The Association between Serum Cancer Antigen 125 (CA 125) and Risk of Lung Cancer in Females: Assessing the Possibilities for Early Detection

Background: Few studies have closely examined the relationship between CA 125 and lung cancer. This study is expected to provide more understanding about CA 125 and its role as a potential predictor for lung cancer risk. Objectives: To evaluate: i) the association between CA 125 and lung cancer; ii) if the associations differ by potential effect modifier (smoking status); and iii) if the association between CA 125 and lung cancer differs by lung cancer stage (early vs. advanced). Methods: The present research was conducted using secondary data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) randomized controlled trial (RCT). The associations between explanatory variables and lung cancer were evaluated using multivariable logistic regression. Each multivariable logistic regression model was adjusted for age, education, current body mass index (BMI), family history of lung cancer, personal history of cancer, chronic obstructive pulmonary disease (COPD), average number of cigarettes smoked per day and number of years smoked. Results: The study demonstrated that CA 125 is significantly and independently associated with lung cancer and that CA 125 is associated with early-stage lung cancer. It was found that an elevated CA 125 level was associated with a higher risk of lung cancer in individuals who smoked. Although the study demonstrated promising results, CA 125 did not have a large effect on the study’s lung cancer risk prediction models. Conclusion: CA 125 is not a strong enough predictor to be used as an indicator in lung cancer screening alone, however it may be useful in a panel of complimentary biomarkers. Future research is needed to explore whether a panel of complimentary biomarkers including CA 125 can improve lung cancer risk prediction

Genomic instability as a predictive biomarker for the application of DNA-damaging therapies in gynecological cancer patients

