MicroRNA‐based risk scoring system to identify early‐stage oral squamous cell carcinoma patients at high‐risk for cancer‐specific mortality

ABSTRACTWe have previously constructed a novel microRNA (miRNA)-based prognostic model and cancer-specific mortality risk score formula to predict survival outcome in oral squamous cell carcinoma (OSCC) patients who are already categorized into ‘early-stage’ by the TNM staging system. A total of 836 early-stage OSCC patients were assigned the mortality risk scores. We evaluated the efficacy of various treatment regimens in terms of survival benefit compared to surgery only in patients stratified into high (risk score ≥0) versus low (risk score <0) mortality risk categories. For the high-risk group, surgery with neck dissection significantly improved the 5-year survival to 75% from 46% with surgery only (p<0.001); a Cox proportional hazard model on time-to-death demonstrated a hazard ratio of 0.37 for surgery with neck dissection (95% CI: 0.2-0.6; p=0.0005). For the low-risk group, surgery only was the treatment of choice associated with 5-year survival benefit. Regardless of treatment selected, those with risk score ≥2 may benefit from additional therapy to prevent cancer relapse. We also identified hTERT (human telomerase reverse transcriptase) as a gene target common to the prognostic miRNAs. There was 22-fold increase in the hTERT expression level in patients with risk score ≥2 compared to healthy controls (p<0.0005). Overexpression of hTERT was also observed in the patient-derived OSCC organoid compared to that of normal organoid. The DNA cancer vaccine that targets hTERT expressing cells currently undergoing rigorous clinical evaluation for other tumors can be repurposed to prevent cancer recurrence in these high-risk early-stage oral cancer patients.

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MicroRNA-Based Cancer Mortality Risk Scoring System and hTERT Expression in Early-Stage Oral Squamous Cell Carcinoma

Journal of Oncology ◽

10.1155/2021/8292453 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Angela J. Yoon ◽

Regina M. Santella ◽

Shuang Wang ◽

David I. Kutler ◽

Richard D. Carvajal ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

High Risk ◽

Oral Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Risk Score ◽

Mortality Risk ◽

Survival Benefit ◽

Risk Group ◽

Early Stage ◽

Htert Expression

We have previously constructed a novel microRNA (miRNA)-based prognostic model and cancer-specific mortality risk score formula to predict survival outcome in oral squamous cell carcinoma (OSCC) patients who are already categorized into “early-stage” by the TNM staging system. A total of 836 early-stage OSCC patients were assigned the mortality risk scores. We evaluated the efficacy of various treatment regimens in terms of survival benefit compared to surgery only in patients stratified into high (risk score ≥0) versus low (risk score <0) mortality risk categories. For the high-risk group, surgery with neck dissection significantly improved the 5-year survival to 75% from 46% with surgery only ( p < 0.001 ); a Cox proportional hazard model on time-to-death demonstrated a hazard ratio of 0.37 for surgery with neck dissection (95% CI: 0.2–0.6; p = 0.0005 ). For the low-risk group, surgery only was the treatment of choice associated with 5-year survival benefit. Regardless of treatment selected, those with risk score ≥2 may benefit from additional therapy to prevent cancer relapse. We also identified hTERT (human telomerase reverse transcriptase) as a gene target common to the prognostic miRNAs. There was 22-fold increase in the hTERT expression level in patients with risk score ≥2 compared to healthy controls ( p < 0.0005 ). Overexpression of hTERT was also observed in the patient-derived OSCC organoid compared to that of normal organoid. The DNA cancer vaccine that targets hTERT-expressing cells currently undergoing rigorous clinical evaluation for other tumors can be repurposed to prevent cancer recurrence in these high-risk early-stage oral cancer patients.

