Key Immune Profiles in Cancer Therapy

Our team just published our latest work in Nature revealing how patients' own antibodies can make or break their response to checkpoint immunotherapy. The Question: Why do some cancer patients experience dramatic tumor shrinkage when they received immunotherapy while others see no benefit? Our Approach: Using REAP (Rapid Extracellular Antigen Profiling), we screened blood samples from 374 cancer patients for autoantibodies against 6,000+ proteins. Key Findings: · Cancer patients have an extraordinarily diverse “autoantibody reactome.” We detected ~3,000 unique autoantibody reactivities and clearly had not achieved saturation. · Patients with anti-interferon antibodies were up to 40x more likely to respond to treatment. This is a complete reversal from COVID-19 where these same antibodies increase mortality by 20-200 fold. · Novel finding: Anti-TL1A antibodies enhance treatment by preventing T cell apoptosis in the TME · Red flag: 10% of non-responders had antibodies against BMP receptors, revealing a previously unknown barrier to treatment success Conclusions: Treatment-modifying autoantibodies act as a roadmap for developing better therapies. We can now design drugs that mimic beneficial antibodies or counteract harmful ones, potentially improving outcomes for any patient who receives immunotherapy. This work was only possible through incredible collaboration between the Fred Hutchinson Cancer Center, the Yale Cancer Center, and my company Seranova Bio. Special recognition to lead author Yile Dai and the entire team who made this vision a reality. Read the full paper here: https://lnkd.in/dRxYd4bC

Published in Nature (2023), Bastian Kruse et al. (Thomas Tüting lab) demonstrate that adoptively transferred CD4+ T cells alone—but not CD8+ T cells—can eradicate melanoma tumors completely lacking both MHC class I and II expression. These findings challenge the current paradigm of cancer immunotherapy, which predominantly focuses on CD8+ cytotoxic T cells whose effectiveness is limited by MHC loss and immunosuppressive TME. Historically viewed merely as ‘helper’ cells, CD4+ T cells instead have a critical yet underappreciated capacity for antitumor immunity independent of CD8+ cells. Intriguingly, CD4+ T cells do not directly infiltrate tumors in the same way CD8+ T cells do. Rather, they profoundly reshape the tumor immune landscape by recruiting and functionally reprogramming myeloid cells. These myeloid cells mature into potent interferon-activated APCs and robust iNOS-expressing tumoricidal effectors. This study uncovers exciting therapeutic opportunities by revealing the potential of CD4+ T cells to complement CD8+ T cells and NK cells, paving the way for innovative strategies against immune-evasive cancers.

#ScienceSaturday ❓ Why does immunotherapy work incredibly well for some melanoma patients, but not for others? ➡️ A new study in Cancer Cell takes a closer look at the immune cells that matter most during immunotherapy. Researchers tracked melanoma-specific CD8+ T cells (the immune system’s cancer killers) in patients receiving anti-PD-1 treatment before surgery. ➡️ They found that not all T cells are created equal. Patients who responded best had a special group of T cells marked by a protein called T-bet. These cells weren’t “burned out,” but they weren’t fully fresh either, instead, they were in a powerful in-between state that allowed them to expand, stay active, and attack tumors when PD-1 therapy removed the brakes. ➡️ In contrast, patients who didn’t respond had more terminally exhausted T cells, immune cells that were too worn down to bounce back, even with immunotherapy. ➡️ The team also discovered that these fate decisions start early, in the lymph nodes, before T cells ever reach the tumor. And importantly, ongoing exposure to tumor antigens helped sustain effective immune responses, helping explain why neoadjuvant (pre-surgery) immunotherapy can be so effective. ➡️ Together, these findings show that which T cells are present, not just how many, can predict response to immunotherapy, and could guide better biomarkers and treatment strategies in the future. 🌟 Kudos to the researchers for uncovering how immune cell “identity” shapes success with cancer immunotherapy! Lynn Schuchter Penn Medicine, University of Pennsylvania Health System Ravi Amaravadi Penn Medicine Cancer 🔗 Read more in Cancer Cell: https://lnkd.in/enNpEicD

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. Interesting new study in Cell Press Reports Medicine: https://lnkd.in/e69kDM2d

