Clonal somatic copy number altered driver events inform drug sensitivity in high-grade serous ovarian cancer

DeepCCDS: Interpretable Deep Learning Framework for Predicting Cancer Cell Drug Sensitivity through Characterizing Cancer Driver Signals

Accurate characterization of cellular states is the foundation for precise prediction of drug sensitivity in cancer cell lines, which in turn is fundamental to realizing precision oncology. However, current deep learning approaches have limitations in characterizing cellular states. They rely solely on isolated genetic markers, overlooking the complex regulatory networks and cellular mechanisms that underlie drug responses. To address this limitation, this work proposes DeepCCDS, a Deep learning framework for Cancer Cell Drug Sensitivity prediction through Characterizing Cancer Driver Signals. DeepCCDS incorporates a prior knowledge network to characterize cancer driver signals, building upon the self-supervised neural network framework. The signals can reflect key mechanisms influencing cancer cell development and drug response, enhancing the model's predictive performance and interpretability. DeepCCDS has demonstrated superior performance in predicting drug sensitivity compared to previous state-of-the-art approaches across multiple datasets. Benefiting from integrating prior knowledge, DeepCCDS exhibits powerful feature representation capabilities and interpretability. Based on these feature representations, we have identified embedding features that could potentially be used for drug screening in new indications. Further, this work demonstrates the applicability of DeepCCDS on solid tumor samples from The Cancer Genome Atlas. This work believes integrating DeepCCDS into clinical decision-making processes can potentially improve the selection of personalized treatment strategies for cancer patients.

Unveiling drug resistance pathways in high-grade serous ovarian cancer(HGSOC): recent advances and future perspectives

High-Grade Serous Ovarian Carcinoma (HGSOC) represents the most prevalent and lethal subtype of ovarian cancer, with approximately 225,000 new cases reported globally each year and a five-year survival rate of merely 49.1%. The clinical management of HGSOC encounters substantial challenges, primarily attributable to its intricate drug resistance mechanisms, which involve multiple biological processes, including tumor cell heterogeneity, microenvironment remodeling, gene mutations, and drug efflux. This study systematically reviews the most recent advancements in HGSOC drug resistance research, concentrating on the molecular biological foundations of resistance mechanisms, innovative detection strategies, and potential therapeutic approaches. The research indicates that HGSOC drug resistance constitutes a complex process characterized by multifactorial interactions, involving aberrant cell signaling pathways, dynamic alterations in the tumor microenvironment, and specific expressions of molecular markers. In this review, we systematically analyzed and investigated the intricate biological behaviors associated with HGSOC drug resistance, which not only enhances the understanding of disease progression but also provides essential theoretical foundations for the development of more precise and effective targeted therapies. This review firstly illustrated the detailed drug resistance cellular and molecular mechanisms underlying HGSOC chemotherapy, which can pave the way for future studies in HGSOC drug resistance practices.

Plasma metabolomic signatures for copy number variants and COVID-19 risk loci in Northern Finland populations

Copy number variants (CNVs) are an important class of genomic variation known to be important for human physiology and diseases. Here we present genome-wide metabolomic signatures for CNVs in two Finnish cohorts-The Northern Finland Birth Cohort 1966 (NFBC 1966) and NFBC 1986. We have analysed and reported CNVs in over 9,300 individuals and characterised their dosage effect (CNV-metabolomic QTL) on 228 plasma lipoproteins and metabolites. We have reported reference (normal physiology) metabolomic signatures for up to ~ 2.6 million COVID-19 GWAS results from the National Institutes of Health (NIH) GRASP database, including for outcomes related to COVID-19 death, severity, and hospitalisation. Furthermore, by analysing two exemplar genes for COVID-19 severity namely LZTFL1 and OAS1, we have reported here two additional candidate genes for COVID-19 severity biology, (1) NFIX, a gene related to viral (adenovirus) replication and hematopoietic stem cells and (2) ACSL1, a known candidate gene for sepsis and bacterial inflammation. Based on our results and current literature we hypothesise that (1) charge imbalance across the cellular membrane between cations (Fe2+, Mg2+ etc.) and anions (e.g. ROS, hydroxide ion from cellular Fenton reactions, superoxide etc.), (2) iron trafficking within and between different cell types e.g., macrophages and (3) systemic oxidative stress response (e.g. lipid peroxidation mediated inflammation), together could be of relevance in severe COVID-19 cases. To conclude, our unique atlas of univariate and multivariate metabolomic signatures for CNVs (~ 7.2 million signatures) with deep annotations of various multi-omics data sets provide an important reference knowledge base for human metabolism and diseases.