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Histopathological risk scoring system as a tool for predicting lymph nodal metastasis in penile squamous cell carcinoma

Pathology ◽

10.1016/j.pathol.2019.08.003 ◽

2019 ◽

Vol 51 (7) ◽

pp. 696-704 ◽

Cited By ~ 2

Author(s):

Akash P. Sali ◽

Santosh Menon ◽

Gagan Prakash ◽

Vedang Murthy ◽

Ganesh Bakshi ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Squamous Cell ◽

Scoring System ◽

Nodal Metastasis ◽

Lymph Nodal Metastasis ◽

Risk Scoring ◽

Penile Squamous Cell Carcinoma ◽

Risk Scoring System

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Novel risk scoring system for metastatic renal cell carcinoma patients treated with cabozantinib

Cancer Treatment and Research Communications ◽

10.1016/j.ctarc.2021.100393 ◽

2021 ◽

pp. 100393

Author(s):

Dylan J. Martini ◽

Meredith R. Kline ◽

Yuan Liu ◽

Julie M. Shabto ◽

Bradley C. Carthon ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Metastatic Renal Cell Carcinoma ◽

Renal Cell ◽

Scoring System ◽

Risk Scoring ◽

Risk Scoring System

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The REASON score: an epigenetic and clinicopathologic score to predict risk of poor survival in patients with early stage oral squamous cell carcinoma

Biomarker Research ◽

10.1186/s40364-021-00292-x ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Chi T. Viet ◽

Gary Yu ◽

Kesava Asam ◽

Carissa M. Thomas ◽

Angela J. Yoon ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Oral Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Risk Score ◽

Squamous Cell ◽

Early Stage ◽

Genome Wide Association Studies ◽

Gene Methylation ◽

Poor Survival ◽

Risk Of Death

Abstract Background Oral squamous cell carcinoma (OSCC) is a capricious cancer with poor survival rates, even for early-stage patients. There is a pressing need to develop more precise risk assessment methods to appropriately tailor clinical treatment. Genome-wide association studies have not produced a viable biomarker. However, these studies are limited by using heterogeneous cohorts, not focusing on methylation although OSCC is a heavily epigenetically-regulated cancer, and not combining molecular data with clinicopathologic data for risk prediction. In this study we focused on early-stage (I/II) OSCC and created a risk score called the REASON score, which combines clinicopathologic characteristics with a 12-gene methylation signature, to predict the risk of 5-year mortality. Methods We combined data from an internal cohort (n = 515) and The Cancer Genome Atlas (TCGA) cohort (n = 58). We collected clinicopathologic data from both cohorts to derive the non-molecular portion of the REASON score. We then analyzed the TCGA cohort DNA methylation data to derive the molecular portion of the risk score. Results 5-year disease specific survival was 63% for the internal cohort and 86% for the TCGA cohort. The clinicopathologic features with the highest predictive ability among the two the cohorts were age, race, sex, tobacco use, alcohol use, histologic grade, stage, perineural invasion (PNI), lymphovascular invasion (LVI), and margin status. This panel of 10 non-molecular features predicted 5-year mortality risk with a concordance (c)-index = 0.67. Our molecular panel consisted of a 12-gene methylation signature (i.e., HORMAD2, MYLK, GPR133, SOX8, TRPA1, ABCA2, HGFAC, MCPH1, WDR86, CACNA1H, RNF216, CCNJL), which had the most significant differential methylation between patients who survived vs. died by 5 years. All 12 genes have already been linked to survival in other cancers. Of the genes, only SOX8 was previously associated with OSCC; our study was the first to link the remaining 11 genes to OSCC survival. The combined molecular and non-molecular panel formed the REASON score, which predicted risk of death with a c-index = 0.915. Conclusions The REASON score is a promising biomarker to predict risk of mortality in early-stage OSCC patients. Validation of the REASON score in a larger independent cohort is warranted.