[ES] El curso natural de los tumores va acompañado de la acumulación progresiva de alteraciones genómicas, propiciando una cadena de eventos que resultan en inestabilidad genómica (IG). Éste fenómeno, caracterizado por alteraciones en el número de copias, constituye un hallmark genómico con impacto pronóstico más allá de la histología y otras características moleculares del tumor. En el ámbito de la investigación en oncología ginecológica, la IG ha ganado fuerza en los últimos años, permitiendo la estratificación de pacientes de acuerdo al pronóstico y la respuesta a agentes que dañan el ADN, como las terapias basadas en platinos y los inhibidores de PARP. En el cáncer de ovario, en particular, se ha descrito un subgrupo molecular caracterizado por alta incidencia de alteraciones en el número de copias relacionado con un mejor pronóstico y respuesta a quimioterapia. Esta correlación presenta la IG como un buen marcador predictivo y pronóstico. Así, un modelo basado en la IG trasladable a la práctica clínica constituirá una herramienta útil para la optimización de la toma de decisiones. La era de la medicina personalizada llegó de la mano de los estudios integrativos, donde las técnicas de alto rendimiento se aplican de manera combinada para obtener una visión molecular global de los tumores, completando y complementando la caracterización clásica a nivel anatómico e histológico. Esta tesis propone un estudio global de la IG como biomarcador pronóstico y predictivo de respuesta en cáncer ginecológico, haciendo hincapié en el cáncer de ovario seroso de alto grado y cáncer de endometrio. A través de la aplicación de estrategias basadas en NGS con la adaptación de pipelines de análisis disponibles obtuvimos los perfiles de IG de muestras de tejido fijadas en formol y embebidas en parafina, de una manera fiable, portable y coste efectiva, combinando herramientas de machine learning para ajustar modelos predictivos y pronósticos. Partiendo de esta premisa, ajustamos y validamos, en cohortes clínicas bien caracterizadas, tres modelos a partir de los datos ómicos individuales y un modelo integrativo (Scarface Score) que demostró la capacidad de predecir la respuesta a agentes que dañan el ADN en un escenario clínico concreto de pacientes con cáncer de ovario seroso de alto grado. Paralelamente, desarrollamos y validamos un algoritmo basado en el perfil de mutaciones, con impacto pronóstico, en cáncer de endometrio. Este algoritmo consiguió una estratificación que respondía al perfil de IG de los pacientes. Finalmente, se caracterizó un panel de líneas celulares de cáncer de ovario a nivel de respuesta, genético y genómico. Se interrogó el estatus de la vía de recombinación homóloga y su asociación a patrones de IG, completando el perfil molecular y estableciendo las bases para futuros estudios preclínicos y clínicos. Los resultados obtenidos en esta tesis doctoral presentan herramientas de gran valor para el manejo clínico en cuanto a la búsqueda de una medicina personalizada. Adicionalmente, diferentes estudios para trasladar el modelo predictivo a otros escenarios clínicos pueden ser explorados, usando como base el planteado, pero restableciendo puntos de corte nuevos y específicos.[CA] El curs natural dels tumors va acompanyat de l'acumulació progressiva d'alteracions genòmiques, propiciant una cadena d'esdeveniments que resulten en inestabilitat genòmica (IG). Aquest fenomen, caracteritzat per la presencia de alteracions en el nombre de cópies, constitueix un hallmark genòmic amb impacte pronòstic més enllà de la histologia i altres característiques moleculars del tumor. En l'àmbit de la recerca en oncologia ginecològica, la IG ha guanyat força en els últims anys, permetent l'estratificació de pacients d'acord amb el pronòstic i la resposta d'agents que danyen l'ADN, com les teràpies basades en platins i els inhibidors de PARP. En el càncer d'ovari en particular, s'ha descrit un subgrup molecular caracteritzat per una alta incidència d'alteracions en el nombre de còpies relacionat amb un millor pronòstic i resposta a quimioteràpia. Aquesta correlació presenta la IG com un marcador predictiu i pronòstic adeqüat. Així, un model basat en la IG traslladable a la pràctica clínica constituirà una eina útil per a l'optimització de la presa de decisions. L'era de la medicina personalitzada va arribar de la mà dels estudis integratius, on les tècniques d'alt rendiment s'apliquen de manera combinada per a obtenir una visió molecular global dels tumors, completant i complementant la caracterització clàssica a nivell anatòmic i histològic. Aquesta tesi proposa un estudi global de la IG com a biomarcador pronòstic i predictiu de resposta en càncer ginecològic, posant l'accent en el càncer d'ovari serós d'alt grau i càncer d'endometri. A través de la aplicación d'estratègies basades en NGS amb l'adaptació de pipelines d'anàlisis disponibles, vam obtenir els perfils de IG de mostres de teixit fixades en formol i embegudes en parafina d'una manera fiable, portable i cost efectiva, combinant eines de machine learning per a ajustar models