Age-related genomic alterations and chemotherapy sensitivity in osteosarcoma: insights from cancer genome profiling analyses

BACKGROUND

Osteosarcoma, the most common primary bone malignancy, has a complex genetic basis and two incidence peaks. In younger patients, the standard treatment involves wide surgical resection combined with adjuvant chemotherapy; however, the role of chemotherapy in elderly patients remains controversial. The aims of this study were to investigate genetic differences between younger and elderly patients with osteosarcoma and to identify genetic signatures associated with chemotherapy response.

METHODS

Genetic alterations were analyzed using cancer genome profiling data for 204 patients with osteosarcoma obtained from the Center for Cancer Genomics and Advanced Therapeutics.

RESULTS

The mutation spectrum was consistent with previous results for osteosarcoma. CCNE1, MCL1, MYC, and RB1 alterations were significantly associated with a younger age, while CDK4, CDKN2A, CDKN2B, H3F3A, KMT2D, MDM2, RAC1, and SETD2 alterations were significantly associated with an older age. Age, unsupervised clustering of gene alterations, and MYC amplifications were significantly associated with the response to ifosfamide. Notably, both clustered mutation signatures and MYC amplification were correlated with age.

CONCLUSIONS

These findings suggest that distinct oncogenic mechanisms contribute to differential sensitivity to chemotherapy in younger and elderly patients. Cancer genome profiling may aid in chemotherapy selection, and its early implementation is recommended to optimize treatment strategies.

Genomic instability in ovarian cancer: Through the lens of single nucleotide polymorphisms

Ovarian cancer (OC) is the deadliest gynecological malignancy among all female reproductive cancers. It is characterized by high mortality rate and poor prognosis. Genomic instability caused by mutations, single nucleotide polymorphisms (SNPs), copy number variations (CNVs), microsatellite instability (MSI), and chromosomal instability (CIN) are associated with OC predisposition. SNPs, which are highly prevalent in the general population, show a greater relative risk contribution, particularly in sporadic cancers. Understanding OC etiology in terms of genetic basis can increase the use of molecular diagnostics and provide promising approaches for designing novel treatment modalities. This will help deliver personalized medicine to OC patients, which may soon be within reach. Given the pivotal impact of SNPs in cancers, the primary emphasis of this review is to shed light on their prevalence in key caretaker genes that closely monitor genomic integrity, viz., DNA damage response, repair, cell cycle checkpoints, telomerase maintenance, and apoptosis and their clinical implications in OC. We highlight the current challenges faced in different SNP-based studies. Various computational methods and bioinformatic tools employed to predict the functional impact of SNPs have also been comprehensively reviewed concerning OC research. Overall, this review identifies that variants in the DDR and HRR pathways are the most studied, implying their critical role in the disease. Conversely, variants in other pathways, such as NHEJ, MMR, cell cycle, apoptosis, telomere maintenance, and PARP genes, have been explored the least.

Current trends in sensitizing immune checkpoint inhibitors for cancer treatment

Immune checkpoint inhibitors (ICIs) have dramatically transformed the treatment landscape for various malignancies, achieving notable clinical outcomes across a wide range of indications. Despite these advances, resistance to immune checkpoint blockade (ICB) remains a critical clinical challenge, characterized by variable response rates and non-durable benefits. However, growing research into the complex intrinsic and extrinsic characteristics of tumors has advanced our understanding of the mechanisms behind ICI resistance, potentially improving treatment outcomes. Additionally, robust predictive biomarkers are crucial for optimizing patient selection and maximizing the efficacy of ICBs. Recent studies have emphasized that multiple rational combination strategies can overcome immune checkpoint resistance and enhance susceptibility to ICIs. These findings not only deepen our understanding of tumor biology but also reveal the unique mechanisms of action of sensitizing agents, extending clinical benefits in cancer immunotherapy. In this review, we will explore the underlying biology of ICIs, discuss the significance of the tumor immune microenvironment (TIME) and clinical predictive biomarkers, analyze the current mechanisms of resistance, and outline alternative combination strategies to enhance the effectiveness of ICIs, including personalized strategies for sensitizing tumors to ICIs.

The Role of Circulating Tumor DNA in Ovarian Cancer

Ovarian cancer is the deadliest of all gynecological diseases because its diagnosis and treatment still pose many problems. Surgical excision, hormone therapy, radiation, chemotherapy, or targeted therapy for eradicating the main tumor and halting the spread of metastases are among the treatment options available to individuals with ovarian cancer, depending on the disease's stage. Tumor DNA that circulates in a patient's bodily fluids has been studied recently as a possible novel biomarker for a number of cancers, as well as a means of quantifying tumor size and evaluating the efficacy of cancer therapy. The most significant alterations that we could find in the ctDNA of ovarian cancer patients-such as chromosomal instability, somatic mutations, and methylation-are discussed in this review. Additionally, we talk about the utility of ctDNA in diagnosis, prognosis, and therapy response prediction for these patients.

Ecological and evolutionary dynamics to design and improve ovarian cancer treatment

Ovarian cancer ecosystems are exceedingly complex, consisting of a high heterogeneity of cancer cells. Development of drugs such as poly ADP-ribose polymerase (PARP) inhibitors, targeted therapies and immunotherapies offer more options for sequential or combined treatments. Nevertheless, mortality in metastatic ovarian cancer patients remains high because cancer cells consistently develop resistance to single and combination therapies, urging a need for treatment designs that target the evolvability of cancer cells. The evolutionary dynamics that lead to resistance emerge from the complex tumour microenvironment, the heterogeneous populations, and the individual cancer cell's plasticity. We propose that successful management of ovarian cancer requires consideration of the ecological and evolutionary dynamics of the disease. Here, we review current options and challenges in ovarian cancer treatment and discuss principles of tumour evolution. We conclude by proposing evolutionarily designed strategies for ovarian cancer, with the goal of integrating such principles with longitudinal, quantitative data to improve the treatment design and management of drug resistance. KEY POINTS/HIGHLIGHTS: Tumours are ecosystems in which cancer and non-cancer cells interact and evolve in complex and dynamic ways. Conventional therapies for ovarian cancer inevitably lead to the development of resistance because they fail to consider tumours' heterogeneity and cellular plasticity. Eco-evolutionarily designed therapies should consider cancer cell plasticity and patient-specific characteristics to improve clinical outcome and prevent relapse.

Single nucleotide and copy number variants of cancer driver genes inform drug response in multiple cancers

Due to the heterogeneity of cancer, precision medicine has been a major challenge for cancer treatment. Determining medication regimens based on patient genotypes has become a research hotspot in cancer genomics. In this study, we aim to identify key biomarkers for targeted therapies based on single nucleotide variants (SNVs) and copy number variants (CNVs) of genes. The experiment is carried out on 7 cancers on the Encyclopedia of Cancer Cell Lines (CCLE) dataset. Considering the high mutability of driver genes which result in abundant mutated samples, the effect of data sparsity can be eliminated to a large extent. Therefore, we focus on discovering the relationship between driver mutation patterns and three measures of drug response, namely area under the curve (AUC), half maximal effective concentration (EC50), and log2-fold change (LFC). First, multiple statistical methods are applied to assess the significance of difference in drug response between sample groups. Next, for each driver gene, we analyze the extent to which its mutations can affect drug response. Based on the results of multiple hypothesis tests and correlation analyses, our main findings include the validation of several known drug response biomarkers such as BRAF, NRAS, MAP2K1, MAP2K2, and CDKN2A, as well as genes with huge potential to infer drug responses. It is worth emphasizing that we identify a list of genes including SALL4, B2M, BAP1, CCDC6, ERBB4, FOXA1, GRIN2A, and PTPRT, whose impact on drug response spans multiple cancers and should be prioritized as key biomarkers for targeted therapies. Furthermore, based on the statistical p-values and correlation coefficients, we construct gene-drug sensitivity maps for cancer drug recommendation. In this work, we show that driver mutation patterns could be used to tailor therapeutics for precision medicine.

