Genomics-Enabled Precision Medicine for Cancer

Sequencing of high-frequency mutated genes in breast cancer (BRCA) and associated-functions analysis

OBJECTIVE

Mutations or aberrant expression of genes in an organism tend not to be completely random and this cumulative effect predisposes to the development of malignant tumours. This study aims to reveal the possible aberrant expression of high frequency mutated genes, and then to investigate their role in development, prognosis, signalling pathway function and drug resistance in breast cancer.

METHODS

The mutated genes in breast cancer (BRCA) clinical samples were identified and detected by high-throughput sequencing. High-frequency mutant genes were counted. Gene expression profiles and the relationship with prognosis were analysed throughout TCGA database. qRT-PCR was used to analyse the mRNA levels of the six high-frequency mutant genes in BRCA tissues and cell lines. IHC was used to analyse the protein levels of the six high-frequency mutant genes in BRCA tissues. The linear interaction, single-cell layer clustering status and the influence in immune cell infiltration degree among these six high-frequency mutant genes were analysed by bioinformatics analysis. The STITCH and cMAP datasets were used for high-frequency mutant gene interaction networks, association signalling pathway enrichment and drug-transcriptome analyses. The effects of trastuzumab on the proliferative capacity of breast cancer cells, as well as on the expression of six high-frequency mutated genes were determined by CCK8 assay.

RESULTS

The genes that were statistically found to have high-frequency mutations in the samples recruited in the present study by high-throughput sequencing analysis included TP53, PIK3CA, NF1, TBX3, BRCA1 and BRCA2. The expression profiles of these genes and the correlation with prognosis were further demonstrated using the TCGA database: the trend in this study was similar to that of BRCA in TCGA. The mRNA and protein expression of these genes showed that the expression of TP53, NF1, TBX3, BRCA1 and BRCA2 was higher in tumor samples than that in normal samples, with an opposite trend for PIK3CA, a similar trend was observed in BRCA cell lines. The protein expressions of TP53, NF1, TBX3, BRCA1 and BRCA2 displayed the same trend by IHC. Other correlation results include 1) the single cell layer clustering of these six genes resulted in significant clustering with few overlapping regions; 2) these six genes showed different degrees of influence on BRCA immune cell infiltration; 3) these six genes showed a significant correlation between each other; 4) the network of each gene had partially overlapping molecules; and 5) the PI3K pathway was a key association pathway in BRCA. Finally, the cell proliferation ability results confirmed the optimal concentration of trastuzumab and its effect on mRNA expression of these six genes.

High Expression of Fas-Associated Factor 1 Indicates a Poor Prognosis in Non-Small-Cell Lung Cancer

Fas-associated factor 1 (FAF1) is a death-promoting protein identified as an interaction partner of the death receptor Fas. The downregulation and mutation of FAF1 have been reported in a variety of human tumors, but there have been few studies on lung cancer. Here, we investigated the prognostic significance of FAF1 expression in non-small-cell lung cancer (NSCLC), and whether aberrant FAF1 expression may be involved in the pathogenesis and prognosis of NSCLC. FAF1 expression was examined in NSCLC specimens as well as human lung cancer cell lines. In addition, changes in cell viability and apoptosis upon regulating FAF1 expression were investigated in lung cancer cell lines. As a result, high FAF1 expression was significantly associated with a poor prognosis in NSCLC. In lung cancer cell lines, FAF1 downregulation hindered cell viability and tended to promote early apoptosis. In conclusion, this is the first study of the clinical significance of FAF1 in NSCLC, showing that FAF1 overexpression is associated with a poor prognosis in NSCLC and that FAF1 acts as a dangerous factor rather than an apoptosis promoter in NSCLC.

Multi-omics strategies for personalized and predictive medicine: past, current, and future translational opportunities

Precision medicine is driven by the paradigm shift of empowering clinicians to predict the most appropriate course of action for patients with complex diseases and improve routine medical and public health practice. It promotes integrating collective and individualized clinical data with patient specific multi-omics data to develop therapeutic strategies, and knowledgebase for predictive and personalized medicine in diverse populations. This study is based on the hypothesis that understanding patient's metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic and predictive biomarkers and optimal paths providing personalized care for diverse and targeted chronic, acute, and infectious diseases. This study briefs emerging significant, and recently reported multi-omics and translational approaches aimed to facilitate implementation of precision medicine. Furthermore, it discusses current grand challenges, and the future need of Findable, Accessible, Intelligent, and Reproducible (FAIR) approach to accelerate diagnostic and preventive care delivery strategies beyond traditional symptom-driven, disease-causal medical practice.

Clinical implementation of an oncology-specific family health history risk assessment tool

BACKGROUND

The presence of hereditary cancer syndromes in cancer patients can have an impact on current clinical care and post-treatment prevention and surveillance measures. Several barriers inhibit identification of hereditary cancer syndromes in routine practice. This paper describes the impact of using a patient-facing family health history risk assessment platform on the identification and referral of breast cancer patients to genetic counselling services.

METHODS

This was a hybrid implementation-effectiveness study completed in breast cancer clinics. English-literate patients not previously referred for genetic counselling and/or gone through genetic testing were offered enrollment. Consented participants were provided educational materials on family health history collection, entered their family health history into the platform and completed a satisfaction survey. Upon completion, participants and their clinicians were given personalized risk reports. Chart abstraction was done to identify actions taken by patients, providers and genetic counsellors.

RESULTS

Of 195 patients approached, 102 consented and completed the study (mean age 55.7, 100 % women). Sixty-six (65 %) met guideline criteria for genetic counseling of which 24 (36 %) were referred for genetic counseling. Of those referred, 13 (54 %) participants attended and eight (33 %) completed genetic testing. On multivariate logistic regression, referral was not associated with age, cancer stage, or race but was associated with clinical provider (p = 0.041). Most providers (71 %) had higher referral rates during the study compared to prior. The majority of participants found the experience useful (84 %), were more aware of their health risks (83 %), and were likely to recommend using a patient-facing platform to others (69 %).

CONCLUSIONS

65 % of patients attending breast cancer clinics in this study are at-risk for hereditary conditions based on current guidelines. Using a patient-facing risk assessment platform enhances the ability to identify these patients systematically and with widespread acceptability and recognized value by patients. As only a third of at-risk participants received referrals for genetic counseling, further understanding barriers to referral is needed to optimize hereditary risk assessment in oncology practices.

TRIAL REGISTRATION

NIH Clinical Trials registry, NCT04639934 . Registered Nov 23, 2020 -- Retrospectively registered.

Practical Considerations and Challenges for Germline Genetic Testing in Patients With Prostate Cancer: Recommendations From the Germline Genetics Working Group of the PCCTC

Germline genetic testing is now routinely recommended for patients with prostate cancer (PCa) because of expanded guidelines and options for targeted treatments. However, integrating genetic testing into oncology and urology clinical workflows remains a challenge because of the increased number of patients with PCa requiring testing and the limited access to genetics providers. This suggests a critical unmet need for genetic services outside of historical models. This review addresses current guidelines, considerations, and challenges for PCa genetic testing and offers a practical guide for genetic counseling and testing delivery, with solutions to help address potential barriers and challenges for both providers and patients. As genetic and genomic testing become integral to PCa care, developing standardized systems for implementation in the clinic is essential for delivering precision oncology to patients with PCa and realizing the full scope and impact of genetic testing.

Chronic inflammation: key player and biomarker-set to predict and prevent cancer development and progression based on individualized patient profiles

A strong relationship exists between tumor and inflammation, which is the hot point in cancer research. Inflammation can promote the occurrence and development of cancer by promoting blood vessel growth, cancer cell proliferation, and tumor invasiveness, negatively regulating immune response, and changing the efficacy of certain anti-tumor drugs. It has been demonstrated that there are a large number of inflammatory factors and inflammatory cells in the tumor microenvironment, and tumor-promoting immunity and anti-tumor immunity exist simultaneously in the tumor microenvironment. The typical relationship between chronic inflammation and tumor has been presented by the relationships between Helicobacter pylori, chronic gastritis, and gastric cancer; between smoking, development of chronic pneumonia, and lung cancer; and between hepatitis virus (mainly hepatitis virus B and C), development of chronic hepatitis, and liver cancer. The prevention of chronic inflammation is a factor that can prevent cancer, so it effectively inhibits or blocks the occurrence, development, and progression of the chronic inflammation process playing important roles in the prevention of cancer. Monitoring of the causes and inflammatory factors in chronic inflammation processes is a useful way to predict cancer and assess the efficiency of cancer prevention. Chronic inflammation-based biomarkers are useful tools to predict and prevent cancer.