Distribution of genetic alterations in high-risk early-stage cervical cancer patients treated with postoperative radiation therapy

Excellent concordance of the molecular classification between preoperative biopsy and final hysterectomy in endometrial carcinoma

OBJECTIVE

The 2023 International Federation of Gynecology and Obstetrics classification with molecular classification shows superior discriminatory ability compared to staging systems lacking molecular data. However, the accuracy of endometrial biopsy data in molecular classification remains uncertain. This study aimed to assess the concordance of molecular classifications between preoperative biopsy and hysterectomy to predict prognosis before surgical staging.

METHODS

Endometrial biopsies and corresponding hysterectomy specimens were collected at the National Cancer Center Hospital between 2012 and 2023. Immunohistochemistry for p53 and mismatch repair (MMR) proteins and next-generation sequencing of all exons of polymerase epsilon (POLE) were performed. Given the limited number of POLE mut cases in prior studies, we prepared a POLE mut-enriched cohort. Cohen's kappa estimates were used to determine concordance for molecular and clinicopathological subgroup assignments.

RESULTS

Among 70 patients classified into four molecular subtype groups, 33 exhibited POLE mutations, 15 showed loss of MMR protein expression, 13 had p53-abnormality, and 9 had no specific molecular profile. Concordance between biopsy and hysterectomy specimens was 100% (κ = 1.000). In contrast, histological types and grades between biopsy and surgical specimens showed moderate and substantial agreement (κ = 0.420 and κ = 0.780, respectively).

CONCLUSIONS

Molecular subtypes were completely consistent with those derived from surgical specimens, demonstrating high concordance between preoperative and postoperative molecular classifications. This suggests that endometrial biopsies could reliably predict prognosis. Future studies should investigate how biopsy-based molecular profiling influences treatment planning and patient outcomes.

Genomic profiles of Japanese patients with vulvar squamous cell carcinoma

The incidence of vulvar carcinoma varies by race; however, it is a rare disease, and its genomic profiles remain largely unknown. This study examined the characteristics of vulvar squamous cell carcinoma (VSCC) in Japanese patients, focusing on genomic profiles and potential racial disparities. The study included two Japanese groups: the National Cancer Center Hospital (NCCH) group comprised 19 patients diagnosed between 2015 and 2023, and the Center for Cancer Genomics and Advanced Therapeutics group comprised 29 patients diagnosed between 2019 and 2022. Somatic mutations were identified by targeted or panel sequencing, and TP53 was identified as the most common mutation (52-81%), followed by HRAS (7-26%), CDKN2A (21-24%), and PIK3CA (5-10%). The mutation frequencies, except for TP53, were similar to those of Caucasian cohorts. In the NCCH group, 16 patients of HPV-independent tumors were identified by immunohistochemistry and genotyping. Univariate analysis revealed that TP53-mutated patients were associated with a poor prognosis (log-rank test, P = 0.089). Japanese VSCC mutations resembled those of Caucasian vulvar carcinomas, and TP53 mutations predicted prognosis regardless of ethnicity. The present findings suggest potential molecular-targeted therapies for select VSCC patients.

Precision unveiled: Synergistic genomic landscapes in breast cancer-Integrating single-cell analysis and decoding drug toxicity for elite prognostication and tailored therapeutics

BACKGROUND

Globally, breast cancer, with diverse subtypes and prognoses, necessitates tailored therapies for enhanced survival rates. A key focus is glutamine metabolism, governed by select genes. This study explored genes associated with T cells and linked them to glutamine metabolism to construct a prognostic staging index for breast cancer patients for more precise medical treatment.

METHODS

Two frameworks, T-cell related genes (TRG) and glutamine metabolism (GM), stratified breast cancer patients. TRG analysis identified key genes via hdWGCNA and machine learning. T-cell communication and spatial transcriptomics emphasized TRG's clinical value. GM was defined using Cox analyses and the Lasso algorithm. Scores categorized patients as TRG_high+GM_high (HH), TRG_high+GM_low (HL), TRG_low+GM_high (LH), or TRG_low+GM_low (LL). Similarities between HL and LH birthed a "Mixed" class and the TRG_GM classifier. This classifier illuminated gene variations, immune profiles, mutations, and drug responses.

RESULTS

Utilizing a composite of two distinct criteria, we devised a typification index termed TRG_GM classifier, which exhibited robust prognostic potential for breast cancer patients. Our analysis elucidated distinct immunological attributes across the classifiers. Moreover, by scrutinizing the genetic variations across groups, we illuminated their unique genetic profiles. Insights into drug sensitivity further underscored avenues for tailored therapeutic interventions.

CONCLUSION

Utilizing TRG and GM, a robust TRG_GM classifier was developed, integrating clinical indicators to create an accurate predictive diagnostic map. Analysis of enrichment disparities, immune responses, and mutation patterns across different subtypes yields crucial subtype-specific characteristics essential for prognostic assessment, clinical decision-making, and personalized therapies. Further exploration is warranted into multiple fusions between metrics to uncover prognostic presentations across various dimensions.

Emerging biomarkers and molecular targets for precision medicine in cervical cancer

Cervical cancer remains a significant global health burden, necessitating innovative approaches for improved diagnostics and personalized treatment strategies. Precision medicine has emerged as a promising paradigm, leveraging biomarkers and molecular targets to tailor therapy to individual patients. This review explores the landscape of emerging biomarkers and molecular targets in cervical cancer, highlighting their potential implications for precision medicine. By integrating these biomarkers into comprehensive diagnostic algorithms, clinicians can identify high-risk patients at an earlier stage, enabling timely intervention and improved patient outcomes. Furthermore, the identification of specific molecular targets has paved the way for the development of targeted therapies aimed at disrupting key pathways implicated in cervical carcinogenesis. In conclusion, the evolving landscape of biomarkers and molecular targets presents exciting opportunities for advancing precision medicine in cervical cancer. By harnessing these insights, clinicians can optimize treatment selection, enhance patient outcomes, and ultimately transform the management of this devastating disease.

Utility of the revised FIGO2023 staging with molecular classification in endometrial cancer

OBJECTIVES

Molecular classification was introduced in endometrial cancer staging following the transition of the International Federation of Gynecology and Obstetrics (FIGO) 2008 to FIGO2023. In the early stages, p53 abnormal endometrial carcinoma with myometrial involvement was upstaged to stage IICm, in addition to the downstaging of POLE mutation endometrial cancer to stage IAm. This study compared the goodness of fit and discriminatory ability of FIGO2008, FIGO2023 without molecular classification (FIGO2023), and FIGO2023 with molecular classification (FIGO2023m); no study has been externally validated to date.

METHODS

The study included 265 patients who underwent initial surgery at the National Cancer Center Hospital between 1997 and 2019 and were pathologically diagnosed with endometrial cancer. The three classification systems were compared using Harrell's concordance index (C-index), Akaike information criterion (AIC), and time-dependent receiver operating characteristic (ROC) curves. A higher C-index score and a lower AIC value indicated a more accurate model.

RESULTS

Among the three classification systems, FIGO2023m had the lowest AIC value (FIGO2023m: 455.925; FIGO2023: 459.162; FIGO2008: 457.901), highest C-index (FIGO2023m: 0.768; FIGO2023: 0.743; FIGO2008: 0.740), and superior time-dependent ROC curves within 1 year after surgical resection. In the stage IIIC, patients with p53 abnormalities had considerably lower 5-year overall survival than those with a p53 wild-type pattern (24.3% vs. 83.7%, p = 0.0005).

CONCLUSIONS

FIGO2023m had the best discriminatory ability compared with FIGO2008 and FIGO2023. Even in advanced stages, p53 status was a poor prognostic factor. When feasible, molecular subtypes can be added to the staging criteria to allow better prognostic prediction in all stages.

Rare FGFR fusion genes in cervical cancer and transcriptome-based subgrouping of patients with a poor prognosis

BACKGROUND

Although cervical cancer is often characterized as preventable, its incidence continues to increase in low- and middle-income countries, underscoring the need to develop novel therapeutics for this disease.This study assessed the distribution of fusion genes across cancer types and used an RNA-based classification to divide cervical cancer patients with a poor prognosis into subgroups.

MATERIAL AND METHODS

RNA sequencing of 116 patients with cervical cancer was conducted. Fusion genes were extracted using StarFusion program. To identify a high-risk group for recurrence, 65 patients who received postoperative adjuvant therapy were subjected to non-negative matrix factorization to identify differentially expressed genes between recurrent and nonrecurrent groups.

RESULTS

We identified three cases with FGFR3-TACC3 and one with GOPC-ROS1 fusion genes as potential targets. A search of publicly available data from cBioPortal (21,789 cases) and the Center for Cancer Genomics and Advanced Therapeutics (32,608 cases) showed that the FGFR3 fusion is present in 1.5% and 0.6% of patients with cervical cancer, respectively. The frequency of the FGFR3 fusion gene was higher in cervical cancer than in other cancers, regardless of ethnicity. Non-negative matrix factorization identified that the patients were classified into four Basis groups. Pathway enrichment analysis identified more extracellular matrix kinetics dysregulation in Basis 3 and more immune system dysregulation in Basis 4 than in the good prognosis group. CIBERSORT analysis showed that the fraction of M1 macrophages was lower in the poor prognosis group than in the good prognosis group.

CONCLUSIONS

The distribution of FGFR fusion genes in patients with cervical cancer was determined by RNA-based analysis and used to classify patients into clinically relevant subgroups.

Utility of molecular subtypes and genetic alterations for evaluating clinical outcomes in 1029 patients with endometrial cancer

BACKGROUND

We investigated the utility of a molecular classifier tool and genetic alterations for predicting prognosis in Japanese patients with endometrial cancer.

METHODS

A total of 1029 patients with endometrial cancer from two independent cohorts were classified into four molecular subtype groups. The primary and secondary endpoints were relapse-free survival (RFS) and overall survival (OS), respectively.

RESULTS

Among the 265 patients who underwent initial surgery, classified according to immunohistochemistry, patients with DNA polymerase epsilon exonuclease domain mutation had an excellent prognosis (RFS and OS), patients with no specific molecular profile (NSMP) and mismatch repair protein deficiency had an intermediate prognosis, and those with protein 53 abnormal expression (p53abn) had the worst prognosis (P < 0.001). In the NSMP group, mutant KRAS and wild-type ARID1A were associated with significantly poorer 5-year RFS (41.2%) than other genomic characteristics (P < 0.001). The distribution of the subtypes differed significantly between patients with recurrence/progression and classified by sequencing (n = 764) and patients who underwent initial surgery (P < 0.001). Among patients with recurrence/progression, 51.4% had the opportunity to receive molecular targeted therapy.

CONCLUSIONS

A molecular classifier is a useful tool for determining prognosis and eligibility for molecularly targeted therapy in patients with endometrial cancer.

Molecular Markers to Predict Prognosis and Treatment Response in Uterine Cervical Cancer

Uterine cervical cancer is one of the leading causes of cancer-related mortality in women worldwide. Each year, over half a million new cases are estimated, resulting in more than 300,000 deaths. While less-invasive, fertility-preserving surgical procedures can be offered to women in early stages, treatment for locally advanced disease may include radical hysterectomy, primary chemoradiotherapy (CRT) or a combination of these modalities. Concurrent platinum-based chemoradiotherapy regimens remain the first-line treatments for locally advanced cervical cancer. Despite achievements such as the introduction of angiogenesis inhibitors, and more recently immunotherapies, the overall survival of women with persistent, recurrent or metastatic disease has not been extended significantly in the last decades. Furthermore, a broad spectrum of molecular markers to predict therapy response and survival and to identify patients with high- and low-risk constellations is missing. Implementation of these markers, however, may help to further improve treatment and to develop new targeted therapies. This review aims to provide comprehensive insights into the complex mechanisms of cervical cancer pathogenesis within the context of molecular markers for predicting treatment response and prognosis.

Cohort Profile: Chinese Cervical Cancer Clinical Study

Cervical cancer is the fourth most common cancer worldwide, but its incidence varies greatly in different countries. Regardless of incidence or mortality, the burden of cervical cancer in China accounts for approximately 18% of the global burden. The Chinese Cervical Cancer Clinical Study is a hospital-based multicenter open cohort. The major aims of this study include (i) to explore the associations of therapeutic strategies with complications as well as mid- and long-term clinical outcomes; (ii) to widely assess the factors which may have an influence on the prognosis of cervical cancer and then guide the treatment options, and to estimate prognosis using a prediction model for precise post-treatment care and follow-up; (iii) to develop a knowledge base of cervical clinical auxiliary diagnosis and prognosis prediction using artificial intelligence and machine learning approaches; and (iv) to roughly map the burden of cervical cancer in different districts and monitoring the trend in incidence of cervical cancer to potentially inform prevention and control strategies. Patients eligible for inclusion were those diagnosed with cervical cancer, whether during an outpatient visit or hospital admission, at 47 different types of medical institutions in 19 cities of 11 provinces across mainland China between 2004 and 2018. In a total, 63 926 patients with cervical cancer were enrolled in the cohort. Since the project inception, a large number of standardized variables have been collected, including epidemiological characteristics, cervical cancer-related symptoms, physical examination results, laboratory testing results, imaging reports, tumor biomarkers, tumor staging, tumor characteristics, comorbidities, co-infections, treatment and short-term complications. Follow-up was performed at least once every 6 months within the first 5 years after receiving treatment and then annually thereafter. At present, we are developing a cervical cancer imaging database containing Dicom files with data of computed tomography/magnetic resonance imaging examination. Additionally, we are also collecting original pathological specimens of patients with cervical cancer. Potential collaborators are welcomed to contact the corresponding authors, and anyone can submit at least one specific study proposal describing the background, objectives and methods of the study.