scholarly journals A computational network approach to identify predictive biomarkers and therapeutic combinations for anti-PD-1 immunotherapy in cancer

2020 ◽  
Author(s):  
Chia-Chin Wu ◽  
Y Alan Wang ◽  
J Andrew Livingston ◽  
Jianhua Zhang ◽  
P. Andrew Futreal
Keyword(s):  
Gene Network ◽  
Target Genes ◽  
Tumor Response ◽  
Cytotoxic T Cells ◽  
Predictive Biomarkers ◽  
Cancer Genes ◽  
Network Approach ◽  
Tumor Resistance ◽  
Mhc I ◽  
Transcriptomic Data

AbstractBackgroundDespite remarkable success, only a subset of cancer patients have shown benefit from the anti-PD1 therapy. Therefore, there is a growing need to identify predictive biomarkers and therapeutic combinations for improving the clinical efficacy.ResultsBased upon the hypothesis that aberrations of any gene that are close to MHC class I genes in the gene network are likely to deregulate MHC I pathway and affect tumor response to anti-PD1, we developed a network approach to infer genes, pathway, and potential therapeutic target genes associated with response to PD-1/PD-L1 checkpoint immunotherapies in cancer. Our approach successfully identified genes (e.g. B2M and PTEN) and pathways (e.g. JAK/STAT and WNT) known to be associated with anti-PD1 response. Our prediction was further validated by 5 CRISPR gene sets associated with tumor resistance to cytotoxic T cells. Our results also showed that many cancer genes that act as hubs in the gene network may drive immune evasion through indirectly deregulating the MHC I pathway. The integration analysis of transcriptomic data of the 34 TCGA cancer types and our prediction reveals that MHC I-immunoregulations may be tissue-specific. The signature-based score, the MHC I association immunoscore (MIAS), calculated by integration of our prediction and TCGA melanoma transcriptomic data also showed a good correlation with patient response to anti-PD1 for 354 melanoma samples complied from 5 cohorts. In addition, most targets of the 36 compounds that have been tested in clinical trials or used for combination treatments with anti-PD1 are in the top list of our prediction (AUC=0.833). Integration of drug target data with our top prediction further identified compounds that were recently shown to enhance tumor response to anti-PD1, such as inhibitors of GSK3B, CDK, and PTK2.ConclusionOur approach is effective to identify candidate genes and pathways associated with response to anti-PD-1 therapy, and can also be employed for in silico screening of potential compounds to enhances the efficacy of anti-PD1 agents against cancer.

scholarly journals Prediction of biomarkers and therapeutic combinations for anti-PD-1 immunotherapy using the global gene network association

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Chia-Chin Wu ◽  
Y. Alan Wang ◽  
J. Andrew Livingston ◽  
Jianhua Zhang ◽  
P. Andrew Futreal
Keyword(s):  
Tumor Response ◽  
Cytotoxic T Cells ◽  
Gene Signature ◽  
Genetic Alterations ◽  
Cancer Type ◽  
Tumor Resistance ◽  
Patient Response ◽  
Mhc I ◽  
Combination Treatments ◽  
Gene Sets

AbstractOwing to a lack of response to the anti-PD1 therapy for most cancer patients, we develop a network approach to infer genes, pathways, and potential therapeutic combinations that are associated with tumor response to anti-PD1. Here, our prediction identifies genes and pathways known to be associated with anti-PD1, and is further validated by 6 CRISPR gene sets associated with tumor resistance to cytotoxic T cells and targets of the 36 compounds that have been tested in clinical trials for combination treatments with anti-PD1. Integration of our top prediction and TCGA data identifies hundreds of genes whose expression and genetic alterations that could affect response to anti-PD1 in each TCGA cancer type, and the comparison of these genes across cancer types reveals that the tumor immunoregulation associated with response to anti-PD1 would be tissue-specific. In addition, the integration identifies the gene signature to calculate the MHC I association immunoscore (MIAS) that shows a good correlation with patient response to anti-PD1 for 411 melanoma samples complied from 6 cohorts. Furthermore, mapping drug target data to the top genes in our association prediction identifies inhibitors that could potentially enhance tumor response to anti-PD1, such as inhibitors of the encoded proteins of CDK4, GSK3B, and PTK2.

scholarly journals Intratumoral SIRPα-deficient macrophages activate tumor antigen-specific cytotoxic T cells under radiotherapy

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zhen Bian ◽  
Lei Shi ◽  
Koby Kidder ◽  
Ke Zen ◽  
Charlie Garnett-Benson ◽  
...  
Keyword(s):  
T Cells ◽  
Tumor Response ◽  
Cytotoxic T Cells ◽  
Suppressor Cells ◽  
Myeloid Derived Suppressor Cells ◽  
Tumor Resistance ◽  
Treatment Resistant ◽  
Humoral And Cellular Immunity ◽  
Advanced Stages ◽  
Post Radiation

AbstractRadiotherapy (RT)-induced tumoricidal immunity is severely limited when tumors are well-established. Here, we report that depleting SIRPα on intratumoral macrophages augments efficacy of RT to eliminate otherwise large, treatment-resistant colorectal (MC38) and pancreatic (Pan02 and KPC) tumors, inducing complete abscopal remission and long-lasting humoral and cellular immunity that prevent recurrence. SIRPα-deficient macrophages activated by irradiated tumor-released DAMPs exhibit robust efficacy and orchestrate an anti-tumor response that controls late-stage tumors. Upon RT-mediated activation, intratumoral SIRPα-deficient macrophages acquire potent proinflammatory features and conduct immunogenic antigen presentation that confer a tumoricidal microenvironment highly infiltrated by tumor-specific cytotoxic T cells, NK cells and inflammatory neutrophils, but with limited immunosuppressive regulatory T cells, myeloid derived suppressor cells and post-radiation wound-healing. The results demonstrate that SIRPα is a master regulator underlying tumor resistance to RT and provide proof-of-principle for SIRPα-deficient macrophage-based therapies to treat a broad spectrum of cancers, including those at advanced stages with low immunogenicity and metastases.

scholarly journals Harnessing Synthetic Lethal Interactions for Personalized Medicine

2022 ◽  
Vol 12 (1) ◽  
pp. 98
Author(s):  
Grace S. Shieh
Keyword(s):  
Synthetic Lethality ◽  
Predictive Biomarkers ◽  
Cancer Genes ◽  
Tumor Resistance ◽  
Future Directions ◽  
Synthetic Lethal ◽  
The Past ◽  
Synthetic Lethal Interactions ◽  
A Cell ◽  
Cancer Medicine

Two genes are said to have synthetic lethal (SL) interactions if the simultaneous mutations in a cell lead to lethality, but each individual mutation does not. Targeting SL partners of mutated cancer genes can kill cancer cells but leave normal cells intact. The applicability of translating this concept into clinics has been demonstrated by three drugs that have been approved by the FDA to target PARP for tumors bearing mutations in BRCA1/2. This article reviews applications of the SL concept to translational cancer medicine over the past five years. Topics are (1) exploiting the SL concept for drug combinations to circumvent tumor resistance, (2) using synthetic lethality to identify prognostic and predictive biomarkers, (3) applying SL interactions to stratify patients for targeted and immunotherapy, and (4) discussions on challenges and future directions.

823 CD4 T cells are essential for an anti-tumor effect in a B78 murine melanoma tumor model

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A873-A873
Author(s):  
Arika Feils ◽  
Mackenzie Heck ◽  
Anna Hoefges ◽  
Peter Carlson ◽  
Luke Zangl ◽  
...  
Keyword(s):  
T Cells ◽  
Tumor Growth ◽  
Cd4 T Cells ◽  
Tumor Response ◽  
Immune Cell ◽  
Cd8 T Cell ◽  
Mhc I ◽  
Mhc Ii ◽  
Flow Cytometric

