Predicting immunotherapy response through genomics

Cormedi, Marina Candido Visontai; Allen, Eliezer M. Van; Colli, Leandro M.

doi:10.1016/j.gde.2020.11.004

Cited by 15 publications

(10 citation statements)

References 65 publications

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“…15 DPF3 effect on T-cell-mediated cytotoxic and immunotherapy response Previous studies have suggested that mutations within the SWI/SNF complexes, particularly in PBRM1, could contribute to response to immune checkpoint inhibitor therapy in RCC affected individuals, 77,78 but other studies could not find this association; [79][80][81] confirmation studies are needed. 82 Ex vivo data suggest that PBRM1 mutations are associated with enhanced sensitization to killing by T cells, 83 an effect most likely mediated through mSWI/ SNF complex regulation of chromatin accessibility for INF-g target genes. 83 Accordingly, we tested whether DPF3, through activation of the STAT3 pathway, could increase T-cell-mediated killing.…”

Section: Dpf3 Changes Chromatin Accessibility Leading To Altered Gene Expressionmentioning

confidence: 99%

Altered regulation of DPF3, a member of the SWI/SNF complexes, underlies the 14q24 renal cancer susceptibility locus

Colli

Jessop

Myers

et al. 2021

The American Journal of Human Genetics

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Our study investigated the underlying mechanism for the 14q24 renal cell carcinoma (RCC) susceptibility risk locus identified by a genome-wide association study (GWAS). The sentinel single-nucleotide polymorphism (SNP), rs4903064, at 14q24 confers an allele-specific effect on expression of the double PHD fingers 3 (DPF3) of the BAF SWI/SNF complex as assessed by massively parallel reporter assay, confirmatory luciferase assays, and eQTL analyses. Overexpression of DPF3 in renal cell lines increases growth rates and alters chromatin accessibility and gene expression, leading to inhibition of apoptosis and activation of oncogenic pathways. siRNA interference of multiple DPF3-deregulated genes reduces growth. Our results indicate that germline variation in DPF3, a component of the BAF complex, part of the SWI/SNF complexes, can lead to reduced apoptosis and activation of the STAT3 pathway, both critical in RCC carcinogenesis. In addition, we show that altered DPF3 expression in the 14q24 RCC locus could influence the effectiveness of immunotherapy treatment for RCC by regulating tumor cytokine secretion and immune cell activation.

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Section: Dpf3 Changes Chromatin Accessibility Leading To Altered Gene Expressionmentioning

confidence: 99%

Altered regulation of DPF3, a member of the SWI/SNF complexes, underlies the 14q24 renal cancer susceptibility locus

Colli

Jessop

Myers

et al. 2021

The American Journal of Human Genetics

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“…However, this story is far from over. As additional cancer and treatment-line indications are evaluated, it has become clear these agents have limits, often hampered by a variety of resistance mechanisms, including insufficient tumor immunogenicity, MHC dysfunction, T-cell exhaustion, resistance to secondary cytokines such as interferon (IFN)-γ signaling, and barriers on entering the immunosuppressive tumor microenvironment (TME) [5,6].…”

Section: Importance Of the Pd-1/pd-l1 Pathwaymentioning

confidence: 99%

Tumor in the Crossfire: Inhibiting TGF-β to Enhance Cancer Immunotherapy

Tschernia

Gulley

2022

BioDrugs

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Cancer immunotherapy using monoclonal antibodies targeting immune checkpoints has undoubtedly revolutionized the cancer treatment landscape in the last decade. Immune checkpoint inhibitors can elicit long-lasting, previously unheard-of responses in a number of tumor entities. Yet, even in such tumors as metastatic melanoma and non-small cell-lung cancer, in which immune checkpoint inhibition has become the first-line treatment of choice, only a minority of patients will benefit considerably from these treatments. This has been attributed to a number of factors, including an immune-suppressive tumor microenvironment (TME). Using different modalities to break these barriers is of utmost importance to expand the population of patients that benefit from immune checkpoint inhibition. The multifunctional cytokine transforming growth factor-β (TGF-β) has long been recognized as an immune-suppressive factor in the TME. A considerable number of drugs have been developed to target TGF-β, yet most of these have since been discontinued. The combination of anti-TGF-β agents with immune checkpoint inhibitors now has the potential to revive this target as a viable immunomodulatory therapeutic approach. Currently, a limited number of small molecular inhibitor and monoclonal antibody candidates that target TGF-β are in clinical development in combination with the following immune checkpoint inhibitors: SRK 181, an antibody inhibiting the activation of latent TGF-β1; NIS 793, a monoclonal antibody targeting TGF-β; and SHR 1701, a fusion protein consisting of an anti-PD-L1 monoclonal antibody fused with the extracellular domain of human TGF-β receptor II. Several small molecular inhibitors are also in development and are briefly reviewed: LY364947, a pyrazole-based small molecular inhibitor of the serine-threonine kinase activity of TGFβRI; SB-431542, an inhibitor targeting several TGF-β superfamily Type I activin receptor-like kinases as well as TGF-β1-induced nuclear Smad3 localization; and galunisertib, an oral small molecular inhibitor of the TGFβRI kinase. One of the most advanced agents in this area is bintrafusp alfa, a bifunctional fusion protein composed of the extracellular domain of TGF-β receptor II fused to a human IgG1 mAb blocking PD-L1. Bintrafusp alfa is currently in advanced clinical development and as an agent in this space with the most clinical experience, is a focused highlight of this review.

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“…Genomic-based rubrics [11] (e.g., tumor mutational burden [12]), machine learning-based approaches [13], and transcriptomic rubrics [14] have also been studied as potential indicators of therapeutic response, among others [15]. These response evaluation methods, while useful, have traditionally defined response as a change in size of one or more tumors, which may not always be indicative of the true gold standard of cancer treatment in clinical practice: extending patient overall survival (OS) [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Hybridizing mechanistic mathematical modeling with deep learning methods to predict individual cancer patient survival after immune checkpoint inhibitor therapy

Butner,

Dogra,

Chung

et al. 2024

Preprint

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We present a study where predictive mechanistic modeling is used in combination with deep learning methods to predict individual patient survival probabilities under immune checkpoint inhibitor (ICI) therapy. This hybrid approach enables prediction based on both measures that are calculable from mechanistic models (but may not be directly measurable in the clinic) and easily measurable quantities or characteristics (that are not always readily incorporated into predictive mechanistic models). The mechanistic model we have applied here can predict tumor response from CT or MRI imaging based on key mechanisms underlying checkpoint inhibitor therapy, and in the present work, its parameters were combined with readily-available clinical measures from 93 patients into a hybrid training set for a deep learning time-to-event predictive model. Analysis revealed that training an artificial neural network with both mechanistic modeling-derived and clinical measures achieved higher per-patient predictive accuracy based on event-time concordance, Brier score, and negative binomial log-likelihood-based criteria than when only mechanistic model-derived values or only clinical data were used. Feature importance analysis revealed that both clinical and model-derived parameters play prominent roles in neural network decision making, and in increasing prediction accuracy, further supporting the advantage of our hybrid approach. We anticipate that many existing mechanistic models may be hybridized with deep learning methods in a similar manner to improve predictive accuracy through addition of additional data that may not be readily implemented in mechanistic descriptions.

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Predicting immunotherapy response through genomics

Cited by 15 publications

References 65 publications

Altered regulation of DPF3, a member of the SWI/SNF complexes, underlies the 14q24 renal cancer susceptibility locus

Altered regulation of DPF3, a member of the SWI/SNF complexes, underlies the 14q24 renal cancer susceptibility locus

Tumor in the Crossfire: Inhibiting TGF-β to Enhance Cancer Immunotherapy

Hybridizing mechanistic mathematical modeling with deep learning methods to predict individual cancer patient survival after immune checkpoint inhibitor therapy

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