PARP1 rs1805407 Increases Sensitivity to PARP1 Inhibitors in Cancer Cells Suggesting an Improved Therapeutic Strategy

A plasma peptidomic signature reveals extracellular matrix remodeling and predicts prognosis in alcohol-associated hepatitis

BACKGROUND

Alcohol-associated hepatitis (AH) is plagued with high mortality and difficulty in identifying at-risk patients. The extracellular matrix undergoes significant remodeling during inflammatory liver injury and could potentially be used for mortality prediction.

METHODS

EDTA plasma samples were collected from patients with AH (n = 62); Model for End-Stage Liver Disease score defined AH severity as moderate (12-20; n = 28) and severe (>20; n = 34). The peptidome data were collected by high resolution, high mass accuracy UPLC-MS. Univariate and multivariate analyses identified differentially abundant peptides, which were used for Gene Ontology, parent protein matrisomal composition, and protease involvement. Machine-learning methods were used to develop mortality predictors.

RESULTS

Analysis of plasma peptides from patients with AH and healthy controls identified over 1600 significant peptide features corresponding to 130 proteins. These were enriched for extracellular matrix fragments in AH samples, likely related to the turnover of hepatic-derived proteins. Analysis of moderate versus severe AH peptidomes was dominated by changes in peptides from collagen 1A1 and fibrinogen A proteins. The dominant proteases for the AH peptidome spectrum appear to be CAPN1 and MMP12. Causal graphical modeling identified 3 peptides directly linked to 90-day mortality in >90% of the learned graphs. These peptides improved the accuracy of mortality prediction over the Model for End-Stage Liver Disease score and were used to create a clinically applicable mortality prediction assay.

CONCLUSIONS

A signature based on plasma peptidome is a novel, noninvasive method for prognosis stratification in patients with AH. Our results could also lead to new mechanistic and/or surrogate biomarkers to identify new AH mechanisms.

Insertion/deletion (Indel) variant of the goat RORA gene is associated with growth traits

RAR related orphan receptor A (RORA), which encodes the retinoid-acid-related orphan receptor alpha (RORα), is a clock gene found in skeletal muscle. Several studies have shown that RORα plays an important role in bone formation, suggesting that RORA gene may take part in the regulation of growth and development. The purpose of this research is to study the insertion/deletion (indel) variations of the RORA gene and investigate the relationship with the growth traits of Shaanbei white cashmere (SBWC) goats. Herein, the current study identified that the P4-11-bp and P11-28-bp deletion sites are polymorphic among 12 pairs of primers within the RORA gene in the SBWC goats (n = 641). Moreover, the P11-28-bp deletion locus was significantly related to the body height (p = 0.046), height at hip cross (p = 0.012), and body length (p = 0.003). Both of P4-11-bp and P11-28-bp indels showed the moderate genetic diversity (0.25 Collapse

Key Words

• RAR related orphan receptor A (RORA)
• clock gene
• goat
• growth traits
• insertion/deletion

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MESH Headings

• Pregnancy
• Female
• Animals
• Litter Size/genetics
• Goats/physiology
• INDEL Mutation/genetics
• Phenotype

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Affiliation(s)

Qian Zhou College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
Huina Hu College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
Yuta Yang College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
Yuxin Kang College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
Xianyong Lan College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
Xianfeng Wu Institute of Animal Husbandry and Veterinary, Fujian Academy of Agricultural Sciences, Fuzhou, Fujian, China
Zhengang Guo College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China Animal Husbandry and Veterinary Science Institute of Bijie city, Bijie, Guizhou, China
Chuanying Pan College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China

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Differential Sensitivity of Germline and Somatic BRCA Variants to PARP Inhibitor in High-Grade Serous Ovarian Cancer

PURPOSE

The introduction of PARP inhibitors (PARPis) as a treatment option for patients with high-grade serous ovarian cancer (HGSOC) modified the approach of BRCA testing worldwide. In this study, we aim to evaluate the impact of BRCA1 and BRCA2 variants on treatment response and survival outcomes in patients diagnosed in our institution.

