatial and cell-type specific context-dependency of myeloid and T cell interaction difference between Responders and Non-Responders. We defined further cellular neighborhood and the sources of chemokine CXCL9/10-CXCR3 interactions in Responders, emerging targets in ICB, as well as CXCL16-CXCR6, CCL4/5-CCR5, and other underappreciated and potential markers and targets for ICB response in HNSCC. In addition, we have contributed a rich data resource of cell-cell Ligand Receptor interactions for the immunotherapy and HNSCC research community.

Discussion

Our work provides a comprehensive single-cell and spatial atlas of immune cell interactions that correlate with response to ICB in HNSCC. We showcase how integrating multiple technologies and bioinformatics approaches can provide new insights into potential immune-based biomarkers of ICB response. Our results suggested refining future studies using preclinical animal models in a more context-specific manner to elucidate potential underlying mechanisms that lead to improved ICB responses.

Pan-cancer analysis of co-inhibitory molecules revealing their potential prognostic and clinical values in immunotherapy

Background

The widespread use of immune checkpoint inhibitors (anti-CTLA4 or PD-1) has opened a new chapter in tumor immunotherapy by providing long-term remission for patients. Unfortunately, however, these agents are not universally available and only a minority of patients respond to them. Therefore, there is an urgent need to develop novel therapeutic strategies targeting other co-inhibitory molecules. However, comprehensive information on the expression and prognostic value of co-inhibitory molecules, including co-inhibitory receptors and their ligands, in different cancers is not yet available.

Methods

We investigated the expression, correlation, and prognostic value of co-inhibitory molecules in different cancer types based on TCGA, UCSC Xena, TIMER, CellMiner datasets. We also examined the associations between the expression of these molecules and the extent of immune cell infiltration. Besides, we conducted a more in-depth study of VISTA.

Result

The results of differential expression analysis, correlation analysis, and drug sensitivity analysis suggest that CTLA4, PD-1, TIGIT, LAG3, TIM3, NRP1, VISTA, CD80, CD86, PD-L1, PD-L2, PVR, PVRL2, FGL1, LGALS9, HMGB1, SEMA4A, and VEGFA are associated with tumor prognosis and immune cell infiltration. Therefore, we believe that they are hopefully to serve as prognostic biomarkers for certain cancers. In addition, our analysis indicates that VISTA plays a complex role and its expression is related to TMB, MSI, cancer cell stemness, DNA/RNA methylation, and drug sensitivity.

Conclusions

These co-inhibitory molecules have the potential to serve as prognostic biomarkers and therapeutic targets for a broad spectrum of cancers, given their strong associations with key clinical metrics. Furthermore, the analysis results indicate that VISTA may represent a promising target for cancer therapy.

Sex-Related Differences in Immunotherapy Toxicities: Insights into Dimorphic Responses

Significant sex-based differences exist in the immune system and antitumor immune responses, potentially leading to variations in both the efficacy and toxicity of anticancer immunotherapies. Women generally mount stronger innate and adaptive immune responses than men, which can result in more severe immune-related adverse events (irAEs) during treatments with immune checkpoint inhibitors (ICIs). However, the importance of sex dimorphism in the safety of cancer immunotherapy remains underexplored in clinical oncology, despite its profound implications for treatment outcomes. Our review highlights the critical influence of biological sex on pharmacokinetics, pharmacodynamics, and immune responses, shaping ICI efficacy and the prevalence, type, and severity of irAEs. Integrating sex as a critical variable in cancer treatment and clinical trial design is essential for personalizing therapeutic strategies, bridging existing knowledge gaps, and enhancing survival rates and quality of life for patients across genders.

Genetically engineered macrophages reverse the immunosuppressive tumor microenvironment and improve immunotherapeutic efficacy in TNBC

The main challenges in current immunotherapy for triple-negative breast cancer (TNBC) lie in the immunosuppressive tumor microenvironment (TME). Considering tumor-associated macrophages (TAMs) are the most abundant immune cells in the TME, resetting TAMs is a promising strategy for ameliorating the immunosuppressive TME. Here, we developed genetically engineered macrophages (GEMs) with gene-carrying adenoviruses, to maintain the M1-like phenotype and directly deliver the immune regulators interleukin-12 and CXCL9 into local tumors, thereby reversing the immunosuppressive TME. In tumor-bearing mice, GEMs demonstrated targeted enrichment in tumors and successfully reprogramed TAMs to M1-like macrophages. Moreover, GEMs significantly enhanced the accumulation, proliferation, and activation of CD8+ T cells, mature dendritic cells, and natural killer cells within tumors, while diminishing M2-like macrophages, immunosuppressive myeloid-derived suppressor cells, and regulatory T cells. This treatment efficiently suppressed tumor growth. In addition, combination therapy with GEMs and anti-programmed cell death protein 1 further improved interferon-γ+CD8+ T cell percentages and tumor inhibition efficacy in an orthotopic murine TNBC model. Therefore, this study provides a novel strategy for reversing the immunosuppressive TME and improving immunotherapeutic efficacy through live macrophage-mediated gene delivery.

Tumor Heterogeneity and the Immune Response in Non-Small Cell Lung Cancer: Emerging Insights and Implications for Immunotherapy

Resistance to immune checkpoint inhibitors (ICIs) represents a major challenge for the effective treatment of non-small cell lung cancer (NSCLC). Tumor heterogeneity has been identified as an important mechanism of treatment resistance in cancer and has been increasingly implicated in ICI resistance. The diversity and clonality of tumor neoantigens, which represent the target epitopes for tumor-specific immune cells, have been shown to impact the efficacy of immunotherapy. Advances in genomic techniques have further enhanced our understanding of clonal landscapes within NSCLC and their evolution in response to therapy. In this review, we examine the role of tumor heterogeneity during immune surveillance in NSCLC and highlight its spatial and temporal evolution as revealed by modern technologies. We explore additional sources of heterogeneity, including epigenetic and metabolic factors, that have come under greater scrutiny as potential mediators of the immune response. We finally discuss the implications of tumor heterogeneity on the efficacy of ICIs and highlight potential strategies for overcoming therapeutic resistance.

Comprehensive immunophenotyping of gastric adenocarcinoma identifies an inflamed class of tumors amenable to immunotherapies

BACKGROUND

Gastric adenocarcinoma (GAC) imposes a considerable global health burden. Molecular profiling of GAC from the tumor microenvironment perspective through a multi-omics approach is eagerly awaited in order to allow a more precise application of novel therapies in the near future.

METHODS

To better understand the tumor-immune interface of GAC, we identified an internal cohort of 82 patients that allowed an integrative molecular analysis including mutational profiling by whole-exome sequencing, RNA gene expression of 770 genes associated with immune response, and multiplex protein expression at spatial resolution of 34 immuno-oncology targets at different compartments (tumorous cells and immune cells). Molecular findings were validated in 595 GAC from the TCGA and ACRG external cohorts with available multiomics data. Prediction of response to immunotherapies of the discovered immunophenotypes was assessed in 1039 patients with cancer from external cohorts with available transcriptome data.

RESULTS

Unsupervised clustering by gene expression identified a subgroup of GAC that includes 52% of the tumors, the so-called Inflamed class, characterized by high tumor immunogenicity and cytotoxicity, particularly in the tumor center at protein level, with enrichment of PIK3CA and ARID1A mutations and increased presence of exhausted CD8+ T cells as well as co-inhibitory receptors such as PD1, CTLA4, LAG3, and TIGIT. The remaining 48% of tumors were called non-inflamed based on the observed exclusion of T cell infiltration, with an overexpression of VEGFA and higher presence of TP53 mutations, resulting in a worse clinical outcome. A 10-gene RNA signature was developed for the identification of tumors belonging to these classes, demonstrating in evaluated datasets comparable clinical utility in predicting response to current immunotherapies when tested against other published gene signatures.

CONCLUSIONS

Comprehensive immunophenotyping of GAC identifies an inflamed class of tumors that complements previously proposed tumor-based molecular clusters. Such findings may provide the rationale for exploring novel immunotherapeutic approaches for biomarker-enriched populations in order to improve GAC patient's survival.

Overview on biomarkers for immune oncology drugs

Although immune checkpoint inhibitors (ICIs) are widely used in clinical oncology, less than half of treated cancer patients derive benefit from this therapy. Both tumor- and host-related variables are implicated in response to ICIs. The predictive value of PD-L1 expression is confined only to several cancer types, so this molecule is not an agnostic biomarker. Highly elevated tumor mutation burden (TMB) caused either by excessive carcinogenic exposure or by a deficiency in DNA repair is a reliable indicator for ICI efficacy, as exemplified by tumors with high-level microsatellite instability (MSI-H). Other potentially relevant tumor-related characteristics include gene expression signatures, pattern of tumor infiltration by immune cells, and, perhaps, some immune-response modifying somatic mutations. Host-related factors have not yet been comprehensively considered in relevant clinical trials. Microbiome composition, markers of systemic inflammation [e.g., neutrophil-to-lymphocyte ratio (NLR)], and human leucocyte antigen (HLA) diversity may influence the efficacy of ICIs. Studies on ICI biomarkers are likely to reveal modifiable tumor or host characteristics, which can be utilized to direct the antitumor immune defense. Examples of the latter approach include tumor priming to immune therapy by cytotoxic drugs and elevation of ICI efficacy by microbiome modification.

