The benefits of immunotherapy combinations

Schmidt, Charles W.

doi:10.1038/d41586-017-08702-7

Cited by 106 publications

(87 citation statements)

References 7 publications

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“…Forth, quanTIseq analysis of the transcriptomes of patients treated with kinase inhibitors demonstrates profound pharmacological remodeling of the immune contexture. The immunological effects of conventional and targeted therapies came only recently into focus, fostering numerous clinical trials on combinatorial regimens of checkpoint blockers and targeted agents 39 . As bulk RNA-seq is now widely applied to profile fresh-frozen and archived tumor specimens, quanTIseq can be applied to effectively mine these data.…”

Section: Discussionmentioning

confidence: 99%

Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data

Finotello

Mayer

Plattner

et al. 2017

Preprint

173

196

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We introduce quanTIseq, a method to quantify the tumor immune contexture, determined by the type and density of tumor-infiltrating immune cells. quanTIseq is based on a novel deconvolution algorithm for RNA sequencing data that was validated with independent data sets. Complementing the deconvolution output with image data from tissue slides enables in silico multiplexed immunodetection and provides an efficient method for the immunophenotyping of a large number of tumor samples.Cancer immunotherapy with antibodies targeting immune checkpoints has shown durable benefit or even curative potential in various cancers 1,2 . As only a fraction of patients are responsive to immune checkpoint blockers, efforts are underway to identify predictive markers as well as mechanistic rationale for combination therapies with synergistic potential. Thus, comprehensive and quantitative immunological characterization of tumors in a large number of clinical samples is of utmost importance, but it is currently hampered by the lack of simple and efficient methods. Therefore, we developed quanTIseq, a computational pipeline for the quantification of the Tumor Immune contexture using RNA-seq data and images of haematoxylin and eosin (H&E)-stained tissue slides (Fig. 1a). As part of quanTIseq, we first developed a deconvolution algorithm based on constrained least squares regression 12 . We then designed a signature matrix from a compendium of 51 RNA-seq data sets (Supplementary (Fig. 1c).To validate quanTIseq we first used both simulated data and published data. We simulated 1,700RNA-seq data sets from human breast tumors by mixing various numbers of reads from tumor and immune-cell RNA-seq data, considering different immune compositions and sequencing depths.quanTIseq obtained a high correlation between the true and the estimated fractions and accurately quantified tumor content (measured by the fraction of "other" cells) (Supplementary Figure 1). We then validated quanTIseq using experimental data from a previous study 13 , in which peripheral blood mononuclear cell (PBMC) mixtures were subjected to both, RNA-seq and flow cytometry. A high accuracy of quanTIseq estimates was also observed with this data set ( Fig. 1d and Supplementary Figure 2). Additionally, we successfully validated quanTIseq using two previous published data sets (Supplementary Figures 3 and 4).As most of the validation data sets available in the literature are based on microarray data or consider a limited number of phenotypes, we generated RNA-seq and flow cytometry data from mixtures of peripheral-blood immune cells collected from nine healthy donors. Flow cytometry was used to quantify all the immune sub-populations considered by quanTIseq signature matrix except macrophages, which are not present in blood. Comparison between quanTIseq cell estimates and flow cytometry fractions showed a high correlation at a single and multiple cell-type level ( Fig. 1e and Supplementary Figure 5).We then validated quanTIseq using two independent data sets. The first data...

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Section: Discussionmentioning

confidence: 99%

Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data

Finotello

Mayer

Plattner

et al. 2017

Preprint

173

196

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“…Notably, treatment interactions are important in clinical therapeutics and side effects. More than 10,000 clinical trials in the United States alone are studying the effects of drug combinations 1,2,24 . By uncovering relevant interactions and their functions, SAIL can further understanding of interaction mechanisms and the development of combination therapeutics.…”

Section: Discussionmentioning

confidence: 99%

“…Biological responses are shaped by the effects of multiple stimuli in complex environments, and treatment of many diseases involves combination therapies 1,2 . A complex consequence of combination stimuli is the potential occurrence of nonlinear interactions that can dramatically alter the effects of individual treatments.…”

