Over Expressed TKTL1, CIP-2A, and B-MYB Proteins in Uterine Cervix Epithelium Scrapings as Potential Risk Predictive Biomarkers in HR-HPV-Infected LSIL/ASCUS Patients

Abstract

High oncogenic risk human papillomaviruses (HR-HPVs) promote cervical carcinoma development, the fourth most common feminine cancer. A slow oncodevelopmental phase-defined histopathologically as Cervical Intraepithelial Neoplasia (CIN) grades 1-3, or cytologically as Low- or High-grade Squamous Intraepithelial Lesions (LSIL or HSIL)-precedes the malignancy. Cervical carcinoma screenings through HR-HPV genotyping and Pap smears are regularly performed in Western countries. Faulty cytology screening or genotyping or patients' non-compliance with follow-ups can let slip an oncoprogression diagnosis. Novel biomarker tests flanking HR-HPV genotyping and cytology could objectively predict the risk of disease progression thus helping triage LSIL/ASCUS patients. Here, anonymized leftovers of fresh cervical epithelium scrapings from twice (LSIL/ASCUS and HR-HPV DNA)-positive and twice (Pap smear- and HR-HPV DNA)-negative (control) patients in a proteome-preserving solution served to assess the biomarker worth of three cervical carcinoma-related proteins, i.e., B-MYB (or MYBL2), Cancerous Inhibitor of PP2A (CIP-2a), and transketolase-like1 (TKTL1). Leftovers anonymity was strictly kept and storage at -80°C, protein extraction, immunoblotting, and band densitometry were blindly performed. Only after tests completion, the anonymous yet code-corresponding HR-HPV-genotyping and cytology data allowed to assign each sample to the twice-positive or twice-negative group. Descriptive statistics showed that the three proteins levels significantly increased in the twice-positive vs. twice-negative scrapings. Diagnostic ROC curve analysis identified each protein's Optimal Decision Threshold (OTD) showing that TKTL1 and CIP-2a are stronger risk predictive biomarkers (Sensitivity, 0.91-0.93; Specificity, 0.77-0.83) than B-MYB. Logistic Regression coupled with Likelihood-Ratio Tests confirmed that a highly significant relation links increasing TKTL1/CIP-2a/B-MYB protein levels in twice-positive cervical scrapings to the risk of HR-HPV-driven oncoprogression. Finally, a 3 year clinical follow-up showed that 13 patients (50% of total) of the twice-positive group with biomarker values over OTDs compliantly underwent scheduled colposcopy and biopsy. Of these, 11 (i.e., 84.7%) received a positive histological diagnosis, i.e., CIN1 (n = 5; 38.5%) or CIN2/CIN2+ (n = 6; 46,2%). Therefore, TKTL1/CIP-2a/B-MYB protein levels could objectively predict oncoprogression risk in twice (HR-HPV- and Pap smear)-positive women. Further studies will assess the translatability of these findings into clinical settings.

Keywords: ASCUS; B-MYB; CIP-2a; LSIL; TKTL1; human cervical carcinoma; oncogenic papillomaviruses; predictive biomarker.

Figure 1

The different expression of putative biomarker proteins B-MYB, CIP-2a, and TKTL1 can be detected in Western immunoblots of cervical epithelium scraping leftovers from twice (LSIL/ASCUS and HR-HPV DNA)-positive and twice (HR-HPV DNA- and Pap-test)-negative patients. The typical immunoblots show also as positive controls (PC) the same proteins as expressed by pre-metastatic human cervical carcinoma C4-I cells. The loading control (LC) protein is β-actin. The samples were run on SDS-PAGE and blotted as detailed in the Materials and Methods section.

Figure 2

The different expression of putative biomarker proteins B-MYB (A), CIP-2a (B), and TKTL1 (C) is significantly increased in twice (LSIL/ASCUS and HR-HPV DNA)-positive fresh cervical epithelium scraping leftovers vs. twice (normal Pap test/HPV DNA-negative, i.e., controls)-negative samples. The data are shown as side by side univariate plots of each protein specific band densitometry values as skeletal notched box charts including from the first to the third quartiles, with the median indicated by a black transverse line, and the minimum and maximum values as whiskers with end caps. The box notch indicates the 95% confidence interval (CI) of the median. A blue line enclosed in a blue dashed diamond indicates the mean value ± SEM. According to Wilcoxon's Z test, the levels of statistical significance of twice-positive vs. twice-negative (control) samples are: B-MYB (top panel), P = 0.0023; CIP-2a (middle panel), P < 0.0004; and TKTL1 (bottom panel), P < 0.0001 (see Table 2).

Figure 3

The receiver operating characteristic (ROC) curve analysis of the putative biomarker proteins B-MYB (A), CIP-2a (B), and TKTL1 (C). Details concerning the several diagnostic parameters reckoned from each ROC curve are reported in Table 3 (q. v.). In all three panels the value of the area under the curve (AUC) is shown between brackets as well as the no discrimination (chance) line joining the bottom left angle with the top right angle, the area of which is 0.5. In all the three instances the AUC values significantly differ (P < 0.0001) from the no discrimination (chance) area value (see also Table 3). TPF, true positive fraction or Sensitivity. FPF, false positive fraction (1-Specificity).

Figure 4

The concurrent changes in the proportion values of Sensitivity, Specificity, and Youden's J index (Youden's statistic) in relation to increasing densitometric integrated intensity values (xE4) of each specific protein band (A-C) examined. Optimal Decision Thresholds (ODTs) of the densitometric intensity values for each putative biomarker tested are indicated by vertical dashed lines and coincide with the highest value of the corresponding Jouden's J index.

Figure 5

The three binary Logistic Regression curves reach convergence fitting the double-negative (or control) densitometric integrated intensity values with the double (LSIL/ASCUS and HR-HPV DNA)-positive values for each specific protein tested (A-C). The curves show the relationship in terms of Log Odd ratios between a categorical dependent outcome variable (“0” or normal vs. “1” or active oncogenesis) and the continuously increasing values of the predictor or independent variable, that is the specific band densitometry values of each protein studied. The binary Logistic Regression parameters (or coefficient) estimates of the three curves are reported in Table 4. The results of the Likelihood Ratio Tests (LRT or Effect of Model) (not shown here: see Table 5 for details) indicate a statistically significant impact of the increasing independent predictor (i.e., densitometry values) values on the dependent outcome “1” (i.e., ongoing HPV-driven oncogenesis).