Exome sequencing shows same pattern of clonal tumor mutational burden, intratumor heterogenicity and clonal neoantigen between autologous tumor and Vigil product

Retrospective data support overall survival (OS) advantage to high clonal tumor mutation burden (cTMB), high clonal neoantigen load (cNEO) and low intratumor heterogeneity (ITH) in cancer patients who receive immunotherapy. In order to explore this relationship prospectively with Vigil, a triple function targeted immunotherapy involving ovarian cancer patients in long term follow up of the Phase 2b VITAL trial, we developed an exome sequencing procedure and associated bioinformatics pipeline to determine clonal signal patterns. DNA libraries containing exome sequences tagged with unique molecular identifiers (UMI) were prepared from paired samples and sequenced on Illumina sequencers to high coverage depths of ~ 930X (tumor) and ~ 130X (normal). Raw sequence reads were processed into optimized binary alignment map (BAM) files, using the UMI information. The BAM files were inputted into modules for calling MHC-I alleles, annotating single nucleotide variants (SNVs) and small insertions/deletions (InDels), and for determination of allelic copy number. The outputs were used to predict the sequence of peptide neoantigens and to perform clonality analysis in order to assign each SNV and InDel in a patient tumor sample to a primary clone or subclone. The Clonal Neoantigen pipeline was further assessed using whole exome Illumina sequencing data from three previously published studies. Evaluation of the pipeline using synthetic sequencing data from a sub-clonal deconvolution tool benchmarking study, showed positive predictive value (PPV) and positive percent agreement (PPA) of > 97.5% and > 96.5%, respectively, for SNV and InDel detection with minimum requirements for variant density and allele fraction. Haplotype calls from the Clonal Neoantigen pipeline MHC-I/ MHC-II typing module matched a published benchmark for 91.5% of the calls in a sample of 99 patients. Analysis of exome sequencing data from 14 patients with advanced melanoma revealed a strong correlation between cTMB values determined by the Clonal Neoantigen pipeline as compared to those calculated from the published data (R2 = 0.99). Following validation, the wet lab process and Clonal Neoantigen pipeline was applied to a set of matched normal, tumor, and Vigil product samples from 9 (n = 27 samples) ovarian cancer subjects entered into the VITAL (CL-PTL-119) trial. Results demonstrated marked correlation (R2 = 0.98) of cTMB between tumor used to construct Vigil and Vigil product. Correlation between tumor and Vigil for the cNEO and ITH metrics, showed R2 values of 0.95 and 0.87, respectively. The consistency of the Clonal Neoantigen pipeline results with previously published data as well as the agreement between results for tumor and Vigil for the entire system provide a strong basis of support for utilization of this pipeline for prospective determination of cTMB, cNEO, and ITH values in clinical tumor tissue in order to explore possible correlative relationships with clinical response parameters.

Clinical outcomes and molecular characteristics of lung-only and liver-only metastatic pancreatic cancer: results from a real-world evidence database

BACKGROUND

Previous research demonstrates longer survival for patients with lung-only metastatic pancreatic adenocarcinoma (mPDAC) compared to liver-only mPDAC. The objective of this study is to understand the survival differences, impact of chemotherapy, and associated genomic features of mPDAC that is isolated to either the liver or lung.

PATIENTS AND METHODS

Longitudinal clinical outcomes and molecular sequencing data were retrospectively analyzed across 831 patients with PDAC across all stages whose tumors first metastasized to the liver or lung. Survival differences were evaluated using Cox regression. Mutational frequency differences were evaluated using Fisher's exact test.

RESULTS

Median overall survival (mOS) was shorter in patients with liver-only metastasis (1.3y [1.2-1.4], n = 689) compared to lung-only metastasis (2.1y [1.9-2.5], n = 142) (P = .000000588, HR = 2.00 [1.53-2.63]. Survival differences were observed regardless of choice of 1st-line standard-of-care therapy. For 5-fluorouracil-based therapies, mOS for liver-only mPDAC was 1.4y [1.3-1.6] (n = 211) compared to 2.1y [1.8-3.3] for lung-only mPDAC (n = 175) (P = .008113, HR = 1.75 [1.16-2.65]). For gemcitabine/nab-paclitaxel therapy, mOS for liver-only mPDAC was 1.2y [1.1-1.5] (n = 175) compared to 2.1y [1.6-3.4] for lung-only disease (n = 32) (P = .01863, HR = 1.84 [1.11-3.06]). PDAC tumors with liver-only metastases were modestly enriched (unadjustable P < .05) for: TP53 mutations, MYC amplifications, inactivating CDK2NA alterations, inactivating SMAD alterations, and SWI/SWF pathway mutations. PDAC tumors with lung-only metastases were enriched for: STK11 mutations, CCND1 amplifications, and GNAS alterations.

CONCLUSION

Patients with lung-only mPDAC demonstrate an improved prognosis relative to those with liver-only mPDAC. Responses to chemotherapy do not explain these differences. Organotropic metastatic tumor diversity is mirrored at the molecular level in PDAC.

Sex hormones, the anticancer immune response, and therapeutic opportunities

Sex-based differences have been observed in the incidence and prognosis of various cancers, as well as in the response to immune check point inhibitors (ICIs). These disparities are partially attributed to sex-based differences in the molecular characteristics of the anticancer immune response, which are largely influenced by sex hormones. Here, we provide a comprehensive overview on how sex hormones affect innate and adaptive immunity and contribute to shaping the features of tumor immune microenvironment and response to anticancer immunotherapy. We also discuss the promising potential and challenges of combining sex hormone manipulation with anticancer immunotherapy as new therapeutic strategy. We surmise that a sex-based perspective should be part of precision medicine approaches, and sex hormones manipulation provides opportunities for innovative immune therapeutic approaches.

Pharmacological activation of STAT1-GSDME pyroptotic circuitry reinforces epigenetic immunotherapy for hepatocellular carcinoma

BACKGROUND

Genomic screening uncovered interferon-gamma (IFNγ) pathway defects in tumours refractory to immune checkpoint blockade (ICB). However, its non-mutational regulation and reversibility for therapeutic development remain less understood.

OBJECTIVE

We aimed to identify ICB resistance-associated druggable histone deacetylases (HDACs) and develop a readily translatable combination approach for patients with hepatocellular carcinoma (HCC).

DESIGN

We correlated the prognostic outcomes of HCC patients from a pembrolizumab trial (NCT03419481) with tumourous cell expressions of all HDAC isoforms by single-cell RNA sequencing. We investigated the therapeutic efficacy and mechanism of action of selective HDAC inhibition in 4 ICB-resistant orthotopic and spontaneous models using immune profiling, single-cell multiomics and chromatin immunoprecipitation-sequencing and verified by genetic modulations and co-culture systems.

RESULTS

HCC patients showing higher HDAC1/2/3 expressions exhibited deficient IFNγ signalling and poorer survival on ICB therapy. Transient treatment of a selective class-I HDAC inhibitor CXD101 resensitised HDAC1/2/3high tumours to ICB therapies, resulting in CD8+T cell-dependent antitumour and memory T cell responses. Mechanistically, CXD101 synergised with ICB to stimulate STAT1-driven antitumour immunity through enhanced chromatin accessibility and H3K27 hyperacetylation of IFNγ-responsive genes. Intratumoural recruitment of IFNγ+GZMB+cytotoxic lymphocytes further promoted cleavage of CXD101-induced Gasdermin E (GSDME) to trigger pyroptosis in a STAT1-dependent manner. Notably, deletion of GSDME mimicked STAT1 knockout in abolishing the antitumour efficacy and survival benefit of CXD101-ICB combination therapy by thwarting both pyroptotic and IFNγ responses.

CONCLUSION

Our immunoepigenetic strategy harnesses IFNγ-mediated network to augment the cancer-immunity cycle, revealing a self-reinforcing STAT1-GSDME pyroptotic circuitry as the mechanistic basis for an ongoing phase-II trial to tackle ICB resistance (NCT05873244).

Tertiary Lymphoid Structures in Pancreatic Cancer are Structurally Homologous, Share Gene Expression Patterns and B-cell Clones with Secondary Lymphoid Organs, but Show Increased T-cell Activation

Tertiary lymphoid structures (TLS) in cancer are considered ectopic hotspots for immune activation that are similar to lymphoid follicles in secondary lymphoid organs (SLO). This study elucidates shared and TLS/SLO-specific features in pancreatic ductal adenocarcinoma (PDAC). TLS abundance was related to superior survival and T-cell abundance in 110 treatment-naïve PDAC samples, underlining their clinical relevance. Immunofluorescence microscopy identified structural homologies between TLSs and SLOs. In RNA expression analyses of laser-microdissected TLSs and paired SLOs, we observed largely overlapping expression patterns of immune-related gene clusters but distinct expression patterns of T-cell and complement-associated genes. Immune cells in TLS expressed essential markers of germinal center formation. Increased activation of tumor-draining lymph nodes in patients with high numbers of TLSs highlights the relevance of these tumor-related structures to systemic immune response. In line with this, we identified an overlap of expanded B-cell receptor clonotypes in TLSs and SLOs, which suggests a vivid cross-talk between the two compartments. We conclude that combined therapeutic approaches exploiting TLS-mediated antitumor immune responses may improve susceptibility of PDAC to immunotherapy.

Machine learning identifies clinical tumor mutation landscape pathways of resistance to checkpoint inhibitor therapy in NSCLC

BACKGROUND

Immune checkpoint inhibitors (CPIs) have revolutionized cancer therapy for several tumor indications. However, a substantial fraction of patients treated with CPIs derive no benefit or have short-lived responses to CPI therapy. Identifying patients who are most likely to benefit from CPIs and deciphering resistance mechanisms is therefore essential for developing adjunct treatments that can abrogate tumor resistance.

