Assessment of variation in immunosuppressive pathway genes reveals TGFBR2 to be associated with prognosis of estrogen receptor-negative breast cancer after chemotherapy

Association of germline genetic variants with breast cancer-specific survival in patient subgroups defined by clinic-pathological variables related to tumor biology and type of systemic treatment

BACKGROUND

Given the high heterogeneity among breast tumors, associations between common germline genetic variants and survival that may exist within specific subgroups could go undetected in an unstratified set of breast cancer patients.

METHODS

We performed genome-wide association analyses within 15 subgroups of breast cancer patients based on prognostic factors, including hormone receptors, tumor grade, age, and type of systemic treatment. Analyses were based on 91,686 female patients of European ancestry from the Breast Cancer Association Consortium, including 7531 breast cancer-specific deaths over a median follow-up of 8.1 years. Cox regression was used to assess associations of common germline variants with 15-year and 5-year breast cancer-specific survival. We assessed the probability of these associations being true positives via the Bayesian false discovery probability (BFDP < 0.15).

RESULTS

Evidence of associations with breast cancer-specific survival was observed in three patient subgroups, with variant rs5934618 in patients with grade 3 tumors (15-year-hazard ratio (HR) [95% confidence interval (CI)] 1.32 [1.20, 1.45], P = 1.4E-08, BFDP = 0.01, per G allele); variant rs4679741 in patients with ER-positive tumors treated with endocrine therapy (15-year-HR [95% CI] 1.18 [1.11, 1.26], P = 1.6E-07, BFDP = 0.09, per G allele); variants rs1106333 (15-year-HR [95% CI] 1.68 [1.39,2.03], P = 5.6E-08, BFDP = 0.12, per A allele) and rs78754389 (5-year-HR [95% CI] 1.79 [1.46,2.20], P = 1.7E-08, BFDP = 0.07, per A allele), in patients with ER-negative tumors treated with chemotherapy.

CONCLUSIONS

We found evidence of four loci associated with breast cancer-specific survival within three patient subgroups. There was limited evidence for the existence of associations in other patient subgroups. However, the power for many subgroups is limited due to the low number of events. Even so, our results suggest that the impact of common germline genetic variants on breast cancer-specific survival might be limited.

DAGM: A novel modelling framework to assess the risk of HER2-negative breast cancer based on germline rare coding mutations

BACKGROUND

Breast cancers can be divided into HER2-negative and HER2-positive subtypes according to different status of HER2 gene. Despite extensive studies connecting germline mutations with possible risk of HER2-negative breast cancer, the main category of breast cancer, it remains challenging to obtain accurate risk assessment and to understand the potential underlying mechanisms.

METHODS

We developed a novel framework named Damage Assessment of Genomic Mutations (DAGM), which projects rare coding mutations and gene expressions into Activity Profiles of Signalling Pathways (APSPs).

FINDINGS

We characterized and validated DAGM framework at multiple levels. Based on an input of germline rare coding mutations, we obtained the corresponding APSP spectrum to calculate the APSP risk score, which was capable of distinguish HER2-negative from HER2-positive cases. These findings were validated using breast cancer data from TCGA (AUC = 0.7). DAGM revealed that HER2 signalling pathway was up-regulated in germline of HER2-negative patients, and those with high APSP risk scores had exhibited immune suppression. These findings were validated using RNA sequencing, phosphoproteome analysis, and CyTOF. Moreover, using germline mutations, DAGM could evaluate the risk for HER2-negative breast cancer, not only in women carrying BRCA1/2 mutations, but also in those without known disease-associated mutations.

INTERPRETATION

The DAGM can facilitate the screening of subjects at high risk of HER2-negative breast cancer for primary prevention. This study also provides new insights into the potential mechanisms of developing HER2-negative breast cancer. The DAGM has the potential to be applied in the prevention, diagnosis, and treatment of HER2-negative breast cancer.

