Single-cell characterization of anti-LAG-3 and anti-PD-1 combination treatment in patients with melanoma

BackgroundRelatlimab plus nivolumab (anti-lymphocyte-activation gene 3 plus anti-programmed death 1 [anti-LAG-3+anti-PD-1]) has been approved by the FDA as a first-line therapy for stage III/IV melanoma, but its detailed effect on the immune system is unknown.MethodsWe evaluated blood samples from 40 immunotherapy-naive or prior immunotherapy-refractory patients with metastatic melanoma treated with anti-LAG-3+anti-PD-1 in a phase I trial using single-cell RNA and T cell receptor sequencing (scRNA+TCRαβ-Seq) combined with other multiomics profiling.ResultsThe highest LAG3 expression was noted in NK cells, Tregs, and CD8+ T cells, and these cell populations underwent the most significant changes during the treatment. Adaptive NK cells were enriched in responders and underwent profound transcriptomic changes during the therapy, resulting in an active phenotype. LAG3+ Tregs expanded, but based on the transcriptome profile, became metabolically silent during the treatment. Last, higher baseline TCR clonality was observed in responding patients, and their expanding CD8+ T cell clones gained a more cytotoxic and NK-like phenotype.ConclusionAnti-LAG-3+anti-PD-1 therapy has profound effects on NK cells and Tregs in addition to CD8+ T cells.Trial registrationClinicalTrials.gov (NCT01968109)FundingCancer Foundation Finland, Sigrid Juselius Foundation, Signe and Ane Gyllenberg Foundation, Relander Foundation, State funding for university-level health research in Finland, a Helsinki Institute of Life Sciences Fellow grant, Academy of Finland (grant numbers 314442, 311081, 335432, and 335436), and an investigator-initiated research grant from BMS.

Evolution and modulation of antigen-specific T cell responses in melanoma patients

Analyzing antigen-specific T cell responses at scale has been challenging. Here, we analyze three types of T cell receptor (TCR) repertoire data (antigen-specific TCRs, TCR-repertoire, and single-cell RNA + TCRαβ-sequencing data) from 515 patients with primary or metastatic melanoma and compare it to 783 healthy controls. Although melanoma-associated antigen (MAA) -specific TCRs are restricted to individuals, they share sequence similarities that allow us to build classifiers for predicting anti-MAA T cells. The frequency of anti-MAA T cells distinguishes melanoma patients from healthy and predicts metastatic recurrence from primary melanoma. Anti-MAA T cells have stem-like properties and frequent interactions with regulatory T cells and tumor cells via Galectin9-TIM3 and PVR-TIGIT -axes, respectively. In the responding patients, the number of expanded anti-MAA clones are higher after the anti-PD1(+anti-CTLA4) therapy and the exhaustion phenotype is rescued. Our systems immunology approach paves the way for understanding antigen-specific responses in human disorders.

Previous studies have characterized the diversity and dynamics of the T cell receptor (TCR) repertoire in patients with solid cancer. Here, by analyzing TCR repertoire data from multiple datasets, the authors report that melanoma-associated antigen-specific TCRs can be used to separate metastatic melanoma patients from healthy controls and to follow anti-tumor responses in patients treated with immunotherapy.

Abstract 1488: Viral molecular mimicry influences the antitumor immune response in murine and human melanoma

Molecular mimicry is known to be one of the leading mechanisms by which infectious agents may induce autoimmunity. However, whether a similar mechanism triggers anti-tumor immune response is unexplored, and the role of anti-viral T-cells infiltrating the tumor has remained anecdotal. To address this question, we first developed a bioinformatic tool to identify tumor peptides with high similarity to viral epitopes. Using peptides identified by this tool, we showed that, in mice, viral pre-existing immunity enhanced the efficacy of cancer immunotherapy via molecular mimicry. Specifically, when treated with a cancer vaccine consisting of peptides with a high degree of homology with specific viral peptides, the mice with induced pre-existing immunity to these viral peptides showed significantly better anti-tumor response.To understand whether this mechanism could partly explain immunotherapy-response in humans, we analyzed a cohort of melanoma patients undergoing PD1 treatment with high IgG titer for Cytomegalovirus (CMV). In this cohort of patients, we showed that high level of CMV-antibodies was associated with a prolonged progression free survival, and found that in some cases PBMCs could cross-react with both melanoma and CMV homologous peptides. Finally, T-cell TCR sequencing revealed expansion of the same CD8+ T-cell clones, when PBMCs were pulsed with tumor- or homologous viral peptides.In conclusion, we have demonstrated that pre-existing immunity and molecular mimicry could explain part of the response observed in immunotherapy. Most importantly, we have developed a tool able to identify tumor antigens and neoantigens based on their similarity to pathogen antigens, in order to exploit molecular mimicry and cross-reactive T-cells in cancer vaccine development.

Citation Format: Jacopo Chiaro, Henna Kasanen, Thomas Whalley, Cristian Capasso, Sara Feola, Mikaela Grönholm, Firas Hamdan, Karita Peltonen, Micaela Hernberg, Siru Mäkelä, Hanna Karhapää, Paul Brown, Beatriz Martins, Manlio Fusciello, Erkko Ylösmäki, Anna Kreutzman, Satu Mustjoki, Barbara Szomolay, Vincenzo Cerullo. Viral molecular mimicry influences the antitumor immune response in murine and human melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1488.

Modelling of killer T-cell and cancer cell subpopulation dynamics under immuno- and chemotherapies

Each patient's cancer has a unique molecular makeup, often comprised of distinct cancer cell subpopulations. Improved understanding of dynamic processes between cancer cell populations is therefore critical for making treatment more effective and personalized. It has been shown that immunotherapy increases the survival of melanoma patients. However, there remain critical open questions, such as timing and duration of immunotherapy and its added benefits when combined with other types of treatments. We introduce a model for the dynamics of active killer T-cells and cancer cell subpopulations. Rather than defining the cancer cell populations based on their genetic makeup alone, we consider also other, non-genetic differences that make the cell populations either sensitive or resistant to a therapy. Using the model, we make predictions of possible outcomes of the various treatment strategies in virtual melanoma patients, providing hypotheses regarding therapeutic efficacy and side-effects. It is shown, for instance, that starting immunotherapy with a denser treatment schedule may enable changing to a sparser schedule later during the treatment. Furthermore, combination of targeted and immunotherapy results in a better treatment effect, compared to mono-immunotherapy, and a stable disease can be reached with a patient-tailored combination. These results offer better understanding of the competition between T-cells and cancer cells, toward personalized immunotherapy regimens.