PATIENTS AND METHODS

In this study, we used a machine learning approach that used the US-based nationwide de-identified Flatiron Health and Foundation Medicine non-small cell lung carcinoma (NSCLC) clinico-genomic database to identify genomic markers that predict clinical responses to CPI therapy. In total, we analyzed data from 4,433 patients with NSCLC.

RESULTS

Analysis of pretreatment genomic data from 1,511 patients with NSCLC identified. Of the 36 genomic signatures identified, 33 exhibited strong predictive capacity for CPI response (n=1150) compared with chemotherapy response (n=361), while three signatures were prognostic. These 36 genetic signatures had in common a core set of four genes (BRAF, BRIP1, FGF10, and FLT1). Interestingly, we observed that some (n=19) of the genes in the signatures (eg, TP53, EZH2, KEAP1 and FGFR2) had alternative mutations with contrasting clinical outcomes to CPI therapy. Finally, the genetic signatures revealed multiple biological pathways involved in CPI response, including MAPK, PDGF, IL-6 and EGFR signaling.

CONCLUSIONS

In summary, we found several genomic markers and pathways that provide insight into biological mechanisms affecting response to CPI therapy. The analyses identified novel targets and biomarkers that have the potential to provide candidates for combination therapies or patient enrichment strategies, which could increase response rates to CPI therapy in patients with NSCLC.

Clonal driver neoantigen loss under EGFR TKI and immune selection pressures

Neoantigen vaccines are under investigation for various cancers, including epidermal growth factor receptor (EGFR)-driven lung cancers1,2. We tracked the phylogenetic history of an EGFR mutant lung cancer treated with erlotinib, osimertinib, radiotherapy and a personalized neopeptide vaccine (NPV) targeting ten somatic mutations, including EGFR exon 19 deletion (ex19del). The ex19del mutation was clonal, but is likely to have appeared after a whole-genome doubling (WGD) event. Following osimertinib and NPV treatment, loss of the ex19del mutation was identified in a progressing small-cell-transformed liver metastasis. Circulating tumour DNA analyses tracking 467 somatic variants revealed the presence of this EGFR wild-type clone before vaccination and its expansion during osimertinib/NPV therapy. Despite systemic T cell reactivity to the vaccine-targeted ex19del neoantigen, the NPV failed to halt disease progression. The liver metastasis lost vaccine-targeted neoantigens through chromosomal instability and exhibited a hostile microenvironment, characterized by limited immune infiltration, low CXCL9 and elevated M2 macrophage levels. Neoantigens arising post-WGD were more likely to be absent in the progressing liver metastasis than those occurring pre-WGD, suggesting that prioritizing pre-WGD neoantigens may improve vaccine design. Data from the TRACERx 421 cohort3 provide evidence that pre-WGD mutations better represent clonal variants, and owing to their presence at multiple copy numbers, are less likely to be lost in metastatic transition. These data highlight the power of phylogenetic disease tracking and functional T cell profiling to understand mechanisms of immune escape during combination therapies.

Hallmarks of artificial intelligence contributions to precision oncology

The integration of artificial intelligence (AI) into oncology promises to revolutionize cancer care. In this Review, we discuss ten AI hallmarks in precision oncology, organized into three groups: (1) cancer prevention and diagnosis, encompassing cancer screening, detection and profiling; (2) optimizing current treatments, including patient outcome prediction, treatment planning and monitoring, clinical trial design and matching, and developing response biomarkers; and (3) advancing new treatments by identifying treatment combinations, discovering cancer vulnerabilities and designing drugs. We also survey AI applications in interventional clinical trials and address key challenges to broader clinical adoption of AI: data quality and quantity, model accuracy, clinical relevance and patient benefit, proposing actionable solutions for each.

ImmuneLENS characterizes systemic immune dysregulation in aging and cancer

Recognition and elimination of pathogens and cancer cells depend on the adaptive immune system. Thus, accurate quantification of immune subsets is vital for precision medicine. We present immune lymphocyte estimation from nucleotide sequencing (ImmuneLENS), which estimates T cell and B cell fractions, class switching and clonotype diversity from whole-genome sequencing data at depths as low as 5× coverage. By applying ImmuneLENS to the 100,000 Genomes Project, we identify genes enriched with somatic mutations in T cell-rich tumors, significant sex-based differences in circulating T cell fraction and demonstrated that the circulating T cell fraction in patients with cancer is significantly lower than in healthy individuals. Low circulating B cell fraction was linked to increased cancer incidence. Finally, circulating T cell abundance was more prognostic of 5-year cancer survival than infiltrating T cells.