FUNDING

This work was supported by the National Key Research and Development Program of China (grant no. 2018YFC0910406 and 2018AAA0103302 to CZ); the National Natural Science Foundation of China (grant no. 81202076 and 82072939 to MY, 81871513 to KW); the Guangzhou Science and Technology Program key projects (grant no. 2014J2200007 to MY, 202002030236 to KW); the National Key R&D Program of China (grant no. 2017YFC1309100 to CL); Shenzhen Science and Technology Planning Project (grant no. JCYJ20170817095211560 574 to YN); and the Natural Science Foundation of Guangdong Province (grant no. 2017A030313882 to KW and S2013010012048 to MY); Hefei National Laboratory for Physical Sciences at the Microscale (grant no. KF2020009 to GN); and RGC General Research Fund (grant no. 17114519 to YQS).

Genetic Influences in Breast Cancer Drug Resistance

Breast cancer is the most common cancer in adult women aged 20 to 50 years. The therapeutic regimens that are commonly recommended to treat breast cancer are human epidermal growth factor receptor 2 (HER2) targeted therapy, endocrine therapy, and systemic chemotherapy. The selection of pharmacotherapy is based on the characteristics of the tumor and its hormone receptor status, specifically, the presence of HER2, progesterone receptors, and estrogen receptors. Breast cancer pharmacotherapy often gives different results in various populations, which may cause therapeutic failure. Different types of congenital drug resistance in individuals can cause this. Genetic polymorphism is a factor in the occurrence of congenital drug resistance. This review explores the relationship between genetic polymorphisms and resistance to breast cancer therapy. It considers studies published from 2010 to 2020 concerning the relationship of genetic polymorphisms and breast cancer therapy. Several gene polymorphisms are found to be related to longer overall survival, worse relapse-free survival, higher pathological complete response, and increased disease-free survival in breast cancer patients. The presence of these gene polymorphisms can be considered in the treatment of breast cancer in order to shape personalized therapy to yield better results.

Modeling and comparing data mining algorithms for prediction of recurrence of breast cancer

Breast cancer is the most common invasive cancer and the second leading cause of cancer death in women. and regrettably, this rate is increasing every year. One of the aspects of all cancers, including breast cancer, is the recurrence of the disease, which causes painful consequences to the patients. Moreover, the practical application of data mining in the field of breast cancer can help to provide some necessary information and knowledge required by physicians for accurate prediction of breast cancer recurrence and better decision-making. The main objective of this study is to compare different data mining algorithms to select the most accurate model for predicting breast cancer recurrence. This study is cross-sectional and data gathering of this research performed from June 2018 to June 2019 from the official statistics of Ministry of Health and Medical Education and the Iran Cancer Research Center for patients with breast cancer who had been followed for a minimum of 5 years from February 2014 to April 2019, including 5471 independent records. After initial pre-processing in dataset and variables, seven new and conventional data mining algorithms have been applied that each one represents one kind of data mining approach. Results show that the C5.0 algorithm possibly could be a helpful tool for the prediction of breast cancer recurrence at the stage of distant recurrence and nonrecurrence, especially in the first to third years. also, LN involvement rate, Her2 value, Tumor size, free or closed tumor margin were found to be the most important features in our dataset to predict breast cancer recurrence.

Eight immune-related genes predict survival outcomes and immune characteristics in breast cancer

Advancements in immunotherapy have improved our understanding of the immune characteristics of breast cancer. Here, we analyzed gene expression profiles and clinical data obtained from The Cancer Genome Atlas database to identify genes that were differentially expressed between breast tumor tissues and normal breast tissues. Comparisons with the Immunology Database and Analysis Portal (ImmPort) indicated that many of the identified differentially expressed genes were immune-related. Risk scores calculated based on an eight-gene signature constructed from these immune-related genes predicted both overall survival and relapse-free survival outcomes in breast cancer patients. The predictive value of the eight-gene signature was validated in different breast cancer subtypes using external datasets. Associations between risk score and breast cancer immune characteristics were also identified; in vitro experiments using breast cancer cell lines confirmed those associations. Thus, the novel eight-gene signature described here accurately predicts breast cancer survival outcomes as well as immune checkpoint expression and immune cell infiltration processes.