Section: Introductionmentioning

confidence: 99%

Deciphering the combinatorial interaction landscape

Cappuccio

Jensen

Hartmann

et al. 2019

Preprint

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From cellular activation to drug combinations, the control of biological systems involves multiple stimuli that can elicit complex nonlinear interactions. To elucidate the functions and logic of stimulus interactions, we developed SAIL (Synergistic/Antagonistic Interaction Learner). SAIL uses a machine learning classifier trained to categorize interactions across a complete taxonomy of possible combinatorial effects. The strategy resolves the most informative interactions, and helps infer their functions and regulatory mechanisms. SAIL-predicted interaction mechanisms controlling key immune functions were experimentally validated. SAIL can integrate results from multiple datasets to derive general properties of how cells respond to multiple stimuli. Using public immunological datasets, we assembled a fine-grained landscape of~30000 interactions. Analysis of the landscape shows the context-dependent functions of individual modulators, and reveals a probabilistic algebra that links the separate and combined stimulus effects. SAIL is available through a user friendly interface to resolve the effect of stimulus and drug combinations .

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“…183,185-187 In several instances, vaccination is further combined with standard treatment regimens including conventional chemotherapy, 117,188-191 radiation therapy, 52,192-195 and targeted anticancer agents, 196-199 or with various immunotherapeutic interventions. 200-205 The latter include (1) immune checkpoint blockers such as the anti-PD-1 mAbs pembrolizumab and nivolumab, 206-208 the anti-PD-L1 mAbs durvalumab and atezolizumab, 209-211 and the anti-CTLA4 mAb ipilimumab; 137,186,212-215 (2) immunostimulatory antibodies such as utomilumab, which stimulates TNF receptor superfamily member 9 (TNFRSF9; best known as 4-1BB or CD137) signaling, 28,216-218 or the CD27 agonist varlilumab; 28,216,219,220 and immunomodulatory agents such as lenalidomide. 221-224 In line with preclinical and clinical data demonstrating that multi-epitope vaccines are generally more powerful than their single-epitope counterparts, 117,225 the most common vaccination strategy employed by these studies consists in targeting simultaneously multiple TAAs (20 studies).…”

Section: Ongoing Clinical Trialsmentioning

confidence: 99%

Trial watch: Peptide-based vaccines in anticancer therapy

et al. 2018

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Peptide-based anticancer vaccination aims at stimulating an immune response against one or multiple tumor-associated antigens (TAAs) following immunization with purified, recombinant or synthetically engineered epitopes. Despite high expectations, the peptide-based vaccines that have been explored in the clinic so far had limited therapeutic activity, largely due to cancer cell-intrinsic alterations that minimize antigenicity and/or changes in the tumor microenvironment that foster immunosuppression. Several strategies have been developed to overcome such limitations, including the use of immunostimulatory adjuvants, the co-treatment with cytotoxic anticancer therapies that enable the coordinated release of damage-associated molecular patterns, and the concomitant blockade of immune checkpoints. Personalized peptide-based vaccines are also being explored for therapeutic activity in the clinic. Here, we review recent preclinical and clinical progress in the use of peptide-based vaccines as anticancer therapeutics.Abbreviations: CMP: carbohydrate-mimetic peptide; CMV: cytomegalovirus; DC: dendritic cell; FDA: Food and Drug Administration; HPV: human papillomavirus; MDS: myelodysplastic syndrome; MHP: melanoma helper vaccine; NSCLC: non-small cell lung carcinoma; ODD: orphan drug designation; PPV: personalized peptide vaccination; SLP: synthetic long peptide; TAA: tumor-associated antigen; TNA: tumor neoantigen

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The benefits of immunotherapy combinations

Cited by 106 publications

References 7 publications

Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data

Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data

Deciphering the combinatorial interaction landscape

Trial watch: Peptide-based vaccines in anticancer therapy

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