Immunotherapy and its development for gynecological (Ovarian, endometrial and cervical) tumors: From immune checkpoint inhibitors to chimeric antigen receptor (car)-T cell therapy

Gynecological tumors are malignancies with both high morbidity and mortality. To date, only a few chemotherapeutic agents have shown efficacy against these cancer types (only ovarian cancer responds to several agents, especially platinum-based combinations). Within this context, the discovery of immune checkpoint inhibitors has led to numerous clinical studies being carried out that have also demonstrated their activity in these cancer types. More recently, following the development of chimeric antigen receptor (CAR)-T cell therapy in hematological malignancies, this strategy was also tested in solid tumors, including gynecological cancers. In this article, we focus on the molecular basis of gynecological tumors that makes them potential candidates for immunotherapy. We also provide an overview of the main immunotherapy studies divided by tumor type and report on CAR technology and the studies currently underway in the area of gynecological malignancies

Potential Application of Chimeric Antigen Receptor (CAR)-T Cell Therapy in Renal Cell Tumors

Currently, renal cell carcinoma is characterized by encouraging benefits from immunotherapy that have led to significant results in treatment outcome. The approval of nivolumab primarily as second-line monotherapy and, more recently, the approval of new combination therapies as first-line treatment have confirmed the importance of immunotherapy in this type of tumor. In this context, the chimeric antigen receptor (CAR)-T represents a further step forward in the field of immunotherapy. Initially tested on hematological malignancies, this new therapeutic approach is also becoming a topic of great interest for solid tumors. Although the treatment has several advantages over previous T-cell receptor-dependent immunotherapy, it is facing some obstacles in solid tumors such as a hostile tumor microenvironment and on-tumor/off-tumor toxicities. Several strategies are under investigation to overcome these problems, but the approval of CAR-T cell therapy is still some way off. In renal cancer, the significant advantages obtained from immune checkpoint inhibitors represent a good starting point, but the potential nephrological toxicity of CAR-T cell therapy represents an important risk. In this review, we provide the rationale and preliminary results of CAR-T cell therapy in renal cell malignancies

Circulating tumor cell gene expression and plasma AR gene copy number as biomarkers for castration-resistant prostate cancer patients treated with cabazitaxel

Background: Cabazitaxel improves overall survival (OS) in metastatic castration-resistant prostate cancer (mCRPC) patients progressing after docetaxel. In this prospective study, we evaluated the prognostic role of CTC gene expression on cabazitaxel-treated patients and its association with plasma androgen receptor (AR) copy number (CN). Methods: Patients receiving cabazitaxel 20 or 25 mg/sqm for mCRPC were enrolled. Digital PCR was performed to assess plasma AR CN status. CTC enrichment was assessed using the AdnaTest EMT-2/StemCell kit. CTC expression analyses were performed for 17 genes. Data are expressed as hazard ratio (HR) or odds ratio (OR) and 95% CI. Results: Seventy-four patients were fully evaluable. CTC expression of AR-V7 (HR=2.52, 1.24–5.12, p=0.011), AKR1C3 (HR=2.01, 1.06–3.81, p=0.031), AR (HR=2.70, 1.46–5.01, p=0.002), EPCAM (HR=3.75, 2.10–6.71, p< 0.0001), PSMA (HR=2.09, 1.19–3.66, p=0.01), MDK (HR=3.35, 1.83–6.13, p< 0.0001), and HPRT1 (HR=2.46, 1.44–4.18, p=0.0009) was significantly associated with OS. ALDH1 (OR=5.50, 0.97–31.22, p=0.05), AR (OR=8.71, 2.32–32.25, p=0.001), EPCAM (OR=7.26, 1.47–35.73, p=0.015), PSMA (OR=3.86, 1.10–13.50, p=0.035), MDK (OR=6.84, 1.87–24.98, p=0.004), and HPRT1 (OR=7.41, 1.82–30.19, p=0.005) expression was associated with early PD. AR CN status was significantly correlated with AR-V7 (p=0.05), EPCAM (p=0.02), and MDK (p=0.002) expression. In multivariable model, EPCAM and HPRT1 CTC expression, plasma AR CN gain, ECOG PS=2, and liver metastases and PSA were independently associated with poorer OS. In patients treated with cabazitaxel 20 mg/sqm, median OS was shorter in AR-V7 positive than negative patients (6.6 versus 14 months, HR=3.46, 1.47–8.17], p=0.004). Conclusions: Baseline CTC biomarkers may be prognosticators for cabazitaxel-treated mCRPC patients. Cabazitaxel at lower (20 mg/sqm) dose was associated with poorer outcomes in AR-V7 positive patients compared to AR-V7 negative patients in a post hoc subgroup analysis. Trial registration: Clinicaltrials.govNCT03381326. Retrospectively registered on 18 December 2017