BackgroundMice bearing B78 melanoma tumors can be cured using an in situ vaccine (ISV) regimen that includes radiation (RT) together with immunocytokine (tumor-targeting mAb conjugated to IL-2). B78 melanoma cells, derived from B16 cells, express minimal to no MHC-I but express MHC-II upon IFN-g/TNF-a stimulation. Although B78 cells are primarily MHC-I-deficient, an increased CD8 T cell infiltration into the tumor microenvironment (TME) has been shown following ISV.1 To further investigate the potential role of specific immune cell lineages in the B78 anti-tumor response to ISV, immune subset depletion studies and flow cytometric analyses were performed.MethodsC57BL/6 mice bearing B78 tumors were depleted of immune cell subsets with mAbs (anti-CD4, anti-CD8, anti-NK1.1, or Rat IgG control) for 3 weeks during the course of treatment. Treatment groups included no treatment, RT (12 Gy), or ISV (RT D0 and immunocytokine D5-D9). 6 mice/group (repeated three times) were followed for survival/tumor growth, and flow cytometry studies included 4 mice/group, sacrificed on D8 and D13 following the start of ISV.ResultsMice depleted of CD4 T cells during the course of ISV showed a significant reduction of anti-tumor effect as compared to mice treated with ISV/Rat IgG (pConclusionsThese studies suggest that CD4 T cells are essential for an anti-tumor response in the B78 melanoma model. In vivo depletion data show that CD4 T cells, but not CD8 or NK cells, are required for a decrease in tumor growth via ISV. Flow cytometric analyses suggest an interplay between CD4 and CD8 T cells as indicated by a decrease in CD8/IFN-g expression following ISV in the absence of CD4 T cells. The role that MHC-I and MHC-II expression plays in this CD4/CD8 T cell anti-tumor response is under investigation. In future studies, B78 melanoma may serve as a critical syngeneic model for development of more effective immunotherapy treatment regimens.Ethics ApprovalAll animal experiments were performed in accordance with protocols approved by Animal Care and Use Committees of the University of Wisconsin-Madison.ReferenceMorris Z, Guy E, Francis D, et al. In situ tumor vaccination by combining local radiation and tumor-specific antibody or immunocytokine treatments. Cancer Res 2016;76(13):3929-3941.

scholarly journals Depicting the genetic architecture of pediatric cancers through an integrative gene network approach

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Clara Savary ◽  
Artem Kim ◽  
Alexandra Lespagnol ◽  
Virginie Gandemer ◽  
Isabelle Pellier ◽  
...  
Keyword(s):  
Gene Network ◽  
Genetic Architecture ◽  
Network Approach ◽  
Pediatric Cancers

scholarly journals Circulating miRNAs as a Predictive Biomarker of the Progression from Prediabetes to Diabetes: Outcomes of a 5-Year Prospective Observational Study

2020 ◽  
Vol 9 (7) ◽  
pp. 2184 ◽  
Author(s):  
Iwona Sidorkiewicz ◽  
Magdalena Niemira ◽  
Katarzyna Maliszewska ◽  
Anna Erol ◽  
Agnieszka Bielska ◽  
...  
Keyword(s):  
Pathway Analysis ◽  
Mirna Target ◽  
Target Genes ◽  
Quantitative Polymerase Chain Reaction ◽  
Area Under The Curve ◽  
Predictive Biomarkers ◽  
Predictive Biomarker ◽  
Circulating Mirnas ◽  
Serum Samples ◽  
Roc Curve Analysis

Due to a global increase in the prevalence of type 2 diabetes mellitus (T2DM), there is an urgent need for early identification of prediabetes, as these people have the highest risk of developing diabetes. Circulating miRNAs have shown potential as progression biomarkers in other diseases. This study aimed to conduct a baseline comparison of serum-circulating miRNAs in prediabetic individuals, with the distinction between those who later progressed to T2DM and those who did not. The expression levels of 798 miRNAs using NanoString technology were examined. Spearman correlation, receiver operating characteristic (ROC) curve analysis, and logistic regression modeling were performed. Gene ontology (GO) and canonical pathway analysis were used to explore the biological functions of the miRNA target genes. The study revealed that three miRNAs were upregulated in the serum samples of patients who later progressed to T2DM. Pathway analysis showed that the miRNA target genes were mainly significantly enriched in neuronal NO synthase (nNOS) signaling in neurons, amyloid processing, and hepatic cholestasis. ROC analysis demonstrated that miR-491-5p, miR-1307-3p, and miR-298 can be introduced as a diagnostic tool for the prediction of T2DM (area under the curve (AUC) = 94.0%, 88.0%, and 84.0%, respectively). Validation by real-time quantitative polymerase chain reaction (qRT-PCR) confirmed our findings. The results suggest that circulating miRNAs can potentially be used as predictive biomarkers of T2DM in prediabetic patients.

scholarly journals Novel agents and immune invasion in Hodgkin lymphoma

Hematology ◽  
2019 ◽  
Vol 2019 (1) ◽  
pp. 243-248 ◽  
Author(s):  
Reid W. Merryman ◽  
Ann LaCasce
Keyword(s):  
T Cells ◽  
Hodgkin Lymphoma ◽  
Cytotoxic T Cells ◽  
Rapid Development ◽  
Predictive Biomarkers ◽  
Treatment Resistance ◽  
Brentuximab Vedotin ◽  
Antibody Drug Conjugate ◽  
Barr Virus ◽  
Epstein Barr

Abstract The approval of brentuximab vedotin (BV) and the PD-1 inhibitors nivolumab and pembrolizumab has dramatically improved outcomes for patients with relapsed or refractory (R/R) classic Hodgkin lymphoma (HL). With the goal of increasing long-term disease control rates and decreasing late toxicities, these agents are currently being tested in earlier phases of treatment in combination with chemotherapy agents. In the R/R setting, our expanding understanding of HL’s various mechanisms of immune evasion and treatment resistance has spurred a growing number of rationally designed combination trials. Beyond BV and PD-1 blockade, other novel therapies have demonstrated encouraging preliminary results, including targeted agents, like the CD25 antibody-drug conjugate ADCT-301, and cellular therapies, including CD30 chimeric antigen receptor T cells and Epstein-Barr virus (EBV)-directed cytotoxic T cells. These trials, coupled with the rapid development of prognostic and predictive biomarkers, should drive additional breakthroughs that promise safer and more effective therapies for patients with HL in the future.

scholarly journals Predictive biomarkers of platinum and taxane resistance using the transcriptomic data of 1816 ovarian cancer patients

2020 ◽  
Vol 156 (3) ◽  
pp. 654-661 ◽  
Author(s):  
János Tibor Fekete ◽  
Ágnes Ősz ◽  
Imre Pete ◽  
Gyula Richárd Nagy ◽  
Ildikó Vereczkey ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Patients ◽  
Predictive Biomarkers ◽  
Transcriptomic Data ◽  
Taxane Resistance

scholarly journals Bayes Factor-Based Regulatory Gene Network Analysis of Genome-Wide Association Study of Economic Traits in a Purebred Swine Population

Genes ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 293 ◽  
Author(s):  
Lee ◽  
Kang ◽  
Kim
Keyword(s):  
Association Study ◽  
Gene Network ◽  
Bayes Factor ◽  
Target Genes ◽  
Genome Wide Association Study ◽  
Function Analysis ◽  
Regulatory Gene ◽  
Genome Wide Association ◽  
Regulatory Sequence ◽  
Genome Wide

: Early stage prediction of economic trait performance is important and directly linked to profitability of farm pig production. Genome-wide association study (GWAS) has been applied to find causative genomic regions of traits. This study established a regulatory gene network using GWAS for critical economic pig characteristics, centered on easily measurable body fat thickness in live animals. We genotyped 2,681 pigs using Illumina Porcine SNP60, followed by GWAS to calculate Bayes factors for 47,697 single nucleotide polymorphisms (SNPs) of seven traits. Using this information, SNPs were annotated with specific genes near genome locations to establish the association weight matrix. The entire network consisted of 226 nodes and 6,921 significant edges. For in silico validation of their interactions, we conducted regulatory sequence analysis of predicted target genes of transcription factors (TFs). Three key regulatory TFs were identified to guarantee maximum coverage: AT-rich interaction domain 3B (ARID3B), glial cell missing homolog 1 (GCM1), and GLI family zinc finger 2 (GLI2). We identified numerous genes targeted by ARID3B, associated with cellular processes. GCM1 and GLI2 were involved in developmental processes, and their shared target genes regulated multicellular organismal process. This system biology-based function analysis might contribute to enhancing understanding of economic pig traits.

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