Correction: Lung cancer multi-omics digital human avatars for integrating precision medicine into clinical practice: the LANTERN study

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PTEN and Gynecological Cancers

PTEN is a tumour suppressor gene, and its loss of function is frequently observed in both heritable and sporadic cancers. It is involved in a great variety of biological processes, including maintenance of genomic stability, cell survival, migration, proliferation and metabolism. A better understanding of PTEN activity and regulation has therefore emerged as a subject of primary interest in cancer research. Gynaecological cancers are variously interested by PTEN deregulation and many perspective in terms of additional prognostic information and new therapeutic approaches can be explored. Here, we present the most significant findings on PTEN in gynaecological cancers (ovarian, endometrial, cervical, vulvar and uterine cancer) focusing on PTEN alterations incidence, biological role and clinical implications

Radiomics and Radiogenomics of Ovarian Cancer: Implications for Treatment Monitoring and Clinical Management

: Ovarian cancer, one of the deadliest gynecologic malignancies, is characterized by high intra- and inter-site genomic and phenotypic heterogeneity. The traditional information provided by the conventional interpretation of diagnostic imaging studies cannot adequately represent this heterogeneity. Radiomics analyses can capture the complex patterns related to the microstructure of the tissues and provide quantitative information about them. This review outlines how radiomics and its integration with other quantitative biological information, like genomics and proteomics, can impact the clinical management of ovarian cancer

Ovarian cancer beyond imaging: integration of AI and multiomics biomarkers

Abstract High-grade serous ovarian cancer is the most lethal gynaecological malignancy. Detailed molecular studies have revealed marked intra-patient heterogeneity at the tumour microenvironment level, likely contributing to poor prognosis. Despite large quantities of clinical, molecular and imaging data on ovarian cancer being accumulated worldwide and the rise of high-throughput computing, data frequently remain siloed and are thus inaccessible for integrated analyses. Only a minority of studies on ovarian cancer have set out to harness artificial intelligence (AI) for the integration of multiomics data and for developing powerful algorithms that capture the characteristics of ovarian cancer at multiple scales and levels. Clinical data, serum markers, and imaging data were most frequently used, followed by genomics and transcriptomics. The current literature proves that integrative multiomics approaches outperform models based on single data types and indicates that imaging can be used for the longitudinal tracking of tumour heterogeneity in space and potentially over time. This review presents an overview of studies that integrated two or more data types to develop AI-based classifiers or prediction models. Relevance statement Integrative multiomics models for ovarian cancer outperform models using single data types for classification, prognostication, and predictive tasks. Key points • This review presents studies using multiomics and artificial intelligence in ovarian cancer. • Current literature proves that integrative multiomics outperform models using single data types. • Around 60% of studies used a combination of imaging with clinical data. • The combination of genomics and transcriptomics with imaging data was infrequently used. Graphical Abstrac

Ovarian cancer beyond imaging: integration of AI and multiomics biomarkers

Acknowledgements: Figure 1 was created in the Mind the Graph platform (www.mindthegraph.com).High-grade serous ovarian cancer is the most lethal gynaecological malignancy. Detailed molecular studies have revealed marked intra-patient heterogeneity at the tumour microenvironment level, likely contributing to poor prognosis. Despite large quantities of clinical, molecular and imaging data on ovarian cancer being accumulated worldwide and the rise of high-throughput computing, data frequently remain siloed and are thus inaccessible for integrated analyses. Only a minority of studies on ovarian cancer have set out to harness artificial intelligence (AI) for the integration of multiomics data and for developing powerful algorithms that capture the characteristics of ovarian cancer at multiple scales and levels. Clinical data, serum markers, and imaging data were most frequently used, followed by genomics and transcriptomics. The current literature proves that integrative multiomics approaches outperform models based on single data types and indicates that imaging can be used for the longitudinal tracking of tumour heterogeneity in space and potentially over time. This review presents an overview of studies that integrated two or more data types to develop AI-based classifiers or prediction models. Relevance statement Integrative multiomics models for ovarian cancer outperform models using single data types for classification, prognostication, and predictive tasks. Key points • This review presents studies using multiomics and artificial intelligence in ovarian cancer. • Current literature proves that integrative multiomics outperform models using single data types. • Around 60% of studies used a combination of imaging with clinical data. • The combination of genomics and transcriptomics with imaging data was infrequently used. Graphical Abstract

Ovarian cancer beyond imaging: integration of AI and multiomics biomarkers.

