Niraparib is an oral poly(adenosine diphosphate ADP-ribose) polymerase (PARP) 1/2 inhibitor that has shown clinical activity in patients with ovarian cancer. We sought to evaluate the efficacy of ...niraparib versus placebo as maintenance treatment for patients with platinum-sensitive, recurrent ovarian cancer.
In this randomized, double-blind, phase 3 trial, patients were categorized according to the presence or absence of a germline BRCA mutation (gBRCA cohort and non-gBRCA cohort) and the type of non-gBRCA mutation and were randomly assigned in a 2:1 ratio to receive niraparib (300 mg) or placebo once daily. The primary end point was progression-free survival.
Of 553 enrolled patients, 203 were in the gBRCA cohort (with 138 assigned to niraparib and 65 to placebo), and 350 patients were in the non-gBRCA cohort (with 234 assigned to niraparib and 116 to placebo). Patients in the niraparib group had a significantly longer median duration of progression-free survival than did those in the placebo group, including 21.0 vs. 5.5 months in the gBRCA cohort (hazard ratio, 0.27; 95% confidence interval CI, 0.17 to 0.41), as compared with 12.9 months vs. 3.8 months in the non-gBRCA cohort for patients who had tumors with homologous recombination deficiency (HRD) (hazard ratio, 0.38; 95% CI, 0.24 to 0.59) and 9.3 months vs. 3.9 months in the overall non-gBRCA cohort (hazard ratio, 0.45; 95% CI, 0.34 to 0.61; P<0.001 for all three comparisons). The most common grade 3 or 4 adverse events that were reported in the niraparib group were thrombocytopenia (in 33.8%), anemia (in 25.3%), and neutropenia (in 19.6%), which were managed with dose modifications.
Among patients with platinum-sensitive, recurrent ovarian cancer, the median duration of progression-free survival was significantly longer among those receiving niraparib than among those receiving placebo, regardless of the presence or absence of gBRCA mutations or HRD status, with moderate bone marrow toxicity. (Funded by Tesaro; ClinicalTrials.gov number, NCT01847274 .).
Precision or personalized cancer medicine is a clinical approach that strives to customize therapies based upon the genomic profiles of individual patient tumors. Machine learning (ML) is a ...computational method particularly suited to the establishment of predictive models of drug response based on genomic profiles of targeted cells. We report here on the application of our previously established open-source support vector machine (SVM)-based algorithm to predict the responses of 175 individual cancer patients to a variety of standard-of-care chemotherapeutic drugs from the gene-expression profiles (RNA-seq or microarray) of individual patient tumors. The models were found to predict patient responses with >80% accuracy. The high PPV of our algorithms across multiple drugs suggests a potential clinical utility of our approach, particularly with respect to the identification of promising second-line treatments for patients failing standard-of-care first-line therapies.
Accumulating evidence suggests that somatic stem cells undergo mutagenic transformation into cancer initiating cells. The serous subtype of ovarian adenocarcinoma in humans has been hypothesized to ...arise from at least two possible classes of progenitor cells: the ovarian surface epithelia (OSE) and/or an as yet undefined class of progenitor cells residing in the distal end of the fallopian tube.
Comparative gene expression profiling analyses were carried out on OSE removed from the surface of normal human ovaries and ovarian cancer epithelial cells (CEPI) isolated by laser capture micro-dissection (LCM) from human serous papillary ovarian adenocarcinomas. The results of the gene expression analyses were randomly confirmed in paraffin embedded tissues from ovarian adenocarcinoma of serous subtype and non-neoplastic ovarian tissues using immunohistochemistry. Differentially expressed genes were analyzed using gene ontology, molecular pathway, and gene set enrichment analysis algorithms.
Consistent with multipotent capacity, genes in pathways previously associated with adult stem cell maintenance are highly expressed in ovarian surface epithelia and are not expressed or expressed at very low levels in serous ovarian adenocarcinoma. Among the over 2000 genes that are significantly differentially expressed, a number of pathways and novel pathway interactions are identified that may contribute to ovarian adenocarcinoma development.
Our results are consistent with the hypothesis that human ovarian surface epithelia are multipotent and capable of serving as the origin of ovarian adenocarcinoma. While our findings do not rule out the possibility that ovarian cancers may also arise from other sources, they are inconsistent with claims that ovarian surface epithelia cannot serve as the origin of ovarian cancer initiating cells.
The majority of ovarian cancer biomarker discovery efforts focus on the identification of proteins that can improve the predictive power of presently available diagnostic tests. We here show that ...metabolomics, the study of metabolic changes in biological systems, can also provide characteristic small molecule fingerprints related to this disease.
