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1,452 result(s) for "Chan, Timothy A"
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The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy
Checkpoint inhibitor-based immunotherapies that target cytotoxic T lymphocyte antigen 4 (CTLA4) or the programmed cell death 1 (PD1) pathway have achieved impressive success in the treatment of different cancer types. Yet, only a subset of patients derive clinical benefit. It is thus critical to understand the determinants driving response, resistance and adverse effects. In this Review, we discuss recent work demonstrating that immune checkpoint inhibitor efficacy is affected by a combination of factors involving tumour genomics, host germline genetics, PD1 ligand 1 (PDL1) levels and other features of the tumour microenvironment, as well as the gut microbiome. We focus on recently identified molecular and cellular determinants of response. A better understanding of how these variables cooperate to affect tumour–host interactions is needed to optimize the implementation of precision immunotherapy.This Review discusses recent work demonstrating that immune checkpoint inhibitor efficacy is affected by a combination of factors involving tumour genomics, host germline genetics, programmed cell death 1 ligand 1 (PDL1) levels and other features of the tumour microenvironment, as well as the gut microbiome.
Evolutionary divergence of HLA class I genotype impacts efficacy of cancer immunotherapy
Functional diversity of the highly polymorphic human leukocyte antigen class I (HLA-I) genes underlies successful immunologic control of both infectious disease and cancer. The divergent allele advantage hypothesis dictates that an HLA-I genotype with two alleles with sequences that are more divergent enables presentation of more diverse immunopeptidomes1–3. However, the effect of sequence divergence between HLA-I alleles—a quantifiable measure of HLA-I evolution—on the efficacy of immune checkpoint inhibitor (ICI) treatment for cancer remains unknown. In the present study the germline HLA-I evolutionary divergence (HED) of patients with cancer treated with ICIs was determined by quantifying the physiochemical sequence divergence between HLA-I alleles of each patient’s genotype. HED was a strong determinant of survival after treatment with ICIs. Even among patients fully heterozygous at HLA-I, patients with an HED in the upper quartile respond better to ICIs than patients with a low HED. Furthermore, HED strongly impacts the diversity of tumor, viral and self-immunopeptidomes and intratumoral T cell receptor clonality. Similar to tumor mutation burden, HED is a fundamental metric of diversity at the major histocompatibility complex–peptide complex, which dictates ICI efficacy. The data link divergent HLA allele advantage to immunotherapy efficacy and unveil how ICI response relies on the evolved efficiency of HLA-mediated immunity.
Next-generation sequencing: unraveling genetic mechanisms that shape cancer immunotherapy efficacy
Immunity is governed by fundamental genetic processes. These processes shape the nature of immune cells and set the rules that dictate the myriad complex cellular interactions that power immune systems. Everything from the generation of T cell receptors and antibodies, control of epitope presentation, and recognition of pathogens by the immunoediting of cancer cells is, in large part, made possible by core genetic mechanisms and the cellular machinery that they encode. In the last decade, next-generation sequencing has been used to dissect the complexities of cancer immunity with potent effect. Sequencing of exomes and genomes has begun to reveal how the immune system recognizes \"foreign\" entities and distinguishes self from non-self, especially in the setting of cancer. High-throughput analyses of transcriptomes have revealed deep insights into how the tumor microenvironment affects immunotherapy efficacy. In this Review, we discuss how high-throughput sequencing has added to our understanding of how immune systems interact with cancer cells and how cancer immunotherapies work.
Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy
Immunotherapy works by activating the patient's own immune system to fight cancer. For effective tumor killing, CD8 + T cells recognize tumor peptides presented by human leukocyte antigen class I (HLA-I) molecules. In humans, there are three major HLA-I genes ( HLA-A, HLA-B , and HLA-C ). Chowell et al. asked whether germline HLA-I genotype influences how T cells recognize tumor peptides and respond to checkpoint inhibitor immunotherapies (see the Perspective by Kvistborg and Yewdell). They examined more than 1500 patients and found that heterozygosity at HLA-I loci was associated with better survival than homozygosity for one or more HLA-I genes. Thus, specific HLA-I mutations could have implications for immune recognition and for the design of epitopes for cancer vaccines and immunotherapies. Science , this issue p. 582 ; see also p. 516 Human leukocyte antigen superfamilies predict immunotherapy response. CD8 + T cell–dependent killing of cancer cells requires efficient presentation of tumor antigens by human leukocyte antigen class I (HLA-I) molecules. However, the extent to which patient-specific HLA-I genotype influences response to anti–programmed cell death protein 1 or anti–cytotoxic T lymphocyte–associated protein 4 is currently unknown. We determined the HLA-I genotype of 1535 advanced cancer patients treated with immune checkpoint blockade (ICB). Maximal heterozygosity at HLA-I loci (“A,” “B,” and “C”) improved overall survival after ICB compared with patients who were homozygous for at least one HLA locus. In two independent melanoma cohorts, patients with the HLA-B44 supertype had extended survival, whereas the HLA-B62 supertype (including HLA-B*15:01) or somatic loss of heterozygosity at HLA-I was associated with poor outcome. Molecular dynamics simulations of HLA-B*15:01 revealed different elements that may impair CD8 + T cell recognition of neoantigens. Our results have important implications for predicting response to ICB and for the design of neoantigen-based therapeutic vaccines.
