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      L1CAM expression as a predictor of platinum response in high‐risk endometrial carcinoma

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          Abstract

          For high‐risk endometrial cancer (EC) patients, adjuvant chemotherapy is recommended to improve outcome. Yet, predictive biomarkers for response to platinum‐based chemotherapy (Pt‐aCT) are currently lacking. We tested expression of L1 cell‐adhesion molecule (L1CAM), a well‐recognised marker of poor prognosis in EC, in tumour samples from high‐risk EC patients, to explore its role as a predictive marker of Pt‐aCT response. L1CAM expression was determined using RT‐qPCR and immunohistochemistry in a cohort of high‐risk EC patients treated with Pt‐aCT and validated in a multicentric independent cohort. The association between L1CAM and clinicopathologic features and L1CAM additive value in predicting platinum response were determined. The effect of L1CAM gene silencing on response to carboplatin was functionally tested on primary L1CAM‐expressing cells. Increased L1CAM expression at both genetic and protein level correlated with high‐grade, non‐endometrioid histology and poor response to platinum treatment. A predictive model adding L1CAM to prognostic clinical variables significantly improved platinum response prediction (C‐index 78.1%, P = .012). In multivariate survival analysis, L1CAM expression was significantly associated with poor outcome (HR: 2.03, P = .019), potentially through an indirect effect, mediated by its influence on response to chemotherapy. In vitro, inhibition of L1CAM significantly increased cell sensitivity to carboplatin, supporting a mechanistic link between L1CAM expression and response to platinum in EC cells. In conclusion, we have demonstrated the role of L1CAM in the prediction of response to Pt‐aCT in two independent cohorts of high‐risk EC patients. L1CAM is a promising candidate biomarker to optimise decision making in high‐risk patients who are eligible for Pt‐aCT.

          Abstract

          What's new?

          Endometrial cancer patients with high‐risk disease typically receive adjuvant platinum‐based chemotherapy. So far, no biomarkers can predict platinum chemotherapy response, one of the most important factors affecting prognosis. Here, the authors report their findings that overexpression of the transmembrane protein L1CAM predicts poor response to platinum‐based chemotherapy in high‐risk endometrial cancer patients. Inhibiting L1CAM sensitised cultured cells to carboplatin, supporting a mechanistic link between L1CAM expression and response to platinum therapy. Assessment of L1CAM could therefore improve the treatment selection process, allowing patients who are less likely to benefit from platinum to pursue alternative treatment options.

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          Most cited references39

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          Cancer Statistics, 2021

          Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths in the United States and compiles the most recent data on population-based cancer occurrence. Incidence data (through 2017) were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2018) were collected by the National Center for Health Statistics. In 2021, 1,898,160 new cancer cases and 608,570 cancer deaths are projected to occur in the United States. After increasing for most of the 20th century, the cancer death rate has fallen continuously from its peak in 1991 through 2018, for a total decline of 31%, because of reductions in smoking and improvements in early detection and treatment. This translates to 3.2 million fewer cancer deaths than would have occurred if peak rates had persisted. Long-term declines in mortality for the 4 leading cancers have halted for prostate cancer and slowed for breast and colorectal cancers, but accelerated for lung cancer, which accounted for almost one-half of the total mortality decline from 2014 to 2018. The pace of the annual decline in lung cancer mortality doubled from 3.1% during 2009 through 2013 to 5.5% during 2014 through 2018 in men, from 1.8% to 4.4% in women, and from 2.4% to 5% overall. This trend coincides with steady declines in incidence (2.2%-2.3%) but rapid gains in survival specifically for nonsmall cell lung cancer (NSCLC). For example, NSCLC 2-year relative survival increased from 34% for persons diagnosed during 2009 through 2010 to 42% during 2015 through 2016, including absolute increases of 5% to 6% for every stage of diagnosis; survival for small cell lung cancer remained at 14% to 15%. Improved treatment accelerated progress against lung cancer and drove a record drop in overall cancer mortality, despite slowing momentum for other common cancers.
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            Decision curve analysis: a novel method for evaluating prediction models.