The Geriatric G8 Score Is Associated with Survival Outcomes in Older Patients with Advanced Prostate Cancer in the ADHERE Prospective Study of the Meet-URO Network

Introduction: Androgen receptor pathway inhibitors (ARPIs) have been increasingly offered to older patients with prostate cancer (PC). However, prognostic factors relevant to their outcome with ARPIs are still little investigated. Methods and Materials: The Meet-URO network ADHERE was a prospective multicentre observational cohort study evaluating and monitoring adherence to ARPIs metastatic castrate-resistant PC (mCRPC) patients aged ≥70. Cox regression univariable and multivariable analyses for radiographic progression-free (rPFS) and overall survival (OS) were performed. Unsupervised median values and literature-based thresholds where available were used as cut-offs for quantitative variables. Results: Overall, 234 patients were enrolled with a median age of 78 years (73–82); 86 were treated with abiraterone (ABI) and 148 with enzalutamide (ENZ). With a median follow-up of 15.4 months (mo.), the median rPFS was 26.0 mo. (95% CI, 22.8–29.3) and OS 48.8 mo. (95% CI, 36.8–60.8). At the MVA, independent prognostic factors for both worse rPFS and OS were Geriatric G8 assessment ≤ 14 (p < 0.001 and p = 0.004) and PSA decline ≥50% (p < 0.001 for both); time to castration resistance ≥ 31 mo. and setting of treatment (i.e., post-ABI/ENZ) for rPFS only (p < 0.001 and p = 0.01, respectively); age ≥78 years for OS only (p = 0.008). Conclusions: Baseline G8 screening is recommended for mCRPC patients aged ≥70 to optimise ARPIs in vulnerable individuals, including early introduction of palliative care

On Metalenses with Arbitrarily Wide Field of View

Metalenses are nanostructured surfaces that mimic the functionality of optical elements. Many exciting demonstrations have already been made, for example, focusing into diffraction-limited spots or achromatic operation over a wide wavelength range. The key functionality that is yet missing, however, and that is most important for applications such as smartphones or virtual reality, is the ability to perform the imaging function with a single element over a wide field of view. Here, by relaxing the constraint on diffraction-limited resolution, we demonstrate the ability of single-layer metalenses to perform wide field of view (WFOV) imaging while maintaining high resolution suitable for most applications. We also discuss the WFOV physical properties and, in particular, we show that such a WFOV metalens mimics a spherical lens in the limit of infinite radius and infinite refractive index. Finally, we use Fourier analysis to explain the dependence of the FOV on the numerical aperture

Identification of single nucleotide variants using position-specific error estimation in deep sequencing data.

Background Targeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs).Methods To address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to model position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson model-based statistical framework for SNV detection.Results Our tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic positions and compare the results to digital droplet PCR experiments.Conclusions AmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve

Identification of single nucleotide variants using position-specific error estimation in deep sequencing data

Background Targeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs). Methods To address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to model position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson model-based statistical framework for SNV detection. Results Our tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic positions and compare the results to digital droplet PCR experiments. Conclusions AmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve

Identification of single nucleotide variants using position-specific error estimation in deep sequencing data

BACKGROUND: Targeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs). METHODS: To address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to model position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson model-based statistical framework for SNV detection. RESULTS: Our tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic positions and compare the results to digital droplet PCR experiments. CONCLUSIONS: AmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve