Beta-Validation of a Non-Invasive Method for Simultaneous Detection of Early-Stage Female-Specific Cancers

Objectives In this report, we assessed the accuracy of our previously developed method for simultaneous diagnosis of the four female cancers of the Breast, Endometrium, Cervix, and Ovary, in a clinical set-up with blinded protocol. Materials and Methods Our test protocol combined global serum metabolome profiling wherein data was analyzed with machine learning algorithms to extract metabolite signatures that correlated with early-stage cancers. High-resolution mass spectrometry was employed to profile the serum metabolome and the resulting data were subjected to a pre-processing pipeline to obtain the data set. The data was then analyzed using artificial intelligence algorithms to identify early-stage cancer metabolic signatures. Results Overall, a total of 1000 blinded samples were analyzed by generating the serum metabolome profiles, followed by sequential algorithms for cancer detection and multiclass cancer type identification. Of these 1000 samples, 797 were identified as cancer positive, while, 203 samples were identified as cancer-negative. The multiclass algorithm was then applied to the 797 cancer-positive samples, to distinguish between samples that were from patients with either endometrial, breast, cervical, or ovarian cancer. After completion of the analysis, the sample code was broken to estimate the accuracy of the results. Concerning the identification of samples that were cancer-positive, the sensitivity obtained was 99.6% whereas the specificity was 100%. For the second stage of analysis which involves ‘tissue of origin’, all 107 breast cancer samples were correctly identified without any false calls. The accuracy for identification of cervical and ovarian cancers was between 95-96% for each, whereas 91% for endometrial cancer. Conclusions Our present study validates the performance of our method for the early-stage detection of female-specific cancers in a clinical setting. Importantly, the algorithms for cancer detection and ‘tissue of origin’ prediction, which were initially trained using samples from Caucasian patients, retained the accuracy on samples from Indian women patients. This suggests that the performance of these algorithms was minimally influenced by variables such as ethnicity and race. Present results, therefore, also underscore the potential clinical utility of our method for early-stage diagnosis of cancers that are specific to females. ### Competing Interest Statement A.G, G.S., Z.S. and N.S. are fulltime employees of PredOmix Technologies Private Limited. K.V.S.R. is a cofounder and owns stock in both PredOmix Technologies Private Limited and PredOmix Health Sciences Pte. Ltd. A.G., Z.S., and NS. own stock in PredOmix Health Sciences Pte. Ltd. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Panimalar Medical College Hospital & Research Institute - Institutional Human Ethics Committee (Protocol No. PMCHRI-IHEC-034) gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors.