bims-tumhet 2022-07-31 papers

Issue of 2022–07–31
eight papers selected by
Sergio Marchini, Humanitas Research

Int J Environ Res Public Health. 2022 Jul 14. pii: 8577. [Epub ahead of print]19(14):

Recent Insights into PARP and Immuno-Checkpoint Inhibitors in Epithelial Ovarian Cancer.

Antonios Revythis, Anu Limbu, Christos Mikropoulos, Aruni Ghose, Elisabet Sanchez, Matin Sheriff, Stergios Boussios.

Ovarian cancer is one of the most common gynecologic cancers and has the highest mortality rate of any other cancer of the female reproductive system. Epithelial ovarian cancer (EOC) accounts for approximately 90% of all ovarian malignancies. The standard therapeutic strategy includes cytoreductive surgery accompanied by pre- or postoperative platinum-based chemotherapy. Nevertheless, up to 80% of the patients relapse within the following 12-18 months from the completion of the treatment and then receive first-line chemotherapy depending on platinum sensitivity. Mutations in BRCA1/2 genes are the most significant molecular aberrations in EOC and serve as prognostic and predictive biomarkers. Poly ADP-ribose polymerase (PARP) inhibitors exploit defects in the DNA repair pathway through synthetic lethality. They have also been shown to trap PARP1 and PARP2 on DNA, leading to PARP-DNA complexes. Olaparib, rucaparib, and niraparib have all obtained Food and Drug Administration (FDA) and/or the European Medicine Agency (EMA) approval for the treatment of EOC in different settings. Immune checkpoint inhibitors (ICI) have improved the survival of several cancers and are under evaluation in EOC. However, despite the success of immunotherapy in other malignancies, the use of antibodies inhibiting the immune checkpoint programmed cell death (PD-1) or its ligand (PD-L1) obtained modest results in EOC so far, with median response rates of up to 10%. As such, ICI have not yet been approved for the treatment of EOC. We herein provided a comprehensive insight into the most recent progress in synthetic lethality PARP inhibitors, along with the mechanisms of resistance. We also summarised data regarding the role of immune checkpoint inhibitors, the use of vaccination therapy, and adoptive immunotherapy in treating epithelial ovarian cancer.

Keywords: BRCA mutations; PARP inhibitors; adoptive immunotherapy; homologous recombination deficiency; immune checkpoint inhibitors; ovarian cancer; vaccines

DOI: https://doi.org/10.3390/ijerph19148577

Int J Mol Sci. 2022 Jul 23. pii: 8125. [Epub ahead of print]23(15):

The Molecular Mechanisms of Actions, Effects, and Clinical Implications of PARP Inhibitors in Epithelial Ovarian Cancers: A Systematic Review.

Chien-Hui Lau, Kok-Min Seow, Kuo-Hu Chen.

Ovarian cancer is the most lethal gynecologic malignancy in the United States. Some patients affected by ovarian cancers often present genome instability with one or more of the defects in DNA repair pathways, particularly in homologous recombination (HR), which is strictly linked to mutations in breast cancer susceptibility gene 1 (BRCA 1) or breast cancer susceptibility gene 2 (BRCA 2). The treatment of ovarian cancer remains a challenge, and the majority of patients with advanced-stage ovarian cancers experience relapse and require additional treatment despite initial therapy, including optimal cytoreductive surgery (CRS) and platinum-based chemotherapy. Targeted therapy at DNA repair genes has become a unique strategy to combat homologous recombination-deficient (HRD) cancers in recent years. Poly (ADP-ribose) polymerase (PARP), a family of proteins, plays an important role in DNA damage repair, genome stability, and apoptosis of cancer cells, especially in HRD cancers. PARP inhibitors (PARPi) have been reported to be highly effective and low-toxicity drugs that will tremendously benefit patients with HRD (i.e., BRCA 1/2 mutated) epithelial ovarian cancer (EOC) by blocking the DNA repair pathways and inducing apoptosis of cancer cells. PARP inhibitors compete with NAD+ at the catalytic domain (CAT) of PARP to block PARP catalytic activity and the formation of PAR polymers. These effects compromise the cellular ability to overcome DNA SSB damage. The process of HR, an essential error-free pathway to repair DNA DSBs during cell replication, will be blocked in the condition of BRCA 1/2 mutations. The PARP-associated HR pathway can also be partially interrupted by using PARP inhibitors. Grossly, PARP inhibitors have demonstrated some therapeutic benefits in many randomized phase II and III trials when combined with the standard CRS for advanced EOCs. However, similar to other chemotherapy agents, PARP inhibitors have different clinical indications and toxicity profiles and also face drug resistance, which has become a major challenge. In high-grade epithelial ovarian cancers, the cancer cells under hypoxia- or drug-induced stress have the capacity to become polyploidy giant cancer cells (PGCCs), which can survive the attack of chemotherapeutic agents and start endoreplication. These stem-like, self-renewing PGCCs generate mutations to alter the expression/function of kinases, p53, and stem cell markers, and diploid daughter cells can exhibit drug resistance and facilitate tumor growth and metastasis. In this review, we discuss the underlying molecular mechanisms of PARP inhibitors and the results from the clinical studies that investigated the effects of the FDA-approved PARP inhibitors olaparib, rucaparib, and niraparib. We also review the current research progress on PARP inhibitors, their safety, and their combined usage with antiangiogenic agents. Nevertheless, many unknown aspects of PARP inhibitors, including detailed mechanisms of actions, along with the effectiveness and safety of the treatment of EOCs, warrant further investigation.

Keywords: PARP inhibitor; niraparib; olaparib; ovarian cancer; rucaparib

DOI: https://doi.org/10.3390/ijms23158125

Front Genet. 2022 ;13 906158

Spatial Transcriptomics for Tumor Heterogeneity Analysis.

Qiongyu Li, Xinya Zhang, Rongqin Ke.

The molecular heterogeneity of cancer is one of the major causes of drug resistance that leads to treatment failure. Thus, better understanding the heterogeneity of cancer will contribute to more precise diagnosis and improved patient outcomes. Although single-cell sequencing has become an important tool for investigating tumor heterogeneity recently, it lacks the spatial information of analyzed cells. In this regard, spatial transcriptomics holds great promise in deciphering the complex heterogeneity of cancer by providing localization-indexed gene expression information. This study reviews the applications of spatial transcriptomics in the study of tumor heterogeneity, discovery of novel spatial-dependent mechanisms, tumor immune microenvironment, and matrix microenvironment, as well as the pathological classification and prognosis of cancer. Finally, future challenges and opportunities for spatial transcriptomics technology's applications in cancer are also discussed.

Keywords: gene expression profiling; single-cell sequencing; spatial transcriptomics; tumor heterogeneity; tumor microenvironment

DOI: https://doi.org/10.3389/fgene.2022.906158

Lancet Oncol. 2022 Aug;pii: S1470-2045(22)00139-5. [Epub ahead of print]23(8): e374-e384

Clinical research in ovarian cancer: consensus recommendations from the Gynecologic Cancer InterGroup.

participants of the 6th Gynecologic Cancer InterGroup (GCIG) Ovarian Cancer Consensus Conference on Clinical Research

The Gynecologic Cancer InterGroup (GCIG) sixth Ovarian Cancer Conference on Clinical Research was held virtually in October, 2021, following published consensus guidelines. The goal of the consensus meeting was to achieve harmonisation on the design elements of upcoming trials in ovarian cancer, to select important questions for future study, and to identify unmet needs. All 33 GCIG member groups participated in the development, refinement, and adoption of 20 statements within four topic groups on clinical research in ovarian cancer including first line treatment, recurrent disease, disease subgroups, and future trials. Unanimous consensus was obtained for 14 of 20 statements, with greater than 90% concordance in the remaining six statements. The high acceptance rate following active deliberation among the GCIG groups confirmed that a consensus process could be applied in a virtual setting. Together with detailed categorisation of unmet needs, these consensus statements will promote the harmonisation of international clinical research in ovarian cancer.

DOI: https://doi.org/10.1016/S1470-2045(22)00139-5

Comput Struct Biotechnol J. 2022 ;20 3718-3728

BoBafit : A copy number clustering tool designed to refit and recalibrate the baseline region of tumors' profiles.

G Mazzocchetti, A Poletti, V Solli, E Borsi, M Martello, I Vigliotta, S Armuzzi, B Taurisano, E Zamagni, M Cavo, C Terragna.

Human cancer arises from a population of cells that have acquired a wide range of genetic alterations, most of which are targets of therapeutic treatments or are used as prognostic factors for patient's risk stratification. Among these, copy number alterations (CNAs) are quite frequent. Currently, several molecular biology technologies, such as microarrays, NGS and single-cell approaches are used to define the genomic profile of tumor samples. Output data need to be analyzed with bioinformatic approaches and particularly by employing computational algorithms. Molecular biology tools estimate the baseline region by comparing either the mean probe signals, or the number of reads to the reference genome. However, when tumors display complex karyotypes, this type of approach could fail the baseline region estimation and consequently cause errors in the CNAs call. To overcome this issue, we designed an R-package, BoBafit , able to check and, eventually, to adjust the baseline region, according to both the tumor-specific alterations' context and the sample-specific clustered genomic lesions. Several databases have been chosen to set up and validate the designed package, thus demonstrating the potential of BoBafit to adjust copy number (CN) data from different tumors and analysis techniques. Relevantly, the analysis highlighted that up to 25% of samples need a baseline region adjustment and a redefinition of CNAs calls, thus causing a change in the prognostic risk classification of the patients. We support the implementation of BoBafit within CN analysis bioinformatics pipelines to ensure a correct patient's stratification in risk categories, regardless of the tumor type.

Keywords: BAF, B-allele frequency; Baseline region; Bioinformatic pipeline; Breast cancer; CN, Copy number; CNAs, Copy number alterations; CNVs, Copy Number Variations; CR, Correction Factor; Clustering methods; Copy number alteration; Data correction; F-CL, Final Chromosome List; FISH, Fluorescence In Situ Hybridization; HD, Hyperdiploidy; HR, High Risk; LOH, Loss of Heterozygosity; MM, Multiple Myeloma; Multiple myeloma; NGS, Next Generation Sequencing; R-ISS, Revised International Staging System; S-CL, Starting Chromosome List; SNP, Single-Nucleotide Polymorphism; SR, Standard Risk; WES, Whole Exome Sequencing; WGD, Whole-genome doubling

DOI: https://doi.org/10.1016/j.csbj.2022.06.062

Life (Basel). 2022 Jul 12. pii: 1037. [Epub ahead of print]12(7):

Mass Spectrometry Imaging Spatial Tissue Analysis toward Personalized Medicine.

Juliana P L Gonçalves, Christine Bollwein, Kristina Schwamborn.

Novel profiling methodologies are redefining the diagnostic capabilities and therapeutic approaches towards more precise and personalized healthcare. Complementary information can be obtained from different omic approaches in combination with the traditional macro- and microscopic analysis of the tissue, providing a more complete assessment of the disease. Mass spectrometry imaging, as a tissue typing approach, provides information on the molecular level directly measured from the tissue. Lipids, metabolites, glycans, and proteins can be used for better understanding imbalances in the DNA to RNA to protein translation, which leads to aberrant cellular behavior. Several studies have explored the capabilities of this technology to be applied to tumor subtyping, patient prognosis, and tissue profiling for intraoperative tissue evaluation. In the future, intercenter studies may provide the needed confirmation on the reproducibility, robustness, and applicability of the developed classification models for tissue characterization to assist in disease management.

Keywords: cancer research; mass spectrometry imaging; pathology; personalized medicine; proteomics; tissue typing

DOI: https://doi.org/10.3390/life12071037

Nat Rev Drug Discov. 2022 Jul 29.

A route to rational anticancer combinations.

M Teresa Villanueva.

Keywords: Biotechnology; Cancer; Drug discovery; Personalized medicine; Therapeutics

DOI: https://doi.org/10.1038/d41573-022-00133-y

Ther Adv Med Oncol. 2022 ;14 17588359221113270

Approaches to spatially resolving the tumour immune microenvironment of hepatocellular carcinoma.

Felix Marsh-Wakefield, Angela L Ferguson, Ken Liu, Cositha Santhakumar, Geoffrey McCaughan, Umaimainthan Palendira.

Hepatocellular carcinoma (HCC) is a common and deadly cancer worldwide. Many factors contribute to mortality and place an individual at high risk of developing HCC, including viral infection, alcohol intake, metabolic-associated disease, autoimmunity and genetic liver disorders. Although there are many therapeutics available, much about this disease remains to be understood. This is most evident when investigating the tumour microenvironment (TME). Both innate and adaptive immune cells have been associated with carcinogenesis within the TME of HCC patients. The ability to interrogate the TME more thoroughly with spatial technologies continues to improve, both at the experimental and analytical stages. This review provides insight into technologies available to investigate the TME, and how such technologies are beneficial for improving our understanding of HCC carcinogenesis.

Keywords: hepatocellular carcinoma; histopathology; immunohistochemistry; transcriptomics; tumour microenvironment

DOI: https://doi.org/10.1177/17588359221113270