METHODS

A total of 805 HGSOC samples underwent BRCA1 and BRCA2 variant detection by using next-generation sequencing (NGS). Among them, a pathogenic alteration was detected in 104 specimens. Clinicopathological features and germline status were recovered, and alteration types were further characterized. The clinical significance of variant type in terms of response to chemotherapy and to PARPis as well as overall survival were evaluated using univariate analysis.

RESULTS

In our cohort, 13.2% of the HGSOC samples harbored a pathogenic BRCA1 or BRCA2 variant, among which 58.7% were inherited. No difference was observed between germline and somatic variants in terms of the gene altered. Interestingly, patients with somatic variants only (no germline) demonstrated better outcomes under PARPi treatment compared to those with germline ones.

CONCLUSION

The determination of the inheritance or acquisition of BRCA1 and BRCA2 alterations could provide valuable information for improving management strategies and predicting the outcome of patients with HGSOC.

The genetic polymorphisms of immune-related genes contribute to the susceptibility and survival of lymphoma

BACKGROUND

Though immunological abnormalities have been proven involved in the pathogenesis of lymphoma, the underlying mechanism remains unclear.

METHODS

We investigated 25 single nucleotide polymorphisms (SNPs) of 21 immune-related genes and explored their roles in lymphoma. The genotyping assay of the selected SNPs was used by the Massarray platform. Logistic regression and Cox proportional hazards models were used to analyze the associations of SNPs and the susceptibility of lymphoma or clinical characteristics of lymphoma patients. In addition, Least Absolute Shrinkage and Selection Operator regression was used to further analyze the relationships with the survival of lymphoma patients and candidate SNPs, and the significant difference between genotypes was verified by the expression of RNA.

RESULTS

By comparing 245 lymphoma patients with 213 healthy controls, we found eight important SNPs related to the susceptibility of lymphoma, which were involved in JAK-STAT, NF-κB and other functional pathways. We further analyzed the relationships between SNPs and clinical characteristics. Our results showed that both IL6R (rs2228145) and STAT5B (rs6503691) significantly contributed to the Ann Arbor stages of lymphoma. And the STAT3 (rs744166), IL2 (rs2069762), IL10 (rs1800871), and PARP1 (rs907187) manifested a significant relationship with the peripheral blood counts in lymphoma patients. More importantly, the IFNG (rs2069718) and IL12A (rs6887695) were associated with the overall survival (OS) of lymphoma patients remarkably, and the adverse effects of GC genotypes could not be offset by Bonferroni correction for multiple comparison in rs6887695 especially. Moreover, we determined that the mRNA expression levels of IFNG and IL12A were significantly decreased in patients with shorter-OS genotypes.

CONCLUSIONS

We used multiple methods of analysis to predict the correlations between lymphoma susceptibility, clinical characteristics or OS with SNPs. Our findings reveal that immune-related genetic polymorphisms contribute to the prognosis and treatment of lymphoma, which may serve as promising predictive targets.

Association of haplotype and linkage disequilibrium of PARP1 polymorphisms rs1136410, rs1805405 and rs3219088 with gallbladder cancer

BACKGROUND

Previously, we have reported that PARP1 rs1136410 is significantly associated with increased the risk of gallbladder cancer.

AIM

We aimed to investigate the association of PARP1 rs1805405 and rs3219088 polymorphisms with risk of GBC and also association of the haplotype and combined effect of PARP1 SNPs (rs1805405 G/A, rs3219088 G/T and rs1136410 A/G). We have also investigated the expression profile of PARP1 and its correlation with polymorphisms, clinical parameters and overall survival.

METHODS

PARP1 polymorphisms were genotyped by PCR-RFLP and the expression profile of PARP1 at mRNA level was analyzed by semi-quantitative PCR. Overall survival was analyzed using Kaplan-Meier plot and Cox-regression analysis.