Reciprocal fine-tuning of progesterone and prolactin-regulated gene expression in breast cancer cells

Progesterone and prolactin are two key hormones involved in development and remodeling of the mammary gland. As such, both hormones have been linked to breast cancer. Despite the overlap between biological processes ascribed to these two hormones, little is known about how co-expression of both hormones affects their individual actions. Progesterone and prolactin exert many of their effects on the mammary gland through activation of gene expression, either directly (progesterone, binding to the progesterone receptor [PR]) or indirectly (multiple transcription factors being activated downstream of prolactin, most notably STAT5). Using RNA-seq in T47D breast cancer cells, we characterized the gene expression programs regulated by progestin and prolactin, either alone or in combination. We found significant crosstalk and fine-tuning between the transcriptional programs executed by each hormone independently and in combination. We divided and characterized the transcriptional programs into four broad categories. All crosstalk/fine-tuning shown to be modulated by progesterone was dependent upon the expression of PR. Moreover, PR was recruited to enhancer regions of all regulated genes. Interestingly, despite the canonical role for STAT5 in transducing prolactin-signaling in the normal and lactating mammary gland, very few of the prolactin-regulated transcriptional programs fine-tuned by progesterone in this breast cancer cell line model system were in fact dependent upon STAT5. Cumulatively, these data suggest that the interplay of progesterone and prolactin in breast cancer impacts gene expression in a more complex and nuanced manner than previously thought, and likely through different transcriptional regulators than those observed in the normal mammary gland. Studying gene regulation when both hormones are present is most clinically relevant, particularly in the context of breast cancer.

Transforming growth factor beta receptor II (TGFBR2) promoter region polymorphism in Brazilian breast cancer patients: association with susceptibility, clinicopathological features, and interaction with TGFB1 haplotypes

PURPOSE

Transforming growth factor beta (TGFβ) has paradoxical effects in breast cancer (BC), inhibiting initial tumors while promoting aggressive ones. A polymorphism on TGFBR2 promoter region (G-875A, rs3087465) increases TGFβ type II receptor expression and is protective against cancer. Previously, we have shown that TGFB1 variants have subtype-specific roles in BC. This work sought to investigate the association between TGFBR2 and susceptibility and clinicopathological features in BC subgroups.

METHODS

TGFBR2 G-875A was analyzed through PCR-RFLP in 388 BC patients and 405 neoplasia-free women. Case-control analyses as well as interaction with TGFB1 haplotypes previously associated with BC were tested through age-adjusted logistic regression. Correlations between G-875A and clinicopathological parameters were assessed through Kendall's Tau-b test. All statistical tests were two-tailed (α = 0.05).

RESULTS

TGFBR2 G-875A was protective against BC in additive, genotypic, and dominant models. In subgroup-stratified analyses, these effects were greater in hormonal receptor-positive and luminal-A tumors, but were not significant in other subgroups. Logistic models including TGFB1 variants showed that in luminal-A tumors, G-875A retained its significance while TGFB1 haplotype showed a trend towards significance; otherwise, in HER2⁺ tumors TGFB1 variants remained significant while TGFBR2 showed a trend for association. There was no interaction between these genes. In correlation analyses, G-875A positively correlated with histopathological grade in total sample, and a trend towards significance was observed in triple-negative BCs.

CONCLUSION

These results indicate that G-875A is a protective factor against BC, especially from luminal-A subtype, but may promote anaplasia in established tumors, consistent with TGFβ signaling roles in BC.

Identification of targets for prostate cancer immunotherapy

BACKGROUND

We performed profiling of the immune microenvironment of castration-resistant (CRPC) and castration-sensitive (CSPC) prostate cancer (PC) in order to identify novel targets for immunotherapy.