            Diagnostic and prognostic models are typically evaluated with measures of accuracy that do not address clinical consequences. Decision-analytic techniques allow assessment of clinical outcomes but often require collection of additional information and may be cumbersome to apply to models that yield a continuous result. The authors sought a method for evaluating and comparing prediction models that incorporates clinical consequences,requires only the data set on which the models are tested,and can be applied to models that have either continuous or dichotomous results. The authors describe decision curve analysis, a simple, novel method of evaluating predictive models. They start by assuming that the threshold probability of a disease or event at which a patient would opt for treatment is informative of how the patient weighs the relative harms of a false-positive and a false-negative prediction. This theoretical relationship is then used to derive the net benefit of the model across different threshold probabilities. Plotting net benefit against threshold probability yields the "decision curve." The authors apply the method to models for the prediction of seminal vesicle invasion in prostate cancer patients. Decision curve analysis identified the range of threshold probabilities in which a model was of value, the magnitude of benefit, and which of several models was optimal. Decision curve analysis is a suitable method for evaluating alternative diagnostic and prognostic strategies that has advantages over other commonly used measures and techniques.
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              Integrated Genomic Characterization of Endometrial Carcinoma

              Summary We performed an integrated genomic, transcriptomic, and proteomic characterization of 373 endometrial carcinomas using array- and sequencing-based technologies. Uterine serous tumors and ~25% of high-grade endometrioid tumors have extensive copy number alterations, few DNA methylation changes, low ER/PR levels, and frequent TP53 mutations. Most endometrioid tumors have few copy number alterations or TP53 mutations but frequent mutations in PTEN, CTNNB1, PIK3CA, ARID1A, KRAS and novel mutations in the SWI/SNF gene ARID5B. A subset of endometrioid tumors we identified had a dramatically increased transversion mutation frequency, and newly identified hotspot mutations in POLE. Our results classified endometrial cancers into four categories: POLE ultramutated, microsatellite instability hypermutated, copy number low, and copy number high. Uterine serous carcinomas share genomic features with ovarian serous and basal-like breast carcinomas. We demonstrated that the genomic features of endometrial carcinomas permit a reclassification that may impact post-surgical adjuvant treatment for women with aggressive tumors.
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                Author and article information

                Contributors
                stefano.calza@unibs.it
                eliana.bignotti@asst-spedalicivili.it
                Journal
                Int J Cancer
                Int J Cancer
                10.1002/(ISSN)1097-0215
                IJC
                International Journal of Cancer
                John Wiley & Sons, Inc. (Hoboken, USA )
                0020-7136
                1097-0215
                10 May 2022
                15 August 2022
                : 151
                : 4 ( doiID: 10.1002/ijc.v151.4 )
                : 637-648
                Affiliations
                [ 1 ] Department of Medical and Surgical Specialties, Radiological Sciences and Public Health University of Brescia Brescia Italy
                [ 2 ] Department of Clinical and Experimental Sciences University of Brescia Brescia Italy
                [ 3 ] Department of Radiation Oncology Radboud University Medical Center Nijmegen The Netherlands
                [ 4 ] Department of Pathology ASST Spedali Civili of Brescia Brescia Italy
                [ 5 ] Division of Obstetrics and Gynecology ASST Spedali Civili di Brescia Brescia Italy
                [ 6 ] Unit of Biostatistics and Bioinformatics, Department of Molecular and Translational Medicine University of Brescia Brescia Italy
                [ 7 ] Department of Obstetrics and Gynaecology Radboud University Medical Center Nijmegen The Netherlands
                Author notes
                [*] [* ] Correspondence

                Eliana Bignotti, Division of Obstetrics and Gynecology, ASST Spedali Civili di Brescia, Piazzale Spedali Civili 1, 25123 Brescia, Italy.

                Email: eliana.bignotti@ 123456asst-spedalicivili.it

                Stefano Calza, Unit of Biostatistics and Bioinformatics, Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy.

                Email: stefano.calza@ 123456unibs.it

                Author information
                https://orcid.org/0000-0001-7916-2704
                https://orcid.org/0000-0001-6873-7832
                https://orcid.org/0000-0003-4661-9979
                https://orcid.org/0000-0002-6138-1236
                https://orcid.org/0000-0001-6380-6181
                Article
                IJC34035
                10.1002/ijc.34035
                9321598
                35429348
                990753b7-e385-4c88-9913-f204fda90f5e
                © 2022 The Authors. International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 04 March 2022
                : 10 November 2021
                : 29 March 2022
                Page count
                Figures: 5, Tables: 2, Pages: 12, Words: 7504
                Funding
                Funded by: Associazione Chiara Andreoli Onlus
                Funded by: Fondazione Beretta , doi 10.13039/100017727;
                Funded by: Italian Ministry of University
                Award ID: 20178S4EK9
                Categories
                Tumor Markers and Signatures
                Tumor Markers and Signatures
                Custom metadata
                2.0
                15 August 2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.1.7 mode:remove_FC converted:26.07.2022

                Oncology & Radiotherapy
                high‐risk endometrial cancer,l1cam,platinum‐based adjuvant treatment,precision medicine

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