RESULTS

Haplotype analysis of the PARP1 polymorphisms revealed that AGG, AAG and GGT haplotypes are significantly associated with decreased risk of GBC, while AAT, AGT, GGG and GAG haplotypes are significantly associated with increased risk of GBC. Patients with T1+T2 and treated with chemotherapy having risk genotypes of rs1805405 have decreased overall survival. Upregulation of PARP1 is significantly associated with longer overall survival in patients with GBC with different clinical parameters. SNPs rs1136410 and rs1805405 genotypes are significantly associated with PARP1 expression.

CONCLUSION

Haplotype analysis suggests that PARP1 may have a potential role in gallbladder carcinogenesis.

Indel mutations of sheep PLAG1 gene and their associations with growth traits

Pleiomorphic adenoma gene 1 (PLAG1) is mainly expressed in embryonic development, and it is reported to take an effect on the growth performance of mice, cattle, pigs, and sheep. To explore how conservative the PLAG1 is in different sheep breeds, the effects of the two indel variants on the growth traits of the Chinese Luxi blackhead (LXBH) sheep were firstly detected. The P2-del 30 bp and P4-del 45 bp indel loci of the sheep PLAG1 gene were significantly related to 15 growth traits (P < 0.05). Genotype ID and genotype II were dominant for the P2-del 30 bp and P4-del 45 bp loci, respectively. The above findings indicated that the two indel mutations in the ovine PLAG1 gene were suggested to become the molecular markers for the selection of economic traits in sheep.

Causal Discovery in High-dimensional, Multicollinear Datasets

As the cost of high-throughput genomic sequencing technology declines, its application in clinical research becomes increasingly popular. The collected datasets often contain tens or hundreds of thousands of biological features that need to be mined to extract meaningful information. One area of particular interest is discovering underlying causal mechanisms of disease outcomes. Over the past few decades, causal discovery algorithms have been developed and expanded to infer such relationships. However, these algorithms suffer from the curse of dimensionality and multicollinearity. A recently introduced, non-orthogonal, general empirical Bayes approach to matrix factorization has been demonstrated to successfully infer latent factors with interpretable structures from observed variables. We hypothesize that applying this strategy to causal discovery algorithms can solve both the high dimensionality and collinearity problems, inherent to most biomedical datasets. We evaluate this strategy on simulated data and apply it to two real-world datasets. In a breast cancer dataset, we identified important survival-associated latent factors and biologically meaningful enriched pathways within factors related to important clinical features. In a SARS-CoV-2 dataset, we were able to predict whether a patient (1) had Covid-19 and (2) would enter the ICU. Furthermore, we were able to associate factors with known Covid-19 related biological pathways.

Essential Regression: A generalizable framework for inferring causal latent factors from multi-omic datasets

High-dimensional cellular and molecular profiling of biological samples highlights the need for analytical approaches that can integrate multi-omic datasets to generate prioritized causal inferences. Current methods are limited by high dimensionality of the combined datasets, the differences in their data distributions, and their integration to infer causal relationships. Here, we present Essential Regression (ER), a novel latent-factor-regression-based interpretable machine-learning approach that addresses these problems by identifying latent factors and their likely cause-effect relationships with system-wide outcomes/properties of interest. ER can integrate many multi-omic datasets without structural or distributional assumptions regarding the data. It outperforms a range of state-of-the-art methods in terms of prediction. ER can be coupled with probabilistic graphical modeling, thereby strengthening the causal inferences. The utility of ER is demonstrated using multi-omic system immunology datasets to generate and validate novel cellular and molecular inferences in a wide range of contexts including immunosenescence and immune dysregulation.

Neurological Complications Acquired During Pediatric Critical Illness: Exploratory "Mixed Graphical Modeling" Analysis Using Serum Biomarker Levels

OBJECTIVES

Neurologic complications, consisting of the acute development of a neurologic disorder, that is, not present at admission but develops during the course of illness, can be difficult to detect in the PICU due to sedation, neuromuscular blockade, and young age. We evaluated the direct relationships of serum biomarkers and clinical variables to the development of neurologic complications. Analysis was performed using mixed graphical models, a machine learning approach that allows inference of cause-effect associations from continuous and discrete data.