predictius i pronòstics. Partint d'aquesta premissa, vam ajustar i validar, en cohortes clíniques ben caracteritzades, tres models a partir de les dades omiques individuals i un model integratiu (Scarface Score) que va demostrar la capacitat de predir la resposta a agents que danyen l'ADN en un escenari clínic concret de pacients amb càncer d'ovari serós d'alt grau. Paral·lelament, desenvoluparem i validarem un algoritme basat en el perfil de mutacions amb impacte pronòstic en càncer d'endometri. Aquest algoritme va aconseguir una estratificació que responia al perfil de IG dels pacients. Finalment, es va caracteritzar un panell de línies cel·lulars de càncer d'ovari a nivell de resposta, genètic i genòmic. Es varen interrogar l'estatus de la via de recombinació homòloga i la seua associació a patrons de IG, completant el perfil molecular i establint les bases per a futurs estudis preclínics i clínics. Els resultats obtinguts en aquesta tesi doctoral presenten eines de gran valor per al maneig clínic en quant a la cerca d'una medicina personalitzada. Addicionalment, diferents estudis per a traslladar el model predictiu a altres escenaris clínics poden ser plantejats, usant com a base el propost però restablint punts de tall nous i específics.[EN] The natural course of tumors matches the progressive accumulation of genomic alterations, triggering a cascade of events that results in genomic instability (GI). This phenomenon includes copy number alterations and constitutes a genomic hallmark that defines specific outcomes beyond histology and other molecular features of the tumor. In the context of gynaecologic oncology research, GI has gained strength in the last years allowing the stratification of patients according to prognosis and response to certain DNA-damaging agents, such as platinum-based therapies and PARP inhibitors. Particularly in ovarian and endometrial cancers, it has been described a molecular subgroup characterized by high copy number alterations (CNA) related to good prognosis and better response to chemotherapy. This relationship highlights GI as a predictive and prognostic biomarker. Hence, a GI-based model translated into clinical practice would constitute a tool for optimizing clinical decision-making. The era of personalised medicine arrived together with the coming of integrative studies, where results of high-throughput techniques are combined to obtain a comprehensive molecular landscape of the diseases, bringing a new paradigm to characterize the tumors beyond classical anatomic and histological characteristics. This thesis proposes a global study of the phenomenon of GI as a prognostic and predictive biomarker of treatment response in gynaecological cancers, mainly focused on high-grade ovarian cancer and endometrial cancer. Through the development of an NGS-based strategy with the adaptation of available pipelines of analysis, we obtained GI profiles on formalin-fixed paraffin-embedded samples in a reliable, portable, and cost-effective approach, with the combination of Machine Learning tools to fit prognostic and predictive models based on the integration of omic data. Based on that premise, we fit and validated, in well-characterized clinical cohorts, three single-source models and an integrative ensemble model (Scarface Score) that proved to be able to predict response to DNA-damaging agents in a clinical scenario of High-Grade Serous Ovarian Cancer. In addition, a mutational-based algorithm (12g algorithm) with prognostic impact was developed and validated for endometrial cancer patients. This algorithm achieved a GI-based stratification of patients. Finally, a panel of ovarian cancer cell lines was characterized at the response, genetic and genomic level, interrogating homologous recombination repair pathway status and its associated GI profiles, completing the molecular landscape, and establishing the basis and breeding ground of future preclinical and clinical studies. The results reported in this Doctoral Thesis provide valuable clinical management tools in the accomplishment of a reliable tailored therapy. Additionally, future studies in different tumor types and drugs for implementation of the predictive model can be planned, using as a base the defined one but re-establishing new and specific cut-offs.The present doctoral thesis was partially funded by GVA Grants “Subvencions per a la realització de projectes d’i+d+i desenvolupats per grups d’investigació emergents (GV/2020/158)” and “Ayudas para la contratación de personal investigador en formación de carácter predoctoral” (ACIF/2016/008), “Beca de investigación traslacional Andrés Poveda 2020” from GEICO group and Phase II clinical trial (POLA: NCT02684318, EudraCT 2015-001141-08, 03.10.2015). This study was awarded the Prize “Antonio Llombart Rodriguez-FINCIVO 2020” from the Royal Academy of Medicine of the Valencian CommunityLópez Reig, R. (2023). Genomic instability as a predictive biomarker for the application of DNA-damaging therapies in gynecological cancer patients [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19902