METHODS

PD-L1 and CD3/CD8 immunohistochemistry, PD-L1/2 fluorescent in situ hybridization, tumor mutation burden, microsatellite instability, and RNA-seq of 395 immune-related genes were performed in 19 CRPC and CSPC. Targeted genomic sequencing and fusion analysis were performed in 17 of these specimens.

RESULTS

CD276, PVR, and NECTIN2 were highly expressed in PC. Comparison of CRPC versus CSPC and primary versus metastatic tissue revealed the differential expression of immunostimulatory, immunosuppressive, and epithelial-to-mesenchymal transition (EMT)-related genes. Unsupervised clustering of differentially expressed genes yielded two final clusters best segregated by CRPC and CSPC status.

CONCLUSION

CD276 and the alternative checkpoint inhibition PVR/NECTIN2/CD226/TIGIT pathway emerged as relevant to PC checkpoint inhibition target development.

Array comparative genomic hybridization identifies high level of PI3K/Akt/mTOR pathway alterations in anal cancer recurrences

Genomic alterations of anal squamous cell carcinoma (ASCC) remain poorly understood due to the rarity of this tumor. Array comparative genomic hybridization and targeted gene sequencing were performed in 49 cases of ASCC. The most frequently altered regions (with a frequency greater than 25%) were 10 deleted regions (2q35, 2q36.3, 3p21.2, 4p16.3, 4p31.21, 7q36.1, 8p23.3, 10q23.2, 11q22.3, and 13q14.11) and 8 gained regions (1p36.33, 1q21.1, 3q26.32, 5p15.33, 8q24.3, 9q34.3, 16p13.3, and 19p13.3). The most frequent minimal regions of deletion (55%) encompassed the 11q22.3 region containing ATM, while the most frequent minimal regions of gain (57%) encompassed the 3q26.32 region containing PIK3CA. Recurrent homozygous deletions were observed for 5 loci (ie, TGFR2 in 4 cases), and recurrent focal amplifications were observed for 8 loci (ie, DDR2 and CCND1 in 3 cases, respectively). Several of the focal amplified genes are targets for specific therapies. Integrated analysis showed that the PI3K/Akt/mTOR signaling pathway was the pathway most extensively affected, particularly in recurrences compared to treatment‐naive tumors (64% vs 30%; P = .017). In patients with ASCC recurrences, poor overall survival (OS) was significantly correlated with a large number of altered regions (P = .024). These findings provide insight into the somatic genomic alterations in ASCC and highlight the key role of the druggable PI3K/Akt/mTOR signaling pathway.

Association of multiple genetic variants with breast cancer susceptibility in the Han Chinese population

We selected 13 tag single nucleotide polymorphisms (tSNPs) to investigate whether they were associated with breast cancer risk in the Chinese Han population. Upon statistical analyses of clinical data from 551 patients and 577 controls, we found that six of the 13 SNPs were associated with breast cancer; namely, rs4973768(Odds ratio (OR) = 1.30, 95% confidence interval (CI) =1.01-1.67), rs981782(OR =1.30, 95% CI=1.01-1.66), rs1432679(OR =0.84, 95% CI=0.70-0.99), rs10759243(OR=1.30, 95%CI=1.09-1.55), rs10822013(OR =1.18, 95% CI=1.00-1.39) and rs704010(OR =1.63, 95% CI=1.04-2.56). When stratified based on breast cancer subtype, our analyses revealed that three SNPs (rs981782, rs10759243 and rs704010) correlated with ER+ breast cancer, while another three (rs4973768, rs1432679 and rs10822013) correlated with ER- breast cancer. We obtained similar results while investigating the correlation of SNPs with PR status or clinical stage. Our results suggest that associations identified between SNPs and breast cancer through genome-wide association studies (GWAS) may not always be generalizable across races.

Tumor exome sequencing and copy number alterations reveal potential predictors of intrinsic resistance to multi-targeted tyrosine kinase inhibitors

Multi-targeted tyrosine kinase inhibitors (TKIs) have broad efficacy and similar FDA-approved indications, suggesting shared molecular drug targets across cancer types. Irrespective of tumor type, 20-30% of patients treated with multi-targeted TKIs demonstrate intrinsic resistance, with progressive disease as a best response. We conducted a retrospective cohort study to identify tumor (somatic) point mutations, insertion/deletions, and copy number alterations (CNA) associated with intrinsic resistance to multi-targeted TKIs. Using a candidate gene approach (n=243), tumor next-generation sequencing and CNA data was associated with resistant and non-resistant outcomes. Resistant individuals (n=11) more commonly harbored somatic point mutations in NTRK1, KDR, TGFBR2, and PTPN11 and CNA in CDK4, CDKN2B, and ERBB2 compared to non-resistant (n=26, p<0.01). Using a random forest classification model for variable reduction and a decision tree classification model, we were able to differentiate intrinsically resistant from non-resistant patients. CNA in CDK4 and CDKN2B were the most important analytical features, implicating the cyclin D pathway as a potentially important factor in resistance to multi-targeted TKIs. Replication of these results in a larger, independent patient cohort has potential to inform personalized prescribing of these widely utilized agents.

Predicting radiotherapy response for patients with soft tissue sarcoma by developing a molecular signature

Soft tissue sarcomas are rare and aggressive tumors arising from connective tissues. Adjuvant radiotherapy is a commonly used treatment approach for the majority of sarcomas. We attempted to identify a gene signature that can predict radiosensitive patients who are most likely to have a better treatment response from radiotherapy, compared with disease progression. Using the publicly available data of soft tissue sarcoma from The Cancer Genome Atlas, we developed a cross-validation procedure to identify a predictive gene signature for radiosensitivity. The results showed that the predicted radiosensitive patients who received radiotherapy had significantly improved treatment response. We further provide supportive evidence to validate our sensitivity prediction. Results showed that the predicted radiosensitive patients who received radiotherapy had significantly improved survival than patients who did not. ROC analysis showed that the developed gene signature had a powerful prediction on treatment response. We further found that predicted radiosensitive patients who received radiotherapy had a significantly reduced rate of new tumor events. Finally, we validated our gene signature using a hierarchical cluster analysis, and found that the predicted sensitivities were well-matched with results from the cluster analysis. These results are consistent with our expectation, suggesting that the identified gene signature and radiosensitivity prediction are effective. The genes involved in the signature may provide a molecular basis for prognostic studies and radiotherapy target discovery.

Breast tumor DNA methylation patterns associated with smoking in the Carolina Breast Cancer Study

PURPOSE

Tobacco smoking is a risk factor in several cancers, yet its roles as a putative etiologic exposure or poor prognostic factor in breast cancer are less clear. Altered DNA methylation contributes to breast cancer development and may provide a mechanistic link between smoking and gene expression changes leading to cancer development or progression.

METHODS

Using a cancer-focused array, we examined methylation at 933 CpGs in 517 invasive breast tumors in the Carolina Breast Cancer Study to determine whether methylation patterns differ by exposure to tobacco smoke. Multivariable generalized linear regression models were used to compare tumor methylation profiles between smokers and never smokers, overall, or stratified on hormone receptor (HR) status.

RESULTS

Modest differences in CpG methylation were detected at p < 0.05 in breast tumors from current or ever smokers compared with never smokers. In stratified analyses, HR- tumors from smokers exhibited primarily hypomethylation compared with tumors from never smokers; hypomethylation was similarly detected within the more homogeneous basal-like subtype. Most current smoking-associated CpG loci exhibited methylation levels in former smokers that were intermediate between those in current and never smokers and exhibited progressive changes in methylation with increasing duration of smoking. Among former smokers, restoration of methylation toward baseline (never smoking) levels was observed with increasing time since quitting. Moreover, smoking-related hypermethylation was stronger in HR+ breast tumors from blacks than in whites.

CONCLUSIONS

Our results suggest that breast tumor methylation patterns differ with tobacco smoke exposure; however, additional studies are needed to confirm these findings.

Expression of ERβ gene in breast carcinoma and the relevance in neoadjuvant therapy

In the present study, we examined the expression of the estrogen receptor β (ERβ) gene in breast cancer and its relevance in neoadjuvant therapy. In total, 120 breast cancer patients who were hospitalized in the Departments of Breast Disease and Medical Oncology served as the subjects of this study. The subjects were diagnosed with breast cancer phase II to phase IIIA, as confirmed by aspiration biopsy and iconography. The patients were divided into two groups in a randomized control manner, with 60 patients in each group. The experimental group was administered the taxotere + epirubicin + cyclophosphamide (TEC) plan for 3–4 cycles of chemotherapy before the modified radical operation of breast cancer. In the control group, no TEC chemotherapy was carried out prior to operation. Instead, the breast lesion was removed directly by operation. After the operation, the IHC method was used to stain the ERβ protein in the lesion tissue. The patients were classified according to whether the basement membrane was broken through; 5 cases had non-infiltrative carcinoma and 115 cases had infiltrative carcinoma. According to the pathology of the lesion, 114 cases had breast ductal carcinoma, 2 cases had mucinous breast carcinoma (of which there were 2 cases combined with ductal carcinoma), and 4 cases had breast lobular carcinoma. The ERβ gene was found to be expressed in normal and breast cancer tissues. When ERβ gene expression was compared before and after the chemotherapy, its expression was significantly increased in breast cancer tissues, which shows a significant statistical difference (P<0.05). In the experimental group, the expression of ERβ gene in carcinoma tissue was significantly lower than that in the control group, and differences were statistically significant (P<0.05). Therefore, expression of the ERβ gene in breast carcinoma tissues was high. The application of adjuvant chemotherapy before the modified radical operation for breast carcinoma can significantly lower the level of ERβ expression. The expression levels of ERβ gene in the carcinoma tissue of the patients can be treated as the evaluation index for neoadjuvant chemotherapy. Regarding targeted therapy and corresponding drug development for breast carcinoma, ERβ can act as one of the specific drug targets.

DNA methylation detection at single base resolution using targeted next generation bisulfite sequencing and cross validation using capillary sequencing

With a purpose of accurate and simultaneous determination of DNA methylation from multiple loci in multiple samples, here, we are demonstrating a method to aid rapid DNA methylation detection of genomic sequences. Using genomic DNA of peripheral blood from 14 healthy individuals, DNA methylation in 465 CpG sites from 12 loci of genes (ADAM22, ATF2, BCR, CD83, CREBBP, IL12B, IL17RA, MAP2K2, RBM38, TGFBR2, TGFBR3, and WNT5A) was analysed by targeted next generation bisulfite sequencing. Analysed region for three genes, BCR, IL17RA and RBM38 showed an absolute mean DNA methylation of 25.6%, 89.2% and 38.9% respectively. Other nine gene loci were unmethylated and exhibited <10% absolute mean DNA methylation. Two genes, IL17RA and RBM38 were technically validated using direct capillary sequencing and results were comparable with positive correlation (P=0.0088 & P<0.0001 respectively) in the CpG sites for DNA methylation. All CpG sites analysed from RBM38 genes locus displayed 95% limits of agreement for DNA methylation measurements from the two methods. The present approach provides a fast and reliable DNA methylation quantitative data at single base resolution with good coverage of the CpG sites under analysis in multiple loci and samples simultaneously. Use of targeted next generation bisulfite sequencing may provide an opportunity to explore genes in the discovery panel for biomarker identification and facilitate functional validation.