DESIGN

Secondary analysis of a previous prospective observational study.

SETTING

PICU, single quaternary-care center.

PATIENTS

Individuals admitted to the PICU, younger than18 years old, with intravascular access via an indwelling catheter.

INTERVENTIONS

None.

MEASUREMENTS

About 101 patients were included in this analysis. Serum (days 1-7) was analyzed for glial fibrillary acidic protein, ubiquitin C-terminal hydrolase-L1, and alpha-II spectrin breakdown product 150 utilizing enzyme-linked immunosorbent assays. Serum levels of neuron-specific enolase, myelin basic protein, and S100 calcium binding protein B used in these models were reported previously. Demographic data, use of selected clinical therapies, lengths of stay, and ancillary neurologic testing (head CT, brain MRI, and electroencephalogram) results were recorded. The Mixed Graphical Model-Fast-Causal Inference-Maximum algorithm was applied to the dataset.

MAIN RESULTS

About 13 of 101 patients developed a neurologic complication during their critical illness. The mixed graphical model identified peak levels of the neuronal biomarker neuron-specific enolase and ubiquitin C-terminal hydrolase-L1, and the astrocyte biomarker glial fibrillary acidic protein to be the direct causal determinants for the development of a neurologic complication; in contrast, clinical variables including age, sex, length of stay, and primary neurologic diagnosis were not direct causal determinants.

CONCLUSIONS

Graphical models that include biomarkers in addition to clinical data are promising methods to evaluate direct relationships in the development of neurologic complications in critically ill children. Future work is required to validate and refine these models further, to determine if they can be used to predict which patients are at risk for/or with early neurologic complications.

Association of T2285C polymorphism in PARP1 gene coding region with its expression, activity and NSCLC risk along with prognosis

Poly (ADP-ribose) polymerase-1(PARP1), a DNA repair gene, is the crucial player in the maintenance of genome integrity. T2285C polymorphism in coding region of PARP1 has been reported to be associated with susceptibility to tumors. We explored the relation and mechanism of T2285C polymorphism of PARP1 to its expression and activity along with risk and prognosis in NSCLC. mRNA expression was measured using qRT-PCR assay or collected from TCGA dataset. Protein expression was examined with immunoblotting assay. Genotypes were determined by PCR-RFLP and sequencing approaches. PARP1 activity was determined with enzyme activity assay. Regulation of SIRT7 to PARP1 were determined by over-expression and small interference experiment. Association of PARP1 T2285C polymorphism with NSCLC risk was evaluated via multiple logistic regression analysis. Comparison of treatment response and PFS of NSCLC patients among different genotypes or regimens was made by Chi-square test. Results indicated that mRNA and protein expression of PARP1 dramatically increased in NSCLC tissues in comparison to paired para-carcinoma tissues (P<0.05). TC/CC mutant genotypes were associated with markedly enhanced PARP1 mRNA level compared with TT genotype (P=0.011). No significant difference was discovered in PARP1 protein expression among TT, TC or CC genotypes (P>0.05). Subjects with variant allele C had higher risk of NSCLC in comparison to allele T carriers [odds ratio (OR) =1.560; P=0.000]. NSCLC patients carrying mutational TC or CC genotypes were correlated with unfavorable response to platinum-based chemotherapy (TT vs.TC vs.CC, P=0.010), and shorter PFS compared to TT genotype (TT vs.TC vs.CC, P=0.009). T2285C mutation of PARP1 resulted in the enhancement of its mRNA, but the decrease of enzyme activity in tumor cell. Overexpression of SIRT7 attenuated PARP1 expression and activity. These findings suggest the variant allele C of T2285C polymorphism of PARP1 linked to an increase of NSCLC risk, and unfavorable efficacy and prognosis of NSCLC patients with platinum-based chemotherapy, which might be associated with enhancement of its mRNA expression and the diminishment of activity. Identification of PARP1 T2285C polymorphism and mRNA expression may be the promising way for the individualized treatment of NSCLC.