Selecting Differentially Expressed Genes from Microarray Experiments

High throughput technologies, such as gene expression arrays and protein mass spectrometry, allow one to simultaneously evaluate thousands of potential biomarkers that distinguish different tissue types. Of particular interest here is cancer versus normal organ tissues. We consider statistical methods to rank genes (or proteins) in regards to differential expression between tissues. Various statistical measures are considered and we argue that two measures related to the Receiver Operating Characteristic Curve are particularly suitable for this purpose. We also propose that sampling variability in the gene rankings be quantified and suggest using the “selection probability function”, the probability distribution of rankings for each gene. This is estimated via the bootstrap. A real data set derived from gene expression arrays of 23 normal and 30 ovarian cancer tissues are analyzed. Simulation studies are also used to assess the relative performance of different statistical gene ranking measures and our quantification of sampling variability. Our approach leads naturally to a procedure for sample size calculations appropriate for exploratory studies that seek to identify differentially expressed genes

Vibrational Spectroscopy Prospects in Frontline Clinical Diagnosis

The key experimental results from this research are the viable and cost effective methods of diagnosing oral and pancreatic cancer with accuracies over 90%. Furthermore, development of the molecular windowing method to further narrow down the origins of those cancer biomarkers and further improve accuracy.Many papers are being published demonstrating how vibrational spectral biomarkers can be used to diagnose a whole variety of diseases, from cancers to colitis. However, much of the research, proposed as discovering a useful tool for clinical diagnosis, has not yet been widely utilised in clinical practice. This is due mainly to the lack or reproducibility of the findings and current lack of relating the spectral observation to a root biological cause. This thesis aims to highlight the inconsistencies between studies and propose an improved process for spectral biomarker identification, including suggestions for follow up studies to discover the foundation of the spectral change. This thesis reassesses, and adds to, ground covered by previous reviews regarding sample preparation, patient selection and multivariate analysis.Resultantly, this thesis brings light to the need, and suggests solutions, for:• a method to standardise results between detection devices,• knowledge of the additional requirements for using biomarkers for disease monitoring/prognosis,• understanding the biological root cause for the spectral shift.These promising results and suggestions for combined methodology improvements will provide guidance to enable this burgeoning research field to improve patient outcome in the clinical sphere

Early Detection of Ovarian Cancer in Samples Pre-Diagnosis Using CA125 and MALDI-MS Peaks

Aim: A nested case-control discovery study was undertaken 10 test whether information within the serum peptidome can improve on the utility of CA125 for early ovarian cancer detection. Materials and Methods: High-throughput matrix-assisted laser desorption ionisation mass spectrometry (MALDI-MS) was used to profile 295 serum samples from women pre-dating their ovarian cancer diagnosis and from 585 matched control samples. Classification rules incorporating CA125 and MS peak intensities were tested for discriminating ability. Results: Two peaks were found which in combination with CA125 discriminated cases from controls up to 15 and 11 months before diagnosis, respectively, and earlier than using CA125 alone. One peak was identified as connective tissue-activating peptide III (CTAPIII), whilst the other was putatively identified as platelet factor 4 (PF4). ELISA data supported the down-regulation of PF4 in early cancer cases. Conclusion: Serum peptide information with CA125 improves lead time for early detection of ovarian cancer. The candidate markers are platelet-derived chemokines, suggesting a link between platelet function and tumour development

A quantitative performance study of two automatic methods for the diagnosis of ovarian cancer.

We present a quantitative study of the performance of two automatic methods for the early detection of ovarian cancer that can exploit longitudinal measurements of multiple biomarkers. The study is carried out for a subset of the data collected in the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). We use statistical analysis techniques, such as the area under the Receiver Operating Characteristic (ROC) curve, for evaluating the performance of two techniques that aim at the classification of subjects as either healthy or suffering from the disease using time-series of multiple biomarkers as inputs. The first method relies on a Bayesian hierarchical model that establishes connections within a set of clinically interpretable parameters. The second technique is a purely discriminative method that employs a recurrent neural network (RNN) for the binary classification of the inputs. For the available dataset, the performance of the two detection schemes is similar (the area under ROC curve is 0.98 for the combination of three biomarkers) and the Bayesian approach has the advantage that its outputs (parameters estimates and their uncertainty) can be further analysed by a clinical expert.This research was funded by Cancer Research UK and the Eve Appeal Gynaecological Cancer Research Fund (grant ref. A12677) and was supported by the National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre. UKCTOCS was core funded by the Medical Research Council, Cancer Research UK, and the Department of Health with additional support from the Eve Appeal, Special Trustees of Bart's and the London, and Special Trustees of UCLH. We also acknowledge support by the grant of the Ministry of Education and Science of the Russian Federation Agreement No. 074-02-2018-330. I.P.M. and M.A.V. acknowledge the financial support of the Spanish Ministry of Economy and Competitiveness (projects TEC2015-69868-C2-1-R and TEC2017-86921-C2-1-R)

Ovarian cancer marker HE4 in hormone-related gynecological conditions and diagnosis of ovarian granulosa cell tumors