Clinicians have spent years guessing who will benefit from immunotherapy. But this paper introduces a machine learning-driven scoring system that dramatically improves pan-cancer prediction accuracy. The Problem: - Immunotherapy outcomes, particularly with immune checkpoint blockade (ICB), vary significantly across and within cancer types. - Existing biomarkers like PD-L1 and TMB lack universal predictive value across pan-cancer settings. - Complex tumor immune microenvironments (TIMEs) demand more robust, scalable analytic tools for accurate prediction. What the Authors Did: - Developed the iMLGAM R package, integrating machine learning, gene-pair analysis, and genetic algorithms. - Employed ensemble learning with models like Elastic Net, SVM, KNN, and Random Forest, optimized via genetic algorithms. - Validated predictive performance across multiple independent cohorts using multiomics and immune profiling techniques. Main Findings: - iMLGAM score reliably predicts ICB therapy response across pan-cancer cohorts, with lower scores linked to better outcomes. - Tumors with low iMLGAM scores show stronger immune cell infiltration, increased T cell activity, and favorable mutational signatures. - Notably, CEP55 was identified as a key gene promoting immune evasion; its knockdown reduced tumor aggressiveness and improved T cell function. - In vivo CEP55 suppression combined with anti-PD1 therapy significantly enhanced survival in mouse models. - iMLGAM outperformed 12 existing immunotherapy predictive signatures across multiple datasets. Implications for Cell & Gene Therapy - iMLGAM provides a ready-to-use, accurate scoring system for guiding personalized immunotherapy decisions. - The model's reliance on gene-pair analysis allows for platform-independent application, (enhancing clinical utility). - Integration into clinical workflows could minimize ineffective ICB treatment, reducing costs and avoiding toxicity. - CEP55 could be the next big immunotherapy target, potentially augmenting gene-targeted combination therapies. Kudos to the authors - great job! Anything else you'd add? Drop it in the comments.

🔎 Breaking Ground in Hepatocellular Cancer Immunotherapy! 🚀 A new study has unlocked a major breakthrough: Identifying neoantigen-reactive T cells and TCRs for personalized immunotherapy in HCC! 🔬 Key Findings from the Study: ✅ 542 candidate neoantigens were identified from tumors of 7 HCC patients, of which 78 were selected for detailed T cell stimulation experiments. ✅ T cell reactivity confirmed against 14 neoantigens, with the strongest responses found in liver flushes and tumor-draining lymph nodes rather than in TILs. This is a paradigm shift, as these locations appear to harbor highly reactive, neoantigen-specific T cells. ✅ Memory-enriched T cell populations identified using single-cell RNA sequencing: 🔹 CD4+ effector memory (TEM) and central memory (TCM) T cells were enriched in liver flushes, expressing cytotoxic markers like GZMK and NKG7, suggesting their potential for robust anti-tumor responses. 🔹 Exhausted tissue-resident memory CD8+ T cells (TRM), expressing PD-1, CD39, and CXCR6, were predominantly found in draining lymph nodes—a key insight for overcoming immune suppression. ✅ Tumor-reactive T cell receptors (TCRs) successfully isolated and validated! 🔹 The study confirmed that SBNO2, FANCA, SNTG2, SLCO2B1, REEP6, and CTNNB1 mutations could trigger T cell responses, with the SBNO2 mutation leading to strong antigen-specific recognition. 🔹 The most expanded CD8+ TCR clones were found in liver flushes, reinforcing the importance of this tissue compartment in future TCR-based therapies. ✅ Neoantigen-reactive TCRs were genetically reconstructed and transferred into patient-derived T cells, leading to: 🔹 Strong CD4+ and CD8+ T cell activation upon exposure to tumor neoantigens. 🔹 Increased cytokine production (IFNγ, TNFα, IL-2) and degranulation (CD107α), indicating cytotoxic function. 🔹 High TCR avidity against mutated peptides, while showing minimal response to wild-type counterparts—confirming tumor-specific recognition. 🔥 Why This Matters? 💡 Most HCC tumors have a low mutational burden, making it challenging to identify targetable neoantigens. However, this study reveals that: ✅ Liver flushes and lymph nodes serve as reservoirs of functional, tumor-reactive T cells, which could be leveraged for adoptive T cell therapy. ✅ Neoantigen-specific TCRs can be engineered to enhance immune responses, providing a potential alternative to immune checkpoint blockade. ✅ T cell exhaustion in HCC is reversible—targeting the right subsets of memory T cells may significantly improve treatment efficacy. 🚀 Next Steps in Research & Application: 🔹 Further validation of shared neoantigens like PIK3CA and SBNO2 to expand patient eligibility for TCR-based therapies. 🔹 Optimizing T cell persistence & function through gene editing (TCR affinity tuning, resistance to immune suppression). 🔹 Combining TCR therapy with checkpoint inhibitors or metabolic reprogramming to overcome tumor resistance.

Hot Topic of the Week:  Surface Antigen Camouflage and Antigen Expression Loss Tumors often use complex mechanisms to evade immune detection, two of which are surface antigen camouflage and loss of antigen expression. These evasion strategies weaken the immune system's ability to recognize and eliminate cancer cells, and therefore pose a major challenge to immunotherapy. (1) Surface antigen camouflage mechanism Cancer cells can mask their antigens by changing or hiding the molecular structure on their surface. Glycosylation is a key mechanism, in which cancer cells modify their surface proteins by adding sugar molecules to mask the recognition of immune cells. In addition, cancer cells can also use overexpression of surface molecules such as CD47 (known as the "don't eat me" signal) to inhibit macrophage-mediated phagocytosis. This camouflage protects tumors from immune surveillance and creates an immune-tolerant microenvironment. (2) Loss of antigen expression Tumors can evade immune detection by downregulating or completely losing the expression of key tumor-associated antigens (TAAs) or tumor-specific antigens (TSAs). This mechanism is particularly common in T cell-mediated immune responses, as T cells rely primarily on antigen presentation to recognize and attack cancer cells. The loss of antigen expression may occur through mutation, epigenetic modification, or selection pressure of immune response, resulting in the inability of antigen-presenting cells to effectively detect tumor cells. This phenomenon is also the main reason for the resistance of immunotherapies such as CAR-T cells targeting specific antigens. Taken together, these immune evasion strategies together highlight the dynamic interaction between cancer cells and the immune system. Understanding these mechanisms can provide important help in the development of next-generation immunotherapies. For example, scientists can choose to target glycosylation pathways, enhance antigen presentation, or design new CAR-T cells to recognize a wider range of antigens, bringing hope to overcome immune resistance. References [1] Anoop Kallingal et al., J Cancer Res Clin Oncol 2023 (doi: 10.1007/s00432-023-04737-8) [2] Kailin Yang et al., Nature Reviews Clinical Oncology 2023 (https://lnkd.in/e7j2Apah) #ImmuneEvasion #CancerImmunotherapy #AntigenCamouflage #TumorResistance #CAR_Therapy #ImmunoOncology #CancerResearch #InnovationInMedicine #TumorMicroenvironment

Tumor-intrinsic IFNα and CXCL10 are critical for immunotherapeutic efficacy by recruiting and activating T lymphocytes in tumor microenvironment Tumor immunotherapies targeting PD-(L)1 exhibit anti-tumor efficacy in only 10–30% of patients with various cancers. Literature has demonstrated that a “hot tumor” which contains high T lymphocytes in the tumor microenvironment exhibits a better response to immunotherapies than a “cold tumor.” This study aimed to investigate whether tumor-intrinsic IFNα and CXCL10 determine the recruitment and activation of CD8+ T cells to become “hot tumor.” In this study, we found that CXCL10 overexpressed in a variety of tumors including lung, colon, and liver tumors with a correlation with PD-L1. High PD-L1 and CXCL10 are associated with better survival rates in tumor patients receiving immunotherapies. IFNs-downstream transcriptional factor IRF-1 and STAT1 were correlated with PD-L1 and CXCL10 expression. We demonstrated that IRF-1 and STAT1 were both bound with the promoters of PD-L1 and CXCL10, sharing the same signaling pathway and determining IFNs-mediated PD-L1 and CXCL10 expression. In addition, IFNα significantly increased activation marker IFNγ in PBMCs, promoting M1 type monocyte differentiation, CD4+ T, and CD8+ T cell activation. Particularly, we found that CD8+ T lymphocytes abundantly expressed CXCR3, a receptor of CXCL10, by flow cytometry, indicating that tumor-intrinsic CXCL10 potentially recruited CD8+ T in tumor microenvironment. To demonstrate the hypothesis, immunotherapy-sensitive CT26 and immunotherapy-resistant LL/2 were used and we found that CT26 cells exhibited higher IFNα, IFNγ, CXCL10, and PD-L1 levels compared to LL/2, leading to higher IFNγ expression in mouse splenocytes. https://lnkd.in/drJiD_8y

T cells need a little help to infiltrate tumors, persist, and adapt the appropriate phenotype while avoiding exhaustion. The unexpected answer? Interleukin-9 (IL-9). The kicker being, IL-9 receptor (IL-9R) expression is very low. Leveraging endogenous expression is therefore limited. In back-to-back papers (links in comments), teams associated with Carl June and Anusha Kalbasi show compelling evidence that endowing T cells with ectopic IL-9 signaling can generate superior anti-tumor activity. Importantly, whereas other cytokines used for driving T cell expansion/activity such as IL-2 and IL-15 are limited by systemic in vivo toxicity, IL-9 was demonstrated to be safe even at very high doses. It should be mentioned that IL-9 has a complex biology with both anti- and pro-tumor activities. It will be interesting to see how the beneficial IL-9 effects can be fully leveraged, which likely will be context dependent. #Immunotherapy #CARTcell #TILtherapy