A Pipeline for Integrated Theory and Data-Driven Modeling of Biomedical Data

Genome sequencing technologies have the potential to transform clinical decision making and biomedical research by enabling high-throughput measurements of the genome at a granular level. However, to truly understand mechanisms of disease and predict the effects of medical interventions, high-throughput data must be integrated with demographic, phenotypic, environmental, and behavioral data from individuals. Further, effective knowledge discovery methods must infer relationships between these data types. We recently proposed a pipeline (CausalMGM) to achieve this. CausalMGM uses probabilistic graphical models to infer the relationships between variables in the data; however, CausalMGM's graphical structure learning algorithm can only handle small datasets efficiently. We propose a new methodology (piPref-Div) that selects the most informative variables for CausalMGM, enabling it to scale. We validate the efficacy of piPref-Div against other feature selection methods and demonstrate how the use of the full pipeline improves breast cancer outcome prediction and provides biologically interpretable views of gene expression data.

Tanshinone&nbsp;I and simvastatin inhibit melanoma tumour cell growth by regulating poly (ADP ribose) polymerase&nbsp;1 expression

Melanoma is one of the most aggressive forms of skin tumour with poor prognosis; no effective therapy has been established for melanoma at the metastatic stage. The present study aimed to investigate the role of poly (ADP ribose) polymerase (PARP) inhibitors (PARPis) and PARP1 expression in melanoma progression. In addition, whether high PARP1 expression was associated with poor overall survival in melanoma, and whether a combination effect existed between PARPis and other anti-tumour compounds (e.g., sunitinib) was analysed. The PARP1 expression was detected using western blot analysis and the proliferation of cells was detected with a colony formation assay. In addition, cell viability assays and xenograft tumor experiments were conducted. The results of the present study demonstrated that sunitinib reduced PARP1 expression and proliferation of melanoma cells. Notably, one of the PARPis, veliparib, reversed the inhibitory effect of sunitinib on PARP1 expression and proliferation, indicating that inhibition of PARP1 enzyme activity by PARPi may be different from the inhibition of PARP1 expression in melanoma cell biological function. To further confirm the relationship between PARP1 expression and tumour cell proliferation, seven compounds, including common approved drugs and natural Chinese medicine monomers, were screened, and the results demonstrated that simvastatin and tanshinone I exerted an inhibitory effect on PARP1 expression and melanoma cell proliferation, and their combination was more effective than simvastatin alone in vivo. The results indicated that simvastatin and tanshinone I inhibited melanoma and renal tumour cells by regulating PARP1 expression, and in addition to the enzyme activity of PARP1, the expression of PARP1 protein may serve a role in tumour progression.

ERCC1, PARP-1, and AQP1 as predictive biomarkers in colon cancer patients receiving adjuvant chemotherapy

BACKGROUND

The recognition of high-risk colon cancer patients prone to chemoresistant and recurrent disease is a challenge.

OBJECTIVES

We aimed to assess the immunohistochemical expression of ERCC1, PARP-1, and AQP1 in 60 cases of stage II and III colon cancer who underwent curative resection and adjuvant chemotherapy. Their predictive role of tumor progression and disease-free survival (DFS) was analyzed.

METHODS

The immunohistochemical expression of ERCC1, PARP-1, and AQP1 in 60 cases of stage II and III colon cancer who underwent curative resection and adjuvant chemotherapy was studied. The collected data on the overall survival (OS), disease-free survival (DFS), and the response to the chemotherapy were analyzed.

RESULTS

Positive nuclear ERCC1 expression was identified in 58.3% of the patients, ERCC1 expression was significantly associated with left-sided tumors (P< 0.01). Moreover, its expression was significantly associated with the aggressive tumor characteristics including high grade, lymph node metastasis and advanced tumor stage (P< 0.001 for each). High nuclear PARP-1 expression was observed in 63.3% of the cases, and its expression was significantly associated with tumor grade and lymph node metastasis (P= 0.003 for each). Positive membranous AQP1 expression was identified in 41.7% of patients, and it was associated with high grade, lymph node metastasis and advanced tumor stage (P< 0.001 for each). During the follow-up period, 23 patients (38.3%) exhibited a tumor progression; this was significantly associated with positive ERCC1, high PARP-1, and negative AQP1 expression. Statistics of the survival data revealed that shorter DFS was significantly associated with positive ERCC1, high PARP-1, and positive AQP1 expression (P= 0.005, 0.016, 0.002, respectively).