AI Model for Predicting Anti-PD1 Response in Melanoma Using Multi-Omics Biomarkers

BACKGROUND

Immune checkpoint inhibitors (ICIs) have demonstrated significantly improved clinical efficacy in a minority of patients with advanced melanoma, whereas non-responders potentially suffer from severe side effects and delays in other treatment options. Predicting the response to anti-PD1 treatment in melanoma remains a challenge because the current FDA-approved gold standard, the nonsynonymous tumor mutation burden (nsTMB), offers limited accuracy.

METHODS

In this study, we developed a multi-omics-based machine learning model that integrates genomic and transcriptomic biomarkers to predict the response to anti-PD1 treatment in patients with advanced melanoma. We employed least absolute shrinkage and selection operator (LASSO) regression with 49 biomarkers extracted from tumor-normal whole-exome and RNA sequencing as input features. The performance of the multi-omics AI model was thoroughly compared to that of nsTMB alone and to models that use only genomic or transcriptomic biomarkers.

RESULTS

We used publicly available DNA and RNA-seq datasets of melanoma patients for the training and validation of our model, forming a meta-cohort of 449 patients for which the outcome was recorded as a RECIST score. The model substantially improved the prediction of anti-PD1 outcomes compared to nsTMB alone, with an ROC AUC of 0.7 in the training set and an ROC AUC of 0.64 in the test set. Using SHAP values, we demonstrated the explainability of the model's predictions on a per-sample basis.

CONCLUSIONS

We demonstrated that models using only RNA-seq or multi-omics biomarkers outperformed nsTMB in predicting the response of melanoma patients to ICI. Furthermore, our machine learning approach improves clinical usability by providing explanations of its predictions on a per-patient basis. Our findings underscore the utility of multi-omics data for selecting patients for treatment with anti-PD1 drugs. However, to train clinical-grade AI models for routine applications, prospective studies collecting larger melanoma cohorts with consistent application of exome and RNA sequencing are required.

Predicting immunotherapy response of advanced bladder cancer through a meta-analysis of six independent cohorts

Advanced bladder cancer patients show very variable responses to immune checkpoint inhibitors (ICIs) and effective strategies to predict response are still lacking. Here we integrate mutation and gene expression data from 707 advanced bladder cancer patients treated with anti-PD-1/anti-PD-L1 to build highly accurate predictive models. We find that, in addition to tumor mutational burden (TMB), enrichment in the APOBEC mutational signature, and the abundance of pro-inflammatory macrophages, are major factors associated with the response. Paradoxically, patients with high immune infiltration do not show an overall better response. We show that this can be explained by the activation of immune suppressive mechanisms in a large portion of these patients. In the case of non-immune-infiltrated cancer subtypes, we uncover specific variables likely to be involved in the response. Our findings provide information for advancing precision medicine in patients with advanced bladder cancer treated with immunotherapy.

Towards the Prediction of Responses to Cancer Immunotherapy: A Multi-Omics Review

Tumor treatment has undergone revolutionary changes with the development of immunotherapy, especially immune checkpoint inhibitors. Because not all patients respond positively to immune therapeutic agents, and severe immune-related adverse events (irAEs) are frequently observed, the development of the biomarkers evaluating the response of a patient is key for the application of immunotherapy in a wider range. Recently, various multi-omics features measured by high-throughput technologies, such as tumor mutation burden (TMB), gene expression profiles, and DNA methylation profiles, have been proved to be sensitive and accurate predictors of the response to immunotherapy. A large number of predictive models based on these features, utilizing traditional machine learning or deep learning frameworks, have also been proposed. In this review, we aim to cover recent advances in predicting tumor immunotherapy response using multi-omics features. These include new measurements, research cohorts, data sources, and predictive models. Key findings emphasize the importance of TMB, neoantigens, MSI, and mutational signatures in predicting ICI responses. The integration of bulk and single-cell RNA sequencing has enhanced our understanding of the tumor immune microenvironment and enabled the identification of predictive biomarkers like PD-L1 and IFN-γ signatures. Public datasets and machine learning models have also improved predictive tools. However, challenges remain, such as the need for large and diverse clinical datasets, standardization of multi-omics data, and model interpretability. Future research will require collaboration among researchers, clinicians, and data scientists to address these issues and enhance cancer immunotherapy precision.

Spatial proximity of CD8+ T cells to tumor cells predicts neoadjuvant therapy efficacy in breast cancer

The spatial proximity of CD8+ T cells to tumor cells critically influences the efficacy of neoadjuvant therapy (NAT) in breast cancer (BC). In this study, we evaluated whether the presence of CD8+ T cells and other immune cells near cancer cells predicts treatment outcomes across various BC subtypes. We analyzed pre- and post-NAT biopsies from 104 BC patients using multiplex immunofluorescence (mIF) and immunohistochemistry (IHC) to assess the distribution of immune markers, including CD8+ T cells, CD68+ macrophages, FoxP3+ regulatory T cells. Our findings revealed that a higher percentage of CD8+ T cells within 20 µm of cancer cells (N20-CD8+ T cells) was strongly correlated with improved pathological complete response (pCR), disease-free survival (DFS), and overall survival (OS), regardless of tumor subtype or NAT regimen. Moreover, a positive correlation between CXCL9 expression and N20-CD8+ T cells suggests that CXCL9 may facilitate the recruitment of CD8+ T cells to tumor cells. Our study emphasizes the link between immune cell composition and location, and patient outcomes with NAT. Focusing on the spatial dynamics of CD8+ T cells could significantly advance personalized treatment strategies and the development of targeted immunotherapies in BC.

Characteristics of a CCL21 Gene-Modified Dendritic Cell Vaccine Utilized for a Clinical Trial in Non-Small Cell Lung Cancer

The treatment of non-small cell lung cancer has made major strides with the use of immune checkpoint inhibitors; however, there remains a significant need for therapies that can overcome immunotherapy resistance. Dendritic cell (DC) vaccines have been proposed as a therapy that can potentially enhance the antitumor immune response. We have embarked on a phase I clinical trial of a vaccine consisting of monocyte-derived DCs (moDC) modified to express the chemokine C-C motif chemokine ligand 21 (CCL21-DC) given in combination with pembrolizumab. In this study, we report a comprehensive characterization of this CCL21-DC vaccine and interrogate the effects of multiple factors in the manufacturing process. We show that the cellular makeup of the CCL21-DC vaccine is heterogeneous because of the presence of passenger lymphocytes at a proportion that is highly variable among patients. Single-cell RNA sequencing of vaccines revealed further heterogeneity within the moDC compartment, with cells spanning a spectrum of DC phenotypes. Transduction with a CCL21-containing adenoviral vector augmented CCL21 secretion by moDCs, but otherwise had a minimal effect on vaccine characteristics. A single freeze-thaw cycle for stored vaccines was associated with minor alterations to the DC phenotype, as was the use of healthy donors rather than patient autologous blood. Our results highlight important considerations for the production of DC vaccines and identify underexplored factors that may affect their efficacy and immunologic impact.

Identification and Phenotypic Characterization of Neoantigen-Specific Cytotoxic CD4+ T Cells in Endometrial Cancer

Tumor-reactive CD4+ T cells often accumulate in the tumor microenvironment (TME) in human cancer, but their functions and roles in antitumor responses remain elusive. Here, we investigated the immunopeptidome of HLA class II-positive (HLA-II+) endometrial cancer with an inflamed TME using a proteogenomic approach. We identified HLA-II neoantigens, one of which induced polyclonal CD4+ tumor-infiltrating lymphocyte responses. We then experimentally demonstrated that neoantigen-specific CD4+ tumor-infiltrating lymphocytes lyse target cells in an HLA-II-dependent manner. Single-cell transcriptomic analysis of the TME coupled with T-cell receptor sequencing revealed the presence of CD4+ T-cell clusters characterized by CXCL13 expression. The CXCL13+ clusters contained two subclusters with distinct cytotoxic gene expression patterns. The identified neoantigen-specific CD4+ T cells were found exclusively in one of the CXCL13+ subclusters characterized by granzyme B and CCL5 expression. These results demonstrate the involvement of tumor-reactive CD4+ T cells with cytotoxic function in immune surveillance of endometrial cancer and reveal their transcriptomic signature.

Immunogenomic determinants of exceptional response to immune checkpoint inhibition in renal cell carcinoma

Immune checkpoint inhibitors can lead to 'exceptional', durable responses in a subset of persons. However, the molecular basis of exceptional response (ER) to immunotherapy in metastatic clear cell renal cell carcinoma (mccRCC) has not been well characterized. Here we analyzed pretherapy genomic and transcriptomic data in treatment-naive persons with mccRCC treated with standard-of-care immunotherapies: (1) combination of programmed cell death protein and ligand 1 (PD1/PDL1) and cytotoxic T lymphocyte-associated protein 4 inhibitors (IO/IO) or (2) combination of PD1/PDL1 and vascular endothelial growth factor (VEGF) receptor inhibitors (IO/VEGF). In the IO/IO cohort, clonal neoantigen load was significantly higher in persons with ER. In the IO/VEGF cohort, ER participants displayed strong enrichment of B cell receptor signaling-related pathways, tertiary lymphoid structure (TLS) signatures and evidence of increased metabolic activity. Our results suggest that ER may be related to clonal neoantigen-driven cytotoxic T cell responses and TLS formation in tumor microenvironments. Therapeutic combinations that elicit both T cell-directed and B cell-directed antitumor immunity may be important to achieve exceptional benefit to IO-based treatment in ccRCC.