In this work, new approaches to automatic classification of metabolomic data produced from sera of ovarian cancer patients and benign controls are investigated. The performance of support vector machines (SVM) for the classification of liquid chromatography/time-of-flight mass spectrometry (LC/TOF MS) metabolomic data focusing on recognizing combinations or "panels" of potential metabolic diagnostic biomarkers was evaluated. Utilizing LC/TOF MS, sera from 37 ovarian cancer patients and 35 benign controls were studied. Optimum panels of spectral features observed in positive or/and negative ion mode electrospray (ESI) MS with the ability to distinguish between control and ovarian cancer samples were selected using state-of-the-art feature selection methods such as recursive feature elimination and L1-norm SVM.
Three evaluation processes (leave-one-out-cross-validation, 12-fold-cross-validation, 52-20-split-validation) were used to examine the SVM models based on the selected panels in terms of their ability for differentiating control vs. disease serum samples. The statistical significance for these feature selection results were comprehensively investigated. Classification of the serum sample test set was over 90% accurate indicating promise that the above approach may lead to the development of an accurate and reliable metabolomic-based approach for detecting ovarian cancer.
Extremely rare circulating tumor cell (CTC) clusters are both increasingly appreciated as highly metastatic precursors and virtually unexplored. Technologies are primarily designed to detect single ...CTCs and often fail to account for the fragility of clusters or to leverage cluster-specific markers for higher sensitivity. Meanwhile, the few technologies targeting CTC clusters lack scalability. Here, we introduce the Cluster-Wells, which combines the speed and practicality of membrane filtration with the sensitive and deterministic screening afforded by microfluidic chips. The >100,000 microwells in the Cluster-Wells physically arrest CTC clusters in unprocessed whole blood, gently isolating virtually all clusters at a throughput of >25 mL/h, and allow viable clusters to be retrieved from the device. Using the Cluster-Wells, we isolated CTC clusters ranging from 2 to 100+ cells from prostate and ovarian cancer patients and analyzed a subset using RNA sequencing. Routine isolation of CTC clusters will democratize research on their utility in managing cancer.
Quality of life (QOL) has become an important complementary endpoint in cancer clinical studies alongside more traditional assessments (eg, tumour response, progression-free survival, overall ...survival). Niraparib maintenance treatment has been shown to significantly improve progression-free survival in patients with recurrent ovarian cancer. We aimed to assess whether the benefits of extending progression-free survival are offset by treatment-associated toxic effects that affect QOL.
The ENGOT-OV16/NOVA trial was a multicentre, double-blind, phase 3, randomised controlled trial done in 107 study sites in the USA, Canada, Europe, and Israel. Patients with recurrent ovarian cancer who were in response to their last platinum-based chemotherapy were randomly assigned (2:1) to receive either niraparib (300 mg once daily) as a maintenance treatment or placebo. Randomisation was stratified based on time to progression after the penultimate platinum-based regimen, previous use of bevacizumab, and best response (complete or partial) to the last platinum-based regimen with permuted-block randomisation (six in each block) using an interactive web response system. The trial enrolled two independent cohorts on the basis of germline BRCA (gBRCA) mutation status (determined by BRACAnalysis Testing, Myriad Genetics, Salt Lake City, UT, USA). The primary endpoint of the trial was progression-free survival, and has already been reported. In this study, we assessed patient-reported outcomes (PROs) in the intention-to-treat population using the Functional Assessment of Cancer Therapy–Ovarian Symptoms Index (FOSI) and European QOL five-dimension five-level questionnaire (EQ-5D-5L). We collected PROs from trial entry every 8 weeks for the first 14 cycles and every 12 weeks thereafter. If a patient discontinued, we collected PROs at discontinuation and during a postprogression visit 8 weeks (plus or minus 2 weeks) later. We assessed the effect of haematological toxic effects on QOL with disutility analyses of the most common grade 3–4 adverse events (thrombocytopenia, anaemia, and neutropenia) using a mixed model with histology, region, previous treatment, age, planned treatment, and baseline score as covariates. This study is registered with ClinicalTrials.gov, number NCT01847274.
Between Aug 28, 2013, and June 1, 2015, 553 patients were enrolled and randomly assigned to receive niraparib (n=138 in the gBRCAmut cohort, n=234 in the non-gBRCAmut cohort) or placebo (n=65 in the gBRCAmut cohort, n=116 in the non-gBRCAmut cohort). The mean FOSI score at baseline was similar between the two groups (range between 25·0–25·6 in the two groups). Overall QOL scores remained stable during the treatment and preprogression period in the niraparib group; no significant differences were observed between the niraparib and placebo group, and preprogression EQ-5D-5L scores were similar between the two groups in both cohorts (0·838 0·0097 in the niraparib group vs 0·834 0·0173 in the placebo group in the gBRCAmut cohort; and 0·833 0·0077 in the niraparib group vs 0·815 0·0122 in the placebo group in the non-gBRCAmut cohort). The most common adverse events reported at screening (baseline) were lack of energy (425 79%; 97 18% reporting severe lack of energy), pain (236 44%), and nausea (118 22%). All symptoms, except nausea, either remained stable or improved over time in the niraparib group. The most common grade 3 or 4 toxicities observed in the niraparib group were haematological in nature: thrombocytopenia (124 34% of 367 patients), anaemia (93 25%), and neutropenia (72 20%); disutility analyses showed no significant QOL impairment associated with these toxic effects.