Ovarian cancer is commonly diagnosed at an advanced stage as the early stages are symptom-free. Despite the development in the fields of surgery and chemotherapy, the prognosis remains poor. In order to improve the diagnostic methods, research on biomarkers such as HE4 (Human epididymis protein 4) is actively ongoing. According to previous studies, HE4 is sensitive in detecting even early stages of epithelial ovarian cancer, yet its specificity needs further studies. Altogether 359 women were included in this study. The aims were to evaluate the performance of serum tumor marker HE4 in benign gynecological conditions and to determine confounding factors in the interpretation of the marker analysis. The usability of epithelial ovarian cancer markers HE4 and CA125 in comparison with inhibin B and AMH was evaluated in the diagnosis and follow-up of ovarian granulosa cell tumors. HE4 serum concentration was not significantly dependent on hormonal factors, which simplifies the interpretation of the serum HE4 assays particularly in women of fertile age. In tubal pregnancies we detected elevated serum HE4 concentrations, and the tubal epithelium showed more intense and continuous immunohistochemical HE4 staining than normal fallopian tubes. Combining HE4 with CA125 improves accuracy in ovarian cancer diagnostics. However, normal serum levels of these epithelial ovarian cancer markers do not exclude other ovarian cancer subtypes, which must be kept in mind particularly in premenopausal women. The best serum marker for the diagnosis and follow-up of ovarian granulosa cell tumors is inhibin B, yet its accuracy can be further improved by combining AMH to the analysis.Munasarjasyöpä on diagnosoitaessa useimmiten laajalle levinnyt, koska alkuvaiheen tauti on oireeton. Tämän vuoksi selviytymisennuste on sekä kirurgisten että lääkkeellisten hoitojen kehityksestä huolimatta edelleen huono. Diagnostiikan parantamiseksi tutkimuksissa on kehitetty uusia kasvainmerkkiaineita, kuten HE4 (Human Epididymis Secretory protein 4). HE4 on osoittautunut tehokkaaksi epiteliaalisen munasarjasyövän varhaisdiagnostiikassa, mutta sen tarkkuudesta tarvitaan lisätutkimuksia. Tutkimukseen osallistui yhteensä 359 naista. Tutkimuksen tarkoituksena oli selvittää kasvainmerkkiaine HE4:n seerumipitoisuuksien vaihtelua erilaisissa hyvänlaatuisissa gynekologisissa tiloissa sekä selvittää mahdollisia virhelähteiden vaikutusta merkkiainepitoisuuksien tulkinnassa. Lisäksi arvioitiin epiteliaalisen munasarjasyövän merkkiaineiden HE4:n ja CA125:n käytettävyyttä munasarjan granuloosasolukasvainten erotusdiagnostiikassa ja seurannassa verrattuna inhibiini B- ja AMH-merkkiaineisiin. HE4-merkkiaineen seerumipitoisuuksissa ei todettu hormonivaikutuksesta, kuten yhdistelmäehkäisypillereistä, kuukautiskierrosta tai koeputkihoitoon liittyvästä munasarjojen stimulaatiosta, johtuvaa merkittävää vaihtelua. Tämä yksinkertaistaa HE4-määritysten tulkintaa etenkin fertiili-ikäisillä naisilla. Munanjohdinraskaudessa sen sijaan totesimme kohonneita HE4-seerumipitoisuuksia sekä lisääntynyttä voimakasta immunohistokemiallista värjäytymistä munanjohtimen epiteelissä. Yhdistämällä HE4- ja CA125-määritykset munasarjasyövän diagnostiikassa on päästy parempaan herkkyyteen ja tarkkuuteen. Etenkin hedelmällisessä iässä olevien naisten kohdalla on kuitenkin muistettava, että näiden epiteliaalisen munasarjasyövän merkkiaineiden viiterajoissa olevat pitoisuudet eivät poissulje harvinaisempia munasarjasyöpätyyppejä. Munasarjan granuloosasolukasvainten diagnostiikassa ja seurannassa toimivin kasvainmerkkiaine on inhibiini B, jonka diagnostista osuvuutta voidaan parantaa yhdistämällä määritykseen AMH-pitoisuuden määritys.Siirretty Doriast

A survey on computational intelligence approaches for predictive modeling in prostate cancer

Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex forconventional statistical techniques to process quickly and eciently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes evolutionary algorithms (also known asmetaheuristic optimisation, nature inspired optimisation algorithms), Artificial Neural Networks, Deep Learning, Fuzzy based approaches, and hybrids of these,as well as Bayesian based approaches, and Markov models. Metaheuristic optimisation approaches, such as the Ant Colony Optimisation, Particle Swarm Optimisation, and Artificial Immune Network have been utilised for optimising the performance of prostate cancer predictive models, and the suitability of these approaches are discussed

Pathway-Based Multi-Omics Data Integration for Breast Cancer Diagnosis and Prognosis.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017