CONCLUSIONS

ERCC1, PARP1, and AQP1 are adverse prognostic biomarkers in stage II-III colon cancer. Moreover, adjuvant chemotherapy may not be beneficial for patients with positive ERCC1, high PARP1, and AQP1-negative tumors. Therefore, we recommend that ERCC1, PARP-1, and AQP1 should be assessed during the selection of the treatment strategy for stage II-III colon cancer patients.

CausalMGM: an interactive web-based causal discovery tool

High-throughput sequencing and the availability of large online data repositories (e.g. The Cancer Genome Atlas and Trans-Omics for Precision Medicine) have the potential to revolutionize systems biology by enabling researchers to study interactions between data from different modalities (i.e. genetic, genomic, clinical, behavioral, etc.). Currently, data mining and statistical approaches are confined to identifying correlates in these datasets, but researchers are often interested in identifying cause-and-effect relationships. Causal discovery methods were developed to infer such cause-and-effect relationships from observational data. Though these algorithms have had demonstrated successes in several biomedical applications, they are difficult to use for non-experts. So, there is a need for web-based tools to make causal discovery methods accessible. Here, we present CausalMGM (http://causalmgm.org/), the first web-based causal discovery tool that enables researchers to find cause-and-effect relationships from observational data. Web-based CausalMGM consists of three data analysis tools: (i) feature selection and clustering; (ii) automated identification of cause-and-effect relationships via a graphical model; and (iii) interactive visualization of the learned causal (directed) graph. We demonstrate how CausalMGM enables an end-to-end exploratory analysis of biomedical datasets, giving researchers a clearer picture of its capabilities.

Association of PARP1 polymorphisms with response to chemotherapy in patients with high-risk neuroblastoma

The genetic aetiology and the molecular mechanisms that characterize high‐risk neuroblastoma are still little understood. The majority of high‐risk neuroblastoma patients do not take advantage of current induction therapy. So far, one of the main reasons liable for cancer therapeutic failure is the acquisition of resistance to cytotoxic anticancer drugs, because of the DNA repair system of tumour cells. PARP1 is one of the main DNA damage sensors involved in the DNA repair system and genomic stability. We observed that high PARP1 mRNA level is associated with unfavourable prognosis in 3 public gene expression NB patients’ datasets and in 20 neuroblastomas analysed by qRT‐PCR. Among 4983 SNPs in PARP1, we selected two potential functional SNPs. We investigated the association of rs907187, in PARP1 promoter, and rs2048426 in non‐coding region with response chemotherapy in 121 Italian patients with high‐risk NB. Results showed that minor G allele of rs907187 associated with induction response of patients (P = .02) and with decrease PARP1 mRNA levels in NB cell line (P = .003). Furthermore, rs907187 was predicted to alter the binding site of E2F1 transcription factor. Specifically, allele G had low binding affinity with E2F1 whose expression positively correlates with PARP1 expression and associated with poor prognosis of patients with NB. By contrast, we did not find genetic association for the SNP rs2048426. These data reveal rs907187 as a novel potential risk variant associated with the failure of induction therapy for high‐risk NB.

