Jun Kang

Prediction of homologous recombination deficiency from Oncomine Comprehensive Assay Plus correlating with SOPHiA DDM HRD Solution

Objective Poly(ADP-ribose) polymerase (PARP) inhibitors are used for targeted therapy for ovarian cancer with homologous recombination deficiency (HRD). In this study, we aimed to develop a homologous recombination deficiency prediction model to predict the genomic integrity (GI) index of the SOPHiA DDM HRD Solution from the Oncomine Comprehensive Assay (OCA) Plus. We also tried to a find cut-off value of the genomic instability metric (GIM) of the OCA Plus that correlates with the GI index of the SOPHiA DDM HRD Solution. Methods We included 87 cases with high-grade ovarian serous carcinoma from five tertiary referral hospitals in Republic of Korea. We developed an HRD prediction model to predict the GI index of the SOPHiA DDM HRD Solution. As predictor variables in the model, we used the HRD score, which included percent loss of heterozygosity (%LOH), percent telomeric allelic imbalance (%TAI), percent large-scale state transitions (%LST), and the genomic instability metric (GIM). To build the model, we employed a penalized logistic regression technique. Results The final model equation is -21.77 + 0.200 × GIM + 0.102 × %LOH + 0.037 × %TAI + 0.261 × %LST. To improve the performance of the prediction model, we added a borderline result category to the GI results. The accuracy of our HRD status prediction model was 0.958 for the test set. The accuracy of HRD status using GIM with a cut-off value of 16 was 0.911. Conclusion The Oncomine Comprehensive Assay Plus provides a reliable biomarker for homologous recombination deficiency.

Prognostic Significance of the Immune Microenvironment in Endometrial Cancer

This study used artificial intelligence (AI)-based analysis to investigate the immune microenvironment in endometrial cancer (EC). We aimed to evaluate the potential of AI-based immune metrics as prognostic biomarkers. In total, 296 cases with EC were classified into 4 molecular subtypes: polymerase epsilon ultramutated (POLEmut), mismatch repair deficiency (MMRd), p53 abnormal (p53abn), and no specific molecular profile (NSMP). AI-based methods were used to evaluate the following immune metrics: total tumor-infiltrating lymphocytes (TIL), intratumoral TIL, stromal TIL, and tumor cells using Lunit SCOPE IO, as well as CD4+, CD8+, and FOXP3+ T cells using immunohistochemistry (IHC) by QuPath. These 7 immune metrics were used to perform unsupervised clustering. PD-L1 22C3 IHC expression was also evaluated. Clustering analysis demonstrated 3 distinct immune microenvironment groups: immune active, immune desert, and tumor dominant. The immune-active group was highly prevalent in POLEmut, and it was also seen in other molecular subtypes. Although the immune-desert group was more frequent in NSMP and p53mut, it was also detected in MMRd and POLEmut. POLEmut showed the highest levels of CD4+ and CD8+ T cells, total TIL, intratumoral TIL, and stromal TIL with the lowest levels of FOXP3+/CD8+ ratio. In contrast, p53abn in the immune-active group showed higher FOXP3+/CD4+ and FOXP3+/CD8+ ratios. The immune-active group was associated with favorable overall survival and recurrence-free survival. In the NSMP subtype, a significant association was observed between immune active and better recurrence-free survival. The PD-L1 22C3 combined positive score (CPS) showed significant differences among the 3 groups, with the immune-active group having the highest median CPS and frequency of CPS ≥ 1%. The immune microenvironment of EC was variable within molecular subtypes. Within the same immune microenvironment group, significant differences in immune metrics and T cell composition were observed according to molecular subtype. AI-based immune microenvironment groups served as prognostic markers in ECs, with the immune-active group associated with favorable outcomes.

Roles of Cancer Histology Type and HPV Genotype in HPV ctDNA Detection at Baseline in Cervical Cancer: Implications for Tumor Burden Assessment

Introduction: Human papillomavirus circulating tumor DNA (HPV ctDNA) is a promising biomarker for monitoring cervical cancer. HPV ctDNA level at baseline (before treatment) reflects tumor burden. However, reported HPV ctDNA detection rates at baseline have shown variations across studies, suggesting the existence of other potential contributing factors. This study aimed to identify additional factors that might influence HPV ctDNA detection at baseline, focusing on histology type and HPV genotypes (high-risk genotypes HPV16 and HPV18). Methods: We retrospectively analyzed blood samples at baseline prior to treatment from 92 patients diagnosed with HPV16- or HPV18-associated cervical cancer (FIGO IA2–IIIC2) between 2013 and 2020. HPV ctDNA was evaluated using digital droplet PCR. Results: HPV ctDNA was detected at baseline in 41.3% of cases. Locally advanced cervical cancers had a higher (p = 0.028) detection rate at baseline than early stage cervical cancers. HPV ctDNA positivity was significantly (p = 0.048) higher for HPV18 (60%) than for HPV16 (34.3%). Adenocarcinoma/adenosquamous carcinoma had a higher HPV ctDNA detection rate at baseline (54.2%) than squamous cell carcinoma (36.8%) but not significantly (p = 0.212) higher. Conclusion: This study found the impact of histology and HPV genotype on HPV ctDNA at baseline in cervical cancer. HPV18 and adenocarcinoma were associated with a higher baseline HPV ctDNA detection rate. These results suggest the need for different HPV ctDNA approaches for analyzing tumor burden. This finding may also serve as a useful reference for posttreatment surveillance studies.