Tumor cell enrichment by tissue suspension improves sensitivity to copy number variation in diffuse gastric cancer with low tumor content

The detection of copy number variations (CNVs) and somatic mutations in cancer is important for the selection of specific drugs for patients with cancer. In cancers with sporadic tumor cells, low tumor content prevents the accurate detection of somatic alterations using targeted sequencing. To efficiently identify CNVs, we performed tumor cell enrichment using tissue suspensions of formalin-fixed paraffin-embedded (FFPE) tissue sections with low tumor cell content. Tumor-enriched and residual fractions were separated from FFPE tissue suspensions of intestinal and diffuse-type gastric cancers containing sporadic tumor cells, and targeted sequencing was performed on 225 cancer-related genes. Sequencing of a targeted panel of cancer-related genes using tumor-enriched fractions increased the number of detectable CNVs and the copy number of amplified genes. Furthermore, CNV analysis using the normal cell-enriched residual fraction as a reference for CNV scoring allowed targeted sequencing to detect CNV characteristics of diffuse-type gastric cancer with low tumor content. Our approach improves the CNV detection rate in targeted sequencing with tumor enrichment and the accuracy of CNV detection in archival samples without paired blood.

A novel defined programmed cell death related gene signature for predicting the prognosis of serous ovarian cancer

PURPOSE

This study aims to explore the contribution of differentially expressed programmed cell death genes (DEPCDGs) to the heterogeneity of serous ovarian cancer (SOC) through single-cell RNA sequencing (scRNA-seq) and assess their potential as predictors for clinical prognosis.

METHODS

SOC scRNA-seq data were extracted from the Gene Expression Omnibus database, and the principal component analysis was used for cell clustering. Bulk RNA-seq data were employed to analyze SOC-associated immune cell subsets key genes. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were utilized to calculate immune cell scores. Prognostic models and nomograms were developed through univariate and multivariate Cox analyses.

RESULTS

Our analysis revealed that 48 DEPCDGs are significantly correlated with apoptotic signaling and oxidative stress pathways and identified seven key DEPCDGs (CASP3, GADD45B, GNA15, GZMB, IL1B, ISG20, and RHOB) through survival analysis. Furthermore, eight distinct cell subtypes were characterized using scRNA-seq. It was found that G protein subunit alpha 15 (GNA15) exhibited low expression across these subtypes and a strong association with immune cells. Based on the DEGs identified by the GNA15 high- and low-expression groups, a prognostic model comprising eight genes with significant prognostic value was constructed, effectively predicting patient overall survival. Additionally, a nomogram incorporating the RS signature, age, grade, and stage was developed and validated using two large SOC datasets.

CONCLUSION

GNA15 emerged as an independent and excellent prognostic marker for SOC patients. This study provides valuable insights into the prognostic potential of DEPCDGs in SOC, presenting new avenues for personalized treatment strategies.

Onco-Circuit Addiction and Onco-Nutrient mTORC1 Signaling Vulnerability in a Model of Aggressive T Cell Malignancy

How genetic lesions drive cell transformation and whether they can be circumvented without compromising function of non-transformed cells are enduring questions in oncology. Here we show that in mature T cells-in which physiologic clonal proliferation is a cardinal feature- constitutive MYC transcription and Tsc1 loss in mice modeled aggressive human malignancy by reinforcing each other's oncogenic programs. This cooperation was supported by MYC-induced large neutral amino acid transporter chaperone SLC3A2 and dietary leucine, which in synergy with Tsc1 deletion overstimulated mTORC1 to promote mitochondrial fitness and MYC protein overexpression in a positive feedback circuit. A low leucine diet was therapeutic even in late-stage disease but did not hinder T cell immunity to infectious challenge, nor impede T cell transformation driven by constitutive nutrient mTORC1 signaling via Depdc5 loss. Thus, mTORC1 signaling hypersensitivity to leucine as an onco-nutrient enables an onco-circuit, decoupling pathologic from physiologic utilization of nutrient acquisition pathways.

