Novel immunotherapeutic approaches in head and neck cancer

Neal, Molly Heft; Haring, Catherine T.; Mann, Jacqueline E.; Brenner, J. Chad; Spector, Matthew E.; Swiecicki, Paul

doi:10.20517/2394-4722.2019.32

Cited by 10 publications

(12 citation statements)

References 79 publications

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“…There are several common biomarkers of immune-checkpoint inhibitor (ICI) under evaluation, including tumor-infiltrating lymphocytes (TIL), interferon gamma (IFN-

), and tumor mutational burden (TMB) [ 23 ]. The other ICI, anti-LAG-3 (pelatlimab), is currently being evaluated in phase I/IIA [ 50 ] (ClinicalTrials.gov Identifier: NCT01968109) and II-IVA [ 94 ] (NCT04080804) studies.…”

Section: Discussionmentioning

confidence: 99%

A Transcriptomic Analysis of Head and Neck Squamous Cell Carcinomas for Prognostic Indications

Chi

Hsiao

et al. 2021

JPM

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Survival analysis of the Cancer Genome Atlas (TCGA) dataset is a well-known method for discovering gene expression-based prognostic biomarkers of head and neck squamous cell carcinoma (HNSCC). A cutoff point is usually used in survival analysis for patient dichotomization when using continuous gene expression values. There is some optimization software for cutoff determination. However, the software’s predetermined cutoffs are usually set at the medians or quantiles of gene expression values. There are also few clinicopathological features available in pre-processed datasets. We applied an in-house workflow, including data retrieving and pre-processing, feature selection, sliding-window cutoff selection, Kaplan–Meier survival analysis, and Cox proportional hazard modeling for biomarker discovery. In our approach for the TCGA HNSCC cohort, we scanned human protein-coding genes to find optimal cutoff values. After adjustments with confounders, clinical tumor stage and surgical margin involvement were found to be independent risk factors for prognosis. According to the results tables that show hazard ratios with Bonferroni-adjusted p values under the optimal cutoff, three biomarker candidates, CAMK2N1, CALML5, and FCGBP, are significantly associated with overall survival. We validated this discovery by using the another independent HNSCC dataset (GSE65858). Thus, we suggest that transcriptomic analysis could help with biomarker discovery. Moreover, the robustness of the biomarkers we identified should be ensured through several additional tests with independent datasets.

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“…There are several common biomarkers of immune-checkpoint inhibitor (ICI) under evaluation, including tumor-infiltrating lymphocytes (TIL), interferon gamma (IFN-

Section: Discussionmentioning

confidence: 99%

A Transcriptomic Analysis of Head and Neck Squamous Cell Carcinomas for Prognostic Indications

Chi

Hsiao

et al. 2021

JPM

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“…Despite advances in diagnostics, treatment, and close follow-up by serum Tumor Markers, the 5-year progression-free survival (PFS) or survival rates for recurrent/metastatic (R/M) disease -have not significantly changed over the past years [3][4][5]24]. Recent treatments include NIVOLUMAB -B and PEMBROLIZUMAB -C, in addition to formerly used Methotrexate, Docetaxel and Cetuximab [25]. In a randomized, open-label, phase 3 study at 97 medical centers in 20 countries, they demonstrated a prolongation of overall survival (OS) and favorable safety of Pembrolizumab in patients with recurrent or metastatic HNSCC.…”

Section: Discussionmentioning

confidence: 99%

Prognosis and survival prediction by tumor markers in head and neck cancer patients

Meirovitz¹,

Gross²,

Leibovici³

et al. 2021

Otorhinolaryngol Head Neck Surg

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Background: Establishing prognostic factors is very important in treatment of cancer patients. In Head and Neck Cancer-(HNC) patients, diagnosis occurs usually in already advanced disease, therefore, there is a special need to early detect the disease and its recurrence. Aim:We aimed to evaluate the survival (OS, DFS) and its prediction by a panel of serum Tumor Markers: CEA (Carcino Embryonic Antigen), SCC (Squamous Cell Carcinoma Antigen), TPS (Tissue Polypeptide Specific Antigen), CYFRA 21-1 (Cytokeratin 19 Fragment) and sIL-2R (soluble Interleukin 2 Receptor) in HNC patients, for establishing prognosis in those patients. Patients and methods:We evaluated 293 blood samples from 194 HNC patients. Blood Tumor Markers levels were evaluated by conventional ELISA assays, at the first date of diagnosis and during follow-up. Comparison of marker levels between 76 healthy subjects and HNC patients, alive vs dead, response to therapy and survival -were performed.Results: Significantly lower levels of all Tumor Markers were demonstrated in patients alive, as opposed to all those who died. HNC patients who died 0-24 m after entering the study, were found to be the most sensitive time parameter to evaluate, as compared to 0-12 m, 0-36m, 0-48m or 0-60 m. We compared the alive and dead patients according to gender and found more male patients than females. Serum levels of all Tumor Markers of our panel were higher before therapy and decreased significantly thereafter, in patients responding to various therapies. The Tumor Markers sIL-2R and Cyfra 21-1 levels, were best correlated to prediction of patient's survival. Patients having low levels had the best clinical outcome and a longer survival -according to ROC analysis. Conclusion:CEA, SCC, TPS, CYFRA 21-1 and sIL-2R are all useful Tumor Markers in the management of HNC patients. They assessed response to therapy, were prognostic for recurrence earlier than CT and predicted survival. From our panel of Tumor Markers, sIL-2R and Cyfra 21-1, proved to be the most sensitive predictors of advanced disease with poor prognosis vs those with a good and longer survival.

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“…Currently, there are several common biomarkers of ICI under evaluation, which include tumor-infiltrating lymphocytes (TIL), interferon gamma (IFN-γ), and tumor mutational burden (TMB) [23]. The other ICI, anti-LAG-3 (pelatlimab), is currently evaluated under the phase I/IIA [32](ClinicalTrials.gov Identifier: NCT01968109) and II-IVA [64](ClinicalTrials.gov Identifier: NCT04080804) studies.…”

Section: Discussionmentioning

confidence: 99%

A Global Genome-wide Scan With Optimal Cutoff Mining for Emerging Biomarkers in Head and Neck Squamous Cell Carcinoma

Chi

Hsiao

et al. 2020

Preprint

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Background: The survival analysis of the Cancer Genome Atlas (TCGA) dataset is a well-known method to discover the gene expression-based prognostic biomarkers of head and neck squamous cell carcinoma (HNSCC). In order to utilize a continuous gene expression for survival analysis, it is necessary to determine a cutoff point by the dichotomization of the patients. There is some optimization software for cutoff determination. However, those predetermined cutoffs by software usually set at the median, 1/4 quantile, or 3/4 quantile of RNA sequencing (RNA -Seq) value to nd a significant P-value of the Kaplan-Meier curve. There are few clinicopathological features available on their pre-processed data sets.Methods: We developed a comprehensive work flow by R script, running on the Rstudio platform. It includes data retrieving and pre-processing, feature selection, cutoff mining engine, Kaplan-Meier survival analysis, Cox proportional hazard modeling, and biomarker discovery.Results: Using this work flow on the TCGA HNSCC cohort, we scanned human protein-coding genes (20,500) programmatically. After adjustment with other confounders, we found that the clinical tumor stage and the surgical margin involvement are independent risk factors in patient survival. According to the resulting tables with Bonferroni adjusted P-value under optimal cutoff as well as hazard ratio (>= 1:5), there were ten candidate biomarkers, named as DKK1, CAMK2N1, STC2, PGK1, SURF4, USP10, NDFIP1, FOXA2, STIP1, and DKC1, which are significantly associated with the poor prognosis of overall survival (OS). At the same time, the other ten genes were over-expressed in the better survival patients (with hazard ratio <= 0:5), named as ZNF557, ZNF266, IL19, MYO1H, FCGBP, LOC148709, EVPLL, PNMA5, IQCN (previous name as KIAA1683), and NPB. Further validations are warranted.Conclusions: We suggested this analysis tool equipped with an optimal cutoff finder will help with biomarker discovery of protein-coding genes, in terms of tumor-agnostic therapy.

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Novel immunotherapeutic approaches in head and neck cancer

Cited by 10 publications

References 79 publications

A Transcriptomic Analysis of Head and Neck Squamous Cell Carcinomas for Prognostic Indications

A Transcriptomic Analysis of Head and Neck Squamous Cell Carcinomas for Prognostic Indications

Prognosis and survival prediction by tumor markers in head and neck cancer patients

A Global Genome-wide Scan With Optimal Cutoff Mining for Emerging Biomarkers in Head and Neck Squamous Cell Carcinoma

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