ATRX, DAXX or MEN1 mutant pancreatic neuroendocrine tumors are a distinct alpha-cell signature subgroup
The commonly mutated genes in pancreatic neuroendocrine tumors (PanNETs) are ATRX , DAXX , and MEN1 . We genotyped 64 PanNETs and found 58% carry ATRX , DAXX , and MEN1 mutations (A-D-M mutant PanNETs) and this correlates with a worse clinical outcome than tumors carrying the wild-type alleles of all three genes (A-D-M WT PanNETs). We performed RNA sequencing and DNA-methylation analysis to reveal two distinct subgroups with one consisting entirely of A-D-M mutant PanNETs. Two genes differentiating A-D-M mutant from A-D-M WT PanNETs were high ARX and low PDX1 gene expression with PDX1 promoter hyper-methylation in the A-D-M mutant PanNETs. Moreover, A-D-M mutant PanNETs had a gene expression signature related to that of alpha-cells (FDR q -value < 0.009) of pancreatic islets including increased expression of HNF1A and its transcriptional target genes. This gene expression profile suggests that A-D-M mutant PanNETs originate from or transdifferentiate into a distinct cell type similar to alpha cells. In pancreatic neuroendocrine tumors (PanNETs) ATRX, DAXX, and MEN1 are commonly mutated (A-D-M mutant PanNETs). Here, the authors find in a cohort of PanNETS 58% are A-D-M mutant PanNETs, with a worse clinical outcome and differences in gene expression and methylation compared to A-D-M wild type cases- these gene expression differences suggest that A-D-M mutant PanNETs potentially originate from a cell type similar to alpha cells.
Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer
Immune checkpoint inhibitors, which unleash a patient's own T cells to kill tumors, are revolutionizing cancer treatment. To unravel the genomic determinants of response to this therapy, we used whole-exome sequencing of non–small cell lung cancers treated with pembrolizumab, an antibody targeting programmed cell death-1 (PD-1). In two independent cohorts, higher nonsynonymous mutation burden in tumors was associated with improved objective response, durable clinical benefit, and progression-free survival. Efficacy also correlated with the molecular smoking signature, higher neoantigen burden, and DNA repair pathway mutations; each factor was also associated with mutation burden. In one responder, neoantigen-specific CD8+ T cell responses paralleled tumor regression, suggesting that anti–PD-1 therapy enhances neoantigen-specific T cell reactivity. Our results suggest that the genomic landscape of lung cancers shapes response to anti–PD-1 therapy.
Genetic Basis for Clinical Response to CTLA-4 Blockade in Melanoma
In a comparison of tumors from patients with melanoma who benefitted from blockade of cytotoxic T-lymphocyte antigen 4 (CTLA-4) with tumors from patients who did not benefit, tumor neoantigens were detected that were strongly associated with a response. Immune checkpoint blockade has led to durable antitumor effects in patients with metastatic melanoma, non–small-cell lung cancer, and other tumor types, but the factors determining whether a patient will have a response remain elusive. 1 , 2 The fully human monoclonal antibodies ipilimumab and tremelimumab block cytotoxic T-lymphocyte antigen 4 (CTLA-4), resulting in T-cell activation. Some studies have established correlations between outcomes with ipilimumab and peripheral-blood lymphocyte count, markers of T-cell activation, 3 an “inflammatory” microenvironment, 4 , 5 and maintenance of high-frequency T-cell receptor clonotypes. 6 The relationship among the genomic landscape of the tumor, the mutational load, and the benefit from treatment remains obscure. . . .
Aging‐related cell type‐specific pathophysiologic immune responses that exacerbate disease severity in aged COVID‐19 patients
Coronavirus disease 2019 (COVID‐19) is especially severe in aged patients, defined as 65 years or older, for reasons that are currently unknown. To investigate the underlying basis for this vulnerability, we performed multimodal data analyses on immunity, inflammation, and COVID‐19 incidence and severity as a function of age. Our analysis leveraged age‐specific COVID‐19 mortality and laboratory testing from a large COVID‐19 registry, along with epidemiological data of ~3.4 million individuals, large‐scale deep immune cell profiling data, and single‐cell RNA‐sequencing data from aged COVID‐19 patients across diverse populations. We found that decreased lymphocyte count and elevated inflammatory markers (C‐reactive protein, D‐dimer, and neutrophil–lymphocyte ratio) are significantly associated with age‐specific COVID‐19 severities. We identified the reduced abundance of naïve CD8 T cells with decreased expression of antiviral defense genes (i.e., IFITM3 and TRIM22) in aged severe COVID‐19 patients. Older individuals with severe COVID‐19 displayed type I and II interferon deficiencies, which is correlated with SARS‐CoV‐2 viral load. Elevated expression of SARS‐CoV‐2 entry factors and reduced expression of antiviral defense genes (LY6E and IFNAR1) in the secretory cells are associated with critical COVID‐19 in aged individuals. Mechanistically, we identified strong TGF‐beta‐mediated immune–epithelial cell interactions (i.e., secretory‐non‐resident macrophages) in aged individuals with critical COVID‐19. Taken together, our findings point to immuno‐inflammatory factors that could be targeted therapeutically to reduce morbidity and mortality in aged COVID‐19 patients. Our study identified that elevated inflammatory markers (C‐reactive protein, D‐dimer, and neutrophil–lymphocyte ratio) are significantly associated with age‐specific COVID‐19 severities. Mechanistically, we identified that reduced abundance of naïve CD8 T cells and type I and II interferon deficiencies are associated with severity of COVID‐19 in aged individuals. We identified strong TGF‐beta‐mediated immune–epithelial cell interactions in aged individuals with critical COVID‐19.
G-quadruplex DNA drives genomic instability and represents a targetable molecular abnormality in ATRX-deficient malignant glioma
Mutational inactivation of ATRX (α-thalassemia mental retardation X-linked) represents a defining molecular alteration in large subsets of malignant glioma. Yet the pathogenic consequences of ATRX deficiency remain unclear, as do tractable mechanisms for its therapeutic targeting. Here we report that ATRX loss in isogenic glioma model systems induces replication stress and DNA damage by way of G-quadruplex (G4) DNA secondary structure. Moreover, these effects are associated with the acquisition of disease-relevant copy number alterations over time. We then demonstrate, both in vitro and in vivo, that ATRX deficiency selectively enhances DNA damage and cell death following chemical G4 stabilization. Finally, we show that G4 stabilization synergizes with other DNA-damaging therapies, including ionizing radiation, in the ATRX-deficient context. Our findings reveal novel pathogenic mechanisms driven by ATRX deficiency in glioma, while also pointing to tangible strategies for drug development. ATRX deficiency is linked to genomic stability in cancer cells. Here, the authors show that ATRX inactivation induces G-quadruplex formation, leading to genome-wide DNA damage, and the use of G-quadruplex stabilisers can be exploited therapeutically in ATRX deficient gliomas.
Immune selection determines tumor antigenicity and influences response to checkpoint inhibitors
In cancer, evolutionary forces select for clones that evade the immune system. Here we analyzed >10,000 primary tumors and 356 immune-checkpoint-treated metastases using immune dN/dS, the ratio of nonsynonymous to synonymous mutations in the immunopeptidome, to measure immune selection in cohorts and individuals. We classified tumors as immune edited when antigenic mutations were removed by negative selection and immune escaped when antigenicity was covered up by aberrant immune modulation. Only in immune-edited tumors was immune predation linked to CD8 T cell infiltration. Immune-escaped metastases experienced the best response to immunotherapy, whereas immune-edited patients did not benefit, suggesting a preexisting resistance mechanism. Similarly, in a longitudinal cohort, nivolumab treatment removes neoantigens exclusively in the immunopeptidome of nonimmune-edited patients, the group with the best overall survival response. Our work uses dN/dS to differentiate between immune-edited and immune-escaped tumors, measuring potential antigenicity and ultimately helping predict response to treatment. Immune dN/dS is the ratio of nonsynonymous to synonymous mutations in the MHC-bound immunopeptidome. Application of immune dN/dS to cancer datasets shows that it distinguishes immune evasion and escape and predicts response to immunotherapies.