The two sides of chromosomal instability: drivers and brakes in cancer

Chromosomal instability (CIN) is a hallmark of cancer and is associated with tumor cell malignancy. CIN triggers a chain reaction in cells leading to chromosomal abnormalities, including deviations from the normal chromosome number or structural changes in chromosomes. CIN arises from errors in DNA replication and chromosome segregation during cell division, leading to the formation of cells with abnormal number and/or structure of chromosomes. Errors in DNA replication result from abnormal replication licensing as well as replication stress, such as double-strand breaks and stalled replication forks; meanwhile, errors in chromosome segregation stem from defects in chromosome segregation machinery, including centrosome amplification, erroneous microtubule-kinetochore attachments, spindle assembly checkpoint, or defective sister chromatids cohesion. In normal cells, CIN is deleterious and is associated with DNA damage, proteotoxic stress, metabolic alteration, cell cycle arrest, and senescence. Paradoxically, despite these negative consequences, CIN is one of the hallmarks of cancer found in over 90% of solid tumors and in blood cancers. Furthermore, CIN could endow tumors with enhanced adaptation capabilities due to increased intratumor heterogeneity, thereby facilitating adaptive resistance to therapies; however, excessive CIN could induce tumor cells death, leading to the "just-right" model for CIN in tumors. Elucidating the complex nature of CIN is crucial for understanding the dynamics of tumorigenesis and for developing effective anti-tumor treatments. This review provides an overview of causes and consequences of CIN, as well as the paradox of CIN, a phenomenon that continues to perplex researchers. Finally, this review explores the potential of CIN-based anti-tumor therapy.

Allele-specific transcriptional effects of subclonal copy number alterations enable genotype-phenotype mapping in cancer cells

Subclonal copy number alterations are a prevalent feature in tumors with high chromosomal instability and result in heterogeneous cancer cell populations with distinct phenotypes. However, the extent to which subclonal copy number alterations contribute to clone-specific phenotypes remains poorly understood. We develop TreeAlign, which computationally integrates independently sampled single-cell DNA and RNA sequencing data from the same cell population. TreeAlign accurately encodes dosage effects from subclonal copy number alterations, the impact of allelic imbalance on allele-specific transcription, and obviates the need to define genotypic clones from a phylogeny a priori, leading to highly granular definitions of clones with distinct expression programs. These improvements enable clone-clone gene expression comparisons with higher resolution and identification of expression programs that are genomically independent. Our approach sets the stage for dissecting the relative contribution of fixed genomic alterations and dynamic epigenetic processes on gene expression programs in cancer.

CCT2 prevented β-catenin proteasomal degradation to sustain cancer stem cell traits and promote tumor progression in epithelial ovarian cancer

BACKGROUND

Epithelial ovarian cancer (EOC) is featured by rapid progression and dismal outcomes clinically. Chaperonin Containing TCP1 Subunit 2 (CCT2) was identified as a crucial regulator for tumor progression, however, its exact role in EOC remained largely unknown.

METHODS

CCT2 expression and prognostic value in EOC samples were assessed according to TCGA dataset. Proliferation and mobility potentials were assessed by CCK8, colony-formation, wound healing, and Transwell assays. Cancer stem cell (CSC) traits were evaluated by RT-PCR, WB assays, sphere-forming assay and chemoresistance analysis. Bioinformatic analysis, co-IP assays and ubiquitin assays were performed to explore the mechanisms of CCT2 on EOC cells.

RESULTS

CCT2 highly expressed in EOC tissues and predicted poor prognosis of EOC patients by TCGA analysis. Silencing CCT2 significantly restrained cell proliferation, migration, and invasion. Moreover, CCT2 could effectively trigger epithelial-mesenchymal transition to confer extensive invasion potentials to EOC cells, Importantly, CCT2 positively correlated with CSC markers in EOC, and CCT2 knockdown impaired CSC traits and sensitize EOC cells to conventional chemotherapy regimens. Contrarily, overexpressing CCT2 achieved opposite results. Mechanistically, CCT2 exerted its pro-oncogene function by triggering Wnt/β-catenin signaling. Specifically, CCT2 could recruit HSP105-PP2A complex, a well-established dephosphorylation complex, to β-catenin via direct physical interaction to prevent phosphorylation-induced proteasomal degradation of β-catenin, resulting in intracellular accumulation of active β-catenin and increased signaling activity.

CONCLUSIONS

CCT2 was a novel promotor for EOC progression and a crucial sustainer for CSC traits mainly by preventing β-catenin degradation. Targeting CCT2 may represent a promising therapeutic strategy for EOC.

Circulating tumor DNA-based copy-number profiles enable monitoring treatment effects during therapy in high-grade serous carcinoma

Circulating tumor DNA (ctDNA) analysis has emerged as a promising tool for detecting and profiling longitudinal genomics changes in cancer. While copy-number alterations (CNAs) play a major role in cancers, treatment effect monitoring using copy-number profiles has received limited attention as compared to mutations. A major reason for this is the insensitivity of CNA analysis for the real-life tumor-fraction ctDNA samples. We performed copy-number analysis on 152 plasma samples obtained from 29 patients with high-grade serous ovarian cancer (HGSC) using a sequencing panel targeting over 500 genes. Twenty-one patients had temporally matched tissue and plasma sample pairs, which enabled assessing concordance with tissues sequenced with the same panel or whole-genome sequencing and to evaluate sensitivity. Our approach could detect concordant CNA profiles in most plasma samples with as low as 5% tumor content and highly amplified regions in samples with ∼1% of tumor content. Longitudinal profiles showed changes in the CNA profiles in seven out of 11 patients with high tumor-content plasma samples at relapse. These changes included focal acquired or lost copy-numbers, even though most of the genome remained stable. Two patients displayed major copy-number profile changes during therapy. Our analysis revealed ctDNA-detectable subclonal selection resulting from both surgical operations and chemotherapy. Overall, longitudinal ctDNA data showed acquired and diminished CNAs at relapse when compared to pre-treatment samples. These results highlight the importance of genomic profiling during treatment as well as underline the usability of ctDNA.

Assessment of MYC and TERT copy number variations in lung cancer using digital PCR

OBJECTIVE

Lung cancer is the second most frequent cancer type and the most common cause of cancer-related deaths worldwide. Alteration of gene copy numbers are associated with lung cancer and the determination of copy number variations (CNV) is appropriate for the discrimination between tumor and non-tumor tissue in lung cancer. As telomerase reverse transcriptase (TERT) and v-myc avian myelocytomatosis viral oncogene homolog (MYC) play a role in lung cancer the aims of this study were the verification of our recent results analyzing MYC CNV in tumor and non-tumor tissue of lung cancer patients using an independent study group and the assessment of TERT CNV as an additional marker.

RESULTS

TERT and MYC status was analyzed using digital PCR (dPCR) in tumor and adjacent non-tumor tissue samples of 114 lung cancer patients. The difference between tumor and non-tumor samples were statistically significant (p < 0.0001) for TERT and MYC. Using a predefined specificity of 99% a sensitivity of 41% and 51% was observed for TERT and MYC, respectively. For the combination of TERT and MYC the overall sensitivity increased to 60% at 99% specificity. We demonstrated that a combination of markers increases the performance in comparison to individual markers. Additionally, the determination of CNV using dPCR might be an appropriate tool in precision medicine.

Overview of Tumor Heterogeneity in High-Grade Serous Ovarian Cancers

Ovarian cancers encompass a group of neoplasms originating from germinal tissues and exhibiting distinct clinical, pathological, and molecular features. Among these, epithelial ovarian cancers (EOCs) are the most prevalent, comprising five distinct tumor histotypes. Notably, high-grade serous ovarian cancers (HGSOCs) represent the majority, accounting for over 70% of EOC cases. Due to their silent and asymptomatic behavior, HGSOCs are generally diagnosed in advanced stages with an evolved and complex genomic state, characterized by high intratumor heterogeneity (ITH) due to chromosomal instability that distinguishes HGSOCs. Histologically, these cancers exhibit significant morphological diversity both within and between tumors. The histologic patterns associated with solid, endometrioid, and transitional (SET) and classic subtypes of HGSOCs offer prognostic insights and may indicate specific molecular profiles. The evolution of HGSOC from primary to metastasis is typically characterized by clonal ITH, involving shared or divergent mutations in neoplastic sub-clones within primary and metastatic sites. Disease progression and therapy resistance are also influenced by non-clonal ITH, related to interactions with the tumor microenvironment and further genomic changes. Notably, significant alterations occur in nonmalignant cells, including cancer-associated fibroblast and immune cells, during tumor progression. This review provides an overview of the complex nature of HGSOC, encompassing its various aspects of intratumor heterogeneity, histological patterns, and its dynamic evolution during progression and therapy resistance.

Diagnostics and treatment of ovarian cancer in the era of precision medicine - opportunities and challenges

Due to predictions of increasing incidences and deaths from ovarian cancer, this neoplasm is a challenge for modern health care. The advent of NGS technology has made it possible to understand the molecular characteristics of many cancers, including ovarian cancer. The data obtained in research became the basis for the development of molecularly targeted therapies thus leading to the entry of NGS analysis into the diagnostic process of oncological patients. This review presents targeted therapies currently in preclinical or clinical trials, whose promising results offer hope for their use in clinical practice in the future. As more therapeutic options emerge, it will be necessary to modify molecular diagnostic regimens to select the best treatment for a given patient. New biomarkers are needed to predict the success of planned therapy. An important aspect of public health is molecular testing in women with a familial predisposition to ovarian cancer enabling patients to be included in prevention programs. NGS technology, despite its high throughput, poses many challenges, from the quality of the diagnostic material used for testing to the interpretation of results and classification of sequence variants. The article highlights the role of molecular testing in ongoing research and also its role in the diagnostic and therapeutic process in the era of personalized medicine. The spread of genetic testing in high-risk groups, the introduction of more targeted therapies and also the possibility of agnostic therapies could significantly improve the health situation for many women worldwide.

Whole Exome Sequencing of Thymoma Patients Exhibiting Exceptional Responses to Pemetrexed Monotherapy

BACKGROUND

Pemetrexed is used for the chemotherapy of advanced thymoma. Exceptional responses of thymoma to pemetrexed treatment are not frequently observed. The underlying genetic mechanism of the exceptional responses remains unclear. We used whole-exome sequencing to explore the specific genomic aberrations that lead to an extreme and durable response.

METHODS

Whole-exome sequencing using NovaSeq6000 (150 bp paired-end sequencing) was performed on nine formalin-fixed paraffin-embedded tissues from patients with advanced thymomas treated with pemetrexed (two exceptional responders and seven typical responders).

RESULTS

We identified 284 somatic single-nucleotide variants (SNVs; 272 missense, 8 missense/splice-site, 3 stop-gain, and 1 stop-gain/splice-site), 34 insertions and deletions (Indels; 33 frameshift and one splice region), and 21 copy number variations (CNVs; 15 gains and six losses). No difference in the number of SNVs variants and distribution of deleterious Indels was observed between the exceptional and typical responders. Interestingly, arm-level chromosomal CNVs (15 gains and six losses) were detected in four patients, including an exceptional responder. The highest number of arm-level CNVs was observed in an exceptional responder.

CONCLUSION

Exceptional responders to pemetrexed for metastatic thymomas may be characterized by arm-level CNVs. Further, whole-genome and RNA sequencing studies should be performed.

High-grade serous ovarian carcinoma organoids as models of chromosomal instability

High-grade serous ovarian carcinoma (HGSOC) is the most genomically complex cancer, characterized by ubiquitous TP53 mutation, profound chromosomal instability, and heterogeneity. The mutational processes driving chromosomal instability in HGSOC can be distinguished by specific copy number signatures. To develop clinically relevant models of these mutational processes we derived 15 continuous HGSOC patient-derived organoids (PDOs) and characterized them using bulk transcriptomic, bulk genomic, single-cell genomic, and drug sensitivity assays. We show that HGSOC PDOs comprise communities of different clonal populations and represent models of different causes of chromosomal instability including homologous recombination deficiency, chromothripsis, tandem-duplicator phenotype, and whole genome duplication. We also show that these PDOs can be used as exploratory tools to study transcriptional effects of copy number alterations as well as compound-sensitivity tests. In summary, HGSOC PDO cultures provide validated genomic models for studies of specific mutational processes and precision therapeutics.