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Profiling high‐risk human papillomavirus (HPV) subtypes in oral squamous cell carcinoma (OSCC)

Histopathology ◽

10.1111/his.14504 ◽

2021 ◽

Vol 79 (S1) ◽

pp. 23-24

Keyword(s):

Squamous Cell Carcinoma ◽

Human Papillomavirus ◽

High Risk ◽

Oral Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Squamous Cell

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Development of a Sex-Specific Risk Scoring System for Predicting Cognitive Normal to Mild Cognitive Impairment (SRSS-CNMCI)

10.21203/rs.3.rs-517520/v1 ◽

2021 ◽

Author(s):

Wen Luo ◽

Hao Wen ◽

Shuqi Ge ◽

Chunzhi Tang ◽

Xiufeng Liu ◽

...

Keyword(s):

Risk Factors ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

High Risk ◽

Scoring System ◽

External Validation ◽

Scoring Systems ◽

Cutoff Point ◽

Risk Scoring ◽

Risk Scoring System

Abstract Objective: We aim to develop a sex-specific risk scoring system for predicting cognitive normal (CN) to mild cognitive impairment (MCI), abbreviated SRSS-CNMCI, to provide a reliable tool for the prevention of MCI.Methods: Participants aged 61-90 years old with a baseline diagnosis of CN and an endpoint diagnosis of MCI were screened from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database with at least one follow-up. Multivariable Cox proportional hazards models were used to identify risk factors associated with conversion from CN to MCI and to build risk scoring systems for male and female groups. Receiver operating characteristic (ROC) curve analysis was applied to determine the risk probability cutoff point corresponding to the optimal prediction effect. We ran an external validation of the discrimination and calibration based on the Harvard Aging Brain Study (HABS) database.Results: A total of 471 participants, including 240 women (51%) and 231 men (49%), aged 61 to 90 years, were included in the study cohort for subsequent primary analysis. The final multivariable models and the risk scoring systems for females and males included age, APOE ε4, Mini-Mental State Examination (MMSE) and Clinical Dementia Rating (CDR). The scoring systems for females and males revealed C statistics of 0.902 (95% CI 0.840-0.963) and 0.911 (95% CI 0.863-0.959), respectively, as measures of discrimination. The cutoff point of high and low risk was 33% in females, and more than 33% was considered high risk, while more than 9% was considered high risk for males. The external validation effect of the scoring systems was good: C statistic 0.950 for the females and C statistic 0.965 for the males. Conclusions: Our parsimonious model accurately predicts conversion from CN to MCI with four risk factors and can be used as a predictive tool for the prevention of MCI.

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Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine

Frontiers in Oral Health ◽

10.3389/froh.2021.794248 ◽

2022 ◽

Vol 2 ◽

Author(s):

Rasheed Omobolaji Alabi ◽

Alhadi Almangush ◽

Mohammed Elmusrati ◽

Antti A. Mäkitie

Keyword(s):

Machine Learning ◽

Squamous Cell Carcinoma ◽

Deep Learning ◽

Oral Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Precision Medicine ◽

Treatment Planning ◽

Squamous Cell ◽

Early Stage ◽

Learning Technology

Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers worldwide and its incidence is on the rise in many populations. The high incidence rate, late diagnosis, and improper treatment planning still form a significant concern. Diagnosis at an early-stage is important for better prognosis, treatment, and survival. Despite the recent improvement in the understanding of the molecular mechanisms, late diagnosis and approach toward precision medicine for OSCC patients remain a challenge. To enhance precision medicine, deep machine learning technique has been touted to enhance early detection, and consequently to reduce cancer-specific mortality and morbidity. This technique has been reported to have made a significant progress in data extraction and analysis of vital information in medical imaging in recent years. Therefore, it has the potential to assist in the early-stage detection of oral squamous cell carcinoma. Furthermore, automated image analysis can assist pathologists and clinicians to make an informed decision regarding cancer patients. This article discusses the technical knowledge and algorithms of deep learning for OSCC. It examines the application of deep learning technology in cancer detection, image classification, segmentation and synthesis, and treatment planning. Finally, we discuss how this technique can assist in precision medicine and the future perspective of deep learning technology in oral squamous cell carcinoma.

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