Personalized Medicine: Leave no Patient Behind; Report From the 2024 Coffey-Holden Prostate Cancer Academy Meeting

INTRODUCTION

The 11th Annual 2024 Coffey - Holden Prostate Cancer Academy (CHPCA) Meeting, was themed "Personalized Medicine: Leave No Patient Behind," and was held from June 20 to 23, 2024 at the University of California, Los Angeles, Luskin Conference Center, in Los Angeles, CA.

METHODS

The CHPCA Meeting is an academy-styled annual conference organized by the Prostate Cancer Foundation, to focus discussion on the most critical emerging research that have the greatest potential to advance knowledge of prostate cancer biology and treatment. The 2024 CHPCA Meeting was attended by 75 academic investigators and included 37 talks across 8 sessions.

RESULTS

The meeting sessions focused on: novel human, mouse and systems biology research models, novel immunotherapies for prostate cancer, efforts to overcome treatment resistance, the role of metabolism and diet in prostate cancer biology and as a therapeutic target, mechanisms that drive differentiation into neuroendocrine cancer subtypes, the evolving prostate cancer epigenome in disease progression and treatment resistance, and machine learning and advanced computational approaches for precision oncology.

DISCUSSION

This article summarizes the presentations and discussions from the 2024 CHPCA Meeting. We hope that sharing this knowledge will inspire and accelerate research into new discoveries and solutions for prostate cancer.

Local ablation disrupts immune evasion in pancreatic cancer

BACKGROUND

Pancreatic cancer (PC) is characterised by late diagnosis, tumour heterogeneity, and a peculiar immunosuppressive microenvironment, leading to poor clinical outcomes. Local ablative techniques have been proposed to treat unresectable PC patients, although their impact on activating the host immune system and overcoming resistance to immunotherapy remains elusive.

METHODS

We dissected the immune-modulatory abilities triggered by local ablation in mouse and human PC models and human specimens, integrating phenotypic and molecular technologies with functional assays.

RESULTS

Local ablation treatment performed in mice bearing orthotopic syngeneic PC tumours triggered tumour necrosis and a short-term inflammatory process characterised by the prompt increase of HMGB1 plasma levels, coupled with an enhanced amount of circulating and tumour infiltrating myeloid cells and increased MHCII expression in splenic myeloid antigen-presenting cells. Local ablation synergised with immunotherapy to restrict tumour progression and improved the survival of PC-bearing mice by evoking a T lymphocyte-dependent anti-tumour immune response. By integrating spatial transcriptomics with histological techniques, we pinpointed how combination therapy could reshape TME towards an anti-tumour milieu characterised by the preferential entrance and colocalization of activated T lymphocytes and myeloid cells endowed with antigen presentation features instead of T regulatory lymphocytes and CD206-expressing tumour-associated macrophages. In addition, treatment-dependent TME repolarization extended to neoplastic cells, promoting a shift from squamous to a more differentiated classical phenotype. Finally, we validated the immune regulatory properties induced by local ablation in PC patients and identified an association of the short-term treatment-dependent increase of neutrophils, NLR and HMGB1 with a longer time to progression.

CONCLUSION

Therefore, local ablation might overcome the current limitations of immunotherapy in PC.

Role of the androgen receptor in melanoma aggressiveness

Malignant melanoma represents the fifth most common cancer in the world and its incidence is rising. Novel therapies targeting receptor tyrosine kinases, kinases and immune checkpoints have been employed with a significant improvement of the overall survival and long-term disease containment. Nevertheless, the disease often progresses and becomes resistant to the therapies. As such, the discovery of new targets and drugs for advanced melanoma still remains a difficult task. Gender disparities, with a female advantage in melanoma incidence and outcome, have been reported. Although emerging studies support the pro-tumorigenic role of androgen/androgen receptor axis in melanoma, the molecular bases of such evidence are still under intense investigation. We now report that ligand activation of the androgen receptor drives melanoma invasiveness and its escape from natural killer-mediated cytotoxic effect. By combining different experimental approaches, we observe that melanoma escape is mediated by the androgen-triggered shedding of the surface molecule MICA. Specific blockade of ADAM10 or androgen receptor impairs the androgen-induced MICA shedding and melanoma immune-escape. Further, the increase in MICA serum levels correlates with a poor outcome in melanoma patients treated with the anti-PD-1 monoclonal antibody, pembrolizumab. At last, melanoma cells depleted of the androgen receptor become more responsive to the most commonly used immunocheckpoint inhibitors, suggesting that the receptor dampens the immunotherapy efficacy. Taken together, our findings identify the androgen receptor as a diagnostic guidance in melanoma and support the repositioning of AR blockers in clinical management of patients.