Efficacy and Safety of Weekly Paclitaxel Plus Vistusertib vs Paclitaxel Alone in Patients With Platinum-Resistant Ovarian High-Grade Serous Carcinoma: The OCTOPUS Multicenter, Phase 2, Randomized Clinical Trial

Importance

Patients with platinum-resistant or refractory ovarian high-grade serous carcinoma (PR-HGSC) have a poor prognosis and few therapeutic options. Preclinical studies support targeting PI3K/AKT/mTOR signaling in this setting, and a phase 1 study of the dual mTORC1/mTORC2 inhibitor vistusertib with weekly paclitaxel showed activity.

Objective

To evaluate whether the addition of vistusertib to weekly paclitaxel improves clinical outcomes in patients with PR-HGSC.

Design, Setting, and Participants

This phase 2, double-blind, placebo-controlled multicenter randomized clinical trial recruited patients from UK cancer centers between January 2016 and March 2018. Patients with PR-HGSC of ovarian, fallopian tube, or primary peritoneal origin and with measurable or evaluable disease (Response Evaluation Criteria in Solid Tumors version 1.1 and/or Gynecological Cancer Intergroup cancer antigen 125 criteria) were eligible. There were no restrictions on number of lines of prior therapy. Data analysis was performed from May 2019 to January 2022.

Interventions

Patients were randomized (1:1) to weekly paclitaxel (80 mg/m2 days 1, 8, and 15 of a 28-day cycle) plus oral vistusertib (50 mg twice daily) or placebo.

Main Outcomes and Measures

The primary end point was progression-free survival in the intention-to-treat population. Secondary end points included response rate, overall survival, and quality of life.

Results

A total of 140 patients (median [range] age, 63 [36-86] years; 17.9% with platinum-refractory disease; 53.6% with ≥3 prior therapies) were randomized. In the paclitaxel plus vistusertib vs paclitaxel plus placebo groups, there was no difference in progression-free survival (median, 4.5 vs 4.1 months; hazard ratio [HR], 0.84; 80% CI, 0.67-1.07; 1-sided P = .18), overall survival (median, 9.7 vs 11.1 months; HR, 1.21; 80% CI, 0.91-1.60) or response rate (odds ratio, 0.86; 80% CI, 0.55-1.36). Grade 3 to 4 adverse events were 41.2% (weekly paclitaxel plus vistusertib) vs 36.7% (weekly paclitaxel plus placebo), and there was no difference in quality of life.

Conclusions and Relevance

In this randomized clinical trial of weekly paclitaxel and dual mTORC1/2 inhibition in patients with PR-HGSC, vistusertib did not improve clinical activity of weekly paclitaxel.

Trial Registration

isrctn.org Identifier: ISRCTN16426935.

Advances in small molecule maintenance therapies for high-grade serous ovarian cancer

INTRODUCTION

Ovarian cancer is one of the most lethal gynecological tumors with a lack of effective treatment modalities especially in advanced/recurrent disease. Nevertheless, recently, new small molecules have emerged as an effective approach for the management of ovarian cancer patients, especially in the maintenance setting.

AREAS COVERED

This review summarizes the role of small molecules used in the management of high-grade serous ovarian cancer. The authors performed a critical review of current evidence and ongoing studies. Of note, tyrosine kinase inhibitors (TKIs) and poly(ADP-ribose) polymerase (PARP) inhibitors are the most intriguing medications in this setting.

EXPERT OPINION

Protein-targeted therapies against tumor tissues have progressed significantly in the last years due to an enhanced knowledge of the biological and molecular processes of carcinogenesis. Treatment with small molecules allows the targeting of specific proteins involved in cancer biology. TKIs seem promising but further data are necessary to assess the pros and cons of adopting this treatment modality. PARP inhibitors represent the new standard of care for ovarian cancer patients harboring either a BRCA mutation or with homologous recombination deficiency (HRD). Interestingly, the accumulation of data has highlighted that PARP inhibitors provide benefits even in patients with HR proficient tumors.