bims-tumhet 2022-02-20 papers

Issue of 2022‒02‒20
five papers selected by
Sergio Marchini
Humanitas Research

Cochrane Database Syst Rev. 2022 Feb 16. 2 CD007929

Poly(ADP-ribose) polymerase (PARP) inhibitors for the treatment of ovarian cancer.

Abigail Tattersall, Neil Ryan, Alison J Wiggans, Ewelina Rogozińska, Jo Morrison.

BACKGROUND: Ovarian cancer is the sixth most common cancer in women world-wide. Epithelial ovarian cancer (EOC) is the most common; three-quarters of women present when disease has spread outside the pelvis (stage III or IV). Treatment consists of a combination of surgery and platinum-based chemotherapy. Although initial responses to chemotherapy are good, most women with advanced disease will relapse. PARP (poly (ADP-ribose) polymerase) inhibitors (PARPi), are a type of anticancer treatment that works by preventing cancer cells from repairing DNA damage, especially in those with breast cancer susceptibility gene (BRCA) variants. PARPi offer a different mechanism of anticancer treatment from conventional chemotherapy.OBJECTIVES: To determine the benefits and risks of poly (ADP-ribose) polymerase) inhibitors (PARPi) for the treatment of epithelial ovarian cancer (EOC).
SEARCH METHODS: We identified randomised controlled trials (RCTs) by searching the Cochrane Central Register of Controlled Trials (Central 2020, Issue 10), Cochrane Gynaecological Cancer Group Trial Register, MEDLINE (1990 to October 2020), Embase (1990 to October 2020), ongoing trials on www.controlled-trials.com/rct, www.clinicaltrials.gov, www.cancer.gov/clinicaltrials, the National Research Register (NRR), FDA database and pharmaceutical industry biomedical literature.
SELECTION CRITERIA: We included trials that randomised women with EOC to PARPi with no treatment, or PARPi versus conventional chemotherapy, or PARPi together with conventional chemotherapy versus conventional chemotherapy alone.
DATA COLLECTION AND ANALYSIS: We used standard Cochrane methodology. Two review authors independently assessed whether studies met the inclusion criteria. We contacted investigators for additional data. Outcomes included overall survival (OS), objective response rate (ORR), quality of life (QoL) and rate of adverse events.
MAIN RESULTS: We included 15 studies (6109 participants); four (3070 participants) with newly-diagnosed, advanced EOC and 11 (3039 participants) with recurrent EOC. The studies varied in types of comparisons and evaluated PARPi. Eight studies were judged as at low risk of bias in most of the domains. Quality of life data were generally poorly reported. Below we present six key comparisons. The majority of participants had BRCA mutations, either in their tumour (sBRCAmut) and/or germline (gBRCAmut), or homologous recombination deficiencies (HRD) in their tumours. Newly diagnosed EOC Overall, four studies evaluated the effect of PARPi in newly-diagnosed, advanced EOC. Two compared PARPi with chemotherapy and chemotherapy alone. OS data were not reported. The combination of PARPi with chemotherapy may have little to no difference in progression-free survival (PFS) (two studies, 1564 participants; hazard ratio (HR) 0.82, 95% confidence interval (CI 0).49 to 1.38; very low-certainty evidence)(no evidence of disease progression at 12 months' 63% with PARPi versus 69% for placebo). PARPi with chemotherapy likely increases any severe adverse event (SevAE) (grade 3 or higher) slightly (45%) compared with chemotherapy alone (51%) (two studies, 1549 participants, risk ratio (RR) 1.13, 95% CI 1.07 to 1.20; high-certainty evidence). PARPi combined with chemotherapy compared with chemotherapy alone likely results in little to no difference in the QoL (one study; 744 participants, MD 1.56 95% CI -0.42 to 3.54; moderate-certainty evidence). Two studies compared PARPi monotherapy with placebo as maintenance after first-line chemotherapy in newly diagnosed EOC. PARPi probably results in little to no difference in OS (two studies, 1124 participants; HR 0.81, 95%CI 0.59 to 1.13; moderate-certainty evidence) (alive at 12 months 68% with PARPi versus 62% for placebo). However, PARPi may increase PFS (two studies, 1124 participants; HR 0.42, 95% CI 0.19 to 0.92; low-certainty evidence) (no evidence of disease progression at 12 months' 55% with PARPi versus 24% for placebo). There may be an increase in the risk of experiencing any SevAE (grade 3 or higher) with PARPi (54%) compared with placebo (19%)(two studies, 1118 participants, RR 2.87, 95% CI 1.65 to 4.99; very low-certainty evidence), but the evidence is very uncertain. There is probably a slight reduction in QoL with PARPi, although this may not be clinically significant (one study, 362 participants; MD -3.00, 95%CI -4.48 to -1.52; moderate-certainty evidence). Recurrent, platinum-sensitive EOC Overall, 10 studies evaluated the effect of PARPi in recurrent platinum-sensitive EOC. Three studies compared PARPi monotherapy with chemotherapy alone. PARPi may result in little to no difference in OS (two studies, 331 participants; HR 0.95, 95%CI 0.62 to 1.47; low-certainty evidence) (percentage alive at 36 months 18% with PARPi versus 17% for placebo). Evidence is very uncertain about the effect of PARPi on PFS (three studies, 739 participants; HR 0.88, 95%CI 0.56 to 1.38; very low-certainty evidence)(no evidence of disease progression at 12 months 26% with PARPi versus 22% for placebo). There may be little to no difference in rates of any SevAE (grade 3 or higher) with PARPi (50%) than chemotherapy alone (47%) (one study, 254 participants; RR 1.06, 95%CI 0.80 to 1.39; low-certainty evidence). Four studies compared PARPi monotherapy as maintenance with placebo. PARPi may result in little to no difference in OS (two studies, 560 participants; HR 0.88, 95%CI 0.65 to 1.20; moderate-certainty evidence)(percentage alive at 36 months 21% with PARPi versus 17% for placebo). However, evidence suggests that PARPi as maintenance therapy results in a large PFS (four studies, 1677 participants; HR 0.34, 95% CI 0.28 to 0.42; high-certainty evidence)(no evidence of disease progression at 12 months 37% with PARPi versus 5.5% for placebo). PARPi maintenance therapy may result in a large increase in any SevAE (51%) (grade 3 or higher) than placebo (19%)(four studies, 1665 participants, RR 2.62, 95%CI 1.85 to 3.72; low-certainty evidence). PARPi compared with chemotherapy may result in little or no change in QoL (one study, 229 participants, MD 1.20, 95%CI -1.75 to 4.16; low-certainty evidence). Recurrent, platinum-resistant EOC Two studies compared PARPi with chemotherapy. The certainty of evidence in both studies was graded as very low. Overall, there was minimal information on the QoL and adverse events.
AUTHORS' CONCLUSIONS: PARPi maintenance treatment after chemotherapy may improve PFS in women with newly-diagnosed and recurrent platinum-sensitive EOC; there may be little to no effect on OS, although OS data are immature. Overall, this is likely at the expense of an increase in SevAE. It is disappointing that data on quality of life outcomes are relatively sparse. More research is needed to determine whether PARPi have a role to play in platinum-resistant disease.

DOI: https://doi.org/10.1002/14651858.CD007929.pub4

Epigenetics. 2022 Feb 13. 1-15

Multi-tissue DNA methylation microarray signature is predictive of gene function.

Xiavan Renaldo Roopnarinesingh, Hunter Porter, Cory Giles, Chase Brown, Constantin Georgescu, Jonathan Wren.

Background Transcriptional correlation networks derived from publicly available gene expression microarrays have been previously shown to be predictive of known gene functions, but less is known about the predictive capacity of correlated DNA methylation at CpG sites. Guilt-by-association co-expression methods can adapted for use with DNA methylation when a representative methylation value is created for each gene. We examine how methylation compares to expression in predicting Gene Ontology terms using both co-methylation and traditional machine learning approaches across different types of representative methylation values per gene. Methods We perform guilt-by-association gene function prediction with a suite of models called Methylation Array Network Analysis, using a network of correlated methylation values derived from over 24,000 samples. In generating the correlation matrix, the performance of different methods of collapsing probe-level data effect on the resulting gene function predictions was compared, along with the use of different regions surrounding the gene of interest. Results Using mean comethylation of a given gene to its annotated term had an overall highest prediction macro-AUC of 0.60 using mean gene body methylation, across all Gene Ontology terms. This was increased using the logistic regression approach with the highest macro-AUC of 0.82 using mean gene body methylation, compared to the naive predictor of 0.72. Conclusion Genes correlated in their methylation state are functionally related. Genes clustered in co-methylation space were enriched for chromatin state, PRC2, immune response, and development-related terms.

Keywords: DNA methylation; epigenetics; gene function prediction

DOI: https://doi.org/10.1080/15592294.2022.2036411

J Biomed Inform. 2022 Feb 15. pii: S1532-0464(22)00041-7. [Epub ahead of print] 104025

Sparse modelling of cancer patients' survival based on genomic copy number alterations.

Khaled Alqahtani, Charles C Taylor, Henry M Wood, Arief Gusnanto.

Copy number alterations (CNA) are structural variation in the genome, in which some regions exhibit more or less than the normal two chromosomal copies. This genomic CNA profile provides critical information in tumour progression and is therefore informative for patients' survival. It is currently a statistical challenge to model patients' survival using their genomic CNA profiles while at the same time identify regions in the genome that are associated with patients' survival. Some methods have been proposed, including Cox proportional hazard (PH) model with ridge, lasso, or elastic net penalties. However, these methods do not take the general dependencies between genomic regions into account and produce results that are difficult to interpret. In this paper, we extend the elastic net penalty by introducing additional penalty that takes into account general dependencies between genomic regions. This new model produces smooth parameter estimates while simultaneously performs variable selection via sparse solution. The results indicate that the proposed method shows a better prediction performance than other models in our simulation study, while enabling us to investigate regions in the genome that are associated with the patients' survival with sensible interpretation. We illustrate the method using a real dataset from a lung cancer cohort and simulated data.

Keywords: Copy number alterations; Cox proportional hazard; Lung cancer; Regression; Sparse solution

DOI: https://doi.org/10.1016/j.jbi.2022.104025

Cancers (Basel). 2022 Jan 26. pii: 627. [Epub ahead of print]14(3):

Chemotherapy Side-Effects: Not All DNA Damage Is Equal.

Winnie M C van den Boogaard, Daphne S J Komninos, Wilbert P Vermeij.

Recent advances have increased survival rates of children and adults suffering from cancer thanks to effective anti-cancer therapy, such as chemotherapy. However, during treatment and later in life they are frequently confronted with the severe negative side-effects of their life-saving treatment. The occurrence of numerous features of accelerated aging, seriously affecting quality of life, has now become one of the most pressing problems associated with (pediatric) cancer treatment. Chemotherapies frequently target and damage the DNA, causing mutations or genome instability, a major hallmark of both cancer and aging. However, there are numerous types of chemotherapeutic drugs that are genotoxic and interfere with DNA metabolism in different ways, each with their own biodistribution, kinetics, and biological fate. Depending on the type of DNA lesion produced (e.g., interference with DNA replication or RNA transcription), the organ or cell type inflicted (e.g., cell cycle or differentiation status, metabolic state, activity of clearance and detoxification mechanisms, the cellular condition or micro-environment), and the degree of exposure, outcomes of cancer treatment can largely differ. These considerations provide a conceptual framework in which different classes of chemotherapeutics contribute to the development of toxicities and accelerated aging of different organ systems. Here, we summarize frequently observed side-effects in (pediatric) ex-cancer patients and discuss which types of DNA damage might be responsible.

Keywords: DNA damage; cancer survivors; cancer treatment; chemotherapy; premature aging

DOI: https://doi.org/10.3390/cancers14030627

BMB Rep. 2022 Feb 16. pii: 5562. [Epub ahead of print]

Recent advances in spatially resolved transcriptomics: challenges and opportunities.

Jongwon Lee, Minsu Yoo, Jungmin Choi.

Single-cell RNA sequencing (scRNA-seq) has greatly advanced our understanding of cellular heterogeneity by profiling individual cell transcriptomes. However, cell dissociation from the tissue structure causes a loss of spatial information, which hinders the identification of intercellular communication networks and global transcriptional patterns present in the tissue architecture. To overcome this limitation, novel transcriptomic platforms that preserve spatial information have been actively developed. Significant achievements in imaging technologies have enabled in situ targeted transcriptomic profiling in single cells at singlemolecule resolution. In addition, technologies based on mRNA capture followed by sequencing have made possible profiling of the genome-wide transcriptome at the 55-100 μm resolution. Unfortunately, neither imaging-based technology nor capturebased method elucidates a complete picture of the spatial transcriptome in a tissue. Therefore, addressing specific biological questions requires balancing experimental throughput and spatial resolution, mandating the efforts to develop computational algorithms that are pivotal to circumvent technology-specific limitations. In this review, we focus on the current state-of-the-art spatially resolved transcriptomic technologies, describe their applications in a variety of biological domains, and explore recent discoveries demonstrating their enormous potential in biomedical research. We further highlight novel integrative computational methodologies with other data modalities that provide a framework to derive biological insight into heterogeneous and complex tissue organization.