Improved Prognostic Stratification With 2023 International Federation of Gynecology and Obstetrics Staging in Endometrial Cancer Reflecting Poor Prognosis of Aggressive Histological Types and p53 Abnormality

This study compares the distribution and prognostic impact of the 2009 and 2023 International Federation of Gynecology and Obstetrics (FIGO) staging systems for endometrial cancer and their impact on the 2022 European Society for Medical Oncology (ESMO) risk classification. Patients were restaged according to the 2009 FIGO staging system, the 2023 FIGO staging system, and the 2023 FIGO staging system with molecular classification. Risk groups were assigned according to the 2022 ESMO guidelines using each staging system. Among 679 patients, 139 (20.5%) experienced stage migration when transitioning from the 2009 FIGO staging system to the 2023 FIGO staging system with molecular classification, with 121 (17.8%) upstaged and 18 (2.7%) downstaged. Most changes were from FIGO stage I to stage II, primarily due to p53 abnormality, aggressive histological type, or extensive/substantial lymphovascular space invasion. Hazard ratios for overall survival, disease-free survival, and event-free survival increased with advancing stage groups in all systems, showing the greatest differences when the 2023 FIGO staging system with molecular classification was used. The newly introduced FIGO stages IC, IIC (both representing aggressive histological types), and IICmp53abn (associated with p53 abnormality) in the 2023 FIGO staging system were associated with worse outcomes, similar to FIGO stage III. The prognostic predictability of the 2022 ESMO risk group was minimally affected by the transition from the 2009 FIGO to the 2023 FIGO staging system, as the factors introduced in the new FIGO system were already incorporated into the 2022 ESMO risk classification. Only 17 (2.5%) patients experienced a change in their assigned risk group. The 2023 FIGO staging system showed improved prognostic stratification over the 2009 FIGO staging system, particularly by reflecting the poor prognosis of aggressive histological types and p53 abnormality.

Prediction of homologous recombination deficiency from cancer gene expression data

Objective Homologous recombination deficiency (HRD) is the main mechanism of tumorigenesis in some cancers. HRD causes abnormal double-strand break repair, resulting in genomic scars. Some scoring HRD tests have been approved as companion diagnostics of polyadenosine diphosphate-ribose polymerase (PARP) inhibitor treatment. This study aimed to build an HRD prediction model using gene expression data from various cancer types. Methods The cancer genome atlas data were used for HRD prediction modeling. A total of 10,567 cases of 33 cancer types were included, and expression data from 5128 out of 20,502 genes were included as predictors. A penalized logistic regression model was chosen as a modeling technique. Results The area under the curve of the receiver operating characteristic curve of HRD status prediction was 0.98 for the training set and 0.93 for the test set. The accuracy of HRD status prediction was 0.93 for the training set and 0.88 for the test set. Conclusions Our study suggests that the HRD prediction model based on penalized logistic regression using gene expression data can be used to select patients for treatment with PARP inhibitors.

HPV ctDNA as a Biomarker for Monitoring Disease Progression in HPV16/18-Associated Cervical Cancer

Introduction: Cervical cancer, primarily driven by oncogenic HPV16/18, often relapses despite standard treatments. HPV circulating tumor DNA (ctDNA), which reflects tumor-derived genetic material in the bloodstream, has emerged as a promising noninvasive biomarker for monitoring disease progression. Methods: A prospective study was conducted on 20 patients with HPV16/18-associated cervical cancer. Posttreatment blood samples were collected, and HPV ctDNA levels were measured using droplet digital PCR. The correlation between HPV ctDNA levels and disease progression was examined. Results: HPV ctDNA was detected in 21% (18/85) of samples, with 6% (5/85) showing positivity. Patients without disease progression (n = 15) were HPV ctDNA negative, indicating a false positivity rate of zero. HPV ctDNA concentrations appeared higher in samples collected before or during disease progression, suggesting a potential association with disease status. Patients with positive HPV ctDNA tended to have shorter progression-free survival compared to those with negative ctDNA. Conclusions: This study suggests that HPV ctDNA may aid in monitoring disease progression in patients with HPV16/18-associated cervical cancer, highlighting the need for further validation.