These PRO data suggest that women who receive niraparib as maintenance treatment for recurrent ovarian cancer after responding to platinum treatment are able to maintain QOL during their treatment when compared with placebo.
TESARO.
The isolation of a patient's metastatic cancer cells is the first, enabling step toward treatment of that patient using modern personalized medicine techniques. Whereas traditional standard-of-care ...approaches select treatments for cancer patients based on the histological classification of cancerous tissue at the time of diagnosis, personalized medicine techniques leverage molecular and functional analysis of a patient's own cancer cells to select treatments with the highest likelihood of being effective. Unfortunately, the pure populations of cancer cells required for these analyses can be difficult to acquire, given that metastatic cancer cells typically reside in fluid containing many different cell populations. Detection and analyses of cancer cells therefore require separation from these contaminating cells. Conventional cell sorting approaches such as Fluorescence Activated Cell Sorting or Magnetic Activated Cell Sorting rely on the presence of distinct surface markers on cells of interest which may not be known nor exist for cancer applications. In this work, we present a microfluidic platform capable of label-free enrichment of tumor cells from the ascites fluid of ovarian cancer patients. This approach sorts cells based on differences in biomechanical properties, and therefore does not require any labeling or other pre-sort interference with the cells. The method is also useful in the cases when specific surface markers do not exist for cells of interest. In model ovarian cancer cell lines, the method was used to separate invasive subtypes from less invasive subtypes with an enrichment of ~ sixfold. In ascites specimens from ovarian cancer patients, we found the enrichment protocol resulted in an improved purity of P53 mutant cells indicative of the presence of ovarian cancer cells. We believe that this technology could enable the application of personalized medicine based on analysis of liquid biopsy patient specimens, such as ascites from ovarian cancer patients, for quick evaluation of metastatic disease progression and determination of patient-specific treatment.
The identification/development of a machine learning-based classifier that utilizes metabolic profiles of serum samples to accurately identify individuals with ovarian cancer.
Serum samples collected ...from 431 ovarian cancer patients and 133 normal women at four geographic locations were analyzed by mass spectrometry. Reliable metabolites were identified using recursive feature elimination coupled with repeated cross-validation and used to develop a consensus classifier able to distinguish cancer from non-cancer. The probabilities assigned to individuals by the model were used to create a clinical tool that assigns a likelihood that an individual patient sample is cancer or normal.
Our consensus classification model is able to distinguish cancer from control samples with 93% accuracy. The frequency distribution of individual patient scores was used to develop a clinical tool that assigns a likelihood that an individual patient does or does not have cancer.
An integrative approach using metabolomic profiles and machine learning-based classifiers has been employed to develop a clinical tool that assigns a probability that an individual patient does or does not have ovarian cancer. This personalized/probabilistic approach to cancer diagnostics is more clinically informative and accurate than traditional binary (yes/no) tests and represents a promising new direction in the early detection of ovarian cancer.
•Predictive models derived from machine learning analyses of serum metabolic profiles can accurately detect ovarian cancer.•Only a minority of the most predictively informative metabolites is currently annotated (7%).•Lipids predominate among the most predictively informative metabolites currently annotated.•The frequency distribution of model-derived patient scores were used to develop a clinical tool for the diagnosis of OC.
Response to the letter to the editor Ban, Dongjo; Housley, Stephen N; Matyunina, Lilya V ...
Gynecologic oncology,
04/2024, Letnik:
183
Journal Article
Gestational trophoblastic disease usually follows a molar pregnancy but can occur also after an abortion or a term pregnancy. In only 10% of cases will treatment be required; and usually, ...single-agent chemotherapy will suffice. In high-risk disease, the multiagent regimen EMA-CO is usually used; and if that fails, most oncologists will use the EMA-EP regimen. If this does not produce a remission, there is no unanimity of opinion as to how to proceed. Numerous salvage regimens are in current use, and some centers do not consider high-dose chemotherapy.
A young woman presented 4 months after a normal spontaneous delivery with an elevated human chorionic gonadotropin level and multiple pulmonary metastases. She failed both the EMA-CO and EMA-EP regimens as well as additional standard chemotherapy. She was then treated with 4 separate courses of high-dose chemotherapy with autologous stem cell support, which produced a complete remission.
Even patients with high-risk gestational trophoblastic disease are usually cured with standard chemotherapy. Patients who fail such treatment should be considered for high-dose chemotherapy.
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