New interpretable machine-learning method for single-cell data reveals correlates of clinical response to cancer immunotherapy

Greene, Evan; Finak, Greg; D’Amico, Leonard A.; Bhardwaj, Nina; Church, Candice D.; Morishima, Chihiro; Ramchurren, Nirasha; Taube, Janis M.; Nghiem, Paul; Cheever, Martin A.; Fling, Steven P.; Gottardo, Raphaël

doi:10.1016/j.patter.2021.100372

Cited by 28 publications

(36 citation statements)

References 67 publications

(148 reference statements)

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“…Finally, a study harnessing the power of a new method for single-cell cytometry investigations, FAUST, uncovered a population of effector memory CD4+ and CD8+ T-cells co-expressing CD28, HLA-DR, and PD-1 in the peripheral blood of MCC patients, which may be a candidate biomarker for response to pembrolizumab. These findings are also in line with the well-known importance of CD28 expression in CD8+ T-cells during anti-PD1 immunotherapy [ 199 ].…”

Section: MCC Immunobiology and Tumor-specific Predictorssupporting

confidence: 86%

Identifying Candidates for Immunotherapy among Patients with Non-Melanoma Skin Cancer: A Review of the Potential Predictors of Response

Zelin

Maronese

Dri

et al. 2022

JCM

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Background: Non-melanoma skin cancer (NMSC) stands as an umbrella term for common cutaneous malignancies, including basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC), together with rarer cutaneous cancers, such as Merkel cell carcinoma (MCC) and other forms of adnexal cancers. The majority of NMSCs can be successfully treated with surgery or radiotherapy, but advanced and metastatic stages may require systemic approaches such as immunotherapy with immune checkpoint inhibitors (ICIs). Summary: Since immunotherapy is not effective in all patients and can potentially lead to severe adverse effects, an important clinical question is how to properly identify those who could be suitable candidates for this therapeutic choice. In this paper, we review the potential features and biomarkers used to predict the outcome of ICIs therapy for NMSCs. Moreover, we analyze the role of immunotherapy in special populations, such as the elderly, immunocompromised patients, organ transplant recipients, and subjects suffering from autoimmune conditions. Key messages: Many clinical, serum, histopathological, and genetic features have been investigated as potential predictors of response in NMSCs treated with ICIs. Although this field of research is very promising, definitive, cost-effective, and reproducible biomarkers are still lacking and further efforts are needed to validate the suggested predictors in larger cohorts.

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Section: MCC Immunobiology and Tumor-specific Predictorssupporting

confidence: 86%

Identifying Candidates for Immunotherapy among Patients with Non-Melanoma Skin Cancer: A Review of the Potential Predictors of Response

Zelin

Maronese

Dri

et al. 2022

JCM

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“…The poorest correlations were observed in cell populations expressed as a smear, without a clear-cut discrimination between the negative and the positive edge. The interpretation of markers expressed as a continuous is subtle in flow cytometry data analysis and clustering machine-learning-based algorithms are in development to address this issue (28). Three immune cell subsets, i.e.…”

Section: Discussionmentioning

confidence: 99%

Combined unsupervised and semi-automated supervised analysis of flow cytometry data reveals cellular fingerprint associated with newly diagnosed pediatric type 1 diabetes

Genzano

Bezzecchi²,

Carnovale³

et al. 2022

Front. Immunol.

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An unbiased and replicable profiling of type 1 diabetes (T1D)-specific circulating immunome at disease onset has yet to be identified due to experimental and patient selection limitations. Multicolor flow cytometry was performed on whole blood from a pediatric cohort of 107 patients with new-onset T1D, 85 relatives of T1D patients with 0-1 islet autoantibodies (pre-T1D_LR), 58 patients with celiac disease or autoimmune thyroiditis (CD_THY) and 76 healthy controls (HC). Unsupervised clustering of flow cytometry data, validated by a semi-automated gating strategy, confirmed previous findings showing selective increase of naïve CD4 T cells and plasmacytoid DCs, and revealed a decrease in CD56brightNK cells in T1D. Furthermore, a non-selective decrease of CD3+CD56+ regulatory T cells was observed in T1D. The frequency of naïve CD4 T cells at disease onset was associated with partial remission, while it was found unaltered in the pre-symptomatic stages of the disease. Thanks to a broad cohort of pediatric individuals and the implementation of unbiased approaches for the analysis of flow cytometry data, here we determined the circulating immune fingerprint of newly diagnosed pediatric T1D and provide a reference dataset to be exploited for validation or discovery purposes to unravel the pathogenesis of T1D.

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“…To identify specific immune subset differences, we performed computational analysis using full annotation using shape-constrained trees (FAUST) 29 , a machine learning algorithm that discovers and annotates statistically relevant cellular phenotypes in an unsupervised manner. For the T cell panel, FAUST identified a single subcluster of CD8 + T cells, and four CD4 + T reg (CD25 + CD127 − ) phenotypes marked by expression of ICOS with combinations of PD-1, TIM3 and HLA-DR as being enriched in HNSCC (Extended Data Fig.…”

Section: Phenotypic Congruence Of Om and Hnsccmentioning

confidence: 99%

Extricating human tumour immune alterations from tissue inflammation

Mair

Erickson

Frutoso

et al. 2022

Nature

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Immunotherapies have achieved remarkable successes in the treatment of cancer, but major challenges remain1,2. An inherent weakness of current treatment approaches is that therapeutically targeted pathways are not restricted to tumours, but are also found in other tissue microenvironments, complicating treatment3,4. Despite great efforts to define inflammatory processes in the tumour microenvironment, the understanding of tumour-unique immune alterations is limited by a knowledge gap regarding the immune cell populations in inflamed human tissues. Here, in an effort to identify such tumour-enriched immune alterations, we used complementary single-cell analysis approaches to interrogate the immune infiltrate in human head and neck squamous cell carcinomas and site-matched non-malignant, inflamed tissues. Our analysis revealed a large overlap in the composition and phenotype of immune cells in tumour and inflamed tissues. Computational analysis identified tumour-enriched immune cell interactions, one of which yields a large population of regulatory T (Treg) cells that is highly enriched in the tumour and uniquely identified among all haematopoietically-derived cells in blood and tissue by co-expression of ICOS and IL-1 receptor type 1 (IL1R1). We provide evidence that these intratumoural IL1R1+ Treg cells had responded to antigen recently and demonstrate that they are clonally expanded with superior suppressive function compared with IL1R1− Treg cells. In addition to identifying extensive immunological congruence between inflamed tissues and tumours as well as tumour-specific changes with direct disease relevance, our work also provides a blueprint for extricating disease-specific changes from general inflammation-associated patterns.

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New interpretable machine-learning method for single-cell data reveals correlates of clinical response to cancer immunotherapy

Cited by 28 publications

References 67 publications

Identifying Candidates for Immunotherapy among Patients with Non-Melanoma Skin Cancer: A Review of the Potential Predictors of Response

Identifying Candidates for Immunotherapy among Patients with Non-Melanoma Skin Cancer: A Review of the Potential Predictors of Response

Combined unsupervised and semi-automated supervised analysis of flow cytometry data reveals cellular fingerprint associated with newly diagnosed pediatric type 1 diabetes

Extricating human tumour immune alterations from tissue inflammation

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