High-grade serous ovarian cancer is the most lethal gynaecological malignancy. Detailed molecular studies have revealed marked intra-patient heterogeneity at the tumour microenvironment level, likely contributing to poor prognosis. Despite large quantities of clinical, molecular and imaging data on ovarian cancer being accumulated worldwide and the rise of high-throughput computing, data frequently remain siloed and are thus inaccessible for integrated analyses. Only a minority of studies on ovarian cancer have set out to harness artificial intelligence (AI) for the integration of multiomics data and for developing powerful algorithms that capture the characteristics of ovarian cancer at multiple scales and levels. Clinical data, serum markers, and imaging data were most frequently used, followed by genomics and transcriptomics. The current literature proves that integrative multiomics approaches outperform models based on single data types and indicates that imaging can be used for the longitudinal tracking of tumour heterogeneity in space and potentially over time. This review presents an overview of studies that integrated two or more data types to develop AI-based classifiers or prediction models.Relevance statement Integrative multiomics models for ovarian cancer outperform models using single data types for classification, prognostication, and predictive tasks.Key points• This review presents studies using multiomics and artificial intelligence in ovarian cancer.• Current literature proves that integrative multiomics outperform models using single data types.• Around 60% of studies used a combination of imaging with clinical data.• The combination of genomics and transcriptomics with imaging data was infrequently used

Ovarian cancer beyond imaging: integration of AI and multiomics biomarkers

Acknowledgements: Figure 1 was created in the Mind the Graph platform (www.mindthegraph.com).AbstractHigh-grade serous ovarian cancer is the most lethal gynaecological malignancy. Detailed molecular studies have revealed marked intra-patient heterogeneity at the tumour microenvironment level, likely contributing to poor prognosis. Despite large quantities of clinical, molecular and imaging data on ovarian cancer being accumulated worldwide and the rise of high-throughput computing, data frequently remain siloed and are thus inaccessible for integrated analyses. Only a minority of studies on ovarian cancer have set out to harness artificial intelligence (AI) for the integration of multiomics data and for developing powerful algorithms that capture the characteristics of ovarian cancer at multiple scales and levels. Clinical data, serum markers, and imaging data were most frequently used, followed by genomics and transcriptomics. The current literature proves that integrative multiomics approaches outperform models based on single data types and indicates that imaging can be used for the longitudinal tracking of tumour heterogeneity in space and potentially over time. This review presents an overview of studies that integrated two or more data types to develop AI-based classifiers or prediction models.Relevance statement Integrative multiomics models for ovarian cancer outperform models using single data types for classification, prognostication, and predictive tasks.Key points• This review presents studies using multiomics and artificial intelligence in ovarian cancer.• Current literature proves that integrative multiomics outperform models using single data types.• Around 60% of studies used a combination of imaging with clinical data.• The combination of genomics and transcriptomics with imaging data was infrequently used. Graphical Abstract</jats:p

Management of stage III and IVa uterine cancer

The prognosis of patients with advanced endometrial cancer is poor with limited therapeutic options. Nevertheless, the integration of molecular features in the clinico-pathological classification of endometrial cancer has significantly refined prognostic risk groups, representing a major breakthrough not only in the management of the disease but also in treatment perspectives. New therapeutic compounds such as target therapies, immunotherapy, and hormonal therapies have emerged for this clinical setting. Furthermore, molecular-driven clinical trials may improve significantly the efficacy of new treatments selecting those patients who are highly likely to respond. This review aims at describing the state of the art of advanced stage III-IVa endometrial cancer management, providing also the most interesting clinical perspectives

L1CAM Expression in Microcystic, Elongated, and Fragmented (MELF) Glands Predicts Lymph Node Involvement in Endometrial Carcinoma

Simple Summary L1CAM overexpression (&gt;= 10%) and the microcystic, elongated, and fragmented (MELF) pattern of invasion have previously been assessed as prognostic factors in endometrial carcinoma. We aimed to assess the relationship between L1CAM expression, MELF glands, and lymph node involvement in endometrial carcinoma, as all these factors are related to epithelial-to-mesenchymal transition. We evaluated L1CAM expression in 58 cases of uterine-confined, low-grade endometrioid carcinomas. We found that most cases (65.5%) expressed L1CAM in a limited manner to MELF glands. Cases with L1CAM expression in &gt;= 10% of the MELF component showed a significantly higher tendency to lymph node spread (p &lt; 0.001), even when adjusted for lymphovascular space invasion, depth of myometrial invasion and p53/mismatch repair status. On this account, L1CAM expression in the MELF component may stratify the prognosis and management in patients with uterine-confined, low-grade carcinomas. In endometrial carcinoma, both L1CAM overexpression and microcystic, elongated and fragmented (MELF) patterns of invasion have been related to epithelial-to-mesenchymal transition and metastatic spread. We aimed to assess the association between L1CAM expression, the MELF pattern, and lymph node status in endometrial carcinoma. Consecutive cases of endometrial carcinoma with MELF pattern were immunohistochemically assessed for L1CAM. Inclusion criteria were endometrioid-type, low-grade, stage T1, and known lymph node status. Uni- and multivariate logistic regression were used to assess the association of L1CAM expression with lymph node status. Fifty-eight cases were included. Most cases showed deep myometrial invasion (n = 42, 72.4%) and substantial lymphovascular space invasion (n = 34, 58.6%). All cases were p53-wild-type; 17 (29.3%) were mismatch repair-deficient. Twenty cases (34.5%) had positive nodes. No cases showed L1CAM positivity in &gt;= 10% of the whole tumor. MELF glands expressed L1CAM at least focally in 38 cases (65.5%). L1CAM positivity in &gt;= 10% of the MELF component was found in 24 cases (41.4%) and was the only significant predictor of lymph node involvement in both univariate (p &lt; 0.001) and multivariate analysis (p &lt; 0.001). In conclusion, L1CAM might be involved in the development of the MELF pattern. In uterine-confined, low-grade endometrioid carcinomas, L1CAM overexpression in MELF glands may predict lymph node involvement

Ovarian Cancer Treatments Strategy: Focus on PARP Inhibitors and Immune Check Point Inhibitors

Ovarian cancer treatment strategy is mainly based on three pillars: cytoreductive surgery, platinum-based chemotherapy, and targeted therapies. The latter in the last decade has provided a remarkable improvement in progression free patients and, hopefully, in overall survival. In particular, poly(adenosine diphosphate-ribose) polymerase (PARP) inhibitors exploit BRCA 1/2 mutations and DNA damage response deficiencies, which are believed to concern up to 50% of high grade epithelial ovarian cancer cases. While these agents have an established role in ovarian cancer treatment strategy in BRCA mutated and homologous recombination deficient patients, an appropriate predictive molecular test to select patients is lacking in clinical practice. At the same time, the impressive results of immunotherapy in other malignancies, have opened the space for the introduction of immune-stimulatory drugs in ovarian cancer. Despite immune checkpoint inhibitors as a monotherapy bringing only modest efficacy when assessed in pretreated ovarian cancer patients, the combination with chemotherapy, anti-angiogenetics, PARP inhibitors, and radiotherapy is believed to warrant further investigation. We reviewed literature evidence on PARP inhibitors and immunotherapy in ovarian cancer treatment