An 11-bp Indel Polymorphism within the CSN1S1 Gene Is Associated with Milk Performance and Body Measurement Traits in Chinese Goats

The casein alpha s1 (CSN1S1) gene encodes α-s1 casein, one of the proteins constituting milk, which affects milk performance, as well as improving the absorption of calcium and bone development in mammals. A previous study found that an 11-bp insertion/deletion (indel) of this gene strongly affected litter size in goats. However, to our knowledge, the relationships between this polymorphism and the milk performance and body measurement traits of goats have not been reported. In this paper, the previously identified indel has been recognized in three Chinese goat breeds, namely the Guanzhong dairy goat (GZDG; n = 235), Shaanbei white cashmere goat (SBWC; n = 1092), and Hainan black goat (HNBG; n = 278), and the following three genotypes have been studied for all of the breeds: insertion/insertion (II), deletion/deletion (DD), and insertion/deletion (ID). The allele frequencies analyzed signified that the frequencies of the "D" allele were higher (47.8%-65.5%), similar to the previous report, which indicates that this polymorphism is genetically stable in different goat breeds. Further analysis showed that this indel was markedly associated with milk fat content, total solids content, solids-not-fat content, freezing point depression, and acidity in GZDG (p < 0.05), and also affected different body measurement traits in all three breeds (p < 0.05). The goats with II genotypes had superior milk performance, compared with the others; however, goats with DD genotypes had better body measurement sizes. Hence, it may be necessary to select goats with an II or DD genotype, based on the desired traits, while breeding. Our study provides information on the potential impact of the 11-bp indel polymorphism of the CSN1S1 gene for improving the milk performance and body measurement traits in goats.

PARP Inhibitor PJ34 Protects Mitochondria and Induces DNA-Damage Mediated Apoptosis in Combination With Cisplatin or Temozolomide in B16F10 Melanoma Cells

PARP-1 inhibition has recently been employed in both mono- and combination therapies in various malignancies including melanoma with both promising and contradicting results reported. Although deeper understanding of the underlying molecular mechanisms may help improving clinical modalities, the complex cellular effects of PARP inhibitors make disentangling of the mechanisms involved in combination therapies difficult. Here, we used two cytostatic agents used in melanoma therapies in combination with PARP inhibition to have an insight into cellular events using the B16F10 melanoma model. We found that, when used in combination with cisplatin or temozolomide, pharmacologic blockade of PARP-1 by PJ34 augmented the DNA-damaging and cytotoxic effects of both alkylating compounds. Interestingly, however, this synergism unfolds relatively slowly and is preceded by molecular events that are traditionally believed to support cell survival including the stabilization of mitochondrial membrane potential and morphology. Our data indicate that the PARP inhibitor PJ34 has, apparently, opposing effects on the mitochondrial structure and cell survival. While, initially, it stimulates mitochondrial fusion and hyperpolarization, hallmarks of mitochondrial protection, it enhances the cytotoxic effects of alkylating agents at later stages. These findings may contribute to the optimization of PARP inhibitor-based antineoplastic modalities.

Feasibility of lung cancer prediction from low-dose CT scan and smoking factors using causal models

Introduction

Low-dose CT (LDCT) is currently used in lung cancer screening of high-risk populations for early lung cancer diagnosis. However, 96% of individuals with detected nodules are false positives.

Methods

In order to develop an efficient early lung cancer predictor from clinical, demographic and LDCT features, we studied a total of 218 subjects with lung cancer or benign nodules. Probabilistic graphical models (PGMs) were used to integrate demographics, clinical data and LDCT features from 92 subjects (training cohort) from the Pittsburgh Lung Screening Study cohort.

Results

Learnt PGMs identified three variables directly (causally) linked to malignant nodules and the largest benign nodule and used them to build the Lung Cancer Causal Model (LCCM), which was validated in a separate cohort of 126 subjects. Nodule and vessel numbers and years since the subject quit smoking were sufficient to discriminate malignant from benign nodules. Comparison with existing predictors in the training and validation cohorts showed that (1) incorporating LDCT scan features greatly enhances predictive accuracy; and (2) LCCM improves cancer detection over existing methods, including the Brock parsimonious model (p<0.001). Notably, the number of surrounding vessels, a feature not previously used in predictive models, significantly improves predictive efficiency. Based on the validation cohort results, LCCM is able to identify 30% of the benign nodules without risk of misclassifying cancer nodules.

Discussion

LCCM shows promise as a lung cancer predictor as it is significantly improved over existing models. Validated in a larger, prospective study, it may help reduce unnecessary follow-up visits and procedures.